PhD Thesis - Rebecca Webster · Rebecca Webster BSc, BEng (Hons) This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia School of Medicine

Complementary investigations of the molecular biology of cancer:

assessment of the role of Grb7 in the proliferation and migration of breast cancer cells;

and prediction and validation of microRNA targets involved in cancer

Rebecca Webster

BSc, BEng (Hons)

This thesis is presented for the degree of Doctor of Philosophy

of The University of Western Australia

School of Medicine and Pharmacology, University of Western Australia

Laboratory for Cancer Medicine, Western Australian Institute for Medical Research

2007

i

ABSTRACT

For this thesis, the molecular biology of cancer was approached from two directions.

Firstly, an investigation was conducted on the role of growth factor receptor-bound

protein 7 (Grb7) in breast cancer. Grb7 is an adapter molecule that binds to a variety of

proteins, including the growth factor receptor and proto-oncogene, ErbB2, and mediates

signalling to downstream pathways. It has been linked to cell migration and an invasive

phenotype, and is of interest as a therapeutic target. To investigate the role of Grb7 in

breast cancer, preliminary experiments were performed that, firstly, determined the

expression of wild-type Grb7 and a splice variant, Grb7V, in a range of cell lines, and

secondly, aided the development of a protocol for treating cells with short interfering

RNA (siRNA) against Grb7 and the ErbB ligand, heregulin (HRG), in a cell system

appropriate for measuring the functional outcomes. Using this protocol in conjunction

with CellTitre (CT) proliferation assays, it was demonstrated that Grb7 does not play a

role in the proliferation of either unstimulated or HRG-stimulated SK-BR-3 breast

cancer cells. Furthermore, using the protocol in conjunction with Boyden chamber

migration assays, it was shown that inhibition of Grb7 expression has a slight

stimulatory effect on HRG-stimulated SK-BR-3 cell migration. Thus, Grb7 was found

to play only a minor role in the migration of SK-BR-3 cells, suggesting that it is not an

ideal anti-cancer target for breast cancers modelled by this cell system.

Concurrently, a second investigation was conducted, which similarly sought insight into

the molecular biology of cancer, but adopted a more strategic approach. Specifically, a

microRNA (miRNA) target prediction program was custom-designed, implemented and

used to predict miRNA target candidates from a data set of human genes implicated in

cancer. miRNAs are ~22 nt non-coding RNAs derived from endogenous genes. They

bind imperfectly to target mRNAs and can regulate gene expression by repressing

translation, reducing target stability and/or inducing target cleavage. miRNAs regulate a

range of biological processes and play important roles in human disease. The miRNA

target prediction program’s top ranking prediction was that EGFR mRNA is a target of

miR-7. It was subsequently experimentally verified that EGFR is a target of miR-7 in

vitro, through studies of the effect of exogenous miR-7 precursor on the activity of

EGFR wild-type and mutant luciferase reporter constructs, and on endogenous protein

levels, in different cancer cell lines. EGFR mRNA was also shown to be reduced

ii

following treatment with exogenous miR-7 precursor using RT-PCR, indicating that

miR-7 reduces EGFR expression at least in part by reducing the stability of EGFR

mRNA. In MDA-MB-468 cells, miR-7 up-regulation had a small, significant inhibitory

effect on cell cycle progression, while in A549 cells, miR-7 up-regulation reduced cell

proliferation, inhibited cell cycle progression at the G1/S checkpoint and induced cell

death, consistent with the observed down-regulation of EGFR. These results provide

evidence for a biologically significant role for the miR-7-mediated regulation of EGFR

expression. A microarray experiment was also performed to identify genes that were

down-regulated following treatment with miR-7 compared to NS precursor. Of 248

down-regulated genes, including EGFR, 37 promising new miR-7 target candidates

were identified. Functional clustering of down-regulated genes and promising target

candidates suggested that miR-7 may have functionally-related targets involved in

processes including cell motility and brain-associated functions. This investigation thus

yielded a program capable of accurately predicting a miRNA target not predicted by any

other target prediction program, verified a previously unknown miRNA:target

interaction with functional consequences in cancer cells and provided the first steps

towards investigating miR-7-mediated regulation in greater depth. Furthermore, EGFR

was, to our knowledge, the first example of a verified miRNA target with target sites

that are not conserved across mammals, an observation with important implications for

computational target prediction and the evolution of miRNA regulatory systems. In

addition, the demonstrated growth inhibitory and cytotoxic effects of miR-7 on lung

cancer cells raise the possibility of a miR-7-based therapeutic for the treatment of

EGFR-overexpressing tumours.

iii

TABLE OF CONTENTS

Abstract i

List of Figures x

List of Tables xiii

Acknowledgements xiv

Statement of contribution xv

Abbreviations xvi

Terminology xxi

Overview xxii

PART 1

CHAPTER 1: PART 1 LITERATURE REVIEW AND INTRODUCTION

1.1 Overview 1

1.2 Literature review 2

1.2.1 Grb7 background 2

1.2.1.1 Structure 2

1.2.1.2 Expression and localisation 4

1.2.2 Grb7 binding partners and functions 5

1.2.2.1 ErbB receptors 7

1.2.2.2 Focal adhesion kinase (FAK) 9

1.2.2.3 Phosphatidylinositol phosphates (PIPs) 11

1.2.2.4 Eph receptor B1 (EphB1) 11

1.2.3 Grb7 in cancer 11

1.2.4 Grb7 as a potential therapeutic target 13

1.3 Project rationale and aims 14

1.4 Hypotheses 15

iv

CHAPTER 2: PART 1 METHODOLOGY

2.1 Cell culture 16

2.2 siRNA transfections 16

2.3 HRG treatment 17

2.4 Treatment and preparation of cells for functional assays 17

2.5 Harvesting of RNA 19

2.6 Harvesting of protein 19

2.7 Reverse transcriptase polymerase chain reaction (RT-PCR) 19

2.8 Western blot 21

2.9 CellTitre (CT) assay 22

2.10 Cell migration assay 22

2.11 Statistical analysis 23

2.12 Software 23

CHAPTER 3: INVESTIGATION OF THE ROLE OF Grb7 IN THE

PROLIFERATION AND MIGRATION OF BREAST CANCER CELLS

3.1 Introduction 24

3.2 Results 27

3.2.1 Grb7 and Grb7V expression 27

3.2.1.1 RNA expression 27

3.2.1.2 Protein expression 28

3.2.2 Development of a protocol for the effective knockdown of

Grb7 using siRNA 29

3.2.2.1 Identification of an effective siRNA against Grb7 29

3.2.2.2 Demonstration that the choice of primers affects the

appearance of a Grb7 RNA knockdown 31

3.2.2.3 Optimisation and characterisation of the Grb7

knockdown 31

3.2.3 Development of a protocol for the treatment of cells with siRNA,

FN and/or HRG, and the performance of functional studies 34

3.2.3.1 Problems associated with transfecting cells in 96-well

plates 34

3.2.3.2 Treatment with HRG 36

v

3.2.3.3 Problems associated with splitting and plating cells for

serum starvation 38

3.2.3.4 Cell counting problem 38

3.2.4 Effect of concurrent treatment with Grb7 siRNA and HRG on

Grb7 protein 40

3.2.5 Effect of Grb7 siRNA and HRG on SK-BR-3 cell proliferation 41

3.2.6 Effect of Grb7 siRNA and HRG on SK-BR-3 cell migration 43

3.2.6.1 Effect of HRG on cell spreading 43

3.2.6.2 Cell migration assays 44

3.3 Discussion 47

Summary 47

Limitations 51

Future directions 51

Bridge 53

PART 2


4.1 Overview 54

4.2 Literature review of miRNAs 55

4.2.1 miRNA biogenesis 55

4.2.2 Mechanisms of miRNA action 57

4.2.3 The functions of miRNAs in normal and diseased cells 58

4.2.3.1 The functions of miRNAs in normal cells 58

4.2.3.2 miRNAs in cancer 61

4.2.4 Clinical applications of miRNA research 64

4.2.5 miRNA target prediction 65

4.2.5.1 Cross-species conservation of the mRNA sequence 66

4.2.5.2 Target site location in the 3’UTR 68

4.2.5.3 High sequence complementarity of target sites to the

5’ end of the miRNA and other sequence considerations 69

4.2.5.4 Low free energy of hybridisation between miRNA and

mRNA target sites 71

vi

4.2.5.5 Accessibility of mRNA target sites to miRNAs 73

4.2.5.6 Presence of multiple miRNA target sites within a 3’UTR 74

4.2.5.7 miRNA and target expression profiles 75

4.2.6 Verification of human miRNA targets 77

4.2.6.1 miRNA up-regulation 77

4.2.6.2 miRNA down-regulation 78

4.2.6.3 Luciferase reporter assays 78

4.2.6.4 Monitoring of endogenous protein levels 78

4.2.6.5 Microarray experiments 79

4.2.6.6 Function studies 79

4.3 miR-7 80

4.3.1 miR-7 background 80

4.3.2 miR-7 targets and functions 81

4.3.2.1 miR-7 in Drosophila 81

4.3.2.2 miR-7 in Homo sapiens 83

4.4 Epidermal Growth Factor Receptor (EGFR) 84

4.4.1 EGFR signalling and function 84

4.4.2 The role of EGFR in cancer 86

4.4.3 Treatment of EGFR-overexpressing cancers 87

4.4.3.1 Monoclonal antibodies 87

4.4.3.2 Tyrosine kinase inhibitors 87

4.5 Project rationale and aims 89


5.1 Cell culture 90

5.2 Plasmids 90

5.3 Transfections 91

5.4 Treatment and preparation of cells for cell proliferation assays 92

5.5 Luciferase reporter assay 93

5.6 RT-PCR 93

5.7 Western blot 94

5.8 Cell counting 94

5.9 Fluorescence-activated cell sorting (FACS) analysis 94

5.10 Harvest and preparation of RNA for microarray assays 95

vii

5.11 Microarray assay and processing of raw data 96

5.12 Statistical analysis 96

5.13 Hardware and software 96

CHAPTER 6: DEVELOPMENT OF A miRNA TARGET PREDICTION PROGRAM

AND THEORETICAL EVALUATION OF ITS PREDICTIONS

6.1 Introduction 98

6.2 Development of a miRNA target prediction program and

target predictions 99

6.2.1 Program design and implementation 99

6.2.1.1 Program outline 99

6.2.1.2 Choice of data sets 103

6.2.1.3 Program parameters 103

6.2.2 Target predictions 103

6.2.2.1 Selection of a target prediction for further scrutiny 104

6.2.3 Further theoretical evaluation of the miR-7:EGFR prediction 107

6.2.3.1 The seed and other sequence considerations 107

6.2.3.2 Target sequence conservation 109

6.2.3.3 Structure and minimum free energy of miRNA

target predictions 112

6.2.3.4 Instability of target sites in the context of the 3’UTR

mRNA structure 114

6.2.3.5 miRNA and target expression profiles 116

6.3 Discussion 118

6.4 Hypotheses 120

CHAPTER 7: EXPERIMENTAL ASSESSMENT OF THE miR-7:EGFR

TARGET PREDICTION

7.1 Introduction 121

7.2 Results 122

7.2.1 Establishment of an optimum reporter assay 122

7.2.1.1 Replication of the results of Lewis et al., 2003 122

7.2.1.2 Perfect target reporter assays 123

viii

7.2.1.3 Perfect target reporter assays with miR-7 up-regulation 125

7.2.2 Assessment of the miR-7:EGFR prediction using EGFR-Wt and

EGFR-Mt plasmids, and miR-7 up-regulation 127

7.2.2.1 Cloning of EGFR-Wt and EGFR-Mt plasmids 127

7.2.2.2 Luciferase assays with EGFR-Wt and EGFR-Mt

plasmids, and miR-7 up-regulation 128

7.2.3 Effect of miR-7 up-regulation on endogenous protein 131

7.2.3.1 EGFR protein 131

7.2.3.2 Other proteins 131

7.3 Discussion 134

CHAPTER 8: THE FUNCTIONAL EFFECT OF miR-7 PRECURSOR IN LUNG

AND BREAST CANCER CELLS


8.2 Results 140

8.2.1 Visual assessment of miR-7-treated cells 140

8.2.2 Quantification of differences in cell proliferation 141

8.2.2.1 Optimisation of CT assay and pilot experiments 141

8.2.2.2 Results of cell counting experiments 143

8.2.2.3 Results of CT assays 144

8.2.3 FACS cell cycle analysis 146

8.2.3.1 FACS analysis in A549 cells 146

8.2.3.2 FACS analysis in MDA-MB-468 cells 146

8.3 Discussion 150

CHAPTER 9: MICROARRAY ANALYSIS OF A549 CELLS TRANSFECTED WITH

miR-7 OR NONSENSE PRECURSOR


9.2 Results 157

9.2.1 Preliminary experiments 157

9.2.1.1 Verification of an effect of miR-7 precursor on

EGFR mRNA 157

9.2.1.2 Choice of time-point for RNA harvest 158

ix

9.2.1.3 Preparation of RNA samples for microarrays 159

9.2.2 Microarray results 160

9.2.2.1 Down-regulated genes 160

9.2.2.2 Target predictions in the down-regulated gene set 160

9.2.3 KEGG pathway functional trend analysis 162

9.2.4 Gene Ontology (GO) functional trend analysis 171

9.2.4.1 Cellular component 177

9.2.4.2 Molecular function 177

9.2.4.3 Biological process 177

9.2.4.4 Some non-significant GO terms 178

9.3 Discussion 182

CHAPTER 10: PART 2 GENERAL DISCUSSION

Summary 186

Limitations 188

Implications of major findings 189

Future directions 194

CONCLUSIONS 197

BIBLIOGRAPHY 199

APPENDIX A: Code for the Chapter 6 miRNA target prediction program 225

APPENDIX B: Set of miRNAs and their sequences used for miRNA

target prediction in Chapter 6 244

APPENDIX C: Set of genes used for miRNA target prediction in

Chapter 6 248

APPENDIX D: Set of probes significantly down-regulated by miR-7 in

the Chapter 9 microarray experiment 252

APPENDIX E: Manuscript prepared for publication 267

x

LIST OF FIGURES

Figure 0.1 Diagram of a miRNA:mRNA interaction, defining terminology. xxi

Figure 1.1: Gene structure and protein domains of the Grb7 and Grb7V. 3

Figure 1.2: Signalling involving Grb7 binding partners. 7

Figure 3.1: RNA expression of Grb7 and Grb7V in a panel of cell lines

and a breast cancer cDNA library. 27

Figure 3.2: Western blot showing the Grb7 protein knockdown induced by

SP siRNA in SK-BR-3 and BT-474 cells. 30

Figure 3.3: Western blot showing the Grb7 protein knockdown induced by

SP component siRNAs in SK-BR-3 cells. 30

Figure 3.4: The effect of PCR primers on the appearance of a Grb7 RNA

knockdown in SK-BR-3 cells. 32

Figure 3.5: Western blot showing the effect of different transfection

reagents on Grb7 knockdown in SK-BR-3 cells. 33

Figure 3.6: Western blot showing the effect of SP concentration on Grb7

knockdown in SK-BR-3 cells. 33

Figure 3.7: CT assay of SK-BR-3 cells showing the effect of NS

transfection and media change in 96-well plates. 35

Figure 3.8: Western blots showing the effect of HRG on Grb7

expression over time in SK-BR-3 and BT-474 cells. 37

Figure 3.9: Final protocol for treatment of cells with siRNA and HRG,

and preparation for proliferation and migration assays. 39

Figure 3.10: Western blots showing the effects of concurrent treatment

with SP and HRG on Grb7 and FAK expression in SK-BR-3

and BT-474 cells. 40

Figure 3.11: CT assays showing the effect of siRNA and HRG on the

proliferation of SK-BR-3 cells. 42

Figure 3.12: Photographs showing the effect of siRNA and HRG on

SK-BR-3 cell morphology on FN-coated dishes. 43

Figure 3.13: The effects of siRNA, HRG and serum (FBS) on the

migration of SK-BR-3 cells. 45

Figure 3.14: The effects of siRNA on the migration of HRG-treated

SK-BR-3 cells. 46

xi

Figure 4.1: The biogenesis of miRNAs and siRNAs. 56

Figure 4.2: Mechanisms of action of miRNAs and siRNAs. 58

Figure 4.3: Cross-species sequence alignment of mature miR-7. 80

Figure 4.4: EGFR signalling. 85

Figure 6.1: An example mfold folding of a miRNA and mRNA section

linked by a linker sequence. 101

Figure 6.2: Flow chart for miRNA target prediction procedure. 102

Figure 6.3: Positions of the destabilising elements, EGFR-1A and

EGFR-2A, and putative miR-7 target sites within EGFR. 108

Figure 6.4: Cross-species conservation of putative and verified miRNA

target sites. 111

Figure 6.5: RNAhybrid foldings of putative and verified miRNA

target sites. 114

Figure 6.6: Folded structure of the EGFR 3’UTR mRNA and enlargement

of the putative miR-7 target sites. 116

Figure 7.1: Luciferase assay showing the expression of SMAD1-Wt,

SMAD1-Mt and empty vector plasmids, in HeLa cells. 123

Figure 7.2: Luciferase assay showing the effect of miR-7 inhibitor on the

expression of the perfect miR-7 target plasmid and the empty

vector plasmid in MCF7 cells. 125

Figure 7.3: Luciferase assays showing the effects of miR-7 and NS

precursors on the expression of the perfect miR-7 target

plasmid and the empty vector plasmid, in HeLa cells. 126

Figure 7.4: Composition of inserts for the EGFR-Wt and EGFR-Mt

plasmids. 128

Figure 7.5: Luciferase assays showing the effects of miR-7 and NS

precursors on the expression of EGFR-Wt, EGFR-Mt and

empty vector plasmids in three cell lines. 130

Figure 7.6: The effects of miR-7 and NS precursors on endogenous EGFR

protein levels in MDA-MB-468 cells. 132

Figure 7.7: Western blot showing the effects of miR-7 and NS precursors

on the levels of different proteins in MDA-MB-468 and

A549 cells. 133

Figure 8.1: Photographs of cells treated with LF, miR-7 precursor or NS

precursor. 141

xii

Figure 8.2 CT assays of A549 cells following splitting of cells from 10 cm

dishes into 96-well plates on either day 2 or day 3 after

transfection. 143

Figure 8.3: Quantification of the effects of miR-7 and NS precursors on

A549 cell proliferation. 145

Figure 8.4: Results of FACS analysis experiments in A549 cells. 148

Figure 8.5: Results of FACS analysis experiments in MDA-MB-468 cells. 149

Figure 9.1: RT-PCRs for A549 cells harvested 12 and 24 hours after

transfection with LF, miR-7 precursor or NS precursor. 158

Figure 9.2: RT-PCRs for EGFR and β-actin for the two replicate

experiments chosen for microarray analysis. 159

Figure 9.3: KEGG Apoptosis pathway. 164

Figure 9.4: KEGG Focal adhesion pathway. 165

Figure 9.5: KEGG Regulation of actin cytoskeleton pathway. 166

Figure 9.6: KEGG GnRH signalling pathway. 167

Figure 9.7: KEGG Long-term potentiation pathway. 168

Figure 9.8: KEGG Olfactory transduction pathway. 169

Figure 9.9: DAGs for the GO Cellular component terms for down-regulated

genes and promising targets. 173

Figure 9.10: DAGs for the GO Molecular function terms for down-regulated

genes and promising targets. 174

Figure 9.11: DAG for the GO Biological process terms for down-regulated

genes. 175

Figure 9.12: DAG for the GO Biological process terms for promising targets. 176

Figure 10.1: Model of miR-7 action. 189

xiii

LIST OF TABLES

Table 1.1: Grb7 binding proteins and functions. 5

Table 4.1. Animal miRNA functions. 61

Table 4.2: Predicted and verified miR-7 targets in Drosophila. 82

Table 4.3: Predicted human miR-7 targets. 84

Table 6.1: miRNA targets predicted by the target prediction program. 105

Table 6.2: Seed and sequence characteristics of putative EGFR target sites

and three verified targets. 109

Table 6.3: % sequence match, mfe and p-values for each putative EGFR

target site and the target sites of three verified targets, calculated

by RNAhybrid. 113

Table 6.4: Summary of seed region instability for putative EGFR target

sites and three verified targets. 115

Table 9.1: Top ten miRNA target predictions from the down-regulated

gene set. 162

Table 9.2: KEGG pathways significantly enriched with up- and/or

down-regulated genes. 163

Table 9.3: Down-regulated genes from non-significant GO terms from the

Biological process category. 179

Table 9.4: Down-regulated genes from the non-significant GO term

‘RNA binding’ from the Molecular function category. 181

Appendix C Table: Set of genes used for miRNA target prediction in

Chapter 6. 248

Appendix D Table: Set of probes significantly down-regulated by miR-7

in the Chapter 9 microarray experiment, with miR-7 target

predictions. 252

xiv

ACKNOWLEDGEMENTS

Firstly, I must acknowledge and thank my supervisor, Prof. Peter Leedman, for giving

me the opportunity to work on these fantastic projects and for staying enthusiastic

through it all. Thanks too to Dr Keith Giles for all his intellectual input and for sharing

his valuable lab experience with me. In addition, I am grateful to the many other

members of the lab who have freely offered their time for discussions, technical advice

and generous favours. In particular, thanks to Mike Epis, for teaching me all of the

fundamental lab skills and techniques when I first started out, to Ross McCulloch, for

helping me with my cloning puzzles, and to Christin Down and Esme Hatchell, for

being fun, helpful and supportive lab pals.

I would also like to acknowledge the assistance provided by certain people outside of

the lab that helped to make this investigation more thorough, informative and

interesting. The breast cancer cDNA library used in section 3.2.1.1 was provided by

Dr Jennifer Byrne of the Children’s Medical Research Institute, NSW, Australia. The

SMAD1-Wt, SMAD1-Mt and empty vector plasmids used in section 7.2.1.1 were

provided by Prof. David Bartel from the Massachusetts Institute of Technology. The

microarray assay of Chapter 9 was performed by the Lotterywest State MicroArray

Facility; and the FACS analysis of section 8.2.3 was conducted at the Flow Cytometry

Unit of PathWest Laboratory Medicine WA, Royal Perth Hospital, with the assistance

of Rom Krueger.

Also outside the lab, thank you so much to mum and dad for always being around with

help of every sort, encouragement, lots of good food and good cheer.

And finally, a very powerful and special thank you to kind Ed Wilson. His care,

understanding and advice, and his wonderful helping hands and brain have made me

very happy and productive.

xv

STATEMENT OF CONTRIBUTION

This thesis is a true account of my own research. The text and figures comprising the

body of this thesis are my own composition, and all technical advice and assistance

received has been appropriately acknowledged. To the best of my knowledge, the data

presented is original and has not been previously submitted for a degree at this or any

other university.

The co-authored manuscript, “miR-7 targets EGF receptor signaling”, appears in

Appendix E. Co-authors of this manuscript are estimated to have made the following

contributions: Rebecca Webster: 45%, Keith Giles: 30%, Karina Price: 15%, Peter

Leedman: 9%, John Mattick: 1%.

Rebecca Jane Webster

Coordinating Supervisor, Prof. Peter Leedman

xvi

ABBREVIATIONS

Throughout this thesis, human genes and proteins are referred to using common

abbreviations. To disambiguate these references, the HUGO Gene Nomenclature

Committee (HGNC) convention (Wain, Lush, Ducluzeau, Khodiyar, & Povey, 2004) is

employed below. Where different to the abbreviation used in the text, the HGNC

symbol for each gene is given in brackets following the full gene name.

3’UTR 3’ untranslated region

5’UTR 5’ untranslated region

A adenosine

ADCY9 adenylate cyclase 9

ATP adenosine triphosphate

BCL2 B-cell CLL/lymphoma 2

BPS between PH and SH2

Brn-3b POU domain, class 4, transcription factor 2 (HGNC: POU4F2)

BSA bovine serum albumin

C cytidine

c-Abl v-abl Abelson murine leukemia viral oncogene homolog 1

(HGNC: ABL1)

CALM1 calmodulin 1

CALM3 calmodulin 3

CAMK2D calcium/calmodulin-dependent protein kinase II delta

CAMKII Calcium/calmodulin-dependent protein kinase II [Drosophila]

CASP9 caspase 9, apoptosis-related cysteine peptidase

Cav-1 caveolin 1, caveolae protein, 22 kDa (HGNC: CAV1)

cDNA DNA copy generated by reverse transcription

CEB cytoplasmic extraction buffer

CFLAR CASP8 and FADD-like apoptosis regulator

c-Fos v-fos FBJ murine osteosarcoma viral oncogene homolog

(HGNC: FOS)

c-Jun jun oncogene (HGNC: JUN)

c-Kit v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene

homolog (HGNC: KIT)

xvii

CLL chronic lymphocytic leukaemia

COX-2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H

synthase and cyclooxygenase) (HGNC: PTGS2)

c-Src v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog

(avian) (HGNC: SRC)

CT CellTitre

DAG Directed Acyclic Graph

DNA deoxyribonucleic acid

Drosha ribonuclease III, nuclear (HGNC: RNASEN)

dsRNA double-stranded RNA

E2F1 E2F transcription factor 1

EFNB1 ephrin-B1

EGF epidermal growth factor

EGFR epidermal growth factor receptor

EIF2AK1 eukaryotic translation initiation factor 2-alpha kinase 1

EIF4EBP2 eukaryotic translation initiation factor 4E-binding protein 2

ENX-1 enhancer of zeste homolog 2 (HGNC: EZH2)

EphB1 EPH receptor B1 (HGNC: EPHB1)

ErbB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2,

neuro/glioblastoma derived oncogene homolog (avian)

[Homo sapiens] (HGNC: ERBB2)

ErbB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3

(avian) [Homo sapiens] (HGNC: ERBB3)

ErbB4 v-erb-a erythroblastic leukemia viral oncogene homolog 4

(avian) [Homo sapiens] (HGNC: ERBB4)

ETV6 ets variant gene 6 (TEL oncogene)

ETV7 ets variant gene 7 (TEL2 oncogene)

FACS fluorescence-activated cell sorting

FAK PTK2 protein tyrosine kinase 2 (HGNC: PTK2)

Other designation: focal adhesion kinase

FBS foetal bovine serum

FDA Food and Drug Administration

FN fibronectin

G guanosine

G6f chromosome 6 open reading frame 21 (HGNC: C6orf21)

xviii

Gemin3 DEAD (Asp-Glu-Ala-Asp) box polypeptide 20 (HGNC: DDX20)

GM Grb and mig

GO Gene Ontology

GnRH gonadotrophin-releasing hormone (HGNC: GNRH1)

Grb7 growth factor receptor-bound protein 7 (HGNC: GRB7)

Grb7V growth factor receptor-bound protein 7 variant (HGNC: GRB7V)



GRIN1 glutamate receptor, ionotropic, N-methyl D-aspartate 1

Hand2 heart and neural crest derivatives expressed transcript 2

[Mus musculus]

hAT1R angiotensin II receptor, type 1 (HGNC: AGTR1)

HRG heregulin, alias of neuregulin 1 (HGNC: NRG1)

HuR ELAV (embryonic lethal, abnormal vision, Drosophila)-like 1

(Hu antigen R) (HGNC: ELAVL1)

IR insulin receptor (HGNC: INSR)

KEGG Kyoto Encyclopaedia of Genes and Genomes

k-Ras v-ki-ras2 Kirsten rat sarcoma viral oncogene homolog

(HGNC: KRAS)

LF Lipofectamine 2000

MAPK mitogen-activated protein kinase

MAPK1/2 mitogen-activated protein kinase 1/2 (alias: ERK1/2)

MAP2K1/2 mitogen-activated protein kinase kinase 1/2 (alias: MEK1/2)

mfe minimum free energy of hybridisation

mig-10 mig-10 – (abnormal cell migration) [C. elegans]

miRNA microRNA

mRNA messenger RNA

Mt mutant

MTPN myotrophin [Homo sapiens]

MTT 3-[4,5-dimethylthiozol-2yl]-2,5-diphenyltetrazolium bromide

NADH nicotinamide adenine dinucleotide

NADPH nicotinamide adenine dinucleotide phosphate

NC non-conserved

NFκB nuclear factor of kappa light polypeptide gene enhancer in B-cells

(HGNC: NFKB)

xix

NIK mitogen-activated protein kinase kinase kinase 14

(HGNC: MAP3K14)

N-myc v-myc myelocytomatosis viral related oncogene, neuroblastoma

derived (avian) (HGNC: MYCN)

NS nonsense (used to abbreviate nonsense ‘SMARTpool’ siRNA in

Part 1 and nonsense precursor miRNA in Part 2)

nt nucleotide(s)

ORF open reading frame

P proline

p27 cyclin-dependent kinase inhibitor 1B (HGNC: CDKN1B)

PCR polymerase chain reaction

PDGFR platelet-derived growth factor receptor

PFN2 profilin 2

PGSF1a pituitary gland specific factor 1a (HGNC: C19orf30)

PH pleckstrin homology

PI3K phosphoinositide-3 kinase (HGNC: PIK3)

PIK3CB phosphoinositide-3-kinase, catalytic, beta polypeptide

PIPs phosphatidylinositol phosphates

PIR phosphotyrosine interacting region

PKB protein kinase B

PKC protein kinase C

PLC-γ phospholipase C, gamma (HGNC: PLCG)

PMA phorbol 12-myristate 13-acetate

posn position

pre-miRNA precursor miRNA

pri-miRNA primary miRNA

Pro proline-rich

RA Ras-associating

Raf-1 v-raf-1 murine leukemia viral oncogene homolog 1

(HGNC: RAF1)

RELA v-rel reticuloendotheliosis viral oncogene homolog A, nuclear

factor of kappa light polypeptide gene enhancer in B-cells 3,

p65 (avian) [Homo sapiens]

Ret ret proto-oncogene (HGNC: RET)

RIN RNA integrity number

xx

RISC RNA-induced silencing complex

RNA ribonucleic acid

RNAi RNA interference

Rnd1 Rho family GTPase 1 (HGNC: RND1)

rRNA ribosomal RNA

RT-PCR reverse transcriptase polymerase chain reaction

SH2 Src-homology 2

SH3 Src-homology 3

SHC1 SHC (Src homology 2 domain containing) transforming protein 1

SHPTP2 protein tyrosine phosphatase, non-receptor type 11

(Noonan syndrome 1) (HGNC: PTPN11)

shRNA short hairpin RNA

siRNA short interfering RNA

SMAD1 SMAD family member 1

SNR signal to noise ratio

SP ‘SMARTpool’ siRNA against Grb7

STAT signal transducer and activator of transcription

Tek TEK tyrosine kinase, endothelial (venous malformations,

multiple cutaneous and mucosal) (HGNC: TEK)

TGCT testicular germ cell tumour

TGF-α transforming growth factor alpha (HGNC: TGFA)

TTP zinc finger protein 36, C3H type, homolog (mouse)

[Homo sapiens] (HGNC: ZFP36)

TUNEL terminal deoxynucleotidyl transferase-dUTP nick-end labeling

U uridine

Wt wild-type

Yan anterior open (aop) [Drosophila]

xxi

TERMINOLOGY

The following terminology is used throughout this thesis to describe components of

miRNAs and mRNA sites. Note that by convention, numbering of the nucleotides of

miRNA:mRNA interactions is from the first nucleotide of the miRNA at it’s 5’ end.

Figure 0.1: Diagram of a miRNA:mRNA interaction, defining terminology.

seed A section of the 5’ end of a miRNA. Unless otherwise stated,

it is the 7nt portion of the miRNA from nucleotides 2-8.

seed match A section of an mRNA sequence that is at least partially

complementary to a miRNA seed.

perfect seed match A seed match that is perfectly complementary to the

miRNA seed, i.e. with no mismatches or G:U base pairs.

seed region The section of aligned miRNA and mRNA sequence at the

miRNA seed.

xxii

OVERVIEW

This thesis is divided into two parts, describing two concurrent investigations, both of

which have the same ultimate goal, to gain an understanding of the molecular biology

of cancer, with a view towards the potential application of this knowledge to clinical

problems. The two parts represent two different approaches to this research.

Part 1 describes a research program that was designed to build on previous promising

findings reported in literature. Specifically, in view of published evidence that the

adaptor molecule, Grb7, is involved in cell migration and cancer progression, and is

strongly linked to the oncogene, ErbB2, in many types of cancer, a project was

conducted to extend this research into breast cancer. This involved the study of Grb7

expression in different cancer cells, the development of an experimental protocol

enabling investigation of Grb7 function, and experiments to assess the effect of Grb7

knockdown on breast cancer cell proliferation and migration. There was a sound basis

for this research program.

However, an alternative research approach involves a more systematic effort to select

molecules prior to investigating their role in cancer. This may include an initial

exploratory phase that serves to identify promising new leads that can subsequently be

pursued. In Part 2 of this thesis, an investigation began with the development of a

computer program to predict miRNA targets, with a view to discovering previously

unknown human miRNA targets of possible significance in cancer. This program

yielded many target predictions that could potentially be pursued, and the most

promising of these, the prediction that another ErbB receptor and proto-oncogene,

EGFR, is a target of miR-7, was investigated further. This involved work to

experimentally verify this prediction and determine its functional consequences in

cancer cells, followed by a microarray study to identify other miR-7 target candidates.

Together, these two approaches investigate different aspects of the molecular biology of

cancer and, coincidentally, the ErbB signalling network, which is known to be very

important in a large number of cancers.

1


1.1 Overview

This chapter reviews the literature on the adaptor molecule, growth factor receptor-

bound protein 7 (Grb7), with a view to investigating its possible role in breast cancer. It

begins with a description of the common structure of the Grb7 family proteins, and the

typical roles of the component domains in binding to different classes of molecules,

with the potential implications for Grb7 function. A Grb7 splice variant is also

described. This is followed by a summary of Grb7’s subcellular localisation and its

expression in normal tissues and cancers. Next, all known Grb7 binding partners are

listed, accompanied by the demonstrated or postulated functional effects of their

interactions with Grb7. A selection of the binding partners of particular relevance in cell

migration and cancer are then described in detail with an emphasis on the literature

linking Grb7 to functional roles in each case. Then, an overview of the evidence that

Grb7 plays a role in cancer is presented, including the results of in vitro experiments

and analyses of tumour specimens. In addition, the potential success of a Grb7-targeting

anti-cancer therapeutic is considered. The literature thereby demonstrates that Grb7 is

involved in the progression of numerous cancer types, but that its role in breast cancer is

a tantalising unknown. Hence, the chapter concludes by describing a research program

designed to assess this role.

2

1.2 Literature review

1.2.1 Grb7 background

Grb7 is a 535 amino acid protein belonging to the Grb7 family of adaptor proteins,

comprising Grb7, Grb10 and Grb14. As an adaptor protein, Grb7 lacks intrinsic

enzymatic activity and acts to mediate signal transduction from tyrosine phosphorylated

proteins to downstream signalling pathways.

1.2.1.1 Structure

Grb7 family proteins all have a similar structure that is exemplified by that of Grb7 in

Figure 1.1. This structure consists of three regions: a proline-rich (Pro) region, a central

‘Grb and mig’ (GM) region, and a Src-homology 2 (SH2) domain. Within the GM

region is a putative Ras-associating (RA) region, a pleckstrin homology (PH) domain

and a region between the PH and SH2 domains (BPS).

There is a single splice variant of Grb7, named Grb7V, which, due to an 88 base pair

deletion and a resulting frame shift, is missing the SH2 domain, having a short

hydrophobic sequence in its place (Tanaka et al., 1998), as depicted in Figure 1.1.

The proline-rich region at the amino terminal of Grb7 family proteins is composed of

several PXXP1 repeats and has homology to the Src-homolgy 3 (SH3) domain-binding

sites of other proteins (Kay, Williamson, & Sudol, 2000). Although this region of

Grb10 has been shown to bind to the SH3 domain of c-Abl (Frantz, Giorgetti-Peraldi,

Ottinger, & Shoelson, 1997), no SH3 domain-containing proteins have yet been shown

to bind to the proline-rich region of Grb7.

The GM region is named for its significant sequence homology in all members of the

Grb7 protein family and the Caenorhabditis elegans protein mig-10 (Manser,

Roonprapunt, & Margolis, 1997). mig-10 is involved in cell migration during

embryonic development (Manser & Wood, 1990).

1 PXXP = sequence of amino acids with the form proline-any-any-proline.

3

Figure 1.1: A) Gene structure and B) protein domains of the Grb7 and Grb7V proteins. In A), vertical shaded bands represent Grb7 exons, joined by lines representing introns. Protein coding regions of transcripts are shown in solid colour.

Within the GM region, the PH domain has homology to the pleckstrin domain, which is

found in a range of other proteins and has been shown to mediate protein-protein and

protein-lipid interactions (Lemmon & Ferguson, 2000; Rebecchi & Scarlata, 1998). The

majority of PH domains bind to phospholipids and thus may be involved in functions

such as membrane localisation, conformational changes, vesicle trafficking and

cytoskeletal organization (Lemmon & Ferguson, 2000). The Grb7 PH domain has also

been shown to bind to phospholipids (Shen, Han, & Guan, 2002).

The putative RA domain of Grb7 was suggested from alignments of Grb7 family

members with proteins known to associate with Ras family proteins (Wojcik et al.,

1999). If verified, this domain could link Grb7 proteins to Ras signalling and thus

processes such as cell proliferation, migration and survival (see Hilger and colleagues,

2002). However, focused searches have failed to reveal any such G-proteins binding to

this region of Grb7, and it has been argued that the homology between this domain and

4

the known RA domains of other proteins is only weak (Leavey et al., 1998). Hence, the

function of the putative RA domain in Grb7 proteins is unclear.

The BPS region, also known as the phosphotyrosine interacting region (PIR), is a

stretch of ~50 amino acids that is involved in binding to receptor tyrosine kinases. For

example, all three Grb7 family proteins have been shown to bind to the insulin receptor

(IR) via both their BPS and SH2 domains (W. He, Rose, Olefsky, & Gustafson, 1998;

Kasus-Jacobi, Bereziat, Perdereau, Girard, & Burnol, 2000; Kasus-Jacobi et al., 1998).

The relative importance of the BPS and SH2 domains in such cases is known to depend

on the Grb7 family member and the target protein, but the specifics of the interaction

process and the contribution of each domain to target specificity is not clear.

Finally, at the C-terminus of Grb7 is the SH2 domain, which binds to specific

phosphotyrosine residues on tyrosine kinase receptors and other signalling molecules

(Stein et al., 1994; Thommes, Lennartsson, Carlberg, & Ronnstrand, 1999). It is this

domain that acts as the binding site for the majority of the known Grb7-binding proteins

(Pero, Daly, & Krag, 2003). Grb7V is missing this domain and hence is unable to bind

to these proteins.

1.2.1.2 Expression and localisation

In humans, Grb7 is expressed most abundantly in the pancreas, with moderate levels

also in the kidney, placenta, prostate and intestine, and lower levels in the colon, liver,

lung and testis (Frantz, Giorgetti-Peraldi, Ottinger, & Shoelson, 1997). The Grb7 gene

is located in the 17q12 amplicon that also contains the ErbB2 gene and, variably, a

number of other genes (Kao & Pollack, 2006; Kauraniemi, Barlund, Monni, &

Kallioniemi, 2001; Maqani et al., 2006). This region is amplified in many cancer types

and, as a result, Grb7 is also expressed in a range of tumours and cancer cell lines (Kao

& Pollack, 2006; Kishi et al., 1997; Skotheim et al., 2002; Stein et al., 1994).

On a cellular level Grb7 is mainly localised in the cytoplasm but can also be detected in

regions of the plasma membrane called focal contacts, where transmembrane receptors

called integrins connect the extracellular matrix with the actin cytoskeleton (Han, Shen,

& Guan, 2000).

5

1.2.2 Grb7 binding partners and functions

Grb7 has been shown to bind to a large number of proteins, in the majority of cases via

its SH2 domain, as given in Table 1.1. However, most of these binding partners are

likely to be upstream of Grb7 rather than downstream effectors, and hence the

signalling mediated by this adaptor protein is not completely understood. Nevertheless,

a number of functional studies, together with the functions associated with known

Grb7-binding partners, give an indication of the possible role of Grb7 in the cell. Some

of the implicated signalling pathways are depicted in Figure 1.2.

Table 1.1: Grb7 binding proteins and functions.

Protein

target

Binding

domain

Postulated/demonstrated(*)

function of Grb7:target interaction Reference

EGFR/ SH2 (Margolis et al., 1992)

ErbB2/ SH2 (Stein et al., 1994)

ErbB3/ SH2 (Fiddes et al., 1998)

ErbB4 SH2

for all ErbB receptors:

cell migration, invasion,

proliferation, cell cycle,

apoptosis (Fiddes et al., 1998)

FAK SH2 Grb7 phosphorylation by FAK

shown to be critical for FAK-

regulated cell migration(*)

(Han & Guan, 1999; Han,

Shen, & Guan, 2000)

EphB1 SH2 Grb7 shown to be involved in

EphB1-mediated cell migration(*)

(Han, Shen, Miao, Wang,

& Guan, 2002)

Tek SH2 vascular and haematopoietic

development

(Jones et al., 1999)

PDGFR-β SH2 embryonal development, wound

healing

(Yokote, Margolis,

Heldin, & Claesson-

Welsh, 1996)

Rnd1 SH2 actin cytoskeleton rearrangements,

control of proliferation or migration

(Vayssiere et al., 2000)

Ret SH2 development of the kidney, adrenal

medulla and thyroid gland

(Pandey, Liu, Dixon, Di

Fiore, & Dixit, 1996)

Cav-1 SH2 Grb7 shown to increase cell growth

and EGF-stimulated cell

migration(*)

(H. Lee et al., 2000)

(continued over page)

6

Table 1.1 (continued):

Protein

target

Binding

domain

Postulated/demonstrated(*)

function of Grb7:target interaction Reference

c-Kit SH2 development of haematopoietic

cells, melanoblasts and germ cells

(Thommes, Lennartsson,

Carlberg, & Ronnstrand,

1999)

SHC1 SH2 cell proliferation (Stein et al., 1994)

SHPTP2 SH2 cell cycle progression (Keegan & Cooper, 1996)

G6f SH2 immune system and cellular

recognition

(De Vet, Aguado, &

Campbell, 2003)

IR SH2 &

BPS

insulin signalling (Kasus-Jacobi, Bereziat,

Perdereau, Girard, &

Burnol, 2000)

NIK GM EGF/HRG-stimulated activation of

NFκB

(D. Chen et al., 2003)

PIPs PH Grb7 shown to be involved in cell

migration (*)

(Shen, Han, & Guan,

2002)

CALM1 PH angiogenesis and cell motility (H. Li et al., 2005)

7

Figure 1.2: Signalling involving Grb7 binding partners. White squares represent signalling pathways.

A selection of the best-characterised and most relevant Grb7 interactions are discussed

below.

1.2.2.1 ErbB receptors

The ErbB receptors (ErbB1/EGFR, ErbB2, ErbB3 and ErbB4) are a family of receptor

tyrosine kinases that reside at the cell surface and mediate signalling from growth

factors. Binding of a growth factor to the extracellular domain of a receptor induces

receptor dimerisation, and both homodimers, composed of two identical receptors, and

heterodimers, composed of two different receptors, are possible. Dimerisation triggers

the tyrosine kinase activity of the receptors and leads to phosphorylation of the

intracellular domains at specific tyrosine residues. These then act as binding sites for

proteins with SH2 or phosphotyrosine-binding domains, and the ensuing series of

8

protein-protein interactions forms signalling pathways in the cell. ErbB receptors can

activate a number of signalling pathways, including the mitogen-activated protein

kinase (MAPK), phosphoinositide-3-kinase (PI3K) and phospholipase C gamma

(PLC-γ) pathways, and can thereby regulate many important processes such as cell

proliferation, cell survival and migration. See one of the many reviews for more detail

on this topic (Linggi & Carpenter, 2006; Marmor, Skaria, & Yarden, 2004).

The exceptions to this simplified overview of ErbB signal transduction are the ErbB2

receptor, which has no known ligand, and the ErbB3 receptor which has no intrinsic

tyrosine kinase activity and so must be transphosphorylated by a dimerising partner

(Guy, Platko, Cantley, Cerione, & Carraway, 1994). An additional complication is that

the combination of signalling pathways that are activated is dependent on the

composition of the dimers and on the ligand, of which there are many. Ligands for the

epidermal growth factor receptor (EGFR) include epidermal growth factor (EGF),

heparin-binding EGF-like growth factor (HBEGF), transforming growth factor alpha

(TGF-α), amphiregulin (AREG) and betacellulin (BTC). All of these ligands are also

able to stimulate ErbB3 to an equal or lesser extent, and HBEGF and BTC are able to

stimulate ErbB4. In addition, two other ligands of the neuregulin family, heregulin

(HRG) and neuregulin 2 (NRG2), are able to stimulate ErbB3 and ErbB4 only (Beerli &

Hynes, 1996; H. Chang, Riese, Gilbert, Stern, & McMahan, 1997).

Grb7 has been shown to bind to all four members of the ErbB receptor family. It was

originally identified from a search for EGFR-binding proteins (Margolis et al., 1992).

However the significance of the Grb7:EGFR interaction has not been investigated

further. Currently of greater interest is Grb7’s interaction with ErbB2. The two genes

are adjacent in the genome, separated by less than 10 kb, making it very likely that any

amplification of one will be accompanied by that of the other. Moreover, correlation of

Grb7 and ErbB2 protein expression has been observed in breast cancer cell lines and

primary tumours (Kao & Pollack, 2006; Stein et al., 1994), as well as a number of other

cancer types (Kishi et al., 1997; Skotheim et al., 2002).

Stein and colleagues (1994) conducted the original study that demonstrated the ability

of Grb7 to bind via its SH2 domain to tyrosine phosphorylated ErbB2. Grb7 was shown

to bind to ErbB2 in serum starved, non-stimulated SK-BR-3 and BT-474 cells, and also

in EGF-stimulated SK-BR-3 cells. The binding of Grb7 to ErbB2 in serum-starved cells

9

was suggested to be a result of a basal level of phosphorylated ErbB2, often seen in

breast cancer cell lines (Janes, Daly, deFazio, & Sutherland, 1994). However, Grb7 was

not tyrosine phosphorylated either in the non-stimulated or EGF-stimulated cells,

despite the demonstrated activation of the ErbB2 receptor in the latter case. Fiddes and

colleagues (1998) also failed to detect tyrosine phosphorylation of Grb7 following

interaction with stimulated ErbB receptors. This study also demonstrated that Grb7 is

able to bind to both ErbB3 and ErbB4 receptors. In SK-BR-3 cells, the ErbB3/ErbB4

ligand, HRG, increased the formation and phosphorylation of ErbB2:ErbB3

heterodimers and led to the binding of Grb7 to both receptors. Similar results were

observed in BT-474 cells. HRG increased the formation of ErbB2:ErbB4 heterodimers

and led to the binding of Grb7 to ErbB2, ErbB3 and ErbB4 receptors. However, when

Grb7 phosphorylation was examined in SK-BR-3 cells, no change was observed

following HRG treatment. On the other hand, further experiments found that Grb7 was

tyrosine phosphorylated following EGF-stimulation of NIH/3T3 mouse fibroblast cells

transfected with a chimeric receptor composed of the ErbB2 intracellular domain

attached to the EGFR extracellular domain. Tanaka and colleagues (2000) also reported

Grb7 tyrosine phosphorylation in EGF-stimulated oesophageal carcinoma cells.

These results have a number of possible explanations, as outlined by Stein and

colleagues (1994). One is that Grb7 is able to perform its adaptor protein role without

being tyrosine phosphorylated. It is also possible that Grb7:ErbB signalling is context

dependent and varies with binding partner, dimer composition and/or ligand. The nature

of ErbB signalling through Grb7 is unclear at this stage.

1.2.2.2 Focal adhesion kinase (FAK)

FAK is a cytoplasmic protein tyrosine kinase that is found at focal contacts and plays an

important role in integrin signalling and cell migration (reviewed by Schlaepfer and

colleagues, 2004). It has also been implicated in integrin-mediated regulation of cell

survival and cell cycle progression (Frisch, Vuori, Ruoslahti, & Chan-Hui, 1996; J. H.

Zhao, Reiske, & Guan, 1998). FAK is activated and tyrosine phosphorylated upon cell

adhesion, specifically, the binding of integrins to extracellular matrix proteins such as

fibronectin (FN) (Burridge, Turner, & Romer, 1992). The phosphorylated residues then

serve as binding sites for SH2 domain-containing proteins (H. C. Chen & Guan, 1994;

Xing et al., 1994). FAK can also be activated in response to signalling from receptor

10

tyrosine kinases, including EGFR and platelet-derived growth factor receptor (PDGFR)

(Sieg et al., 2000).

Grb7 binds via its SH2 domain to tyrosine phosphorylated FAK (Han & Guan, 1999).

This interaction has been shown to contribute to the localisation of Grb7 to focal

contacts, where interactions with other proteins may stimulate cell migration (Han,

Shen, & Guan, 2000; Shen & Guan, 2001). However, studies have also suggested that

Grb7 is a downstream effector of FAK, and that the Grb7:FAK complex plays a crucial

role in integrin-mediated cell migration. Grb7 binds to FAK in a cell adhesion-

dependent manner (Han & Guan, 1999), and is tyrosine phosphorylated upon either

overexpression of FAK or replating of cells on FN in FAK positive, but not FAK

negative, cells (Han, Shen, & Guan, 2000). In addition, Tanaka and colleagues (2000)

demonstrated that FN-dependent phosphorylation of endogenous Grb7 in oesophageal

carcinoma cells was abolished by an anti-integrin antibody. These and other studies

strongly implicate Grb7 in FAK and integrin signalling.

In addition, variations in Grb7 expression have been shown to influence cell migration

on FN. Han and colleagues (1999) demonstrated that overexpression of Grb7 in cells

significantly increased migration on FN and, in a later study (2000), that FAK was

necessary for this effect. Conversely, Tanaka and colleagues (2000) demonstrated that

ectopic expression of a Grb7 mutant lacking the SH2 domain significantly inhibited

both endogenous Grb7 phosphorylation and cell migration on FN.

The involvement of Grb7 in the regulation of cell migration recalls the homology

between the central GM domain of Grb7 and the C. elegans mig-10 protein, which is

also involved in migration (Manser & Wood, 1990). It is possible that this domain of

Grb7 may bind downstream signalling proteins to continue an integrin-FAK-Grb7

signalling cascade. There is some evidence for a role of the GM domain in cell

migration (Han, Shen, & Guan, 2000). However, more research is required in this area.

In terms of other possible functions of the Grb7:FAK interaction, two studies have

failed to find evidence for a role for this complex in cell cycle progression (Reiske,

Zhao, Han, Cooper, & Guan, 2000; Shen & Guan, 2001).

11

1.2.2.3 Phosphatidylinositol phosphates (PIPs)

A study by Shen and colleagues (2002) has demonstrated that Grb7 binds to membrane

phospholipids, preferentially those of the PIP class, via its PH domain. This study also

found that such interactions appear to be involved in FAK signalling and cell migration.

For example, the binding of Grb7 to PIPs was enhanced by cell adhesion and was also

necessary for the phosphorylation of Grb7 by FAK. Furthermore, interaction with PIPs

was shown to be crucial for Grb7’s role in cell migration in this system. From several

different experiments, the working hypothesis is that phosphorylated FAK binds to

Grb7 and PI3K and recruits them to focal contacts. Activated PI3K increases the

production of PIPs which increases the binding of Grb7 to PIPs. This induces a

conformational change in Grb7 that allows phosphorylation by FAK, and thus

downstream signalling leading to an increase in cell migration.

1.2.2.4 Eph receptor B1 (EphB1)

The Eph receptors, like the ErbB receptors, are membrane receptor tyrosine kinases.

One member of this family is EphB1, which has been implicated in the regulation of

cell adhesion and migration (Huynh-Do et al., 2002). Han and colleagues (2002)

showed that Grb7 can bind via its SH2 domain to tyrosine phosphorylated EphB1 and

that this interaction was enhanced by treatment with the EphB1 ligand, ephrin-B1

(EFNB1), which induces autophosphorylation of EphB1. Grb7 was also shown to be

tyrosine phosphorylated by EphB1. In addition, co-transfection of cells with EphB1 and

Grb7 was found to enhance cell migration on FN, while co-transfection with the Grb7

SH2 domain rather than Grb7 prevented EphB1-induced migration, suggesting a

possible role for the Grb7:EphB1 complex in cell migration.

1.2.3 Grb7 in cancer

There is a great deal of evidence to suggest that Grb7 plays a role in some cancers. For

example, Grb7 expression is significantly increased in testicular germ cell tumours

(TGCTs) and oesophageal carcinomas compared to corresponding normal tissues

(McIntyre et al., 2005; Tanaka et al., 1997), and can be used to differentiate TGCT

subtypes (Hofer et al., 2005). It is also negatively correlated with distant recurrence-free

survival in breast cancer (Cobleigh et al., 2005), and has been linked to lymph node

12

metastases in pancreatic cancer (Tanaka et al., 2006), and cancer stage in chronic

lymphocytic leukaemia (Haran et al., 2004). In the last example, 88% of stage IV

chronic lymphocytic leukaemias were found to overexpress Grb7 compared to only

18% of stage I cancers.

Grb7V has also been linked to the progression of oesophageal carcinoma. A study by

Tanaka and colleagues (1998) found that 40% of the Grb7-positive oesophageal

carcinomas tested also expressed the Grb7V isoform, and that this was associated with

an invasive and metastatic phenotype. Grb7-positive lymph node metastases were also

found to have higher Grb7V expression than the original tumours. This study also

showed that inhibition of both Grb7 and Grb7V inhibited cell invasion through

matrigel. This invasive phenotype may arise from constitutive tyrosine phosphorylation

of Grb7V, which was observed in oesophageal cancer cells to persist even in serum-

starved, quiescent cells.

There is also substantial in vitro evidence that supports a role for Grb7 in cancer, in

particular, in cancer cell migration. Studies have shown in different cell lines that

overexpression of Grb7 enhances cell migration towards FN, while overexpression of

the dominant-negative Grb7 SH2 domain inhibits cell migration towards FN (Han &

Guan, 1999; Tanaka et al., 2000). Additional studies have tested whether Grb7 has an

effect on processes other than migration. Grb7 does not appear to play a role in cell

cycle progression (Reiske, Zhao, Han, Cooper, & Guan, 2000; Shen & Guan, 2001).

However, its role in cell proliferation is still unclear. One study found that Grb7

antisense RNA had no effect on the proliferation of oesophageal carcinoma cells

(Tanaka et al., 1998), while another study found that co-transfection of 293T human

embryonic kidney cells with caveolin 1 (Cav-1), c-Src and Grb7 enhanced anchorage-

independent growth (H. Lee et al., 2000).

Grb7 is also associated with several proteins that have been linked to cancer, including

ErbB2, FAK, EphB1 and c-Kit (Han & Guan, 1999; Han, Shen, Miao, Wang, & Guan,

2002; Stein et al., 1994; Thommes, Lennartsson, Carlberg, & Ronnstrand, 1999).

Of particular interest is Grb7’s interaction with ErbB2 since, as stated previously, the

Grb7 gene is located within the 17q12 ErbB2 amplicon and, as a result, is co-amplified

and co-overexpressed with ErbB2 in numerous cancers, including gastric cancer,

13

Barrett’s carcinoma, squamous cell oesophageal carcinoma, and breast cancer (Kishi et

al., 1997; Stein et al., 1994; Tanaka et al., 1997; Walch et al., 2004). In one study,

co-expression of Grb7 and ErbB2 was found to occur in ~33% of breast cancers (Stein

et al., 1994). In these cases, there may be greatly amplified signalling in Grb7 and

ErbB2’s common signalling pathway. This is significant because ErbB2 is an important

oncogene, and numerous studies have shown correlations between ErbB2 amplification

or expression and poor clinical outcome in a range of cancers (reviewed by Nicholson,

Gee, & Harper, 2001). In breast cancer, ErbB2 amplification is associated with relapse,

poor response to chemotherapy and shortened survival, as reviewed by Ross and

Fletcher (1998). In addition, co expression of Grb7 and ErbB2 has been shown to be

correlated with shortened overall survival and time to relapse in breast cancer (Slamon

et al., 1987).

1.2.4 Grb7 as a potential therapeutic target

It appears, then, that there is much evidence that Grb7 is involved in cancer, and for this

reason, it has been proposed as a potential therapeutic target (Pero et al., 2002). This

possibility is of particular interest in view of Grb7’s link to ErbB2, and the success of

Herceptin (trastuzumab), an ErbB2-targeted monoclonal antibody that was approved by

the Food and Drug administration (FDA) for the treatment of breast cancer in 1998.

Clinical trials have demonstrated a 15-26% response rate to Herceptin monotherapy and

a 49% response rate to combination treatment with Herceptin and chemotherapy, in

patients with ErbB2-overexpressing breast cancers with very poor prognosis (Baselga et

al., 1996; Cobleigh et al., 1999; Slamon et al., 2001). These results indicate that ErbB2

signalling is critical to certain cancers and that some are also susceptible to inhibition of

this signalling.

Therefore, targeting downstream Grb7 may also be an effective treatment approach

(Pero et al., 2002). Furthermore, inhibition of ErbB2 signalling at two points in the

pathway using combination anti-Grb7 and anti-ErbB2 treatment could provide

additional benefits in cancers in which this pathway is amplified. The fact that Grb7

also has binding partners besides ErbB2 that are involved in cancer also strengthens the

case for a Grb7-targeting drug. Grb7 is also expressed in only a small number of normal

tissues, which could mean fewer side-effects (Frantz, Giorgetti-Peraldi, Ottinger, &

Shoelson, 1997).

14

1.3 Project rationale and aims

Grb7 has been implicated in cell migration in certain cancers, its overexpression has

been associated with an invasive phenotype, and it is currently under investigation as a

potential therapeutic target. It is therefore very important that the signalling and

functional role of this protein and its variant, Grb7V, are well understood.

The functional role of Grb7 has been investigated in oesophageal cancer (Tanaka et al.,

1997, 1998; Tanaka et al., 2000), with some associations also made between Grb7

expression and certain clinical features in pancreatic cancer, TGCTs and chronic

lymphocytic leukaemia. However, at the outset of this project, no studies had

investigated the functional role of Grb7 in breast cancer. Breast cancer may be a good

target for anti-Grb7 therapy as it frequently exhibits co-amplification and co-

overexpression of Grb7 and binding partner ErbB2, and has been successfully treated

using ErbB2-targeted therapy (Baselga et al., 1996). Grb7 also has several other binding

partners, including FAK, PDGFR-β and c-Kit, that are involved in oncogenesis and

cancer progression, and may be expressed in breast cancer (Coltrera, Wang, Porter, &

Gown, 1995; Han & Guan, 1999; Han, Shen, Miao, Wang, & Guan, 2002; Lark et al.,

2005; P. H. Tan et al., 2005; Thommes, Lennartsson, Carlberg, & Ronnstrand, 1999).

Therefore, this project was designed to examine the functional role of Grb7 in breast

cancer, in particular, its role in cell proliferation and migration, in view of previous

studies indicating a role for Grb7 in these functions in non-breast cancer cells (Han &

Guan, 1999; H. Lee et al., 2000; Tanaka et al., 2006). Thus the project aims were:

1. To investigate the expression of Grb7 and Grb7V in a range of different cell types,

and identify a suitable breast cancer cell line model,

2. To develop a protocol for the treatment and preparation of breast cancer cells for

functional studies,

3. To investigate the role of Grb7 in the proliferation of breast cancer cells, and

4. To investigate the role of Grb7 in the migration of breast cancer cells.

15

1.4 Hypotheses

For aims 3 and 4, two hypotheses were evaluated:

Hypothesis 1: Inhibition of Grb7 expression leads to reduced cell proliferation in breast

cancer cells.

Hypothesis 2: Inhibition of Grb7 expression leads to reduced cell migration in breast

cancer cells.

16


2.1 Cell culture

The following American Type Culture Collection (ATCC) human cell lines were used

in Part 1 of this thesis: SK-BR-3 (HTB-30, breast adenocarcinoma), BT-474 (HTB-20,

ductal breast carcinoma), NCI-N87 (CRL-5822, gastric carcinoma), MDA-MB-453

(HTB-131, metastatic carcinoma of the breast), MDA-MB-468 (HTB-132,

adenocarcinoma of the breast), MCF7 (HTB-22, adenocarcinoma of the breast), HeLa

(CCL-2, adenocarcinoma of the cervix) and LNCaP (CRL-1740, prostate carcinoma).

Apart from MCF7, the above cell lines were routinely cultured in high glucose

Dulbecco’s modified Eagles medium (DMEM) (Invitrogen, Corp.) supplemented with

5% foetal bovine serum (FBS) (Invitrogen, Corp.) and treated with 50 U/mL penicillin

and 50 µg/mL streptomycin. The MCF7 cell line was cultured in high glucose RPMI

1640 media (Invitrogen, Corp.) supplemented as above. In addition, the human

mammary epithelial cell line, HMEC, (CC-2551, Cambrex, Corp.) was cultured in

MCDB Medium 170 plus supplements (Invitrogen, Corp.).

2.2 siRNA transfections

siRNA was routinely transfected into cells using Lipofectamine 2000 Reagent (LF)

(Invitrogen, Corp.). For experiments in 6-well plates, cells were plated in 2 mL of

growth media lacking penicillin and streptomycin at 300x103 cells/dish, 24 hours prior

to transfection. Stock transfection mixes were made according to the LF manufacturer’s

instructions ("Transfecting siRNA into Mammalian Cells Using Lipofectamine 2000",

2002), using 5 µL of LF per well and siRNA to a final concentration of 10 nM unless

otherwise stated. To transfect cells, the growth media was removed and replaced with 2

mL of fresh media lacking penicillin and streptomycin, plus 500 µL of the appropriate

transfection mix. Cells were incubated at 37°C for 4 hours, after which time the media

was replaced with 2 mL of fresh growth media lacking penicillin and streptomycin.

These transfections were scaled up for 10 cm dishes by using 1.5x106 SK-BR-3

cells/dish, and multiplying transfection and LF volumes by a factor of 6.

17

For section 3.2.2.3, experiments were also conducted using siPORT NeoFX

Transfection Agent (NeoFX) (Ambion, Inc.) and Oligofectamine Reagent (OF)

(Invitrogen, Corp.). These transfections were performed in 6-well plates according to

the manufacturer’s instructions for these reagents, with 3 µL of OF or 5 µL of NeoFX

per well.

The siRNAs used in this investigation included siGENOME SMARTpool reagent for

Human Grb7, NM_005310 (SP) (Dharmacon, Inc.) and siCONTROL Non-Targeting

siRNA Pool (Dharmacon, Inc.). The siRNA components of the Grb7 SMARTpool were

duplex 1 (sense sequence 5’- AGA AGU GCC UCA GAU AAU AUU -3’), duplex 2

(sense sequence 5’- UAG UAA AGG UGU ACA GUG AUU -3’), duplex 3 (sense

sequence 5’- UGC AGA AAG UGA AGC AUU AUU -3’), and duplex 4 (sense

sequence 5’- GCG CCG AUC UGG CCU CUA UUU -3’).

2.3 HRG treatment

The effects of HRG were studied using Recombinant Human HRG-β1 (Sigma-Aldrich,

Inc.), which comprised the EGF domain of the HRG-β1 protein. This product, supplied

as a powder, was reconstituted in sterile Dulbecco’s Phosphate Buffered Saline (PBS)

(Invitrogen, Corp.) with 0.5% bovine serum albumin (Sigma-Aldrich, Inc.). Treatment

involved serum-starvation of cells in media containing 0.5% FBS for 24 hours prior to

the addition of HRG. HRG was used at a final concentration of 1 nM unless otherwise

stated.

2.4 Treatment and preparation of cells for functional assays

Cells were seeded in 10 cm dishes and transfected with 10 nM siRNA using LF, as

described in section 2.2. As stated, the cell media was changed 4 hours after

transfection. For the case of experiments involving HRG treatment, the replacement

media contained 0.5% FBS, while for all other cases, replacement media was normal

growth media containing 5% FBS. For experiments in each case, media with the same

FBS content was used throughout the protocol as appropriate, unless otherwise stated.

The day of the transfection was defined as day 0.

18

On day 1, 24 hours after transfection, the cells in each 10 cm dish were split for seeding

into plates suitable for the different assays to be performed. For the case of proliferation

experiments, cells were split using trypsin, while for the case of migration experiments,

cells were split using a 10 mM solution of ethylenediamine tetraacetic acid in PBS

(PBS/EDTA). Splitting using PBS/EDTA involved washing cells twice with PBS,

incubating cells for up to 2 hours at 37°C in 5 mL of PBS/EDTA, and quenching with

20 to 30 mL of quenching media (serum-free growth media containing 5% bovine

serum albumin (BSA)). Cell suspensions were counted four times each using a

Neubauer Counting Chamber (Weber Scientific International) and diluted with media to

achieve suitable cell concentrations for seeding into the different plate sizes. For the

case of HRG experiments, each cell suspension was then divided into two tubes, and

HRG was added to one to achieve a final concentration of 1 nM. For the case of

experiments not involving HRG treatment, cells from a dish of non-siRNA-transfected

cells were treated with phorbol 12-myristate 13-acetate (PMA) (Sigma-Aldrich, Inc.) at

a final concentration of 50 nM, as a positive control for inhibited cell proliferation.

For all experiments, cells from each condition were plated into 6-well plates at

500x103 cells/well in 2 mL of media for protein harvest on day 2, as described in

section 2.6. The resulting protein samples were used to confirm siRNA-induced Grb7

knockdown using Western blot, as described in section 2.8, for all functional

experiments performed. For proliferation experiments, cells were also plated into

96-well plates at 1.5x103 cells/well in 100 µL of media for CellTitre (CT) assays. One

96-well plate was seeded per time point with a minimum of five replicate wells per

condition. 100 µL of media alone was also added to five wells on each plate, to be used

as blanks in the CT assay. The CT assay protocol is described in section 2.9. For

migration experiments, cells were also plated into the 24-well plate-format chambers

and control wells for migration assays, as described in section 2.10.

The protocol for the treatment of cells with both siRNA and HRG, and the preparation

of cells for functional studies is diagrammatically summarised in Figure 3.9.

19

2.5 Harvesting of RNA

RNA was harvested from cell lines using TRIzol Reagent (Invitrogen, Corp.) according

to the manufacturer’s instructions, and was quantitated using a NanoDrop ND-1000

Spectrophotometer (Biolab Australia Ltd).

2.6 Harvesting of protein

Protein was routinely harvested by adding 150 µL of 1x Passive Lysis Buffer (PLB)

(Promega, Corp.) to each well of cells in 6-well plates and standing at -20°C for 5

minutes. For section 3.2.1.2 of the investigation, other lysis buffers were used. These

were cytoplasmic extraction buffer (CEB) (10 mM HEPES pH 7.6, 40 mM KCL, 3 mM

MgCl2, 5% glycerol, 0.2% Nonidet P-40) and mid-RIPA (radioimmune precipitation

assay) lysis buffer (150 mM NaCl, 25 mM Tris pH 8.0, 1% Nonidet P-40,

Deoxycholate, 0.5% (w/v)). For these buffers, cells were washed with PBS prior to

protein harvest. Protein was quantitated using the Bio-rad Protein Assay (Bio-Rad

Laboratories, Inc.) according to the manufacturer’s instructions, with absorbance

readings taken at 595 nm using a Fluostar OPTIMA Microplate Reader (BMG

LABTECH Pty Ltd).

2.7 Reverse transcriptase polymerase chain reaction (RT-PCR)

First, for each RNA sample, 1 µg of RNA was heated with 1 µL of Random Hexamers

(Promega, Corp.) in water to 12 µL, at 70°C for 10 min, then quenched on ice for 5 min.

Each sample was then reverse transcribed to complementary DNA (cDNA) in a 25 µL

reaction (15 U avian myeloblastosis virus reverse transcriptase (AMV-RT), 1x

AMV-RT buffer, 1 mM dNTPs, 40 U RNasin Ribonuclease Inhibitor (Promega, Corp.),

10 mM dithiothreitol (DTT)) in a series of heating steps (37°C for 45 min, 42°C for

30 min, 75°C for 15 min). Samples were then cooled on ice. For all RT reactions

performed, parallel reactions lacking the AMV-RT enzyme were also performed and

used in subsequent PCR reactions to ascertain the level of genomic DNA in the

samples. No significant genomic DNA was evident in any of the RNA samples used for

this thesis.

20

PCRs were performed using 2 µL of each cDNA sample, prepared as described above,

in a 20 µL reaction (1 U Platinum Taq DNA Polymerase, 1x PCR Buffer, 1.5 mM

MgCl2 (Invitrogen, Corp.), 0.2 mM dNTPs (Promega, Corp.), 0.05 µg of both forward

and reverse primers). 10 ng of a breast cancer cDNA library was also used as a test

sample for Figure 3.1. This library was derived from a metastatic infiltrating ductal

breast carcinoma, supplied by Dr Jennifer Byrne of the Children’s Medical Research

Institute, NSW, Australia. Nuclease-free water was used as a negative PCR control.

Plasmid DNA containing full-length Grb7 was used as a positive control in Grb7 PCRs.

Five Grb7 primers were used in this thesis. The forward primers were #429-Fd

(5’- GCC TGG AGG AAG AAG ACA AAC CAC -3’), #443-Fd (5’- GCA GTC CTC

CCT CAC AGA -3’) and #364-Fd (5’- GGC CTC TAT TAC TCC ACC AA -3’). The

reverse primers were #430-Rvs (5’- CTC CTC ATC CCG TCC CCT GTG G -3’) and

#365-Rvs (5’- ATG GAT GCA GAT GGC GAG AC -3’). The Grb7 primers #429-Fd

and #430-Rvs have the same sequences as the Grb7 primers used by Tanaka and

colleagues (1998). The primers used for the β-actin loading control were

#50-Fd (5’- GCC AAC ACA GTG CTG TCT GG -3’) and

#51-Rvs (5’- TAC TCC TGC TTG CTG ATC CA -3’).

PCR cycling was performed using a PTC-200 Peltier Thermal Cycler (GeneWorks Pty

Ltd). PCR conditions were varied for different samples and primers. However, PCRs

using the β-actin primers, #50-Fd and #51-Rvs, were usually performed with an

annealing temperature (TA) of 58°C, over 25 cycles. The Grb7 PCR result of Figure 3.1

was obtained using the #429-Fd and #430-Rvs primers, with a TA of 64°C, over 30

cycles. The Grb7 PCR result of Figure 3.4B was obtained using the #364-Fd and

#365-Rvs primers, with a TA of 60°C, over 27 cycles. In Figure 3.4C, the Grb7 PCR

result obtained using the #364-Fd and #430-Rvs primers was conducted with a TA of

64°C, over 32 cycles; while that obtained using the #443-Fd and #430-Rvs primers was

conducted with a TA of 64°C, over 34 cycles. PCR products were separated by

electrophoresis in 1-2% agarose gels, which were then stained with ethidium bromide

and viewed with an ultraviolet (UV) transilluminator.

With the aim of sequencing the PCR products in the upper and lower bands of the Grb7

PCR result of Figure 3.1, each of the bands amplified from the HMEC cell line was

21

stabbed with a pipette tip, which was then stirred into a new PCR mix. Five PCRs were

performed in this way for each band. The products were run on a 1% agarose gel to

confirm the isolation of the two bands. Bands were then gel purified using an

UltraClean GelSpin DNA Purification Kit (Mo Bio Laboratories, Inc.) according to the

manufacturer’s instructions. The resulting DNA was sequenced by automated dideoxy

sequencing at the Department of Clinical Immunology, Royal Perth Hospital.

2.8 Western blot

Western blot was performed using the XCell SureLock Mini-Cell system and reagents

from Invitrogen, Corp.. Protein samples were prepared for electrophoresis by adding

appropriate volumes of 4x NuPAGE LDS Sample Buffer, 10x NuPAGE Reducing

Agent and water to 20 µg of each protein sample, for equal final volumes. Samples

were heated at 70°C for 10 minutes, centrifuged briefly and chilled on ice. Proteins

were separated by electrophoresis using 10% NuPAGE Bis-Tris Gels in 1x NuPAGE

MES SDS Running Buffer at 125 V for 21/2 hours at 4°C. Proteins were then transferred

to polyvinylidene difluoride (PVDF) membranes (Roche Diagnostics Corp.) in

1x NuPAGE Transfer Buffer, prepared with 20% methanol and 0.1% NuPAGE

Antioxidant, at 15 V for 16 hours.

Immunoblotting was performed with an initial 15 min membrane wash in TBST

(20 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.1% Tween-20), followed by membrane

blocking in a solution of 5% skim milk in TBST for 1 hour, incubation with primary

antibody diluted in blocking solution for 1 hour, a second 1 hour blocking step,

incubation with secondary antibody diluted in blocking solution for 1 hour, a third

1 hour blocking step, and a brief final wash in TBST. The primary antibodies used were

the β-actin antibody (Abcam, cat. # ab20272) (1:15000), GRB7 C-20 (Santa Cruz

Biotechnology, Inc., cat. # sc-606) (1:1000), GRB7 N-20 (Santa Cruz Biotechnology,

Inc., cat. # sc-607) (1:1000), FAK C-20 (Santa Cruz Biotechnology, Inc., cat. # sc-558)

(1:200), and c-erbB2/HER-2/neu Ab-15 (Neomarkers Inc., cat. # MS-599-P1) (1:1000).

Each primary antibody was used with the appropriate secondary antibody, either Mouse

(1:15000) or Rabbit (1:5000) IgG Horseradish Peroxidase Linked Whole Antibody

(Amersham Australia, Pty Ltd.). Protein was detected using ECL Plus Western Blotting

Detection Reagents and Hyperfilm ECL (Amersham Australia, Pty Ltd.) according to

the manufacturer’s instructions.

22

2.9 CellTitre (CT) assay

CT assays were performed on cells that had been treated and set up in 96-well plates as

described in section 2.4. The assay involved adding 15 µL of CellTitre 96 Aqueous One

Solution Cell Proliferation Assay (Promega, Corp.) reagent to each well to be assayed,

including the five blank wells. A Fluostar OPTIMA Microplate Reader (BMG

LABTECH Pty Ltd) was then used to shake the plate for 3 sec, before measuring the

absorbance for each well at 492 nm. The mean absorbance of the five blank wells was

subtracted from the mean absorbance of the replicate wells for each treatment condition

to give the final measurements.

2.10 Cell migration assay

Cell migration assays were performed on treated cell suspensions, prepared as described

in section 2.4, using QCM-FN Quantitative Cell Migration Assay Fibronectin kits

(Chemicon International). A single kit could be used to assay six cell suspension

samples. For each sample, 80x103 cells in 500 µL of quenching media were seeded into

each of a fibronectin (FN)-coated migration chamber, a BSA-coated chamber, a

FN-coated well and an uncoated well, all in 24-well plate format. The wells beneath

migration chambers were filled with 300 µL of quenching media. To treat cells in

migration chambers with FBS or HRG, this media was supplemented with either 5%

FBS or 1 nM HRG, while the cell suspensions within chambers were not directly

treated. Cells in control wells were treated with 5% FBS or 1 nM HRG as usual. After

48 hours, the migration chambers were removed from their wells and non-migrated

cells were swabbed from the insides of the chambers using the supplied cotton wool

buds. Chambers were then placed in 500 µL of the kit’s crystal violet Cell Stain

Solution for 30 min to stain migrated cells. Chambers were cleaned using water and

cotton wool buds to remove excess stain from the membranes and the chambers

themselves. Cells in control wells were washed with PBS, stained with Cell Stain

Solution and washed further to remove excess stain. Both migration chambers and

control wells were observed under a microscope and five different fields of view were

photographed at 10x magnification for each migration chamber. The cells in each field

of view were counted manually and the mean and standard deviation of the five counts

for each chamber were calculated. A single field of view was also photographed for one

23

FN-coated control well for each condition in the migration experiment of Figure 3.14.

The number of cells in these two fields of view were counted and used to normalise the

corresponding mean migration chamber counts.

2.11 Statistical analysis

Student’s t-test (two-tailed, unpaired) was used to determine the statistical significance

of the differences between conditions for both CT and migration assays. Statistical

significance was defined at the standard 5% level.

2.12 Software

Diagrams were drawn using the R statistical package v 2.5.0 (Ihaka & Gentelman,

1996).

24

CHAPTER 3: INVESTIGATION OF THE ROLE OF Grb7 IN THE

PROLIFERATION AND MIGRATION OF BREAST CANCER CELLS

3.1 Introduction

The ultimate goal of this investigation was to determine the role of Grb7 in the

proliferation and migration of breast cancer cells. To achieve this goal, it was first

necessary to conduct a survey of the expression of Grb7 and Grb7V in a range of

different cell types, in particular, breast cancer cells, in order to identify a suitable cell

line for use in this investigation. Therefore, a panel of different cell types was

assembled that included five breast cancer cell lines of different subtypes, a breast

cancer cDNA library and a normal breast cell line, as well as gastric, cervical and

prostate cancer cell lines for comparison. Both the RNA and protein expression of Grb7

and Grb7V was assessed in these cells using RT-PCR and western blot.

The next step was to develop an experimental protocol that could be used to treat cells

and prepare them for functional studies. An experimental approach involving Grb7

down-regulation, as opposed to up-regulation, was most appropriate, as it best suited the

study of the role of Grb7 in Grb7-overexpressing breast cancer cell lines. These cell

lines are of particular interest as they represent a group of breast cancers that may

benefit from Grb7-targeted therapy. Hence, from a clinical perspective, this approach

also tested the potential effects of an anti-Grb7 drug on breast cancer cells.

Down-regulation of Grb7 was to be achieved through RNA interference (RNAi), a

cellular mechanism that can rapidly and effectively eliminate specific RNAs from cells.

Central to this mechanism are 23-25 nt, double-stranded RNAs called short interfering

RNAs (siRNAs). During RNAi, one strand of an siRNA binds with perfect

complementarity to a target mRNA and triggers events leading to target cleavage.

Afterwards, the siRNA strand can go on to bind to other target instances, making this a

very efficient process. This area is reviewed in detail by Kumar and Clarke (2007).

In the laboratory, RNAi can be exploited to obtain a knockdown of an RNA or protein

of interest by transfecting cells with specifically designed artificial siRNAs. This

technique is now highly valued as a simple and effective way to knock down specific

25

RNAs and proteins, and hence is becoming widely used in signalling and functional

studies in favour of alternatives such as morpholinos, antisense oligonucleotides,

dominant-negative mutants and inhibitory peptides (Huppi, Martin, & Caplen, 2005).

Therefore, an experimental protocol was developed, which involved treatment of cells

with siRNA such that the optimal knockdown of Grb7 was achieved, and preparation of

cells for functional studies within the constraints imposed by this treatment.

In addition, the investigation was to study the role of Grb7 under two sets of conditions:

normal growth conditions, and conditions of stimulated ErbB signalling, achieved

through the use of the ErbB3/ErbB4 ligand, HRG. HRG was the most suitable ligand

for stimulation of ErbB2 signalling as it has been shown in SK-BR-3 breast cancer cells

to induce the formation of ErbB2:ErbB3 heterodimers in strong preference to

ErbB1:ErbB3 heterodimers, and lead to the tyrosine phosphorylation of both ErbB2 and

ErbB3 (Tzahar et al., 1996). In addition, Grb7 has been shown to associate with ErbB2

and ErbB3 after treatment with HRG (Fiddes et al., 1998). Therefore, the experimental

protocol was adapted to accommodate treatment with HRG for the second set of

experiments.

With respect to the techniques employed for the functional studies in this investigation,

the CellTitre (CT) assay was used for measuring cell proliferation, while invasion and

migration were measured using an assay based on the Boyden chamber technique.

The CT assay is a colourimetric method in which cells are incubated with a small

amount of CT reagent for 1-4 hours, during which time a tetrazolium compound present

in the reagent is converted to a coloured product by NADH or NADPH in viable cells.

This product is quantitated through absorbance readings to give a measurement that is

proportional to the number of live cells present. This assay is efficient, flexible and safe,

with no volatile organic solvent or radioactive isotopes required, unlike the MTT and

[3H]thymidine incorporation assays.

In the Boyden chamber migration assay, cells migrate through a porous membrane at

the base of a chamber over a period of hours to days, at the end of which they are

stained and quantitated, either by cell counting or through optical density measurements

of eluted stain. In this study, migration experiments were conducted using FN-coated

membranes, in view of the evidence for Grb7’s involvement in FN-stimulated integrin-

26

mediated cell migration, as discussed in section 1.2.2.2. While there are alternatives to

the Boyden chamber assay, such as the wound assay (L. G. Rodriguez, Wu, & Guan,

2004) and techniques that use sophisticated microscope equipment and computer

software to monitor the movement of cells (Han, Shen, & Guan, 2000), the Boyden

chamber assay is the most widely accepted technique in the literature for the study of

cell migration and invasion.

This chapter thus addresses all four project aims.

27

3.2 Results

3.2.1 Grb7 and Grb7V expression

3.2.1.1 RNA expression

The RNA levels of Grb7 and Grb7V were determined in different cell lines using

RT-PCR on RNA extracts, and in a breast cancer cDNA library2 using PCR alone. The

products were run on an agarose gel to allow comparison of Grb7 and Grb7V RNA

levels (Figure 3.1).

Figure 3.1: RNA expression of Grb7 (505 bp) and Grb7V (417 bp) in a panel of cell lines, and a breast cancer (BC) cDNA library, with β-actin loading control (203 bp).

Of the breast cancer samples tested, Grb7 was present at high levels in the SK-BR-3 and

BT-474 cell lines, and the breast cancer cDNA library. Grb7V RNA was also present in

each of these samples, but at lower levels than wild-type Grb7. In addition, Grb7, but

not Grb7V, RNA was present at low levels in the breast cancer cell lines,

MDA-MB-453 and MCF7, and the normal breast cell line, HMEC. However, neither

Grb7 nor Grb7V were detected in the breast cancer cell line, MDA-MB-468.

2 The breast cancer cDNA library was derived from a metastatic infiltrating ductal breast carcinoma, kindly supplied by Dr Jennifer Byrne of the Children’s Medical Research Institute, NSW, Australia.

28

Of the non-breast cancer cell lines, the gastric cancer cell line, NCI-N87, expressed both

Grb7 and Grb7V at high levels in approximately equal proportions. Grb7, but not

Grb7V, RNA was also present at low levels in the prostate cancer cell line, LNCaP,

while neither isoform was detected in the cervical cancer cell line, HeLa.

Similar results were obtained using a higher PCR cycle number and less stringent

conditions, with the exception of faint additional Grb7V bands present in the

MDA-MB-453 and HMEC samples.

Sequencing of gel purified SK-BR-3 cDNA taken from the upper and lower bands of a

replicate of the gel shown in Figure 3.1 verified that the bands were composed of Grb7

and Grb7V cDNA respectively.

3.2.1.2 Protein expression

In separate experiments, the expression of Grb7 protein in the cell lines studied above

was examined using Western blot, and found to be similar to the expression of Grb7

RNA. SK-BR-3, BT-474 and NCI-N87 cells were all shown to have high levels of Grb7

protein, while expression was barely detectable in MDA-MB-453 and MCF7 cells, and

undetectable in MDA-MB-468 and HeLa cells. In contrast to Grb7 RNA, however,

Grb7 protein was also undetectable in LNCaP and HMEC cells.

Western blots for Grb7 were performed using a primary antibody against the

N-terminus of Grb7 that has been published to detect both the Grb7 and Grb7V

isoforms (Tanaka et al., 1998). However, only a single band corresponding to full-

length Grb7 was evident on Western blot under all experimental conditions tested,

including different cell lines (SK-BR-3, BT-474 and NCI-N87), lysis buffers

(Mid-RIPA and CEB) and cell densities (50 x103, 300 x103 and 800 x103 cells/well in

6-well plates) (see for example Figures 3.2, 3.3, 3.5, 3.6, 3.8 and 3.10). Different

Western blot conditions were also tried including different primary and secondary

antibody concentrations, different batches of primary antibody, different concentrations

of skim milk blocking solution, and different protein amounts and exposure times.

In summary, both Grb7 and Grb7V RNA are expressed in a range of cell types,

including several breast cancer cell lines and a breast cancer cDNA library, while only

29

Grb7 protein can be detected on Western blot. In addition, this survey identified both

SK-BR-3 and BT-474 as Grb7-overexpressing breast cancer cell lines suitable for use in

this study. Of these two cell lines, SK-BR-3 was chosen as the model for this

investigation as it was most readily accessible at low passage numbers.

3.2.2 Development of a protocol for the effective knockdown of Grb7 using siRNA

The aim of the next part of the investigation was to develop a protocol for the

transfection of breast cancer cells with siRNA against Grb7, and subsequent assessment

of cell proliferation and migration. The first phase of this process involved

identification of an effective siRNA against Grb7 and optimisation of the transfection

conditions.

3.2.2.1 Identification of an effective siRNA against Grb7

At the outset of the project, two siRNAs against Grb7, designed by a past member of

the laboratory, were provided for use in this investigation. While these siRNAs had not

been shown to cause a convincing Grb7 knockdown, they had appeared to have a

functional effect on breast cancer cells, inducing growth inhibition, cell cycle arrest and

apoptosis (Balmer, 2004). Much time was spent trying to optimise transfection

conditions to achieve a knockdown of Grb7 using these siRNAs. Western blot

conditions were also varied for best sensitivity to differences in protein levels.

However, no Grb7 knockdown was ever observed and the putative functional effect

observed by Balmer (2004) was shown to be a non-specific effect resulting from

impurities introduced during the in-house production of the siRNAs.

Therefore, Grb7 siRNA was purchased from the SMARTpool line of siRNAs from

Ambion, Inc.. A SMARTpool siRNA is a pool of four siRNAs designed against the

RNA of a single gene, in this case Grb7. A nonsense SMARTpool siRNA was also

purchased, to be used as a negative control. The Grb7 SMARTpool siRNA (SP) was

shown to induce a substantial, knockdown of Grb7 protein in both SK-BR-3 and

BT-474 cells, while the nonsense SMARTpool siRNA (NS) did not affect the Grb7

protein level (Figure 3.2).

30

Figure 3.2: Western blot showing the Grb7 protein knockdown (60 kDa) induced by SP siRNA in SK-BR-3 and BT-474 cells on day 2 after transfection. siRNA was used at 10 nM.

At a later point, the four SP component siRNAs were purchased individually and tested

to determine their relative potencies in knocking down Grb7 (Figure 3.3). Three of the

four components (#1, #3 and #4) effectively knocked down Grb7, but #4 was the most

potent. The fact that a single siRNA could effectively knock down Grb7 meant that a

lower concentration of siRNA could be used, and that off-target target effects would be

less likely in the functional studies to follow.

Figure 3.3: Western blot showing the Grb7 protein knockdown (60 kDa) induced by SP component siRNAs in SK-BR-3 cells on day 2 after transfection. SP and NS were used at 10 nM, while the SP components #1, #2, #3 and #4 were used at 2.5 nM.

31

3.2.2.2 Demonstration that the choice of primers affects the appearance of a Grb7

RNA knockdown

The SP siRNA was able to knock down Grb7 protein very effectively and so, logically,

should also have knocked down Grb7 RNA. However, initially, a Grb7 RNA

knockdown could not be detected, though many different RNA harvesting and RT-PCR

conditions were tested. Parallel reactions lacking the AMV-RT enzyme demonstrated

that this was not due to amplification of genomic DNA in the Grb7 knockdown sample.

Even the more sensitive real-time RT-PCR technique could not detect a Grb7

knockdown. Then, upon purchase of the individual SP component siRNAs and their

sequences, it became clear that the set of primers used for these initial experiments

spanned a section of Grb7 RNA that did not include any of the SP siRNA target sites.

Hence, it was possible that the PCR was amplifying cut sections of Grb7 cDNA in the

SP sample, which would account for the unexpectedly high signal. Therefore, different

primer sets were designed that spanned at least one siRNA target site. With the new

primers, RT-PCR did reveal a Grb7 RNA knockdown, and furthermore, the knockdown

appeared more pronounced when the primers spanned more than one siRNA target site

(Figure 3.4). These results demonstrate that the appearance of an siRNA-mediated RNA

knockdown can depend on the primers used for PCR amplification and suggest that

primers should span siRNA cut sites.

3.2.2.3 Optimisation and characterisation of the Grb7 knockdown

The first step in optimising the siRNA transfection protocol was to determine the most

suitable transfection reagent, specifically, the one that offered the best transfection

efficiency with minimal toxicity to the cells.

The first transfection reagent tested was Lipofectamine 2000 (LF). This reagent was

toxic to SK-BR-3 cells even when used at 60% of the volume recommended by the

manufacturer for siRNA transfection. However, this problem was largely circumvented

by changing the media on the cells 4 hours after transfection. This step did not reduce

the magnitude of the Grb7 knockdown, as asserted in the LF instruction booklet (2002)

and as confirmed experimentally. However, in the interest of exposing the cells to a less

toxic reagent, two other transfection reagents were also tested, Oligofectamine (OF) and

NeoFX.

32

Figure 3.4: The effect of PCR primers on the appearance of a Grb7 RNA knockdown in SK-BR-3 cells. siRNA was used at 10 nM. RNA was harvested on day 1 after transfection. Grb7 plasmid and H2O were used as positive and negative PCR controls respectively. A) Positions of SP component target sites and PCR primers within Grb7. B) RT-PCR from siRNA-treated SK-BR-3 cells using original Grb7 primers (Grb7 protein knockdown was confirmed for this experiment, see Figure 3.10A), and C) using two different sets of Grb7 primers spanning siRNA target sites.

33

As shown in Figure 3.5, no Grb7 knockdown was observed using OF and only a small

knockdown was observed using NeoFX. Therefore, as the only reagent associated with

a substantial Grb7 knockdown, LF was considered the best reagent for these

experiments.

Figure 3.5: Western blot showing the effect of different transfection reagents on Grb7 knockdown (60 kDa) in SK-BR-3 cells on day 2 after transfection. siRNA was used at 10 nM.

To determine the minimal concentration of siRNA required, SK-BR-3 cells were treated

with different concentrations of SP using LF. Western blot showed a very substantial

Grb7 protein knockdown, even at a concentration of 10 nM (Figure 3.6). The

knockdown was only slightly greater for a concentration of 50 nM, and there was no

apparent difference in knockdown between the 50 and 100 nM concentrations.

Therefore, from this point further, SP siRNA was used at a concentration of 10 nM.

Figure 3.6: Western blot showing the effect of SP concentration on Grb7 knockdown (60 kDa) in SK-BR-3 cells on day 2 after transfection. β-actin was used as a loading control (42 kDa).

34

To characterise the timing of the knockdown, both Grb7 RNA and protein were

monitored over a period of days following treatment with siRNA. SP induced a Grb7

RNA knockdown that was evident only 4 hours after transfection and was still present

on day 2 after transfection. A Grb7 protein knockdown was observed from day 1

through to day 3 with no signs that it was diminishing at this time.

3.2.3 Development of a protocol for the treatment of cells with siRNA, FN and/or HRG,

and the performance of functional studies

The second phase of protocol development involved modification of the basic

transfection protocol to enable concurrent treatment with other agents and a final cell

setup appropriate for the different functional studies.

With regard to the cell setup for each of the functional assays, the CT proliferation

assay is generally performed on cells seeded in 96-well plates, while the Boyden

chamber migration assay requires suspensions of treated cells to be added directly to

migration chambers. As the migration assays usually take between 2 and 48 hours

(QCM-FN manual, 1999), a Grb7 knockdown would ideally be present at the beginning

of a migration assay.

The other treatments under consideration were FN and HRG. FN can be simply applied

as a layer to cell dishes or plates prior to the addition of cells and hence did not require

any special arrangement. For treatment with HRG, however, cells are generally serum

starved for 24 hours prior to treatment (Chausovsky et al., 2000; Fiddes et al., 1998).

3.2.3.1 Problems associated with transfecting cells in 96-well plates

The simplest protocol for the cell proliferation experiments would involve transfection

of cells directly in the 96-well plates, in which they would later be assayed. However, it

was found that when the media was changed, both during transfection and 4 hours later,

many cells were washed away, despite extreme care. This occurred in four different cell

lines: SK-BR-3, BT-474, MDA-MB-468 and A549. Several techniques for removing

the media were tried including slow pipetting, suctioning with a fine glass cannula, and

inverting the plate onto a tissue. The techniques tested for the replacement of the media

involved pipetting in single drops or slowly pipetting in a stream onto the side of the

35

well. Simply diluting the transfection media by adding fresh media on top was not

sufficient to prevent the cytotoxic effects of LF.

Figure 3.7 shows the results of a series of CT assays performed over time for three

groups of cells subject to different conditions: NS transfection using LF followed by a

media change at 4 hours, media change only, or a control condition with no transfection

or media change. The three groups of cells originated from the same suspension plated

out on day -1. After treatment on day 0, the control cells were more numerous than

those in the groups that had undergone a media change. This difference was more

pronounced at day 1 after transfection. By day 3, the cells in the ‘media change only’

group had begun to recover but their number was still only 60% of that of the control

cells. The standard deviations for the ‘media change only’ group were also larger,

possibly as a result of varying numbers of cells being washed away in replicate wells.

The transfected cells did not recover from the media change by day 3, possibly because

of a weakened condition following transfection.

Figure 3.7: CT assay of SK-BR-3 cells showing the effect of NS transfection and media change in 96-well plates. Cells were seeded in replicate plates at 1.5x103 cells/well on day -1 and either treated with 10 nM NS siRNA followed by a media change (NS + change), subjected to a media change alone (NT + change) or left untouched (NT – change) on day 0, 24 hours later. CT assays were performed on replicate plates at specific time points over five days. Values are mean absorbance – blank absorbance (media only) ± SD (n=5).

36

Because of this problem, it was decided that cells would be plated and treated in 10 cm

dishes and then split, counted and seeded into 96-well plates at a later time (see

Chapter 2 for more detailed methods). The proportion of cells lost during transfection

and media changes in 10 cm dishes was found to be negligible, and differences in cell

numbers were compensated for at the cell counting and seeding steps. The degree of

Grb7 protein knockdown on day 2 after transfection was shown not to be affected by

splitting of the cells on day 1. This protocol also suited the migration assay, as cells

could be treated in 10 cm dishes and seeded into the migration chambers at any time

deemed appropriate.

3.2.3.2 Treatment with HRG

To determine the effects of HRG on breast cancer cells, SK-BR-3 and BT-474 cells

were serum starved for 24 hours in serum-free media then treated with different

concentrations of HRG for different amounts of time.

At a concentration of 1 nM, HRG induced a multi-phase response in the level of Grb7

protein in SK-BR-3 cells as shown in Figure 3.8A. For 5 to 10 minutes after HRG

treatment, the Grb7 protein level was reduced relative to that of non-treated cells. It

returned to that of untreated cells by 30 minutes and was elevated after 1 hour. But by

6 hours, Grb7 protein level was again reduced, this time to a very low level that

remained unchanged to the final 48 hour time-point. A further experiment demonstrated

that the drop occurred between 2 and 3 hours after HRG treatment. This response was

accompanied by a similar response in the level of ErbB2 protein, which remained fairly

constant for the first hour, became slightly elevated at 6 hours and began to drop by

12 hours. From this time, the level dropped progressively to the final 48 hour time-point

(Figure 3.8A).

The same time-course experiment conducted in BT-474 cells showed a similar but

much less pronounced effect of HRG on Grb7 protein level (Figure 3.8B). The level

appeared slightly raised at the 1 hour time point and slightly reduced from the 12 to

48 hour time-points.

At a concentration of 0.01 nM, HRG had no effect on Grb7 protein level in either

SK-BR-3 or BT-474 at any of the time points tested.

37

Figure 3.8: Western blots showing the effect of HRG on Grb7 expression (60 kDa) over time in A) SK-BR-3 and B) BT-474 cells on day 2 after transfection. HRG was used at 1 nM. β-actin was used as a loading control (42 kDa).

Difficulties arose with the need to treat cells with both HRG and siRNA and incorporate

the new treatment step into the experimental protocol. As HRG treatment was shown to

affect Grb7 protein level within minutes, it was desirable to treat cells with siRNA first

and then with HRG at a later time, once the siRNA-induced Grb7 knockdown was in

effect. Therefore, following treatment with siRNA, cells needed not only to be split,

counted and seeded into 96-well plates or migration chambers, but also serum starved

for 24 hours and treated with HRG. A protocol was proposed in which cells would be

transfected on day 0 at 0 hours, undergo a media change to serum-free media at 4 hours,

and be treated with HRG on day 1 at 30 hours, by which time a significant Grb7

knockdown would be present.

38

3.2.3.3 Problems associated with splitting and plating cells for serum starvation

When the proposed protocol was put into practice, it was found that the split cells would

not attach to the new plates in the absence of serum. One potential solution to this

problem was to use a gentler splitting reagent for the cells. Trypsin, the reagent

routinely used for cell splitting, causes the detachment of cells through enzymatic

cleavage of membrane proteins. This mode of action could make the recovery and

re-attachment of cells slower and more difficult than if the cells were detached non-

enzymatically. Therefore, the non-enzymatic splitting reagent, PBS/EDTA, was tried in

place of trypsin. EDTA is a chelator that depletes the media of divalent cations,

including the calcium ions that are necessary for cadherin-mediated cell adhesion. In the

absence of calcium ions, this adhesion is lost and cells detach from the dish with their

membrane proteins intact.

Experiments showed that cells split using PBS/EDTA were able to attach to plates

much quicker and better in serum-free media and low-serum media than those split

using trypsin. However, the detachment of cells under PBS/EDTA was very slow, even

when used at 10x the normal concentration, taking more than 2 hours on some

occasions. Cells also needed to be close to 100% confluent, otherwise they would not

detach within 2 hours. This was not ideal, both because cells were submersed in

PBS/EDTA for hours and because the Grb7 knockdown was found to be much less

pronounced at high cell densities. It was also common for cells to come away in sheets

or clumps that required aggressive pipetting to separate and reduced the accuracy of cell

counting and seeding. The clumping of cells was reduced if a large volume of media

was used to resuspend cells, however the lower concentration of cells in the resulting

suspension meant that cell counting was less accurate. Therefore, it was decided that

media with 0.5% foetal bovine serum (FBS) would be used in place of serum-free

media, and that trypsin would continue to be used for CT assay experiments, but that

PBS/EDTA would be used for migration assay experiments.

3.2.3.4 Cell counting problem

Another problem encountered with this protocol was that each different transfection

condition needed to undergo separate cell counting prior to seeding, as opposed to the

standard protocol in which an original stock of cells is counted once, seeded, treated and

39

studied. This meant that inaccuracies in the estimation of cell counts manifested as

different starting cell densities for the different conditions. To try to minimise this error,

the cell suspensions for each condition were counted four times each and the resulting

counts were averaged to obtain a final estimate of the cell concentration. The final

protocol is represented in Figure 3.9. Detailed methods are given in section 2.4.

Figure 3.9: Final protocol for treatment of cells with siRNA and HRG, and preparation for proliferation and migration assays.

40

3.2.4 Effect of concurrent treatment with Grb7 siRNA and HRG on Grb7 protein

As shown above, when used independently, SP and HRG induce a reduction in Grb7

protein level at a 48 hour time-point. Figure 3.10 shows that, in cells in which Grb7 has

been knocked down by SP, HRG induces an additional reduction in Grb7 protein in

both SK-BR-3 and BT-474 cells. In SK-BR-3 cells, the expression of FAK protein was

not affected by either SP or HRG. Plating of cells on FN did not affect either the normal

level of Grb7 protein or changes in this level induced by SP or HRG.

Figure 3.10: Western blots showing the effects of concurrent treatment with SP and HRG on A) Grb7 (60 kDa) and FAK (125 kDa) expression in SK-BR-3 cells, and B) Grb7 expression in BT-474 cells on day 2 after transfection. siRNA was used at 10 nM, HRG was used at 1 nM. β-actin was used as a loading control (42 kDa).

41

3.2.5 Effect of Grb7 siRNA and HRG on SK-BR-3 cell proliferation

Using the optimised cell treatment and setup protocol of Figure 3.9, CT assay

experiments were performed to examine the effects of Grb7 knockdown and HRG on

the proliferation of SK-BR-3 cells.

The CT assay itself was first optimised with respect to its duration (30, 60, 90, 120 min)

and the volume of CT reagent used (5, 15, 30 µL). A volume of 15 µL of reagent/well

with an assay duration of 60 min was found to give the most appropriate readings for

the growth of SK-BR-3 cells over 8 days. The experimental duration of 8 days enabled

cells to be monitored throughout the period of Grb7 knockdown, shown to last for at

least 3 days, and beyond into a period in which downstream effects could potentially

continue, up to the point at which the cells reached confluency.

Next, a series of experiments was conducted to examine the effects of siRNA-mediated

Grb7 knockdown on SK-BR-3 cells under normal growth conditions. No significant

difference was present between the SP and NS conditions in any of the four

experimental replicates individually. Furthermore, from the average growth curve for

the four replicates (Figure 3.11A), no significant difference was present between the SP,

NS and NT conditions at any time-point, demonstrating that SK-BR-3 cell proliferation

was not affected by Grb7 knockdown. The protein kinase C (PKC)-activator, PMA, was

also used in this series of experiments as a positive control for reduced cell

proliferation, as it has been shown to induce growth arrest in SK-BR-3 cells

(Blagosklonny, 1998). Figure 3.11A shows that the CT assay was able to detect a

significant reduction in cell proliferation for PMA-treated cells.

The next series of experiments examined the effect of siRNA-mediated Grb7

knockdown in media with only 0.5% serum in the presence or absence of HRG. The

average growth curve for the three replicate experiments is given in Figure 3.11B. As

expected, the growth of cells in all treatment conditions was substantially less in media

with 0.5% serum compared to 5% serum, as used in the first series of experiments.

Again, there was no significant difference between any of the treatment conditions at

any time-point, demonstrating that SK-BR-3 cell proliferation was not affected by either

Grb7 siRNA knockdown or HRG.

42

Figure 3.11: CT assays showing the effect of siRNA and HRG on the proliferation of SK-BR-3 cells for A) untreated cells and cells treated with either 10 nM siRNA or 50 nM PMA, in media with 5% serum. B) untreated cells and cells treated with siRNA, serum starved in 0.5% serum for 24 hours in the presence or absence of 1 nM HRG. Values are mean absorbance – blank absorbance (media only) ± SD (n=5). Results are representative of at least three independent experiments.

43

3.2.6 Effect of Grb7 siRNA and HRG on SK-BR-3 cell migration

Next, the protocol developed for migration assay experiments was used to investigate

the role of Grb7 in SK-BR-3 cell migration.

3.2.6.1 Effect of HRG on cell spreading

In conducting experiments with HRG, an early observation was that HRG increased

SK-BR-3 cell spreading on both FN-coated (Figure 3.12) and uncoated dishes. Grb7

knockdown using either SP or the SP component, #4, did not appear to affect this

spreading from visual examination. These observations were made on more than fifteen

occasions.

Figure 3.12: Photographs showing the effect of siRNA and HRG on SK-BR-3 cell morphology on FN-coated dishes (10x magnification) on day 3 after transfection. siRNA was used at 10 nM, HRG was used at 1 nM.

44

3.2.6.2 Cell migration assays

Due to their expense, only four migration assay kits were available for this study.

Therefore, the first two kits were used for pilot experiments, while the second two were

used to address the question of the effect of Grb7 knockdown on SK-BR-3 migration.

The two pilot experiments determined suitable cell numbers and migration times for the

assay and also broadly assessed the effect of siRNA and HRG on SK-BR-3 cell

migration. In these experiments, quantitation of cell migration was attempted using a

technique for measurement of the optical density of eluted stain. However, due to the

poor design of the migration chambers, non-migrated cells and stain could accumulate

under a ledge, inaccessible to cleaning implements. The stain was also difficult to

remove from small and intricate parts of the chambers. As a result, when the stain was

eluted from the migrated cells on the membrane, stain was also eluted from non-

migrated cells and removed from the plastic so that it contributed significant and

variable background signal that reduced the sensitivity of the assay and, in many cases,

overwhelmed the signal from the migrated cells. Therefore, for the third and fourth

migration experiments, this technique was abandoned in favour of counting migrated

cells under the microscope.

In the third migration experiment, six different treatment conditions were tested. Cells

were transfected with either #4 or NS siRNA and suspended in media containing either

0.5% serum, 5% serum or 0.5% serum plus HRG. Counting of migrated cells from each

condition demonstrated that treatment with HRG significantly increased cell migration

by up to 12-fold compared to 0.5% serum alone, for both #4- and NS-transfected cells

(p = 1x10-3 and p = 2x10-8 respectively; Figure 3.13B). There was no significant

difference in migration between cells plated in 0.5% and 5% serum. Cells transfected

with #4 siRNA showed slightly but significantly greater cell counts than NS-transfected

cells in both 0.5% and 5% serum (p = 3x10-3 and p = 8x10-4 respectively). However,

this could possibly be explained by the addition of different numbers of cells to the

migration chambers at the start of the assay. No migration was observed in the BSA-

coated control chambers for any of the conditions.

45

Figure 3.13: The effects of siRNA, HRG and serum (FBS) on the migration of SK-BR-3 cells. siRNA was used at 10 nM, HRG was used at 1 nM. A) Photographs (10x magnification) and B) cell counts of migrated cells on FN-coated membranes on day 2 after transfection. Values are mean counts per field of view ± SD (n=3).

The fourth migration experiment tested two treatment conditions in triplicate chambers.

Cells were transfected with either #4 or NS siRNA and both groups were treated with

HRG in media with 0.5% serum. To compensate for any differences between the

46

numbers of cells added to the migration chambers for the two transfection conditions,

migrated cell counts were normalised to cell counts from FN-coated control wells plated

with the same stocks of transfected cells. The normalisation factor indicated that the

concentration of the suspension of #4-transfected cells had been 20% greater than that

of the NS-transfected cells. Nevertheless, the normalised migrated cell counts of

#4-transfected cells were still significantly greater than those of NS-transfected cells

(p = 4x10-4; Figure 3.14B). No migration was observed in the BSA-coated control

chambers for either condition.

Figure 3.14: The effects of siRNA on the migration of HRG-treated SK-BR-3 cells. siRNA was used at 10 nM, HRG was used at 1 nM. A) Photographs (10x magnification) and B) cell counts of migrated cells on FN-coated membranes on day 2 after transfection. Values are mean counts per field of view ± SD (n=3).

47

3.3 Discussion

Summary

In Part 1 of this thesis, the role of Grb7 in breast cancer cells was investigated,

beginning with a survey of the expression of Grb7 and Grb7V in cancer cells, followed

by the development and optimisation of experimental procedures, and culminating in

experiments to assess the effect of Grb7 knockdown and the ErbB ligand, HRG, on

breast cancer cell proliferation and migration. Thus, all four of the project aims were

achieved. Several important findings were made.

The first stage of the investigation confirmed the expression of Grb7 in the breast

cancer cell lines BT-474, SK-BR-3 and MDA-MB-453, as observed previously (Kao &

Pollack, 2006; Kauraniemi, Barlund, Monni, & Kallioniemi, 2001), and determined the

expression level of Grb7 in a range of other cell lines. It also demonstrated the

expression of Grb7V RNA in breast and gastric cancer cell lines and a breast cancer

cDNA library, providing the first evidence of this isoform outside of oesophageal

cancer (Tanaka et al., 1998), although the inability to detect Grb7V protein raises the

possibility that it may not be translated in the cell lines tested. These experiments also

identified SK-BR-3 and BT-474 as Grb7-overexpressing breast cancer cell lines suitable

for this investigation.

The second stage of the investigation resulted in a protocol for the treatment of cells

with Grb7 siRNA and HRG in a setup suitable for subsequent functional studies that

overcame numerous technical difficulties.

While optimising this protocol, it was found that the ability of RT-PCR to detect a Grb7

RNA knockdown is dependent on the PCR primers used, in a way that suggests that

primers must span an siRNA target site. This would indicate that pieces of cleaved

target mRNA can persist in the cell for at least 24 hours before being degraded. This

finding, if shown to be generalisable, would have important implications for

methodology in this area, in determining the PCR primers that can be used for any

particular siRNA.

48

In addition, HRG was shown to induce a reduction in ErbB2 protein and a multi-phase

response in the level of Grb7 protein in SK-BR-3 cells. The former result verifies the

previous observation that HRG down-regulates ErbB2 in SK-BR-3 cells (Guerra-

Vladusic, Vladusic, Tsai, & Lupu, 2001). In relation to the latter result, a recent study in

MCF7 cells showed that HRG induced phosphorylation of ErbB2 within 1 minute,

which led to the transcription of a number of genes, including the transcription factors

c-Fos and DUSP1, by 20 mins, and a subsequent rise in the corresponding protein

levels. Protein levels fell back to original levels by 90 mins, possibly as a result of

negative feedback signalling, at which time experiments were discontinued (Nagashima

et al., 2007). This expression pattern very closely fits the observed expression of Grb7

after HRG treatment. Hence, the results of this study suggest that Grb7 expression may

be modulated by HRG through the same mechanism as proteins such as c-Fos and

DUSP1.

In the final stage of the investigation, the role of Grb7 in the proliferation and migration

of SK-BR-3 cells was examined using the optimised experimental protocol.

Cell proliferation was not significantly affected by siRNA knockdown of Grb7 under

either normal growth conditions in media with 5% serum, or under conditions of serum-

starvation in media with only 0.5% serum. The proliferation of serum-starved cells was

also not significantly affected by treatment with HRG or with a combination of Grb7

siRNA and HRG. Based on these results, there is no indication that Grb7 plays a role in

the proliferation of either unstimulated or HRG-stimulated breast cancer cells.

Recently, two other studies were published on the functional effects of Grb7 inhibition

in SK-BR-3 cells, including its effect on cell proliferation. The first study, by Kao and

Pollack (2006), found that Grb7 siRNA did not affect proliferation when cells were

treated in media with 10% serum, but inhibited proliferation when cells were treated in

media with only 2% serum. In the latter case, there was no effect on apoptosis, but some

evidence of inhibition of cell cycle progression. The second study, by Pero and

colleagues (2007), found that peptides designed against the Grb7 SH2 domain inhibited

cell proliferation in serum-free media.

However, there are several differences between the experiments in these studies that

could have affected the outcomes. For example, in Kao and Pollack’s study of serum-

49

starved cells, the numbers of cells in each of the treatment conditions, including the

control condition, were decreasing for the duration of the experiment. The authors

attribute this to a 2-fold decrease in the S-phase fraction and a 2-fold increase in the

apoptotic fraction, resulting from serum-starvation. Thus the cells in these experiments

were under considerably more stress than those in the experiments of the present study,

for which there was no evidence of a reduction in cell number from CT assays. This

difference may have led to an altered response of the cells to Grb7 inhibition.

In addition, Pero and colleagues used peptides against Grb7 rather than Grb7 siRNA for

their experiments and found that these inhibited the proliferation of MDA-MB-231 cells

that do not express Grb7. Although other cell lines not expressing Grb7 were unaffected

by the peptides, this observation raises the possibility that a non-specific effect

influenced the results of these experiments.

However, on the assumption that these differences did not have a critical impact on the

experiment outcomes, it is possible that the varying responses to Grb7 inhibition were

due to differences in growth conditions. Specifically, experiments conducted in

different serum levels, in the same cell line, and even within a single study, indicate that

Grb7 has no role in SK-BR-3 cell proliferation in media with normal serum levels, but

may stimulate proliferation in low-serum media.

With regard to the results of the proliferation assays incorporating HRG treatment,

many studies have demonstrated that HRG induces growth inhibition and apoptosis in

SK-BR-3 cells (Le et al., 2000; F. J. Xu et al., 1997), while many others have shown

that HRG induces an increase in cell proliferation in SK-BR-3 cells (Aguilar et al.,

1999; Yen et al., 2000). In the present study, no significant change in proliferation was

observed upon HRG treatment, even though it was shown to induce a reduction in Grb7

expression in the same experiments, indicating that it succeeded in stimulating the cells.

These results, together with the evidence in the literature, suggest that this may be

another case in which the response of cells to treatment is dependent on the precise

conditions used, such as the serum level, cell confluency, or HRG concentration. The

lack of effect of co-treatment with HRG and Grb7 siRNA is consistent with HRG

having no effect on cell proliferation. Thus, Hypothesis 1 of Part 1 of this thesis, that

inhibition of Grb7 expression leads to reduced cell proliferation in breast cancer cells, is

not supported by the results of this investigation.

50

In contrast, when it came to examining the effects of the different treatments on

SK-BR-3 cell migration, HRG had a striking effect. HRG visibly enhanced cell

spreading on tissue culture plates under all conditions tested. In addition, migration

experiments performed using the optimised protocol showed that HRG significantly

increased the migration of serum-starved cells by up to 12-fold. In contrast, an increase

in serum to 5% had no significant effect on cell migration. These results are consistent

with published observations, as HRG has been shown to increase cell aggregation and

invasion in SK-BR-3 cells (M. Tan, Grijalva, & Yu, 1999; F. J. Xu et al., 1997).

Grb7 siRNA had no visible effect on cell spreading under any of the conditions tested.

Further experimentation revealed that the number of migrated cells was slightly but

significantly greater for cells treated with Grb7 siRNA than with NS siRNA,

independent of serum level and treatment with HRG. Thus, Hypothesis 2 of Part 1 of

this thesis, that inhibition of Grb7 expression leads to reduced cell migration in breast

cancer cells, is not supported by the results of this investigation.

The role of Grb7 in the migration of SK-BR-3 cells has not previously been studied.

However, inhibition of Grb7 has been shown to significantly reduce cell migration in

oesophageal and pancreatic cancer cell lines, and Grb7 expression has been linked to

the metastasis of these cancers in vivo (Tanaka et al., 2006; Tanaka et al., 2000). Hence,

the increase in migration observed in response to Grb7 knockdown is the opposite of its

effects in other cancer cells.

However, a number of scenarios are consistent with all of these results. For example,

Grb7 has a large number of known binding partners and is involved in many signalling

pathways. It is possible that one of these binding partners, or one that is yet to be

discovered, plays a role in the inhibition of cell migration. Under certain conditions, this

anti-migratory signalling could predominate, leading to an increase in migration upon

Grb7 knockdown. Alternatively, Grb7 could bind to pro-migratory proteins to little or

no effect, and in doing so, block access to other proteins, acting as a dominant-negative.

In summary, on the assumption that this effect will be replicated in future studies, it is

concluded that Grb7 knockdown does not significantly inhibit the migration of

SK-BR-3 cells either in the presence or absence of HRG, but rather has a mild

stimulatory effect.

51

Limitations

During the development of the protocol for the functional experiments, some problems

were encountered that necessitated an extra step in the protocol to split, count and

reseed cells at a point between treatment with siRNA and the functional assays

themselves. Disruption of the cells at this point was not ideal, as the resulting change in

cell density and growth conditions and the additional stress to the cells could have

altered the response to the treatment, even though the Grb7 knockdown was shown to

be unaffected by this step. As a Grb7 knockdown was already present at the time of

splitting, the counting and reseeding of cells also had the potential to compensate for

differences in cell number resulting from this knockdown, although the cell counts were

not significantly different between conditions. The need to split and count cells from

each condition separately also meant that the counting error would be different for each

condition and could mimic real differences between the responses of cells to the

different conditions, although this was minimised with replicate cell counts. Hence,

although the extra splitting step in the protocol was necessary, error was introduced in

this step that may have compromised the sensitivity of the assay and impacted the

outcomes of the experiments.

However, the main factor that limits the interpretation of the results of this investigation

is that experiments focused solely on the SK-BR-3 cell line as the model for Grb7-

overexpressing breast cancer cells. The results from this cell line can not necessarily be

generalised to other Grb7-overexpressing cancer cells, which may have very different

characteristics. This is especially evident from the finding of both this and other

published studies, that even within the SK-BR-3 line, cells respond differently to Grb7

inhibition under different growth conditions.

Future directions

The primary limitation of this investigation, described above, suggests two possible

lines of future work. Firstly, conditions that can alter the response of SK-BR-3 cells to

ligand treatments and Grb7 inhibition, such as the serum content of the growth media,

must be identified before this cell line is used for further experiments in this area.

Secondly, to more completely characterise the role of Grb7 in breast cancer, studies

should be conducted in cell lines other than SK-BR-3. The survey of Grb7 expression

52

presented in section 3.2.1 provided much information to aid the choice of cell lines for

such studies. For example, the breast cancer cell line, BT-474, was identified as another

cell line that expresses Grb7 at high levels and that could be used in place of SK-BR-3

in a study similar to the present study. Such a study would be worthwhile to determine

whether Grb7 knockdown has a similar effect in cell lines with similar Grb7 levels. The

expression survey also showed that several cell lines express Grb7 protein at low levels,

including the breast cancer cell lines, MDA-MB-453 and MCF7. Experiments in these

cell lines could assess how sensitive such cancer cells are to the effects of Grb7

inhibition, a characteristic that may reflect their dependence on signalling pathways

involving Grb7, as well as whether the effects are the same as in highly Grb7-

overexpressing cells. Cell lines with no detectable Grb7 RNA or protein, such as

MDA-MB-468, could also be used to potentially provide evidence that there are no non-

specific effects of Grb7 siRNA.

Another avenue for future work would involve investigation of the responses of breast

cancer cells to Grb7 knockdown in the presence of ligands besides HRG, that stimulate

other signalling pathways in which Grb7 is involved. Ligands could include EGF,

PDGF, and the EphB1 ligand, EFNB1. These ligands are known to be overexpressed in

some cancers (Blume-Jensen & Hunter, 2001; Coltrera, Wang, Porter, & Gown, 1995;

Kataoka et al., 2002) and hence, such experiments could test the role of Grb7 under

different tumour conditions. Experiments could take a similar approach to that used in

this project, with the use of siRNA to down-regulate Grb7, and CT and Boyden

chamber assays to measure the effects on cell proliferation and migration.

In conclusion, this investigation has succeeded in achieving all four of the aims set,

thereby extending the knowledge of the role of Grb7 in breast cancer. The expression of

Grb7 and Grb7V was determined in a range of cells, and it was demonstrated that

knockdown of Grb7 with siRNA has no effect on SK-BR-3 cell proliferation, but mildly

stimulates SK-BR-3 cell migration. Therefore, from the present study, there is no

evidence that a Grb7-targeted drug, such as that under development by Pero and

colleagues (2002), would be of benefit as a therapeutic in Grb7-overexpressing breast

cancer. In addition to this finding, this investigation highlighted and overcame a number

of practical issues associated with the use of siRNA, serum starvation and the CT and

Boyden chamber assays, that could greatly help investigators using these techniques in

the future.

53

The investigation conducted in Part 1 was based on the literature published on Grb7

characteristics, binding partners, functional roles and links to cancer, and succeeded in

extending this knowledge to the role of Grb7 in breast cancer.

Concurrently, the molecular biology of cancer was investigated from a different

direction, that of the prediction and verification of miRNA targets of potential

significance in cancer. This investigation took a more strategic approach, involving an

initial exploratory stage to generate miRNA target predictions and hypotheses for

further study, followed by a verification stage, and additional studies to both examine

initial findings in greater depth and explore their broader context. This approach was

most appropriate in this case, as very little research had been conducted in the area of

miRNAs when the investigation began, particularly on topics relevant to the

investigation, such as computational target prediction, human miRNA targets and their

functions, and the involvement of miRNAs in cancer. This approach thus opened up a

new area for study.

The second investigation is now presented in Part 2.

54


4.1 Overview

This chapter reviews the literature relevant to an investigation of miRNA target

prediction, target verification and function, and provides background information on

two molecules of particular interest. It begins by introducing the area of miRNAs,

including a brief description of miRNA biogenesis and mechanisms of action, and an

overview of the functions of human and animal miRNAs, with special attention given to

the roles that miRNAs have been shown to play in cancer. The potential clinical

implications of these roles are also discussed. Then, the area of miRNA target

prediction is discussed. This section deals in particular with the evidence for the

usefulness of each of seven criteria proposed in the literature, that may be of use in

target prediction. This is followed by a summary of the experimental techniques that

have been used to verify miRNA:target interactions. Next, a review of the literature on

one specific miRNA, miR-7, and the epidermal growth factor receptor (EGFR) is

presented. These two molecules become the major focus of this project part way

through the investigation. Then, in the final section of this chapter, an exploratory

project is proposed to investigate miRNA target prediction, with a view to discovering

previously unknown human miRNA targets, possibly of significance in cancer.

55

4.2 Literature review of miRNAs

The first miRNA, lin-4, was discovered in C. elegans in 1993 (R. C. Lee, Feinbaum, &

Ambros, 1993). But it was not until 2001 that large numbers of miRNAs were

discovered in multiple species, including humans (Lagos-Quintana, Rauhut, Lendeckel,

& Tuschl, 2001; Lau, Lim, Weinstein, & Bartel, 2001; R. C. Lee & Ambros, 2001). By

the latest estimate, miRNAs constitute ~3% of genes in humans, flies and worms

(Bartel, 2004) and regulate at least 30% of all human genes (Lewis, Burge, & Bartel,

2005). For such a pervasive regulatory network, there is still a great deal that is

unknown. However, publications regarding miRNAs have risen from 40 in 2002 to

1093 in 20063. The field is developing at a rapid pace and much progress is being made

towards a more complete understanding of miRNAs, their targets and their roles in

cellular functions.

miRNAs are often compared to siRNAs, which were used in Part 1 of this thesis. Both

are ~22 nt RNA molecules and utilise similar cellular machinery for their maturation

and action. Hence, an extended comparison of miRNAs and siRNAs is provided here in

order to more clearly illustrate the characteristics of miRNAs.

4.2.1 miRNA biogenesis

miRNAs are encoded in miRNA genes, which are located all across the genome.

Approximately 30% are located in intergenic regions and are thought to be transcribed

independently, but the majority are located in transcriptional units. Consistent with this,

miRNAs are often co-expressed with their host genes (Baskerville & Bartel, 2005; A.

Rodriguez, Griffiths-Jones, Ashurst, & Bradley, 2004). Some miRNAs are also found in

clusters that are transcribed as polycistronic transcripts (Baskerville & Bartel, 2005). It

has been suggested that miRNAs appearing in clusters may be functionally related,

though this has not been validated (V. N. Kim & Nam, 2006).

3 NCBI Pubmed database, http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed.

56

Figure 4.1: The biogenesis of miRNAs and siRNAs, adapted from He and Hannon (2004). miRNA:miRNA* denotes the miRNA and its complementary strand.

miRNAs are distinguished from siRNAs by their biogenesis. siRNAs are processed

from double-stranded RNA (dsRNA) precursors, which can be endogenously produced

or exogenously provided (V. N. Kim & Nam, 2006). In contrast, miRNAs are

transcribed by RNA polymerase II as primary transcripts, several kilobases long, called

primary miRNAs (pri-miRNAs) (Yoontae Lee, Jeon, Lee, Kim, & Kim, 2002). These

are cleaved in the nucleus by the RNase III enzyme, Drosha, to release an ~70 nt

precursor miRNA (pre-miRNA) (Y. Lee et al., 2003). This pre-miRNA forms a stem-

loop or “hairpin” structure, one arm of which contains the mature miRNA sequence.

Next, the pre-miRNA is transported to the cytoplasm by Exportin 5, where it is cleaved

again by another RNase III enzyme, Dicer-1, into an imperfect ~22 nt duplex

(Hutvagner et al., 2001; Yi, Qin, Macara, & Cullen, 2003). The miRNA duplex may

then encounter an RNA-induced silencing complex (RISC). At this point, the duplex is

unwound and one strand is rapidly degraded. This leaves the mature single-stranded

miRNA, which is incorporated into the RISC. Importantly, there is a strong bias for the

strand with lower 5’-end stability to be incorporated into the RISC instead of its

complementary strand (Brown & Sanseau, 2005). The RISC is a ribonucleoprotein

effector complex for the miRNA. There are several types of RISC with different

components, reflecting their diverse functions. The human miRNA-containing RISC

57

contains the helicase, Gemin3, an Argonaute protein that can bind both single-stranded

and double-stranded RNA, and a number of other protein factors (Mourelatos et al.,

2002). These processes are illustrated in Figure 4.1.

4.2.2. Mechanisms of miRNA action

The mechanisms of action of both miRNAs and siRNAs are depicted in Figure 4.2. As

very little is understood in this area, the following overview is merely an outline of the

current working model.

There are three different mechanisms of action of miRNAs: target cleavage, inhibition

of translation and reduction of target stability. Firstly, both miRNAs and siRNAs can

direct cleavage of their target mRNAs by endonucleases. A requirement for this

cleavage is near-perfect base-pairing between the miRNA or siRNA and target mRNA.

This is the primary mode of action for siRNAs and plant miRNAs. Only a minority of

animal miRNAs exhibit perfect binding to their targets, there being only a few isolated

examples (Yekta, Shih, & Bartel, 2004).

Secondly, the primary mode of action for miRNAs is inhibition of translation. This was

first proposed following observations that many miRNAs are able to inhibit target

protein with little or no effect on target mRNA (R. C. Lee, Feinbaum, & Ambros, 1993;

Moss, Lee, & Ambros, 1997; Wightman, Ha, & Ruvkun, 1993). The majority of

evidence suggests that the inhibition is most likely to involve a reduction in the rate of

translation initiation (Valencia-Sanchez, Liu, Hannon, & Parker, 2006). However, there

is also evidence that inhibition occurs at a later stage in translation (Nelson,

Hatzigeorgiou, & Mourelatos, 2004) and, in fact, the mechanism is not well understood

at all. Perfect or near-perfect base-pairing is not required for inhibition of translation.

However, there are certain sequence and structural requirements that contribute to

miRNA specificity, to be discussed in section 4.2.5. siRNAs can also adopt this mode of

action with imperfect targets (Doench, Petersen, & Sharp, 2003; Hutvagner & Zamore,

2002).

Thirdly, a recently discovered mechanism of action is reduction of mRNA stability. Jing

and colleagues first proposed that interactions between miR-16, the RISC, and the

sequence-specific protein TTP were required for degradation of mRNAs containing

58

destabilising sequences called AU-rich elements (Jing et al., 2005). miRNAs have now

also been shown to reduce target mRNA as well as protein, despite insufficient

miRNA:target base-pairing for target cleavage (Lim et al., 2005a). A study conducted

by Wu and colleagues demonstrates that, in mammals, miR-125b and let-7 both speed

up the removal of mRNA poly-A tails, which would be expected to facilitate

degradation of the forming mRNA (Wu, Fan, & Belasco, 2006). This action was found

to be independent of miRNA-mediated translation inhibition and so was not a resulting

downstream effect. This study is supported by several others, as reviewed by Valencia-

Sanchez and colleagues (2006). It is possible that siRNAs could also decrease mRNA

stability, however this is yet to be investigated.

Figure 4.2: Mechanisms of action of miRNAs and siRNAs.

4.2.3 The functions of miRNAs in normal and diseased cells

4.2.3.1 The functions of miRNAs in normal cells

The functions of the majority of miRNAs are unknown. However, a number of miRNA

targets have now been revealed and miRNAs have been linked to many important

processes. In addition, some trends have become apparent.

59

An accumulation of evidence has led to a general belief that miRNAs play an important

role in the control of development. This notion began with the founding members of the

miRNA family, lin-4 and let-7 in C. elegans, shown to be involved in the timing of

early larval developmental transitions (Reinhart et al., 2000; Wightman, Ha, & Ruvkun,

1993). Since then, several other specific miRNAs have also been shown to regulate

integral processes such as apoptosis, cell proliferation, differentiation, and timing of

gene expression during development in a number of different organisms (Brennecke,

Hipfner, Stark, Russell, & Cohen, 2003; C. Z. Chen, Li, Lodish, & Bartel, 2004;

Reinhart et al., 2000). Significantly, in these cases, the miRNAs have highly specific

spatial and/or temporal expression patterns that coincide with their point of action. Also

supporting this trend are a number of studies of organisms that have mutations in their

versions of the Dicer-1 gene, and hence, are unable to generate mature miRNAs. Mutant

worms, zebrafish and mice all exhibit developmental abnormalities (Giraldez et al.,

2005; Ketting et al., 2001; W. J. Yang et al., 2005). Furthermore, analyses of the

function annotation of predicted and verified miRNA targets, such as Gene Ontology

(GO) terms, have shown that targets are enriched for genes involved in development in

Arabidopsis, Drosophila and human (Enright et al., 2003; John et al., 2004; Lewis,

Burge, & Bartel, 2005; M. W. Rhoades et al., 2002).

The same GO analyses also identified a second, though related, trend towards genes

involved the regulation of transcription. A number of transcriptional regulators have

also been verified as miRNA targets. (Yekta, Shih, & Bartel, 2004).

Both of these trends are much stronger in plants than in animals. In one study, 69% of

predicted plant miRNA targets were found to be transcription factors involved in

developmental patterning or cell differentiation (M. W. Rhoades et al., 2002). In

humans, on the other hand, two function annotation analyses of different sets of

predicted miRNA targets found that target predictions were enriched by between 3- and

6-fold for genes involved in development and in the regulation of transcription (John et

al., 2004; Lewis, Burge, & Bartel, 2005). While significant, these enrichments are much

smaller than those seen in plants. A number of other function annotation terms were

also over-represented in the predicted target genes in these studies, although the results

of the two studies did not completely overlap. Importantly, both studies emphasised

that, in contrast to plant targets, the predicted human miRNA targets encompassed a

60

very broad range of functions. The verified miRNA targets in Table 4.1 give an

indication of the diversity of miRNA functions in animals.

In addition to trends in the functions of miRNAs collectively, it has also been suggested

that there may be functional trends in the target genes of individual miRNAs. Targeting

a functional pathway at a number of different sites could dramatically increase the

efficiency of inhibition of that function. John and colleagues tested this idea with

function annotation analyses of all predicted vertebrate target genes of individual

miRNAs. These analyses showed that some miRNAs did have enrichment for predicted

targets within a functional group. For example, there was enrichment for the term

“transcription factor” in miR-208 targets (6-fold), and “small GTPase mediated signal

transduction” in miR-105 targets (5-fold). Furthermore, there is experimental evidence

for some cases in the literature. For example, miR-2 has been shown to target the

pro-apoptotic genes grim, reaper and sickle in Drosophila, suggesting that it may be

involved in apoptosis (Stark, Brennecke, Russell, & Cohen, 2003). So, some miRNAs

can target multiple genes within a functional pathway, although it is unclear whether

this results in a coordinated response or how commonly this occurs. An extension of

this idea is that of miRNA regulatory modules in which multiple miRNAs target

multiple functionally related mRNAs for a coordinated functional outcome.

In summary, miRNAs are often involved in the processes of development and

regulation of transcription. However, in animals, these roles are not as central as in

plants, and miRNA targets additionally cover a broad range of functions. It is also

possible that some miRNAs form modules of miRNAs and targets that act together to

perform a particular function.

61

Table 4.1: Animal miRNA functions. Hs = Homo sapiens, Mm = Mus musculus, Dm = Drosophila melanogaster, Ce = Caenorhabditis elegans. miRNA Species Target Biological Role Reference

miR-375 Hs MTPN Regulation of insulin

secretion

(Poy et al., 2004)

miR-15a/

miR-16-1

Hs BCL2 Promotion of apoptosis (Cimmino et al., 2005)

miR-155 Hs hAT1R Hypothesised role in renin-

angiotensin system

(Martin, Lee,

Buckenberger,

Schmittgen, & Elton,

2006)

miR-125 Hs ErbB2,

ErbB3

Promotion of cell growth,

migration and invasion

(Scott et al., 2007)

miR-20/

miR-17-5p

Hs E2F1 Control of cell proliferation (O'Donnell, Wentzel,

Zeller, Dang, &

Mendell, 2005)

miR-1 Mm Hand2 Control of differentiation

and proliferation during

cardiogenesis

(Y. Zhao, Samal, &

Srivastava, 2005)

miR-181 Mm - Promotion of B-cell

differentiation

(C. Z. Chen, Li,

Lodish, & Bartel,

2004)

bantam Dm hid Control of proliferation and

inhibition of cell death

(Brennecke, Hipfner,

Stark, Russell, &

Cohen, 2003)

miR-14 Dm - Fat metabolism and

inhibition of cell death

(P. Xu, Vernooy, Guo,

& Hay, 2003)

lin-4 Ce lin-14 Developmental timing (Wightman, Ha, &

Ruvkun, 1993)

4.2.3.2 miRNAs in cancer

As described above, miRNAs have been strongly linked to development, with many

miRNAs thought to be involved in associated processes such as differentiation,

62

proliferation and cell death. However, these processes are also central in cancer. Hence,

it has been proposed that abnormalities in miRNA-mediated regulation might contribute

to the generation or maintenance of cancer. Much evidence now substantiates this

hypothesis.

For example, examination of miRNA expression profiles in normal and cancerous

tissues has revealed patterned differences that may represent molecular changes

important in oncogenesis. Lu and colleagues demonstrated, using miRNA microarrays,

that a large proportion of miRNAs (129 of 217, p < 0.05) are down-regulated in cancers

compared to normal tissues (Lu et al., 2005). In accordance with this, other studies have

reported down-regulation of specific miRNAs in tumours compared to normal tissues,

for both colorectal and lung cancer (Michael, SM, Van Holst Pellekaan, Young, &

James, 2003; Takamizawa et al., 2004). Further, Lu and colleagues were able to

generate a classifier capable of distinguishing cancers from normal tissues based on

their miRNA expression profiles. The accuracy of the classifier was excellent and far

exceeded that of classifiers based on mRNA expression profiles. This is consistent with

numerous observations of cancer-specific miRNA expression profiles in many types of

cancer including B-cell chronic lymphocytic leukaemia (CLL), primary glioblastoma,

and breast, colon and papillary thyroid carcinoma (Calin et al., 2004; Ciafre et al., 2005;

H. He et al., 2005; Iorio et al., 2005; Michael, SM, Van Holst Pellekaan, Young, &

James, 2003). Some of these studies additionally demonstrated links between the

expression of particular miRNAs and pathological features of the cancer. It has also

been shown that cancers can be classified into tissue of origin and even cell lineage

groups using miRNA expression profiles (Lu et al., 2005). It has been proposed that

these tissue-specific miRNA “fingerprints” may reflect oncogenic changes in

developmental pathways characteristic of tissue type, and may hold valuable

information about transformation in different tissues (Esquela-Kerscher & Slack, 2006).

There is also genetic evidence linking miRNAs to a role in cancer. It has been shown

that 52.5% of miRNA genes are located in cancer-associated genomic regions or in

fragile sites (Calin et al., 2004). In addition, a high proportion of genomic loci

containing miRNA genes have been found to exhibit DNA copy number alterations in

ovarian cancer (37.1%), breast cancer (72.8%) and melanoma (85.9%) (L. Zhang et al.,

2006). These studies suggest direct mechanisms that may contribute to aberrant miRNA

63

expression in cancer. Such high proportions of affected miRNAs also suggest their

widespread involvement in cancer.

Numerous studies also link particular miRNAs to cancer. Several of these cases have

been thoroughly investigated and convincingly support the hypothesis that miRNAs

play important roles in cancer. Possibly the best-known example is that of miR-15a and

miR-16-1. The genes for these two miRNAs are located in a cluster at chromosome

13q14, a region that is deleted in more than half of B-cell CLLs as well as in a large

proportion of mantle cell lymphomas, multiple myelomas and prostate cancers. Both

miRNA genes are deleted or down-regulated in ~65% of CLL cases (Calin et al., 2002).

In a study by Cimmino and colleagues (2005), miR-15a and miR-16-1 were shown to

directly inhibit expression of the anti-apoptotic protein B-cell CLL/lymphoma 2

(BCL2), commonly up-regulated in CLL and other cancers. In addition, the expression

of both miRNAs was found to be inversely correlated with that of BCL2 in CLL. miR-

15a and miR-16-1 also induced apoptosis in a leukaemia cell line via down-regulation

of BCL2. As yet, no other mechanism has been shown to account for BCL2 up-

regulation in CLL. In another study, Calin and colleagues discovered a germline point

mutation in the common primary precursor of miR-15a and miR-16-1 in two patients

with CLL, which was linked to lower miRNA levels in vitro and in vivo (Calin et al.,

2005). A number of miRNAs, including miR-15a and miR-16-1, were also identified,

whose expression profiles could distinguish between cases of CLL with different

prognostic factors.

Another miRNA that is well-studied in the context of cancer is let-7. Originally studied

for its role in C. elegans development, it has been shown to be frequently down-

regulated in human lung cancers and lung cancer cell lines compared to normal lung

tissue (Johnson et al., 2005; Takamizawa et al., 2004). In one study, down-regulation of

let-7 was also found to be correlated with poor prognosis (Takamizawa et al., 2004).

Furthermore, over-expression of let-7 in the lung adenocarcinoma cell line, A549,

caused growth suppression, consistent with a role for let-7 as a tumour suppressor in

lung cancer. Supporting all of these results, let-7 can directly target and inhibit the

expression of Ras family oncogenes in both worm and human (Johnson et al., 2005).

Activating mutations and overexpression of Ras causes transformation of human cells

(Bos, 1989). This finding therefore provides a plausible mechanism for the observed

effects of let-7 in human lung cancer.

64

In summary, evidence from miRNA expression profiles, genetic analysis and examples

of the actions of specific miRNAs in cancer indicates that miRNAs can play important

roles in cancer. In fact, the application of miRNA-related findings to clinical areas, such

as diagnosis and therapy, is already under investigation.

4.2.4 Clinical applications of miRNA research

The study of miRNAs has yielded fresh insight into diseases such as cancer and raised

the possibility of new approaches to therapy and improvements in the assessment of

diagnosis, prognosis and disease susceptibility.

miRNA-based cancer therapies could utilise miRNA inhibitors against over-expressed

oncogenic miRNAs, or alternatively, mimics of tumour suppressor miRNAs. These

approaches have the advantage over similar oligonucleotide-based approaches such as

RNAi and antisense treatments, that miRNAs can have multiple targets. By altering the

level of a single miRNA, many functionally related targets may be affected, inducing a

more dramatic effect on the cell. In this way, miRNA treatments may act like “single

drug cocktails”. On the other hand, multiple functionally unrelated targets may induce

more diverse and/or serious side effects.

Unfortunately, the realisation of miRNA-based therapeutics faces many of the same

obstacles encountered for other nucleic acid-based therapies, including drug stability

and nuclease resistance, intracellular delivery and unwanted cellular responses. See

Dykxhoorn and Lieberman (2006) for a review of these issues.

However, independent of their future as therapeutics, miRNAs appear to hold a great

deal of information in their expression profiles that may be exploited. Discriminators

based on miRNA expression, that can distinguish between cancerous and normal

tissues, have already been identified (Lu et al., 2005; Yanaihara et al., 2006).

Furthermore, miRNA expression may also be able to distinguish pre-malignant from

normal tissues (Bottoni et al., 2005; H. He et al., 2005; Michael, SM, Van Holst

Pellekaan, Young, & James, 2003) and inform cancer management through specific

prognostic markers (Iorio et al., 2005; Takamizawa et al., 2004). With greater accuracy

and specificity than discriminators based on mRNA expression and a smaller set of

genes to probe, miRNA discriminators may aid the development of cheaper and faster

65

diagnostic and prognostic tests. In addition, it is possible that germline mutations in

miRNAs or their target sites could provide a means to assess the susceptibility of

healthy individuals to specific diseases, as hinted at by the results of several groups

(Calin et al., 2005; H. He et al., 2005).

There is much to investigate in the field of miRNAs, and there is great motivation to do

so, from a clinical as much as from a purely theoretical standpoint.

miRNAs have been implicated in many important functions. However, the majority of

their targets are yet to be determined. A common way to predict and determine miRNA

functions involves identification of the targets through which they perform these

functions. Because of the focus of this project on the possible roles of miRNAs in

cancer, it was considered important to first illustrate in some detail, the computational

and methodological approaches that have recently been used to begin to identify

miRNA targets, and that are extensively used in this research program.

4.2.5 miRNA target prediction

Target prediction is an essential step in characterising the function of a miRNA. While

some studies have tackled the problem experimentally (Boutla, Delidakis, & Tabler,

2003; Nakamoto, Jin, O'Donnell W, & Warren, 2005; Vatolin, Navaratne, & Weil,

2006), the most common approach is bioinformatic in nature. Many computer

algorithms have now been designed to predict miRNA targets, including TargetScanS,

miRanda, RNAhybrid and PicTar (John et al., 2004; Krek et al., 2005; Lewis, Burge, &

Bartel, 2005; Rehmsmeier, Steffen, Hochsmann, & Giegerich, 2004). Programs

generally cycle through a set of mRNAs in a given set for all known miRNAs,

screening each mRNA sequence for possible miRNA binding sites, based on proposed

criteria for miRNA:mRNA interaction. However, this approach is not straightforward

for two reasons.

Firstly, as miRNAs are very short and do not bind with perfect or near perfect

complementarity to their targets, it is difficult to detect real miRNA target candidates

(the signal) over coincidental matches (the background noise) based on sequence

complementarity alone. The signal to noise ratio (SNR) is a measure of the specificity

66

of a target prediction algorithm in this context. Therefore, additional insight is required

to enable efficient target prediction.

Secondly, the set of verified miRNA targets is still quite small. The rules proposed for

target prediction have been based on characteristics common to these few examples.

Therefore, these rules may overfit the data set and not generalise to the greater

population of miRNA targets. Hence, it is possible that current prediction programs are

missing large groups of targets that differ slightly from the original few. Deviating from

the formula is risky, however, because without a guiding hypothesis, the false positive

rate is likely to increase. Therefore, more information about the molecular biology of

miRNA:mRNA interactions is required.

Nevertheless, prediction algorithms have had a great deal of success in predicting

functional miRNA targets and a number of advances in prediction criteria have

significantly improved their accuracy. Several “rules” have been surmised from

statistical analysis and empirical evidence to be important for prediction of miRNA

targets. Some are still speculative and this list is constantly evolving. According to the

rules, the following characteristics make for a promising miRNA:target candidate:

1. Cross-species conservation of the mRNA sequence,

2. Target site location in the 3’ untranslated region (3’UTR),

3. High sequence complementarity of target sites to the 5’ end of the miRNA,

4. Low free energy of hybridisation between miRNA and mRNA target sites,

5. Accessibility of mRNA target sites to miRNAs,

6. The presence of multiple miRNA target sites within the 3’UTR,

7. Co-expression of miRNA and target in vivo.

Each of these criteria is discussed in detail in the following sections.

4.2.5.1 Cross-species conservation of the mRNA sequence

The vast majority of miRNA target prediction programs include a cross-species

conservation filter of some description to restrict the mRNA sequence search set. The

stringency of this filter varies from requiring entirely conserved 3’UTRs (Krek et al.,

2005) to requiring only local conservation over a portion of the miRNA match (Lewis,

67

Burge, & Bartel, 2005), and from requiring conservation across eight vertebrate species

(Krek et al., 2005) to conservation across two Drosophila species only (Stark,

Brennecke, Russell, & Cohen, 2003). The primary motivation is that there is a great

deal of evidence that limiting the search set to conserved sequences enriches it for true

miRNA targets (John et al., 2004; Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003).

For example, in one study, the SNR was found to increase from 2:1 for conservation

across human and mouse, to 4.6:1 for conservation across human, mouse, rat and

pufferfish (Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003). An added benefit of a

conservation filter is the reduction of the search set.

The rationale for this criterion is that because miRNAs themselves are very well

conserved across evolution, their target sites and possibly the presence of other

necessary cis regulatory elements in the 3’UTR might be under similar selective

pressure to preserve the potentially important regulatory interaction. The conservation

filter criterion is not derived from a fundamental restriction on the interaction of

miRNAs and targets. An analysis of human microarray expression data suggests that

many non-conserved target site predictions are actually functional miRNA targets

(Sood, Krek, Zavolan, Macino, & Rajewsky, 2006). In addition, the discovery of more

than 100 primate-specific miRNAs and also a small number of human-specific miRNAs

indicates that there are probably a large number of species-specific miRNA targets

(Bentwich et al., 2005; Berezikov et al., 2006). All of these studies suggest that there is

likely to be a number of human miRNA targets that are not extensively conserved

across species and that it would be desirable to be free of the conservation restriction.

A few groups have now tried to develop algorithms that do not rely on cross-species

conservation (Robins, Li, & Padgett, 2005; Saetrom, Snove, & Saetrom, 2005) and

hence will not overlook non-conserved targets. These programs have the added

advantage that they do not automatically exclude potential conserved targets with

incomplete sequences or incorrect alignments.

To summarise, while the predictive value of a conservation filter has not been

challenged, there is a trend to acknowledge that non-conserved miRNA targets are

likely to exist.

68

4.2.5.2 Target site location in the 3’UTR

Target prediction programs are usually restricted to search 3’UTRs only. This bias

stems from the observation that the first animal miRNA target sites discovered were

located in 3’UTRs (Brennecke, Hipfner, Stark, Russell, & Cohen, 2003; Moss, Lee, &

Ambros, 1997; Pasquinelli et al., 2000; Wightman, Ha, & Ruvkun, 1993). This practice

has been questioned, one reason being that the target sites of plant miRNAs are usually

located in coding regions (Jones-Rhoades & Bartel, 2004; M. W. Rhoades et al., 2002)

and have also been predicted in 5’UTRs (Sunkar & Zhu, 2004). However, evidence is

now accumulating that suggests that 3’UTRs do hold the majority of animal miRNA

targets, but that there may be some target sites within coding regions as well.

At least three studies using target prediction programs have found a substantial increase

in SNR for 3’UTR search sets over more encompassing search sets (John et al., 2004;

Lewis, Burge, & Bartel, 2005; Stark, Brennecke, Russell, & Cohen, 2003). One of these

studies also detected a significant, though much lower, signal above noise for a coding

region search set but little to no signal for a 5’UTR search set (Lewis, Burge, & Bartel,

2005). Another presented a statistical analysis that predicted significant numbers of

targets in 3’UTRs, but far fewer than expected in coding regions (Rehmsmeier, Steffen,

Hochsmann, & Giegerich, 2004). An analysis of microarray data came to a similar

conclusion (Lim et al., 2005b). It was shown that the set of genes down-regulated upon

transfection of miRNA duplexes was enriched for miRNA target sites. The enrichment

was found to be greatest for 3’UTRs, but was also significant for coding regions.

Another study, taking an experimental approach to target prediction, monitored shifts in

mRNA abundance in polyribosome profiles following miRNA knockdown (Nakamoto,

Jin, O'Donnell W, & Warren, 2005). For miRNA targets predicted using this process,

the miRNA match sites were often found in the target 3’UTR and also, frequently in the

coding region.

The only study with evidence to the contrary used a population-based statistical

approach to determine characteristics of miRNA:mRNA interactions. This study

reported no tendency for miRNA targets to be located in the 3’UTR, instead finding that 2/3 of miRNA match sites were within coding regions (Smalheiser & Torvik, 2004).

69

The available data provides adequate justification for restricting target searches to

3’UTRs to reduce the false positive rate. The data is inconclusive as to whether miRNA

target sites occur in the coding region and 5’UTR, and so, as for the conservation filter,

with utilising a 3’UTR restriction comes the possibility of overlooking some real

miRNA targets.

4.2.5.3 High sequence complementarity of target sites to the 5’ end of the miRNA

and other sequence considerations

The very nature of miRNA:target interactions implies that the mRNA sequence plays a

critical role in target recognition. Sequence analysis formed the basis of the original

target prediction algorithms and continues to be an essential step in all standard

prediction programs.

The most important characteristic of a putative miRNA target site is that it has high

complementarity to the 5’ end of the miRNA, particularly in the region known as the

“seed”, defined in this thesis as miRNA nucleotides 2-84. The importance of the seed to

miRNA:target interactions has been verified on many occasions using different

approaches and has become a commonly acknowledged requirement to the point where

it has been referred to as the “obligatory” seed (Bentwich, 2005).

Not only do the majority of verified miRNA targets have sections of perfect

complementarity to the miRNA 5’ end, but the 5’ ends of miRNAs are also better

conserved than their 3’ ends, suggesting a particular importance for this region (Lim,

Glasner, Yekta, Burge, & Bartel, 2003). The significance of the seed has also been

explored computationally. One analysis involved running a target prediction program

requiring perfect seed matches at different positions along the length of the miRNA,

1-7, 2-8, 3-9 and so on (Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003). The result

was that the SNR was greatest for seed positions at the 5’ end of the miRNA and was at

a maximum for a seed position at nucleotides 2-8. This suggests that the section of the

miRNA between nucleotides 2 and 8 is the most important for target recognition. This

result was later corroborated using a machine-learning algorithm (Saetrom, Snove, &

Saetrom, 2005).

4 Precise definitions of the terms relating to miRNA and target structure used in this thesis are given in the Terminology section on page xxi.

70

Several mutation studies have been performed to elucidate the sequence requirements

for miRNA targets (Brennecke, Stark, Russell, & Cohen, 2005; Doench & Sharp, 2004;

Kiriakidou et al., 2004; Lai, Tam, & Rubin, 2005). All support the conclusion that a

seed match is of central importance for miRNA:target functionality. However, some

mutants with deviations from the perfect 7 nt seed match were also found to cause

partial repression. Mismatches, single nucleotide loops and G:U base-pairs in the seed

region all reduced repression. However, the effect varied with the position of the

mutation, the identity of a loop nucleotide, and the miRNA sequence, and, in a few

cases, significant repression was still observed. This is consistent with the presence of

single nucleotide loops in the seed regions of the verified C. elegans miRNA:target

pairs let-7:lin-41 and lin-4:lin-14 (Rehmsmeier, Steffen, Hochsmann, & Giegerich,

2004).

Brennecke and colleagues also investigated the minimum seed match length required

for functionality (Brennecke, Stark, Russell, & Cohen, 2005) and found that a perfect

7 nt seed match with no predicted 3’ binding was sufficient for translational repression.

In addition, strong 3’ binding could compensate for a seed match as short as 4 nt. From

the findings of this study and the work of others (Doench & Sharp, 2004; Lai, Tam, &

Rubin, 2005), Brennecke and colleagues recognised three classes of miRNA target site.

1. 5’-dominant canonical sites:

Sites of this class exhibit strong binding to the 5’ end of the miRNA, but also bind

well to the 3’ end of the miRNA. The majority of verified animal miRNA targets

are of this form. Examples include the sites for miR-7 in hairy in Drosophila,

(Stark, Brennecke, Russell, & Cohen, 2003) and for let-7 in lin-41 in C. elegans

(Reinhart et al., 2000).

2. 5’-dominant seed sites:

Sites of this class have a perfect miRNA seed match at least 7 nt long, with little or

no predicted binding to the 3’ end of the miRNA. There are no examples of verified

seed site targets as yet. However, there is evidence that three 7 nt sequences known

as Brd boxes found within the Drosophila Bearded 3’UTR may be seed sites for

miR-4 and miR-79 (Lai, 2002).

71

3. 3’-dominant compensatory sites:

3’-dominant miRNA target sites are dependent on strong binding to the 3’ end of

the miRNA to compensate for weak 5’ binding. This class includes target sites with

short seed matches of 4-6 nts and seed matches of 7-8 nts with G:U base-pairs,

loops or mismatches. Sequence analysis suggests that these sites are less common

than 5’-dominant sites. Examples include sites for miR-2 in grim and sickle in

Drosophila (Stark, Brennecke, Russell, & Cohen, 2003) and sites for let-7 in lin-41

in C. elegans (Reinhart et al., 2000).

Target prediction methods should ideally enable detection of all of these classes of

target site. The miRanda algorithm (John et al., 2004) allows G:U base-pairs and

mismatches in the seed region. However, the TargetScanS algorithm has come closest

to this goal by allowing 3’UTRs with only a single 6 nt seed and also offering flexible

parameters for restrictions on G:U base-pairs in the seed and weak 5’ binding (Lewis,

Burge, & Bartel, 2005). This comes at the cost of a greater reliance on conservation.

A final sequence consideration is that miRNA targets are more likely to have

adenosines in positions 1 and 9, regardless of the identity of the corresponding bases in

the miRNA (Lewis, Burge, & Bartel, 2005). This may reflect the preference of a

cofactor that is required for interaction.

In conclusion, a requirement for target predictions to have good or perfect

complementarity to the miRNA seed is very worthwhile and at present is the only rule

capable of predicting 5’-dominant seed sites. This is not an ideal approach for detecting

the rarer 3’-dominant target sites. However, their increased complementarity to the 3’

ends of miRNAs suggests an alternative approach. In addition, the presence of

adenosines flanking the seed is another criterion which may improve target prediction.

4.2.5.4 Low free energy of hybridisation between miRNA and mRNA target sites

mRNA sequence will affect both the strength of miRNA:mRNA binding and the

structure of the resulting duplex. Either of these could be important predictors,

depending on the nature of the interactions of miRNAs and mRNAs with each other and

with other proteins. Therefore, consideration of the consequences of mRNA sequence at

a higher level than simple complementarity may be beneficial.

72

RNA-folding programs such as mfold (Zuker, 2003), RNAfold (as used by Lewis and

colleagues, (2003)) and PairFold (Andronescu, Aguirre-Hernandez, Condon, & Hoos,

2003) can be useful here. These programs predict RNA secondary structures and

provide estimates of the free energy of hybridisation based on knowledge of known

RNA structures and thermodynamic modelling. Factors such as the base composition of

the interacting base-pairs, and the possibility of G:U base-pairs and loops in the

structure are taken into account.

It is fairly common to include a free energy calculation step in target prediction

programs (Burgler & Macdonald, 2005; Doench & Sharp, 2004; Enright et al., 2003;

Kiriakidou et al., 2004; Krek et al., 2005; Lewis, Shih, Jones-Rhoades, Bartel, & Burge,

2003; Stark, Brennecke, Russell, & Cohen, 2003). This is usually performed after an

initial sequence analysis step imposing a seed match requirement. In general, the free

energy of hybridisation of a particular duplex is compared to a cutoff score, chosen for a

good compromise between sensitivity and specificity. Duplexes with free energy values

below the cutoff continue through to subsequent analysis steps.

As discussed in the previous section, miRNA:target pairs are likely to have strong

binding in the seed region. Consistent with this, Doench and colleagues (2004) showed

that the degree of target repression is correlated with the free energy of hybridisation of

the first eight nucleotides, considering features such as loops and mismatches of

different nucleotides in different positions. This result says much for the use of free

energy calculations in addition to seed complementarity in target prediction. However,

the main reason for calculating free energy values is to assess the quality of the entire

miRNA:mRNA interaction. Although no published studies have estimated the value of

a free energy criterion to target prediction, intuitively, it could be a powerful tool.

However, there is a problem that can undermine the usefulness of free energy values

under some conditions. It has been shown that G:U base pairs in the seed region reduce

the ability of a miRNA to repress an mRNA beyond the level that would be expected

from free energy values (Doench & Sharp, 2004). An extreme example of this problem

arises in the target prediction program RNAhybrid (Rehmsmeier, Steffen, Hochsmann,

& Giegerich, 2004). RNAhybrid’s main criterion for prediction is good free energy of

binding. Its top predictions for Drosophila include miR-92a:tailless and miR-210:hairy,

which have two and three G:U base-pairs in the seed region respectively. Two separate

73

studies report that more than one G:U base-pair in the seed region completely

eliminates repression (Brennecke, Stark, Russell, & Cohen, 2005; Doench & Sharp,

2004), suggesting that these predictions may not be functional.

In terms of the value of secondary structure to target prediction, it has been suggested

that functional miRNA:mRNA pairs may require a central loop, possibly for binding a

cofactor. Although studies have provided experimental evidence for this (Doench &

Sharp, 2004; Kiriakidou et al., 2004), the existence of multiple target sites that do not

satisfy this criterion make it unlikely to be a strict requirement (Brennecke, Stark,

Russell, & Cohen, 2005).

In summary, the folding of miRNA:mRNA duplexes provides free energy of

hybridisation values, which are likely to improve target prediction. However, it is less

likely that a central loop is required for interaction. As no other predictive structural

features have been proposed, the miRNA:mRNA folds themselves are less useful at this

stage.

4.2.5.5 Accessibility of mRNA target sites to miRNAs

The accessibility of a potential miRNA target site is also a consideration for target

prediction. This is dependent on the free energy of hybridisation and on structural

elements, such as stable stem arrangements and unstable free bases present in loop and

bubble arrangements, in the region of the target site. It can therefore be assessed

through folding of the mRNA sequence on a larger scale, in the absence of the miRNA.

Examination of verified target sites has revealed that virtually all of them are located in

unstable stretches of mRNA, suggesting that an mRNA structure criterion could be

useful in target prediction (Y. Zhao, Samal, & Srivastava, 2005). Robins and colleagues

tested this idea with a target prediction algorithm that employed a criterion based on the

local stability of the mRNA seed match sites (Robins, Li, & Padgett, 2005). This

algorithm was quite successful, having sufficient specificity that it did not require a

conservation filter.

The consideration of mRNA target structure is a relatively new proposal and has not

been thoroughly investigated. However, it does appear to offer a different approach to

target prediction, independent of other criteria.

74

4.2.5.6 Presence of multiple miRNA target sites within a 3’UTR

Just as many mRNAs are regulated by combinations of transcription factors, much

evidence suggests that some mRNAs have multiple miRNA target sites. Doench and

colleagues first showed, using experiments with reporter constructs containing different

numbers of imperfect target sites, that multiple miRNAs can target a single mRNA and

that this occurs in a synergistic manner (Doench, Petersen, & Sharp, 2003). In a less

artificial system, there are two branches of this idea to consider. The first is that a single

miRNA can have multiple target sites on a single mRNA. The second is that multiple

miRNAs can have target sites on a single mRNA. Both of these possibilities could

affect the results of target prediction.

With respect to the first possibility, it has been observed that for many of the known

miRNA:target pairs, the miRNA is predicted or has been shown to target multiple sites

within the mRNA (Burgler & Macdonald, 2005; Stark, Brennecke, Russell, & Cohen,

2003). In addition, Lai and colleagues showed, using in vivo reporter transgene

experiments, that 3’UTRs with multiple seed sites were generally repressed to a greater

degree than those with single seed sites, even though the single seed sites often had

better pairing to the miRNA (Lai, Tam, & Rubin, 2005). Furthermore, Kloosterman and

colleagues (2004) found that, in zebrafish embryos, when either of the two let-7 target

sites in lin-41 was changed to a miR-221 target site, then both let-7 and miR-221 were

required for significant lin-41 repression, suggesting that targets that are not functional

in isolation can function in combination.

Kloosterman and colleagues’ study also lends weight to the hypothesis that different

miRNAs are able to act concurrently on the same mRNA target. Indeed, several studies

also predict that many mRNAs contain target sites for multiple miRNAs (John et al.,

2004; Krek et al., 2005; Stark, Brennecke, Russell, & Cohen, 2003). Furthermore, there

is experimental evidence that, in mouse, there is cooperative regulation of myotrophin

(Mtpn) by a set of three different miRNAs (Krek et al., 2005). This would allow the cell

to fine-tune regulation of gene expression with cell specific expression of miRNAs at

different levels. It has also been proposed that miRNAs can act as part of regulatory

modules in which multiple miRNAs regulate a group of mRNA targets associated with

a particular function (Yoon & De Micheli, 2005), although none have been verified as

yet.

75

In terms of target prediction, these possibilities could mean that algorithms may achieve

greater accuracy by weighting predicted miRNA:target pairs by the number of match

sites within the target. Alternatively, multiple weak target sites that would not be

predicted in isolation, might be considered in combination. There are programs that

have been designed specifically to consider combinations of target sites. One example is

the program PicTar (Krek et al., 2005). This program uses a maximum likelihood

approach for scoring multiple target sites and makes some predictions that are verified

in cell lines. In another study, Yoon and colleagues (2005) present a program that

predicts miRNA regulatory modules using a parallel distributed processing framework.

This program predicts a module of significance in cancer and produces evidence from

the literature.

In conclusion, while it is established that miRNAs can mediate repression through a

single target site, it is likely that they can also bind to multiple sites within the same

target, and possible that these can act synergistically. Target prediction programs should

allow for multiple target sites to influence the scoring of miRNA:target pairs. The

cooperativity of miRNAs is a promising line of research that is still in its early stages.

4.2.5.7 miRNA and target expression profiles

In order for a miRNA and target to interact in vivo, the two must be co-expressed. For

this reason, it is a good idea to confirm this for any miRNA:target prediction. However,

it has also been suggested that for some tissue-specific miRNAs, mRNA expression

profiles may have predictive power as well.

The expression of many miRNAs appears to be limited spatially to particular cell types

and tissues, and/or temporally to particular developmental stages (Babak, Zhang,

Morris, Blencowe, & Hughes, 2004; C. G. Liu et al., 2004). It is possible then, that

exclusive miRNA and target expression profiles contribute to miRNA specificity by

ensuring that target matches occurring by chance are not co-expressed with the

matching miRNA and so do not have functional consequences in vivo.

In support of this idea are a number of microarray studies of miRNA and mRNA

expression profiles across a range of tissues (Farh et al., 2005; Lim et al., 2005a; Sood,

Krek, Zavolan, Macino, & Rajewsky, 2006). These studies show that miRNA targets

76

tend to be expressed in the same tissues as their matching miRNA, but at lower levels

than their expression in other tissues. Presumably, these reduced levels are due to

miRNA-mediated reduction in target mRNA. However, a similar study by Babak and

colleagues (2004) did not find a correlation between miRNA and mRNA levels from

microarrays of mouse organs and tissues. As they suggest, the majority of targets in this

study may have been regulated by translational repression, or the target predictions may

have been erroneous. Critics of this idea also argue that few miRNAs are likely to be

truly tissue-specific (Shivdasani, 2006).

It has been suggested that a correlation between miRNA and target expression profiles

be taken advantage of in target prediction algorithms (Krek et al., 2005; Rajewsky,

2006). While this has not been attempted to date, many microarray experiments are

being performed to determine the expression profiles of miRNAs and mRNAs, which

will certainly aid this process (Babak, Zhang, Morris, Blencowe, & Hughes, 2004; C. G.

Liu et al., 2004; Sun et al., 2004).

In conclusion, miRNA and mRNA expression profiles may be of use in target

prediction algorithms in some cases, though their predictive power is still in question.

Regardless, it is important to check whether miRNAs are co-expressed with predicted

targets to determine whether they are likely interact in vivo.

This review has attempted to convey the weight of evidence for and against the use of

each of the seven target prediction criteria and the value to place on agreement with

each one. As stated at the beginning of this section, there are still relatively few verified

miRNA targets on which these criteria are based and the area is poorly understood. As a

reflection of this, target prediction programs are typically not very accurate, with

estimates of false positive rates ranging from 22% to 39% (Enright et al., 2003; Krek et

al., 2005; Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003). Therefore, it is

extremely important to validate miRNA target predictions experimentally, ideally in a

physiologically relevant system.

77

4.2.6 Verification of human miRNA targets

Following target prediction, the natural progression is to validate predicted targets

experimentally. Progress in this area has been relatively slow, hampered by the lack of a

high-throughput assay. However, with the increasing interest in miRNA functions,

research in this area is providing new approaches to target validation.

To validate a predicted miRNA target, evidence can be accumulated from a number of

different in vitro approaches, utilising endogenous miRNAs and/or miRNA up- or

down-regulation. Approaches include luciferase reporter assays, monitoring of

endogenous target levels, microarray analysis and function studies.

4.2.6.1 miRNA up-regulation

miRNAs can be up-regulated in cells by transfection with synthetic miRNAs or

plasmids expressing miRNAs.

Synthetic miRNAs are partially double-stranded RNA duplexes. They are designed to

maximise activation of the miRNA sense strand and hence mimic endogenous miRNA

precursors. They have been used widely and demonstrated to reliably knockdown target

protein (Johnson et al., 2005; Lim et al., 2005a; Martin, Lee, Buckenberger, Schmittgen,

& Elton, 2006; Wang & Wang, 2006; Yu, Raabe, & Hecht, 2005).

For long-term studies, plasmid-based miRNA expression systems can be used to

continuously express miRNAs for an extended period. Such systems have also been

widely used (Dickins et al., 2005; Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003;

Stark, Brennecke, Russell, & Cohen, 2003; Takamizawa et al., 2004; Zeng, Wagner, &

Cullen, 2002). The DNA constructs generally contain the hairpin portion of the miRNA

precursor that includes the mature miRNA sequence. The hairpin RNA will be

processed within the cell into duplexes containing the mature sequence. Constructs can

utilise endogenous promoters such as those for RNA polymerase II or III (Dickins et al.,

2005; Takamizawa et al., 2004). Alternatively, inducible promoters can be used to

enable miRNA production to be controlled.

78

Another option is to use a retroviral or adenoviral expression system for stable

transfection of cells (C. Z. Chen, Li, Lodish, & Bartel, 2004; Lewis, Shih, Jones-

Rhoades, Bartel, & Burge, 2003).

4.2.6.2 miRNA down-regulation

miRNAs can be down-regulated with siRNAs, antisense DNA oligonucleotides or

miRNA inhibitor duplexes. All of these techniques have been presented in the literature

to effectively down-regulate miRNAs (Davis, Lollo, Freier, & Esau, 2006; Johnson et

al., 2005; Krek et al., 2005). Most commonly used are the ‘Anti-miR’ miRNA inhibitors

produced by Ambion, Inc. (Cheng, Byrom, Shelton, & Ford, 2005; Johnson et al., 2005;

Martin, Lee, Buckenberger, Schmittgen, & Elton, 2006). These are RNA-based

sequence-specific inhibitors, chemically modified to increase their stability.

Both up- and down-regulation of miRNAs can be used in conjunction with the target

validation approaches described in the sections to follow.

4.2.6.3 Luciferase reporter assays

Luciferase reporter assays are by far the most common way to validate miRNA targets

(Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003; Lim et al., 2005a; Martin, Lee,

Buckenberger, Schmittgen, & Elton, 2006; Yu, Raabe, & Hecht, 2005). Reporter

constructs generally contain a portion of the predicted target 3’UTR, including the

miRNA target sites, cloned downstream of a luciferase reporter gene. Luciferase

activity of the wild-type construct is compared to that of an analogous construct with

the predicted target sites mutated. If the 3’UTR is a target of the miRNA, then in the

presence of the miRNA, the wild-type reporter is inhibited and luciferase activity is

reduced relative to vector activity. The miRNA will not bind to the mutant reporter, and

hence, luciferase activity in this case is unaffected.

4.2.6.4 Monitoring of endogenous protein levels

Another approach to target validation involves monitoring the effects of miRNA up- or

down-regulation on the endogenous protein levels of predicted targets, often using

Western blot (Krek et al., 2005; Martin, Lee, Buckenberger, Schmittgen, & Elton, 2006;

79

O'Donnell, Wentzel, Zeller, Dang, & Mendell, 2005). If a predicted target is a real

target, its protein levels will change in response to changes in miRNA levels.

4.2.6.5 Microarray experiments

As an alternative to protein levels, mRNA levels may be measured for changes resulting

from miRNA up- or down-regulation, using microarrays (Wang & Wang, 2006). As

well as contributing to validation of miRNA target predictions, this approach also

allows many more potential targets to be identified at once. A trend towards

functionally related targets may also suggest specific roles for the miRNA.

4.2.6.6 Function studies

It can also be beneficial to investigate the function of the miRNA in relation to that of

the predicted target. If up- or down-regulating a miRNA induces a cellular response that

is consistent with the demonstrated effect of a converse change in the level of the

predicted target protein, then in combination with evidence of a miRNA:mRNA

interaction, this suggests that the miRNA not only inhibits the expression of the

predicted target, but does so to the extent that the behaviour of the cell is affected

(Krutzfeldt, Poy, & Stoffel, 2006; Y. Zhao, Samal, & Srivastava, 2005). There is

overlap between this approach to experimental target evaluation and investigation of the

function of a miRNA. In fact, similar studies have been used as the first step in an

investigation of the function of a miRNA with unknown targets (Cheng, Byrom,

Shelton, & Ford, 2005; Takamizawa et al., 2004).

In conclusion, an accumulation of evidence from a combination of different

experimental approaches can validate a miRNA target in vitro and provide evidence for

the biological significance of the interaction.

80

In this project, a miRNA target prediction study flagged miR-7 and EGFR as of

particular interest, and they subsequently became a focus of investigation. Therefore, to

put this work in context, some background information on these two molecules is now

provided.

4.3 miR-7

4.3.1 miR-7 background

The mature sequence of miR-7 is conserved across human, rat, mouse, chicken,

pufferfish and fly, although its length varies between 21 and 23 nt for different species,

as shown in Figure 4.3. In humans, miR-7 is 22 nt in length.

Homo sapiens 5’- UGGAAGACUAGUGAUUUUGUUG -3’ Rattus norvegicus 5’- UGGAAGACUAGUGAUUUUGUU -3’ Mus musculus 5’- UGGAAGACUAGUGAUUUUGUUG -3’ Gallus gallus 5’- UGGAAGACUAGUGAUUUUGUUG -3’ Fugu rubripes 5’- UGGAAGACUAGUGAUUUUGUU -3’ Drosophila melanogaster 5’- UGGAAGACUAGUGAUUUUGUUGU -3’

Figure 4.3: Cross-species sequence alignment of mature miR-7 (Griffiths-Jones, Grocock, van Dongen, Bateman, & Enright, 2006).

Three separate human miR-7 precursor genes exist across the genome. Their mature

miR-7 sequences are identical and are denoted miR-7-1, miR-7-2 and miR-7-3 (Lagos-

Quintana, Rauhut, Lendeckel, & Tuschl, 2001).

miR-7-1 is located in intron 16 of the heterogeneous nuclear ribonucleoprotein K

(HNRPK) gene on chromosome 9 (A. Rodriguez, Griffiths-Jones, Ashurst, & Bradley,

2004). It is oriented in the same direction as HNRPK, suggesting that the expression of

the two may be linked. HNRPK has been implicated in cell proliferation, chromatin

remodelling, transcription, splicing and translation (Bomsztyk, Van Seuningen, Suzuki,

Denisenko, & Ostrowski, 1997; Mandal et al., 2001; Michelotti, Michelotti, Aronsohn,

& Levens, 1996). It is ubiquitously expressed in normal tissues (A. Rodriguez,

Griffiths-Jones, Ashurst, & Bradley, 2004) and is also overexpressed in some cancers

(Dejgaard et al., 1994; Pino et al., 2003).

81

miR-7-2 is located within an intron of a non-coding transcription unit on chromosome

15 (A. Rodriguez, Griffiths-Jones, Ashurst, & Bradley, 2004).

miR-7-3 is located within intron 2 of the pituitary gland specific factor 1a gene

(PGSF1a) on chromosome 19 (Baskerville & Bartel, 2005; A. Rodriguez, Griffiths-

Jones, Ashurst, & Bradley, 2004). PGSF1a is expressed primarily in the pituitary gland

but also at low levels in the pancreas (Tanaka et al., 2002). The expression of PGSF1a

and miR-7-3 are highly correlated (correlation coefficient, 0.961), consistent with

miR-7-3 being processed from the PGSF1a primary transcript (Baskerville & Bartel,

2005). The function of PGSF1a is not clear at this stage.

The expression of miR-7 is almost brain-specific (Baskerville & Bartel, 2005; Sempere

et al., 2004), with very high levels in the pituitary in particular (Sood, Krek, Zavolan,

Macino, & Rajewsky, 2006), but with low expression also in the spleen (Sempere et al.,

2004). miR-7 is also expressed in some cancer cell lines (Jiang, Lee, Gusev, &

Schmittgen, 2005; , "miRNA Research Guide", 2005). In particular, miR-7-3 expression

was found to be increased 122-fold in the colorectal cancer cell line SW620, compared

to its mean expression in other cell lines assayed (Jiang, Lee, Gusev, & Schmittgen,

2005).

4.3.2 miR-7 targets and functions

4.3.2.1 miR-7 in Drosophila

A number of studies have provided insight into the function of miR-7 in Drosophila.

Indeed, ten miR-7 targets have now been verified in vitro and/or in vivo, as listed in

Table 4.2. All of the targets in this table are functionally related, belonging to the Notch

signalling pathway. Notch signalling mediates local cell-cell communication and

regulates many different cell fate decisions throughout development in all invertebrate

and vertebrate species. It can act to either induce or repress a particular cell fate,

depending on the circumstances, and can affect cell proliferation and cell death, as

reviewed by Lai (2004).

Two studies have investigated the functional effect of miR-7 in Drosophila in vivo.

Both found that ectopic expression of miR-7 induced developmental defects

82

characteristic of loss of the Notch pathway function (Lai, Tam, & Rubin, 2005; Stark,

Brennecke, Russell, & Cohen, 2003). These included notching of the wing margin,

thickened wing veins, increased bristle density and tufted bristles. These studies suggest

that miR-7 plays an important role in Drosophila development. This is consistent with

the observed expression of miR-7 in the developing Drosophila embryo (Aravin et al.,

2003).

This knowledge of the function of miR-7 in Drosophila gives a valuable background to

the much sparser work on the subject in humans.

Table 4.2: Predicted and verified miR-7 targets in Drosophila.

Target Status Reference

anterior open (aop, alias Yan) Predicted (Enright et al., 2003; X. Li &

Carthew, 2005)

hairy (h) Predicted,

Verified

(Rajewsky & Socci, 2004;

Stark, Brennecke, Russell, &

Cohen, 2003)

Twin of m4 (Tom) Predicted,

Verified

(Lai, 2002; Lai, Tam, &

Rubin, 2005)

Bearded (Brd) Verified (Lai, Tam, & Rubin, 2005)

Brother of Bearded A (BobA) Verified (Lai, Tam, & Rubin, 2005)

fringe (fng) Verified (Robins, Li, & Padgett, 2005)

E(spl) region transcript m3 (HLHm3) Predicted,

Verified

(Lai, 2002; Lai, Tam, &

Rubin, 2005)

E(spl) region transcript m4 (m4) Verified (Lai, Tam, & Rubin, 2005;

Stark, Brennecke, Russell, &

Cohen, 2003)

E(spl) region transcript m5 (m5) Verified (Lai, Tam, & Rubin, 2005;

Robins, Li, & Padgett, 2005)

E(spl) region transcript mδ (mδ) Verified (Lai, Tam, & Rubin, 2005)

E(spl) region transcript mγ (mγ) Verified (Lai, Tam, & Rubin, 2005)

83

4.3.2.2 miR-7 in Homo sapiens

miR-7 has no verified targets in humans and its function is unknown. However, many

miR-7 targets have been predicted by different algorithms, as listed in Table 4.3. No

algorithm predicts a predominance of Notch-related targets in humans. In fact, there

does not appear to be any obvious functional trend among these predictions. John and

colleagues do note that their set of miR-7 target predictions is enriched with genes

linked with the GO term ‘RNA binding proteins’. However, a noted lack of overlap

between the predictions of different algorithms (Rajewsky, 2006) raises the question of

whether this finding generalises to the true population of miR-7 targets.

miR-7 has also been linked to human cancer. Cheng and colleagues demonstrated that a

miR-7 inhibitor significantly reduced proliferation of A549 lung cancer cells and

significantly increased apoptosis in HeLa cervical cancer cells (Cheng, Byrom, Shelton,

& Ford, 2005), suggesting a possible role for miR-7 as an oncogene in these

circumstances. Consistent with this, miR-7-2 has been shown to exhibit increased copy

number in both breast tumours and melanoma (L. Zhang et al., 2006).

In summary, the knowledge of miR-7 function in humans is slim, leaving great scope

for further investigation.

84

Table 4.3: Predicted human miR-7 targets.

Predicted target gene Gene ID References

spermatogenesis associated 2 SPATA2 1, 2,3

Kruppel-like factor 4 (gut) KLF4 1, 2, 3

O-linked N-acetylglucosamine (GlcNAc) transferase OGT 1, 2, 3

polymerase (DNA-directed), epsilon 4 (p12 subunit) POLE4 1,3

round spermatid basic protein 1 RSBN1 1,3

KIAA1920 KIAA1920 1

ATP-binding cassette, sub-family G (WHITE), member 4 ABCG4 1, 2, 3

chromosome 13 open reading frame 8 C13orf8 1, 3

insulin receptor substrate 2 IRS2 1, 3

muskelin MKLN1 2

KIAA0247 KIAA0247 2

v-raf-1 murine leukemia viral oncogene homolog 1 RAF1 1, 2, 3

glyoxalase 1 GLO1 2

heterogeneous nuclear ribonucleoprotein H1 (H) HNRPH1 2

stress-associated endoplasmic reticulum protein 1 SERP1 1, 3

checkpoint suppressor 1 CHES1 1, 3

N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1 NDST1 1, 3

phospholipase C, beta 1 (phosphoinositide-specific) PLCB1 1, 3

bone morphogenetic protein receptor, type II BMPR2 1, 3

1. (Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003), 2. (John et al., 2004), 3. (Krek et al., 2005)

4.4 Epidermal Growth Factor Receptor (EGFR)

4.4.1 EGFR signalling and function

EGFR is a member of the ErbB family of receptor tyrosine kinases introduced in

Chapter 1 of this thesis. As depicted in Figure 4.4, it is involved in several signalling

pathways including the MAPK, PI3K, PLC-γ and signal transducer and activator of

transcription (STAT) pathways, and can stimulate cell survival, cell cycle progression,

proliferation, migration and angiogenesis, depending on its dimerisation partner and the

ligands present, as described by Normanno and colleagues (2005).

85

Figure 4.4: EGFR signalling, taken from (Dannenberg, Lippman, Mann, Subbaramaiah, & DuBois, 2005).

EGFR is expressed in a range of tissues including the placenta, skin, spleen, liver,

stomach and testis (Ge et al., 2005), and is also widely expressed in the brain, including

the amygdala, hypothalamus, hippocampus, cortex, cerebellum and pituitary (Ferrer et

al., 1996).

In normal human tissues, EGFR plays an important role in the maintenance of

epithelium and wound healing (Nakamura, Sotozono, & Kinoshita, 2001), and is also

involved in a number of other processes such as regulation of vascular smooth muscle

cell function (Kalmes, Daum, & Clowes, 2001), regulation of nitric oxide biosynthesis

(B. Liu & Neufeld, 2003) and regulation of the synthesis of gonadotropins in the

pituitary (Roelle et al., 2003). EGFR has also been shown to be required for correct

development in mice, particularly in the development of the cardiac valve, nervous

system and epithelial tissues (Miettinen et al., 1995; Sibilia et al., 2003; Threadgill et

al., 1995).

86

4.4.2 The role of EGFR in cancer

EGFR is involved in many processes that are often dysregulated in cancer, and

numerous studies have shown that EGFR signalling plays an important role in

oncogenesis and cancer progression, as reviewed by Laskin and Sandler (2004).

Clinical studies have implicated EGFR in a wide range of cancers including lung,

prostate, bladder, colorectal, pancreatic, breast, ovarian and cervical cancers, cancers of

the head and neck, melanoma, neuroblastoma, glioma and meningioma, with over a

third of solid tumours expressing EGFR (see (Kuan, Wikstrand, & Bigner, 2001; Laskin

& Sandler, 2004; Nicholson, Gee, & Harper, 2001; Normanno et al., 2005).

These cancers show a variety of EGFR abnormalities. In many cases, there is over-

expression of EGFR protein, and, in some cancer types, this is frequently a result of

gene amplification (Salomon, Brandt, Ciardiello, & Normanno, 1995). This can

magnify EGFR signalling and is associated with very aggressive, invasive and

metastatic cancers, and an overall poor prognosis in many cancer types (Dassonville et

al., 1993; Galizia et al., 2006; Hirsch et al., 2003). For example, a large study of patients

with laryngeal squamous cell carcinoma found that EGFR level was strongly correlated

with the incidence of relapse and death, with a 5-year survival rate of 81% in patients

with EGFR-negative tumours compared to 25% in patients with EGFR-positive tumours

(Maurizi et al., 1996). Furthermore, when EGFR is co-overexpressed with EGFR

ligands such as EGF and TGF-α, an autocrine loop can form, leading to constitutive

activation of EGFR (Yarden, 2001). In line with this, co-expression of EGFR and its

ligands is also associated with a worse prognosis in some cancers (Yamanaka et al.,

1993; Yonemura et al., 1992).

Numerous EGFR variants with activating mutations have also been observed in EGFR-

overexpressing cancers of the breast, ovary, prostate, lung and brain, as described by

Kuan and colleagues (2001). The most common of these, EGFRvIII, is missing a large

portion of the extracellular domain and has ligand-independent constitutive activity

(Batra et al., 1995).

87

4.4.3 Treatment of EGFR-overexpressing cancers

When applied to the treatment of EGFR-overexpressing cancers, conventional cancer

therapies such as radiotherapy and chemotherapy generally give disappointing results,

as evidenced by the poorer prognosis of EGFR-overexpressing cancers, while

subjecting patients to serious side-effects (Nicholson, Gee, & Harper, 2001). In recent

times, many drugs have been developed to specifically target EGFR, with the theory

that this strategy would more effectively inhibit cancer progression in EGFR-positive

tumours and cause milder side-effects.

4.4.3.1 Monoclonal antibodies

One approach to targeted therapy is to use monoclonal antibodies against the

extracellular domain of EGFR. These antibodies compete with EGFR ligands for

binding to EGFR and, once bound, prevent activation of the receptor and downstream

signalling. They may also induce receptor internalisation and thus reduce the number of

EGFRs on the cell surface (Prenzel, Fischer, Streit, Hart, & Ullrich, 2001). Cetuximab

is one such monoclonal antibody that was approved by the FDA in 2004 for the

treatment of EGFR-positive metastatic colorectal cancer. It has been shown to improve

survival time in several different cancer types both alone and in combination with

radiotherapy or certain chemotherapeutic agents, though it is only a small subset of

tumours that respond to this treatment (Bonner et al., 2006; Cunningham et al., 2004; E.

S. Kim et al., 2003). Other EGFR-specific antibodies are also currently undergoing

clinical testing (Crombet et al., 2001; Vanhoefer et al., 2004).

4.4.3.2 Tyrosine kinase inhibitors

The other major approach to EGFR-targeted therapy is to design low molecular weight

tyrosine kinase inhibitors. These small molecules bind at or near the ATP-binding site

on the intracellular kinase domain of EGFR and block downstream signalling. There are

many of these currently undergoing clinical testing. Two, gefitinib and erlotinib, were

approved by the FDA for treatment of non-small cell lung cancer, and in the case of

erlotinib, later for treatment of pancreatic cancer. However, despite the promising

results of early studies (Ciardiello et al., 2000; Shepherd et al., 2005), these two drugs

have not met expectations, failing to confer a survival benefit in advanced non-small

88

cell lung cancer in combination with chemotherapy in phase III clinical trials (Herbst et

al., 2004; Herbst et al., 2005). Gefitinib has also been shown to have no significant

effect in breast cancer (von Minckwitz et al., 2005). However, some studies have found

gefitinib and erlotinib to be of benefit in certain cancer types. For example, erlotinib has

been shown to improve overall survival and progression-free survival in combination

with chemotherapy in pancreatic cancer in phase III clinical trials (Moore et al., 2007).

Many of the EGFR-targeted therapies developed to date have had only partial success in

a subset of EGFR-positive tumours. This may be due to redundancy in the signalling

pathways used for cell growth and survival or the presence of EGFR mutants that are

not bound or affected by the drugs (Learn et al., 2004; Pao, Miller et al., 2005), to list

just two possible explanations. Development of EGFR-targeted therapies remains a

promising strategy for treating certain cancers. However, new approaches are required

to overcome the shortcomings of existing therapies.

89

4.5 Project rationale and aims

At the outset of this project, only three miRNA targets had been identified

experimentally in C. elegans and no means existed to predict miRNA targets in animals.

Therefore, an investigation of miRNA target prediction was proposed with the goal of

identifying undiscovered miRNA targets. The investigation was to have an original

focus on the identification of human, cancer-related miRNA targets. As no miRNA

targets had yet been predicted or identified in humans, this was a new area to be

explored. In addition, with the laboratory’s background and experience in the molecular

biology of cancer, the question of whether miRNAs regulated the expression of cancer-

related genes was of great interest. This question was particularly valid given that

miRNAs had been shown to be involved in processes such as cell proliferation and cell

death in Drosophila (Brennecke, Hipfner, Stark, Russell, & Cohen, 2003; P. Xu,

Vernooy, Guo, & Hay, 2003). Furthermore, two specific miRNAs had been linked to

CLL (Calin et al., 2002). However, no study had been published that focused on human,

cancer-related miRNA targets. This investigation therefore had the potential to advance

the understanding of miRNA targets in two new areas. Thus, the aims of this project

were:

1. To design and implement a computer algorithm to predict miRNA targets,

2. To use the computer program to search a range of human, cancer-related genes for

miRNA target candidates,

3. To evaluate one target prediction experimentally,

4. In the case that the target prediction is verified, to investigate the functional

significance of miRNA:target interaction

5. To conduct a microarray experiment to determine the molecular response of cells to

up-regulation of the miRNA of interest, in order to identify other miRNA target

candidates and investigate their functional trends.

Work conducted towards each of these aims is described and discussed in Chapters 6

through 9.

90


5.1 Cell culture

The following ATCC cell lines were used in Part 2 of this thesis: A549 (CCL-185, lung

carcinoma), MDA-MB-468, MCF7 and HeLa. The latter three cell lines and their

culture conditions were described in section 2.1 of this thesis. The A549 cell line was

cultured in high glucose DMEM, supplemented with 5% FBS and treated with 50 U/mL

penicillin and 50 µg/mL streptomycin.

5.2 Plasmids

The plasmids used in section 7.2.1.1 of this investigation, SMAD1-Wt, SMAD1-Mt and

their empty vector, were provided by Prof. David Bartel from the Massachusetts

Institute of Technology and have been described previously (Lewis, Shih, Jones-

Rhoades, Bartel, & Burge, 2003). Briefly, the vector used was a modified

pGL3-Control vector (Genbank accession number U47296) with an added multiple

cloning site into which inserts were cloned. The SMAD1-Wt plasmid contained a

section of the SMAD family member 1 (SMAD1, RefSeq accession number

NM_005900) 3’UTR, that included two predicted target sites for hsa-miR-26a

(miRBase accession number MIMAT0000082), while the SMAD1-Mt plasmid

contained an analogous insert with three point substitutions in each of the predicted

target site seeds.

EGFR-Wt and EGFR-Mt plasmids were constructed from the pGL3 MCS ‘3’ vector

(Giles, 2004), a modified pGL3-Control vector into which a multiple cloning site had

been inserted at the XbaI restriction site, downstream of the firefly (Photinus pyralis)

luciferase (luc+) coding sequence. Inserts were generated by amplifying plasmid DNA

containing the full EGFR 3’UTR, using the following PCR primers: EGFR-Wt-Fd

(5’- TAA CTA GTA GCA CAA GCC ACA AGT CTT CCA -3’), EGFR-Wt-Rvs

(5’- ATG GGC CCT GGA AGA CAA ACA AGT CAG TCT -3’), EGFR-Mt-Fd

(5’- TAA CTA GTA GCA CAA GCC ACA AGA CGT ACA -3’), EGFR-Mt-Rvs

(5’- ATG GGC CCT GTA CGT CAA ACA AGT CAG TCT -3’) with a TA of 60°C,

over 35 cycles. The EGFR-Wt-Fd and -Rvs primers amplified a 304 bp section of the

91

EGFR 3’UTR between indices 445 and 748, containing predicted miR-7 target sites #1

and #2, with 5’ SpeI and 3’ ApaI restriction sites. The EGFR-Mt-Fd and -Rvs primers

amplified the same region of EGFR but introduced three point substitutions in each

predicted miR-7 seed site into the amplified DNA. PCR products were gel purified

using the UltraClean GelSpin DNA Purification Kit (Mo Bio Laboratories, Inc.).

Purified EGFR segments and pGL3 MCS ‘3’ vector were digested with the SpeI and

ApaI enzymes, and ligated so as to insert the EGFR segments into the unique SpeI and

ApaI restriction sites within the vector’s multiple cloning site.

The perfect miR-7 target plasmid used in sections 7.2.1.2 and 7.2.1.3 was constructed

from the unmodified pGL3-Control vector and inserts comprising the sequence

perfectly complementary to miR-7, a BamH1 restriction site to facilitate screening for

the presence of such small inserts during the cloning process, and 5’ and 3’ ends

suitable for ligating directly into the XbaI site of the vector. Inserts were generated by

annealing sense (m7-report-Fd: 5’- CTA GAC AAC AAA ATC ACT AGT CTT CCA

GGA TCC T -3’) and antisense (m7-report-Rvs: 5’- CTA GAG GAT CCT GGA AGA

CTA GTG ATT TTG TTG T -3’) oligonucleotides with 5’ phosphate groups. This

involved combining 1 µg of each oligonucleotide in 1 mL of water and incubating at

90°C for 3 min, then 37°C for 1 hour. The annealed oligonucleotides were ligated into

XbaI-digested, Shrimp Alkaline Phosphatase (SAP)-treated vector.

Throughout the investigation, the pRL-SV40 reporter plasmid (Genbank accession

number AF025845), encoding the Sea Pansy (Renilla reniformis) Renilla luciferase

enzyme (Rluc), was used as an internal control.

For all plasmids, insert sequences were confirmed by automated dideoxy sequencing at

the Department of Clinical Immunology, Royal Perth Hospital.

5.3 Transfections

For luciferase reporter assay experiments, cells were plated in 24-well plates in 500 µL

of growth media lacking penicillin and streptomycin, 24 hours prior to transfection.

HeLa, MCF7, MDA-MB-468 and A549 cells were plated at 30x103, 60 x103, 50x103

and 30x103 cells/well respectively. Cells were cotransfected with a firefly luciferase

reporter plasmid, the Renilla luciferase plasmid as an internal control and, in some

92

cases, miRNA precursor and/or inhibitor, using Lipofectamine 2000 Reagent (LF)

(Invitrogen, Corp.). Stock transfection mixes were made according to the LF

manufacturer’s instructions, routinely containing 200 ng of firefly luciferase reporter

plasmid, 1 ng of Renilla luciferase plasmid, and 3 µL of LF per well. miRNA precursors

were used at a final concentration of 30 nM unless otherwise stated. The miRNA

inhibitor was used at final concentrations of 5, 10 and 30 pmol/well. To transfect cells,

the growth media was removed and replaced with 500 µL of fresh media lacking

penicillin and streptomycin, plus 100 µL of the appropriate transfection mix. Cells were

incubated at 37°C for 4 hours, after which time the media was replaced with 500 µL of

fresh growth media lacking penicillin and streptomycin. Triplicate wells were

transfected for each condition.

For experiments involving Western blot, RT-PCR, functional studies and microarray

assays, miRNA precursors were transfected into cells alone using LF. For these cases,

transfections were scaled up to 6-well plates, 6 cm dishes and 10 cm dishes by using

100x103 A549 and 300x103 MDA-MB-468 cells/well in 6-well plates, 300x103 A549

and 800 x103 MDA-MB-468 cells in 6 cm dishes and 1x106 A549 cells in 10 cm dishes,

and multiplying transfection and LF volumes by factors of 5, 10 and 30 respectively.

The miRNA precursors used in this investigation were the Pre-miR miRNA Precursor

Molecule, hsa-miR-7-1 and the Pre-miR miRNA Precursor Molecule Negative

Control #1 (NS) (Ambion, Inc.). The miRNA inhibitor used was the Anti-miR miRNA

Inhibitor, hsa-miR-7-1 (Ambion, Inc.).

5.4 Treatment and preparation of cells for cell proliferation assays

A549 cells were seeded in 10 cm dishes and transfected with 30 nM miRNA precursor

using LF as described in section 5.3. At a later time, the cells in each 10 cm dish were

split for seeding into plates suitable for the different assays to be performed. The final

functional studies were performed with cells split 6 hours after transfection. The growth

media was not changed at 4 hours for this series of experiments. Cell suspensions were

counted four times each using a Neubauer Counting Chamber (Weber Scientific

International), and diluted with media lacking penicillin and streptomycin, to achieve

suitable cell concentrations for seeding into the different plate sizes.

93

Cells from each condition were plated into 6-well plates at 300x103 cells/well in 2 mL

of media for protein harvest on day 2, as described in section 2.6. Protein samples were

used to confirm that the transfection was successful using Western blot, as described in

sections 2.8 and 5.7. Cells were also plated into 96-well plates at 1x103 cells/well in

100 µL of media for CT assays, as described in section 2.4. The CT assay protocol is

given in section 2.9. Cells were also plated into 6 cm dishes at 300x103 cells/dish, with

one dish per condition, for observation and cell counting.

5.5 Luciferase reporter assay

Luciferase reporter assays were performed on protein from cells transfected as

described in section 5.3. Protein was harvested 24 hours after transfection by removing

the cell media, adding 50 µL of Passive Lysis Buffer to each well of transfected cells

and storing plates at -20°C for at least 5 minutes. Cell lysates were transferred to tubes

and spun in a centrifuge to settle any solid matter.

Luciferase assays were performed using a Dual-Luciferase Reporter Assay System kit

(Promega, Corp.). 30 µL of each cell lysate was added to a well of a black plastic

96-well plate. A negative control well was also set up, containing 30 µL of Passive

Lysis Buffer. A Fluostar OPTIMA Microplate Reader (BMG LABTECH Pty Ltd) was

used to deliver assay reagents to each well (50 µL of Luciferase Assay Substrate,

reconstituted in Luciferase Assay Buffer II, followed by 50 µL of Stop & Glow

Reagent) and read the luciferase activity, according to the manufacturer’s instructions.

For all reporter assays, firefly luciferase readings were normalised against Renilla

luciferase readings. For experiments involving miRNA precursor or inhibitor

treatments, these normalised readings were additionally normalised against control

wells not subjected to miRNA precursor or inhibitor treatment.

5.6 RT-PCR

RT-PCRs were performed as described in section 2.7. The same primers were again

used to amplify β-actin (#50-Fd and #51-Rvs). The primers used for EGFR were

#293-Fd (5’- CAC CGA CTA GCC AGG AAG TA -3’) and #294-Rvs (5’- AAG CTT

94

CTT CCT TGT TGG AAG AGC CCA TTG A -3’). PCRs for EGFR were performed

with a TA of 60°C over 26 cycles.

5.7 Western blot

Protein for Western blot was harvested as described in section 2.6. Western blot

performed as described in section 2.8, using the following primary antibodies with their

corresponding secondary antibodies: EGFR antibody (Abcam, cat. # ab31325) (1:2000),

Raf-1 (C-12) (Santa Cruz Biotechnology, Inc., cat. # sc-133) (1:500), Cox-2 (29) (Santa

Cruz Biotechnology, Inc., cat. # sc-199999) (1:500), HuR/ELAVL1 antibody (Abcam,

cat. # ab28660) (1:2000) and p27 KIP 1 antibody (Abcam, cat. # ab45872).

Quantitation of X-ray band intensities for Figure 7.6 was performed using the Bio-Rad

ChemiDoc XRS system in white light transillumination mode and the Quantity One

Software v 4.5.0.

5.8 Cell counting

Three days after transfection with miRNA precursor (see section 5.4), A549 cells were

observed under a microscope using a 10x objective, and five representative fields of

view were photographed for each condition. Cells in each field of view were counted

manually, and the mean and standard deviation for the five counts was calculated. The

experiment was performed three times, with one 6 cm dish per condition each time.

5.9 Fluorescence-activated cell sorting (FACS) analysis

Three days after transfection with miRNA precursor in 6 cm dishes (see section 5.3),

A549 cells were harvested for FACS analysis. The media was removed from each dish

and put aside. Cells were washed once with PBS, which was removed and added to the

original cell media. Cells were trypsinised and, together with the PBS wash and original

cell media, were spun in a centrifuge at 1300 rpm. The media was removed and cells

were washed in 5 mL of fresh media. Approximately half of the cell suspension was set

aside for protein lysis and Western blot to confirm that the transfection had down-

regulated EGFR. The remaining cells were spun again and the media was removed.

While gently vortexing, 1.5 mL of ice-cold PBS was added drop-wise to the cell pellet,

95

followed by 3 mL of ice-cold 95% ethanol. The cell suspension was stored at 4°C for at

least 16 hours. To stain cells, the cell suspension was spun down, washed in 1 mL of

cold PBS and resuspended in 500 µL of propidium iodide solution (69 µM Propidium

Iodide (Sigma-Aldrich Inc.) in 38 mM sodium citrate pH 7.4). Cell suspensions were

left on ice for at least 30 min before analysis. FACS analysis was performed with a

Coulter EPICS XL-MCL (Coulter, Hialeah, FL) flow cytometer, and MultiPlus AV

MultiParameter data analysis software (Pheonix Flow Systems, San Diego, CA) at the

Flow Cytometry Unit of Royal Perth Hospital. Experiments were performed four times

each, with one replicate per condition each time.

5.10 Harvest and preparation of RNA for microarray assays

Microarray samples were prepared from A549 cells that had been transfected with

30 nM miR-7 or NS precursor in 10 cm dishes as described in section 5.3. Cells were

harvested 24 hours after transfection, using TRIzol Reagent (Invitrogen, Corp.)

according to the manufacturer’s instructions up to the point of RNA precipitation in

isopropanol. RNA samples were then purified using the RNeasy Mini Kit (QIAGEN)

according to the manufacturer’s instructions, and eluted in 30 µL of RNase-free water.

The Lotterywest State MicroArray Facility recommends that RNA samples meet certain

standards for purity, integrity and concentration, specifically, a 260/280 absorbance

ratio between 1.8 and 2.1, an 18s/28s rRNA ratio between 1.6 and 1.9, and an RNA

integrity number (RIN) greater than 9.5. Samples are also required to contain at least

20 µg of RNA at a concentration greater than 1 µg/µL. Each of the samples used for the

microarray assays met all of these criteria. Sample concentration and 260/280 ratio were

determined using a NanoDrop ND-1000 Spectrophotometer (Biolab Australia Ltd).

Assessment of the 18s/28s rRNA ratio and RIN of each sample was performed by the

Lotterywest State MicroArray Facility using a 2100 Bioanalyzer (Agilent Technologies,

Inc.).

EGFR down-regulation was confirmed for both replicate experiments used for

microarray assay using RT-PCR as described in sections 2.7 and 5.6.

96

5.11 Microarray assay and processing of raw data

Microarray assays were performed by the Lotterywest State MicroArray Facility using

Human Genome U133 Plus 2.0 Affymetrix array chips according to their standard

protocol (Peeva, 2003).

The raw data was processed using the GeneSifter software (VizX Labs, Seattle, USA).

An ‘All groups must pass’ restriction was imposed, with a threshold quality score of ‘P’

(Present) required for inclusion in the analysis. The data was normalised to the all

means fluorescence and was log2 transformed. Pairwise comparison of the probe values

of miR-7-treated and NS-treated sample data sets was performed using Student’s t-tests

(two-tailed, unpaired), and was used to identify transcripts that were significantly up- or

down-regulated with miR-7 treatment (p < 0.05) by at least a factor of 2.

5.12 Statistical analysis

Student’s t-test (two-tailed, unpaired) was used to determine the statistical significance

of the differences between conditions in all cases for which the distributions of the data

sets satisfied the assumptions of this test (Sheskin, 2007). For other cases, the non-

parametric Mann-Whitney U-test was used. Statistical significance was defined at the

standard 5% level, except in the case of the Gene Ontology (GO) analysis of section

9.2.4, for which significance was defined at a level of 1%.

5.13 Hardware and software

The computer used throughout this investigation was a Macintosh PowerBook G4

version 2.1 with a 550 MHz PowerPC G4 processor and 256 MB RAM, running Mac

OS X 10.2.3 (Apple Computer, Inc.).

The miRNA target prediction program developed in Chapter 6 was written using

MATLAB 6.5 Student Version, Release 13 (The MathWorks, Inc.).

RNA secondary structures and minimum free energy of hybridisation values were

predicted by mfold v 3.0 (http://mfold.burnet.edu.au/) (Zuker, 2003), and

97

RNAhybrid and RNAcalibrate v 2.1 (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/)

(Rehmsmeier, Steffen, Hochsmann, & Giegerich, 2004).

Cross-species sequence alignments were generated using ClustalW

(http://www.ebi.ac.uk/clustalw) (Chenna et al., 2003).

Raw microarray data was processed using the GeneSifter software (VizX Labs, Seattle,

USA, http://www.genesifter.net/web/). Functional annotation reports and z-scores were

also generated using the GeneSifter software.

The GO analysis of microarray data was performed using the GeneSifter software and

GOTree Machine (http://bioinfo.vanderbilt.edu/webgestalt) (B. Zhang, Schmoyer,

Kirov, & Snoddy, 2004).

Investigation of the enrichment of gene sets for predicted miRNA targets was conducted

using the L2L Microarray Analysis Tool (http://depts.washington.edu/l2l/about.html)

(Newman & Weiner, 2005).

Promising miRNA target candidates were identified in section 9.2.2.2 with the help of

TargetScan v 3.0 (http://www.targetscan.org/) (Lewis, Burge, & Bartel, 2005; Lewis,

Shih, Jones-Rhoades, Bartel, & Burge, 2003), PicTar (http://pictar.bio.nyu.edu/) (Krek

et al., 2005) and miRanda (http://www.microrna.org/mammalian/index.html) (John et

al., 2004).

Diagrams were drawn using the R statistical package v 2.5.0 (Ihaka & Gentelman,

1996).

98

CHAPTER 6: DEVELOPMENT OF A miRNA TARGET PREDICTION PROGRAM

AND THEORETICAL EVALUATION OF ITS PREDICTIONS

6.1 Introduction

The first goal of this project was to design and implement a computer algorithm to

predict miRNA targets. When this goal was set, no literature had been published on the

possibility of employing an iterative computational approach to miRNA target

prediction in animals, and hence no precedent existed on which to base the approach.

While generic programs for sequence comparison such as blastn were in existence, they

offered very little control over match parameters. With very few verified targets on

which to base a model for miRNA:target interactions, the rules governing these

interactions were virtually unknown, and the restrictions imposed by sequence

comparison programs made any target prediction investigation extremely limited.

Therefore, to address the first aim of this project, a computer program was designed and

implemented that offered complete control over prediction parameters and the

flexibility to enable it to be updated with future advances in the understanding of

miRNA:target interactions and target prediction. This program was a valuable tool with

which to pursue the second aim of this project, to screen a range of human, cancer-

related mRNAs for possible miRNA targets.

This chapter describes the prediction program in its final form and presents some of its

predictions. Predictions are evaluated according to various published criteria and the

most promising are highlighted. Finally, the top prediction is evaluated with regard to

recent advances in target prediction and newly proposed selection criteria.

99

6.2 Development of a miRNA target prediction program and target predictions

6.2.1 Program design and implementation

6.2.1.1 Program outline

The first version of the miRNA target prediction program incorporated calculations of

the complementarity of miRNA and mRNA sequences, allowing gaps, loops and G:U

base-pairs, and was followed by a conservation screen of high rating sites. The program

was later updated to incorporate the findings of three studies published after its initial

development, to give the final version presented here. (Doench, Petersen, & Sharp,

2003; Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003; Stark, Brennecke, Russell, &

Cohen, 2003). The program was written in the MATLAB language (see section 5.13).

The flow chart for the miRNA target prediction procedure is given in Figure 6.2. In

brief, the program cycles through each mRNA with each miRNA. For each

miRNA:mRNA pair, the following steps are carried out:

1. The mRNA sequence is screened for acceptable matches with the miRNA seed. If

more than a user-defined minimum number of miRNA seed matches are found

within the mRNA sequence, the program aligns the entire miRNA with a portion of

the mRNA extended about the seed match. This alignment then undergoes four

different complementarity checks:

a. The complementarity between the miRNA and the exactly aligned mRNA

sequence is first computed.

b. A single nucleotide, non-matching loop is introduced into the mRNA sequence

at the first position following the seed match, and the complementarity is

computed as for the first check. This process is repeated for loop positions

along the length of the mRNA sequence and the maximum complementarity

over all iterations is returned.

c. Step b is repeated, but with a loop of two nucleotides rather than a single

nucleotide in the mRNA sequence.

d. Step b is repeated, but with a single nucleotide loop in the miRNA sequence

rather than the mRNA sequence.

100

If the maximum of these computed complementarity scores exceeds a certain

threshold, the miRNA:mRNA pair is retained in a database.

2. After cycling through all of the miRNAs and mRNAs, the program returns an array

of miRNA:mRNA pairs satisfying complementarity and seed match criteria, ranked

by their maximum complementarity score, together with the accompanying

sequence and evaluation data for each match site, in a user-defined format. The data

can be saved both as a delimited text file able to be opened as a spreadsheet in

Excel, and as a MATLAB data structure, which enables the database to be easily

sorted and searched at the command line. Several MATLAB functions were also

written to facilitate this method of database searching.

3. From here, final target predictions are obtained by subjecting the generated target

candidates to the following computations:

a. Target candidates are screened for conservation of the entire site sequence

across human, mouse and rat using the alignment program Clustal W (Chenna

et al., 2003).

b. The minimum free energy of hybridisation (mfe) of the miRNA and its

predicted mRNA target sites from position 1 of the seed match to the length of

the miRNA plus five extra nucleotides, is calculated using the mfold program

(Zuker, 2003).

Because the original version of mfold was unable to fold two individual sequences at

once, the two needed to be connected using a linker sequence. Using the same approach

as Stark and colleagues (2003), sequences submitted to mfold consisted of the predicted

mRNA target sequence extending the length of the miRNA plus an additional five

nucleotides in the 5’ direction, followed by the linker sequence, GCGGGGACGC,

followed by the miRNA sequence (Figure 6.1). The linker sequence forms a hairpin

structure that forces together the seed region of the miRNA:mRNA pair, allowing an

estimate of the mfe.

101

Figure 6.1: An example mfold folding of a miRNA and mRNA section linked by a linker sequence.

The program allows the user to vary the following parameters:

1. The minimum number of seed matches required,

2. The length and position of seed matches within the miRNA,

3. Whether to allow G:U base-pairs in the seed, and

4. The complementarity threshold score.

This flexibility was very valuable in the investigation of validated and predicted targets

throughout this project. The values chosen for these parameters for the final target

prediction run are specified in section 6.2.1.3.

The final code for the miRNA target prediction program and the functions to facilitate

effective command line database searching is given in Appendix A.

102

Figure 6.2: Flow chart for miRNA target prediction procedure.

103

6.2.1.2 Choice of data sets

The miRNA data set used was the full set of 132 human miRNAs as identified at

December 2003, obtained from the Rfam database (Griffiths-Jones, Bateman, Marshall,

Khanna, & Eddy, 2003) (Appendix B).

The mRNA data set comprised 54 genes flagged as potentially involved in cancer by

previous laboratory work and published studies (Bertucci et al., 2004) (Appendix C).

The screen was limited to the 3’UTRs of these genes. In each case, the longest 3’UTR

was chosen from the NCBI Entrez Gene database.

6.2.1.3 Program parameters

The final list of target predictions was obtained by running the program on the miRNA

and mRNA data sets described above with the following criteria, determining those

matches which could plausibly indicate target interactions:

1. A minimum of two perfect 7 nt miRNA seed matches in the 3’UTR,

2. At least one match site with greater than 65% overall complementarity to the

miRNA,

3. At least one match site with an mfe less than -20.0 kcal/mol.

6.2.2 Target predictions

A list of the 23 miRNA target predictions is given in Table 6.1. This table also includes

five miRNA targets verified in either human or fly for comparison (Lewis, Shih, Jones-

Rhoades, Bartel, & Burge, 2003; Stark, Brennecke, Russell, & Cohen, 2003). The

program is able to predict two of these verified targets, excluding three as a result of its

strict criteria. hairy was excluded as a result of the double seed site requirement, N-myc

because its overall complementarity score does not exceed the threshold value and

Brn-3b because its mfe does not exceed the threshold value. With the criteria employed,

the 23 target predictions made by this program rank very well against the verified

targets.

104

6.2.2.1 Selection of a target prediction for further scrutiny

The prediction program’s top ranking prediction was for EGFR as a target of miR-7.

This prediction rated higher than any of the verified miRNA targets tested with the

same criteria. One of the predicted miR-7 target sites in EGFR is as good as or better

than all of the verified miRNA targets tested in both sequence complementarity and

mfe. In addition, while the single verified miRNA target that equals this EGFR site in

sequence complementarity, Drosophila hairy, possesses only one miRNA target site,

EGFR has two predicted target sites for miR-7. The second predicted target site may

also mediate repression and could be particularly important in view of evidence that

multiple target sites can act synergistically (Doench, Petersen, & Sharp, 2003). Neither

of the predicted EGFR target sites are conserved across human, mouse and rat.

However, as the value of sequence conservation was still poorly understood at this time,

the presence of two potential miR-7 target sites within EGFR, and the excellent

complementarity score of the first, presented strong reasons to proceed in an

investigation of the miR-7:EGFR prediction.

The miRNA target prediction program described above was developed at a very early

point in the evolution of miRNA target prediction programs. Since that time, this area

has burgeoned in light of new empirical findings and theoretical advances.

Nevertheless, as will be seen in the brief review that follows, the miR-7:EGFR

prediction selected using the program developed for this project stands up well to both

adaptations of original prediction criteria and to new prediction criteria suggested more

recently.

105

Table 6.1: miRNA targets predicted by the target prediction program. Some verified targets are also included and are shaded grey.

% Sequence match mfe (kcal/mol) Conserved?

mRNA miRNA 7 nt seed matches Site 1 Site 2 Site 3 Site 1 Site 2 Site 3 Site 1 Site 2 Site 3

EGFR hsa-miR-7 2 81.0% 61.9% -26.6 -21.5 No No hairy (1) dme-miR-7 1 81.0% -25.3 Yes†

ENX-1 (2) hsa-miR-101 2 77.3% 77.3% -24.5 -21.5 Yes Yes

ESR1 hsa-miR-22 2 50.0% 77.3% -20.8 -27.2 Yes No

ESR1 hsa-miR-130a 2 75.0% 45.0% -22.1 -16.0 Yes No

ErbB3 hsa-miR-17-5p* 3 54.2% 75.0% 58.3% -22.5 -30.2 -28.7 No No No

FADS1 hsa-miR-186 2 52.2% 73.9% -17.8 -22.2 No No

ErbB3 hsa-miR-20* 3 59.1% 72.7% 50.0% -20.4 -27.7 -26.2 No No No

ESR1 hsa-miR-18 2 68.2% 72.7% -26.8 -26.8 Yes No

Brn-3b (2) hsa-miR-23 3 71.4% 61.9% 57.1% -17.1 -17.0 -18.6 Yes Yes Yes

ErbB3 hsa-miR-106* 3 58.3% 70.8% 58.3% -22.5 -24.7 -23.2 No No No

SMAD1 (2) hsa-miR-26a 2 63.6% 68.2% -22.0 -21.9 Yes Yes

NOTCH2 hsa-miR-16* 2 59.1% 68.2% -20.8 -19.5 No No

ELAVL4 hsa-miR-132 2 68.2% 59.1% -21.1 -20.7 No No

ESR1 hsa-miR-20* 2 68.2% 50.0% -22.1 -23.2 No No

ErbB3 hsa-miR-93 3 63.6% 68.2% 59.1% -19.2 -30.9 -28.3 No No No

CTTN hsa-miR-182 2 63.6% 68.2% -21.8 -22.8 Yes No

NOTCH2 hsa-miR-15a* 2 63.6% 68.2% -25.1 -18.8 No No

PPARBP hsa-miR-205 3 45.5% 59.1% 68.2% -20.0 -19.2 -23.3 No Yes No

MED25 hsa-miR-185 2 66.7% 66.7% -19.8 -25.3 No No (continued over page)

106


% Sequence match mfe (kcal/mol) Conserved?

mRNA miRNA 7 nt seed matches Site 1 Site 2 Site 3 Site 1 Site 2 Site 3 Site 1 Site 2 Site 3

GATA4 hsa-miR-185 2 66.7% 61.1% -22.8 -19.8 No No

ESR1 hsa-miR-106* 2 66.7% 50.0% -27.2 -19.7 No No

ESR1 hsa-miR-145 2 66.7% 45.8% -28.7 -15.5 No No

ELAVL1 hsa-miR-27b 2 65.0% 65.0% -21.7 -21.6 No No

ELAVL1 hsa-miR-147 3 60.0% 60.0% 65.0% -23.0 -23.0 -20.9 Yes Yes No

N-myc (2) hsa-miR-101 2 59.1% 63.6% -20.9 -20.2 Yes Yes

* miRNA families with the same seed include miR-17-5p/20/106 and miR-16/15a † Drosophila targets were considered to be conserved if the entire 3’UTR was identical for D. melanogaster and D. pseudoobscura. (1): (Stark, Brennecke, Russell, & Cohen, 2003), (2): (Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003)

107

6.2.3 Further theoretical evaluation of the miR-7:EGFR prediction

After proceeding with experimental evaluation of the miR-7:EGFR prediction, several

new criteria for target prediction were proposed in the literature. As a result, a more

extensive theoretical evaluation of the miR-7:EGFR prediction became possible. In the

coming sections, the miR-7:EGFR prediction and three experimentally verified miRNA

targets are assessed according to the new criteria, side-by-side for comparison. The

experimentally verified targets are: the human hsa-miR101 target, ENX-1, (Lewis, Shih,

Jones-Rhoades, Bartel, & Burge, 2003), mouse mmu-miR-1 target, Hand2, (Y. Zhao,

Samal, & Srivastava, 2005) and fly dme-miR-7 target, hairy (Stark, Brennecke, Russell,

& Cohen, 2003).

6.2.3.1 The seed and other sequence considerations

One development in miRNA target prediction is that more study has been dedicated to

determining the exact seed and sequence requirements for miRNA:target interaction, as

well as variations on the standard model of a miRNA target. It has now been shown that

sites with as little as a single 7 nt seed match and no 3’ complementarity are able to act

as targets, as are sites with weak seed matches of 6 nt, or 7 nt with a G:U base pair, in

the presence of strong 3’ binding (Brennecke, Stark, Russell, & Cohen, 2005).

In light of these findings, EGFR sites #1 and #2 look even more promising, having

perfect seed matches of 9 nt and 8 nt respectively, and considerable 3’ complementarity.

In addition, these findings raise the possibility that some non-standard putative target

sites were missed by the original target search. Therefore, a search of the EGFR 3’UTR

for weaker seed matches was conducted. This search did not reveal any 7 nt seeds with

a single G:U base pair. However, two 6 nt seed matches were identified, with

complementarity to positions 2 to 7 of miR-7. The first, denoted site #3, has an

additional match at position 1, making for a non-standard 7 nt seed match as described

by Lewis and colleagues (2005). The second, denoted site #4, has no match at either

position 1 or 8.

The EGFR 3’UTR also contains two AU-rich elements, EGFR-1A and EGFR-2A, that

have been shown to play a role in regulating EGFR mRNA stability in breast cancer

108

(Balmer et al., 2001). However, all four putative miRNA target sites lie 3’ of these two

regions.

Figure 6.3: Positions of the destabilising elements, EGFR-1A and EGFR-2A, and putative miR-7 target sites within EGFR. Seed match lengths are given in brackets. ORF = open reading frame. Base-pair numbering is from the start of the EGFR 3’UTR (RefSeq number NM_005228).

Because the two new putative miR-7 target sites have comparatively weak seed

matches, it might be predicted that strong 3’ complementarity would be required for

miRNA binding. The overall complementarity for these sites, given in Table 6.2,

suggests that 3’ binding would not be very strong, although the mfe may be a better

indicator of this (see section 6.2.3.3). At this stage however, sites #3 and #4 appear less

promising as miR-7 targets than sites #1 and #2. Nevertheless, they are worth further

consideration given their context and in view of the constantly evolving understanding

of miRNA:target interactions.

In comparison, the target sites of ENX-1 and hairy have long, 8 or 9 nt perfect seed

matches, similar to those of EGFR sites #1 and #2. On the other hand, the Hand2 target

site has only a non-standard 7 nt seed match extending from position 1 to 7. This is the

same form as the EGFR site #3 seed match. However, the overall complementarity of

the Hand2 site is better than that of the EGFR site #3, a feature that may be important

for its functionality. Nevertheless, the finding of two additional putative target sites,

giving a total of four, further supports the EGFR:miR-7 prediction.

109

Another sequence consideration that has come to light since the original analysis is that

miRNA targets are more likely to have adenosines in positions 1 and 9 (Lewis, Burge,

& Bartel, 2005). Each of the putative EGFR target sites have an adenosine in at least

one of these two positions as given in Table 6.2, site #1 having adenosines in both

positions 1 and 9. Therefore the putative EGFR target sites follow this predictive trend.

Table 6.2: Seed and sequence characteristics of putative EGFR target sites and three verified targets5.

miRNA mRNA Site #

Seed match

(length)

% Sequence

match

A in

posn 1?

A in

posn 9?

hsa-miR-7 EGFR 1 1-9 (9 nt) 77.3% Yes Yes

2 1-8 (8 nt) 63.6% Yes No

3 1-7 (7 nt) 54.5% Yes No

4 2-7 (6 nt) 45.4% No Yes

hsa-miR-101 ENX-1 1 2-9 (8 nt) 77.3% No Yes

2 1-8 (8 nt) 77.3% Yes Yes

mmu-miR-1 Hand2 1 1-7 (7 nt) 47.6% Yes No

dme-miR-7 hairy 1 1-9 (9 nt) 81.0% Yes Yes

6.2.3.2 Target sequence conservation

Since the beginning of this investigation, it has become more generally accepted that

non-conserved miRNA targets are also likely to exist. In addition, the standard

requirement for target sequence conservation has been refined. Specifically, it is now

considered that it is conservation of the seed region of the putative target site that is of

primary importance, with conservation of the remainder of the sequence of lesser

importance.

Alignment of the putative EGFR target sites across human, chimp, mouse, rat and dog

demonstrate that all four sites, including the seed regions, are perfectly conserved

between human and chimp, but that there is only minimal conservation of these sites 5 The Rfam miRNA database downloaded in early 2003 included hsa-miR-7 defined as a 21 nt miRNA. At a later date, this database was revised such that miR-7 became defined as a 22 nt miRNA with an additional ‘U’ on its 3’ end, as it appears in (Lim, Glasner, Yekta, Burge, & Bartel, 2003). Hence, from here, the miR-7 sequence used will be the currently defined 22 nt sequence.

110

across the other species (Figure 6.4). While the site #1 seed region is also conserved to

dog and the site #3 seed region to rat, the seed regions of sites #2 and #4 are not

conserved beyond chimp.

In contrast, all target sites for the three verified miRNA targets are well conserved along

their entire length across five species, with perfect conservation in the seed regions.

These three examples represent the majority of verified miRNA targets in this respect.

Therefore, although the four EGFR sites do exhibit a certain degree of cross-species

sequence conservation, they do not conform to the conservation norm. However, this

does not preclude EGFR from serious consideration as a miRNA target, as discussed

with reference to the literature in section 4.2.5.1.

111

EGFR site #1 H. sapiens AGGAGCACAAGCCACAAGUCUUCCA P. troglodytes AGGAGCACAAGCCACAAGUCUUCCA C. familiaris AGAAGCAAGGGUCA-GAGUCUUCCA M. musculus GACAG-------------------- R. norvegicus GACAG-------------------- ** EGFR site #2 H. sapiens GUUAGACUGACUUGUUUGUCUUCCA P. troglodytes GUUAGACUGACUUGUUUGUCUUCCA C. familiaris AUCGGACCUAAUU-------UUCC A M. musculus AUUAGACUUCCUUCUAUGUUUUCUG R. norvegicus AUUAGACUACCUUUUAUGUUUUCUG * *** ** *** EGFR site #3 H. sapiens AUUUUUACUUCAAUGGGCUCUUCCA P. troglodytes AUUUUUACUUCAAUGGGCUCUUCCA C. familiaris AUUUUAUUUCUCGUGGGCUUUUCCA M. musculus AUUUGAUU---GAUGCACUCUUGUA R. norvegicus AUUUGAUU---CAUGCACUCUUCCA **** ** ** ** * EGFR site #4 H. sapiens A----AACGGAGGGGAUGGAAUUCUUCCU P. troglodytes A----AACGGAGGGGAUGGAAUUCUUCCU C. familiaris A----AAUGCAGGCG-UAGACUUCUUCUU M. musculus AGAGGAAUGACGGGG-UAGAAUUUUCCCU R. norvegicus A---GAAUGACUGGG-UAGAAUUUUCCCU * ** * * * * ** ** * * * ENX-1 #1 H. sapiens AGCUUCAGGAACCUCGAGUACUGUG P. troglodytes AGCUUCAGGAACCUCGAGUACUGUG C. familiaris AGCUUCAGGAACCUCGAGUACUGUG M. musculus AGCUUCAGGAACCUUGAGUACUGUG R. norvegicus AGCUUCAGGAACCUUGAGUACUGUG ************** ********** ENX-1 #2 H. sapiens AAUUCUGAAUUUGCAAAGUACUGUA P. troglodytes AAUUCUGAAUUUGCAAAGUACUGUA C. familiaris AAUUCUGAAUUUGCAAAGUACUGUA M. musculus AAUUCUGAAUUUGCAAAGUACUGUA R. norvegicus AAUUUUGAAUUUGCAAAGUACUGUA **** ******************** Hand2 M. musculus UGGAUAUUUGAAGAAAAGCAUUCCA R. norvegicus UGGAUAUUUGAAGAAAAGCAUUCCA C. familiaris UGGAUAUUUGAAGAAAAGCAUUCCA H. sapiens UGGAUAUUUGAAGAAAAGCAUUCCA P. troglodytes UGGAUAUUUGAAGAAAAGCAUUCCA ************************* hairy D. melanogaster ACAGCAAAU-CAGCAAAAGUCUUCCA D. simulans ACAGCAAAU-CAGCGAAAGUCUUCCA D. pseudoobscura ACAGCAAAA-CAGAAAAAGUCUUCCA T. castanaeum ACAGCAAGA-UCAUUCAUGUCUUCCA A. gambiae GCGACAAAAAUCACUAACGUCUUCCA * *** * ********

Figure 6.4: Cross-species conservation of putative and verified miRNA target sites. Bases conserved with the species in which the target was identified are shaded grey. Bases conserved across all species are marked with a star. Seed sites are underlined.

112

6.2.3.3 Structure and minimum free energy of miRNA target predictions

Since the original target search, RNA-folding programs have been adapted and

developed to address the needs of miRNA target prediction. They now have the ability

to fold two individual sequences together and a number of artefacts and shortcomings

related to early approaches to miRNA:mRNA folding have been eliminated.

One program, RNAhybrid, was developed to be used as a miRNA target prediction tool

in itself (Rehmsmeier, Steffen, Hochsmann, & Giegerich, 2004). To this end, it also

calculates p-values for predicted target sites, taking into account the lengths of the two

sequences and the number of sites predicted, to allow mfe values to be easily

interpreted.

The miR-7:EGFR prediction and the three verified miRNA:target pairs were submitted

to RNAhybrid with the requirement that match sites bind at positions 2 to 7.

Appropriate species-specific background sequence parameters computed using

RNAcalibrate were also submitted for the p-value calculation (Table 6.3). RNAhybrid

identified all four of the putative miR-7:EGFR target sites. Compared to ENX-1, Hand2

and hairy, all four EGFR sites have relatively good mfe values that fall below the

-16.6 kcal/mol mfe of the verified Hand2 target site. EGFR target site #1 also has a

p-value < 0.05, indicating that according to this algorithm, this site is not likely to have

arisen by chance.

113

Table 6.3: % sequence match, mfe and p-values for each putative EGFR target site and the target sites of three verified targets, calculated by RNAhybrid. The % match score includes G:U base-pairs and is determined from the optimal folding predicted from RNAhybrid. The number of G:U base-pairs is given in brackets.

miRNA mRNA Site #

% Sequence

match (G:U)

mfe

(kcal/mol) p-value

hsa-miR-7 EGFR 1 86.4% (1) -25.3 0.031

2 72.7% (1) -20.2 0.347

3 54.5% (1) -18.5 0.687

4 72.7% (4) -18.6 0.770

hsa-miR-101 ENX-1 1 81.8% (2) -24.6 0.002

2 72.7% (0) -19.8 0.013

mmu-miR-1 Hand2 1 76.2% (3) -16.6 -*

dme-miR-7 hairy 1 82.6% (1) -29.0 0.007

*A p-value for the Hand2 target site could not be calculated as background sequence parameters were unavailable for mouse.

114

hsa-miR-7:EGFR site #1 hsa-miR-7:EGFR site #2

5’

mfe: -25.3 kcal/mol

G AG

CA

C A A GC C A C

A A G U C U U C C AG

UGGAAGACUAGUG

AUUUU

GU

UG

5’

mfe: -20.2 kcal/mol

U UA

GAC U

G AC

UUG

U UUG U C U U C C A U

UGGAAGACUA

GUGA

UUU

UGU

UG

hsa-miR-7:EGFR site #3 hsa-miR-7:EGFR site #4

5’

mfe: -18.5 kcal/mol

U UCA

AU

G GGC U C U U C C A

A

UGGAAGAC

UA

GUG

AUUU

UGUU

G

5’

mfe: -18.6 kcal/mol

A AA

CG

GA

GGG

G A UG

GA

A UU

CU

UC

C U

UG

GA

AG

ACU

AG

UGAU

UU

UG

UUG

hsa-miR-101:ENX-1 site #1 hsa-miR-101:ENX-1 site #2

5’

mfe: -24.6 kcal/mol

G CU

UC

AGGA

A C CU C G

AG

UA

CU

GU

GG

UA

CA

GU

AC

UGUGA

UAAC

UG

AA

G

5’

mfe: -19.8 kcal/mol

G CA

GU

UUG

A A AUU

C UGA

A U UUG

CAAA

GU

AC

UG

UA

A

UA

CA

GU

ACUG

UG

AUA

AC

UGA

AG

mmu-miR-1:Hand2

5’

mfe: -16.6 kcal/mol

G UGG A

UA

UU

UGA

AG A A

AAG C A U U C C A U

UGGAAUGUA

AAGA

AG

UA

UGU

A

dme-miR-7:hairy

5’

mfe: -29.0 kcal/mol

A AC

AG

CA

AAU

C A GC A

A AA G U C U U C C A

A

UGGAAGACUAG

UGAUU

UU

GU

UG

U

Figure 6.5: RNAhybrid foldings of putative and verified miRNA target sites. The mRNA strand is below the miRNA strand in each case.

6.2.3.4 Instability of target sites in the context of the 3’UTR mRNA structure

A new idea that arose after the original search, was that the functionality of a target site

may be influenced by its stability in the context of the greater mRNA structure. Robins

and colleagues (2005) proposed a target prediction criterion that requires the mRNA

mRNA

miRNA

115

structure of a putative target to have at least three consecutive unbound nucleotides in

the seed region, as depicted in Figure 6.6. They reported that this criterion improves the

accuracy of target prediction in Drosophila.

Hence, the 3’UTR structures of EGFR and the three verified targets were obtained using

mfold, and the target sites were tested with this criterion (Table 6.4).

Table 6.4: Summary of seed region instability for putative EGFR target sites and three verified targets.

miRNA mRNA Site #

Three consecutive

unbound nts in seed?

hsa-miR-7 EGFR 1 Yes

2 No

3 No

4 Yes

hsa-miR-101 ENX-1 1 No

2 No

mmu-miR-1 Hand2 1 No

dme-miR-7 hairy 1 Yes

While the hairy target site and two of the putative EGFR target sites do satisfy the

instability criterion, ENX-1 and Hand2 do not. The predictive value of this criterion in

species other than Drosophila has not been tested. But while clearly not a requirement

for miRNA target interaction, target site instability may still enable an interaction to

occur more readily, perhaps thereby enhancing repression. With this possibility, the

EGFR site instability is encouraging.

116

Figure 6.6: Folded structure of the EGFR 3’UTR mRNA and enlargement of the putative miR-7 target sites. Sites extend between short line markers. Seed nucleotides are represented by black dots.

6.2.3.5 miRNA and target expression profiles

Since the original target search, several authors have emphasised the importance of co-

expression of miRNA and target for a biologically significant interaction (Farh et al.,

2005; Lim et al., 2005a; Sood, Krek, Zavolan, Macino, & Rajewsky, 2006).

In normal tissues, miR-7 is predominantly expressed in the brain, but is also expressed

at lower levels in the spleen (Baskerville & Bartel, 2005; Sempere et al., 2004). Within

the brain, it has been shown to be the most highly expressed miRNA in the pituitary

(Sood, Krek, Zavolan, Macino, & Rajewsky, 2006).

117

EGFR has also been shown to be expressed in these tissues (Ferrer et al., 1996; Ge et

al., 2005). To focus on the pituitary, the location of highest miR-7 expression, numerous

studies have found that EGFR is expressed at varying levels in the pituitary (Chaidarun,

Eggo, Sheppard, & Stewart, 1994; Onguru et al., 2004; Theodoropoulou et al., 2004).

One study detected EGFR in only 10% of normal human pituitaries (Chaidarun, Eggo,

Sheppard, & Stewart, 1994), while another found that, of eight normal human

pituitaries, two showed strong EGFR expression and six showed weak EGFR

expression (Onguru et al., 2004).

One explanation for the different EGFR levels observed in normal pituitaries is that

EGFR expression changes over time, possibly due to changes in cell function or stress.

As it is possible that miR-7 also varies over time, EGFR and miR-7 expression levels

should ideally be evaluated in the same samples. Only one study has presented both

miRNA and mRNA expression profiles of the same samples (Lu et al., 2005).

Unfortunately, no brain or spleen tissues were assayed. However, of the tissues that

were assayed, some did express both EGFR and miR-7 at levels above the minimum

threshold value, including normal pancreas and bladder and tumours of the colon,

bladder, pancreas, kidney and lung, supporting the co-expression of EGFR and miR-7

in these tissues.

However, another consideration here is that there are several different cell types within

the whole tissues analysed in the studies described so far. Hence it is possible that

EGFR and miR-7 are spatially separated within different cell types in vivo. On the other

hand, there are also human cancer cell lines that have been reported, in separate studies,

to express both EGFR and miR-7, such as Hep G2, A549 and HeLa (Bai et al., 2006; ,

"miRNA Research Guide", 2005; Timpson, Lynch, Schramek, Walker, & Daly, 2005;

E. B. Yang, Wang, Mack, & Cheng, 1996).

Therefore, though it is not certain at present that EGFR and miR-7 are co-expressed

both spatially and temporally in human tissues, there is much cumulative support for

this and it is likely that EGFR and miR-7 do have opportunity to interact in vivo.

118

6.3 Discussion

The aims of this chapter of the investigation were, firstly, to implement a computer

program to predict miRNA targets and, secondly, to use this program to search a set of

human, cancer-related genes for possible miRNA targets.

To address the first aim, a computer program was written to predict targets based on

seed match requirements, the presence of multiple sites, the location of sites in the

3’UTR, good overall sequence complementarity and low mfe, features that have been

shown in the literature to improve prediction accuracy and/or correlate with enhanced

repression in experimental tests. Though not used as a prediction requirement, the

conservation of predicted target sequences was also assessed and could provide

additional support for a prediction. The resultant program is a very convenient and

flexible tool for prediction of miRNA targets.

To achieve the second aim of this investigation, the program was used to search a set of

human, cancer-related genes, from which it made 23 miRNA target predictions. With

the criteria employed, these target predictions compare very well to verified miRNA

targets. The top prediction was for EGFR as a target of miR-7. This prediction

underwent further theoretical evaluation in response to advances in the understanding of

miRNA targets and new prediction criteria. The outcome of this analysis was even

greater confidence in the miR-7:EGFR prediction.

To summarise the findings of this analysis, the EGFR 3’UTR has four seed match sites

for miR-7, all having some features characteristic of miRNA targets. One, site #1, is

particularly promising with respect to seed match length, overall complementarity and

mfe. Using the most recent information and programs, miR-7 is predicted to bind to this

site with a 9 nt perfect seed match, 86.4% of its nucleotides bound, including one G:U

base-pair, and an mfe of -25.3 kcal/mol. In addition, however, all four sites have an

adenosine in position 1 and/or position 9, and sites #1 and #4 satisfy Robins and

colleagues’ criterion for target instability (Robins, Li, & Padgett, 2005). Also, EGFR

and miR-7 have both been shown to be expressed in the pituitary and other brain

tissues, and hence are likely to have the opportunity to interact in vivo.

119

The EGFR target sites are all perfectly conserved between human and chimp, but have

only partial conservation to other species. Although this does not eliminate the

possibility that EGFR is a miR-7 target, it does mean that EGFR is unlike the majority

of miRNA targets in this respect. In fact, it is primarily EGFR’s limited conservation

that has kept published prediction programs, the majority of which have multiple-

species conservation filters, from making this prediction, which is very promising

according to other criteria.

In addition to miR-7:EGFR, the original search also identified a number of other

putative human miRNA targets that are involved in cancer. However, this search

included only a subset of cancer-related genes. Therefore, an interesting direction for

future study would be to conduct more extensive searches, encompassing a greater

number of genes associated with cancer and an updated list of miRNAs. Different and

larger searches are very possible given the flexibility of the prediction program and

would be further facilitated by additional computing power.

In addition to cancer research, the prediction program could also be applied to

investigate miRNA targets involved in normal cells and also in other diseases. miRNAs

have already been linked to cardiac hypertrophy and heart failure, Tourette’s syndrome

and fragile X mental retardation (Abelson et al., 2005; Jin, Alisch, & Warren, 2004; van

Rooij et al., 2006). With miRNAs likely to be involved in a broad range of biological

processes, this is a promising line of research.

Another direction for future work could involve searches using less stringent prediction

criteria. The criteria for the original search were chosen for greater prediction specificity

at the expense of sensitivity, in view of the fact that only a small number of predictions

would be tested experimentally in this investigation. However, a more relaxed search

would produce a larger number of predictions and would allow investigation of weaker

potential target sites that may mediate target repression individually or contribute to

fine-tuning of target repression through cooperativity with other miRNAs.

To follow up on the results of this chapter, however, the next step is to evaluate the

existing predictions experimentally. This process is begun in the next chapter with the

top prediction, miR-7:EGFR.

120

In conclusion, aims 1 and 2 of this investigation have been successfully met, resulting in

a customised miRNA target prediction program and 23 target predictions from a set of

human cancer-related genes. The most promising of these is miR-7:EGFR, which has

many features hypothesised or demonstrated to characterise true miRNA targets.

6.4 Hypotheses

At this point in the investigation, two hypotheses were made:

Hypothesis 1: miR-7 targets and inhibits the expression of EGFR in human cells.

Hypothesis 2: miR-7 affects cell functioning in a way that is consistent with an increase

in the level of EGFR.

Project aims 3 and 4, yet to be achieved, were pursued with a view to evaluating these

specific hypotheses.

121

CHAPTER 7: EXPERIMENTAL ASSESSMENT OF THE miR-7:EGFR TARGET

PREDICTION

7.1 Introduction

In Chapter 6, it was hypothesised that EGFR is a target of miR-7 in human cells, based

on both computational sequence analysis and evaluation in terms of additional proposed

prediction criteria. The aim of the next part of the investigation was to test this

hypothesis experimentally.

Of the target validation techniques discussed in section 4.2.6, a reporter assay approach

was chosen as the most appropriate to begin with. Firstly, at a pragmatic level, the

reporter assay is an efficient way to assess a putative miRNA:target interaction.

Secondly, unlike other approaches, the reporter assay can demonstrate the ability of a

miRNA to inhibit target expression directly, in a sequence-specific manner.

If the results of the reporter assays supported the EGFR target hypothesis, then

additional validation experiments were to be performed. These would involve

monitoring the response of endogenous EGFR protein levels to up-regulation of miR-7.

This approach has the important advantage that the predicted interaction is assessed in a

less artificial, more biologically relevant context than in the reporter assay approach.

miR-7 up-regulation was achieved through transfection of synthetic miR-7 precursors

rather than using a plasmid-based approach. Although miRNA precursors produce only

a transient down-regulation of target protein, the proposed experiment only required

that the knockdown last long enough for it to be easily observed. This was not of great

concern given that EGFR has been shown to be rapidly and effectively knocked down

by siRNA in A549 and SPC-A1 cells for at least 48 hours (M. Zhang et al., 2005).

Therefore, the prolonged miRNA expression achieved with plasmid-based systems was

unnecessary. Furthermore, miRNA precursors are easy to transfect and transfection

efficiency can approach 100% ("Technotes", 2005).

This chapter begins with the setup and optimisation of the reporter assay. This is

followed by assessment of the EGFR target prediction using both reporter assays and

examination of endogenous protein levels.

122

7.2 Results

7.2.1 Establishment of an optimum reporter assay

7.2.1.1 Replication of the results of Lewis et al., 2003

As reporter assays to detect miRNA:target interactions had not previously been

performed in the Laboratory for Cancer Medicine, a positive control was required with

which to validate and optimise the assay. For this purpose, wild-type, mutant and empty

vector plasmids for the published verified target, SMAD family member 1 (SMAD1),

were used6 (Lewis, Shih, Jones-Rhoades, Bartel, & Burge, 2003). Lewis and colleagues

showed that the expression of a construct containing a section of the SMAD1 3’UTR,

including two predicted miR-26a target sites (SMAD1-Wt), was 8-fold lower than that

of an identical construct with the target sites mutated (SMAD1-Mt) in HeLa S3 cells

(p < 0.001). The Laboratory for Cancer Medicine did not have access to stocks of

HeLa S3 cells and so HeLa cells were used instead for these replication experiments. As

both of these cell lines are reported to express miR-7 at a ‘+’ level on a scale from 0 to

‘++++’ ("miRNA Research Guide", 2005), this was considered a reasonable substitution

under the circumstances.

However, the results of Lewis and colleagues could not be replicated, despite numerous

experiments aimed at creating an optimal system for miRNA:target interaction.

Experiments were conducted in 12-well plates and involved testing of different cell

densities (60x103, 80x103, 100x103, 120x103 cells/well), harvest times (24, 30, 36 and

48 hours), firefly luciferase plasmid amounts (0.05, 0.15, 0.5 µg/well), firefly to Renilla

luciferase plasmid ratios (5:1, 15:1, 50:1) and two different preparations of each

plasmid. Figure 7.1 shows the results of one such experiment showing no significant

differences between the expression of any of the three plasmids.

6 SMAD1-Wt, SMAD1-Mt and empty vector plasmids were kindly provided by Prof. David Bartel at the Massachusetts Institute of Technology.

123

Figure 7.1: Luciferase assay showing the expression of SMAD1-Wt, SMAD1-Mt and empty vector plasmids, in HeLa cells. Values are mean normalised luciferase readings (firefly/Renilla) ± SD (n=3). Results are representative of at least three independent experiments.

One possible explanation for this failure was that miR-26a was absent or at a reduced

level in our HeLa cells compared to the HeLa S3 cells used by Lewis and colleagues.

Alternatively, HeLa cells may lack a cofactor present in HeLa S3 cells that is necessary

for miR-26a:SMAD1 interaction. Finally, SMAD1 may not in fact be a functional target

of miR-26a. Therefore, alterations were made to the reporter assay experiment protocol

to eliminate or reduce the likelihood of these possibilities.

7.2.1.2 Perfect target reporter assays

Given the failure of initial attempts to verify a positive control for the reporter assay

experiments, two alterations were made to the approach described above.

One alteration was to use a reporter plasmid containing a perfectly complementary

target site for miR-7, to be compared to an empty vector lacking the targe site, instead

of the SMAD1-Wt and SMAD1-Mt plasmids. This was done so as to be certain of using

an authentic miRNA target. In addition, with a perfectly complementary target,

miRNAs would be predicted to act as siRNAs and inhibit reporter expression through

124

an RNAi-like target cleavage mechanism rather than through translational repression.

As this is a much more efficient way of inhibiting expression, differences in plasmid

expression should be much more pronounced and more readily detected. Similar

plasmids were used by Cheng and colleagues (2005) to check whether co-transfected

miRNA inhibitors were entering the cell and behaving as expected.

The second alteration made to the reporter assay system was to use a different cell line,

MCF7. While HeLa cells are published to express miR-7 at a ‘+’ level above

background, MCF7 cells are published to express miR-7 at a ‘++’ level ("miRNA

Research Guide", 2005). As MCF7 cells should have more miR-7, more inhibition of

target plasmid expression would be predicted, giving a larger difference between vector

and target plasmid signals to detect.

However, once again, none of the reporter assay experiments conducted showed any

significant difference between vector and perfect target expression (Figure 7.2).

Experiments were then performed in the presence and absence of a miR-7 inhibitor.

This inhibitor would be predicted to relieve any inhibition of target expression, thereby

leading to increased target expression, indicated by an increase in luciferase activity.

However, no significant effect was observed (Figure 7.2).

Optimisation experiments were then conducted, involving many combinations of

different cell densities (30x103 or 60x103 cells/well) and different amounts of target and

vector plasmid (50, 100, 200 ng/well), Renilla plasmid (20, 5, 1, 0.2 ng/well) and miR-7

inhibitor (5, 10, 30 pmol/well), with two different preparations for each plasmid.

However, none of these experiments showed any difference between vector and target

reporter activity.

It was considered likely on the basis of these results that there was insufficient miR-7 in

MCF7 cells to significantly affect the perfect target reporter activity.

125

Figure 7.2: Luciferase assay showing the effect of miR-7 inhibitor on the expression of the perfect miR-7 target plasmid and the empty vector plasmid in MCF7 cells. Values are mean normalised luciferase readings (firefly/Renilla), expressed as a ratio of empty vector alone ± SD (n=3). Results are representative of at least three independent experiments.

7.2.1.3 Perfect target reporter assays with miR-7 up-regulation

In view of the above results, the decision was made to artificially up-regulate miR-7 in

the cells so that the target prediction could be assessed using the reporter assays as

originally planned.

As can be seen in Figure 7.3A, in HeLa cells, miR-7 induced a significant reduction

(p < 0.05, Mann-Whitney test) in the expression of a reporter plasmid containing the

perfectly complementary miR-7 target, in a dose-dependent manner, while the

expression of the empty vector was not significantly affected at either concentration.

Figure 7.3B shows firstly that the target plasmid was not significantly affected by

nonsense (NS) precursor. This demonstrates that the observed effect is a direct or

indirect effect of the miR-7 precursor rather than a non-specific effect resulting from the

transfection of small RNA molecules. Furthermore, the addition of a miR-7 inhibitor

partially countered the miR-7-induced reduction in target plasmid expression.

126

Figure 7.3: Luciferase assays showing the effects of miR-7 and NS precursors on the expression of the perfect miR-7 target plasmid and the empty vector plasmid, in HeLa cells. A) The effect of miR-7 precursor concentration on vector and target plasmid expression. B) The effect of concurrent treatment with miR-7 inhibitor on target plasmid expression. Values are mean normalised luciferase readings (firefly/Renilla), expressed as a ratio of empty vector alone, ± SD (n=3). Results are representative of at least three independent experiments.

127

This effect is statistically significant at both 25 and 50 pmol/well (p < 0.05, Mann-

Whitney test). In contrast, the miR-7 inhibitor had no significant effect on the target

plasmid in the absence of miR-7 precursor. These results demonstrate that the effect of

the miR-7 precursor on target plasmid expression was due to the processed, mature

miR-7 and thereby verified that the luciferase reporter assay setup was working as

expected.

7.2.2 Assessment of the miR-7:EGFR prediction using EGFR-Wt and EGFR-Mt

plasmids, and miR-7 up-regulation

Having optimised the luciferase assay with a positive control, the next step was to use

the assay to test for miR-7-mediated repression of EGFR expression.

7.2.2.1 Cloning of EGFR-Wt and EGFR-Mt plasmids

Firstly, plasmids were made using the same design approach as that used by Lewis and

colleagues (2003). The design and cloning of these plasmids is described in detail in

methods section 5.2. Briefly, the EGFR-Wt plasmid contains a section of the EGFR

3’UTR extending between putative miR-7 target sites #1 and #2, cloned downstream of

firefly luciferase in a modified pGL3 vector, while the EGFR-Mt plasmid is identical

except for three point mutations in each of these two seed matches, as depicted in

Figure 7.4.

128

Figure 7.4: Composition of inserts for the EGFR-Wt and EGFR-Mt plasmids. ORF = open reading frame. Base-pair numbering is from the start of the 3’UTR.

7.2.2.2 Luciferase assays with EGFR-Wt and EGFR-Mt plasmids,

and miR-7 up-regulation

A series of experiments using the EGFR-Wt, EGFR-Mt and empty vector plasmids was

next conducted. In HeLa cells, miR-7 was shown to induce a dose-dependent reduction

in the expression of EGFR-Wt relative to that of empty vector (p < 0.05, Mann-Whitney

test), while expression of EGFR-Mt was not significantly affected. This effect was

independent of the plasmid preparation used (Figure 7.5A). Importantly, the EGFR-Wt

plasmid was not significantly affected by NS precursor at either of the concentrations

tested, suggesting a mir-7-specific effect. The same effect was observed in triplicate

experiments.

In addition, miR-7 also significantly reduced the expression of EGFR-Wt but not

EGFR-Mt in MDA-MB-468 breast cancer cells and A549 lung cancer cells (p < 0.05,

Mann-Whitney test). Therefore, the effects of miR-7 on reporter plasmid expression are

not cell-type specific, but rather are observed in a non-EGFR-overexpressing cancer cell

129

line (HeLa) and two EGFR-overexpressing cancer cell lines (MDA-MB-468 and A549)

(Figure 7.5). The effect was less pronounced in MDA-MB-468 cells compared to A549

cells, possibly as a result of the much greater levels of EGFR in MDA-MB-468 cells,

that could ‘soak up’ miR-7 molecules, leaving fewer to interact with target RNA. These

results were replicated at least twice for both the MDA-MB-468 and A549 cell lines.

A meta-analysis, performed on the luciferase readings from all eight of the reporter

assays conducted, confirmed with strong statistical significance that miR-7 reduces

EGFR-Wt expression relative to EGFR-Mt expression (p < 0.01, Mann-Whitney test;

p = 7x10-13, Student’s t-test), while NS has no significant effect at a 0.05 significance

level. The reduction in expression of EGFR-Wt in cells treated with miR-7 compared to

NS precursor is also strongly statistically significant (p < 0.01, Mann-Whitney test;

p = 8x10-16, Student’s t-test).

_________________________________

Figure 7.5 (over page): Luciferase assays showing the effects of miR-7 and NS precursors on the expression of EGFR-Wt, EGFR-Mt and empty vector plasmids in three cell lines. Precursors were used at 30 nM. A) HeLa cells with two plasmid preparations for both EGFR-Wt and EGFR-Mt, B) MDA-MB-468 cells, C) A549 cells. Values are mean normalised luciferase readings (firefly/Renilla), expressed as a ratio of empty vector alone ± SD (n=3). Results are representative of at least three independent experiments.

130

Figure 7.5: Luciferase assays showing the effects of miR-7 and NS precursors on the expression of EGFR-Wt, EGFR-Mt and empty vector plasmids in three cell lines.

131

7.2.3 Effect of miR-7 up-regulation on endogenous protein

The results of the luciferase assays demonstrated that miR-7 is likely to specifically

target the predicted sites in the EGFR 3’UTR. This finding was next investigated further

with a second approach to target validation. This approach involved monitoring changes

in endogenous EGFR protein levels in response to miR-7 up-regulation using the

Western blot technique.

7.2.3.1 EGFR protein

In both A549 and MDA-MB-468 cells, EGFR protein was reduced by miR-7 compared

to NS precursor (Figures 7.6 and 7.7), consistent with the hypothesis that EGFR is a

target of miR-7. Quantitation of the Western blot band intensities showed that the

maximum reduction in protein was 57%, observed on day 3 after transfection.

7.2.3.2 Other proteins

The effect of miR-7 on the levels of several other proteins was examined next, in order

to determine the specificity of the effect of miR-7 on EGFR protein. As observed

previously, treatment of both A549 cells and MDA-MB-468 cells with miR-7 led to a

reduction in EGFR protein on Western blot (Figure 7.7). The same membrane was then

probed for β-actin, Raf-1, HuR, p27 and COX-2.

Raf-1, which is predicted to be a miR-7 target, was down-regulated following

transfection with miR-7 precursor, while β-actin and p27, which are not predicted to

contain miR-7 binding sites, were unaffected by miR-7 precursor. These results are

consistent with predictions. In contrast, COX-2 and HuR were also reduced in response

to miR-7, though they are not predicted to contain miR-7 binding sites.

132

Figure 7.6: The effects of miR-7 and NS precursors on endogenous EGFR protein levels (175 kDa) in MDA-MB-468 cells. Precursors were used at 30 nM. β-actin (42 kDa) was used as a loading control. A) Western blot using protein extracts harvested from cells on days 1 to 6 after transfection, and B) corresponding EGFR band intensity normalised to β-actin. The values above the bars are the percentage reductions in normalised EGFR levels between the miR-7 and NS conditions for that day.

133

Figure 7.7: Western blot showing the effects of miR-7 and NS precursors on the levels of EGFR (175 kDa), Raf-1 (76 kDa), p27 (26 kDa), HuR (36 kDa) and COX-2 (72 kDa) protein in MDA-MB-468 and A549 cells on day 3 after transfection. β-actin (42 kDa) was used as a loading control. Precursors were used at 30 nM.

134

7.3 Discussion

This chapter of the investigation aimed to experimentally test the hypothesis that EGFR

is a target of miR-7 in vitro. In order to achieve this aim, a reporter assay system was

first established. This involved the design and cloning of EGFR wild-type and mutant

plasmids, trouble-shooting of several problems encountered in the development of the

experimental protocol, and verification of the system with a positive control.

Ultimately, reporter assay experiments showed that transfection with miR-7 precursor

inhibited the expression of EGFR-Wt but not EGFR-Mt plasmid in three different

cancer cell lines. This result demonstrates that EGFR is down-regulated by miR-7 in a

sequence-specific manner. Further, an additional series of experiments conducted in

EGFR-overexpressing lung (A549) and breast (MDA-MB-468) cancer cell lines

revealed that transfection with miR-7 precursor led to a reduction in endogenous EGFR

protein by up to 57% in the case of MDA-MB-468 cells, as measured on Western blot.

Therefore, the hypothesis that EGFR is a target of miR-7 in vitro is supported by results

obtained using two different experimental approaches.

When these experiments were performed, EGFR was, to our knowledge, the only

verified miRNA target that is not conserved across mammals. Since then, a second

example, the miR-155 target, angiotensin II receptor type 1 (hAT1R), has been

published (Martin, Lee, Buckenberger, Schmittgen, & Elton, 2006), which also supports

the existence of target sites that are not extensively conserved. The decision not to

exclude non-conserved 3’UTR sequences was a deliberate and critical choice at the

earlier stage of development of the target prediction program. The exciting finding

reported here validates this decision and reveals the program to be capable of providing

an accurate prediction not provided by any other known prediction program. It therefore

provides motivation to include non-conserved sequences in target searches and to

continue to develop new target prediction approaches that are less reliant on sequence

conservation. The implications of this finding will be considered further in the general

discussion in Chapter 10.

In addition to this major finding, this chapter also presented evidence of the effects of

the miR-7 precursor on the endogenous levels of proteins other than EGFR, specifically,

Raf-1, β-actin, p27, COX-2 and HuR.

135

Raf-1 is predicted by the target prediction program from Chapter 6 and the published

target prediction programs TargetScanS, miRanda and PicTar to be a target of miR-7

(John et al., 2004; Krek et al., 2005; Lewis, Burge, & Bartel, 2005). It has two predicted

miR-7 binding sites, one of which has a seed match conserved across human, mouse, rat

and dog, and has an estimated mfe of -21.7 kcal/mol as calculated by RNAhybrid.

Consistent with this prediction, endogenous Raf-1 protein was reduced following

transfection with miR-7 precursor in both A549 and MDA-MB-468 cells. This result

supports the validity of the experimental approach and also provides evidence that

Raf-1 is a miR-7 target, although confirmation of this proposition would be the subject

of a different research program.

None of the other four proteins examined in this experiment were predicted to be miR-7

targets. The observation that protein levels of both β-actin and p27 were unaffected by

miR-7 demonstrates that it acts with a certain degree of specificity. In contrast, COX-2

and HuR protein levels were reduced in response to miR-7. It is most likely however,

that these results only reflect a limitation of the experimental approach, which cannot

distinguish between down-regulation resulting from the direct action of a miRNA and

down-regulation resulting from downstream effects of such actions. Hence, it is entirely

possible that the down-regulation of COX-2 and HuR are indirect effects of miR-7. In

fact, there is evidence in the literature to support this idea. There is known to be cross-

talk between EGFR and COX-2, with EGFR positively regulating COX-2 expression

via the MAPK pathway and the c-Jun oncogene (Dannenberg, Lippman, Mann,

Subbaramaiah, & DuBois, 2005). In addition, Raf-1 is part of the MAPK signalling

cascade and could also potentially influence COX-2 expression. HuR has also been

linked to the MAPK signalling pathway (Lin et al., 2006; X. Yang et al., 2004),

although neither EGFR, Raf-1 or COX-2 are known to regulate HuR expression.

However, HuR expression may be altered as an indirect effect of other as yet

unidentified miR-7 targets. In addition, in MDA-MB-468 cells, HuR is reduced by both

the miR-7 and the negative control precursor suggesting that in this cell line the down-

regulation may be part of a general response to the addition of small RNA molecules.

One factor that could be argued to limit the interpretation of the results in this chapter is

that the miR-7 precursor is used for both the final reporter assays and the experiments

on endogenous protein. While miRNA up-regulation is a very useful technique, it has

the disadvantage that it may create artificially favourable conditions for miRNA:mRNA

136

interaction. Targets verified under such conditions may not necessarily be regulated

in vivo. Most importantly in this case, miRNA up-regulation experiments present the

cells with miRNA levels that may be higher than endogenous levels. This is important

because it has been shown that predicted targets that are not repressed by endogenous

miRNA levels may be repressed with inflated levels of exogenous miRNA (Doench &

Sharp, 2004). It is not clear what concentration of miRNA precursor could be

considered physiologically relevant, particularly as miRNAs are expressed at different

levels according to the miRNA, the cell type and the state of the cell. Furthermore,

miRNAs can be hugely overexpressed in disease states. For example, miR-7 expression

was shown to be 122-fold higher in the colorectal cancer cell line, SW620, than the

mean miR-7 expression in the other cell lines assayed (Jiang, Lee, Gusev, &

Schmittgen, 2005). Nevertheless, in an effort to address this issue, miR-7 precursor was

used predominantly at a conservative concentration of 30 nM. This concentration was

chosen on the basis of the few publications reporting the use of miRNA precursors.

Ambion, Inc., the manufacturer of the miRNA precursors used, recommends

concentrations of up to 100 nM ("Pre-miR miRNA Precursor Specification Sheet",

2005), while Wang and Wang (2006) used a miRNA precursor concentration of 30 nM.

Thus, given the conservative level of precursor used, it seems unlikely that excessive

repression was a problem. However, to examine this issue definitively, a future program

of research could use a range of cell lines from normal and cancerous cells of different

tissues, expressing miR-7 and ideally also EGFR at low levels, and involve the

treatment of these cell lines with a miR-7 inhibitor. The inhibitor should relieve miR-7-

mediated repression and, if EGFR is a target of miR-7 at endogenous levels, cause the

EGFR protein level to increase.

Another consideration in relation to the biological significance of the miR-7:EGFR

interaction is whether it can have a functional effect in cells. This will not necessarily be

the case, with many factors impacting the response of a cell to such a change. If it is the

case, however, this would provide further supporting evidence for a biologically

significant interaction. This possibility will be discussed further and explored

experimentally in Chapter 8.

In conclusion, this chapter saw the validation of Hypothesis 1 of Part 2 of this thesis,

that EGFR is a target of miR-7 in vitro. Down-regulation of EGFR by miR-7 was

sequence-specific and showed some specificity in assays of endogenous protein. The

137

interaction was verified in a number of different cancer cell lines. In addition, at the

time of this finding, EGFR was the first case, to our knowledge, of a human miRNA

target verified in vitro, for which target sites are not conserved across mammals,

implicating the examination of non-conserved sequences as a serious line of

investigation, and motivating the inclusion of non-conserved sequences in prediction

programs.

138

CHAPTER 8: THE FUNCTIONAL EFFECT OF miR-7 PRECURSOR IN LUNG

AND BREAST CANCER CELLS

8.1 Introduction

In Chapter 7, it was demonstrated that EGFR is a target of miR-7 in vitro. The aim of

this next chapter of the investigation was to determine the functional effect of miR-7 in

human cells. Specifically, the investigation was to test Hypothesis 2 of Part 2 of this

thesis, that miR-7 affects cell functioning in a way that is consistent with an increase in

the level of EGFR. With this aim, the investigation had the potential to provide further

evidence for the miR-7:EGFR interaction and to support its biological significance.

The experimental approach was chosen to take advantage of the system established in

Chapter 7 for examination of the effect of miR-7 on EGFR protein. Hence, functional

studies were performed on A549 and MDA-MB-468 cells transfected with miR-7

precursor. The duration of the miR-7-mediated EGFR knockdown, shown to be at least

6 days, was again considered long enough for this part of the investigation, given that

the majority of EGFR functional studies reported by other groups lasted no more than 6

days after treatment (Bai et al., 2006; G. C. Chang et al., 2004; Janmaat, Rodriguez,

Gallegos-Ruiz, Kruyt, & Giaccone, 2006; M. Zhang et al., 2005).

As has been described, EGFR is involved in many signalling pathways and plays

important roles in a range of cellular processes including cell growth and viability, cell

cycle regulation, migration and angiogenesis. The likely functional effect of miR-7

treatment was predicted from studies of the EGFR inhibitors ZD1839 (Iressa), gefitinib,

AG-1478 and AG-1517, vector-based short hairpin RNA (shRNA) against EGFR and

EGFR siRNA, in the relevant cell lines.

Of four studies in A549 cells, all showed that treatment with EGFR inhibitor reduced

cell growth, as measured by colony assay, cell counting, or MTT assay (Bai et al., 2006;

G. C. Chang et al., 2004; Janmaat, Rodriguez, Gallegos-Ruiz, Kruyt, & Giaccone, 2006;

M. Zhang et al., 2005). In addition, studies using shRNA against EGFR or the selective

EGFR tyrosine kinase inhibitor, ZD1839, observed an increase in G1 phase population

and a decrease in S phase population, consistent with inhibition of cell cycle

139

progression at the G1/S checkpoint (Bai et al., 2006; G. C. Chang et al., 2004). ZD1839

was also shown to induce a concurrent reduction in the G2/M phase population (G. C.

Chang et al., 2004). These same two studies also reported a cytotoxic effect in response

to EGFR inhibitors. One showed that shRNA against EGFR induced apoptosis (Bai et

al., 2006). In the other study, however, observations of cell morphology suggested that

ZD1839 inhibited cell growth through a cytostatic mechanism at concentrations less

than 10 µM and through a cytotoxic mechanism only at high concentrations. TUNEL

assay demonstrated that this cytotoxic effect was due to apoptosis at a ZD1839

concentration of 25 µM (G. C. Chang et al., 2004). These observations are consistent

with the results of another study of ZD1839 in different cell lines (Ciardiello et al.,

2000), although ZD1839 did not induce apoptosis in MDA-231 breast cancer cells

either in vitro or in vivo (Anderson, Ahmad, Chan, Dobson, & Bundred, 2001).

A single study of the effects of the EGFR quinazoline inhibitors AG-1478 and AG-1517

in MDA-MB-468 cells demonstrated that both treatments effectively inhibited colony

formation in soft agarose in this cell line. However, these treatments only inhibited

proliferation of cell monolayers by 20% (Busse et al., 2000).

The present investigation focussed on the effects of miR-7 on cell growth and the cell

cycle. It was predicted that miR-7 would inhibit cell growth, inhibit cell cycle

progression at the G1/S checkpoint and possibly have a cytotoxic effect in A549 cells,

and minimally inhibit cell growth in MDA-MB-468 cell monolayers.

Changes in cell growth were assessed visually and using two different quantification

techniques: a cell-counting technique and the CT assay. Routine visual assessment of

the number and morphology of cells can provide valuable information on their growth

and viability. Extending this technique to photograph and count cells offers a way to

quantify observed changes in cell number without the need of special expertise or

optimisation time. However, the CT assay has several advantages over cell counting,

including ease, speed, and a potentially more precise measure of live cell number.

Changes in the cell cycle were assessed using fluorescence-activated cell sorting

(FACS) analysis. This technique involves harvesting and staining of cells followed by

flow cytometry analysis to determine the proportion of cells in each phase of the cell

cycle. It is an established and widely accepted technique.

140

8.2 Results

8.2.1 Visual assessment of miR-7-treated cells

Cells from two EGFR-overexpressing cell lines, A549 and MDA-MB-468, were

transfected with either LF, miR-7 precursor or NS precursor and observed for up to a

week. For each cell line, this was performed at least five times.

For MDA-MB-468 cells, there was no indication of any functional effect of miR-7 from

visual inspection alone, in any replicate (Figure 8.1A). For A549 cells, however,

transfection of miR-7 precursor induced a dramatic reduction in cell proliferation, an

increase in the number of floating dead cells, and changes in cell morphology compared

to transfection with LF alone, as assessed visually (Figure 8.1B). A smaller reduction in

cell proliferation was also observed with transfection of NS precursor compared to LF

alone. These effects were most pronounced on day 3 after transfection.

On close examination of cell morphology, A549 cells transfected with miR-7 precursor

appeared rounded up compared to untreated cells and cells treated with NS precursor or

LF alone, which were more spindle shaped (Figure 8.1C). This altered morphology is

typical of sick or dying cells.

From repeated observation of treated A549 cells, it was apparent that the presence of

neighbouring cells conferred some resistance to cell death, growth arrest and

morphology change. Confluent patches of cells within a dish often survived transfection

with miR-7 precursor while more sparsely distributed cells elsewhere in the same dish

died. However, cell confluency did not affect the response of MDA-MB-468 cells to

miR-7 precursor, and so has no significant implications for the results in this cell line.

141

Figure 8.1: Photographs of cells treated with LF, miR-7 precursor or NS precursor on day 3 after transfection. Precursors were used at 30 nM. A) MDA-MB-468 and B) A549 cells (10x magnification) and C) A549 cells (20x magnification).

8.2.2 Quantification of differences in cell proliferation

8.2.2.1 Optimisation of CT assay and pilot experiments

The first technique used to quantify the observed differences in cell proliferation was

the CT assay. Much experience with CT assay experiments was gained during the

investigation of the role of Grb7 in breast cancer, described in Part 1 of this thesis. This

experience was useful when some of the same difficulties that arose in experiments on

siRNA-treated cells were also encountered in this set of experiments on miRNA

precursor-treated cells.

142

Briefly, due to the toxicity of the LF transfection reagent, cell viability is much greater

if the media is changed 4 hours after transfection. However, a large proportion of cells

are washed from the 96-well plate wells used for this assay during media changes. The

solution adopted in Part 1 was to transfect cells and change the media in 10 cm dishes,

then to split the transfected cells and seed them into 96-well plates at a later time for CT

assays. A number of issues with this approach were raised in Part 1 and it was

suggested that additional sources of error resulting from this method had the potential to

cause erroneous results. Nevertheless, the CT assay approach was employed again, with

the hope that the large differences in cell number, visible with the naked eye, would be

easily detected above background error.

Therefore, pilot experiments were performed to determine the best time-point at which

to split cells into the 96-well plates after transfection. Initially, it was reasoned that the

assay should begin with a knockdown already present so that the cells would be affected

from the outset of the assay. A good knockdown of EGFR was shown to be present on

day 2 after transfection, with the maximum knockdown at day 3 (Figure 7.6). Since the

knockdown persisted at least until day 6, a continued effect might be predicted beyond

day 3. Thus, in initial experiments, cells were split into the 96-well plates at either day 2

or day 3. However, the CT assays did not show any difference in proliferation between

conditions in experiments conducted with splits on these days (Figure 8.2A and B).

It became apparent when counting suspension cells following the splits, however, that

the act of splitting, counting and replating cells at these times was compensating for

differences already present in cell numbers. To overcome this, experiments were

conducted in which 10 cm dishes were split just 6 hours after transfection. As the

suspension cell counts after the split were found to be similar for different transfection

conditions, 6 hours was considered a suitable splitting time. After splitting, transfected

cells were seeded into 96-well plates for CT assays and also into 6 cm dishes for

observation and parallel cell counting experiments. Through observation of the 6 cm

dishes, it was determined that the split itself did not eliminate the previously observed

effects of the different treatments on cell proliferation. This method also enabled the

same stock of transfected cells to be monitored concurrently using two different

quantification techniques.

143

Figure 8.2: CT assays of A549 cells following splitting of cells from 10 cm dishes into 96-well plates on either A) day 2 or B) day 3 after transfection. Precursors were used at 30 nM. Values are mean absorbance – blank absorbance (media only) ± SD (n=5).

8.2.2.2 Results of cell counting experiments

Figure 8.3A and 8.3B show the results of a single representative of three replicate

transfection experiments, with the proliferation of the treated cells measured using both

the cell counting technique (A) and the CT assay (B).

144

For all replicates, cell counts were significantly and substantially lower for miR-7-

treated cells than for both LF- and NS-treated cells (p < 0.01, Student’s t-test). Cell

counts were also significantly lower for NS-treated cells than LF-treated cells (p < 0.01,

Student’s t-test), though the magnitude of this difference was considerably less.

Meta-analysis of the triplicate cell counting experiments showed that cell counts were,

on average, 61% lower for miR-7-treated cells than for LF-treated cell counts and 20%

lower for NS-treated cells than for LF-treated cells (p < 0.001, Student’s t-test), as

shown in Figure 8.3C.

8.2.2.3 Results of CT assays

Figure 8.3B shows the CT assay growth curves obtained for cells from the same

experiment as those counted for Figure 8.3A. CT readings for both miR-7- and NS-

treated cells were significantly lower than for LF-treated cells at day 3 after transfection

(p < 0.001, Student’s t-test), suggestive of reduced cell proliferation. However, there

was no significant difference between the CT readings for miR-7- and NS-treated cells

at day 3, even though from visual assessment of the cells, this was the day at which cell

death and growth inhibition were the most pronounced.

A meta-analysis of the day 3 CT readings normalised to the mean reading of LF-treated

cells was conducted over all experimental replicates. This analysis showed that the

readings of miR-7-treated cells were, on average, 34% lower than those of LF-treated

cells, while the readings of NS-treated cells were, on average, 32% lower than those of

LF-treated cells (p < 0.001, Student's t-test). However, there was still no significant

difference between miR-7 and NS readings at a significance level of p = 0.05.

Although in two of the three replicate experiments the miR-7 and NS readings diverged

at later times, meta-analysis of day 5 CT readings normalised to mean LF readings over

all experimental replicates showed no significant difference between these two

conditions.

145

Figure 8.3: Quantification of the effects of miR-7 and NS precursors on A549 cell proliferation. Precursors were used at 30 nM. A) Cell counting results (mean cells per field of view ± SD, n=5) and B) corresponding CT assay results (mean absorbance – blank absorbance ± SD, n=5) of a representative experimental replicate. C) Cell counting results over all experimental replicates. Bars represent mean % difference in cell counts compared to LF ± SD (n=3).

146

8.2.3 FACS cell cycle analysis

FACS cell cycle analysis was performed at least three times for both MDA-MB-468

and A549 cells. Cells were taken for analysis on day 3 after transfection.

8.2.3.1 FACS analysis in A549 cells

Figure 8.4 presents the cell cycle profiles (A) and derived phase population data (B) of a

representative of four replicate FACS experiments in A549 cells. These figures show

that miR-7-treated cells had a higher proportion of cells in G0/G1 phase and lower

proportions of cells in both G2/M and S phases compared with either LF- or NS-treated

cells.

Figure 8.4C summarises the differences in the phase populations for the three

treatments normalised to LF over all replicate experiments. A meta-analysis showed

that miR-7-treated cells had on average 33% more cells in G0/G1 phase, 49% fewer cells

in G2/M phase and 39% fewer cells in S phase than NS-treated cells. Each of these

differences was statistically significant (p < 0.05, Student’s t-test). The meta-analysis

did not show any significant differences between the phase populations of LF- and NS-

transfected cells.

These results are consistent with miR-7 inhibiting cell cycle progression at the G1/S

checkpoint in A549 cells.

8.2.3.2 FACS analysis in MDA-MB-468 cells

Figure 8.5 displays the cell cycle profiles (A) and derived phase population results (B)

of a representative of three replicate FACS experiments in MDA-MB-468 cells. Some

differences between the phase profiles for the three treatments are evident in these

figures, that follow the same pattern as seen in A549 cells (Figure 8.4A and B). Again,

miR-7-treated cells had a greater proportion of cells in G0/G1 phase and lower

proportions of cells in G2/M and S phases compared to both LF- and NS-treated cells,

although the magnitudes of the differences were smaller than those observed in A549

cells.

147

Figure 8.5C summarises the differences in the phase populations for the three

treatments normalised to LF over all replicate experiments. A meta-analysis showed

that miR-7-treated cells had on average 10% more cells in G0/G1 phase, 11% fewer cells

in G2/M phase and 17% fewer cells in S phase than NS-treated cells. These differences

were statistically significant across the triplicate experiments (p < 0.05, Mann-Whitney

test). There were no significant differences between the cell cycle phase populations of

LF- and NS-treated cells.

148

Figure 8.4: Results of FACS analysis experiments in A549 cells. Precursors were used at 30 nM. FACS analysis was performed on day 3 after transfection. A) Cell cycle phase profiles and B) derived phase population data for a representative experimental replicate. C) FACS results over all A549 experimental replicates. Bars represent mean % difference in phase populations for miR-7-treated cells compared to NS-treated cells ± SD (n=4).

149

Figure 8.5: Results of FACS analysis experiments in MDA-MB-468 cells. Precursors were used at 30 nM. FACS analysis was performed on day 3 after transfection. A) Cell cycle phase profiles and B) derived phase population data for a representative experimental replicate. C) FACS results over all MDA-MB-468 experimental replicates. Bars represent mean % difference in phase populations for miR-7-treated cells compared to NS-treated cells ± SD (n=3).

150

8.3 Discussion

In this chapter of the investigation, functional studies were performed with the aim of

determining whether the down-regulation of EGFR by miR-7 could have functional

consequences in EGFR-overexpressing cancer cell lines. In A549 cells, miR-7 inhibited

cell cycle progression at the G1/S checkpoint, reduced the G2/M phase population, and

inhibited cell growth by 41% relative to the NS precursor control, as measured by cell

counting. In addition, dramatic changes in cell morphology and the presence of dead

floating cells in the media suggested that miR-7 also had a cytotoxic effect in A549

cells. In MDA-MB-468 cells, miR-7 precursor inhibited cell cycle progression at the

G1/S checkpoint. However, there was no significant effect on G2/M phase population,

cell growth or morphology, and there was no apparent cytotoxic effect from visual

examination, indicating that the effects of miR-7 are cell-type specific. These

observations are consistent with the predicted responses of A549 and MDA-MB-468

cells to down-regulation of EGFR. The results of this chapter therefore strongly support

Hypothesis 2 of Part 2 of this thesis, that a change in miR-7 level alters cell functioning

in a way that is consistent with a converse change in EGFR level.

However, a number of issues are raised by the results of this chapter that warrant some

discussion. Firstly, there is the incongruity between the measurements of the magnitude

of A549 growth inhibition obtained using cell counting and the CT assay. In the cell

photographs taken at day 3 after transfection given in Figure 8.1, there is a clear

difference in cell number between the miR-7-treated and the NS-treated cells. The cell

counting results at day 3 after transfection are consistent with this observation,

measuring 41% fewer cells in miR-7- compared to NS-treated dishes. However, meta-

analysis of CT assay replicates failed to show any significant difference between

miR-7- and NS-transfected cells at either day 3 or day 5 after transfection. This could be

due to the fact that the CT assay gives a reading proportional to the metabolism of the

cells rather than the cell number per se. If cell metabolism were to increase with miR-7

treatment, a reduction in cell number may not be detected. A solution to this problem

would be to use a thymidine incorporation assay rather than the CT assay. However, a

second explanation for the incongruity is considered more likely, that is, that in the

scaling down of the experiment from 10 cm and 6 cm dishes to 96-well plates, the

cytotoxic and/or cytostatic effects of miR-7 were eliminated due to differences in cell

density. It was observed during this investigation that more confluent patches of cells

151

tended to be protected from miR-7-induced cell death. Because of this, the cells were

plated at a fairly low density in larger dishes. In 96-well plates, cells needed to be plated

at a greater density due to the small well size and the greater relative error that would be

introduced by cell counting, dilution and plating. Hence, differences in cell confluency

are thought to be the cause of the different results obtained for the two quantification

methods.

It is curious that the impact of miR-7 upon cell function was much greater in A549 cells

than in MDA-MB-468 cells. This result is consistent with reports of MDA-MB-468 cell

response to other EGFR inhibitors (Busse et al., 2000), as described in the introduction

to this chapter. Hence, the minimal responses observed are likely due to the cell line

rather than the treatment. One possibility is that the results reflect an ability of

MDA-MB-468 cells to utilise signalling pathways independent of EGFR for cell growth

and survival. Another point to consider is that MDA-MB-468 cells are known to be

growth inhibited by EGF and hence do not act according to a typical model of EGFR

signalling (Filmus, Pollak, Cailleau, & Buick, 1985). Therefore, they may not respond

to EGFR knockdown in the same way as other EGFR-overexpressing cell lines. In

either case, the observations made in MDA-MB-468 cells demonstrate that miR-7 does

not kill all cell types indiscriminately at the concentration used.

However, while both the literature and the experimental results of this investigation

strongly support the theory that the functional effects of miR-7 result from miR-7-

mediated down-regulation of EGFR, it remains possible that they have instead been

elicited through down-regulation of other miR-7 targets. As the most prominent

example, consider the potential miR-7 target, Raf-1, which has been flagged by several

published prediction programs, as mentioned previously. Raf-1 forms part of the MAPK

signalling pathway and is involved in cell growth and differentiation. However, in

contrast to EGFR inhibition, Raf-1 inhibition does not induce apoptosis or have any

cytotoxic effect in A549 cells (Kato-Stankiewicz et al., 2002). Therefore, it is more

likely that miR-7 impacts cell function through EGFR, though naturally, the down-

regulation of other miR-7 targets, including Raf-1, may contribute to the functional

response of cells to miR-7.

There is only one published study that provides experimental evidence for the function

of human miR-7. This study, by Cheng and colleagues (2005), examined the effects of a

152

panel of miRNA inhibitors on cell proliferation and apoptosis. The miR-7 inhibitor was

found to significantly inhibit proliferation of A549 cells and induce apoptosis in HeLa

cells. Neither of these results is consistent with the conclusions made in this chapter.

However, there are several differences between Cheng’s study and the present study

that may be responsible for their opposing findings. One difference is the use of miR-7

inhibitor in the Cheng study compared to miR-7 precursor in this investigation. miRNA

up- and down-regulation may not necessarily have exactly inverse effects on cells. It is

also possible that the cells used by Cheng and colleagues had different characteristics to

those used for this investigation, as it is generally known that cells can vary greatly

between labs and with passage number and growth conditions (for example, see

Matsumoto and colleagues (1979)). For instance, the level of endogenous miR-7 in

Cheng’s cells may have been different. In Chapter 7 of this investigation, no

endogenous miR-7 could be detected using reporter assays with miR-7 inhibitor, in

either HeLa or A549 cells. A higher level in Cheng’s cells could have meant that a

different regulatory system was operating. Cheng’s cells may also have had different

levels of EGFR or even a different complement of miR-7 targets expressed, which

would have changed the sum effect of miR-7 inhibition. On the other hand, Cheng and

colleagues did not show that endogenous miR-7 was able to increase the level of a

luciferase target plasmid. It is therefore possible that there was insufficient endogenous

miR-7 to have a significant inhibitory effect on its targets and that the functional effects

of miR-7 inhibitor were non-specific. It is also possible that the miR-7 inhibitor did not

enter the cell or effectively inhibit its targets, as it was assumed to do by the authors. In

both of these cases, the effect of the miR-7 inhibitor would be largely non-specific and

unrelated to the function of miR-7. Finally, Cheng and colleagues offer no target

predictions or theory to support their findings. In contrast, in the present investigation,

the functional effect of miR-7 was predicted following the verification of EGFR as a

miR-7 target, prior conducting the experiments.

The possibility that miR-7 may have different effects in different environments makes it

even more relevant to pursue an understanding of miR-7’s targets and the signalling

pathways that it affects. Future work in this area could include an investigation of the

effects of miR-7 on the downstream effectors of EGFR, such as proteins in the MAPK,

PI3K and STAT pathways, and members of the cyclin and caspase proteins, as well as

proteins linked to any other miR-7 targets that are identified in the future. Experiments

153

to inhibit or stimulate various pathways or proteins could also help to elucidate the

mode of action of miR-7.

An effort should also be made to identify more miR-7 targets. Currently, the most

efficient way to identify target candidates is through a combination of computational

target prediction and microarray analysis. In Chapter 9, a microarray experiment is

conducted with this goal.

In conclusion, this chapter investigated the functional effect of treatment of EGFR-

overexpressing lung and breast cancer cells with miR-7 precursor, and thus achieved

aim 4 of this study. The results supported Hypothesis 2 of Part 2 of this thesis, as miR-7

precursor was found to induce functional changes consistent with EGFR down-

regulation. The results of this chapter not only reinforce the verification of EGFR as a

target of miR-7 in vitro, but also provide evidence that this interaction could have

biological significance. Having demonstrated this, it was very timely to go on to explore

other potential miR-7 targets, to further understand the signalling pathways that miR-7

affects, and to put the miR-7:EGFR interaction into a context in which miR-7 sits at the

centre of a regulatory system, potentially having multiple functional consequences.

154

CHAPTER 9: MICROARRAY ANALYSIS OF A549 CELLS TRANSFECTED WITH

miR-7 OR NONSENSE PRECURSOR

9.1 Introduction

In Chapter 7, EGFR was shown to be a target of miR-7 in vitro. Then in Chapter 8,

up-regulation of miR-7 was shown to inhibit cell cycle progression and induce cell

death in A549 lung cancer cells, a result consistent with down-regulation of EGFR.

These findings call for further investigation into the role and mode of action of miR-7.

One important issue to address is the possibility of other unidentified miR-7 targets

influencing the system. It has been predicted that miRNAs have up to 200 targets each

(Lim et al., 2005b). Hence, it is likely that miR-7 alters cell signalling via multiple

routes, of which EGFR is just one.

Microarray analysis is an ideal technique with which to approach this issue. Unlike

other techniques, microarrays offer high-throughput testing and hence have the ability

to rapidly identify many miRNA target candidates. At the same time, they can provide

experimental evidence towards the verification of these candidates. In addition, they

have the ability to provide a large quantity of data, which enables an analysis of

functional trends to be performed. As a number of miRNAs have been found to have

multiple targets within a functional group (John et al., 2004; Stark, Brennecke, Russell,

& Cohen, 2003), examination of functional trends in down-regulated genes could

provide insight into the roles of miR-7 in human cells.

Two published studies by Lim and colleagues (2005a) and Wang and Wang (2006)

have successfully used microarrays to identify miRNA target candidates and examine

functional trends, setting a precedent for this experiment. Modelled on these studies, the

experiment for this project involved microarrays of RNA samples from A549 cells

transfected with either miR-7 or NS precursor. Differences between the RNA profiles of

miR-7-treated cells and the reference profiles of the NS-treated cells were then

determined and analysed computationally. NS-treated cells were chosen as the reference

condition to control for changes in gene expression resulting from the transfection

process and the delivery of small RNA molecules to the cells. Two biological replicates

were to be performed to improve the reliability of the results.

155

In terms of the outcomes of the differential expression analysis, it was predicted that

previously predicted and verified miR-7 targets, some of which are presented in

Table 4.3, would appear in the down-regulated gene set. With EGFR verified as a

miR-7 target, and Raf-1 protein demonstrated to be reduced in response to miR-7 in

Chapter 7, these two genes were predicted to be the most likely to appear.

With regard to the functional trend analysis, an important assumption was made that

could potentially greatly impact the interpretation of the results. This assumption was

that the set of genes down-regulated in response to miR-7 treatment would be enriched

for miR-7 targets. This may not be the case if the majority of down-regulated genes are

indirectly affected by miR-7 or if few miR-7 targets are down-regulated through

enhanced mRNA degradation resulting from cleavage or reduced stability. Both Lim

and colleagues (2005a) and Wang and Wang (2006) showed that, in their experiments,

sets of genes down-regulated in response to treatment with a miRNA precursor were

enriched with the predicted targets of that miRNA, justifying this initial assumption. It

was important, however, to confirm that this assumption was justified for the

experiments of the present study. Therefore, the experimental plan included preliminary

experiments to facilitate the choice of an appropriate time point for RNA harvest, and

an analysis of the down-regulated gene set. Given that the above assumption is

confirmed, then the following hypotheses can be assessed.

The first hypothesis was that a significant subset of miR-7 targets are involved in

functions similar to those of EGFR, such as cell proliferation, cell cycle regulation, cell

motility and cell death. Targets may even act within the same signalling pathway in a

similar manner to the pro-apoptotic miR-2 targets grim, reaper and sickle in Drosophila

(Stark, Brennecke, Russell, & Cohen, 2003).

The second hypothesis was that a significant subset of targets are RNA binding

proteins, given that John and colleagues (2004) found a preponderance of such genes in

their set of mir-7 target predictions.

The third was that a significant subset of targets are involved in development, in view of

the involvement of miRNAs in the development of multiple species (C. Z. Chen, Li,

Lodish, & Bartel, 2004; Wightman, Ha, & Ruvkun, 1993; Y. Zhao, Samal, &

156

Srivastava, 2005), in particular, of miR-7 in Drosophila development (Stark,

Brennecke, Russell, & Cohen, 2003).

The fourth was for targets in the Notch signalling pathway, another trend observed in

Drosophila miR-7 targets (Stark, Brennecke, Russell, & Cohen, 2003).

Finally, the fifth hypothesis was for targets with functions associated with the brain,

given that miR-7 is quite specifically expressed in the brain (Baskerville & Bartel,

2005; Sempere et al., 2004) and that predicted miR-7 targets were found by one study to

be typically expressed in the brain (Sood, Krek, Zavolan, Macino, & Rajewsky, 2006).

However, given the paucity of information on human miR-7, it was also considered

possible that trends towards unpredicted functions would be observed or alternatively,

that there would be no convincing functional trend. Computational analyses have

suggested that miRNAs can have a broad range of functions (John et al., 2004) and that

particular miRNAs may have no tendency towards functionally related targets (Lim et

al., 2005a).

This chapter describes the preparation, results and analysis of a microarray experiment

designed to identify promising miR-7 target candidates and to determine any functional

trends within this set.

157

9.2 Results

9.2.1 Preliminary experiments

9.2.1.1 Verification of an effect of miR-7 precursor on EGFR mRNA

Before undertaking the microarray experiment, it was desirable to establish that miR-7

is able to reduce the mRNA level of a target. This would not only justify a fundamental

assumption of the planned microarray experiment, but would provide a positive control

with which to optimise the transfection and preparation of samples for the microarray.

As the only verified human miR-7 target, EGFR was chosen for this test.

A549 cells were transfected with either miR-7 or NS precursor and RNA was harvested

at both 12 and 24 hours following transfection. RT-PCRs for EGFR and β-actin were

then conducted on these samples. As seen in Figure 9.1, EGFR mRNA was reduced by

miR-7 at both 12 and 24 hours, while β-actin mRNA was unaffected.

This result verified that miRNAs can down-regulate target mRNA levels. Specifically,

it showed that miR-7 at least partially regulates EGFR by either inducing the cleavage

or reducing the stability of its mRNA.

158

Figure 9.1: RT-PCRs for A549 cells harvested 12 and 24 hours after transfection with LF, miR-7 precursor or NS precursor, using A) EGFR (259 bp) and B) β-actin (203 bp) primers. Precursors were used at 30 nM.

9.2.1.2 Choice of time-point for RNA harvest

The above result also informed the choice of time point for the microarray experiment.

As EGFR levels were reduced by approximately the same amount at 12 and 24 hours

after miR-7 transfection (Figure 9.1), either of these time points could be used to detect

target down-regulation. Therefore, the final choice between these two time-points was

made based on the results of Wang and Wang (2006), who demonstrated that for time

points between 8 and 72 hours after transfection, the enrichment of down-regulated

genes with predicted miRNA targets progressively decreased, while the number of

predicted miRNA targets rapidly increased. A 24 hour-time point was judged likely to

provide the optimal trade-off between the quantity of data obtained and its enrichment

for miRNA targets, in view of the aims of this investigation. Wang and Wang (2006)

found 11 predicted miRNA targets out of 134 down-regulated genes using a 24 hour

time-point.

159

Following optimisation of the cell density, setup and timing for the transfection

procedure, the final transfection experiments were performed and RNA was harvested

and prepared for the microarrays.

9.2.1.3 Preparation of RNA samples for microarrays

The RNA samples used for the microarrays were prepared from two separate

transfection experiments, in which A549 cells were transfected with either 30 nM miR 7

or NS precursors, giving two biological replicates for each of the treatment conditions.

Cells were harvested 24 hours after transfection and RNA was extracted and purified.

Each of the four samples met all of the criteria recommended by the Lotterywest State

MicroArray Facility for RNA purity, integrity and concentration. The EGFR mRNA

knockdown for each of the two chosen replicate experiments are given in Figure 9.2.

Figure 9.2: RT-PCRs for EGFR (259 bp) and β-actin (203 bp) for the two replicate experiments chosen for microarray analysis. A549 cells were transfected with LF, or 30 nM miR-7 or NS precursor, and harvested 24 hours after transfection. A) Replicate 1, B) Replicate 2.

160

9.2.2 Microarray results

The microarray assays were performed by the Lotterywest State MicroArray Facility

using Human Genome U133 Plus 2.0 Affymetrix array chips (see section 5.11). The

resulting raw data was taken and analysed using the GeneSifter software (see section

5.13).

9.2.2.1 Down-regulated genes

Significance testing was used to compile lists of the genes in the miR-7 experimental

condition that were significantly down- or up-regulated (p < 0.05) by at least 2-fold

compared to the NS control condition. 248 genes were significantly down-regulated and

199 genes were significantly up-regulated. The list of significantly down-regulated

genes is given in Appendix D.

All three probes for EGFR on the microarray chip showed significant down-regulation

of EGFR, by 3.13-, 3.07- and 2.87-fold respectively. This result is supported by the

EGFR mRNA knockdown observed in the same samples using RT-PCR (Figure 9.2).

In addition, Raf-1 was down-regulated by 3.47-fold, also consistent with prediction.

Neither HuR nor COX-2 were significantly down-regulated in this experiment. As

miRNA target mRNA is more likely to be reduced at early time points, this result

supports the suggestion that the reduction in HuR and COX-2 protein observed in

Chapter 7 was a result of downstream effects of miR-7 on true targets.

9.2.2.2 Target predictions in the down-regulated gene set

One assumption underlying this experiment was that down-regulated genes would be

enriched for miRNA targets. To verify this, the set of 248 down-regulated genes was

submitted to the program L2L (Newman & Weiner, 2005), which determines the

enrichment of a given data set for putative miRNA targets predicted by the miRanda

algorithm (John et al., 2004). The set of recognised genes was found to be enriched

2.18-fold (p = 0.025) with miR-7 target predictions, but not with target predictions for

any other human miRNA. Therefore, the initial assumption was justified.

161

In order to identify the most promising miR-7 target candidates within the set of down-

regulated genes, the 3’UTRs of these genes were searched for miR-7 target match sites.

Multiple different target prediction programs were used, including the program written

for this investigation (see Chapter 6), miRTarget (Wang & Wang, 2006), miRanda

(John et al., 2004), PicTar (Krek et al., 2005) and TargetScan (Lewis, Burge, & Bartel,

2005). This increased the sensitivity of target prediction and allowed target candidates

predicted more than once to be identified as especially promising.

The number of predicted target sites within down-regulated 3’UTRs that were either

conserved across human, mouse, rat and dog, or non-conserved by this definition, were

also identified using the TargetScan program. The full results of these searches are

given in Appendix D. Within this table, 49 promising miR-7 target candidates are

shaded in grey. These candidates include 30 down-regulated genes that are predicted by

at least one published miRNA target prediction program. Due to the conservation

restrictions imposed by these programs, these predicted targets all have some degree of

cross-species conservation. However, as demonstrated with the example of EGFR, non-

conserved sites cannot be discounted. Therefore, nine down-regulated genes that were

predicted by the Chapter 6 prediction program, but which are not conserved by

TargetScan standards, are also included in the list. In addition, four genes were

identified that were down-regulated by more than 3-fold and predicted by TargetScan to

each have two non-conserved potential target sites. These were not predicted by the

Chapter 6 program because in each case, the seed match of one target site extended

from nucleotides 1 to 7 rather than 2 to 8. However, this selection criterion was relaxed

for these four genes in view of their significant down-regulation and hence they also

appear in the list. Of these promising targets, the top ten targets, that are predicted by at

least three published programs, are given in Table 9.1.

162

Table 9.1: Top ten miRNA target predictions from the down-regulated gene set.

Fold change Gene Name Gene ID

# published predictions

10.03 proteasome (prosome, macropain) activator subunit 3 (PA28 gamma; Ki)

PSME3 3

8.16 polymerase (DNA-directed), epsilon 4 (p12 subunit)

POLE4 3

4.32 plectin 1, intermediate filament binding protein 500kDa

PLEC1 3

3.89 cytoskeleton-associated protein 4 CKAP4 3

3.47 v-raf-1 murine leukemia viral oncogene homolog 1

RAF1/ Raf-1

4

2.87 CCR4-NOT transcription complex, subunit 8 CNOT8 3

2.72 calponin 3, acidic CNN3 3

2.69 capping protein (actin filament) muscle Z-line, alpha 1

CAPZA1 3

2.63 profilin 2 PFN2 4

2.07 ADP-ribosylation factor 4 ARF4 3

9.2.3 KEGG pathway functional trend analysis

Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways are diagrammatic

representations of molecular signalling networks, such as those involved in various

cellular processes (Kanehisa & Goto, 2000). The GeneSifter software was used to

identify KEGG pathways that were significantly enriched for up- and/or down-regulated

genes (Table 9.2).

Most of the significant KEGG pathways in Table 9.2 were enriched with down-

regulated genes, while five were enriched only with up-regulated genes. Although the

up-regulated pathways may provide some hints as to the downstream effects and hence

mode of action of miR-7, the aim of this analysis was to investigate trends in the

functions of miR-7 targets. Therefore, the significantly up-regulated pathways will not

be discussed here.

163

Table 9.2: KEGG pathways significantly enriched with up- and/or down-regulated genes. Pathways containing only a single gene from the up- or down-regulated gene set have not been included. z > 1.96 for significance of p < 0.05.

Number of genes z-score

KEGG Pathway Up Down Up Down

Pyrimidine metabolism 3 6 2.81 4.22

Epithelial cell signalling in H. pylori infection 2 4 2.79 4.16

GnRH signalling pathway 2 6 1.50 4.04

Glycerolipid metabolism 0 4 -0.68 3.50

VEGF signalling pathway 1 4 0.64 3.05

Regulation of actin cytoskeleton 4 8 1.94 2.97

Focal adhesion 5 8 2.71 2.93

Long-term potentiation 1 4 0.57 2.87

beta-Alanine metabolism 0 2 -0.45 2.72

DNA polymerase 1 2 1.82 2.72

Purine metabolism 4 6 2.69 2.66

Olfactory transduction 0 2 -0.50 2.31

Dorso-ventral axis formation 1 2 1.54 2.31

Apoptosis 0 4 -0.88 2.25

Gap junction 1 4 0.27 2.20

Glycosphingolipid metabolism 2 2 2.84 1.70

Type II diabetes mellitus 2 1 2.84 0.44

mTOR signalling pathway 3 1 4.37 0.38

TGF-beta signalling pathway 3 0 3.12 -1.08

On the coming pages, the diagrams of six KEGG pathways that are significantly

enriched with down-regulated genes are presented.

164

Figure 9.3: KEGG Apoptosis pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

165

Figure 9.4: KEGG Focal adhesion pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

166

Figure 9.5: KEGG Regulation of actin cytoskeleton pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

167

Figure 9.6: KEGG GnRH signalling pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

168

Figure 9.7: KEGG Long-term potentiation pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

169

Figure 9.8: KEGG Olfactory transduction pathway. Red text denotes genes that were either up- or down-regulated in the microarray experiment. Red stars denote genes that were down-regulated. Grey boxes describe putative miR-7 targets.

170

Many of the significantly down-regulated KEGG pathways have in common the MAPK

signalling pathway, including the ‘Gonadotropin-releasing hormone (GnRH)

signalling’, ‘Regulation of actin cytoskeleton’, ‘Focal adhesion’, ‘Long-term

potentiation’, ‘Dorso-ventral axis formation’ and ‘Gap junction’ pathways. These

pathways were all found to be enriched with down-regulated genes, including EGFR,

Raf-1 and mitogen-activated protein kinase kinase 2 (MAP2K2) from the MAPK

signalling pathway. Although MAP2K2 is not a predicted miR-7 target, this thesis has

shown that EGFR is a target and that Raf-1 is a very promising target candidate, with

experimental evidence supporting its prediction by multiple programs. The above

KEGG pathways also contain the up-regulated gene, mitogen-activated protein kinase 1

(MAPK1), from the MAPK signalling pathway. However, with the exception of the

‘Focal adhesion’ pathway, they were not significantly enriched for up-regulated genes.

Calcium signalling also features in some of the KEGG pathways, and is common to the

‘GnRH signalling’, ‘Long-term potentiation’ and ‘Olfactory transduction’ pathways.

Two calcium signalling genes were down-regulated, both of which are miR-7 target

candidates. Calmodulin 3 (CALM3) has two non-conserved putative target sites and

was down-regulated 7.1-fold. Calcium/calmodulin-dependent protein kinase II delta

(CAMK2D) also has two non-conserved putative target sites. Both were predicted by

the Chapter 6 program to be miR-7 targets.

Neither the ‘MAPK’ nor the ‘Calcium signalling’ KEGG pathways were themselves

significantly enriched with down-regulated genes. Nevertheless, down-regulated genes

from these pathways formed the basis of many significant functional trends.

The first functional trend in the down-regulated gene set was towards the ‘Apoptosis’

KEGG pathway (Figure 9.3). Three of the genes down-regulated in this pathway,

PIK3CB, RELA and CFLAR, are anti-apoptotic, consistent with the cell death observed

upon miR-7 treatment, although these are not predicted miR-7 targets. The only

predicted target found in this pathway is caspase 9 (CASP9). However, one thing to

note about this and other KEGG pathways is that they are not comprehensive. Here,

EGFR is just one of the proteins involved in apoptosis that is excluded from the KEGG

pathway, giving the false impression that the only predicted miR-7 target that could

affect apoptosis is CASP9.

171

Another functional trend in the down-regulated gene set was towards the ‘Focal

adhesion’ and ‘Regulation of actin cytoskeleton’ KEGG pathways (Figures 9.4 and 9.5).

These pathways are interconnected and both are central to cell motility. Both also

involve MAPK signalling including EGFR and Raf-1. In addition, the ‘Regulation of

actin cytoskeleton’ pathway contains the top ten target candidate profilin 2 (PFN2). The

‘Focal adhesion’ pathway also contains the protein zyxin (ZYX). ZYX is not considered

a promising miR-7 target candidate, although it does contain a single non-conserved

seed match site.

Also among the significantly down-regulated KEGG pathways were three with brain-

associated functions: ‘GnRH signalling’, ‘Long-term potentiation’ and ‘Olfactory

transduction’ (Figures 9.6 to 9.8). All involve calcium signalling and contain the

predicted miR-7 targets CALM3 and CAMK2D. In addition to calcium signalling, the

‘GnRH signalling’ pathway also involves MAPK signalling and a third signalling

pathway that uses the second messenger, cyclic AMP (cAMP). From this signalling

pathway, the protein adenylate cyclase 9 (ADCY9) has been predicted by two published

programs to be a miR-7 target. The ‘Long-term potentiation’ KEGG pathway also

involves MAPK signalling.

The ‘Notch signalling’ KEGG pathway was not significantly enriched with down-

regulated genes. In fact, no genes in this pathway were significantly down-regulated in

the microarray. A single gene in this pathway was up-regulated, ADAM

metallopeptidase domain 17 (ADAM17).

9.2.4 Gene Ontology (GO) functional trend analysis

GO is another system of gene annotation that differs from the KEGG system in that it

does not include information about the interactions or relationships between molecules.

Rather, each gene is simply assigned GO terms that they are associated with, for three

different categories: Biological process, Molecular function and Cellular compartment.

Therefore, GO analysis gives a different perspective on the data than KEGG analysis,

and hence, not only has the potential to reveal different trends, but also to support the

results of the KEGG analysis.

172

The online program GOTree Machine (B. Zhang, Schmoyer, Kirov, & Snoddy, 2004)

identifies GO terms that are significantly over-represented in a given set of genes. It

calculates a p-value for each of these terms and generates a Directed Acyclic Graph

(DAG) to diagrammatically represent the relationships between them. This program

was run for both the whole set of 248 down-regulated genes and on a subset of 49 mir-7

target candidates. The target candidate set comprised all down-regulated genes that

were predicted by at least one program or had at least two conserved or non-conserved

target sites according to TargetScan.

The larger down-regulated gene set was chosen to give a more powerful analysis, less

subject to random fluctuations. This set was shown to be enriched for miR-7 targets (see

section 9.2.2.2), and was also likely to contain targets that were not included in the

target candidate set. However, it was also likely to contain a larger proportion of non-

targets than the target candidate set, and hence its analysis could pick up trends in

downstream effects as well as in targets. An analysis of the target candidate set, on the

other hand, would be less likely to pick up trends in downstream effects and could be

more sensitive to smaller trends in target functions. Hence, the results of both of these

analyses and the comparison of the two was informative.

DAGs for both down-regulated and target candidate gene sets are given in Figures 9.9

to 9.12.

173

Figure 9.9: DAGs for the GO Cellular component terms for A) down-regulated genes and B) promising targets. Red terms are significantly enriched, p < 0.01.

174

Figure 9.10: DAGs for the GO Molecular function terms for A) down-regulated genes and B) promising targets. Red terms are significantly enriched, p < 0.01.

175

Figure 9.11: DAG for the GO Biological process terms for down-regulated genes. Red terms are significantly enriched, p < 0.01.

176

Figure 9.12: DAG for the GO Biological process terms for promising targets. Red terms are significantly enriched, p < 0.01.

177

9.2.4.1 Cellular component

Only three Cellular component terms were significantly over-represented in both the

down-regulated and target candidate gene sets: ‘Organelle outer membrane’,

‘Mitochondrial membrane’ and ’Mitochondrial outer membrane’. These categories each

contained only two or three of the 49 target candidate genes. In general, miR-7 target

candidates occupied a range of cellular components.

9.2.4.2 Molecular function

The Molecular function DAGs for the two data sets show a branch of terms stemming

from ‘Protein binding’ that was significantly over-represented in both. Importantly, two

of the terms in this branch were over-represented at greater significance in the target

candidate set than in the down-regulated gene set: ‘Cytoskeletal protein binding’

(p = 6.5x10-5 vs 8.8x10-3) and ‘Actin binding’ (p = 7.9x10-5 vs 9.1x10-3 ). This suggests

that these terms describe real trends in miR-7 target functions.

Another term that was common to both the down-regulated and target candidate DAGs

was ‘Polyphosphate-glucose phosphotransferase activity’.

9.2.4.3 Biological process

There were four main trends evident in the Biological process DAGs. Firstly, the term

‘Positive regulation of MAPK activity’ was significantly over-represented in both data

sets, but at greater significance in the target candidate set (p = 4.21x10-3 vs 7.92x10-3).

Secondly, both DAGs have a branch ending in the significant terms ‘Negative

regulation of translation’ and ‘Negative regulation of translation initiation’. Again, both

of these terms were more significant in the target candidate set analysis (p = 1.34x10-4

vs 2.39x10-3, and p = 5.37x10-5 vs 9.73x10-4). This trend was based on two genes,

EIF4EBP2 and EIF2AK1. The first is a promising target, predicted by two published

programs, while the second has two non-conserved seed sites.

Thirdly, was a trend present only in the down-regulated data set, consisting of a branch

of eight significantly over-represented terms ending in the term ‘Positive regulation of

178

I-kappaB kinase/NF-kappaB cascade’ (p = 2.97x10-4). As this term describes six genes

in the down-regulated data set, none of which are predicted targets in the target

candidate set, it is most likely that this trend results from downstream effects of

repression of miR-7 targets.

Finally, the term ‘Cell organization and biogenesis’ was significantly over-represented

in both data sets. This term was less significant in the target candidate data set than the

down-regulated set (p = 5.19x10-3 vs 3.64x10-4). This may have been because it is quite

a broad term and may have encompassed both trends in target candidates and

contributions from non-targets with less closely related functions. According to its

definition, this term is associated with “the processes involved in the assembly and

arrangement of cell structures” (Ashburner et al., 2000).

With the increased sensitivity of the target candidate analysis, a trend was also apparent

in a branch of sub-terms under the ‘Cell organization and biogenesis’ heading. This

branch included the significant terms ‘Actin cytoskeleton organization and biogenesis’

(p = 8.85x10-3) and ‘Regulation of actin polymerization and/or depolymerization’

(p = 5.02x10-3).

9.2.4.4 Some non-significant GO terms

A small number of miR-7 targets with related functions could, in reality, bring about big

changes in a cell, but may not be picked up as a significant trend in these analyses.

Therefore, some of the GO terms that were not significantly over-represented in the

down-regulated data set but that were relevant to the hypotheses of this chapter were

searched for miR-7 targets.

Firstly, a range of Biological process GO terms describing some of the processes that

EGFR is associated with were examined: ‘Cell proliferation’, ‘Apoptosis’, ‘Cell cycle’

and ‘Cell motility’. As given in Table 9.3, there were many down-regulated genes

associated with these terms, including several predicted miR-7 targets. This was

particularly true of the term ‘Cell proliferation’, with predicted miR-7 targets EGFR,

Raf-1, PRKRIR, and CNOT8.

179

Secondly, the Biological process GO term ‘Development’ was associated with 18

down-regulated genes. Of these, the following six are predicted miR-7 targets: EGFR,

PAPPA, IDE, ZNF313, TRIM14, CAMK2D.

Finally, the Molecular function GO term ‘RNA binding’ was associated with six down-

regulated genes, as given in Table 9.4. None of these genes are predicted miR-7 targets.

Only TIMM50 has a single non-conserved seed site.

Table 9.3: Down-regulated genes from non-significant GO terms from the Biological

process category. * denotes a promising target prediction from Appendix D, 1xNC denotes genes with one non-conserved miR-7 match site.

GO Gene Name Gene ID Target?

Cell proliferation

insulin-like growth factor binding protein 4 IGFBP4

cell division cycle 25B CDC25B

chemokine (C-X-C motif) ligand 5 CXCL5

protein-kinase, interferon-inducible double stranded RNA dependent inhibitor, repressor of (P58 repressor)

PRKRIR *

CCR4-NOT transcription complex, subunit 8 CNOT8 *

epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)

EGFR *

deoxythymidylate kinase (thymidylate kinase)

DTYMK 1xNC

v-raf-1 murine leukemia viral oncogene homolog 1

RAF1/ Raf-1

*

Cell motility filamin A, alpha (actin binding protein 280) FLNA

phosphatidic acid phosphatase type 2B PPAP2B

capping protein (actin filament) muscle Z-line, alpha1

CAPZA1 *

epidermal growth factor receptor EGFR *

Cell cycle cell division cycle 25B CDC25B

SH3-domain binding protein 4 SH3BP4 1xNC

ubiquitin-like, containing PHD and RING finger domains, 1

UHRF1 1xNC


180



Cell cycle activating transcription factor 5 ATF5

neuroblastoma, suppression of tumorigenicity 1

NBL1 1xNC

interleukin enhancer binding factor 3, 90kDa -

HECT domain containing 3 HECTD3 1xNC

epidermal growth factor receptor EGFR *

protein phosphatase 2 (formerly 2A), regulatory subunit A (PR 65), beta isoform

PPP2R1B 1xNC

deoxythymidylate kinase (thymidylate kinase)

DTYMK 1xNC

Apoptosis ring finger and FYVE-like domain containing 1 RFFL 2xNC

p21/Cdc42/Rac1-activated kinase 1 (STE20 homolog, yeast)

PAK1 1xNC

sphingosine-1-phosphate lyase 1 SGPL1

caspase 9, apoptosis-related cysteine peptidase

CASP9 *

CASP8 and FADD-like apoptosis regulator CFLAR

v-rel reticuloendotheliosis viral oncogene homolog A

RELA


PPP2R1B 1xNC

v-raf-1 murine leukemia viral oncogene homolog 1

RAF1/ Raf-1

*

sequestosome 1 SQSTM1 1xNC

glyoxalase I GLO1 *

lipopolysaccharide-induced TNF factor LITAF

Development pregnancy-associated plasma protein A, pappalysin 1

PAPPA *

filamin A, alpha (actin binding protein 280) FLNA

aldehyde dehydrogenase 3 family, member A2

ALDH3A2

insulin-like growth factor binding protein 4 IGFBP4

CDC42 effector protein (Rho GTPase binding) 4

CDC42EP4


181



Development calcium/calmodulin-dependent protein kinase (CaM kinase) II delta

CAMK2D 2xNC

insulin-degrading enzyme IDE *

zinc finger protein 313 ZNF313 *

tumor necrosis factor, alpha-induced protein 2

TNFAIP2 1xNC

laminin, gamma 2 LAMC2

Wolfram syndrome 1 (wolframin) WFS1

transmembrane protein 97 TMEM97

epidermal growth factor receptor EGFR * protein phosphatase 2 (formerly 2A),

regulatory subunit A (PR 65), beta PPP2R1B 1xNC


PPP2R1B 1xNC

Treacher Collins-Franceschetti syndrome 1 TCOF1

tripartite motif-containing 14 TRIM14 *

sequestosome 1 SQSTM1 1xNC

polyhomeotic-like 2 (Drosophila) PHC2

Table 9.4: Down-regulated genes from the non-significant GO term ‘RNA binding’ from the Molecular function category. 1xNC denotes genes with one non-conserved miR-7 match site.


RNA binding exosome component 2 EXOSC2

translocase of inner mitochondrial membrane 50 homolog (S. cerevisiae)

TIMM50 1xNC

interleukin enhancer binding factor 3, 90kDa -

matrin 3 MATR3

peroxisome proliferative activated receptor, gamma, coactivator-related 1

PPRC1

high density lipoprotein binding protein (vigilin)

HDLBP

182

9.3 Discussion

To complete the planned course of this investigation, a microarray experiment was

conducted that aimed to identify promising miR-7 target candidates and to assess

whether they display any functional trends.

Preliminary RT-PCR experiments set the stage for the microarray experiment, most

importantly, demonstrating that miR-7 precursor is capable of reducing the mRNA level

of a target, EGFR, thereby justifying continuation with the microarray experiment. This

result also gave the insight that miR-7 inhibits EGFR expression at least in part by

inducing cleavage or reducing the stability of its mRNA.

The subsequent microarray experiment was successful, both technically and in that the

results satisfied the fundamental assumption of the experiment that the set of down-

regulated genes would be enriched for miR-7 targets. As hypothesised, the microarray

data showed that EGFR and Raf-1 were down-regulated and thus provides further

verification evidence. The data also provided supporting evidence for many other

promising miR-7 target candidates, of which ten are particularly promising, including

POLE4, PLEC1 and PFN2.

Analysis of the functional annotation of the data showed that three of the five

hypotheses made at the outset of the experiment were not supported. There was no

significant trend towards genes of RNA-binding proteins or genes involved in

development for either the set of down-regulated genes or a set of predicted miR-7

targets. There were also no genes down-regulated from the Notch signalling pathway.

One of the remaining hypotheses was that there is a significant subset of miR-7 targets

with functions similar to those of EGFR. This hypothesis is supported by the results of

the GO analysis, which showed that the set of miR-7 target candidates was enriched for

genes involved in positive regulation of MAPK signalling. This is one of the major

signalling pathways through which EGFR exerts its effects on cell function, as depicted

in Figure 4.4. In addition, many of the significantly down-regulated KEGG pathways

involve MAPK signalling through EGFR and the probable miR-7 target candidate,

Raf-1. The MAPK pathway is involved in a range of processes including cell division,

survival, motility and differentiation (reviewed by Roux and colleagues, 2004).

183

Targeting of this pathway at multiple points by miR-7 could have significant and

diverse effects in both normal and cancerous cells.

One of the significantly down-regulated KEGG pathways was the ‘Apoptosis’ pathway.

This is consistent with the cell death observed upon treatment with miR-7 precursor in

Chapter 8. One of the target candidates in this pathway is the pro-apoptotic CASP9.

Down-regulation of CASP9 clearly did not dominate the net functional effect of miR-7

in this investigation. However, one can speculate that under different cellular conditions

or in a different cell type, its down-regulation may be more influential and that miR-7

may instead induce an anti-apoptotic effect, consistent with the results of Cheng and

colleagues, as discussed in Chapter 8 (Cheng, Byrom, Shelton, & Ford, 2005).

Two other significantly down-regulated KEGG pathways were the ‘Regulation of actin

cytoskeleton’ and ‘Focal adhesion’ pathways, both of which are involved in cell

motility and include EGFR and Raf-1. Consistent with these findings, there was also a

significant over-representation of GO terms relating to actin cytoskeleton organisation

and biogenesis in the miR-7 target candidate set. Furthermore, five of the top ten target

predictions are, like EGFR, associated with the GO term ‘Cytoskeletal protein binding’

and a sixth, that has not yet been assigned any molecular function GO terms, is called

cytoskeleton-associated protein 4 (CKAP4). These results support the hypothesis that

miR-7 targets are enriched with genes involved in cell motility, a function in which

EGFR is involved.

GO terms describing two other EGFR-related functions, ‘Cell proliferation’ and ‘Cell

cycle regulation’, were not significantly over-represented in the down-regulated gene

set, although both contained down-regulated target candidates.

The final hypothesis was that there is a significant subset of miR-7 targets that have

functions associated with the brain, in view of miR-7’s almost brain-specific expression

profile. This hypothesis is supported by the finding that three KEGG pathways

describing brain-associated processes were significantly enriched with down-regulated

genes: the ‘Long-term potentiation’ pathway, the ‘Olfactory transduction’ pathway and

the ‘GnRH signalling’ pathway. Long-term potentiation is associated with memory and

learning, olfactory transduction involves the activation of olfactory receptor neurons

through stimulation of odour receptors, and GnRH signalling controls the synthesis and

184

secretion of gonadotropins from the anterior pituitary, which are required for correct

reproductive function (R. Rhoades & Pflanzer, 1996). The GnRH signalling pathway is

particularly noteworthy because of its localisation in the pituitary, a tissue in which

miR-7 is expressed at very high levels (Sood, Krek, Zavolan, Macino, & Rajewsky,

2006). All three of these pathways contain two down-regulated miR-7 target candidates,

CALM3 and CAMK2D, that form part of a branch of a calcium signalling pathway.

Calcium signalling is important for a great variety of neuronal processes and both

CALM3 and CAMK2D are expressed in the brain (Fischer et al., 1988; Kamata,

Takeuchi, & Fukunaga, 2006). These findings support the possibility that miR-7 has

some brain-associated functions.

Therefore, several trends in the functions of miR-7 target candidates emerged from the

functional annotation analysis of the microarray data. Beyond these trends, however,

both the set of down-regulated genes and the set of miR-7 target candidates

encompassed a broad range of functions, consistent with other studies of human

miRNA target predictions (John et al., 2004; Lewis, Burge, & Bartel, 2005).

However, the microarray approach to target prediction has some limitations. Firstly, it is

likely that the down-regulated gene set contained some genes that are not miRNA

targets, including those regulated downstream of true miRNA targets. In this

investigation, computational target prediction was used to try to filter out most of these.

Secondly, the microarray approach is likely to fail to identify some authentic miRNA

targets. In this investigation, this would have been the case for miR-7 targets that are

regulated primarily at the translational level, targets that are not present in A549 cells

and targets that are not significantly down-regulated at the 24 hour time-point due to

differences in degradation times. Targets would also have been missed if they were

down-regulated by less than the 2-fold cutoff level, or were excluded at the

computational target prediction step. Therefore, it is likely that miR-7 targets exist in

addition to those predicted in this investigation.

The next step for this study should be to experimentally evaluate the miR-7 target

candidates identified in this chapter. Firstly, RT-PCR should be performed for the most

promising of these with the original microarray RNA samples, so as to verify the

microarray results. Then, luciferase reporter assays should be performed to test for

185

sequence-specific regulation by miR-7. This work would lead to a greater understanding

of the direct effect of miR-7 on cell signalling.

The functional trend analysis presented in this chapter also suggests a number of future

directions. Firstly, the finding that miR-7 target candidates are enriched with genes

involved in cell motility strengthens the motivation to investigate the effect of miR-7 on

cell migration and invasion, as suggested in Chapter 8. Secondly, it is important to

pursue the role of miR-7 in the brain. A more relevant cell line for such a study would

be a brain cell line, ideally one expressing miR-7. This would enable miR-7 targets that

are not expressed in lung cancer cells to be identified and would provide a closer

approximation of the environment in which miR-7 normally operates for functional

studies. Such an investigation could shed light on the role of miR-7 in normal brain

cells. In addition, a similar investigation could test the possibility that abnormal miR-7-

mediated gene regulation may be linked to cancer or other diseases in the brain.

The microarray experiment described in this chapter provided a huge amount of

information about the targets and functional role of miR-7 in A549 lung cancer cells. It

has indicated a context for the miR-7:EGFR interaction, and, in doing so, has broadened

the scope of the project beyond the role of EGFR to other potential miR-7 targets. Some

specific focal points for future studies have also been suggested by this work. The fifth

and final aim of the investigation has now been achieved.

186

CHAPTER 10: PART 2 GENERAL DISCUSSION

Summary

This project was designed in response to growing evidence that miRNAs are far more

common than was previously thought and may play important and diverse roles in

animals. At the time that this project was initiated, however, no attempts had been made

to predict animal miRNA targets computationally and no human miRNA targets had

been identified, although two miRNAs had been linked to chronic lymphocytic

leukaemia (Calin et al., 2002). This project sought to advance the understanding of

miRNAs with an original focus on identifying human cancer-related miRNA targets.

The aims of the project were as follows:

1. To design and implement a computer algorithm to predict miRNA targets,

2. To use the computer program to search a range of human, cancer-related genes for

miRNA target candidates,

3. To evaluate one target prediction experimentally,

4. In the case that the target prediction is verified, to investigate the functional

significance of miRNA:target interaction,

5. To conduct a microarray experiment to determine the molecular response of cells to

up-regulation of the miRNA of interest, in order to identify other miRNA target

candidates and investigate their functional trends.

All five of these aims were achieved.

Firstly, a computer program for miRNA target prediction was written, as described in

Chapter 6. Created prior to the first published prediction program, it offered great

freedom to choose parameter values and criteria, and was later updated to incorporate

new knowledge of miRNA target prediction. The program predicted 23 putative

miRNA targets from a data set of human genes thought to be involved in cancer. Of

these, the top ranking prediction was for EGFR as a target of miR-7. A thorough

theoretical evaluation performed at a later point in the project, using several recently

proposed prediction criteria, found that this prediction compared very well to other

verified miRNA targets. The EGFR 3’UTR has four potential miRNA target sites, two

of which have seed matches of length in excess of 7 nt. The best site, site #1, has 86.4%

187

complementarity to miR-7, including a single G:U base pair, and an mfe of

-25.3 kcal/mol, according to the optimal folded structure predicted by RNAhybrid. Sites

#1, #2 and #3 all have an adenosine in position 1, and sites #1 and #4 have an adenosine

in position 9, two common characteristics of verified miRNA targets. In addition, sites

#1 and #4 are predicted to bind to unstable portions of EGFR mRNA at their 5’ ends, a

feature that may facilitate miRNA binding. With respect to cross-species sequence

conservation, all four sites are perfectly conserved between human and chimp. The 9 nt

seed match of site #1 is also conserved to dog and the 7 nt seed match of site #3 is also

conserved to rat, giving these sites the same degree of conservation as the verified

human miR-155 target, hAT1R (Martin, Lee, Buckenberger, Schmittgen, & Elton,

2006). EGFR and miR-7 have also both been shown to be expressed in the pituitary and

other brain tissues, and are likely to have the opportunity to interact in vivo.

In Chapter 7, it was experimentally verified that EGFR is a target of miR-7 in vitro.

Exogenous miR-7 precursor inhibited the expression of a luciferase reporter plasmid

containing a section of the wild-type EGFR 3’UTR, but not of an analogous reporter

with mutations in two of the predicted miR-7 target seed sites, in a dose-dependent

manner. No difference in expression of the two plasmids was observed following

treatment with the control NS precursor. This result was replicated in three cancer cell

lines including both EGFR-overexpressing (MDA-MB-468 and A549) and non-

overexpressing (HeLa) cell lines, and demonstrated a sequence-specific effect of miR-7.

miR-7 also reduced the level of endogenous EGFR protein in both A549 and

MDA-MB-468 cells. In MDA-MB-468 cells, EGFR protein was reduced by 57%.

In Chapter 8, functional studies in A549 cells found that miR-7 inhibited cell growth by

41% compared to NS precursor, inhibited cell cycle progression at the G1/S checkpoint,

and induced a change in cell morphology and cell death. Although there were no visible

effects of miR-7 on MDA-MB-468 cells, FACS analysis detected small but significant

changes in cell cycle phase populations consistent with inhibition of cell cycle

progression at the G1/S checkpoint. These functional responses to miR-7 precursor are

all consistent with a reduction in EGFR levels, as published in the literature for these

cell lines. Hence these results further support the miR-7:EGFR interaction and also

suggest that it may have biological significance.

188

In Chapter 9, a microarray experiment found that 248 genes were down-regulated by

more than 2-fold in miR-7-precursor-treated cells compared to NS-precursor-treated

cells. This down-regulated set was shown to be enriched with predicted miR-7 targets.

EGFR was down-regulated by between 3- and 3.5-fold, consistent with its verified

target status. This result also showed that miR-7-mediated down-regulation of EGFR

occurs at least in part through the cleavage and/or degradation of EGFR mRNA. Many

additional miR-7 target candidates were identified from the microarray screen, some of

which were predicted by up to four published prediction programs. The most promising

candidates included Raf-1, PFN2, PLEC1, PSME3 and POLE4. Finally, analysis of the

KEGG pathways and GO terms associated with the down-regulated genes suggested

that miR-7 targets are enriched with genes involved in functions similar to those of

EGFR, including cell motility, as well as in brain-associated functions.

Limitations

The limitations of the particular research approach adopted in this study have already

been considered within individual chapters of this thesis. However one overarching

limitation relates to the ability to infer the biological significance of the findings.

Although an accumulation of evidence from a combination of different experimental

approaches can validate a miRNA target in vitro and provide evidence for biological

significance of the interaction, as achieved in this project, in vitro findings cannot

necessarily be generalised to the in vivo case. In particular, in this case, the inability to

perform experiments using endogenous miR-7 leaves open the possibility that the

miR-7 levels used may have been higher than physiologically relevant levels, and hence

more conducive to interaction with EGFR mRNA. In addition, the cellular environment

in which miR-7 is normally expressed and active may be quite different to that used for

experimentation. Hence it is possible that endogenous miR-7 may have different effects

on a cell than exogenous miR-7 in a non-miR-7-expressing cell. Furthermore, for a

miRNA target to be regulated in vivo, it must also be co-expressed with the miRNA,

together with all necessary cofactors and regulatory elements. In addition, the

miRNA:target interaction must be strong enough that the target is repressed by the

miRNA in the presence of competing sites on other targets of the miRNA. Also, there is

the question of whether the target is down-regulated to a great enough extent in this

environment to influence cell functioning. As these issues cannot be addressed in an in

vitro system, the approach limits the interpretation of the results.

189

Implications of major findings

The results generated in this project suggest a model for miR-7 action in which miR-7

inhibits the MAPK signalling pathway at two points by targeting both EGFR and

downstream Raf-1 (Figure 10.1). This model would explain all of the functional

outcomes observed upon up-regulation of miR-7 and is consistent with the literature.

Figure 10.1: Model of miR-7 action. White squares represent signalling pathways.

In fact, a link between miR-7 and MAPK signalling via EGFR has also been reported

recently in Drosophila. Following extensive experiments with transgenic flies, Li and

colleagues (2005) proposed a model for the differentiation of progenitor cells to

photoreceptors in the developing eye, in which the level of a Notch pathway

transcription factor, Yan, is maintained at a steady state level through reciprocal

inhibition with miR-7. Activation of EGFR can switch the state of the system,

triggering differentiation by inducing the degradation of Yan via the MAPK pathway.

Hence, in this system, EGFR indirectly regulates miR-7 expression. However, this exact

regulatory system may not operate in human cells, as one predicted human ortholog of

190

Yan, ETV7, does not contain any miR-7 seed matches at all, and the other, ETV6,

contains only a single non-conserved seed match. As a verified miR-7 target in humans,

EGFR will also not play the same role as in this Drosophila system. It would seem that

the roles of this collection of molecules have shifted with the evolution of different

species, but that miR-7 has continued to be involved with EGFR and MAPK signalling.

On the other hand, although the majority of miR-7 targets verified in Drosophila are,

like Yan, involved in Notch signalling (Table 4.2), there is no evidence either from

target predictions in the literature or the results of the microarray experiment of

Chapter 9 to indicate that miR-7 has any extensive role in Notch signalling in humans.

This suggests that genes may gain or lose miRNA target sites over the course of

evolution and that miRNA regulatory systems may be quite different between species.

This idea fits with the lack of strong conservation of the EGFR target sites. When

EGFR was verified as a miR-7 target in vitro, it was, to our knowledge, the first

example of a miRNA target for which the target sites were not conserved across

mammals. Subsequently, another example was published, the miR-155 target, hAT1R,

that also undermines sequence conservation as a characteristic of all miRNA targets

(Martin, Lee, Buckenberger, Schmittgen, & Elton, 2006). In fact, now there are also

known to be more than 100 primate-specific miRNAs and even a small number of

human-specific miRNAs (Bentwich et al., 2005; Berezikov et al., 2006), suggesting the

existence of many miRNA targets that are not extensively conserved. Together with the

results of these studies, the verification of EGFR as a target of miR-7 encourages

broader target searches including the usually overlooked non-conserved sequences.

These findings also have consequences for our understanding of the impact of miRNAs

in the evolution of species. Previously, the assumption was that because miRNAs

themselves are highly conserved, their interactions must be of great importance and so

their targets will also be conserved to preserve these interactions. However, the

existence of non-conserved targets suggests another, less restrictive alternative. That is,

that while miRNAs themselves remain highly conserved to maintain certain very

important interactions and/or to avoid affecting potentially hundreds of targets, mRNA

sequences are freer to mutate such that targets may arise and recede around the miRNAs

across evolution. This would enable much more rapid evolution in miRNA signalling.

191

Such changes could even be responsible for important differences between species,

particularly considering the important roles that miRNAs play in development.

The model depicted in Figure 10.1 is a highly simplified picture, as EGFR is involved

in numerous other signalling pathways that will be affected upon its down-regulation,

and miR-7 may have hundreds of additional targets (Lim et al., 2005b). Take the miR-7

target candidates identified in Chapter 9 for example. If demonstrated to be targets, a

number of these would be directly or indirectly associated with EGFR and the MAPK

pathway, being involved in similar cancer-related processes such as cell proliferation

and cell motility. In addition, it has recently been shown that mmu-miR-7b targets the

transcription factor Fos in the mouse hypothalamus following chronic hyperosmolar

stimulation (H. J. Lee, Palkovits, & Young, 2006). The human homolog, c-Fos, is an

oncogene that is frequently over-expressed in many types of cancer (Milde-Langosch,

2005). In fact, it is downstream of the MAPK signalling pathway and has been shown to

be a biomarker for the action of EGFR inhibitors such as gefitinib and erlotinib (Jimeno

et al., 2006). Though c-Fos has not been verified as a miR-7 target in humans, there is

some conservation of the target sites of mouse Fos to human c-Fos, and hence it is

possible that this is the case.

This trend in the functions of miR-7 target candidates, together with the Figure 10.1

model of miR-7 action and the functional effects of miR-7 precursor on lung cancer

cells observed in Chapter 8, are all consistent with a role for miR-7 as a tumour

suppressor. If this is the case, the loss of normal miR-7 expression or activity in the

brain and, in particular, the pituitary, could contribute to oncogenesis or cancer

progression by allowing EGFR and other oncogenic targets to be more freely translated.

Certainly EGFR and MAPK signalling play pivotal roles in many brain tumours, as

described by Reardon and Wen (2006). In glioblastoma, EGFR amplification is

associated with poor prognosis (Smith et al., 2001) and its dysregulation is associated

with resistance to radiotherapy (cited by Halatsch, (2006)). EGFR is also up-regulated

in a large proportion of pituitary tumours (Onguru et al., 2004; Theodoropoulou et al.,

2004), and has been shown to be involved in pituitary cell growth (Vlotides et al.,

2006). Furthermore, a recent study demonstrated, using microarray miRNA expression

profiling, that miR-7-3 is significantly down-regulated in pituitary adenomas compared

to normal pituitary samples (Bottoni et al., 2007). Indeed, miR-7-3 expression level was

found to be predictive of pituitary adenoma. The results of these studies are consistent

192

with the findings of this thesis and support the proposed model of miR-7 action

described above.

Another implication of the findings of this thesis is the possibility that miR-7 may be of

use as a therapeutic for the treatment of EGFR-overexpressing cancers of any tissue

origin. EGFR-overexpressing cancers represent a large proportion of human cancers

(Salomon, Brandt, Ciardiello, & Normanno, 1995) and existing treatments based on

EGFR inhibition have shown some clinical success in certain cancer subgroups, making

EGFR a good therapeutic target (Bonner et al., 2006; Moore et al., 2007; Van Cutsem et

al., 2007).

In addition to these responding subgroups however, miR-7 may also be of use in the

treatment of other subgroups for which treatments are generally ineffective and new

approaches are required. One example is a subgroup of non-small-cell lung cancer, in

which tumours both overexpress EGFR and exhibit a mutation in the k-Ras gene that

causes intrinsic activity of the MAPK pathway (Janmaat, Kruyt, Rodriguez, &

Giaccone, 2003; Janmaat, Rodriguez, Gallegos-Ruiz, Kruyt, & Giaccone, 2006).

Patients with this type of tumour generally do not respond well to treatment, including

the EGFR inhibitors gefitinib and erlotinib (Pao, Wang et al., 2005; Rodenhuis &

Slebos, 1992). Studies in cell lines have shown that this resistance is primarily a result

of persistent MAPK activity (Janmaat, Kruyt, Rodriguez, & Giaccone, 2003), and that

the growth inhibitory effects of gefitinib and U0126, an inhibitor of MAP2K1/2, are

additive when used in combination (Janmaat, Rodriguez, Gallegos-Ruiz, Kruyt, &

Giaccone, 2006). This suggests that simultaneous inhibition of EGFR and the MAPK

pathway could be a successful therapeutic strategy for these tumours. With EGFR and

potentially also Raf-1 as miR-7 targets, this exactly describes the hypothesised action of

miR-7. Therefore, miR-7 may be of value not only in strongly EGFR-dependent cancers

but also in certain treatment-resistant cancers. In addition, the fact that EGFR and Raf-1

are both part of the MAPK pathway means that a miR-7-based therapeutic could

potentially provide a coordinated attack on this pathway, which drives many cancers.

miR-7 would also be able to down-regulate both wild-type EGFR and the known

mutant EGFR variants. In this way, it could overcome the problem of drug resistance

resulting from certain mutant receptors (Learn et al., 2004; Pao, Miller et al., 2005).

Finally, unlike other EGFR-targeted therapies, a miR-7-based therapeutic would target

193

EGFR at the pre-translation stage. This different approach could be complementary to

other treatments, and may lead to effective combination therapy.

The focus of this project was on the involvement of miRNAs in cancer, which proved to

be a very worthwhile line of investigation. However, the results of this project also give

some hints as to the role of miR-7 in normal brain, where it is naturally expressed. In

particular, the functional analysis of the microarray data set suggested possible roles for

miR-7 in GnRH signalling, olfactory transduction and long-term potentiation.

That long-term potentiation appears in this list is noteworthy as a number of studies

have recently implicated miRNAs in long-term memory and raised much interest in this

area (Ashraf, McLoon, Sclarsic, & Kunes, 2006; Schratt et al., 2006; Vo et al., 2005).

Some putative miR-7 targets have also been linked to long-term potentiation. MAPK

signalling, initiated in this case by activation of the glutamate receptor (GRIN1) rather

than EGFR, stimulates the expression of transcriptional regulators and synaptic growth

proteins involved in long-term potentiation (Miyamoto, 2006).

Calcium signalling is also linked to long-term potentiation and synaptic plasticity. Two

members of a calcium signalling pathway, CALM3 and CAMK2D, were down-

regulated in the microarray experiment in Chapter 9, both having two non-conserved

putative miR-7 target sites. These target candidates are particularly interesting in light

of a model proposed for the involvement of miRNAs in synaptic protein synthesis in

Drosophila. In this model, a miRNA:RISC complex regulates the translation of

Drosophila CaMKII mRNA at the synapse, in response to synaptic stimulation

mediated by brain-derived neurotrophic factor (BDNF) (Ashraf, McLoon, Sclarsic, &

Kunes, 2006). In humans, both CAMK2D and CALM3 are expressed in the brain, and

one splice variant of CAMK2D may be involved in the expression of BDNF in the

substantia nigra (Fischer et al., 1988; Kamata, Takeuchi, & Fukunaga, 2006).

In terms of GnRH signalling, GnRH has been shown to induce transactivation of EGFR

followed by activation of Ras and MAPK1/2 in a cell line derived from the pituitary, a

tissue with high expression of miR-7 (Grosse, Roelle, Herrlich, Hohn, & Gudermann,

2000; Roelle et al., 2003). This is also an association worth pursuing.

194

Future directions

There are many possible directions that this research could take in the future, as

described throughout this thesis. The priority is to determine whether endogenous

miR-7 can significantly inhibit the expression of EGFR, as does exogenous miR-7. If

so, this would provide support for the biological significance of the interaction. The

simplest experimental design would involve a similar series of experiments as

performed in this project, including reporter assays, western blot analysis and functional

studies, but in a miR-7-expressing cell line and employing miR-7 inhibitor rather than

miR-7 precursor.

To continue in the same vein as this project, and with the aim of assessing the prospect

of a miR-7-based therapeutic, additional functional studies could then be performed to

further characterise the extent of the functional effect of miR-7 on cancer cells.

Experiments could again utilise miR-7 precursor or inhibitor and involve assays of

anchorage-dependent and independent growth, such as cell counting assays and colony

survival assays in soft agar, as well as assays of cell migration and invasion, such as

Boyden chamber assays or wound assays. In addition, the cell death observed upon

treatment with miR-7 precursor in Chapter 8 could be further characterised as apoptosis

or necrosis using a caspase assay or flow cytometry with Annexin-FITC staining.

Cytotoxicity assays such as cell counting, cell titre assay and colony-forming assays

could also be used to determine whether miR-7 precursor sensitises cancer cells to other

EGFR inhibitors, such as gefitinib and erlotinib, or other chemotherapeutic agents, such

as cisplatin, doxorubicin and paclitaxel. All of these experiments could also be

performed in multiple different cell lines to assess the functional effect of miR-7 in

different types of cancer and in normal cells.

Finally, with promising results, the effects of miR-7 on in vivo tumour growth could be

tested in a mouse model. The model could be created by injecting athymic nude mice

with cells from a miR-7-sensitive cell line. Mice could then be treated with either a

miR-7-based drug or control solution, and the diameter and/or volume of tumours from

the two groups measured and compared over time. Such a study would help to assess

the potential efficacy and side effects of a miR-7-based therapeutic in vivo.

195

Another program for future research could investigate whether miR-7 is involved in or

associated with oncogenesis or cancer progression. One possible approach would be to

use microarray miRNA expression profiling, in view of Bottoni and colleagues’

promising finding that miR-7 expression level can differentiate between normal

pituitary and pituitary adenoma (Bottoni et al., 2007). This study could be extended to

different brain regions and tumour types, using a similar methodology with additional

screening of the expression of EGFR and other putative targets. Differential expression

of miR-7 between normal and cancerous tissues or any relationship between miR-7,

EGFR and the presence or malignancy of brain tumours could be of diagnostic or

prognostic significance.

Another approach would be to sequence DNA from brain tumours for mutations within

or near to the EGFR target sites or the miR-7 primary precursor that could potentially

prevent correct processing or action of miR-7. Such a study could also reveal germline

mutations in these regions that could be responsible for inherited susceptibility to brain

tumours. Using this approach, He and colleagues (2005) found germline mutations in

putative miRNA target sites in the c-Kit oncogene that were associated with

significantly reduced levels of c-Kit mRNA and protein in papillary thyroid carcinomas.

Knowledge of the targets and signalling pathways affected by miR-7 would be very

useful in all areas of future study on this topic. It would help to determine whether and

when miR-7 is likely to be an effective cancer treatment, as well as what side effects it

may cause. It would also facilitate investigation of the role of miR-7 in normal tissues

and the possibility that it is involved in oncogenesis. Hence, this is another important

area for future work. One aspect of this area is the identification of more human miR-7

targets. There are many target predictions to investigate, in particular, the promising

candidates identified in the microarray experiment of Chapter 9. In addition, the target

prediction program of Chapter 6 and other published prediction programs may be used

to predict hundreds more miR-7 targets. Experiments such as those conducted in

Chapter 7 could then be performed to determine whether candidates are true miR-7

targets.

Another aspect of this area is the determination of whether any other miRNAs could

target EGFR or other putative miR-7 targets. In the case of EGFR, TargetScan outputs a

list of 36 seed families in addition to miR-7 for which there is at least one match in the

196

3’UTR. These include one miRNA with three match sites (miR-502), three miRNAs

and one miRNA family with two match sites (miR-491, miR-370, miR-492,

miR-93.hd/291-3p/294/295/302/372/373/520), and two miRNAs with single match sites

that are conserved across human, dog, rat and mouse (miR-27 and miR-128). Hence it is

quite possible that EGFR could be targeted by multiple miRNAs. These may be

spatially or temporally separated from miR-7 or they may form part of a module of

functionally related targets and cooperating miRNAs. This area could be investigated

through target prediction followed by target verification experiments, perhaps focussing

on other brain-expressed miRNAs or miRNAs linked to cancer.

Finally, the role of miR-7 in the brain could be explored. This study would be

influenced by the identification of other brain-expressed miR-7 targets. However, the

target candidates identified in this project have already suggested that miR-7 may be

involved in long-term potentiation, olfactory transduction and GnRH signalling. To

assess these possibilities, experiments could be performed to determine the effect of

miR-7 on dendrite outgrowth, a sign related to synaptic plasticity (Schratt et al., 2006),

or the effect on GnRH-induced signalling in a pituitary gonadotrope cell line, for

example. On the other hand, it may be more productive to elucidate the signalling

pathways affected by miR-7 in the brain before embarking on functional experiments.

The findings of this project are original and significant in terms of their potential

implications to computational miRNA target prediction, the role of miR-7 in cancer and

normal brain function, and the possible future of miR-7 as the basis for an anti-cancer

therapeutic. They have provided broad scope for future work.

197

CONCLUSIONS

This thesis presented two investigations of the molecular biology of cancer, each with a

different focus within this field.

From the investigation in Part 1 of this project, it is concluded that Grb7 plays no role in

the proliferation of either unstimulated or HRG-stimulated SK-BR-3 breast cancer cells,

but that inhibition of Grb7 expression has a small stimulatory effect on the migration of

HRG-stimulated SK-BR-3 cells. This study therefore indicated that a Grb7-targeting

therapeutic would not be an appropriate treatment for breast cancers modelled by the

SK-BR-3 cell line.

For Part 2 of this thesis, the molecular biology of cancer was studied with a strategic

approach to direct experimental investigations. A combination of computational

miRNA target prediction, and theoretical and experimental evaluation led to the finding

that miR-7 targets EGFR mRNA in intact cells in a sequence-specific manner, and

down-regulates endogenous EGFR protein and mRNA at least in part by inducing

mRNA cleavage and/or reducing mRNA stability. This interaction is likely to have

biological significance, with miR-7 shown to inhibit proliferation and cell cycle

progression at the G1/S checkpoint and to induce cell death in EGFR-overexpressing

lung cancer cells, effects consistent with EGFR knockdown. EGFR was, to our

knowledge, the first miRNA target to be identified for which target sites are not

extensively conserved across mammals. In addition to EGFR mRNA, miR-7 is also

likely to target the mRNA of many of the genes identified in the microarray study as

being down-regulated in response to miR-7 and possessing putative miR-7 target sites,

such as Raf-1, PFN2, PLEC1, PSME3 and POLE4. miR-7 may also have functionally-

related targets involved in processes including cell motility and processes associated

with the brain.

These findings have many and far-reaching implications. The lack of extensive

conservation of the EGFR target sites indicates that the majority of computational target

prediction programs currently fail to detect a potentially large group of miRNA targets

through the use of conservation filters and thus encourages the inclusion of non-

conserved sequences in target prediction attempts. It also supports a more flexible

198

model of the evolution of miRNA regulatory systems. In addition, these findings

suggest the exciting possibility of the development of a miR-7-based therapeutic for the

treatment of EGFR-overexpressing cancers, furthering the collective endeavour towards

the development of therapeutics targeted towards specific cancer profiles for individual

clinical cases.

The prediction and verification approach taken in the second part of the project was

very successful in identifying several entirely new areas for study. The strength of this

novel approach not withstanding, however, both parts of this thesis yielded important

insights into the molecular biology of cancer, and the potential usefulness of Grb7-

targeting and miR-7-mimicing therapeutics in different types of cancer. It seems likely

then that in the future, strategic approaches to certain research questions, facilitated by

the use of computer prediction programs, will continue to complement investigations

driven by synthesis of research literature.

Another common thread in the two investigations of this thesis is that of ErbB

signalling, as Grb7 has been shown to bind to all four ErbB receptors and is involved in

ErbB2 signalling, while a link has now been made between a miRNA (miR-7) and an

ErbB receptor (EGFR). The convergence of these two investigations with relatively

disparate beginnings serves to further highlight this signalling network in the context of

the literature; its complexity, its importance in cancer and the likelihood that it could be

involved in effective targeted cancer therapy.

199

BIBLIOGRAPHY

Abelson, J. F., Kwan, K. Y., O'Roak, B. J., Baek, D. Y., Stillman, A. A., Morgan, T.

M., et al. (2005). Sequence variants in SLITRK1 are associated with Tourette's

syndrome. Science, 310(5746), 317-320.

Aguilar, Z., Akita, R. W., Finn, R. S., Ramos, B. L., Pegram, M. D., Kabbinavar, F. F.,

et al. (1999). Biologic effects of heregulin/neu differentiation factor on normal

and malignant human breast and ovarian epithelial cells. Oncogene, 18(44),

6050-6062.

Anderson, N. G., Ahmad, T., Chan, K., Dobson, R., & Bundred, N. J. (2001). ZD1839

(Iressa), a novel epidermal growth factor receptor (EGFR) tyrosine kinase

inhibitor, potently inhibits the growth of EGFR-positive cancer cell lines with or

without erbB2 overexpression. Int J Cancer, 94(6), 774-782.

Andronescu, M., Aguirre-Hernandez, R., Condon, A., & Hoos, H. H. (2003). RNAsoft:

A suite of RNA secondary structure prediction and design software tools.

Nucleic Acids Res, 31(13), 3416-3422.

Aravin, A. A., Lagos-Quintana, M., Yalcin, A., Zavolan, M., Marks, D., Snyder, B., et

al. (2003). The small RNA profile during Drosophila melanogaster

development. Dev Cell, 5(2), 337-350.

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al.

(2000). Gene ontology: tool for the unification of biology. The Gene Ontology

Consortium. Nat Genet, 25(1), 25-29.

Ashraf, S. I., McLoon, A. L., Sclarsic, S. M., & Kunes, S. (2006). Synaptic protein

synthesis associated with memory is regulated by the RISC pathway in

Drosophila. Cell, 124(1), 191-205.

Babak, T., Zhang, W., Morris, Q., Blencowe, B. J., & Hughes, T. R. (2004). Probing

microRNAs with microarrays: tissue specificity and functional inference. Rna,

10(11), 1813-1819.

Bai, L., Zhu, R., Chen, Z., Gao, L., Zhang, X., Wang, X., et al. (2006). Potential role of

short hairpin RNA targeting epidermal growth factor receptor in growth and

sensitivity to drugs of human lung adenocarcinoma cells. Biochem Pharmacol,

71(8), 1265-1271.

200

Balmer, L. A. (2004). Posttranscriptional regulation of the epidermal growth factor

receptor gene expression in human breast cancer cells. University of Western

Australia, Perth.

Balmer, L. A., Beveridge, D. J., Jazayeri, J. A., Thomson, A. M., Walker, C. E., &

Leedman, P. J. (2001). Identification of a novel AU-Rich element in the 3'

untranslated region of epidermal growth factor receptor mRNA that is the target

for regulated RNA-binding proteins. Mol Cell Biol, 21(6), 2070-2084.

Bartel, D. P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function.

Cell, 116(2), 281-297.

Baselga, J., Tripathy, D., Mendelsohn, J., Baughman, S., Benz, C. C., Dantis, L., et al.

(1996). Phase II study of weekly intravenous recombinant humanized anti-

p185HER2 monoclonal antibody in patients with HER2/neu-overexpressing

metastatic breast cancer. J Clin Oncol, 14(3), 737-744.

Baskerville, S., & Bartel, D. P. (2005). Microarray profiling of microRNAs reveals

frequent coexpression with neighboring miRNAs and host genes. Rna, 11(3),

241-247.

Batra, S. K., Castelino-Prabhu, S., Wikstrand, C. J., Zhu, X., Humphrey, P. A.,

Friedman, H. S., et al. (1995). Epidermal growth factor ligand-independent,

unregulated, cell-transforming potential of a naturally occurring human mutant

EGFRvIII gene. Cell Growth Differ, 6(10), 1251-1259.

Beerli, R. R., & Hynes, N. E. (1996). Epidermal growth factor-related peptides activate

distinct subsets of ErbB receptors and differ in their biological activities. J Biol

Chem, 271(11), 6071-6076.

Bentwich, I. (2005). Prediction and validation of microRNAs and their targets. FEBS

Lett.

Bentwich, I., Avniel, A., Karov, Y., Aharonov, R., Gilad, S., Barad, O., et al. (2005).

Identification of hundreds of conserved and nonconserved human microRNAs.

Nat Genet.

Berezikov, E., Thuemmler, F., van Laake, L. W., Kondova, I., Bontrop, R., Cuppen, E.,

et al. (2006). Diversity of microRNAs in human and chimpanzee brain. Nat

Genet, 38(12), 1375-1377.

Bertucci, F., Borie, N., Ginestier, C., Groulet, A., Charafe-Jauffret, E., Adelaide, J., et

al. (2004). Identification and validation of an ERBB2 gene expression signature

in breast cancers. Oncogene, 23(14), 2564-2575.

201

Blagosklonny, M. V. (1998). The mitogen-activated protein kinase pathway mediates

growth arrest or E1A-dependent apoptosis in SKBR3 human breast cancer cells.

Int J Cancer, 78(4), 511-517.

Blume-Jensen, P., & Hunter, T. (2001). Oncogenic kinase signalling. Nature,

411(6835), 355-365.

Bomsztyk, K., Van Seuningen, I., Suzuki, H., Denisenko, O., & Ostrowski, J. (1997).

Diverse molecular interactions of the hnRNP K protein. FEBS Lett, 403(2), 113-

115.

Bonner, J. A., Harari, P. M., Giralt, J., Azarnia, N., Shin, D. M., Cohen, R. B., et al.

(2006). Radiotherapy plus cetuximab for squamous-cell carcinoma of the head

and neck. N Engl J Med, 354(6), 567-578.

Bos, J. L. (1989). ras oncogenes in human cancer: a review. Cancer Res, 49(17), 4682-

4689.

Bottoni, A., Piccin, D., Tagliati, F., Luchin, A., Zatelli, M. C., & degli Uberti, E. C.

(2005). miR-15a and miR-16-1 down-regulation in pituitary adenomas. J Cell

Physiol, 204(1), 280-285.

Bottoni, A., Zatelli, M. C., Ferracin, M., Tagliati, F., Piccin, D., Vignali, C., et al.

(2007). Identification of differentially expressed microRNAs by microarray: a

possible role for microRNA genes in pituitary adenomas. J Cell Physiol, 210(2),

370-377.

Boutla, A., Delidakis, C., & Tabler, M. (2003). Developmental defects by antisense-

mediated inactivation of micro-RNAs 2 and 13 in Drosophila and the

identification of putative target genes. Nucleic Acids Res, 31(17), 4973-4980.

Brennecke, J., Hipfner, D. R., Stark, A., Russell, R. B., & Cohen, S. M. (2003). bantam

encodes a developmentally regulated microRNA that controls cell proliferation

and regulates the proapoptotic gene hid in Drosophila. Cell, 113(1), 25-36.

Brennecke, J., Stark, A., Russell, R. B., & Cohen, S. M. (2005). Principles of

MicroRNA-Target Recognition. PLoS Biol, 3(3), e85.

Brown, J. R., & Sanseau, P. (2005). A computational view of microRNAs and their

targets. Drug Discov Today, 10(8), 595-601.

Burgler, C., & Macdonald, P. M. (2005). Prediction and verification of microRNA

targets by MovingTargets, a highly adaptable prediction method. BMC

Genomics, 6(1), 88.

202

Burridge, K., Turner, C. E., & Romer, L. H. (1992). Tyrosine phosphorylation of

paxillin and pp125FAK accompanies cell adhesion to extracellular matrix: a role

in cytoskeletal assembly. J Cell Biol, 119(4), 893-903.

Busse, D., Doughty, R. S., Ramsey, T. T., Russell, W. E., Price, J. O., Flanagan, W. M.,

et al. (2000). Reversible G(1) arrest induced by inhibition of the epidermal

growth factor receptor tyrosine kinase requires up-regulation of p27(KIP1)

independent of MAPK activity. J Biol Chem, 275(10), 6987-6995.

Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., et al. (2002).

Frequent deletions and down-regulation of micro- RNA genes miR15 and

miR16 at 13q14 in chronic lymphocytic leukemia. PNAS, 99(24), 15524-15529.

Calin, G. A., Liu, C. G., Sevignani, C., Ferracin, M., Felli, N., Dumitru, C. D., et al.

(2004). MicroRNA profiling reveals distinct signatures in B cell chronic

lymphocytic leukemias. Proc Natl Acad Sci U S A, 101(32), 11755-11760.

Calin, G. A., Trapasso, F., Shimizu, M., Dumitru, C. D., Yendamuri, S., Godwin, A. K.,

et al. (2005). Familial cancer associated with a polymorphism in ARLTS1. N

Engl J Med, 352(16), 1667-1676.

Chaidarun, S. S., Eggo, M. C., Sheppard, M. C., & Stewart, P. M. (1994). Expression of

epidermal growth factor (EGF), its receptor, and related oncoprotein (erbB-2) in

human pituitary tumors and response to EGF in vitro. Endocrinology, 135(5),

2012-2021.

Chang, G. C., Hsu, S. L., Tsai, J. R., Liang, F. P., Lin, S. Y., Sheu, G. T., et al. (2004).

Molecular mechanisms of ZD1839-induced G1-cell cycle arrest and apoptosis in

human lung adenocarcinoma A549 cells. Biochem Pharmacol, 68(7), 1453-

1464.

Chang, H., Riese, D. J., 2nd, Gilbert, W., Stern, D. F., & McMahan, U. J. (1997).

Ligands for ErbB-family receptors encoded by a neuregulin-like gene. Nature,

387(6632), 509-512.

Chausovsky, A., Waterman, H., Elbaum, M., Yarden, Y., Geiger, B., & Bershadsky, A.

D. (2000). Molecular requirements for the effect of neuregulin on cell spreading,

motility and colony organization. Oncogene, 19(7), 878-888.

Chen, C. Z., Li, L., Lodish, H. F., & Bartel, D. P. (2004). MicroRNAs modulate

hematopoietic lineage differentiation. Science, 303(5654), 83-86.

Chen, D., Xu, L. G., Chen, L., Li, L., Zhai, Z., & Shu, H. B. (2003). NIK is a

component of the EGF/heregulin receptor signaling complexes. Oncogene,

22(28), 4348-4355.

203

Chen, H. C., & Guan, J. L. (1994). Association of focal adhesion kinase with its

potential substrate phosphatidylinositol 3-kinase. Proc Natl Acad Sci U S A,

91(21), 10148-10152.

Cheng, A. M., Byrom, M. W., Shelton, J., & Ford, L. P. (2005). Antisense inhibition of

human miRNAs and indications for an involvement of miRNA in cell growth

and apoptosis. Nucleic Acids Res, 33(4), 1290-1297.

Chenna, R., Sugawara, H., Koike, T., Lopez, R., Gibson, T. J., Higgins, D. G., et al.

(2003). Multiple sequence alignment with the Clustal series of programs.


Ciafre, S. A., Galardi, S., Mangiola, A., Ferracin, M., Liu, C. G., Sabatino, G., et al.

(2005). Extensive modulation of a set of microRNAs in primary glioblastoma.

Biochem Biophys Res Commun, 334(4), 1351-1358.

Ciardiello, F., Caputo, R., Bianco, R., Damiano, V., Pomatico, G., De Placido, S., et al.

(2000). Antitumor effect and potentiation of cytotoxic drugs activity in human

cancer cells by ZD-1839 (Iressa), an epidermal growth factor receptor-selective

tyrosine kinase inhibitor. Clin Cancer Res, 6(5), 2053-2063.

Cimmino, A., Calin, G. A., Fabbri, M., Iorio, M. V., Ferracin, M., Shimizu, M., et al.

(2005). miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl

Acad Sci U S A.

Cobleigh, M. A., Tabesh, B., Bitterman, P., Baker, J., Cronin, M., Liu, M. L., et al.

(2005). Tumor gene expression and prognosis in breast cancer patients with 10

or more positive lymph nodes. Clin Cancer Res, 11(24 Pt 1), 8623-8631.

Cobleigh, M. A., Vogel, C. L., Tripathy, D., Robert, N. J., Scholl, S., Fehrenbacher, L.,

et al. (1999). Multinational study of the efficacy and safety of humanized anti-

HER2 monoclonal antibody in women who have HER2-overexpressing

metastatic breast cancer that has progressed after chemotherapy for metastatic

disease. J Clin Oncol, 17(9), 2639-2648.

Coltrera, M. D., Wang, J., Porter, P. L., & Gown, A. M. (1995). Expression of platelet-

derived growth factor B-chain and the platelet-derived growth factor receptor

beta subunit in human breast tissue and breast carcinoma. Cancer Res, 55(12),

2703-2708.

Crombet, T., Torres, O., Rodriguez, V., Menendez, A., Stevenson, A., Ramos, M., et al.

(2001). Phase I clinical evaluation of a neutralizing monoclonal antibody against

epidermal growth factor receptor in advanced brain tumor patients: preliminary

study. Hybridoma, 20(2), 131-136.

204

Cunningham, D., Humblet, Y., Siena, S., Khayat, D., Bleiberg, H., Santoro, A., et al.

(2004). Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-

refractory metastatic colorectal cancer. N Engl J Med, 351(4), 337-345.

Dannenberg, A. J., Lippman, S. M., Mann, J. R., Subbaramaiah, K., & DuBois, R. N.

(2005). Cyclooxygenase-2 and epidermal growth factor receptor: pharmacologic

targets for chemoprevention. J Clin Oncol, 23(2), 254-266.

Dassonville, O., Formento, J. L., Francoual, M., Ramaioli, A., Santini, J., Schneider,

M., et al. (1993). Expression of epidermal growth factor receptor and survival in

upper aerodigestive tract cancer. J Clin Oncol, 11(10), 1873-1878.

Davis, S., Lollo, B., Freier, S., & Esau, C. (2006). Improved targeting of miRNA with

antisense oligonucleotides. Nucleic Acids Res, 34(8), 2294-2304.

De Vet, E. C., Aguado, B., & Campbell, R. D. (2003). The adaptor signaling proteins

Grb2 and Grb7 are recruited by human G6f, a novel member of the

immunoglobulin superfamily encoded in the MHC. Biochem J, Pt.

Dejgaard, K., Leffers, H., Rasmussen, H. H., Madsen, P., Kruse, T. A., Gesser, B., et al.

(1994). Identification, molecular cloning, expression and chromosome mapping

of a family of transformation upregulated hnRNP-K proteins derived by

alternative splicing. J Mol Biol, 236(1), 33-48.

Dickins, R. A., Hemann, M. T., Zilfou, J. T., Simpson, D. R., Ibarra, I., Hannon, G. J.,

et al. (2005). Probing tumor phenotypes using stable and regulated synthetic

microRNA precursors. Nat Genet.

Doench, J. G., Petersen, C. P., & Sharp, P. A. (2003). siRNAs can function as miRNAs.

Genes Dev, 17(4), 438-442.

Doench, J. G., & Sharp, P. A. (2004). Specificity of microRNA target selection in

translational repression. Genes Dev, 18(5), 504-511.

Dykxhoorn, D. M., & Lieberman, J. (2006). Running Interference: Prospects and

Obstacles to Using Small Interfering RNAs as Small Molecule Drugs. Annu Rev

Biomed Eng.

Enright, A. J., John, B., Gaul, U., Tuschl, T., Sander, C., & Marks, D. S. (2003).

MicroRNA targets in Drosophila. Genome Biol, 5(1), R1.

Esquela-Kerscher, A., & Slack, F. J. (2006). Oncomirs - microRNAs with a role in

cancer. Nat Rev Cancer, 6(4), 259-269.

Farh, K. K., Grimson, A., Jan, C., Lewis, B. P., Johnston, W. K., Lim, L. P., et al.

(2005). The widespread impact of mammalian MicroRNAs on mRNA

repression and evolution. Science, 310(5755), 1817-1821.

205

Ferrer, I., Alcantara, S., Ballabriga, J., Olive, M., Blanco, R., Rivera, R., et al. (1996).

Transforming growth factor-alpha (TGF-alpha) and epidermal growth factor-

receptor (EGF-R) immunoreactivity in normal and pathologic brain. Prog

Neurobiol, 49(2), 99-123.

Fiddes, R. J., Campbell, D. H., Janes, P. W., Sivertsen, S. P., Sasaki, H., Wallasch, C.,

et al. (1998). Analysis of Grb7 recruitment by heregulin-activated erbB

receptors reveals a novel target selectivity for erbB3. J Biol Chem, 273(13),

7717-7724.

Filmus, J., Pollak, M. N., Cailleau, R., & Buick, R. N. (1985). MDA-468, a human

breast cancer cell line with a high number of epidermal growth factor (EGF)

receptors, has an amplified EGF receptor gene and is growth inhibited by EGF.


Fischer, R., Koller, M., Flura, M., Mathews, S., Strehler-Page, M. A., Krebs, J., et al.

(1988). Multiple divergent mRNAs code for a single human calmodulin. J Biol

Chem, 263(32), 17055-17062.

Frantz, J. D., Giorgetti-Peraldi, S., Ottinger, E. A., & Shoelson, S. E. (1997). Human

GRB-IRbeta/GRB10. Splice variants of an insulin and growth factor receptor-

binding protein with PH and SH2 domains. J Biol Chem, 272(5), 2659-2667.

Frisch, S. M., Vuori, K., Ruoslahti, E., & Chan-Hui, P. Y. (1996). Control of adhesion-

dependent cell survival by focal adhesion kinase. J Cell Biol, 134(3), 793-799.

Galizia, G., Lieto, E., Ferraraccio, F., De Vita, F., Castellano, P., Orditura, M., et al.

(2006). Prognostic significance of epidermal growth factor receptor expression

in colon cancer patients undergoing curative surgery. Ann Surg Oncol, 13(6),

823-835.

Ge, X., Yamamoto, S., Tsutsumi, S., Midorikawa, Y., Ihara, S., Wang, S. M., et al.

(2005). Interpreting expression profiles of cancers by genome-wide survey of

breadth of expression in normal tissues. Genomics, 86(2), 127-141.

Giles, K. M. (2004). Posttranscriptional regulation of p21 gene expression in human

breast cancer cells: identification and characterisation of cis-trans interactions.

University of Western Australia, Perth.

Giraldez, A. J., Cinalli, R. M., Glasner, M. E., Enright, A. J., Thomson, J. M.,

Baskerville, S., et al. (2005). MicroRNAs regulate brain morphogenesis in

zebrafish. Science, 308(5723), 833-838.

Griffiths-Jones, S., Bateman, A., Marshall, M., Khanna, A., & Eddy, S. R. (2003).

Rfam: an RNA family database. Nucleic Acids Res, 31(1), 439-441.

206

Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., & Enright, A. J.

(2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic

Acids Res, 34(Database issue), D140-144.

Grosse, R., Roelle, S., Herrlich, A., Hohn, J., & Gudermann, T. (2000). Epidermal

growth factor receptor tyrosine kinase mediates Ras activation by gonadotropin-

releasing hormone. J Biol Chem, 275(16), 12251-12260.

Guerra-Vladusic, F. K., Vladusic, E. A., Tsai, M. S., & Lupu, R. (2001). Signaling

molecules implicated in heregulin induction of growth arrest and apoptosis.

Oncol Rep, 8(6), 1203-1214.

Guy, P. M., Platko, J. V., Cantley, L. C., Cerione, R. A., & Carraway, K. L., 3rd.

(1994). Insect cell-expressed p180erbB3 possesses an impaired tyrosine kinase

activity. Proc Natl Acad Sci U S A, 91(17), 8132-8136.

Halatsch, M. E., Schmidt, U., Behnke-Mursch, J., Unterberg, A., & Wirtz, C. R. (2006).

Epidermal growth factor receptor inhibition for the treatment of glioblastoma

multiforme and other malignant brain tumours. Cancer Treat Rev, 32(2), 74-89.

Han, D. C., & Guan, J. L. (1999). Association of focal adhesion kinase with Grb7 and

its role in cell migration. J Biol Chem, 274(34), 24425-24430.

Han, D. C., Shen, T. L., & Guan, J. L. (2000). Role of Grb7 targeting to focal contacts

and its phosphorylation by focal adhesion kinase in regulation of cell migration.

J Biol Chem, 275(37), 28911-28917.

Han, D. C., Shen, T. L., Miao, H., Wang, B., & Guan, J. L. (2002). EphB1 associates

with Grb7 and regulates cell migration. J Biol Chem, 277(47), 45655-45661.

Haran, M., Chebatco, S., Flaishon, L., Lantner, F., Harpaz, N., Valinsky, L., et al.

(2004). Grb7 expression and cellular migration in chronic lymphocytic

leukemia: a comparative study of early and advanced stage disease. Leukemia.

He, H., Jazdzewski, K., Li, W., Liyanarachchi, S., Nagy, R., Volinia, S., et al. (2005).

The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci

U S A, 102(52), 19075-19080.

He, L., & Hannon, G. J. (2004). MicroRNAs: small RNAs with a big role in gene

regulation. Nat Rev Genet, 5(7), 522-531.

He, W., Rose, D. W., Olefsky, J. M., & Gustafson, T. A. (1998). Grb10 interacts

differentially with the insulin receptor, insulin-like growth factor I receptor, and

epidermal growth factor receptor via the Grb10 Src homology 2 (SH2) domain

and a second novel domain located between the pleckstrin homology and SH2

domains. J Biol Chem, 273(12), 6860-6867.

207

Herbst, R. S., Giaccone, G., Schiller, J. H., Natale, R. B., Miller, V., Manegold, C., et

al. (2004). Gefitinib in combination with paclitaxel and carboplatin in advanced

non-small-cell lung cancer: a phase III trial--INTACT 2. J Clin Oncol, 22(5),

785-794.

Herbst, R. S., Prager, D., Hermann, R., Fehrenbacher, L., Johnson, B. E., Sandler, A., et

al. (2005). TRIBUTE: a phase III trial of erlotinib hydrochloride (OSI-774)

combined with carboplatin and paclitaxel chemotherapy in advanced non-small-

cell lung cancer. J Clin Oncol, 23(25), 5892-5899.

Hilger, R. A., Scheulen, M. E., & Strumberg, D. (2002). The Ras-Raf-MEK-ERK

pathway in the treatment of cancer. Onkologie, 25(6), 511-518.

Hirsch, F. R., Varella-Garcia, M., Bunn, P. A., Jr., Di Maria, M. V., Veve, R.,

Bremmes, R. M., et al. (2003). Epidermal growth factor receptor in non-small-

cell lung carcinomas: correlation between gene copy number and protein

expression and impact on prognosis. J Clin Oncol, 21(20), 3798-3807.

Hofer, M. D., Browne, T. J., He, L., Skotheim, R. I., Lothe, R. A., & Rubin, M. A.

(2005). Identification of two molecular groups of seminomas by using

expression and tissue microarrays. Clin Cancer Res, 11(16), 5722-5729.

Huppi, K., Martin, S. E., & Caplen, N. J. (2005). Defining and assaying RNAi in

mammalian cells. Mol Cell, 17(1), 1-10.

Hutvagner, G., McLachlan, J., Pasquinelli, A. E., Balint, E., Tuschl, T., & Zamore, P.

D. (2001). A cellular function for the RNA-interference enzyme Dicer in the

maturation of the let-7 small temporal RNA. Science, 293(5531), 834-838.

Hutvagner, G., & Zamore, P. D. (2002). A microRNA in a multiple-turnover RNAi

enzyme complex. Science, 297(5589), 2056-2060.

Huynh-Do, U., Vindis, C., Liu, H., Cerretti, D. P., McGrew, J. T., Enriquez, M., et al.

(2002). Ephrin-B1 transduces signals to activate integrin-mediated migration,

attachment and angiogenesis. J Cell Sci, 115(Pt 15), 3073-3081.

Ihaka, R., & Gentelman, R. (1996). R: A Language for Data Analysis and Graphics.

Journal of Computational and Graphical Statistics, 5(3), 299-314.

Iorio, M. V., Ferracin, M., Liu, C. G., Veronese, A., Spizzo, R., Sabbioni, S., et al.

(2005). MicroRNA gene expression deregulation in human breast cancer.

Cancer Res, 65(16), 7065-7070.

Janes, P. W., Daly, R. J., deFazio, A., & Sutherland, R. L. (1994). Activation of the Ras

signalling pathway in human breast cancer cells overexpressing erbB-2.

Oncogene, 9(12), 3601-3608.

208

Janmaat, M. L., Kruyt, F. A., Rodriguez, J. A., & Giaccone, G. (2003). Response to

epidermal growth factor receptor inhibitors in non-small cell lung cancer cells:

limited antiproliferative effects and absence of apoptosis associated with

persistent activity of extracellular signal-regulated kinase or Akt kinase

pathways. Clin Cancer Res, 9(6), 2316-2326.

Janmaat, M. L., Rodriguez, J. A., Gallegos-Ruiz, M., Kruyt, F. A., & Giaccone, G.

(2006). Enhanced cytotoxicity induced by gefitinib and specific inhibitors of the

Ras or phosphatidyl inositol-3 kinase pathways in non-small cell lung cancer

cells. Int J Cancer, 118(1), 209-214.

Jiang, J., Lee, E. J., Gusev, Y., & Schmittgen, T. D. (2005). Real-time expression

profiling of microRNA precursors in human cancer cell lines. Nucleic Acids Res,

33(17), 5394-5403.

Jimeno, A., Kulesza, P., Kincaid, E., Bouaroud, N., Chan, A., Forastiere, A., et al.

(2006). C-fos assessment as a marker of anti-epidermal growth factor receptor

effect. Cancer Res, 66(4), 2385-2390.

Jin, P., Alisch, R. S., & Warren, S. T. (2004). RNA and microRNAs in fragile X mental

retardation. Nat Cell Biol, 6(11), 1048-1053.

Jing, Q., Huang, S., Guth, S., Zarubin, T., Motoyama, A., Chen, J., et al. (2005).

Involvement of microRNA in AU-rich element-mediated mRNA instability.

Cell, 120(5), 623-634.

John, B., Enright, A. J., Aravin, A., Tuschl, T., Sander, C., & Marks, D. S. (2004).

Human MicroRNA Targets. PLoS Biol, 2(11), e363.

Johnson, S. M., Grosshans, H., Shingara, J., Byrom, M., Jarvis, R., Cheng, A., et al.

(2005). RAS is regulated by the let-7 microRNA family. Cell, 120(5), 635-647.

Jones, N., Master, Z., Jones, J., Bouchard, D., Gunji, Y., Sasaki, H., et al. (1999).

Identification of Tek/Tie2 binding partners. Binding to a multifunctional

docking site mediates cell survival and migration. J Biol Chem, 274(43), 30896-

30905.

Jones-Rhoades, M. W., & Bartel, D. P. (2004). Computational Identification of Plant

MicroRNAs and Their Targets, Including a Stress-Induced miRNA. Mol Cell,

14(6), 787-799.

Kalmes, A., Daum, G., & Clowes, A. W. (2001). EGFR transactivation in the regulation

of SMC function. Ann N Y Acad Sci, 947, 42-54; discussion 54-45.

Kamata, A., Takeuchi, Y., & Fukunaga, K. (2006). Identification of the isoforms of

Ca2+/calmodulin-dependent protein kinase II and expression of brain-derived

209

neurotrophic factor mRNAs in the substantia nigra. J Neurochem, 96(1), 195-

203.

Kanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes.


Kao, J., & Pollack, J. R. (2006). RNA interference-based functional dissection of the

17q12 amplicon in breast cancer reveals contribution of coamplified genes.

Genes Chromosomes Cancer, 45(8), 761-769.

Kasus-Jacobi, A., Bereziat, V., Perdereau, D., Girard, J., & Burnol, A. F. (2000).

Evidence for an interaction between the insulin receptor and Grb7. A role for

two of its binding domains, PIR and SH2. Oncogene, 19(16), 2052-2059.

Kasus-Jacobi, A., Perdereau, D., Auzan, C., Clauser, E., Van Obberghen, E., Mauvais-

Jarvis, F., et al. (1998). Identification of the rat adapter Grb14 as an inhibitor of

insulin actions. J Biol Chem, 273(40), 26026-26035.

Kataoka, H., Tanaka, M., Kanamori, M., Yoshii, S., Ihara, M., Wang, Y. J., et al.

(2002). Expression profile of EFNB1, EFNB2, two ligands of EPHB2 in human

gastric cancer. J Cancer Res Clin Oncol, 128(7), 343-348.

Kato-Stankiewicz, J., Hakimi, I., Zhi, G., Zhang, J., Serebriiskii, I., Guo, L., et al.

(2002). Inhibitors of Ras/Raf-1 interaction identified by two-hybrid screening

revert Ras-dependent transformation phenotypes in human cancer cells. Proc

Natl Acad Sci U S A, 99(22), 14398-14403.

Kauraniemi, P., Barlund, M., Monni, O., & Kallioniemi, A. (2001). New amplified and

highly expressed genes discovered in the ERBB2 amplicon in breast cancer by

cDNA microarrays. Cancer Res, 61(22), 8235-8240.

Kay, B. K., Williamson, M. P., & Sudol, M. (2000). The importance of being proline:

the interaction of proline-rich motifs in signaling proteins with their cognate

domains. Faseb J, 14(2), 231-241.

Keegan, K., & Cooper, J. A. (1996). Use of the two hybrid system to detect the

association of the protein-tyrosine-phosphatase, SHPTP2, with another SH2-

containing protein, Grb7. Oncogene, 12(7), 1537-1544.

Ketting, R. F., Fischer, S. E. J., Bernstein, E., Sijen, T., Hannon, G. J., & Plasterk, R. H.

A. (2001). Dicer functions in RNA interference and in synthesis of small RNA

involved in developmental timing in C. elegans. Genes Dev., 15(20), 2654-2659.

Kim, E. S., Mauer, A. M., Tran, H. T., Liu, D., Gladish, G., Dicke, K., et al. (2003).

Abstract 2581: A phase II study of cetuximab, an epidermal growth factor

receptor (EGFR) blocking antibody, in combination with docetaxel in

210

chemotherapy refractory/resistant patients with advanced non-small cell lung

cancer: final report. Paper presented at the American Society for Clinical

Oncology Annual Meeting.

Kim, V. N., & Nam, J. W. (2006). Genomics of microRNA. Trends Genet, 22(3), 165-

173.

Kiriakidou, M., Nelson, P. T., Kouranov, A., Fitziev, P., Bouyioukos, C., Mourelatos,

Z., et al. (2004). A combined computational-experimental approach predicts

human microRNA targets. Genes Dev.

Kishi, T., Sasaki, H., Akiyama, N., Ishizuka, T., Sakamoto, H., Aizawa, S., et al.

(1997). Molecular cloning of human GRB-7 co-amplified with CAB1 and c-

ERBB-2 in primary gastric cancer. Biochem Biophys Res Commun, 232(1), 5-9.

Kloosterman, W. P., Wienholds, E., Ketting, R. F., & Plasterk, R. H. (2004). Substrate

requirements for let-7 function in the developing zebrafish embryo. Nucleic

Acids Res, 32(21), 6284-6291.

Krek, A., Grun, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., et al. (2005).

Combinatorial microRNA target predictions. Nat Genet.

Krutzfeldt, J., Poy, M. N., & Stoffel, M. (2006). Strategies to determine the biological

function of microRNAs. Nat Genet, 38 Suppl, S14-19.

Kuan, C. T., Wikstrand, C. J., & Bigner, D. D. (2001). EGF mutant receptor vIII as a

molecular target in cancer therapy. Endocr Relat Cancer, 8(2), 83-96.

Kumar, L. D., & Clarke, A. R. (2007). Gene manipulation through the use of small

interfering RNA (siRNA): From in vitro to in vivo applications. Adv Drug Deliv

Rev, 59(2-3), 87-100.

Lagos-Quintana, M., Rauhut, R., Lendeckel, W., & Tuschl, T. (2001). Identification of

novel genes coding for small expressed RNAs. Science, 294(5543), 853-858.

Lai, E. C. (2002). Micro RNAs are complementary to 3' UTR sequence motifs that

mediate negative post-transcriptional regulation. Nat Genet, 30(4), 363-364.

Lai, E. C. (2004). Notch signaling: control of cell communication and cell fate.

Development, 131(5), 965-973.

Lai, E. C., Tam, B., & Rubin, G. M. (2005). Pervasive regulation of Drosophila Notch

target genes by GY-box-, Brd-box-, and K-box-class microRNAs. Genes Dev.

Lark, A. L., Livasy, C. A., Dressler, L., Moore, D. T., Millikan, R. C., Geradts, J., et al.

(2005). High focal adhesion kinase expression in invasive breast carcinomas is

associated with an aggressive phenotype. Mod Pathol, 18(10), 1289-1294.

211

Laskin, J. J., & Sandler, A. B. (2004). Epidermal growth factor receptor: a promising

target in solid tumours. Cancer Treat Rev, 30(1), 1-17.

Lau, N. C., Lim, L. P., Weinstein, E. G., & Bartel, D. P. (2001). An abundant class of

tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science,

294(5543), 858-862.

Le, X. F., McWatters, A., Wiener, J., Wu, J. Y., Mills, G. B., & Bast, R. C., Jr. (2000).

Anti-HER2 antibody and heregulin suppress growth of HER2-overexpressing

human breast cancer cells through different mechanisms. Clin Cancer Res, 6(1),

260-270.

Learn, C. A., Hartzell, T. L., Wikstrand, C. J., Archer, G. E., Rich, J. N., Friedman, A.

H., et al. (2004). Resistance to tyrosine kinase inhibition by mutant epidermal

growth factor receptor variant III contributes to the neoplastic phenotype of

glioblastoma multiforme. Clin Cancer Res, 10(9), 3216-3224.

Leavey, S. F., Arend, L. J., Dare, H., Dressler, G. R., Briggs, J. P., & Margolis, B. L.

(1998). Expression of Grb7 growth factor receptor signaling protein in kidney

development and in adult kidney. Am J Physiol, 275(5 Pt 2), F770-776.

Lee, H., Volonte, D., Galbiati, F., Iyengar, P., Lublin, D. M., Bregman, D. B., et al.

(2000). Constitutive and growth factor-regulated phosphorylation of caveolin-1

occurs at the same site (Tyr-14) in vivo: identification of a c-Src/Cav-1/Grb7

signaling cassette. Mol Endocrinol, 14(11), 1750-1775.

Lee, H. J., Palkovits, M., & Young, W. S., 3rd. (2006). miR-7b, a microRNA up-

regulated in the hypothalamus after chronic hyperosmolar stimulation, inhibits

Fos translation. Proc Natl Acad Sci U S A, 103(42), 15669-15674.

Lee, R. C., & Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis

elegans. Science, 294(5543), 862-864.

Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993). The C. elegans heterochronic gene

lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75(5),

843-854.

Lee, Y., Ahn, C., Han, J., Choi, H., Kim, J., Yim, J., et al. (2003). The nuclear RNase

III Drosha initiates microRNA processing. Nature, 425(6956), 415-419.

Lee, Y., Jeon, K., Lee, J.-T., Kim, S., & Kim, V. N. (2002). MicroRNA maturation:

stepwise processing and subcellular localization. EMBO J., 21(17), 4663-4670.

Lemmon, M. A., & Ferguson, K. M. (2000). Signal-dependent membrane targeting by

pleckstrin homology (PH) domains. Biochem J, 350 Pt 1, 1-18.

212

Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005). Conserved seed pairing, often

flanked by adenosines, indicates that thousands of human genes are microRNA

targets. Cell, 120(1), 15-20.

Lewis, B. P., Shih, I. H., Jones-Rhoades, M. W., Bartel, D. P., & Burge, C. B. (2003).

Prediction of mammalian microRNA targets. Cell, 115(7), 787-798.

Li, H., Sanchez-Torres, J., del Carpio, A. F., Nogales-Gonzalez, A., Molina-Ortiz, P.,

Moreno, M. J., et al. (2005). The adaptor Grb7 is a novel calmodulin-binding

protein: functional implications of the interaction of calmodulin with Grb7.

Oncogene, 24(26), 4206-4219.

Li, X., & Carthew, R. W. (2005). A microRNA mediates EGF receptor signaling and

promotes photoreceptor differentiation in the Drosophila eye. Cell, 123(7),

1267-1277.

Lim, L. P., Glasner, M. E., Yekta, S., Burge, C. B., & Bartel, D. P. (2003). Vertebrate

microRNA genes. Science, 299(5612), 1540.

Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., et al.

(2005a). Microarray analysis shows that some microRNAs downregulate large

numbers of target mRNAs. Nature.

Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., et al.

(2005b). Microarray analysis shows that some microRNAs downregulate large

numbers of target mRNAs. Nature, 433(7027), 769-773.

Lin, F. Y., Chen, Y. H., Lin, Y. W., Tsai, J. S., Chen, J. W., Wang, H. J., et al. (2006).

The role of human antigen R, an RNA-binding protein, in mediating the

stabilization of toll-like receptor 4 mRNA induced by endotoxin: a novel

mechanism involved in vascular inflammation. Arterioscler Thromb Vasc Biol,

26(12), 2622-2629.

Linggi, B., & Carpenter, G. (2006). ErbB receptors: new insights on mechanisms and

biology. Trends Cell Biol, 16(12), 649-656.

Liu, B., & Neufeld, A. H. (2003). Activation of epidermal growth factor receptor

signals induction of nitric oxide synthase-2 in human optic nerve head astrocytes

in glaucomatous optic neuropathy. Neurobiol Dis, 13(2), 109-123.

Liu, C. G., Calin, G. A., Meloon, B., Gamliel, N., Sevignani, C., Ferracin, M., et al.

(2004). An oligonucleotide microchip for genome-wide microRNA profiling in

human and mouse tissues. Proc Natl Acad Sci U S A.

213

Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., et al. (2005).

MicroRNA expression profiles classify human cancers. Nature, 435(7043), 834-

838.

Mandal, M., Vadlamudi, R., Nguyen, D., Wang, R. A., Costa, L., Bagheri-Yarmand, R.,

et al. (2001). Growth factors regulate heterogeneous nuclear ribonucleoprotein

K expression and function. J Biol Chem, 276(13), 9699-9704.

Manser, J., Roonprapunt, C., & Margolis, B. (1997). C. elegans cell migration gene

mig-10 shares similarities with a family of SH2 domain proteins and acts cell

nonautonomously in excretory canal development. Dev Biol, 184(1), 150-164.

Manser, J., & Wood, W. B. (1990). Mutations affecting embryonic cell migrations in

Caenorhabditis elegans. Dev Genet, 11(1), 49-64.

Maqani, N., Belkhiri, A., Moskaluk, C., Knuutila, S., Dar, A. A., & El-Rifai, W. (2006).

Molecular dissection of 17q12 amplicon in upper gastrointestinal

adenocarcinomas. Mol Cancer Res, 4(7), 449-455.

Margolis, B., Silvennoinen, O., Comoglio, F., Roonprapunt, C., Skolnik, E., Ullrich, A.,

et al. (1992). High-efficiency expression/cloning of epidermal growth factor-

receptor-binding proteins with Src homology 2 domains. Proc Natl Acad Sci U S

A, 89(19), 8894-8898.

Marmor, M. D., Skaria, K. B., & Yarden, Y. (2004). Signal transduction and

oncogenesis by ErbB/HER receptors. Int J Radiat Oncol Biol Phys, 58(3), 903-

913.

Martin, M. M., Lee, E. J., Buckenberger, J. A., Schmittgen, T. D., & Elton, T. S. (2006).

MicroRNA-155 regulates human angiotensin II type 1 receptor expression in

fibroblasts. J Biol Chem, 281(27), 18277-18284.

Matsumoto, M., Mukai, M., & Tagaya, I. (1979). Variation in susceptibility of HeLa

cell lines to coxsackievirus A9. Arch Virol, 59(3), 213-222.

Maurizi, M., Almadori, G., Ferrandina, G., Distefano, M., Romanini, M. E., Cadoni, G.,

et al. (1996). Prognostic significance of epidermal growth factor receptor in

laryngeal squamous cell carcinoma. Br J Cancer, 74(8), 1253-1257.

McIntyre, A., Summersgill, B., Spendlove, H. E., Huddart, R., Houlston, R., & Shipley,

J. (2005). Activating mutations and/or expression levels of tyrosine kinase

receptors GRB7, RAS, and BRAF in testicular germ cell tumors. Neoplasia,

7(12), 1047-1052.

214

Michael, M. Z., SM, O. C., Van Holst Pellekaan, N. G., Young, G. P., & James, R. J.

(2003). Reduced Accumulation of Specific MicroRNAs in Colorectal Neoplasia.

Mol Cancer Res, 1(12), 882-891.

Michelotti, E. F., Michelotti, G. A., Aronsohn, A. I., & Levens, D. (1996).

Heterogeneous nuclear ribonucleoprotein K is a transcription factor. Mol Cell

Biol, 16(5), 2350-2360.

Miettinen, P. J., Berger, J. E., Meneses, J., Phung, Y., Pedersen, R. A., Werb, Z., et al.

(1995). Epithelial immaturity and multiorgan failure in mice lacking epidermal

growth factor receptor. Nature, 376(6538), 337-341.

Milde-Langosch, K. (2005). The Fos family of transcription factors and their role in

tumourigenesis. Eur J Cancer, 41(16), 2449-2461.

miRNA Research Guide. (2005). (pp. 14-15): Ambion, Inc.

Miyamoto, E. (2006). Molecular mechanism of neuronal plasticity: induction and

maintenance of long-term potentiation in the hippocampus. J Pharmacol Sci,

100(5), 433-442.

Moore, M. J., Goldstein, D., Hamm, J., Figer, A., Hecht, J. R., Gallinger, S., et al.

(2007). Erlotinib plus gemcitabine compared with gemcitabine alone in patients

with advanced pancreatic cancer: a phase III trial of the National Cancer

Institute of Canada Clinical Trials Group. J Clin Oncol, 25(15), 1960-1966.

Moss, E. G., Lee, R. C., & Ambros, V. (1997). The cold shock domain protein LIN-28

controls developmental timing in C. elegans and is regulated by the lin-4 RNA.

Cell, 88(5), 637-646.

Mourelatos, Z., Dostie, J., Paushkin, S., Sharma, A., Charroux, B., Abel, L., et al.

(2002). miRNPs: a novel class of ribonucleoproteins containing numerous

microRNAs. Genes Dev, 16(6), 720-728.

Nagashima, T., Shimodaira, H., Ide, K., Nakakuki, T., Tani, Y., Takahashi, K., et al.

(2007). Quantitative transcriptional control of ErbB receptor signaling

undergoes graded to biphasic response for cell differentiation. J Biol Chem,

282(6), 4045-4056.

Nakamoto, M., Jin, P., O'Donnell W, T., & Warren, S. T. (2005). Physiological

identification of human transcripts translationally regulated by a specific

microRNA. Hum Mol Genet.

Nakamura, Y., Sotozono, C., & Kinoshita, S. (2001). The epidermal growth factor

receptor (EGFR): role in corneal wound healing and homeostasis. Exp Eye Res,

72(5), 511-517.

215

Nelson, P. T., Hatzigeorgiou, A. G., & Mourelatos, Z. (2004). miRNP:mRNA

association in polyribosomes in a human neuronal cell line. Rna, 10(3), 387-394.

Newman, J. C., & Weiner, A. M. (2005). L2L: a simple tool for discovering the hidden

significance in microarray expression data. Genome Biol, 6(9), R81.

Nicholson, R. I., Gee, J. M., & Harper, M. E. (2001). EGFR and cancer prognosis. Eur

J Cancer, 37 Suppl 4, S9-15.

Normanno, N., De Luca, A., Bianco, C., Strizzi, L., Mancino, M., Maiello, M. R., et al.

(2005). Epidermal growth factor receptor (EGFR) signaling in cancer. Gene.

O'Donnell, K. A., Wentzel, E. A., Zeller, K. I., Dang, C. V., & Mendell, J. T. (2005). c-

Myc-regulated microRNAs modulate E2F1 expression. Nature, 435(7043), 839-

843.

Onguru, O., Scheithauer, B. W., Kovacs, K., Vidal, S., Jin, L., Zhang, S., et al. (2004).

Analysis of epidermal growth factor receptor and activated epidermal growth

factor receptor expression in pituitary adenomas and carcinomas. Mod Pathol,

17(7), 772-780.

Pandey, A., Liu, X., Dixon, J. E., Di Fiore, P. P., & Dixit, V. M. (1996). Direct

association between the Ret receptor tyrosine kinase and the Src homology 2-

containing adapter protein Grb7. J Biol Chem, 271(18), 10607-10610.

Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., et al.

(2005). Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is

associated with a second mutation in the EGFR kinase domain. PLoS Med, 2(3),

e73.

Pao, W., Wang, T. Y., Riely, G. J., Miller, V. A., Pan, Q., Ladanyi, M., et al. (2005).

KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or

erlotinib. PLoS Med, 2(1), e17.

Pasquinelli, A. E., Reinhart, B. J., Slack, F., Martindale, M. Q., Kuroda, M. I., Maller,

B., et al. (2000). Conservation of the sequence and temporal expression of let-7

heterochronic regulatory RNA. Nature, 408(6808), 86-89.

Peeva, V. (2003). Affymetrix Protocol - Eukaryotic Target Preparation using Total

RNA, Revision 3: Lotterywest State MicroArray Facility.

Pero, S. C., Daly, R. J., & Krag, D. N. (2003). Grb7-based molecular therapeutics in

cancer. Expert Rev Mol Med, 5, 1-11.

Pero, S. C., Oligino, L., Daly, R. J., Soden, A. L., Liu, C., Roller, P. P., et al. (2002).

Identification of novel non-phosphorylated ligands, which bind selectively to the

SH2 domain of Grb7. J Biol Chem, 277(14), 11918-11926.

216

Pero, S. C., Shukla, G. S., Cookson, M. M., Flemer, S., Jr., & Krag, D. N. (2007).

Combination treatment with Grb7 peptide and Doxorubicin or Trastuzumab

(Herceptin) results in cooperative cell growth inhibition in breast cancer cells.

Br J Cancer, 96(10), 1520-1525.

Pino, I., Pio, R., Toledo, G., Zabalegui, N., Vicent, S., Rey, N., et al. (2003). Altered

patterns of expression of members of the heterogeneous nuclear

ribonucleoprotein (hnRNP) family in lung cancer. Lung Cancer, 41(2), 131-143.

Poy, M. N., Eliasson, L., Krutzfeldt, J., Kuwajima, S., Ma, X., Macdonald, P. E., et al.

(2004). A pancreatic islet-specific microRNA regulates insulin secretion.

Nature, 432(7014), 226-230.

Pre-miR miRNA Precursor Specification Sheet. (2005). Ambion, Inc.

Prenzel, N., Fischer, O. M., Streit, S., Hart, S., & Ullrich, A. (2001). The epidermal

growth factor receptor family as a central element for cellular signal

transduction and diversification. Endocr Relat Cancer, 8(1), 11-31.

QCM-FN manual. (1999).): Chemicon Int.

Rajewsky, N. (2006). microRNA target predictions in animals. Nat Genet, 38 Suppl, S8-

13.

Rajewsky, N., & Socci, N. D. (2004). Computational identification of microRNA

targets. Dev Biol, 267(2), 529-535.

Reardon, D. A., & Wen, P. Y. (2006). Therapeutic advances in the treatment of

glioblastoma: rationale and potential role of targeted agents. Oncologist, 11(2),

152-164.

Rebecchi, M. J., & Scarlata, S. (1998). Pleckstrin homology domains: a common fold

with diverse functions. Annu Rev Biophys Biomol Struct, 27, 503-528.

Rehmsmeier, M., Steffen, P., Hochsmann, M., & Giegerich, R. (2004). Fast and

effective prediction of microRNA/target duplexes. Rna, 10(10), 1507-1517.

Reinhart, B. J., Slack, F. J., Basson, M., Pasquinelli, A. E., Bettinger, J. C., Rougvie, A.

E., et al. (2000). The 21-nucleotide let-7 RNA regulates developmental timing in

Caenorhabditis elegans. Nature, 403(6772), 901-906.

Reiske, H. R., Zhao, J., Han, D. C., Cooper, L. A., & Guan, J. L. (2000). Analysis of

FAK-associated signaling pathways in the regulation of cell cycle progression.

FEBS Lett, 486(3), 275-280.

Rhoades, M. W., Reinhart, B. J., Lim, L. P., Burge, C. B., Bartel, B., & Bartel, D. P.

(2002). Prediction of plant microRNA targets. Cell, 110(4), 513-520.

217

Rhoades, R., & Pflanzer, R. (1996). Human Physiology (3rd ed.): Saunders College

Publishing.

Robins, H., Li, Y., & Padgett, R. W. (2005). Incorporating structure to predict

microRNA targets. Proc Natl Acad Sci U S A, 102(11), 4006-4009.

Rodenhuis, S., & Slebos, R. J. (1992). Clinical significance of ras oncogene activation

in human lung cancer. Cancer Res, 52(9 Suppl), 2665s-2669s.

Rodriguez, A., Griffiths-Jones, S., Ashurst, J. L., & Bradley, A. (2004). Identification of

mammalian microRNA host genes and transcription units. Genome Res,

14(10A), 1902-1910.

Rodriguez, L. G., Wu, X., & Guan, J.-L. (2004). Wound-Healing Assay. In J.-L. Guan

(Ed.), Methods in Molecular Biology, vol 294: Cell Migration: Developmental

Methods and Protocols (pp. 23-29): Humana Press.

Roelle, S., Grosse, R., Aigner, A., Krell, H. W., Czubayko, F., & Gudermann, T.

(2003). Matrix metalloproteinases 2 and 9 mediate epidermal growth factor

receptor transactivation by gonadotropin-releasing hormone. J Biol Chem,

278(47), 47307-47318.

Ross, J. S., & Fletcher, J. A. (1998). The HER-2/neu Oncogene in Breast Cancer:

Prognostic Factor, Predictive Factor, and Target for Therapy. Oncologist, 3(4),

237-252.

Roux, P. P., & Blenis, J. (2004). ERK and p38 MAPK-activated protein kinases: a

family of protein kinases with diverse biological functions. Microbiol Mol Biol

Rev, 68(2), 320-344.

Saetrom, O., Snove, O., Jr., & Saetrom, P. (2005). Weighted sequence motifs as an

improved seeding step in microRNA target prediction algorithms. Rna.

Salomon, D. S., Brandt, R., Ciardiello, F., & Normanno, N. (1995). Epidermal growth

factor-related peptides and their receptors in human malignancies. Crit Rev

Oncol Hematol, 19(3), 183-232.

Schlaepfer, D. D., Mitra, S. K., & Ilic, D. (2004). Control of motile and invasive cell

phenotypes by focal adhesion kinase. Biochim Biophys Acta, 1692(2-3), 77-102.

Schratt, G. M., Tuebing, F., Nigh, E. A., Kane, C. G., Sabatini, M. E., Kiebler, M., et al.

(2006). A brain-specific microRNA regulates dendritic spine development.

Nature, 439(7074), 283-289.

Scott, G. K., Goga, A., Bhaumik, D., Berger, C. E., Sullivan, C. S., & Benz, C. C.

(2007). Coordinate suppression of ERBB2 and ERBB3 by enforced expression

of micro-RNA miR-125a or miR-125b. J Biol Chem, 282(2), 1479-1486.

218

Sempere, L. F., Freemantle, S., Pitha-Rowe, I., Moss, E., Dmitrovsky, E., & Ambros,

V. (2004). Expression profiling of mammalian microRNAs uncovers a subset of

brain-expressed microRNAs with possible roles in murine and human neuronal

differentiation. Genome Biol, 5(3), R13.

Shen, T. L., & Guan, J. L. (2001). Differential regulation of cell migration and cell

cycle progression by FAK complexes with Src, PI3K, Grb7 and Grb2 in focal

contacts. FEBS Lett, 499(1-2), 176-181.

Shen, T. L., Han, D. C., & Guan, J. L. (2002). Association of Grb7 with

phosphoinositides and its role in the regulation of cell migration. J Biol Chem,

277(32), 29069-29077.

Shepherd, F. A., Rodrigues Pereira, J., Ciuleanu, T., Tan, E. H., Hirsh, V.,

Thongprasert, S., et al. (2005). Erlotinib in previously treated non-small-cell

lung cancer. N Engl J Med, 353(2), 123-132.

Sheskin, D. J. (2007). Handbook of parametric and nonparametric statistical

procedures (4th ed.): Chapman & Hall.

Shivdasani, R. A. (2006). MicroRNAs: regulators of gene expression and cell

differentiation. Blood, 108(12), 3646-3653.

Sibilia, M., Wagner, B., Hoebertz, A., Elliott, C., Marino, S., Jochum, W., et al. (2003).

Mice humanised for the EGF receptor display hypomorphic phenotypes in skin,

bone and heart. Development, 130(19), 4515-4525.

Sieg, D. J., Hauck, C. R., Ilic, D., Klingbeil, C. K., Schaefer, E., Damsky, C. H., et al.

(2000). FAK integrates growth-factor and integrin signals to promote cell

migration. Nat Cell Biol, 2(5), 249-256.

Skotheim, R. I., Monni, O., Mousses, S., Fossa, S. D., Kallioniemi, O. P., Lothe, R. A.,

et al. (2002). New insights into testicular germ cell tumorigenesis from gene

expression profiling. Cancer Res, 62(8), 2359-2364.

Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., & McGuire, W. L.

(1987). Human breast cancer: correlation of relapse and survival with

amplification of the HER-2/neu oncogene. Science, 235(4785), 177-182.

Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., et al.

(2001). Use of chemotherapy plus a monoclonal antibody against HER2 for

metastatic breast cancer that overexpresses HER2. N Engl J Med, 344(11), 783-

792.

219

Smalheiser, N. R., & Torvik, V. I. (2004). A population-based statistical approach

identifies parameters characteristic of human microRNA-mRNA interactions.

BMC Bioinformatics, 5(1), 139.

Smith, J. S., Tachibana, I., Passe, S. M., Huntley, B. K., Borell, T. J., Iturria, N., et al.

(2001). PTEN mutation, EGFR amplification, and outcome in patients with

anaplastic astrocytoma and glioblastoma multiforme. J Natl Cancer Inst, 93(16),

1246-1256.

Sood, P., Krek, A., Zavolan, M., Macino, G., & Rajewsky, N. (2006). Cell-type-specific

signatures of microRNAs on target mRNA expression. Proc Natl Acad Sci U S

A, 103(8), 2746-2751.

Stark, A., Brennecke, J., Russell, R. B., & Cohen, S. M. (2003). Identification of

Drosophila MicroRNA Targets. PLoS Biol, 1(3), E60.

Stein, D., Wu, J., Fuqua, S. A., Roonprapunt, C., Yajnik, V., D'Eustachio, P., et al.

(1994). The SH2 domain protein GRB-7 is co-amplified, overexpressed and in a

tight complex with HER2 in breast cancer. Embo J, 13(6), 1331-1340.

Sun, Y., Koo, S., White, N., Peralta, E., Esau, C., Dean, N. M., et al. (2004).

Development of a micro-array to detect human and mouse microRNAs and

characterization of expression in human organs. Nucleic Acids Res, 32(22),

e188.

Sunkar, R., & Zhu, J. K. (2004). Novel and stress-regulated microRNAs and other small

RNAs from Arabidopsis. Plant Cell, 16(8), 2001-2019.

Takamizawa, J., Konishi, H., Yanagisawa, K., Tomida, S., Osada, H., Endoh, H., et al.

(2004). Reduced expression of the let-7 microRNAs in human lung cancers in

association with shortened postoperative survival. Cancer Res, 64(11), 3753-

3756.

Tan, M., Grijalva, R., & Yu, D. (1999). Heregulin beta1-activated phosphatidylinositol

3-kinase enhances aggregation of MCF-7 breast cancer cells independent of

extracellular signal-regulated kinase. Cancer Res, 59(7), 1620-1625.

Tan, P. H., Jayabaskar, T., Yip, G., Tan, Y., Hilmy, M., Selvarajan, S., et al. (2005).

p53 and c-kit (CD117) protein expression as prognostic indicators in breast

phyllodes tumors: a tissue microarray study. Mod Pathol, 18(12), 1527-1534.

Tanaka, S., Mori, M., Akiyoshi, T., Tanaka, Y., Mafune, K., Wands, J. R., et al. (1997).

Coexpression of Grb7 with epidermal growth factor receptor or Her2/erbB2 in

human advanced esophageal carcinoma. Cancer Res, 57(1), 28-31.

220

Tanaka, S., Mori, M., Akiyoshi, T., Tanaka, Y., Mafune, K., Wands, J. R., et al. (1998).

A novel variant of human Grb7 is associated with invasive esophageal

carcinoma. J Clin Invest, 102(4), 821-827.

Tanaka, S., Pero, S. C., Taguchi, K., Shimada, M., Mori, M., Krag, D. N., et al. (2006).

Specific peptide ligand for Grb7 signal transduction protein and pancreatic

cancer metastasis. J Natl Cancer Inst, 98(7), 491-498.

Tanaka, S., Sugimachi, K., Kawaguchi, H., Saeki, H., Ohno, S., & Wands, J. R. (2000).

Grb7 signal transduction protein mediates metastatic progression of esophageal

carcinoma. J Cell Physiol, 183(3), 411-415.

Tanaka, S., Tatsumi, K., Okubo, K., Itoh, K., Kawamoto, S., Matsubara, K., et al.

(2002). Expression profile of active genes in the human pituitary gland. J Mol

Endocrinol, 28(1), 33-44.

Technotes. (2005). (Vol. 12): Ambion, Inc.

Theodoropoulou, M., Arzberger, T., Gruebler, Y., Jaffrain-Rea, M. L., Schlegel, J.,

Schaaf, L., et al. (2004). Expression of epidermal growth factor receptor in

neoplastic pituitary cells: evidence for a role in corticotropinoma cells. J

Endocrinol, 183(2), 385-394.

Thommes, K., Lennartsson, J., Carlberg, M., & Ronnstrand, L. (1999). Identification of

Tyr-703 and Tyr-936 as the primary association sites for Grb2 and Grb7 in the

c-Kit/stem cell factor receptor. Biochem J, 341 ( Pt 1), 211-216.

Threadgill, D. W., Dlugosz, A. A., Hansen, L. A., Tennenbaum, T., Lichti, U., Yee, D.,

et al. (1995). Targeted disruption of mouse EGF receptor: effect of genetic

background on mutant phenotype. Science, 269(5221), 230-234.

Timpson, P., Lynch, D. K., Schramek, D., Walker, F., & Daly, R. J. (2005). Cortactin

overexpression inhibits ligand-induced down-regulation of the epidermal growth

factor receptor. Cancer Res, 65(8), 3273-3280.

Transfecting siRNA into Mammalian Cells Using Lipofectamine 2000. (2002). (pp. 2):

Invitrogen, Inc.

Tzahar, E., Waterman, H., Chen, X., Levkowitz, G., Karunagaran, D., Lavi, S., et al.

(1996). A hierarchical network of interreceptor interactions determines signal

transduction by Neu differentiation factor/neuregulin and epidermal growth

factor. Mol Cell Biol, 16(10), 5276-5287.

Valencia-Sanchez, M. A., Liu, J., Hannon, G. J., & Parker, R. (2006). Control of

translation and mRNA degradation by miRNAs and siRNAs. Genes Dev, 20(5),

515-524.

221

Van Cutsem, E., Peeters, M., Siena, S., Humblet, Y., Hendlisz, A., Neyns, B., et al.

(2007). Open-label phase III trial of panitumumab plus best supportive care

compared with best supportive care alone in patients with chemotherapy-

refractory metastatic colorectal cancer. J Clin Oncol, 25(13), 1658-1664.

van Rooij, E., Sutherland, L. B., Liu, N., Williams, A. H., McAnally, J., Gerard, R. D.,

et al. (2006). A signature pattern of stress-responsive microRNAs that can evoke

cardiac hypertrophy and heart failure. Proc Natl Acad Sci U S A, 103(48),

18255-18260.

Vanhoefer, U., Tewes, M., Rojo, F., Dirsch, O., Schleucher, N., Rosen, O., et al. (2004).

Phase I study of the humanized antiepidermal growth factor receptor

monoclonal antibody EMD72000 in patients with advanced solid tumors that

express the epidermal growth factor receptor. J Clin Oncol, 22(1), 175-184.

Vatolin, S., Navaratne, K., & Weil, R. J. (2006). A Novel Method to Detect Functional

MicroRNA Targets. J Mol Biol, 358(4), 983-996.

Vayssiere, B., Zalcman, G., Mahe, Y., Mirey, G., Ligensa, T., Weidner, K. M., et al.

(2000). Interaction of the Grb7 adapter protein with Rnd1, a new member of the

Rho family. FEBS Lett, 467(1), 91-96.

Vlotides, G., Cruz-Soto, M., Rubinek, T., Eigler, T., Auernhammer, C. J., & Melmed,

S. (2006). Mechanisms for growth factor-induced pituitary tumor transforming

gene-1 expression in pituitary folliculostellate TtT/GF cells. Mol Endocrinol,

20(12), 3321-3335.

Vo, N., Klein, M. E., Varlamova, O., Keller, D. M., Yamamoto, T., Goodman, R. H., et

al. (2005). A cAMP-response element binding protein-induced microRNA

regulates neuronal morphogenesis. Proc Natl Acad Sci U S A, 102(45), 16426-

16431.

von Minckwitz, G., Jonat, W., Fasching, P., du Bois, A., Kleeberg, U., Luck, H. J., et al.

(2005). A multicentre phase II study on gefitinib in taxane- and anthracycline-

pretreated metastatic breast cancer. Breast Cancer Res Treat, 89(2), 165-172.

Walch, A., Specht, K., Braselmann, H., Stein, H., Siewert, J. R., Hopt, U., et al. (2004).

Coamplification and coexpression of GRB7 and ERBB2 is found in high grade

intraepithelial neoplasia and in invasive Barrett's carcinoma. Int J Cancer,

112(5), 747.

Wang, X., & Wang, X. (2006). Systematic identification of microRNA functions by

combining target prediction and expression profiling. Nucleic Acids Res, 34(5),

1646-1652.

222

Wightman, B., Ha, I., & Ruvkun, G. (1993). Posttranscriptional regulation of the

heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C.

elegans. Cell, 75(5), 855-862.

Wojcik, J., Girault, J. A., Labesse, G., Chomilier, J., Mornon, J. P., & Callebaut, I.

(1999). Sequence analysis identifies a ras-associating (RA)-like domain in the

N-termini of band 4.1/JEF domains and in the Grb7/10/14 adapter family.


Wu, L., Fan, J., & Belasco, J. G. (2006). MicroRNAs direct rapid deadenylation of

mRNA. Proc Natl Acad Sci U S A, 103(11), 4034-4039.

Xing, Z., Chen, H. C., Nowlen, J. K., Taylor, S. J., Shalloway, D., & Guan, J. L. (1994).

Direct interaction of v-Src with the focal adhesion kinase mediated by the Src

SH2 domain. Mol Biol Cell, 5(4), 413-421.

Xu, F. J., Stack, S., Boyer, C., O'Briant, K., Whitaker, R., Mills, G. B., et al. (1997).

Heregulin and agonistic anti-p185(c-erbB2) antibodies inhibit proliferation but

increase invasiveness of breast cancer cells that overexpress p185(c-erbB2):

increased invasiveness may contribute to poor prognosis. Clin Cancer Res, 3(9),

1629-1634.

Xu, P., Vernooy, S. Y., Guo, M., & Hay, B. A. (2003). The Drosophila MicroRNA Mir-

14 Suppresses Cell Death and Is Required for Normal Fat Metabolism. Curr

Biol, 13(9), 790-795.

Yamanaka, Y., Friess, H., Kobrin, M. S., Buchler, M., Beger, H. G., & Korc, M. (1993).

Coexpression of epidermal growth factor receptor and ligands in human

pancreatic cancer is associated with enhanced tumor aggressiveness. Anticancer

Res, 13(3), 565-569.

Yanaihara, N., Caplen, N., Bowman, E., Seike, M., Kumamoto, K., Yi, M., et al.

(2006). Unique microRNA molecular profiles in lung cancer diagnosis and

prognosis. Cancer Cell, 9(3), 189-198.

Yang, E. B., Wang, D. F., Mack, P., & Cheng, L. Y. (1996). Genistein, a tyrosine kinase

inhibitor, reduces EGF-induced EGF receptor internalization and degradation in

human hepatoma HepG2 cells. Biochem Biophys Res Commun, 224(2), 309-317.

Yang, W. J., Yang, D. D., Na, S., Sandusky, G. E., Zhang, Q., & Zhao, G. (2005). Dicer

is required for embryonic angiogenesis during mouse development. J Biol

Chem, 280(10), 9330-9335.

223

Yang, X., Wang, W., Fan, J., Lal, A., Yang, D., Cheng, H., et al. (2004). Prostaglandin

A2-mediated stabilization of p21 mRNA through an ERK-dependent pathway

requiring the RNA-binding protein HuR. J Biol Chem, 279(47), 49298-49306.

Yarden, Y. (2001). The EGFR family and its ligands in human cancer. signalling

mechanisms and therapeutic opportunities. Eur J Cancer, 37 Suppl 4, S3-8.

Yekta, S., Shih, I. H., & Bartel, D. P. (2004). MicroRNA-directed cleavage of HOXB8

mRNA. Science, 304(5670), 594-596.

Yen, L., You, X. L., Al Moustafa, A. E., Batist, G., Hynes, N. E., Mader, S., et al.

(2000). Heregulin selectively upregulates vascular endothelial growth factor

secretion in cancer cells and stimulates angiogenesis. Oncogene, 19(31), 3460-

3469.

Yi, R., Qin, Y., Macara, I. G., & Cullen, B. R. (2003). Exportin-5 mediates the nuclear

export of pre-microRNAs and short hairpin RNAs. Genes Dev, 17(24), 3011-

3016.

Yokote, K., Margolis, B., Heldin, C. H., & Claesson-Welsh, L. (1996). Grb7 is a

downstream signaling component of platelet-derived growth factor alpha- and

beta-receptors. J Biol Chem, 271(48), 30942-30949.

Yonemura, Y., Takamura, H., Ninomiya, I., Fushida, S., Tsugawa, K., Kaji, M., et al.

(1992). Interrelationship between transforming growth factor-alpha and

epidermal growth factor receptor in advanced gastric cancer. Oncology, 49(2),

157-161.

Yoon, S., & De Micheli, G. (2005). Prediction of regulatory modules comprising

microRNAs and target genes. Bioinformatics, 21 Suppl 2, ii93-ii100.

Yu, Z., Raabe, T., & Hecht, N. B. (2005). MicroRNA122a Reduces Expression of the

Post-Transcriptionally Regulated Germ Cell Transition Protein 2 (Tnp2)

Messenger RNA (mRNA) by mRNA Cleavage. Biol Reprod.

Zeng, Y., Wagner, E. J., & Cullen, B. R. (2002). Both natural and designed micro

RNAs can inhibit the expression of cognate mRNAs when expressed in human

cells. Mol Cell, 9(6), 1327-1333.

Zhang, B., Schmoyer, D., Kirov, S., & Snoddy, J. (2004). GOTree Machine (GOTM): a

web-based platform for interpreting sets of interesting genes using Gene

Ontology hierarchies. BMC Bioinformatics, 5, 16.

Zhang, L., Huang, J., Yang, N., Greshock, J., Megraw, M. S., Giannakakis, A., et al.

(2006). microRNAs exhibit high frequency genomic alterations in human

cancer. Proc Natl Acad Sci U S A, 103(24), 9136-9141.

224

Zhang, M., Zhang, X., Bai, C. X., Song, X. R., Chen, J., Gao, L., et al. (2005). Silencing

the epidermal growth factor receptor gene with RNAi may be developed as a

potential therapy for non small cell lung cancer. Genet Vaccines Ther, 3, 5.

Zhao, J. H., Reiske, H., & Guan, J. L. (1998). Regulation of the cell cycle by focal

adhesion kinase. J Cell Biol, 143(7), 1997-2008.

Zhao, Y., Samal, E., & Srivastava, D. (2005). Serum response factor regulates a muscle-

specific microRNA that targets Hand2 during cardiogenesis. Nature, 436(7048),

214-220.

Zuker, M. (2003). Mfold web server for nucleic acid folding and hybridization

prediction. Nucleic Acids Res, 31(13), 3406-3415.

225

APPENDIX A

Code for the Chapter 6 miRNA target prediction program

% main.m

% Main miRNA search program designed to predict miRNA targets in

% sequences in FASTA format, contained in separate files.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% User to enter:

% Minimum number of seeds:

min_seeds = 2;

% Minimum seed length:

min_seed_length = 7;

% Allow G:U base-pairs in the miR-7 seed?

allow_gus = 0;

% For the allow_gus = 1 case, have a set of 3 altered

% antisense miR-7 seeds, each with a different C changed to a U (T):

miR7_GU_as_seeds = ['GTTTTCC'; 'GTCTTTC'; 'GTCTTCT'];

% File containing the miRNA sequences:

miRNA_file = 'Rfam homo sapiens miRNAs.txt';

% Folder contaiing the UTR sequences:

sequence_folder = '3UTRs_to_search';

linker = 'GCGGGGACGC';

% Save this search over the previously saved database?

save_over_scores = 1;

% Filename under which to save the matlab results struct:

filename = 'All_scores_2x7nt_date';

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Preallocate space to the UTR_struct

this_UTR = struct('name',{},'sequence',{},'length',{});

all_scores_struct = struct('data',{});

226

% Get miRNA and 3'UTR data:

UTR_names = get_UTR_names(sequence_folder);

miRNA_struct = get_miRNA_table(miRNA_file, min_seed_length);

num_UTRs = length(UTR_names);

num_miRNAs = length(miRNA_struct);

% Cycle through the 3'UTRs:

for UTR_index = 1:num_UTRs

% Get this 3'UTR's data for the struct:

this_UTR = get_UTR_data(UTR_names, UTR_index, sequence_folder);

UTR_length = this_UTR.length

disp(this_UTR.name);

% Use a temporary score struct for each 3'UTR, with 1 entry for

% every miRNA with more than the specified minimum number of seed

% matches. Blank the temp_score_struct ready for the next 3'UTR.

temp_score_struct = struct('UTR_name',{},'UTR_length',{},

'miRNA_name',{},'indices',{},'sequence_slices',{},

'as_sequence_slices',{},'sequence_to_fold',{},'miRNA_sequence'

,{},'ns_percent',[],'ss_percent',[], 'ss_skip_position',[],

'ms_percent',[], 'ms_skip_position',[], 'dss_percent',[],

'dss_skip_position',[]);

% Next entry of the temp_score_struct:

next = 0;

% Cycle through the miRNAs:

for miRNA_index = 1:num_miRNAs

this_miRNA = miRNA_struct(miRNA_index);

miRNA_length = this_miRNA.length;

tofold_length = miRNA_length + 4;

seed_matches = 0;

match_indices = [];

% Find how many times the miRNA can run through the sequence:

num_shifts = UTR_length - min_seed_length + 1;

% Cycle the miRNA seed through the sequence looking for

% matches.

seed_check_start = miRNA_length - min_seed_length;

seed_check_stop = UTR_length - min_seed_length + 1;

for slice_index = seed_check_start:seed_check_stop

227

% Get sequence slice to check for seed match:

seed_slice = this_UTR.sequence(slice_index:(slice_index +

min_seed_length - 1));

% Check for perfect seed matches:

if seed_slice == this_miRNA.as_seed

% Keep a tally of how many times the seed matches in

% the UTR and record the indices of the matches in case

% need to go back to them. The index recorded is that

% corresponding to the first miRNA base i.e. one after

% the seed match on the UTR.

seed_matches = seed_matches + 1;

match_indices(seed_matches) = slice_index +

min_seed_length;

% Otherwise, if allowing G:U base-pairs in the seed, enter

% the following for loop and check the 3'UTR seed_slice

% against each of the altered antisense miRNA seeds.

elseif (allow_gus & strcmp(this_miRNA.name, 'hsa-miR-7'))

for a = 1:size(miR7_GU_as_seeds, 1)

if seed_slice == miR7_GU_as_seeds(a,:)



min_seed_length;

end

end

end

end

% If there were more than the specified minimum number of seed

% matches in the 3'UTR, determine how well the miRNA matches

% along its entire length at the seed match positions.

if seed_matches >= min_seeds

next = next + 1;

temp_score_struct(next).UTR_name =

this_UTR.name(1:(length(this_UTR.name)-4));

temp_score_struct(next).miRNA_name = this_miRNA.name;

temp_score_struct(next).miRNA_sequence =

this_miRNA.sequence;

temp_score_struct(next).indices = match_indices;

temp_score_struct(next).UTR_length = UTR_length;

228

% Get the sequence slices for all seeds and the antisense

% sequence slices to make comparison easier later on:

for i = 1:seed_matches,

if (match_indices(i) <= tofold_length)

short_match = 1;

short_by = tofold_length - match_indices(i) + 1;

else short_match = 0;

end

if (~short_match)

next_slice = this_UTR.sequence((match_indices(i) –

tofold_length):match_indices(i));

next_to_fold =

[next_slice,linker,this_miRNA.sequence];

temp_score_struct(next).sequence_slices =

[temp_score_struct(next).sequence_slices; next_slice];

temp_score_struct(next).as_sequence_slices =

[temp_score_struct(next).as_sequence_slices; make_as(next_slice)];

temp_score_struct(next).sequence_to_fold =

[temp_score_struct(next).sequence_to_fold; next_to_fold];

else

% Pad sequence_slice with 'N's if it is shorter

% than the the designated length of match sequence

% to be folded:

pad_string = '';

for pad = 1:short_by

pad_string = strcat(pad_string, 'N');

end

next_slice = [pad_string

this_UTR.sequence((1:match_indices(i)))];

next_to_fold =

[next_slice,linker,this_miRNA.sequence];





temp_score_struct(next).sequence_to_fold =

[temp_score_struct(next).sequence_to_fold; next_to_fold];

end

end

% The comparisons function determines the complementarity

% of a miRNA and sequence slice when aligned with no

229

% skips/loops, a single skip in the 3'UTR sequence, a

% single skip in the miRNA sequence and a double skip in

% the 3'UTR sequence, and returns the temp_score_struct

% updated with the results.

temp_score_struct = comparisons(temp_score_struct);

end

end

% If there is at least one potential targetting miRNA for the

% 3'UTR, add its temp_score_struct to an all_scores_struct

% containing the high scores:

if (length(temp_score_struct) >= 1)

all_scores_struct(length(all_scores_struct)+1).data =

temp_score_struct;

end

end

% If specified, save the all_scores_struct for later access:

if save_over_scores

disp('saving')

save(filename, 'all_scores_struct');

end

% main_single_UTR_file.m

% Main miRNA search program modified to accept a bulk sequence file

% containing consecutive sequences to search in FASTA format.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% User to enter:

% Minimum number of seeds:

min_seeds = 2;

% Minimum seed length:

min_seed_length = 7;

% Allow G:U base-pairs in the miR-7 seed?

allow_gus = 0;

miR7_GU_as_seeds = ['GTTTTCC'; 'GTCTTTC'; 'GTCTTCT'];

% Include duplicate entries/possible alternate transcripts?

incl_alt_transcr = 0;

230

% File containing the miRNA sequences:

miRNA_file = 'Human_miRNA_sequences.txt';

% Folder contaiing the UTR sequences:

UTR_sequence_file = 'indiv_seqs_from_entrez.txt';

% Length of target sequence to retrieve beyond seed match:

% The final target sequence length will be arbitrary length + 1,

% taking into account the extra base of the miRNA before the seed.

arbitrary_length = 25;

% Save this search over the previously saved database?

save_over_scores = 1;

% Filename under which to save the matlab results struct:

filename = 'uA_scores_2x7nt_date';

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Preallocate space to the UTR_struct

this_UTR = struct('name',{},'ensembl_id',{},'sequence',{},'length',{});

all_scores_struct = struct('data',{});

% Get miRNA data:

miRNA_struct = get_miRNA_table(miRNA_file, min_seed_length);

num_miRNAs = length(miRNA_struct);

% Get 3'UTR data from big file:

fid = fopen(UTR_sequence_file, 'rt');

ids_so_far = [];

count = 0;

dups = 0;

% Cycle through the 3'UTRs in the text file:

while feof(fid) == 0

count = count + 1;

% Get this UTR's data for the struct:

[this_UTR, ids_so_far] = get_UTR_from_big_file_v3(fid, ids_so_far,

incl_alt_transcr);

% If this is the first iteration, or have just identified a

% duplicate, this_UTR struct will be empty - signal to go to the

% next iteration of the while loop:

231

if this_UTR.length == 0

dups = dups + 1;

end

UTR_length = this_UTR.length;

disp(this_UTR.name);

% Use a temporary score struct as in the main.m program.

temp_score_struct = struct('ensembl_id',{},'UTR_name',{},

'UTR_length',{},'miRNA_name',{},'indices',{},'sequence_slices'

,{},'as_sequence_slices',{},'miRNA_sequence',{},'ns_percent'

,[],'ss_percent',[], 'ss_skip_position',[], 'ms_percent',[],

'ms_skip_position',[], 'dss_percent',[],

'dss_skip_position',[]);

% Next entry of the temp_score_struct:

next = 0;

% Cycle through miRNAs:

for miRNA_index = 1:num_miRNAs

seed_matches = 0;

match_indices = [];

this_miRNA = miRNA_struct(miRNA_index);

miRNA_length = this_miRNA.length;

num_shifts = UTR_length - min_seed_length + 1;

seed_check_start = miRNA_length - min_seed_length;

seed_check_stop = UTR_length - min_seed_length + 1;

for slice_index = seed_check_start:seed_check_stop

seed_slice = this_UTR.sequence(slice_index:(slice_index +

min_seed_length - 1));

% Check for perfect miRNA seed matches:

if seed_slice == this_miRNA.as_seed



min_seed_length;

% Otherwise, if specified, check for seed matches allowing

% G:U base-pairs in the seed:

elseif (allow_gus & strcmp(this_miRNA.name, 'hsa-miR-7'))

for a = 1:size(miR7_GU_as_seeds,1)

if seed_slice == miR7_GU_as_seeds(a,:)


232


min_seed_length;

end

end

end

end

% Now determine how well the miRNA matches along its entire

% length at the seed match positions:

if seed_matches >= min_seeds

next = next + 1;

temp_score_struct(next).ensembl_id = this_UTR.ensembl_id;

temp_score_struct(next).UTR_name =

this_UTR.name(1:(length(this_UTR.name)-4));

temp_score_struct(next).miRNA_name = this_miRNA.name;

temp_score_struct(next).miRNA_sequence =

this_miRNA.sequence;

temp_score_struct(next).indices = match_indices;

temp_score_struct(next).UTR_length = UTR_length;

% Get the sequence slices for all seeds:

for i = 1:seed_matches,

if (match_indices(i) <= arbitrary_length)

short_match = 1;

short_by = arbitrary_length - match_indices(i) + 1;

else short_match = 0;

end

if (~short_match)

next_slice = this_UTR.sequence((match_indices(i) –

arbitrary_length):match_indices(i));





else

pad_string = '';

for pad = 1:short_by

pad_string = strcat(pad_string, 'N');

end

next_slice = [pad_string

this_UTR.sequence((1:match_indices(i)))];

233


[ temp_score_struct(next).sequence_slices; next_slice];



end

end

% Perform the miRNA:3'UTR sequence comparisons as in the

% main.m program:

temp_score_struct = comparisons(temp_score_struct);

end

end

if (length(temp_score_struct) >= 1)

all_scores_struct(length(all_scores_struct)+1).data =

temp_score_struct;

end

end

disp(ids_so_far)

disp(count)

disp(dups)

if save_over_scores

disp('saving')

save(filename, 'all_scores_struct');

end

function temp_score_struct = comparisons(temp_score_struct)

% temp_score_struct = comparisons(temp_score_struct)

% Takes the temp_score_struct containing the seed match data for a

% 3'UTR and checks the sequence slice for matches with the miRNAs,

% firstly, with no skips/loops in either the 3'UTR or the miRNA,

% secondly, with a single skip in the 3'UTR sequence, thirdly with a

% single skip in the miRNA and fourthly, with a double skip in the

% sequence. Returns the temp_score_struct updated and expanded with

% the results of the comparisons.

% Called by main program.

234

num_miRNAs = length(temp_score_struct);

% Cycle through the miRNAs that match within this 3'UTR:

for i = 1:num_miRNAs,

num_indices = length(temp_score_struct(i).indices);

% Cycle through each of the miRNA seed matches within the 3'UTR:

for seed_number = 1:num_indices,

one_miRNA = temp_score_struct(i);

% NO SKIPS:

one_miRNA = no_skips(one_miRNA, seed_number);

% SEQUENCE SKIP:

one_miRNA = sequence_skip(one_miRNA, seed_number);

temp_score_struct(i) = one_miRNA;

% miRNA SKIP:

one_miRNA = miRNA_skip(one_miRNA, seed_number);


% DOUBLE SEQUENCE SKIP:

one_miRNA = double_sequence_skip(one_miRNA, seed_number);


end

end

function one_miRNA = no_skips(one_miRNA, seed_number)

% one_miRNA = no_skips(one_miRNA, seed_number)

% Takes a single entry of temp_score_struct (one_miRNA) and the number

% of the seed match within the 3'UTR to check, and computes the

% percentage complementarity of the two sequences, when aligned with

% no skips/loops in either the miRNA or the 3'UTR sequence. Returns

% the one_miRNA struct entry updated and expanded with the result.

% Called by the comparisons function.

length_miRNA = length(one_miRNA.miRNA_sequence);

count = 0;

% Cycle through the bases of the miRNA, counting those that match the

% corresponding base of the antisense 3'UTR sequence:

for k = 1:length_miRNA

if one_miRNA.miRNA_sequence(k) ==

one_miRNA.as_sequence_slices(seed_number,k),

count = count + 1;

end

235

end

one_miRNA.ns_percent(seed_number) = (count/length_miRNA)*100;

function one_miRNA = sequence_skip(one_miRNA,seed_number)

% one_miRNA = sequence_skip(one_miRNA, seed_number)



% percentage complementarity of the two sequences, when aligned with a

% single skip/loop in the 3'UTR sequence. Returns the one_miRNA struct

% entry updated and expanded with the result.



top_count = 0;

% Alter the 3'UTR sequence by deleting a single base at a series of

% different positions, from between the 9th element of the miRNA match

% and the penultimate element, checking the matches for each.

for i = 9:(length_miRNA-1),

count = 0;

old_slice = one_miRNA.as_sequence_slices(seed_number,:);

new_slice = [old_slice(1:(i-1)) old_slice((i+1):(length_miRNA+1))];

% Cycle through the bases of the miRNA, counting matches with the

% antisense sequence slice:


if one_miRNA.miRNA_sequence(k) == new_slice(k),

count = count + 1;

end

end

% If the latest count is greater than the previous high score,

% update the high score:

if count > top_count,

top_count = count;

top_skip = i;

end

end

236

one_miRNA.ss_percent(seed_number) = (top_count/length_miRNA)*100;

one_miRNA.ss_skip_position(seed_number) = top_skip;

function one_miRNA = miRNA_skip(one_miRNA,seed_number)

% one_miRNA = miRNA_skip(one_miRNA,j)




% single skip/loop in the miRNA sequence. Returns the one_miRNA struct




top_count = 0;

% Alter the miRNA sequence by deleting a single base at a series of

% different positions, from between the 9th element of the miRNA and

% the penultimate element, checking the matches for each.


count = 0;

sequence_slice = one_miRNA.as_sequence_slices(seed_number,:);

whole_miRNA = one_miRNA.miRNA_sequence;

skip_miRNA = [whole_miRNA(1:(i-1))

whole_miRNA((i+1):length_miRNA)];

% Cycle through the bases of the miRNA, counting matches with

% the antisense sequence slice:

for k = 1:(length_miRNA-1)

if sequence_slice(k) == skip_miRNA(k),

count = count + 1;

end

end




top_count = count;

top_skip = i;

end

end

237

one_miRNA.ms_percent(seed_number) = (top_count/length_miRNA)*100;

one_miRNA.ms_skip_position(seed_number) = top_skip;

function one_miRNA = double_sequence_skip(one_miRNA, seed_number)

% one_miRNA = double_sequence_skip(one_miRNA, seed_number)




% double skip/loop in the miRNA sequence. Returns the one_miRNA struct




top_count = 0;

% Alter the 3'UTR sequence by deleting two consecutive bases at a

% series of different positions, from between the 9th element of the

% miRNA match and the third last element, checking the matches for

% each.


count = 0;

old_slice = one_miRNA.as_sequence_slices(seed_number,:);

new_slice = [old_slice(1:(i-1)) old_slice((i+2):(length_miRNA+2))];

% Cycle through the bases of the miRNA, counting matches with the

% antisense sequence slice:


if one_miRNA.miRNA_sequence(k) == new_slice(k),

count = count + 1;

end

end




top_count = count;

top_skip = i;

end

end

238

one_miRNA.dss_percent(seed_number) = (top_count/length_miRNA)*100;

one_miRNA.dss_skip_position(seed_number) = top_skip;

function miRNA_struct = get_miRNA_table(miRNA_file, min_seed_length)

% miRNA_struct = get_miRNA_table(miRNA_file)

% Opens the miRNA file or a file of random sequences (miRNA_file) and

% creates a struct with all the miRNA data in it:

% Name, length, seed, sense sequence, antisense sequence.

[miRNA_names miRNA_sequences] = textread(miRNA_file,'%s %s');

% Convert sequence strings to a matrix of individual letters:

miRNA_sequences_mat = char(miRNA_sequences);

% Create struct

for i = 1:length(miRNA_names),

miRNA_struct(i).name = miRNA_names{i};

miRNA_struct(i).sequence = deblank(miRNA_sequences_mat(i,1:end));

miRNA_struct(i).length = length(miRNA_struct(i).sequence);

miRNA_struct(i).seed =

miRNA_struct(i).sequence(2:(2+min_seed_length-1));

miRNA_struct(i).as_seed = make_as(miRNA_struct(i).seed);

miRNA_struct(i).as_sequence = make_as(miRNA_struct(i).sequence);

end

function this_UTR = get_UTR_data(UTR_names, UTR_index, sequence_folder)

% this_UTR = get_UTR_data(UTR_names, UTR_index, sequence_folder)

% Given a table of the 3'UTR sequence files to check (UTR_names), the

% index of the desired filename and the name of the folder in which

% the files are stored, this function reads the indicated file and

% returns its information in a this_UTR struct.

% Called by main program.

% Get the name of the file to open and put it into the UTR_struct

name = UTR_names{UTR_index};

this_UTR.name = name;

% Read the 3'UTR file into a cell array of the strings of each line.

cd (sequence_folder)

UTR_raw = textread(name, '%s', 'headerlines',1);

% Convert it to a character array and put it into the UTR_struct

this_UTR.sequence = char(cat(2, UTR_raw{:}));

this_UTR.length = length(UTR_sequence);

239

% Go back to the home directory

cd ..

function [this_UTR, ids_so_far] = get_UTR_from_big_file_v3(fid,

ids_so_far, incl_alt_transcr)

% [this_UTR, ids_so_far] = get_UTR_from_big_file_v3(fid, ids_so_far,

% incl_alt_transcr)

% This function is called by the main program. It takes the file ID

% (fid) for the (already open) big file of consecutive 3'UTR sequences

% and the Ensembl gene IDs of the sequences that have already been

% checked from this file (ids_so_far) and retrieves the next one in

% the list. It gives you the option to include or ignore entries with

% Ensembl gene IDs that are the same as any previously checked

% entries. If the retrieved entry is to be included,the function

% returns it in a this_UTR struct together with an updated ids_so_far

% with the latest Ensembl gene ID.

name = fgetl(fid);

duplicate = 0;

sequence = fgetl(fid);

ensembl_id = name(2:16);

name_no_id = name(18:end);

num_ids_so_far = size(ids_so_far,1);

fgetl(fid);

disp(name);

if num_ids_so_far > 0

% If want to include all transcripts, then none of them are

% duplicates:

if incl_alt_transcr

duplicate = 0;

% If want to include only one transcript per Ensembl gene ID:

else

for i = 1:num_ids_so_far

% If the id is in the ids_so_far matrix, then its a

% duplicate and the ids_so_far matrix doesn't change:

if ensembl_id == ids_so_far(i,:)

duplicate = 1;

% Else it's not a duplicate with this particular id so

% keep checking.

240

else duplicate = 0;

end

% Once you know it's a duplicate, exit the loop:

if duplicate break; end

end

end

% If this is the first cycle and there are no other ids in the matrix,

% the entry is not a duplicate and so ids_so_far can be updated right

% now.

elseif num_ids_so_far == 0

duplicate = 0;

ids_so_far = ensembl_id;

end

if duplicate

this_UTR.name = {};

this_UTR.ensembl_id = {};

this_UTR.sequence = {};

this_UTR.length = 0;

else

this_UTR.name = name_no_id;

this_UTR.ensembl_id = ensembl_id;

this_UTR.sequence = sequence;

this_UTR.length = length(sequence);

end

% If entry is not a duplicate, update ids_so_far, unless it is the

% first cycle:

if (~duplicate & (num_ids_so_far > 0))

ids_so_far = [ids_so_far; ensembl_id];

end

function UTR_names = get_UTR_names(sequence_folder)

% UTR_names = get_UTR_names(sequence_folder)

% This returns a list of all the filenames ending in .txt in the

% specified folder containing sequences to search. Can then cycle

% through this list to open each 3'UTR file sequentially.

string = [sequence_folder,'/*.txt'];

241

UTR_dir = dir (fullfile(matlabroot,string));

UTR_names = {UTR_dir.name};

function antisense = make_as(sense)

% antisense = make_as(sense)

% Takes a matrix containing the letters of a DNA sequence and returns

% a matrix containing the letters of the antisense of that sequence.

% Called by the get_miRNA_table function and the main program.

num_letters = length(sense);

for i = 1:num_letters,

% First reverse the order of the letters:

reverse_sense(i) = sense(num_letters - i + 1);

% Now exchange letters:

if reverse_sense(i) == 'G'

antisense(i) = 'C';

elseif reverse_sense(i) == 'C'

antisense(i) = 'G';

elseif reverse_sense(i) == 'T'

antisense(i) = 'A';

elseif reverse_sense(i) == 'A'

antisense(i) = 'T';

else antisense(i) = 'N';

end

end

function miRNA_score_struct = collect_miRNA(all_scores_struct,

miRNA_to_find)

% function miRNA_score_struct = collect_miRNA(all_scores_struct,

% miRNA_to_find)

% Cycles through the all_scores_struct, collects any entries for the

% chosen miRNA and puts them into a new miRNA_score_struct.

next_3UTR = 1;

miRNA_score_struct = struct('data',{});

% Cycle through all of the 3'UTR indices within all_scores_struct.

for i = 1:length(all_scores_struct)

% Cycle through all of the miRNA indices within the 3'UTR entry.

242

for j = 1:length(all_scores_struct(i).data)

if strcmp(all_scores_struct(i).data(j).miRNA_name,

miRNA_to_find)

miRNA_score_struct(next_3UTR).data(1) =

all_scores_struct(i).data(j);

next_3UTR = next_3UTR + 1;

end

end

end

function UTR_score_struct = collect_3UTR(all_scores_struct,

UTR_to_find)

% function UTR_score_struct = collect_3UTR(all_scores_struct,

% UTR_to_find)

% Cycles through the all_scores_struct, finds the entry for the

% chosen 3'UTR and makes it into a new UTR_score_struct.

next_3UTR = 1;

UTR_score_struct = struct('data',{});

% Cycle through all of the 3'UTR indices within all_scores_struct:


if strcmp(all_scores_struct(i).data(1).UTR_name, UTR_to_find)

UTR_score_struct(next_3UTR).data = all_scores_struct(i).data;

next_3UTR = next_3UTR + 1;

end

end

function make_scores_doc(all_scores_struct, filename)

% function make_scores_doc(all_scores_struct, filename)

% Takes the all_scores_struct and puts its information into a tab

% delimited text file. The resulting file contains one line for each

% miRNA:mRNA pair with column headings: UTR_name, miRNA_name, index_1,

% max_perc_1, index_2, max_perc_2 etc. Used with microarray data,

% which has the Ensembl ID for each gene included.

% Open a file to write to and enter column headings:

fid = fopen(filename,'wt');

fprintf(fid, 'Ensembl ID\tUTR name\tUTR length\tmiRNA name\tSeed 1

243

index\tSeed 1 %%\tSeed 1 seq\tSeed 2 index\tSeed 2 %%\tSeed 2

seq\tSeed 3 index\tSeed 3 %%\tSeed 3 seq\tSeed 4 index\tSeed 4

%%\tSeed 4 seq\n');

% Cycle through each element of all_scores_struct:


for j = 1:length(all_scores_struct(i).data)

A = all_scores_struct(i).data(j);

fprintf(fid, '%s\t%s\t%d\t%s', A.ensembl_id, A.UTR_name,

A.UTR_length, A.miRNA_name);

% Cycle through each miRNA match site within the 3'UTR:

for k = 1:length(A.indices)

% Get the maximum percentage score for the site:

max_perc = max([A.ns_percent(k) A.ss_percent(k)

A.ms_percent(k) A.dss_percent(k)]);

% Print the data to the file:

fprintf(fid, '\t%d\t%.2f%%\t%s', A.indices(k), max_perc,

A.sequence_slices(k,5:26));

end

fprintf(fid, '\n');

end

end

fclose(fid);

244

APPENDIX B

Set of miRNAs and their sequences used for miRNA target prediction in Chapter 6

hsa-let-7a TGAGGTAGTAGGTTGTATAGTT

hsa-let-7b TGAGGTAGTAGGTTGTGTGGTT

hsa-let-7c TGAGGTAGTAGGTTGTATGGTT

hsa-let-7d AGAGGTAGTAGGTTGCATAGT

hsa-let-7e TGAGGTAGGAGGTTGTATAGT

hsa-let-7f TGAGGTAGTAGATTGTATAGTT

hsa-let-7g TGAGGTAGTAGTTTGTACAGT

hsa-let-7i TGAGGTAGTAGTTTGTGCT

hsa-miR-1d TGGAATGTAAAGAAGTATGTATT

hsa-miR-7 TGGAAGACTAGTGATTTTGTT

hsa-miR-9 TCTTTGGTTATCTAGCTGTATGA

hsa-miR-10A TACCCTGTAGATCCGAATTTGTG

hsa-miR-10b TACCCTGTAGAACCGAATTTGT

hsa-miR-15a TAGCAGCACATAATGGTTTGTG

hsa-miR-15b TAGCAGCACATCATGGTTTACA

hsa-miR-16 TAGCAGCACGTAAATATTGGCG

hsa-miR-17-3p ACTGCAGTGAAGGCACTTGT

hsa-miR-17-5p CAAAGTGCTTACAGTGCAGGTAGT

hsa-miR-18 TAAGGTGCATCTAGTGCAGATA

hsa-miR-19a TGTGCAAATCTATGCAAAACTGA

hsa-miR-19b TGTGCAAATCCATGCAAAACTGA

hsa-miR-20 TAAAGTGCTTATAGTGCAGGTA

hsa-miR-21 TAGCTTATCAGACTGATGTTGA

hsa-miR-22 AAGCTGCCAGTTGAAGAACTGT

hsa-miR-23 ATCACATTGCCAGGGATTTCC

hsa-miR-23b ATCACATTGCCAGGGATTACCAC

hsa-miR-24 TGGCTCAGTTCAGCAGGAACAG

hsa-miR-25 CATTGCACTTGTCTCGGTCTGA

hsa-miR-26a TTCAAGTAATCCAGGATAGGCT

hsa-miR-26b TTCAAGTAATTCAGGATAGGT

245

hsa-miR-27a TTCACAGTGGCTAAGTTCCGCC

hsa-miR-27b TTCACAGTGGCTAAGTTCTG

hsa-miR-28 AAGGAGCTCACAGTCTATTGAG

hsa-miR-29 CTAGCACCATCTGAAATCGGTT

hsa-miR-29b TAGCACCATTTGAAATCAGT

hsa-miR-30a CTTTCAGTCGGATGTTTGCAGC

hsa-miR-30a* TGTAAACATCCTCGACTGGAAGC

hsa-miR-30b TGTAAACATCCTACACTCAGC

hsa-miR-30c TGTAAACATCCTACACTCTCAGC

hsa-miR-30d TGTAAACATCCCCGACTGGAAG

hsa-miR-31 GGCAAGATGCTGGCATAGCTG

hsa-miR-32 TATTGCACATTACTAAGTTGC

hsa-miR-33 GTGCATTGTAGTTGCATTG

hsa-miR-34 TGGCAGTGTCTTAGCTGGTTGT

hsa-miR-92 TATTGCACTTGTCCCGGCCTGT

hsa-miR-93 AAAGTGCTGTTCGTGCAGGTAG

hsa-miR-95 TTCAACGGGTATTTATTGAGCA

hsa-miR-96 TTTGGCACTAGCACATTTTTGC

hsa-miR-98 TGAGGTAGTAAGTTGTATTGTT

hsa-miR-99 AACCCGTAGATCCGATCTTGTG

hsa-miR-100 AACCCGTAGATCCGAACTTGTG

hsa-miR-101 TACAGTACTGTGATAACTGAAG

hsa-miR-103-1 AGCAGCATTGTACAGGGCTATGA

hsa-miR-103-20 AGCAACATTGTACAGGGCTATGA

hsa-miR-105 TCAAATGCTCAGACTCCTGT

hsa-miR-106 AAAAGTGCTTACAGTGCAGGTAGC

hsa-miR-107 AGCAGCATTGTACAGGGCTATCA

hsa-miR-122a TGGAGTGTGACAATGGTGTTTGT

hsa-miR-124a TTAAGGCACGCGGTGAATGCCA

hsa-miR-125a TCCCTGAGACCCTTTAACCTGTG

hsa-miR-125b TCCCTGAGACCCTAACTTGTGA

hsa-miR-126 TCGTACCGTGAGTAATAATGC

hsa-miR-127 TCGGATCCGTCTGAGCTTGGCT

hsa-miR-128a TCACAGTGAACCGGTCTCTTTT

hsa-miR-129b CTTTTTGCGGTCTGGGCTTGCT

246

hsa-miR-130a CAGTGCAATGTTAAAAGGGC

hsa-miR-132 TAACAGTCTACAGCCATGGTCG

hsa-miR-133a TTGGTCCCCTTCAACCAGCTGT

hsa-miR-134 TGTGACTGGTTGACCAGAGGG

hsa-miR-135 TATGGCTTTTTATTCCTATGTGA

hsa-miR-136 ACTCCATTTGTTTTGATGATGGA

hsa-miR-137 TATTGCTTAAGAATACGCGTAG

hsa-miR-138 AGCTGGTGTTGTGAATC

hsa-miR-139 TCTACAGTGCACGTGTCT

hsa-miR-140 AGTGGTTTTACCCTATGGTAG

hsa-miR-141 AACACTGTCTGGTAAAGATGG

hsa-miR-142 CATAAAGTAGAAAGCACTAC

hsa-miR-143 TGAGATGAAGCACTGTAGCTCA

hsa-miR-144 TACAGTATAGATGATGTACTAG

hsa-miR-145 GTCCAGTTTTCCCAGGAATCCCTT

hsa-miR-146 TGAGAACTGAATTCCATGGGTT

hsa-miR-147 GTGTGTGGAAATGCTTCTGC

hsa-miR-148 TCAGTGCACTACAGAACTTTGT

hsa-miR-149 TCTGGCTCCGTGTCTTCACTCC

hsa-miR-150 TCTCCCAACCCTTGTACCAGTG

hsa-miR-152 TCAGTGCATGACAGAACTTGG

hsa-miR-153 TTGCATAGTCACAAAAGTGA

hsa-miR-154 TAGGTTATCCGTGTTGCCTTCG

hsa-miR-181a AACATTCAACGCTGTCGGTGAGT

hsa-miR-181b ACCATCGACCGTTGATTGTACC

hsa-miR-181c AACATTCAACCTGTCGGTGAGT

hsa-miR-182 TTTGGCAATGGTAGAACTCACA

hsa-miR-182* TGGTTCTAGACTTGCCAACTA

hsa-miR-183 TATGGCACTGGTAGAATTCACTG

hsa-miR-184 TGGACGGAGAACTGATAAGGGT

hsa-miR-185 TGGAGAGAAAGGCAGTTC

hsa-miR-186 CAAAGAATTCTCCTTTTGGGCTT

hsa-miR-187 TCGTGTCTTGTGTTGCAGCCG

hsa-miR-188 CATCCCTTGCATGGTGGAGGGT

hsa-miR-189 GTGCCTACTGAGCTGATATCAGT

247

hsa-miR-190 TGATATGTTTGATATATTAGGT

hsa-miR-191 CAACGGAATCCCAAAAGCAGCT

hsa-miR-192 CTGACCTATGAATTGACAGCC

hsa-miR-193 AACTGGCCTACAAAGTCCCAG

hsa-miR-194 TGTAACAGCAACTCCATGTGGA

hsa-miR-195 TAGCAGCACAGAAATATTGGC

hsa-miR-196 TAGGTAGTTTCATGTTGTTGG

hsa-miR-197 TTCACCACCTTCTCCACCCAGC

hsa-miR-198 GGTCCAGAGGGGAGATAGG

hsa-miR-199a CCCAGTGTTCAGACTACCTGTT

hsa-miR-199b CCCAGTGTTTAGACTATCTGTTC

hsa-miR-200b CTCTAATACTGCCTGGTAATGATG

hsa-miR-203 GTGAAATGTTTAGGACCACTAG

hsa-miR-204 TTCCCTTTGTCATCCTATGCCT

hsa-miR-205 TCCTTCATTCCACCGGAGTCTG

hsa-miR-206 TGGAATGTAAGGAAGTGTGTGG

hsa-miR-208 ATAAGACGAGCAAAAAGCTTGT

hsa-miR-210 CTGTGCGTGTGACAGCGGCTG

hsa-miR-211 TTCCCTTTGTCATCCTTCGCCT

hsa-miR-212 TAACAGTCTCCAGTCACGGCC

hsa-miR-213 AACATTCATTGCTGTCGGTGGGTT

hsa-miR-214 ACAGCAGGCACAGACAGGCAG

hsa-miR-215 ATGACCTATGAATTGACAGAC

hsa-miR-216 TAATCTCAGCTGGCAACTGTG

hsa-miR-217 TACTGCATCAGGAACTGATTGGAT

hsa-miR-218 TTGTGCTTGATCTAACCATGT

hsa-miR-219 TGATTGTCCAAACGCAATTCT

hsa-miR-220 CCACACCGTATCTGACACTTT

hsa-miR-221 AGCTACATTGTCTGCTGGGTTTC

hsa-miR-222 AGCTACATCTGGCTACTGGGTCTC

hsa-miR-223 TGTCAGTTTGTCAAATACCCC

hsa-miR-224 CAAGTCACTAGTGGTTCCGTTTA

248

APPENDIX C

Appendix C Table: Set of genes used for miRNA target prediction in Chapter 6.

Symbol Accession number Name

AR NM_000044.2 androgen receptor (dihydrotestosterone

receptor; testicular feminization; spinal and

bulbar muscular atrophy; Kennedy disease)

BDNF NM_170731.3 brain-derived neurotrophic factor

BRCA1 NM_007294.1 breast cancer 1, early onset

C14orf156 NM_031210.3 Homo sapiens hypothetical protein DC50

CDH15 NM_004933.2 cadherin 15, M-cadherin (myotubule)

CDKN1A NM_078467.1 cyclin-dependent kinase inhibitor 1A (p21,

Cip1)

CELSR2 NM_001408.1 cadherin, EGF LAG seven-pass G-type receptor

2 (flamingo homolog, Drosophila)

COL5A2 NM_000393.3 collagen, type V, alpha 2

CSTA NM_005213.2 cystatin A (stefin A)

CTTN NM_005231.2 cortactin

CXCL2 NM_000609.4 chemokine (C-X-C motif) ligand 2

DNAJC12 NM_021800.2 DnaJ (Hsp40) homolog, subfamily C, member

12

EGFR NM_005228.3 epidermal growth factor receptor (erythroblastic

leukemia viral (v-erb-b) oncogene homolog,

avian)

ELAVL1 NM_001419.2 ELAV (embryonic lethal, abnormal vision,

Drosophila)-like 1 (Hu antigen R)

ELAVL4 ENST00000371821 ELAV (embryonic lethal, abnormal vision,

Drosophila)-like 4 (Hu antigen D)

ERBB2 NM_004448.1 v-erb-b2 erythroblastic leukemia viral oncogene

homolog 2, neuro/glioblastoma derived

oncogene homolog (avian)

ERBB3 NM_001982.2 v-erb-b2 erythroblastic leukemia viral oncogene

homolog 3 (avian)


249

Appendix C Table continued: Symbol Accession number Name

ERBB4 NM_005235.1 v-erb-a erythroblastic leukemia viral oncogene

homolog 4 (avian)

ESR1 NM_000125.1 estrogen receptor 1

FADS1 NM_013402.3 fatty acid desaturase 1

G6PD NM_000402.3 glucose-6-phosphate dehydrogenase

GATA4 NM_002052.2 GATA binding protein 4

GRB7 NM_005310.1 growth factor receptor-bound protein 7

HOXA5 NM_019102.2 homeobox A5

IQGAP1 NM_003870.3 IQ motif containing GTPase activating protein 1

ITGA2 NM_002203.2 integrin, alpha 2 (CD49B, alpha 2 subunit of

VLA-2 receptor)

ITGA2B NM_000419.2 integrin, alpha 2b (platelet glycoprotein IIb of

IIb/IIIa complex, antigen CD41)

ITGB3 NM_000212.1 integrin, beta 3 (platelet glycoprotein IIIa,

antigen CD61)

LOX NM_002317.3 lysyl oxidase

LTA NM_000595.2 lymphotoxin alpha (TNF superfamily, member

1)

MAFG NM_002359.2 v-maf musculoaponeurotic fibrosarcoma

oncogene homolog G (avian)

MAP2K6 NM_002758.2 mitogen-activated protein kinase kinase 6

MECP2 NM_004992.2 methyl CpG binding protein 2 (Rett syndrome)

MED25 NM_018019.2 mediator of RNA polymerase II transcription,

subunit 25 homolog (S. cerevisiae)

NAT1 NM_000662.4 N-acetyltransferase 1 (arylamine N-

acetyltransferase)

NFKB1 NM_003998.2 nuclear factor of kappa light polypeptide gene

enhancer in B-cells 1 (p105)

NFKB2 NM_002502.2 nuclear factor of kappa light polypeptide gene

enhancer in B-cells 2 (p49/p100)

NFKBIE NM_004556.1 nuclear factor of kappa light polypeptide gene

enhancer in B-cells inhibitor, epsilon


250


NMYCN NM_005378.4 v-myc myelocytomatosis viral related oncogene,

neuroblastoma derived (avian)

NOTCH1 NM_017617.2 Notch homolog 1, translocation-associated

(Drosophila)

NOTCH2 NM_024408.2 Notch homolog 2 (Drosophila)



NR1D1 NM_021724.1 nuclear receptor subfamily 1, group D, member

1

OAS2 NM_016817.1 2'-5'-oligoadenylate synthetase 2, 69/71kDa

PBEF1 NM_005746.1 pre-B-cell colony enhancing factor 1

PECAM1 NM_000442.2 platelet/endothelial cell adhesion molecule

(CD31 antigen)

PPARBP NM_004774.2 PPAR binding protein

PPARGC1A NM_013261.2 peroxisome proliferator-activated receptor

gamma, coactivator 1 alpha

PPP1R1B NM_032192.2 protein phosphatase 1, regulatory (inhibitor)

subunit 1B (dopamine and cAMP regulated

phosphoprotein, DARPP-32)

PRKRA NM_003690.3 protein kinase, interferon-inducible double

stranded RNA dependent activator

PSMB3 NM_002795.2 proteasome (prosome, macropain) subunit, beta

type, 3

PTEN NM_00314.3 phosphatase and tensin homolog (mutated in

multiple advanced cancers 1)

RAF1 NM_002880.2 v-raf-1 murine leukemia viral oncogene

homolog 1

RAPH1 NM_025252.2 Ras association (RalGDS/AF-6) and pleckstrin

homology domains 1


251


RELA NM_021975.2 v-rel reticuloendotheliosis viral oncogene

homolog A, nuclear factor of kappa light

polypeptide gene enhancer in B-cells 3, p65

(avian)

REN NM_000537.2 renin

RPL19 NM_000981.2 ribosomal protein L19

SCUBE2 NM_020974.1 signal peptide, CUB domain, EGF-like 2

SPEN NM_015001.2 spen homolog, transcriptional regulator

(Drosophila)

STAU1 NM_017454.1 staufen, RNA binding protein, homolog 1

(Drosophila)

TARBP1 NM_005646.2 TAR (HIV-1) RNA binding protein 1

TRIM35 NM_171982.3 tripartite motif-containing 35

252

APPENDIX D Appendix D Table: Set of probes significantly down-regulated by miR-7 in the Chapter 9 microarray experiment, with miR-7 target predictions. A ‘1’ indicates a prediction and does not reflect the number of sites. Predictions are only entered for the first probe of a gene.

TargetScan

Ratio p Gene Identifier Gene Name Gene ID Ch 6

miR-Target

miR-anda PicTar Cons.

Non-cons.

10.84 0.003 BC008745 Cartilage associated protein CRTAP 1 1 10.03 0.026 NM_005789 Proteasome (prosome, macropain) activator

subunit 3 (PA28 gamma; Ki) PSME3 1 1 1 1

8.16 0.008 NM_019896 Polymerase (DNA-directed), epsilon 4 (p12 subunit)

POLE4 1 1 1 1

7.1 0.006 NM_005184 Calmodulin 3 (phosphorylase kinase, delta) CALM3 1 7.05 0.007 BQ876971 cartilage associated protein - 6.98 0.002 NM_019896 Polymerase (DNA-directed), epsilon 4 (p12

subunit) POLE4

6.66 0.013 BC001423 Proteasome (prosome, macropain) activator subunit 3 (PA28 gamma; Ki)

PSME3

6.66 0.002 BE618656 ribosomal protein L37a - 6.48 0.024 NM_006371 Cartilage associated protein CRTAP 6.42 0.007 NM_004862 Lipopolysaccharide-induced TNF factor LITAF 6.29 0.011 BC004155 Ring finger protein 5 RNF5 5.57 0.001 AA683481 Cytochrome b, ascorbate dependent 3 CYBASC3 1 5.55 0.003 BF511231 Tissue factor pathway inhibitor (lipoprotein-

associated coagulation inhibitor) TFPI

5.27 0.001 BF689173 Chromosome 18 open reading frame 10 C18orf10 1 4.97 0.010 AW170571 Copine II CPNE2 4.93 0.006 AB034747 Lipopolysaccharide-induced TNF factor LITAF 4.87 0.003 BC006230 Monoglyceride lipase MGLL 4.65 0.001 BC004170 Polymerase (DNA directed), epsilon 3 (p17

subunit) POLE3 1

4.32 0.019 NM_000445 Plectin 1, intermediate filament binding protein 500kDa

PLEC1 1 1 1 1 1

253

TargetScan


miR-Target


Non-cons.

4.28 0.007 W74580 Transmembrane protein 43 TMEM43 1 1 4.16 0.014 W46406 MICAL-like 1 MICAL-L1 1 3.96 0.010 NM_006708 Glyoxalase I GLO1 1 1 1 3.95 0.006 AL571424 Glutamate receptor, ionotropic, N-methyl D-

asparate-associated protein 1 (glutamate binding)

GRINA 1 1

3.94 0.026 NM_002695 Polymerase (RNA) II (DNA directed) polypeptide E, 25kDa

POLR2E 1

3.91 0.012 AK023289 Nuclear transport factor 2-like export factor 2 NXT2 1 1 1 3.9 0.011 NM_012103 ancient ubiquitous protein 1 - 3.89 0.015 NM_006825 Cytoskeleton-associated protein 4 CKAP4 1 1 1 3.86 0.002 AB040903 Vacuolar protein sorting 13 homolog D (S.

cerevisiae) VPS13D

3.79 0.002 NM_004427 Polyhomeotic-like 2 (Drosophila) PHC2 3.71 0.012 AF258562 Deoxythymidylate kinase (thymidylate

kinase) DTYMK 1

3.7 0.027 BC006383 Glycosylphosphatidylinositol anchor attachment protein 1 homolog (yeast)

GPAA1

3.69 0.004 NM_014252 Solute carrier family 25 (mitochondrial carrier; ornithine transporter) member 15

SLC25A15 1 1 1

3.67 0.014 AI554759 Polymerase (RNA) II (DNA directed) polypeptide E, 25kDa

POLR2E

3.64 0.014 NM_014285 Exosome component 2 EXOSC2 1 3.62 0.006 NM_006795 EH-domain containing 1 EHD1 1 3.61 0.021 AA885297 Scavenger receptor class B, member 2 SCARB2 1 3.61 0.020 N30649 Sequestosome 1 SQSTM1 1 3.57 0.004 AL022316 Cluster Incl. AL022316:Human DNA

sequence from clone 126B4 on chromosome 22q13.2-13.31. Contains two or three novel genes, ESTs, STSs and GSSs

-

3.47 0.027 AF001434 EH-domain containing 1 EHD1

254

TargetScan


miR-Target


Non-cons.

3.47 0.042 BE896490 Transcribed locus SNAP29 3.47 0.020 NM_002880 V-raf-1 murine leukemia viral oncogene

homolog 1 RAF1 1 1 1 1 1

3.41 0.019 NM_012145 Deoxythymidylate kinase (thymidylate kinase)

DTYMK

3.4 0.046 AW409599 Secretory carrier membrane protein 2 SCAMP2 3.39 0.012 NM_014671 Ubiquitin protein ligase E3C UBE3C 1 3.38 0.005 AB011092 Adenylate cyclase 9 ADCY9 1 1 3.37 0.048 NM_015332 NudC domain containing 3 NUDCD3 1 3.29 0.003 D80006 Human mRNA for KIAA0184 gene, partial

cds. -

3.29 0.003 AF226604 Opioid receptor, sigma 1 OPRS1 1 3.26 0.017 AL578116 SET domain containing (lysine

methyltransferase) 8 SETD8

3.25 0.035 NM_014905 Glutaminase GLS 1 3.25 0.004 NM_020679 MIF4G domain containing MIF4GD 3.23 0.008 NM_003461 Zyxin ZYX 1 3.13 0.002 NM_005228 Epidermal growth factor receptor

(erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)

EGFR 1 1

3.11 0.005 NM_014764 DAZ associated protein 2 DAZAP2 1 3.08 0.010 BF690150 Major facilitator superfamily domain

containing 5 MFSD5

3.08 0.013 NM_014788 Tripartite motif-containing 14 TRIM14 1 1 3.07 0.006 AW157070 Epidermal growth factor receptor


EGFR

3.07 0.009 AL031685 Human DNA sequence from clone RP5-963K23 on chromosome 20q13.11-13.2 Contains a KRT18 pseudogene

-

255

TargetScan


miR-Target


Non-cons.

3.06 0.006 NM_003801 Glycosylphosphatidylinositol anchor attachment protein 1 homolog (yeast)

GPAA1

3.04 0.021 AK021599 Chromosome 2 open reading frame 37 C2orf37 3 0.036 AB020645 Glutaminase GLS 3 0.042 NM_000356 Treacher Collins-Franceschetti syndrome 1 TCOF1 2.99 0.001 AA065185 Chromosome 11 open reading frame 24 C11orf24 2.99 0.008 NM_024329 EF-hand domain family, member D2 EFHD2 1 2.98 0.016 AA025858 Cartilage associated protein CRTAP 2.97 0.000 AL515918 Full-length cDNA clone CS0CAP007YD06

of Thymus of Homo sapiens (human) -

2.96 0.005 AL157437 glycosylphosphatidylinositol anchor attachment protein 1 homolog (yeast)

-

2.96 0.007 L11669 Tetracycline transporter-like protein TETRAN 2.94 0.001 NM_001642 Amyloid beta (A4) precursor-like protein 2 APLP2 2.91 0.045 NM_021198 CTD (carboxy-terminal domain, RNA

polymerase II, polypeptide A) small phosphatase 1

CTDSP1

2.91 0.011 AA877820 Translocase of inner mitochondrial membrane 50 homolog (S. cerevisiae)

TIMM50 1

2.9 0.019 Z54367 plectin 1, intermediate filament binding protein 500kDa

-

2.9 0.032 M65254 Protein phosphatase 2 (formerly 2A), regulatory subunit A (PR 65), beta isoform

PPP2R1B 1

2.89 0.012 D55880 CDNA FLJ20717 fis, clone HEP18380 - 2.89 0.034 NM_014600 EH-domain containing 3 EHD3 2.88 0.005 AW182860 EH-domain containing 1 EHD1 2.87 0.028 AF180476 CCR4-NOT transcription complex, subunit 8 CNOT8 1 1 1 1 1 2.87 0.002 NM_016458 Chromosome 8 open reading frame 30A C8orf30A 2.87 0.002 BE878463 Epidermal growth factor receptor


EGFR

256

TargetScan


miR-Target


Non-cons.

2.86 0.042 NM_005866 Opioid receptor, sigma 1 OPRS1 2.85 0.005 NM_021067 GINS complex subunit 1 (Psf1 homolog) GINS1 2.85 0.003 BG168471 Monoglyceride lipase MGLL 2.84 0.020 AI861893 Proline-rich transmembrane protein 3 PRRT3 2.84 0.001 BC001463 Scotin SCOTIN 2.84 0.008 AJ002428 voltage-dependent anion channel 1

pseudogene -

2.83 0.006 BC004820 Chromosome 13 open reading frame 8 C13orf8 1 1 1 2.83 0.012 NM_014030 G protein-coupled receptor kinase interactor

1 GIT1

2.82 0.043 NM_014413 Eukaryotic translation initiation factor 2-alpha kinase 1

EIF2AK1 1 1

2.82 0.007 AA918442 Insulin-degrading enzyme IDE 1 1 1 2.82 0.034 BF339821 Scavenger receptor class B, member 2 SCARB2 2.8 0.009 NM_003197 Homo sapiens transcription elongation factor

B (SIII), polypeptide 1-like (TCEB1L), mRNA. /PROD=transcription elongation factor B polypeptide1-like /FL=gb:NM_003197.2

-

2.8 0.019 BF038366 Transmembrane protein 97 TMEM97 2.8 0.003 NM_004896 Vacuolar protein sorting 26 homolog A

(yeast) VPS26A

2.79 0.024 NM_030912 Tripartite motif-containing 8 TRIM8 2.76 0.011 BG427393 Amyloid beta (A4) precursor-like protein 2 APLP2 2.76 0.021 AA022510 Amyloid beta (A4) precursor-like protein 2 APLP2 2.75 0.023 AI796687 Small optic lobes homolog (Drosophila) SOLH 2.75 0.042 AK026008 WD repeat domain 68 WDR68 1 1 2.74 0.001 U51478 ATPase, Na+/K+ transporting, beta 3

polypeptide ATP1B3

2.72 0.000 AI807004 Calponin 3, acidic CNN3 1 1 1 2.72 0.013 AF151072 Hypothetical protein LOC51255 LOC51255 1

257

TargetScan


miR-Target


Non-cons.

2.71 0.009 BE252813 Eukaryotic translation initiation factor 2, subunit 3 gamma, 52kDa

EIF2S3 1

2.71 0.032 NM_024602 HECT domain containing 3 HECTD3 1 2.71 0.044 AB033026 Pleckstrin homology domain containing,

family H (with MyTH4 domain) member 1 PLEKHH1

2.71 0.007 BC000464 WD repeat domain 45 WDR45 2.7 0.009 NM_025070 Homo sapiens hypothetical protein FLJ22242

(FLJ22242), mRNA. /PROD=hypothetical protein FLJ22242 /FL=gb:NM_025070.1

-

2.7 0.005 AF081567 Protein-kinase, interferon-inducible double stranded RNA dependent inhibitor, repressor of (P58 repressor)

PRKRIR 1

2.69 0.001 NM_006135 Capping protein (actin filament) muscle Z-line, alpha 1

CAPZA1 1 1 1

2.68 0.038 NM_015516 Leucine rich repeat containing 54 LRRC54 2.67 0.047 BF056901 Family with sequence similarity 113,

member B FAM113B

2.66 0.005 AF334812 RAB11 family interacting protein 5 (class I) RAB11FIP5 1 1 2.65 0.034 AF220034 Tripartite motif-containing 8 TRIM8 2.64 0.007 BG165815 Eukaryotic translation initiation factor 2,

subunit 3 gamma, 52kDa EIF2S3

2.64 0.016 AW084125 Transcribed locus - 2.63 0.002 NM_002628 Profilin 2 PFN2 1 1 1 1 2.63 0.019 NM_014328 RUN and SH3 domain containing 1 RUSC1 1 2.63 0.002 AK025566 WAS protein family, member 2 WASF2 2.62 0.015 AL162074 CDC42 effector protein (Rho GTPase

binding) 4 CDC42EP4

2.62 0.016 AI348009 CDNA clone IMAGE:3878236 - 2.62 0.017 NM_003168 Suppressor of Ty 4 homolog 1 (S. cerevisiae) SUPT4H1 1

2.62 0.009 BF195608 TBC1 domain family, member 2B TBC1D2B 1

258

TargetScan


miR-Target


Non-cons.

2.62 0.003 NM_021975 V-rel reticuloendotheliosis viral oncogene homolog A, nuclear factor of kappa light polypeptide gene enhancer in B-cells 3, p

RELA 1

2.61 0.003 AV755778 Protein phosphatase 1, regulatory (inhibitor) subunit 11

PPP1R11 1

2.61 0.021 AW249467 Tripartite motif-containing 47 TRIM47 2.6 0.003 NM_003689 Aldo-keto reductase family 7, member A2

(aflatoxin aldehyde reductase) AKR7A2

2.6 0.009 BE221883 Ubiquitin-conjugating enzyme E2R 2 UBE2R2 1 2.59 0.039 AF052151 Family with sequence similarity 89, member

B FAM89B

2.59 0.011 AL031651 Human DNA sequence from clone RP5-1054A22 on chromosome 20q11.22-12 Contains two isoforms of the gene for TGM2 (transglutaminase 2 (C polypeptide, protein-glutamine-gamma-glutamyltransferase), ESTs, STSs, GSSs and a CpG island /FL=gb:M55153.1 gb:NM_0

TGM2

2.59 0.006 AF090934 maternally expressed 3 - 2.59 0.003 NM_002489 NADH dehydrogenase (ubiquinone) 1 alpha

subcomplex, 4, 9kDa NDUFA4 1

2.58 0.016 U63131 CDC37 cell division cycle 37 homolog (S. cerevisiae)

CDC37 1

2.57 0.046 BC001140 Dual specificity phosphatase 23 DUSP23 2.57 0.004 AB040966 GRAM domain containing 1A GRAMD1A 2.57 0.004 AF147209 interleukin enhancer binding factor 3, 90kDa ILF3

2.57 0.020 BC003586 SVH protein SVH 1 2.57 0.030 AL354612 Transmembrane protein 48 TMEM48 2.55 0.028 AL582808 Chromosome 1 open reading frame 144 C1orf144 1 2.55 0.005 AL534321 DAZ associated protein 2 DAZAP2

259

TargetScan


miR-Target


Non-cons.

2.53 0.002 AF255650 Hippocampus abundant transcript-like 1 HIATL1 1 2.53 0.029 AW183074 Succinate dehydrogenase complex, subunit

C, integral membrane protein, 15kDa SDHC

2.53 0.007 BE780075 Transmembrane emp24-like trafficking protein 10 (yeast)

TMED10 1

2.52 0.019 BG283790 Matrin 3 MATR3 2.5 0.010 NM_016598 Zinc finger, DHHC-type containing 3 ZDHHC3 2.49 0.008 NM_022769 CREB regulated transcription coactivator 3 CRTC3 2.49 0.024 AI935180 DnaJ (Hsp40) homolog, subfamily C,

member 5 DNAJC5

2.48 0.020 AV713053 Chromosome 10 open reading frame 22 C10orf22 2.48 0.019 AI651726 hypothetical protein MGC2752 - 2.47 0.041 AL353715 Human DNA sequence from clone CTD-

3184A7 on chromosome 20 Contains the 5 end of the GMEB2 (KIAA1269) gene for glucocorticoid modulatory element binding protein 2, the gene for SCG10-like protein (SCLIP) (ortholog of rabbit neuroplasticin-2 (NPC2)...

-

2.46 0.030 NM_018238 Multiple substrate lipid kinase MULK 1 2.45 0.001 AV705516 Full-length cDNA clone CS0DL005YA15 of

B cells (Ramos cell line) Cot 25-normalized of Homo sapiens (human)

-

2.45 0.008 NM_006005 Wolfram syndrome 1 (wolframin) WFS1 2.44 0.007 NM_005562 Laminin, gamma 2 LAMC2 2.43 0.031 AA700485 Adaptor-related protein complex 3, mu 1

subunit AP3M1 1

2.43 0.015 BE866854 Full-length cDNA clone CS0DN005YM11 of Adult brain of Homo sapiens (human)

-

2.43 0.026 AA430014 Gap junction protein, alpha 7, 45kDa (connexin 45)

GJA7 1

260

TargetScan


miR-Target


Non-cons.

2.43 0.030 NM_012267 Hsp70-interacting protein HSPBP1 1 2.43 0.046 AI127452 SET domain containing (lysine

methyltransferase) 8 SETD8

2.42 0.004 AF317711 CGI-69 protein CGI-69 1 2.42 0.001 W72053 Trans-golgi network protein 2 TGOLN2 1 2.41 0.021 U88989 Eukaryotic translation initiation factor 4E

binding protein 2 EIF4EBP2 1 1 1 1

2.4 0.044 AB037784 Arylacetamide deacetylase-like 1 AADACL1 1 1 2.4 0.003 AC004685 fatty acid 2-hydroxylase - 2.4 0.035 NM_013245 Vacuolar protein sorting 4 homolog A (S.

cerevisiae) VPS4A 1

2.39 0.004 AA971429 CASP8 and FADD-like apoptosis regulator CFLAR 2.38 0.019 NM_024075 TRNA splicing endonuclease 34 homolog (S.

cerevisiae) TSEN34

2.38 0.004 NM_006291 Tumor necrosis factor, alpha-induced protein 2

TNFAIP2 1

2.37 0.046 NM_005881 Branched chain ketoacid dehydrogenase kinase

BCKDK

2.37 0.035 NM_001247 Ectonucleoside triphosphate diphosphohydrolase 6 (putative function)

ENTPD6 1

2.37 0.015 NM_012230 Zona pellucida glycoprotein 3 (sperm receptor)

ZP3

2.36 0.007 BC001425 Homo sapiens, Similar to differential display and activated by p53, clone MGC:1780, mRNA, complete cds. /PROD=Similar to differential display and activated byp53 /FL=gb:BC001425.1 gb:NM_001826.1 gb:AF274941.1 gb:AF279897.1

-

2.36 0.027 BE966193 hypothetical protein FLJ20445 - 2.36 0.019 BC021861 Interferon epsilon 1 IFNE1 2.36 0.022 NM_014734 KIAA0247 KIAA0247 1 1 1

261

TargetScan


miR-Target


Non-cons.

2.36 0.004 NM_024599 Rhomboid 5 homolog 2 (Drosophila) RHBDF2 2.35 0.002 BC000373 Amyloid beta (A4) precursor-like protein 2 APLP2 2.35 0.006 AF300717 Potassium voltage-gated channel, subfamily

H (eag-related), member 2 KCNH2

2.35 0.045 NM_018683 Zinc finger protein 313 ZNF313 1 1 2.33 0.009 X86428 Homo sapiens PTPA gene for

phosphotyrosyl phosphatase activator, exon 1 and joined CDS

-

2.33 0.022 NM_015062 Peroxisome proliferative activated receptor, gamma, coactivator-related 1

PPRC1

2.33 0.007 AF029750 TAP binding protein (tapasin) TAPBP 2.31 0.038 AL571373 Mitochondrial ribosomal protein L10 MRPL10 2.31 0.002 D28124 Neuroblastoma, suppression of

tumorigenicity 1 NBL1 1

2.3 0.011 NM_001157 Annexin A11 ANXA11 1 1 2.3 0.026 U60521 Caspase 9, apoptosis-related cysteine

peptidase CASP9 1 1 1 1

2.28 0.015 AL136807 Stress-associated endoplasmic reticulum protein 1

SERP1 1 1

2.27 0.042 BE378479 High density lipoprotein binding protein (vigilin)

HDLBP

2.27 0.010 AK024724 Lysophospholipase II LYPLA2 2.27 0.019 AF279903 Ribosomal protein L15 RPL15 1 2.27 0.008 NM_017945 Solute carrier family 35, member A5 SLC35A5 1 2.27 0.038 AF277178 SSU72 RNA polymerase II CTD

phosphatase homolog (S. cerevisiae) SSU72

2.27 0.028 BE734905 Transcribed locus, strongly similar to XP_498718.1 PREDICTED: hypothetical protein XP_498718 [Homo sapiens]

-

2.25 0.005 AU147399 Caveolin 1, caveolae protein, 22kDa CAV1 1 2.25 0.027 AW051856 Filamin A, alpha (actin binding protein 280) FLNA

262

TargetScan


miR-Target


Non-cons.

2.25 0.006 AK025328 Leucine rich repeat containing 59 LRRC59 1 2.25 0.015 NM_014735 PHD finger protein 16 PHF16 1 2.25 0.004 AI659180 Translin TSN 1 2.25 0.040 AB033029 Ubiquitin specific peptidase 31 USP31 2.24 0.043 NM_012068 Activating transcription factor 5 ATF5 2.24 0.032 NM_004996 ATP-binding cassette, sub-family C

(CFTR/MRP), member 1 ABCC1 1

2.24 0.032 AL162069 Hypothetical protein LOC144501 KRT80 2.24 0.011 AF059752 Mannose-P-dolichol utilization defect 1 MPDU1 2.24 0.014 BF107618 prothymosin, alpha (gene sequence 28) - 2.23 0.037 AF077353 Drebrin-like DBNL 1 2.23 0.007 BC005020 Peptidylprolyl isomerase F (cyclophilin F) PPIF 1 1 2.22 0.020 AW290956 Nedd4 family interacting protein 2 NDFIP2 1 2.21 0.008 U56417 1-acylglycerol-3-phosphate O-acyltransferase

1 (lysophosphatidic acid acyltransferase, alpha)

AGPAT1

2.21 0.014 NM_006055 LanC lantibiotic synthetase component C-like 1 (bacterial)

LANCL1

2.21 0.035 NM_025124 Transmembrane protein 134 TMEM134 2.2 0.006 AU154408 P21/Cdc42/Rac1-activated kinase 1 (STE20

homolog, yeast) PAK1 1

2.2 0.013 BE999972 Sphingosine-1-phosphate lyase 1 SGPL1 2.2 0.013 AA707320 Transcribed locus - 2.19 0.012 NM_004969 Insulin-degrading enzyme IDE 2.18 0.035 AL525086 UDP-N-acetyl-alpha-D-

galactosamine:polypeptide N-acetylgalactosaminyltransferase 2 (GalNAc-T2)

GALNT2 1 1

2.17 0.014 AF015593 Ceroid-lipofuscinosis, neuronal 3, juvenile (Batten, Spielmeyer-Vogt disease)

CLN3

2.17 0.000 AA148301 COMM domain containing 7 COMMD7 1

263

TargetScan


miR-Target


Non-cons.

2.17 0.012 AK025504 KIAA0251 protein KIAA0251 1 2.17 0.034 BC003379 Small trans-membrane and glycosylated

protein LOC57228 1 1

2.17 0.004 NM_003132 Spermidine synthase SRM 2.16 0.024 AI373643 BRCA2 and CDKN1A interacting protein BCCIP 2.16 0.005 BC000761 SNAP-associated protein SNAPAP 2.15 0.007 BC002700 Keratin 7 KRT7 2.14 0.021 NM_005787 Asparagine-linked glycosylation 3 homolog

(S. cerevisiae, alpha-1,3-mannosyltransferase)

ALG3

2.14 0.047 BG166705 Chemokine (C-X-C motif) ligand 5 CXCL5 2.14 0.016 AB007935 Immunoglobulin superfamily, member 3 IGSF3 1 1 2.13 0.021 NM_012425 Ras suppressor protein 1 RSU1 1 2.13 0.034 AF151063 Transmembrane protein 69 TMEM69 1 2.13 0.035 AF089744 Xenotropic and polytropic retrovirus receptor XPR1 1

2.12 0.026 AI910895 CDNA clone IMAGE:4157286 - 2.12 0.033 NM_021959 Protein phosphatase 1, regulatory (inhibitor)

subunit 11 PPP1R11

2.12 0.021 NM_006588 Sulfotransferase family, cytosolic, 1C, member 2

SULT1C2

2.12 0.017 BC000464 WD repeat domain 45 WDR45 2.11 0.006 NM_023009 MARCKS-like 1 MARCKSL1 2.1 0.023 U79458 Human WW domain binding protein-2

mRNA, complete cds. /PROD=WW domain binding protein-2 /FL=gb:U79458.1

-

2.1 0.036 NM_005567 Lectin, galactoside-binding, soluble, 3 binding protein

LGALS3BP

2.1 0.000 AA628586 Phosphatidic acid phosphatase type 2B PPAP2B 2.1 0.037 BC003393 Phosphoinositide-3-kinase, catalytic, beta

polypeptide PIK3CB

264

TargetScan


miR-Target


Non-cons.

2.1 0.000 AK000776 Receptor tyrosine kinase-like orphan receptor 1

ROR1

2.1 0.002 BG107676 Stress-associated endoplasmic reticulum protein 1

SERP1

2.09 0.015 T79584 protein phosphatase 2 (formerly 2A), regulatory subunit A (PR 65), beta isoform /FL=gb:AF087438.1 gb:AF163473.1 gb:NM_002716.1 gb:M65254.1

-

2.09 0.020 AI669186 Ring finger and SPRY domain containing 1 RSPRY1 2.08 0.006 BG481877 B-cell CLL/lymphoma 9-like BCL9L 1 2.08 0.034 AK026161 Calcium activated nucleotidase 1 CANT1 1 2.08 0.024 AA029441 Calcium/calmodulin-dependent protein

kinase (CaM kinase) II delta CAMK2D 1 1

2.08 0.002 AL039447 Chromosome 9 open reading frame 48 C9orf48 2.08 0.002 NM_024747 Hermansky-Pudlak syndrome 6 HPS6 2.08 0.012 BF111719 Transcribed locus, strongly similar to

NP_003650.1 alkylglycerone phosphate synthase precursor [Homo sapiens]

-

2.07 0.002 NM_001660 ADP-ribosylation factor 4 ARF4 1 1 1 2.07 0.025 L24521 Full-length cDNA clone CS0DM011YA01 of

Fetal liver of Homo sapiens (human) -

2.07 0.011 AK021918 G protein-coupled receptor 172A GPR172A 2.07 0.012 NM_013348 Potassium inwardly-rectifying channel,

subfamily J, member 14 KCNJ14

2.07 0.032 BE907429 Ribosomal protein S19 binding protein 1 RPS19BP1 2.06 0.034 AL353962 B-cell CLL/lymphoma 9-like BCL9L 2.06 0.010 AI916719 Coronin 6 CORO6 2.06 0.015 AI760772 Ring finger and FYVE-like domain

containing 1 RFFL 1 1

2.06 0.002 BC004288 Zinc finger protein 655 ZNF655

265

TargetScan


miR-Target


Non-cons.

2.06 0.007 BC000487 Zona pellucida glycoprotein 3 (sperm receptor)

ZP3

2.05 0.028 AA534526 Transcribed locus - 2.04 0.036 AL562950 Adaptor-related protein complex 1, mu 1

subunit AP1M1 1

2.04 0.040 NM_012121 CDC42 effector protein (Rho GTPase binding) 4

CDC42EP4

2.04 0.008 BC001282 High mobility group nucleosomal binding domain 4

HMGN4 1

2.04 0.027 NM_015140 Tubulin tyrosine ligase-like family, member 12

TTLL12

2.04 0.001 NM_014052 tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, beta polypeptide

- 1

2.03 0.020 NM_021873 Cell division cycle 25B CDC25B 2.03 0.001 NM_022156 Dihydrouridine synthase 1-like (S.

cerevisiae) DUS1L

2.03 0.024 AF055006 Exocyst complex component 3 EXOC3 2.03 0.029 NM_002149 Hippocalcin-like 1 HPCAL1 2.03 0.023 AL583509 KIAA1545 protein KIAA1545 2.03 0.047 AA621983 Myeloma overexpressed gene (in a subset of

t(11;14) positive multiple myelomas) MYEOV

2.03 0.036 AF015043 SH3-domain binding protein 4 SH3BP4 1 2.03 0.007 AK025578 Ubiquitin-like, containing PHD and RING

finger domains, 1 UHRF1 1

2.02 0.017 BC002430 Aldehyde dehydrogenase 3 family, member A2

ALDH3A2

2.02 0.011 NM_001552 Insulin-like growth factor binding protein 4 IGFBP4 2.01 0.022 NM_019606 Bin3, bicoid-interacting 3, homolog

(Drosophila) BCDIN3

2.01 0.012 AI625550 Filamin A, alpha (actin binding protein 280) FLNA

266

TargetScan


miR-Target


Non-cons.

2.01 0.035 NM_004193 Golgi-specific brefeldin A resistance factor 1 GBF1 2.01 0.020 BF791544 Keratin associated protein 4-7 KRTAP4-7 2.01 0.025 NM_030662 Mitogen-activated protein kinase kinase 2 MAP2K2 2.01 0.009 AI110886 Pregnancy-associated plasma protein A,

pappalysin 1 PAPPA 1 1

2.01 0.029 NM_020182 Transmembrane, prostate androgen induced RNA

TMEPAI

miR-7 targets EGF receptor signaling Webster et al.

1

miR-7 targets EGF receptor signaling

Rebecca J Webster1,2#, Keith M Giles1#, Karina J Price1, John S Mattick3,

and Peter J Leedman1,2*.

1Laboratory for Cancer Medicine, UWA Centre for Medical Research, Western Australian

Institute for Medical Research and 2School of Medicine and Pharmacology, the University

of Western Australia, Perth, WA, Australia, 3Australian Research Council Special Research

Centre for Functional and Applied Genomics, Institute for Molecular Bioscience,

University of Queensland, Brisbane, Queensland, Australia.

#Denotes co-first authors

*Denotes corresponding author, email address: [email protected]

Running Title: miR-7 regulates the EGFR signaling pathway

Key Words: microRNA, miR-7, EGFR, Raf1, human cancer


2

Abstract

The epidermal growth factor receptor (EGFR) is frequently overexpressed in human

cancers and is an important target for therapeutics. MicroRNAs (miRNAs), a class of small,

non-coding, regulatory RNAs, decrease expression of specific target mRNAs via

translational inhibition and/or accelerated mRNA decay. The precise function of many

miRNAs in humans is unclear. The human EGFR mRNA 3’-untranslated region (3’-UTR)

is predicted to contain three miR-7 target sites that are not conserved between humans,

dogs and rodents. MiR-7 is expressed in the brain, pituitary and hypothalamus, and is

underexpressed in tumors arising from these organs. We show that miR-7 acts coordinately

via two functional miR-7 target sites to regulate EGFR mRNA and protein expression in

human cancer cells that overexpress EGFRs, including those derived from lung, breast and

glioblastoma, inducing cell cycle arrest and cell death. In concert, miR-7 regulates the

expression of a number of other genes, including Raf1, a member of the Ras-Raf-MEK-

ERK signaling pathway downstream of EGFR, and genes associated with glioblastoma

formation as well as processes specific to the normal function of central nervous system

(CNS) and pituitary cells. These data suggest that miR-7 can function as a regulator of

EGFR signaling in specific human cell types.


3

The epidermal growth factor receptor (EGFR), a member of the erbB receptor family, is

widely expressed in human tissues and regulates important cellular processes including

proliferation, differentiation and development (1). EGFR overexpression occurs in a range

of solid tumours and is associated with disease progression, resistance to chemotherapy and

radiation therapy, and poor prognosis (2). Consequently, the EGFR and its downstream

signaling effectors are major targets of new therapeutics such as monoclonal antibodies and

tyrosine kinase inhibitors (3). However, clinical responses to existing anti-EGFR agents in

cancer are often limited and thus a major research focus is the development of novel

approaches to block EGFR expression and signaling (4).

MicroRNAs (miRNAs) are short, endogenous, non-coding RNA molecules that

bind via imperfect complementarity to 3’-untranslated regions (3’-UTRs) of target mRNAs,

causing translational repression of the target gene or degradation of the target mRNA (5, 6,

7). MiRNAs are involved in a range of processes that include development and

differentiation (8), proliferation and apoptosis (9), and have been implicated in cancer (10).

Interestingly, more than half of miRNA genes are located at sites in the human genome that

are frequently amplified, deleted or rearranged in cancer (11), suggesting that some

miRNAs may act as oncogenes (‘oncomirs’, 12) or tumour suppressors (reviewed in 10).

For instance, reduced expression of the let-7 family of miRNAs is associated with increased

Ras oncogene expression and reduced survival in patients with non-small cell lung cancer

(NSCLC) (13, 14). In contrast, increased miR-21 expression in gliomas (15), and breast,

colon, lung, pancreas, prostate and stomach cancers (16) is associated with resistance to

apoptosis, reduced chemosensitivity and increased tumor growth (15, 17).

Computational approaches have been developed to predict miRNA targets. These

methods have utilised criteria such as complementarity between target mRNAs and a ‘seed’

region within the miRNA thought to be critical for binding specificity, and conservation of

predicted miRNA-binding sites across 3’-UTRs from multiple species (reviewed in 18, 19).

It has been suggested that miRNAs may have the capacity to regulate hundreds or even

thousands of target mRNAs (20) and that much of this regulation might occur at the level of

mRNA decay (21). Furthermore, specific miRNAs have the potential to regulate expression

of several members of a signaling pathway or cellular process (22). The imperfect

complementarity of miRNA:target interactions means that the identification and functional

validation of true miRNA targets remains a major challenge.


4

In view of the finding that EGFR expression is regulated in part via cis-acting 3’-

UTR mRNA stability sequences (23), we sought to identify miRNAs that could regulate

EGFR gene expression in human cells. Using TargetScan (20) three putative miR-7 target

sites were identified (A, B, C; Fig. 1A), the 3’ end of each site contained the hexamer motif

UCUUCC complementary to the seed region (nt. 2-7) at the 5’ end of human miR-7 (hsa-

miR-7) (Fig. 1B). While miR-7 is normally expressed in the brain, lens, pituitary and

hypothalamus (24, 25, 26), its expression is significantly decreased in pituitary adenomas

and in a panel of CNS cancer cell lines relative to normal CNS tissue (27, 28), suggesting

that it may function as a tumor suppressor in these systems by inhibiting oncogene

expression. Interestingly, the EGFR 3’-UTR is poorly conserved across species with

sequence differences in each of the three putative miR-7 target sites between human, mouse

and rat (Fig. 1B). Binding sites that are not conserved between species are often ignored in

an attempt to reduce the number of false positives in target prediction sets. However, the

evolution of miRNAs and their target mRNAs suggests that this exclusion could also

increase the rate of false negative predictions (19). In mice, miR-7b regulates translation of

the Fos oncogene via a 3’-UTR target site that is not present in human Fos mRNA (29).

To investigate the putative interaction between miR-7 and its predicted EGFR

mRNA 3’-UTR target sites, we first generated reporter vectors containing miRNA target

sequences downstream of the luciferase ORF (Fig 1C): a target site with perfect

complementarity to the miR-7 sequence, EGFR 3’-UTR sequences (A, B, C, D) with

predicted miR-7 target sites, and these same sequences with three point mutations in the

seed match region predicted to disrupt miR-7 binding (Fig. 1D). In HeLa cells transfected

with synthetic miR-7 precursor, expression of the perfect target reporter was reduced, an

effect that was not evident with a negative control miRNA precursor (miR-NC) (Fig. 1E).

Transfection studies using human NSCLC cells (A549, which overexpress EGFRs)

examined the relative contribution of each putative miR-7 target site in the EGFR 3’-UTR

to the regulation of target gene expression. We found that expression of miR-7 reduced

reporter expression via target sites B and C compared to miR-NC, while the corresponding

mutant reporters were not affected (Fig. 1F). In contrast, miR-7 had no effect on reporter

gene expression mediated by the EGFR 3’-UTR target site A (Fig. 1F), despite this site

being a predicted target for miR-7 binding. This suggested that target site A alone was not

a target for miR-7 binding. Interestingly, the presence of target sites B and C (plasmid

construct EGFR D, Fig. 1C) in the same reporter construct conferred additive, but not


5

synergistic, repression with miR-7 that was not observed with the EGFR D mutant reporter

(Fig. 1F). Together, these data indicate that two of the three predicted miR-7-binding sites

in the EGFR mRNA 3’-UTR are likely to be specific targets for miR-7, and furthermore

suggest that target sites B and C may act in an additive fashion to amplify the repression of

EGFR expression by miR-7.

Next, we sought to determine the effect of miR-7 on EGFR mRNA and protein

expression in A549 and EGFR-overexpressing MDA-MB-468 breast cancer cells.

Transfection of miR-7 precursor, but not miR-NC precursor, induced a significant

reduction in EGFR mRNA expression in A549 cells observed at 12 h post-transfection

(Fig. 2A), consistent with miR-7 promoting EGFR mRNA decay. This effect is in contrast

to the results of a study in which miR-7 regulates translation of Fos mRNA in the mouse

hypothalamus (29), suggesting that miR-7 is able to regulate either the stability and/or

translation of target mRNAs. Furthermore, when compared with miR-NC, at 72 h post-

transfection with miR-7 there was a specific reduction in EGFR protein expression in A549

and MDA-MB-468 cells (Fig. 2B), even at low concentrations of miR-7 precursor

(Supplemental Fig. 1A). Similarly, EGFR protein expression was observed to be reduced

by miR-7 transfection in EGFR-positive U87MG glioblastoma cells by

immunofluorescence (Fig. 2C) and immunoblotting (Supplemental Fig. 1B). The latter

result was particularly intriguing given the reported downregulation of miR-7 expression

and the established role for EGFR overexpression in CNS tumors (28, 30). Furthermore,

transfection of A549 cells with miR-7 precursor induced cell cycle arrest at G1 (Fig. 2D),

and caused a significant decrease in A549 cell viability compared with vehicle and miR-

NC transfected A549 cells (Fig. 2E). However, cell death induced by miR-7 precursor

transfection did not appear to involve apoptosis, due to the absence of (a) an apoptotic,

sub-G1 cell population by propidium iodide staining and flow cytometry (Fig. 2D), and (b)

activation of the executioner caspases 3 and 7 (data not shown). Thus, it is likely that miR-

7 expression induces a broad program of gene expression that reduces A549 cell viability

through necrosis.

In view of the evidence that miRNAs can have multiple, functionally-related targets

(22), we performed microarray analysis to identify miR-7 target genes and functional

trends using RNA samples from A549 cells treated with miR-7 or miR-NC. In miR-7-

transfected A549 cells, 248 transcripts were significantly downregulated and 199

transcripts were significantly upregulated by at least 2-fold (p < 0.05) when compared to


6

miR-NC-transfected A549 cells (Supplemental Table 1). Furthermore, there was

significant enrichment (2.18-fold, p = 0.025) for predicted miR-7 target genes, but not for

predicted target genes of any other miRNA, among the recognised set of 248

downregulated genes. The enrichment for putative miR-7 target genes among the genes

downregulated in miR-7-transfected A549 cells is consistent with other studies that

identified miRNA target genes by microarray analysis (31). EGFR was significantly

downregulated by miR-7 for all three microarray chip probes (3.13-, 3.07-, and 2.87-fold),

consistent with the observed reduction in EGFR mRNA expression with miR-7

transfection (Fig. 2A). Interestingly, Raf1, a member of the EGFR-Ras-Raf-MEK-ERK

signaling cascade, was also downregulated by miR-7 (3.47-fold). This result was

confirmed by qRT-PCR in A549 cells treated with miR-7 or miR-NC precursor (Fig. 3A),

suggesting that miR-7 promotes degradation of Raf1 mRNA. TargetScan analysis revealed

that the human Raf1 3’-UTR contains two predicted miR-7 target sites (one conserved, one

non-conserved; Fig. 3B). In transfection studies with A549 cells, miR-7 reduced reporter

activity in cells transfected with a luciferase construct that carried a wild-type Raf1 miR-7

target sequence but not an analogous insert with three point mutations in the seed match

region (Fig. 3C). This indicated that the Raf1 mRNA 3’-UTR is a specific target for

binding of miR-7. Furthermore, Raf1 protein expression was decreased in A549 cells

transfected with miR-7 precursor compared with A549 cells transfected with miR-NC

precursor (Fig. 3D). These data provide evidence that miR-7 directly regulates expression

of Raf1, a downstream effector of EGFR signaling via the Raf-MEK-ERK MAPK cascade,

that is commonly activated by mutations and/or overexpressed in human cancers (32).

To investigate potential functional trends for miR-7 we examined Kyoto

Encyclopedia of Genes and Genomes (KEGG) pathways for significant enrichment of

genes that were downregulated in microarray analysis of A549 cells transfected with miR-

7 precursor (Fig. 4), since these may include actual miR-7 targets. Notably, “Glioma”,

“ErbB signaling pathway”, “GnRH signaling pathway”, “Long-term potentiation” and

“Gap junction” pathways were significantly enriched with genes that were downregulated

by miR-7 transfection (Supplemental Fig. 2A-2E). These are consistent with a role for

miR7 in the regulation of EGFR signaling, and with the brain and pituitary-specific

expression of miR-7 and its downregulation in CNS and pituitary tumors (27, 28). In

addition to the validated target genes EGFR and Raf1, several other downregulated genes

in these pathways contain predicted binding sites for miR-7. These include genes involved


7

in calcium signaling (CALM3 and CAMK2D, downregulated 7.1- and 2.08-fold,

respectively), cytoskeleton reorganisation and nuclear signaling (PAK1, downregulated

2.2-fold), and cAMP synthesis and intracellular signaling (ADCY9, downregulated 3.38-

fold) (Supplemental Table 1).

We have shown that EGFR and its downstream signaling effector Raf1 are direct

targets for miR-7. As with many other miRNAs, miR-7 expression is restricted to specific

tissues suggesting that it has important functions in those systems. In turn, by directly

regulating expression of important signaling molecules in these cells, such as EGFR, Raf1,

and other signaling and structural proteins, our data suggest that miR-7 may exert control

over the development and progression of gliomas, normal ErbB receptor function,

reproduction via the production of pituitary gonatropins, and learning and memory. A role

for miR-7 in these systems is supported by several recent reports. MiR-7 belongs to a

subset of miRNAs that are downregulated in schizophrenia (33). Interestingly, the

predicted targets of the dysregulated miRNAs in this study, as with the mRNAs

downregulated here by miR-7, are over-represented in KEGG functional pathways

including those belonging to “Focal adhesion”, “Regulation of actin cytoskeleton” and

“Gap junction” (Fig. 4), and may determine synaptic plasticity in schizophrenia. MiR-7 has

also been shown to control EGFR signaling in Drosophila photoreceptor cells (34),

whereby upon cell differentiation EGFR signaling triggers ERK-mediated degradation of

the transcription repressor Yan, relieving its repression of miR-7 expression. Similarly,

miR-7 represses Yan expression in photoreceptors via binding to Yan 3’-UTR sequences.

This feedback loop promotes mutually exclusive expression of Yan and miR-7. EGFR is

unlikely to represent a direct target for miR-7 in Drosophila due to the lack of EGFR 3’-

UTR species conservation. Thus, our data are consistent with the notion that miR-7

regulates EGFR signaling, and may exert control over specific cellular pathways in the

tissues in which it is expressed. Furthermore, the reported downregulation of miR-7 in

tumor cells of the CNS and pituitary, together with the ability of miR-7 to downregulate

expression of oncogenes associated with these cancers such as EGFR and Raf1, and to

promote cell cycle arrest and death of cancer cells, suggests that miR-7 may function as a

tumor suppressor in these systems and that therapeutic upregulation of miR-7 expression in

these tumors may inhibit growth and metastasis.


8

References and Notes

1. S. Yano et al. Anticancer Res. 23, 3639 (2003).

2. C. L. Arteaga, J. Clin. Oncol. 19, 32 (2001).

3. C. L. Arteaga, Semin. Oncol. 30, 3 (2003).

4. R. Bianco, T. Troiani, G. Tortora, F. Ciardiello, Endocr. Relat. Cancer 12, 159 (2005).

5. D. P. Bartel, Cell. 116, 281 (2004).

6. J. S. Mattick, I. V. Makunin, Hum. Mol. Genet. 14, 121 (2005).

7. D. T. Humphreys, B. J. Westman, D. I. Martin, T. Preiss, Proc. Natl. Acad. Sci. U.S.A.

102, 16961 (2005).

8. J. F. Chen et al., Nat. Genet. 38, 228 (2006).

9. A. M. Cheng, M. W. Byrom, J. Shelton, L. P. Ford, Nucleic Acids Res. 33, 1290 (2005).

10. B. Zhang, X. Pan, G. P. Cobb, T. A. Anderson, Dev. Biol. 302, 1 (2007).

11. G. A. Calin et al., Proc. Natl. Acad. Sci. U.S.A. 101, 2999 (2004).

12. A. Esquela-Kerscher, F. J. Slack, Nat. Rev. Cancer 6, 259 (2006).

13. S. M. Johnson et al., Cell 120, 635 (2005).

14. J. Takamizawa et al., Cancer Res. 64, 3753 (2004).

15. J. A. Chan, A. M. Krichevsky, K. S. Kosik, Cancer Res. 65, 6029 (2005).

16. S. Volinia, et al., Proc. Natl. Acad. Sci. U.S.A. 103, 2257 (2006).

17. M. L. Si, et al., Oncogene 26, 2799 (2006).

18. N. Rajewsky, Nat. Genet. 38, 8 (2006).

19. P. Maziere, A. J. Enright, Drug Discov. Today 12, 452 (2007).

20. B. P. Lewis, C. B. Burge, D. P. Bartel, Cell 120, 15 (2005).

21. J. Krutzfeldt, et al., Nature 438, 685 (2005).

22. A. Stark, J. Brennecke, R. B. Russell, S. M. Cohen, PLoS Biol. 1, 60 (2003).

23. L. A. Balmer, et al., Mol. Cell. Biol. 21, 2070 (2001).

24. L. F. Sempere, et al., Genome Biol. 5, 13 (2004).

25. P. H. Frederikse, R. Donnelly, L. M. Partyka, Histochem. Cell Biol. 126, 1 (2006).

26. K. K. Farh, et al., Science 310, 1817 (2005).

27. A. Bottoni, et al., J. Cell. Physiol. 210, 370 (2007).

28. A. Gaur, et al., Cancer Res. 67, 2456 (2007).

29. H. J. Lee, M. Palkovits, W. S. Young, Proc. Natl. Acad. Sci. U.S.A. 103, 15669 (2006).

30. M. K. Nicholas, et al., Clin. Cancer Res. 12, 7261 (2006).

31. L. P. Lim, et al., Nature 433, 769 (2005).


9

32. P. J. Roberts, C. J. Der, Oncogene 26, 3291 (2007).

33. D. O. Perkins, et al., Genome Biol. 8, 27 (2007).

34. X. Li, R. W. Carthew, Cell 123, 1267 (2005).

35. The authors acknowledge David Bartel and Lance Ford for advice regarding some of

the early studies. This work was funded by the National Health and Medical Research

Council of Australia. RW is the recipient of a Richard Walter Gibbon Scholarship for

Medical Research from the University of Western Australia.


10

Figure Legends

Figure 1. The non-conserved EGFR 3’-UTR mRNA contains target sites for specific

binding of miR-7. (A) TargetScan software predicts three miR-7 binding sites (A, B, C) in

human EGFR mRNA 3’-UTR. (B) Sequence alignment of putative miR-7 targets in EGFR

mRNA 3’-UTR shows that sites A, B, C are not conserved between human, mouse and rat.

The miR-7 seed target sequence (UCUUCC) is shown in bold and underlined, and

conserved nucleotides are shaded. (C) Schematic representation of luciferase reporter

constructs for consensus miR-7 target and EGFR 3’-UTR miR-7 target sites. (D) Sequence

of wild type (WT) and mutant (MT) EGFR mRNA 3’-UTR miR-7 target sites. (E) HeLa

cells were transfected with consensus miR-7 target 3’-UTR luciferase construct and miR-7

or miR-NC precursor. Relative luciferase expression (firefly normalized to renilla) values

are expressed as a ratio of reporter vector only (±SD). (F) A549 cells were transfected with

WT or MT EGFR target site A, B, C or D 3’-UTR reporter along with miR-7 or miR-NC

precursor. Relative luciferase expression (firefly normalized to renilla) values are the ratio

of miR-7-treated reporter vector compared to miR-NC-treated reporter vector (±SD).

Figure 2. miR-7 regulates EGFR expression and alters cell cycle progression and

viability of A549 NSCLC cells. (A) A549 cells were transfected with miR-7 or miR-NC

precursor and RNA isolated at 12 h for semi-quantitative RT-PCR analysis of EGFR and β-

actin mRNA expression. (B) EGFR and β-actin immunoblot using 15 µg of cytoplasmic

protein extracts from A549 and MDA-MB-468 cells transfected with miR-7 or miR-NC for

3 d. (C) EGFR immunofluorescence from U87MG cells that had been transfected with

miR-7 or miR-NC for 3 d. Cell nuclei are Hoechst-stained and secondary antibody only

reveals no significant immunofluorescence. For comparison of EGFR expression, identical

exposure times were used. (D) A549 cells that had been transfected with miR-7 or miR-NC

for 3 d were analyzed with propidium iodide staining for cell cycle progression. Cell cycle

profile data for the three A549 cell populations (control, miR-7, miR-NC) are shown from a

representative experiment (n=3). (E) Microscopic assessment of viability of A549 cells

transfected with miR-7 or miR-NC by light microscopy (40X magnification) and mean

percentage difference in cell counts (±SD) compared to vehicle only.


11

Figure 3. miR-7 regulates Raf1 expression via specific binding to the Raf1 mRNA 3’-

UTR. (A) qRT-PCR validation of Raf1 mRNA expression following transfection of A549

cells for 24 h with miR-7 or miR-NC. Values are fold-change (±SD) in Raf1 mRNA

expression relative to GAPDH mRNA expression between triplicate miR-NC and miR-7

samples. (B) Raf1 mRNA 3’-UTR contains conserved (C) and non-conserved (NC) seed

target sites for miR-7 binding. (C) A549 cells were transfected with WT or MT luciferase-

Raf1 3’-UTR reporter vector and either miR-7 or miR-NC. Values are relative luciferase

expression (firefly normalized to renilla) as a ratio of miR-NC-transfected cells (±SD).

Figure 4. Identification of functional pathways enriched for miR-7 target genes.

KEGG pathways significantly enriched for genes downregulated in A549 cells by miR-7

transfection compared to miR-NC transfection include: “Glioma”, “ErbB signaling

pathway”, GnRH signaling pathway”, “Long-term potentiation”, and “Gap junction”. Z >

1.96 for p < 0.05.


12

Supporting material for:

miR-7 targets EGF receptor signaling

Rebecca J Webster1,2#, Keith M Giles1#, Karina J Price1, John S. Mattick3,

and Peter J Leedman1,2*.

#Denotes co-first authors

*Denotes corresponding author, email address: [email protected]

This PDF file includes: Materials and Methods Figs. S1-S2 Table S1 References


13

Materials and Methods

Cell culture and miRNA precursors. A549, MDA-MB-468, U87MG, U251MG and

HeLa cell lines were obtained from the American Type Culture Collection (ATCC) and

cultured at 37OC in 5% CO2 with DMEM supplemented with 10% fetal bovine serum and

1% penicillin/streptomycin. Synthetic miRNA precursor molecules corresponding to

human miR-7 (Pre-miR miRNA Precursor Product ID: PM10568; Anti-miR miRNA

Inhibitor Product ID: AM10568) and a negative control miRNA (miR-NC; Pre-miR

miRNA Precursor Negative Control #1, Product ID: AM17110; Anti-miR miRNA

Inhibitor Negative Control #1, Product ID: AM17010) were obtained from Ambion.

Luciferase plasmid construction. pGL3-miR-7-report was generated by ligating annealed

DNA oligonucleotides corresponding to a perfect hsa-miR-7 target site (5’-CAA CAA

AAT CAC TAG TCT TCC A-3’ and 5’-TGG AAG ACT AGT GAT TTT GTT G-3’ to

unique SpeI and ApaI sites that were inserted 3’ of the luciferase ORF of pGL3-control

(Promega) firefly luciferase reporter vector (designated pGL3-control-MCS; S1). Wild

type (WT) EGFR target reporter plasmids pGL3-EGFR-A, -B, and -C were generated by

cloning annealed oligonucleotides corresponding to nt. 4214-4260, nt. 4302-4348, and nt.

4585-4631, respectively, of EGFR (GenBank accession number NM_005228) mRNA 3’-

UTR into SpeI and ApaI sites in pGL3-control-MCS. Plasmid pGL3-EGFR-D contained a

PCR-generated EGFR 3’-UTR sequence that spanned the predicted miR-7 target sites B

and C. Mutant (MT) reporters were also generated that included three nucleotide

substitutions to impair binding of the miR-7 seed sequence to its target. Plasmids pGL3-

RAF1-WT and pGL3-RAF1-MT were constructed by cloning annealed DNA

oligonucleotides corresponding to nt. 2965-3030 of the Raf1 mRNA 3’-UTR (GenBank

accession number NM_002880), into the SpeI and ApaI sites in pGL3-control-MCS. The

sequence of all plasmids was confirmed by sequencing.

Transfections and luciferase assays. Cells were seeded 24 hrs prior to transfection in 6-

well plates or 10 cm dishes and transfected using Lipofectamine 2000 (Invitrogen) with

miRNA precursors (Ambion) at final concentrations ranging from 0.1-30 nM. Cells were

harvested at 12-24 h (for RNA extraction) or 3 d (for protein extraction). For reporter

assays, cells were seeded in 24-well plates and transfected using Lipofectamine 2000


14

(Invitrogen) with 100 ng of pGL3-control firefly luciferase reporter DNA and 5 ng of pRL-

CMV renilla luciferase reporter DNA as a transfection control. Lysates were assayed for

firefly and renilla luciferase activities 24 h after transfection using the Dual Luciferase

Report Assay System (Promega) and a Fluostar OPTIMA microplate reader (BMG

Labtech). Expression values were normalized to renilla luciferase and expressed relative to

the average value for each miR-NC-transfected wild type reporter construct.

Semi-quantitative RT-PCR and quantitative real time RT-PCR. Total RNA was

extracted from cell lines with Trizol reagent (Invitrogen) and RNeasy columns (Qiagen)

and treated with DNase I (Promega) to eliminate contaminating genomic DNA. For semi-

quantitative measurement of EGFR and β-actin transcript expression, 1 µg of RNA was

reverse transcribed to cDNA using random hexamers and AMV reverse transcriptase

(Promega). PCR primers for EGFR and β-actin are: EGFR-F, 5’-CAC CGA CTA GCC

AGG AAG TA-3’; EGFR-R, 5’-AAG CTT CTT CCT TGT TGG AAG AGC CCA TTG

A-3’; β-actin-F, 5’-GCC AAC ACA GTG CTG TCT GG-3’; β-actin-R, 5’-TAC TCC TGC

TTG CTG ATC CA-3’. For qRT-PCR, 1 µg of RNA was reverse transcribed with random

hexamers and Thermoscript (Invitrogen). Real-time PCR for Raf1 and GAPDH was

performed using a Corbett 3000 RotorGene instrument (Corbett Research) with QuantiTect

SYBR Green PCR mixture (Qiagen) with primers that were obtained from PrimerBank

(http://pga.mgh.harvard.edu/primerbank/; S2): RAF1-F, 5’-GCA CTG TAG CAC CAA

AGT ACC-3’; RAF1-R, 5’-CTG GGA CTC CAC TAT CAC CAA TA-3’; GAPDH-F, 5’-

ATG GGG AAG GTG AAG GTC G-3’; GAPDH-R, GGG GTC ATT GAT GGC AAC

AAT A-3’. Expression of Raf1 mRNA relative to GAPDH mRNA was determined using

the 2-∆∆CT method (S3).

Western blotting. Cytoplasmic protein extracts were prepared as described (S4), resolved

on NuPAGE 4-12% Bis Tris gels (Invitrogen) and transferred to PVDF (Roche).

Membranes were probed with anti-EGFR mouse monoclonal antibody (1:1000,

Neomarkers Cat# MS-400-P1), anti-Raf-1 mouse monoclonal antibody (1:1000, Santa Cruz

sc-7267), or anti-β-actin mouse monoclonal antibody (1:10,000, Abcam ab6276-100), prior

to detection with ECL Plus detection reagent (General Electric Healthcare) and ECL-

Hyperfilm (General Electric Healthcare).


15

Immunofluorescence. Cells were cultured and transfected on coverslips in 6 well plates,

fixed in ice cold methanol and blocked with 1% BSA/PBS, followed by incubation with

EGFR antibody (1:500, Neomarkers Cat# MS-378-P1). After washing, cells were incubated

with secondary antibody (1:1000, Alexa Fluor 488 goat anti-mouse IgG, Invitrogen Cat#

A11029), with Hoechst dye (1:10,000, Hoechst AG) and coverslips mounted and stained

cells analyzed and photographed with fluorescence microscopy (Olympus IX71S1F-2

microscope) using identical exposures.

Cell cycle analysis. Following trypsinization, cells were permeabilized, stained with

propidium iodide and analysed on a Coulter EPICS XL-MCL (Coulter Corp. flow

cytometer. Cell cycle analysis was performed using MultiPlus AV MultiParameter data

analysis software (Phoenix Flow Systems).

Cell counting. Cells were seeded in 6 cm dishes and assessed 3 d after miR-7 or miR-NC

transfection by light microscopy and five representative fields of view photographed for

each condition. Cells in each field of view were counted manually.

Microarray expression profiling. Total RNA was isolated from A549 cells transfected

with miR-7 or miR-NC using Trizol reagent (Invitrogen) and RNeasy columns (Qiagen)

and assessed using a 2100 Bioanalyzer (Agilent Technologies). Gene expression profiling

was performed by microarray hybridization to Human Genome U133 Plus 2.0 array chips

(Affymetrix). Gene expression data was analyzed using GeneSifter software (VizX Labs).

Data comparisons were from two experimental replicates. Those genes with a p < 0.05 and

that were > 2.0-fold significantly downregulated by miR-7 transfection were selected for

further analysis on the basis that they could represent direct miR-7 targets. MiR-7 target

predictions were performed using miRTarget (S5), miRanda (S6), PicTar (S7) and

TargetScan software (S8). Microarray expression data has been deposited in Gene

Expression Omnibus (GEO) under Accession Number XXX.

Computational investigation of miR-7 binding site enrichment. Investigation of the

enrichment of gene sets for predicted miRNA targets was conducted using the L2L

microarray analysis tool (http://depts.washington.edu/l2l/about.html) (S9).


16

KEGG functional pathway analysis. Analysis of the enrichment of gene sets for

functional KEGG pathways was performed using GeneSifter software (VizX Labs).


17

Supporting References

1. K. M. Giles, et al., J Biol Chem 278, 2937 (Jan, 2003).

2. X. Wang, B. Seed, Nucleic Acids Res 31, 154 (Dec, 2003).

3. K. J. Livak, T. D. Schmittgen, Methods 25, 402 (Dec, 2001).

4. A. M. Thomson, et al., Biotechniques 27, 1032 (Nov, 1999).

5. X. Wang, X. Wang, Nucleic Acids Res 34, 1646 (Mar, 2006).

6. A. J. Enright, et al., Genome Biol 5, 1 (Dec, 2003).

7. A. Krek, et al., Nat Genet 37, 495 (May, 2005).

8. B. P. Lewis, et al., Cell 115, 787 (Dec 2003).

9. J. C. Newman, A. M. Weiner, Genome Biol 6, 81 (Aug, 2005).


18

Supplemental Figures

Supplemental Fig. S1. miR-7 expression alters EGFR protein expression at low

concentrations and in glioblastoma cells.

(A) A549 cells were transfected with miR-7 or miR-NC at final concentrations of 1-30 nM,

cytoplasmic lysates harvested after 3 d and EGFR and β-actin protein expression analysed

by immunoblotting.

(B) U87MG cells were transfected with miR-7 or miR-NC and EGFR and β-actin protein

expression analysed by immunoblotting.

Supplemental Fig. S2. Functional pathways enriched for genes downregulated by

miR-7 expression.

Functional KEGG pathways significantly enriched (z > 1.96 for p < 0.05) for genes

downregulated by transfection of A549 cells with miR-7 include: (A) “Glioma”, (B) “ErbB

signaling pathway”, (C) “GnRH signaling pathway”, (D) “Long-term potentiation”, (E)

“Gap junction”. Genes significantly downregulated in microarray analysis by miR-7 are

indicated with asterisks.

Documents

PhD Thesis - Rebecca Webster · Rebecca Webster BSc, BEng (Hons) This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia School of Medicine