Ttp Lab Tech Talk 051810

High-throughput microRNA functional screening using the Acumen eX3 to identify repressors of a tumorigenic signal

transduction pathway

Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis

Department of Cell Biology

Harvard Medical School

US Acumen Users Group Meeting (UGM)

British Consulate – General

Cambridge, MA

May 18, 2010

mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism

mTOR

RaptorLST8

AminoAcids

GrowthFactors

Energy

RibosomalBiogenesis

mRNATranslationAutophagy

Rapamycin

The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors

mTORC1 is hyperactivated in ~80-90% of all human cancers

Biomarker

Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function

IRS-1

PI3K

PTEN

PIP2 PIP3

PDK1

Akt

CellSurvival Cell

Growth

CellDivision

Cell Membrane Extracellular

Cytosol

mTOR

RaptorLST8

PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis

IRS-1

PI3K

PIP2 PIP3

PDK1

Akt

CellGrowth

CellDivision

Cell Membrane Extracellular

Cytosol

mTOR

RaptorLST8

CellSurvival

ConstitutiveHyperactivation

PTEN is one of the most frequently mutated tumor suppressors in primary human cancers

PTEN LOF

EndometrialCarcinoma(50-80%)

Glioblastoma(50-80%)

Prostate Cancer(50-80%)

Breast Cancer(30-50%)

Generally, PTEN +/- is associated with early-stage disease (e.g. formation/progression), while complete LOF (PTEN -/-) is associated with advanced

stages of cancer (e.g. metastatic disease)

Colon Cancer(30-50%)

Lung Cancer(30-50%)

Molecular Genetics and Prostate Cancer Progression

NormalEpithelium

ProstaticIntraepithelial

Neoplasia(PIN)

InvasiveCarcinoma Metastasis

Loss of 8p21NKX3.1

Loss of 10pPTEN +/-

Loss of 13qRb Loss of 17p

p53Loss Of

Basal CellsLoss Of

Basal LaminaAndrogen-

Independence

Adapted From: Abate-Shen, C. & Shen, MC. (2000) Genes & Dev. 14: 2410-34

Time

Loss of 10pPTEN -/-

Is mTORC1 hyperactivation downstream of PTEN LOF important for prostate cancer formation/progression?

Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP)

Nardella, C. et al. (2009) Sci. Signal. 2: 1-10

PTENpc-/-: PTENloxP/loxP x PB-Cre4

mTorpc-/-: mTorloxP/loxP x PB-Cre4

PB-Cre4 transgenic mice express Cre recombinaseunder the control of the ARR2-probasin promoter,Which is turned on in the prostate epithelium afterpuberty

What about small regulatory RNAs (e.g. microRNAs)?

Biomarker

Kim VN & Siomi MC. (2009) Nat Rev Mol Cell Biol 10: 126-39

microRNA (miRNA) expression is dramatically altered in human cancer

Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressed miRNAs function as oncogenes

Lu, J. et al. Nature 435 (7043): 834-838 Gaur, A. et al. Cancer Res 67: 2456-2468

Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines

miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes

(e.g. Ras and let-7)

Esquela-Kerscher, A & Slack, FJ.(2006) Nat Rev Cancer 6: 259-69

HepG2 Cells:

Human 1° Lung Tumors:

miRNA MimicNeg. Control

let-7 Mimic

Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47

miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes

(e.g. Ras and let-7)

Adapted From: Trang, P et al. (2010) Oncogene 29: 1580-87

Mouse Strain: LSL-K-Ras G12D

This strain carries a latent point mutant allele of Kras2 (K-RasG12D).

Cre-mediated recombination leads to deletion of a transcriptional termination sequence (Lox-Stop-Lox) and expression of the oncogenic protein.

Intranasal infection with Cre adenovirus results in very high frequency of lung tumors at baseline.

Intranasal infection of a lentivirus encoding let-7 reduces lung tumor burden

Jackson, EL et al. (2001) Genes Dev 15: 3243-8

Project: Identify and characterize miRNAs and miRNA inhibitors that repress

the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer.

mTOR

RaptorLST8

PositiveRegulator

RibosomalBiogenesis

mRNATranslationAutophagy

Rapamycin

miRNA-X

NegativeRegulator miRNA-YmiRNA-Z

miRNAInhibitor 1

Phase 1. Acquire miRNA functional screening capabilities

The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening

Facility (HMS)

The microRNA Screeners Consortium @ the ICCB-L

ICCB-LHarvard

Medical School

Ragon Instituteof MGH, MIT and Harvard Immune Disease

Institute

Dana-FarberCancer Institute

Children’s HospitalBoston

Blenis Lab(Cell Bio)

Struhl Lab(BCMP)

Brass LabLieberman Lab

Daley Lab

Chowdhury Lab

Shimaoka Lab

The microRNA Screeners Consortium @ the ICCB-L

• Consortium model allowed for shared purchase and evaluation of miRNA gain-of-function and loss-of-function libraries.

Gain-of-Function Libraries:

miScript miRNA Mimic Library (Qiagen)

Pre-miR miRNA Mimic Library (Ambion)

Loss-of-Function Library:

miRCURY LNA miRNA Knockdown Library (Exiqon)

Phase 2. miRNA 1° Screen Optimization

Primary Screen:

1. Transfection of miRNA gain-of-function and miRNA loss-of-function reagents into PC-3 cells (PTEN -/- human prostate cancer cell line) in a 384-well format.

2. Monitoring of mTORC1 function using an In-Cell Western (ICW) fluorescence-based assay. The screening assay involves antibody-based detection of endogenous ribosomal protein S6 Ser-235/236 phosphorylation (Cell Signaling Technology).

3. Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI).

4. Data is collected using the Acumen eX3 microplate cytometer (TTP LabTech).

20X

40X DroshaDicer

Phase 2. miRNA 1° Screen Optimization

2A. Validation of the 1° screening assay in PC-3 cells

2B. Small RNA transfection protocol for PC-3 cells

2C. siRNA/miRNA positive and negative control selection in PC-3 cells

Matrix WellMate® Microplate Dispenser(Thermo Scientific)

Acumen® eX3 Microplate Cytometer

(TTP LabTech)

PI3K

N

Akt

TSC1/2

mTORC1

S6K1/2

S6

PC-3 Cells (PTEN -/-)

PTEN

Rapamycin

2A. Validation of 1° Screening Assay in PC-3Small Molecule

Plate PC-3 Cells

(384-well)

Small Molecule Pin Transfer

(DMSO vs. Rap)

Fix Permeabilize

Block&

1° Ab

Alexa-488 2° Ab

&PI

DNA Stain

Image&

Data Analysis

Compound TransferRobot

(Epson)

48h 3h 24h Store@

4°C

SerumWithdrawal


Plate Map1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

250cells/well

500cells/well

750cells/well

DMSO

Rapamycin (20 nM)

DMSO

Rap

Heat Map

Mean % p-S6 Active

0 100

Green = ActiveRed = Inactive

Well Scan

Z’=0.852

0102030405060708090

100110

DMSO

Rap

% p

-S6

Ac

tiv

e

Well #

Well Scatter Plot

N = 36


a-p-S6 Ser235/236

a-S6 Total

Merge

Odyssey® Infrared Imaging System

(LI-COR Biosciences)

DMSO

Rap DMSO

Rap DMSO

Rap DMSO

Rap

Rel

ativ

e In

teg

rate

d I

nte

nsi

typ

-S6/

S6

(% C

on

tro

l)

DMSO Rap0

10

20

30

40

50

60

70

80

90

100

110

-99%

Scale to 10 cm plate


(TTP LabTech)

384-well plate

DMSO Rap DMSO Rap DMSO Rap0

10

20

30

40

50

60

70

80

90

100

Me

an

% p

-S6

Ac

tiv

e

250 cells

Z’ Factor: 0.831 0.852 0.716

500 cells 750 cells

-87%

Matrix WellMate® Microplate Dispenser(Thermo Scientific)


(TTP LabTech)

2C. siRNA/miRNA positive and negative control selection for PC-3 cells.

ReverseTransfection

(384-well)

FeedCells

Fix Permeabilize

Block&

1° Ab

Alexa-488 2° Ab

&PI

DNA Stain

Image&

Data Analysis

Bravo AutomatedLiquid Handling

Platform(Velocity 11)

24h 24h 24h Store@

4°C

Optional:SerumStarve

24h

PC-3 Cells (PTEN -/-)

LST8

PI3K

N

Akt

TSC1/2

mTOR

S6K1/2

S6

PTEN

SerumWithdrawal

RISC

Raptor

LST8&

S6K1/2k.d.

2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs

NTC

IGF-1

RIR

S1IR

S2

IRS-1

/2

P13K p

110α

PDK1Rheb

RagA

RagB

RagA/B

RagC

RagD

RagC/D

mTOR

Rapto

r

LST8

Ricto

r

mSin

1S6K

1S6K

2

S6K1/

2

PTENTSC1

TSC2

PRAS400

20

40

60

80

100

120

140

Mea

n %

p-S

6 A

ctiv

e(%

Co

ntr

ol)

Experiment 1NTC siRNA pool vs. siRNA pool positive control panelN = 4/group600 cells/well

Asynchronously-growing (+serum)

Starve (-serum)

LST8: 52%/25%

S6K1/2: 31%/10%

2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs

Experiment 2Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2N = 24/group500-1000 cells/well

Cell siRNA Pool(s)

Number LST8 S6K1/2LST8 + S6K1/2

500 0.310 0.219 0.632

600 0.343 0.471 0.673

700 0.519 0.693 0.780

800 0.666 0.738 0.797

900 0.418 0.673 0.752

1000 0.337 0.702 0.749

Cell siRNA Pool(s)

Number LST8 S6K1/2LST8 + S6K1/2

500 0.671 0.785 0.879

600 0.646 0.763 0.832

700 0.631 0.768 0.820

800 0.709 0.812 0.841

900 0.700 0.803 0.846

1000 0.673 0.755 0.808

(+) serum (-) serum

Z’ Factor0.2 0.9

*

* *

*

* *

Under optimal conditions the Z’-factor values obtained from our siRNA positive control optimization rival those achieved in our small molecule validation study

(Z’ = 0.852)

2C. siRNA/miRNA positive and negative control selection for PC-3 cells.miRNAs

Experiment 1Mock vs. miRNA negative controlsN = 24/group600 cells/well

Asynchronously-growing (+serum)

Starve (-serum)

0

20

40

60

80

100

120

Mea

n %

p-S

6 A

ctiv

e(%

Co

ntr

ol)

Mea

n C

ell N

um

ber

(% C

on

tro

l)0

20

40

60

80

100

120

Q1M A1 A2 E1 E2 Q1M A1 A2 E1 E2

2C. siRNA/miRNA positive and negative control selection for PC-3 cells.Odyssey® WB Validation

siRNA PoolmiRNA

Negative Control

NT

C

S6K

1/2

LS

T8

+ S

6K1/

2

All

Sta

rs (

Qia

gen

)

Pre

-miR

#1

(Am

bio

n)

Pre

-miR

#2

(Am

bio

n)

Scr

amb

led

(E

xiq

on

)

Sen

se m

iR-1

59 (

Exi

qo

n)

NT

C

S6K

1/2

LS

T8

+ S

6K1/

2

All

Sta

rs (

Qia

gen

)

Pre

-miR

#1

(Am

bio

n)

Pre

-miR

#2

(Am

bio

n)

Scr

amb

led

(E

xiq

on

)

Sen

se m

iR-1

59 (

Exi

qo

n)

a-p-S6 Ser235/236

a-S6 Total

Merge

a-S6K1 Total

a-LST8 Total

a-b-Actin Total

TargetKnockdown

BiomarkerRepression

Serum StarveCondition

Final 384-well library plate layout for 1° screen

• 5 source plates/library

• 15 source plates total

• Screen in triplicate = 45 plates

• Screen 2 conditions = 90 plates

• 50 nM concentration

NTC siRNA Pool

S6K1/2 siRNA Pool

LST8 & S6K1/2 siRNA Pools

PLK1 siRNA Pool

miRNA Neg. Control 1

miRNA Neg. Control 2

Empty

miRNA Library Reagents

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Phase 3. Perform miRNA 1° screen

3A. Gain-of-function miRNA mimic libraries (2)

Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Plate-based Heat Map of Raw Mean % p-S6

Active Data

ConditionSerum StarvePlate ID

PL-50684

PL-50685

PL-50686

PL-50687

PL-50688

Conclusions:

1. Hits appear to be evenly distributed

2. Serum starvation sensitization

3. Absence of edge effects

Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Replicate Correlation Plots of Raw

Mean % pS6 Active DataR

ep

lic

ate

A

Replicate B

R2=0.949

Re

pli

ca

te A

Replicate C

R2=0.934

Replicate C

Re

pli

ca

te B

R2=0.944

ConditionSerum Starve

Re

pli

ca

te A

Replicate B

R2=0.939

Re

pli

ca

te A

Replicate C

R2=0.929

Replicate C

Re

pli

ca

te B

R2=0.952

N1: NTC siRNAN2: All Stars siRNAP1: S6K1/2 siRNAP2: LST8+S6K1/2 siRNAX: miRNA Library

Conclusions:

1. Experimental replicates highly correlated.

2. Absence of gross outliers

Screening Data Visualization: miScript miRNA Mimic Library (Qiagen): Plate/Well-Based Scatter Plot Raw

Mean % pS6 Active Data

Serum Starve color by Serum


Conclusions:

1. Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status

2. A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Starve

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Plate/Well

Me

an

% p

S6

Ac

tiv

e

Screening Data Analysis: miScript miRNA Mimic Library (Qiagen): Hit Selection

Serum Starve

Data Analysis Workflow:

1. Z-score normalization relative to miRNA negative control (e.g. AllStars siRNA)

2. One-tailed t-test assuming unequal variance

3. Hit selection: p<0.01

4. “High-confidence” hit selection: Must score in both serum and starve conditions

z = x - m

d

Formula:

Where:

x = raw % pS6 active valuem = miRNA negative control meand = miRNA negative control s.d.

394 388229

Primary ScreenQiagen

“High-confidence” Hits

Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Plate-based Heat Map of Raw Mean % p-S6

Active Data


PL-50689

PL-50690

PL-50691

PL-50692

PL-50693

Conclusions:


2. Serum starvation sensitization


Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Replicate Correlation Plots of Raw

Mean % pS6 Active DataR

ep

lic

ate

A

Replicate B

R2=0.962

Re

pli

ca

te A

Replicate C

R2=0.964

Replicate C

Re

pli

ca

te B

R2=0.970


Re

pli

ca

te A

Replicate B

R2=0.974

Re

pli

ca

te A

Replicate C

R2=0.969

Replicate C

Re

pli

ca

te B

R2=0.968


Conclusions:



Screening Data Visualization: Pre-miR miRNA Mimic Library (Ambion): Plate/Well-Based Scatter Plot Raw

Mean % pS6 Active Data



Conclusions:

1. Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status

2. A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls)

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Starve

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Plate/Well

Me

an

% p

S6

Ac

tiv

e

Screening Data Analysis: Pre-miR miRNA Mimic Library (Ambion): Hit Selection

Serum Starve






z = x - m

d

Formula:

Where:


369 540243

Primary ScreenAmbion


Screening Data Analysis: Gain-of-Function Library Hit Selection Summary

Serum Starve

369 540243




Serum Starve

394 388229




472 miRNA mimics cherry picked for 2° Screen

Phase 3. Perform miRNA 1° screen

3B. Loss-of-function miRNA inhibitor library

Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate-based Heat Map of Raw

Mean % p-S6 Active Data


PL-50694

PL-50695

PL-50696

PL-50697

PL-50698

Conclusions:

1. Few hits compared to gain-of-function miRNA mimic libraries


3. Serum starvation sensitization?


Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Replicate Correlation

Plots of Raw Mean % pS6 Active DataR

ep

lic

ate

A

Replicate B

R2=0.914

Re

pli

ca

te A

Replicate C

R2=0.930

Replicate C

Re

pli

ca

te B

R2=0.950


Re

pli

ca

te A

Replicate B

R2=0.920

Re

pli

ca

te A

Replicate C

R2=0.965

Replicate C

Re

pli

ca

te B

R2=0.928


Conclusions:



Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate/Well-Based

Scatter Plot Raw Mean % pS6 Active Data



Conclusions:

1. Qualitative assessment shows fewer miRNA inhibitor hits compared to miRNA mimic libraries (as expected).

2. Effect of miRNA inhibitors on p-S6 status tends to be less penetrant.

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Starve

Me

an

% p

S6

Ac

tiv

e

Plate/Well

Plate/Well

Me

an

% p

S6

Ac

tiv

e

Screening Data Analysis: miRCURY LNA™ miRNA Knockdown Library (Exiqon): miRNA Hit Selection






z = x - m

d

Formula:

Where:


Serum Starve

118 17441

Primary ScreenExiqon


Screening Data Analysis: Overall Hit Selection Summary

Serum Starve

369 540243




Serum Starve

394 388229




513 total miRNA reagents cherry picked for 2° Screen

Serum Starve

118 17441

Primary ScreenExiqon


miRCURY LNA™ miRNA Knockdown Library

(Exiqon)

Future Directions…

• Phase 4. Perform secondary screen in LNCaP cells to eliminate cell-type specific hits

• Phase 5. Further characterization of mTORC1 function for strongest hits

• Phase 6. Determine mechanism of action for strongest hits

Acknowledgments

John BlenisJanie Zhang

Greg Hoffman

microRNA Screeners Consortium

ICCB-LCaroline Shamu

Sean Johnston

Jen Nale

Katrina Rudnicki

Stewart Rudnicki

Dave Wrobel

TTP LabTechBen Schenker

Cell Signaling Technologies (CST)

Randy Wetzel

EMD Serono

Mei Zhang

Brian Healey

Qiagen

Ambion

Exiqon

Dharmacon/Thermo Scientific

Documents

Ttp Lab Tech Talk 051810