RNA bioinformatics - Göteborgs universitetbio.lundberg.gu.se/courses/ht09/genomics/2009_RNA...RNA bioinformatics 26 IRE: Iron responsive element Essential for oxygen transport, cellular

RNAbioinformatics

Marcela Davila-LopezDepartment of Medical Biochemistry and Cell Biology

Institute of Biomedicine

Medical genomics and bioinformatics, 2009

RNA bioinformatics 2

RNA

mRNA

DNA

Alternative splicing

Translation

ProteinA ProteinB

PolyA tail

5’cap

Mod. / Export

Transcription


Overview

RNA ncRNAImportance disease relatedStructure type

RNA regulatory elements RiboswitchesSECISIREmiRNA

How to predict ncRNA secondary structureMfoldMutual information

How to identify ncRNA genesPattern matching (Patscan)SCFG (CMsearch)Phylogenetic analysis


General concepts


Types and Roles of ncRNAs• mRNA codes for proteins

• A non-coding RNA (ncRNA) is any RNAmolecule that is not translated into a protein

•Genomic stabilityTelomerase

•RNA processing and modificationSpliceosomal snRNAU7 snRNARNAse PRNAse MRP

•Transcription7SK RNA6S RNA

•TranslationtRNAtmRNArRNA

•Protein traffickingSRP RNA

Gisela Storz, Shoshy Altuvia and Karen M. Wasserman (2005)Matera, A.G., R.M. Terns, and M.P. Terns, Nat Rev Mol Cell Biol, 2007.


ncRNA content

Are ncRNAs responsible for the complexity in different organisms?

Huttenhofer, A., P. Schattner, and N. Polacek, Trends Genet, 2005


DiseasePrasanth, K.V. and D.L. Spector, Genes Dev, 2007. Costa, F.F. Drug Discov Today 2009Pandey, A.K., P. Agarwal, K. Kaur, and M. Datta. Cell Physiol Biochem 2009

miR DiabetesMRP RNA Cartilage hair-hypoplasia


DiseaseThiel, C.T., G. Mortier, I. Kaitila, A. Reis, and A. Rauch. Am J Hum Genet 2007

Cartilage hair-hypoplasia

MRP RNA processing of pre-rRNA


Protein - Primary sequenceClustalW

Sequence similarity biological relationsame function


ncRNA - Primary sequence

No sequence conservation,but structural

Covariation: Consistent and compensatory mutations that (often) conserve the structure


A single mutation can radically change the structure

Canonical pairs Non-canonical pairs: GU wobble

http://prion.bchs.uh.edu/bp_type/bp_structure.html


Multibranched loop

Secondary structure

RNA functionality depends on structure

External base

Stem

Loop

Hairpin

Internal loop

Bulge

Pseudoknot


Tertiary structure

RNA tertiary structure comprises interactions of SS:two helicestwo unpaired regionsone unpaired region and a double-stranded helix

Prediction of RNA 3D structure is very difficult and RNA bioinformatics is therefore dominated by the prediction and analysis of secondary structure.


Family structure

tRNA Telomerase RNAP RNA

Each family typically adopts a characteristic secondary structure


However...

Dictyostelium discoideumCandida albicans

Trypanosoma brucei

U1 snRNA

MRP RNA


Examples:RNA regulatory elements

RiboswitchesSECISIREmiRNA


RNA regulatory elements

A cis-regulatory element or cis-element is a region of RNA that regulates the expression of genes located on that same strand.

Trans-regulatory elements are RNAs that may modify the expression of genes, distant from the gene that was originally transcribed to create them.

C D Sm7G

5’ 3’miRNA 5’3’AAUAA AAAAAAAA


Cis and trans regulatory elementsDominski, Z. and W.F. Marzluff. Gene, 2007

Histones

DNA

U7 snRNA

D3

B G

ELsm10

Lsm11 F Symplekin

CPSF-73

CPSF-100

SLBP

ZFP-100

Histone pre-mRNA

Stem-Loop motif of Histone pre-mRNA


Riboswitch2002 Part of an mRNA molecule that can directly bind a small target

molecule, affecting the gene’s activity (Auto-regulation)

• Typically found in the 5’ UTR• Biosynthesis, catabolism and transport of various cellular catabolites

(aminoacids [K,G], cofactors, nucleotides and metal ions)• Most known occur in Bacteria

Tucker, B.J. and R.R. Curr Opin Struct Biol, 2005


Riboswitch examplesSerganov A, Patel DJ. Biochim Biophys Acta. 2009

Transcription Translation

Shine-Dalgarno


Riboswitch identificationHenkin TM. Genes Dev. 2008Mandal M, et al, Cell. 2003

Comparative analysis of upstream regions of several genes:

• BLAST to find UTRs homologous to all UTRs in Bacillus subtilis (e.g)• Inspection for conserved structure RNA-like motifs• Experimental confirmation

Guanine Riboswitch


Selenoproteins

At least 25 selenoproteinsPresent in all lineages of life (bacteria, archaea and eukarya)

Function of most selenoproteins is currently unknown

Prevention of some forms of cancer (?) therapeutic targets (?)

Selenium antioxidant activity chemopreventive, antiinflammatory, and antiviral properties

Moderate selenium deficiency has been linked to:increased cancer and infection risk, male infertility, decrease in immune and thyroid function, and several neurologic conditions, including Alzheimer’s and Parkinson’s disease

Not a cofactor incorporated into the polypeptide chain asselenocysteine [SEC] (21st aa)

Papp, LV, et al. ANTIOXIDANTS & REDOX SIGNALING 2007


SECISKryukov, G.V., et al., Science, 2003

Overall low sequence similarities

Secondary structures are highly conserved and contain consensus sequences that are indispensable for Sec incorporation

Eukaryotic SECIS: non-canonical A-G base pairs K-turn motif



IRE: Iron responsive element

Essential for oxygen transport, cellular respiration, and DNA synthesis

[↓] cellular growth arrest and death anemia, retardation in children

[↑] generate hydroxyl or lipid radicals damage lipid membranes, proteins, and nucleic acids.

hemochromatosis, liver/heart failure

Iron:

Balance: iron-responsive element/iron regulatory protein regulatory system

Muckenthaler MU, Galy B, Hentze MW. AnnuRev Nutr. 2008

Piccinelli P, Samuelsson T, RNA, 2007

26–30 nts (long hairpin) CAGUGN apical loop sequence5’UTR – 3’UTR


IRE regulationMuckenthaler MU, Galy B, Hentze MW. AnnuRev Nutr. 2008


Gene Identificationand

SS prediction


Protein vs RNA identification

Sequence-similarity based

Conserved primary sequence

Protein RNA

Promoters (Pol II)Not Conserved primary sequencePromoters (Pol II, Pol III)Sequence-similarity basedSecondary structure basedComparative genomics


Methods

•Nussinov algorithm•Mfold (prediction of secondary structure)•Analysis of mutual information•Pattern matching•SCFG (Stochastic context-free grammar models)•Phylogenetic analysis

Nussinov algorithm: Find the structure with the most base pairs (dynamic programming)

Drawbacks:Not unique structureTesting all possible structures

numerically impossible


Methods


Zuker folding algorithm (1981): The correct structure is the one with the lowest equilibrium free energy (ΔG) which is the sum of individual contributions from loops, base pairs and other secondary structure elements

Every system seeks to achieve a minimum of free energy (MFE)

However ... The structure with the lowest MFE not always is the biological relevant


Methods


Mutual information: quantity that measures the mutual dependence of the two variables (two positions). The unit of measurement is the bit.

Covarying positions:consitent and compensatory mutations that conserve the structure


Mutual information - example

fxi = fq of one of the 4 bases in column ifxixj = fq of one of the 16 base-pairs in

columns i and jMij = 2 max value informative

= 0 conserved positions not informative

1 2 3 4G G C CG C C GG A C UG U C A

Columns 2-4:GCCGAUUA

fG=1/4 fC=1/4 fGC=1/4fC=1/4 fG=1/4 fCG=1/4fA=1/4 fU=1/4 fAU=1/4fU=1/4 fA=1/4 fUA=1/4

fGC*log2(fGC/fG*fC)1/4*log2(0.25/(0.25*0.25)) = 0.51/4*log2(0.25/(0.25*0.25)) = 0.51/4*log2(0.25/(0.25*0.25)) = 0.51/4*log2(0.25/(0.25*0.25)) = 0.5

MI = 2Columns 1-3:GC fG=4/4 fC=4/4 fGC=4/4 4/4*log2(1/(1*1)) = 0

MI = 0


Mutual information – excercise


Mutual information plot

Diagonals of covarying positions correspond to the four stems of the tRNA. Dashed lines indicate some of the addtional tertiary contacts observed in the yeast tRNA-Phe crytal structure.


Methods


p1 = 5...7GGAA~p1

Patscan: is a pattern matcher (deterministic motifs as well as secondary structure constraints) which searches protein or nucleotide sequence archives

Drawback:Yes/No answer


PatScan - Example

r1={au,ua,gc,cg,gu,ug}

r1~p2[1,0,1]

p1=6...7 ~p1

4...4

p2=8...9

GGG [1,0,0] 3...4

r1={au,ua,gc,cg,gu,ug} p1=6...7GGG [1,0,0]p2=8...94...4r1~p2[1,0,1]3...4

[1,0,0]Mismatch

DeletionInsertion


Methods


Regular grammar primary sequence models

T aS | bT | ɛaT aaS aabS aabaT aabaɛ aaba

S aT | bS

Model repeat regions (ex. FMR-1 triplet repeat region)

S gW1W1 cW2W2 gW3W3 cW4W4 gW5W5 gW6W6 cW7 | aW4 | cW4W7 tW8W8 g

gcg cgg ctggcg cgg agg cgg ctggag agg ctggcg agg cgg ctggcg agg cgg cgg


Methods


Context-free grammar primary sequence models palindromes

S aSa | bSb | aa | bb S aSa aaSaa aabSbaa aabaabaa

RNA secondary structureCAGGAAACUGGCUGCAAAGCGCUGCAACUG

S aW1u | cW1g | gW1c |uW1aW1 aW2u | cW2g | gW2c |uW2aW2 aW3u | cW3g | gW3c |uW3aW3 ggaa | gcaa

G AG AG.CA.UC.G

C AG AU.AC.GG.C

C AG AUxCCxUGxG


Methods


Stochastic regular grammar weighted primary sequence models (probabilistic)

S rW1 S kW1 S nW1

(0,45) (0,45) (0,10)

Hidden markov modelsA

C G

T

ɛβ


Methods


Stochastic context-free grammar Covariance models: probabilistic models that flexibly describe the secondary structure and primary sequences consensus fo an RNA sequence family


Infernal Package

•Search for additional and family-related sequences in sequence databases


CM exampleBuild a model (automatically) from an existing sequence alignment


CM example


Database containing information about ncRNA families and other structured RNA elements.


Structural alignments

Phylogenetic distribution


Methods


- Conserved elements alignment- SCFG Secondary structure- Fold- Phylogenetic evaluation

EVOfold:


miRNA


miRNANegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.

C D Sm7G

5’ 3’miRNA

5’3’

AAUAA AAAAAAAATarget

•SS RNA

•~22 nucleotides

•Inhibit the translation of mRNAs to their protein products by biding tospecific regions in the 3ʼ UTR

•Accounts for ~1% of all transcripts in humans and potentially regulate 10%-30% of all genes.

•Expressed ubiquitously and highly conserved in Metazoans (animal kingdom).


miRNANegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.

C D Sm7G

5’ 3’miRNA

5’3’

AAUAA AAAAAAAA

ApoptosisCell prolifertion Cell differentiationDevelopmentOrganism defense against infectionsTissue morphogenesisRegulation of metabolism

CancerViral infectionsNeurodegenerative disordersCardiac pathologiesMuscle disordersDiabetes

Biological processes Diseases

Target


miRNANegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009. He, L. and G.J. Hannon, Nat Rev Genet 2004

C D Sm7G

5’ 3’miRNA

5’3’


Multiple binding sites lin-4 is partially complementary to 7 sites in the lin-14 3′ UTR


miRNA genesKim VN Nat Rev Mol Cell Biol. 2005Winter J et al Nat Cell Biol. 2009

Exonic miRNAs in non-coding transcripts

Intronic miRNAs in non-coding transcripts

Intronic miRNAs in protein-coding transcripts

SingleClustered


miRNA BiogenesisWinter, J., S. Jung, S. Keller, R.I. Gregory, and S. Diederichs. Nat Cell Biol 2009. Paul S. Meltzer, Nature, 2005

Canonical

Non-Canonical


miRNA structureNegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.

miRNA

miRNA*

Interveningloop

High conservation mature miRNALower conservation loop

Human genome ~11 million hairpins

Hairpin structure


miRNA computational identification

Homology search basedBLASTmiRAling, ProMir, microHARVESTER

Gene findingIdentification of conserved genomic regionsFolding of the identified regions (Mfold, RNAfold)Evalutation of hairpinsmiRseeker, miRscan

Neighbour stem loop (~42% of human miRNA genes are clustered together)Check surroundings of a known miRNA for candidate secondary structures

Comparative genomicsBLAST intergenic sequences of two genomes against each otherFilter based on rules inferred based on known miRNAsmiRFinder

Intragenomic matching (A functional miRNA should have at least a target)miRNAs show perfect complementarity to their targets (?)It simultaneously predicts miRNAs and their targetsmiMatcher


miRNA experimental validation through sequencing

Experimental approach:

– Purify small RNAs (15-35 nt)– Deep sequencing of the RNA library.– Map sequence traces to the genome.

Ruby JG. et al. Genome Res., 2007


miRNA Target predictionNegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.

• Predicting miRNA targets in plants is easier, due to the perfectcomplementarity to the miRNAs

• In animals, perfect complementarity is not common– miRNA seed complementarity (6 to 9 nt)– High false positives rate

• Common approach– Experimental evidences – Validated miRNA/target pairs– Tarbase, miRecords

• Computational methods:– Base-pairing rules and binding sites sequence features– Conservation– Thermodynamics

C D Sm7G

5’ 3’miRNA

5’3’



Base-pairing rulesBartel, D.P. 2009. Cell 2009.

6-9 nt, starting usually at P2P1 is typically unpaired or starts with UOften flanked by AUsually no G:U wobbles (vs regulation)

3’ compensatory sites

Canonical sites

Atypical sites

lsy-6/cog-1 3’UTR

5’ dominant sites

May compensate for insufficient basepairing in the seed


More methods ...Negrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.

Search for conserved seeds in the UTRs across different species

Evaluation of ΔG of predicted duplexes usually < -20 Kcal/molDiscard F(+) but favorable interactions not always correspond to

actual duplex

The targe site on the mRNA not involved in any intramolecular bp

Any existing secondary structure must be first removed

Thermodynamics

Structural accesiblity

Conservation


miRNABartel, D.P. 2009. Cell 2009


miRNA gene expression in cancerNegrini, M., M.S. Nicoloso, and G.A. Calin. CurrOpin Cell Biol 2009.


miRNA in CancerLu, J., et al., Nature, 2005


Carlo Croce 2009

A

B

miR-29b or scrambled oligos injection (5 µg)K562 cells injected SC

Days

Tumor size

Stop

0 3 7 10 14

D

* P<0.003

0

200

400

600

800

1000

1200

1400

1600

1800

0 +3Days +7 +10 +14

Tum

or V

olum

e (m

m3 )

Mock

Scrambled

miR-29b

**

miR-29b

Scrambled

C

Tum

or W

eigh

t (gr

ams)

P<0.001

0

0.2

0.4

0.6

0.8

1

1.2

scrambled miR-29b

(A) Diagram illustrating the experimental design of the mice xenograft experiment.

(B) Graphic representing the tumor volume determinations at the indicated days during the experiment for the three groups; mock (n= 6), scrambled (n=12) and synthetic miR-29b (n=12).

(C) Tumor weight averages between scrambled and synthetic miR-29b treated mice groups at the end of the experiment (Day +14). P-values were obtained using t-test. Bars represent ±S.D.

(D) Photographs of two mice injected with miR-29b (left flank) or scrambled (right flank).

MiR-29b inhibits Leukemic growth in vivo.

miRNAs as tumor suppresors


miR DBs

Published miRNAS

Experimentally suported targets

Prediction of miRNAS targets

miRNA-disease relationships reported in the literature.

Documents

RNA bioinformatics - Göteborgs universitetbio.lundberg.gu.se/courses/ht09/genomics/2009_RNA...RNA bioinformatics 26 IRE: Iron responsive element Essential for oxygen transport, cellular