Systems Biology and Genome Informatics of M. tuberculosis and other infectious diseasesOctober 12-14, 2008 RUSSIA

Molecular Players in Host-Pathogen Interaction: Novel roles for noncoding RNAs

Dr. Vinod Scaria

ScientistGN Ramachandran Knowledge Center for Genome InformaticsInstitute of Genomics and Integrative Biology (IGIB-CSIR)Delhi , INDIA

E-mail: vinods@igib.res.in

Exportin 5

miRNA withRISC

Messenger RNA

pre-miRNA

miRNA-miRNA*

Drosha/Pasha

Dicer

RNAPol II

Polypeptide

AAAAA

pri-miRNATranscript

Transcript

Degradation

P Bodies

Scaria et al. Retrovirology 2006

microRNA Biogenesis and action

?Host-Pathogen Interactions: The role of functional

noncoding RNAs

Host Pathogen Interaction

Host Pathogen Interaction

• Can Human miRNA act as first line of molecular defense?

• Can Human miRNA modulate pathogen proliferation and disease progression?

• Can virus encoded microRNAs regulate cellular processes which culminate in disease ?

Human (Host) Cell

Pathogen

Viral Transcript

Host Transcript

RNAPol II

Host Transcript

Viral TranscriptPolypeptide

MODEL-III

MODEL-IV

MODEL-II

MODEL-I

DROSHA/PASHA

EXPORTIN

DICER RISC

Model of microRNA mediated host-virus crosstalk

Scaria et al. Retrovirology 2006

microRNA Sequences

Computational Pipeline for Prediction of High-Confidence microRNA Targets

Viral Genome Reference Sequence

+

High Confidence Target Prediction using Consensus of 3 Algorithms

• miRanda• RNAhybrid• TargetScan

Sequence Datasets

Computational Target Prediction

Secondary Structure Prediction of messenger RNA

Calculation and Comparison of Thermodynamic Stabilities

High Confidence miRNA-Target Pairs

Verification of Predictions

Verification of thermodynamically feasible microRNA-Target pairs

miRacle is a second generation microRNA

prediction server incorporating target

secondary structure and accessibility

Predicts the thermodynamically feasible microRNA-Target pairs High Accuracy, Significantly reducing on false positives

Case a: Binding Site in the Loop/Unstructured Region

Case b: Binding Site in the Stem

Case c: Binding Site in Stem-Loop

a

bc

miRNA

+

Sequence Based Prediction of potential Target Sites on mRNA

Secondary Structure Prediction of messenger RNA

Calculation and Comparison of Thermodynamic Stabilities

Developed in Collaboration with Dr. Souvik Maiti’s lab

http://miracle.igib.res.in

Five Human microRNAs can possibly target HIV genes.

Targets are Conserved in other HIV-1 Clades also

SEARCH

SELECT ANALYZE

START

Developed in Collaboration with Dr. Beena Pillai’s Group

http://miracle.igib.res.in

microRNAs with putative targets in HIV are expressed variably in T-cell samples

Hariharan et al, Biochem Biophys Res Commun. 2005 Dec 2;337(4):1214-8.

hsa-miR-29ahsa-miR-29b

hsa-miR-149hsa-miR-378hsa-miR-324-5p

Conservation of Targets

low average high*

PBM

C H

eLa

HEK

293T

Markerhsa

-miR-29a

hsa-m

iR-29b

hsa-m

iR-29c

Product sizes (nucleotides) indicated in parentheses include length of T tails added to improve resolution

The extension product is labeled by introduction of alpha-P32-dCTP into the product at positions indicated in bold. The T tail of varying lengths at the 5’ end was used to improve resolution of products

RT

TTTTTTTT

TTTTTTTT

14 mer oligonucleotides were used to capture the miRNA. The primer(Blue) sequence specific extension (green) of each miRNA due to differences at the 3’ end of the oligonucleotide-miRNA hybrid

Methodology

Detection of microRNAs in Human Cell Lines

Dr. Beena Pillai’s Group

Reporter Construct for Validation of microRNA targets

MCS

Reporter Gene

Promoter

Poly A site

microRNA Target region

Reporter Gene

Promoter

Poly A site

microRNA Target region

Transcript

microRNA

ProteinProtein

Transfected in cells along with the miRNA

If the predicted gene IS actually the target for miRNA

If the predicted gene NOT actually the target for miRNA

Protein expression detected using Reporter assay

Clone into the MCS

3’-GGTAAACTTTAGTCAC-5’* * * * *

5’-UAGCACCAUCUGAAAUCGGUUA-3’

3’-TAAACTTTAGCCAA-5’

5’-UAGCACCAUUUGAAAUCAGUGUU-3’

hsa-mir-29b

hsa-mir-29a

****

Design of LNA modified anti-miRNA molecules against hsa-miR-29a and 29b. Red asterisks indicate positions of modification in the backbone of the anti-miRNA molecule

Locked nucleic acid modified anti-miRNA against hsa-miR-29a and hsa-miR-29b restores reporter activity from the Luc-nef clone in a dose dependent manner

SEM for 3 replicates

Validation of the microRNA target using luciferase reporter gene constructs

Dr. Beena Pillai’s Group

hsa-miR-29a and b inhibit the expression of Nef and HIV-1 replication

pCDNA-HA-NefpEGFP-miRNA

+ ++

Control vector29b29a

Actin

HA-Nef

Expression of Nef analyzed by immunoblotting using HA antibody

hsa-miR-29a and hsa-miR-29b miRNA clones inhibit virus production in Jurkat cells.Asterisks in 3E represent significant p-value of 0.014 and 0.016 for inhibition by 29a and 29b respectively, as compared to control

vector

Nef

Tubulin

pNL4.3

pEGFP-miRNA+ ++

29a 29bControl Vector

pEGFP-miRNApNL4.3 + ++

29b29aControl vector

p24

pg/m

l

With Dr. Debasis Mitra’s Group(NCCS Pune)

Human microRNAs target HA and PB2 genes in Influenza A/H5N1 genome

Polymerase PB2

hsa-mir-507

SEGMENT1

responsible for RNA replication and

transcription

hsa-mir-136

SEGMENT4Hemagglutinin (HA)

facilitates entry of the virus into the cell

The target site sequences of the human microRNAs in the Influenza genome are highly conserved

5'---tccaaaaagatgcaaaa 3'||||||| |||||||

3'gtgaggtttt-ccacgtttt 5'

5'---tccaaaaagatgcaaaa 3'||||||| |||||||

3'gtgaggtttt-ccacgtttt 5'

5' -------tcaaaaggcaatagatggagt 3'|||||| ||| |||||||

3' aggtagtagtttt--gtt---tacctca 5'

5' -------tcaaaaggcaatagatggagt 3'|||||| ||| |||||||

3' aggtagtagtttt--gtt---tacctca 5'

hsa-mir-507 target site hsa-mir-136 target site

*Analysis of 357 sequences of H5N1 Segment 1 and 553 sequences of H5N1 segment 4 available at the NCBI Influenza Resource

Target sites of the human microRNAs are highly accessible

hsa-mir-507 target site hsa-mir-136 target site

http://miracle.igib.res.in

The Chicken Genome lacked both of the microRNAs

Virus

Virus

I have my microRNAs

Virus

I’m doomed

OncogenesisOncogenesis

Viral encoded microRNAs

Virus induced epigenetic changes Viral suppression

of RNAi

Viral genome integration and

mutations

Altered host gene expression

Altered host microRNA expression

Regulatory dysfunction

Mechanisms of microRNAs in viral oncogenesis

Scaria and Jadhav, Retrovirology, 2007

GENOME STRUCTURE AND CHROMATIN ORGANISATION

TRANSCRIPTIONAL REGULATION

SPLICING AND RNA EDITING

GENOME SEQUENCE

POST-TRANSCRIPTIONAL REGULATION

PROTEIN INTERACTION AND SIGNALLING

Viral Genome integrationChromosomal InstabilitiesEpigenetic Changes

Viral encoded transcriptional regulators

Virus encoded microRNAs

Virus encoded suppressors of RNAi

Virus encoded proteins and cell signaling mediated by viral infections

microRNA mediated regulation

Host-Pathogen Interaction: An integrative Model for microRNAs in viral oncogenesis

Scaria and Jadhav, Retrovirology. 2007 Nov 24;4(1):82

Structure ?Sequence ?or both ?

Total number of features of type (i) in the -sequence Total number of triplets in the sequence

Content of feature (i) =

AAACCAUUUCUCGCCAGGCUCAUAUGGUGGUUACAAUACUUUAUCACCAGGGCCGAGGCGCUAGUACAGGUGUGGAUCCCCCCCCUCAAC...((((.(((((((.(((((...(((((((...........))))))))))))..)))).......))).))))...............

AACCCGCCCCCCCCAGCGCUGCUUCAGCUUUCGUAGGCGCUGGCAUUGCCGGCGCGGCUGUUGGUAGCAUAGGUGUUGGGAAGGUGCUUG.....((((..(((((((((((((((((....((..((((((((...)))))))).)).))))).)))...)))))))))..)).))...

de novo prediction of microRNAs

Support Vector MachineDatasets

Training andQuality Measures

Model Name Sensitivity Specificity

Model 4.31 68% 87%

Model 3.04 69.7 85.32

Model 4.76 69.3 86

Model 4.01 77% 78%

Model 4.100 67% 78%

Table 1. Sensitivity and Specificity of top 5 models.

Prediction Accuracy in Comparison with other algorithms

The number in brackets following the organism name denotes the total number of entries in miRbase and that following the number of positive predictions is the percentage positive predictions

Prediction of microRNAs using Machine Learning Algorithms

INPUT SEQUENCEINPUT SEQUENCE

RNAfoldRNAfold

Sequence Composition

Sequence Composition

BLASTBLAST

SVM ModelSVM Model

libSVM

SSEARCHSSEARCHRNAfoldRNAfold

OUTPUT

OUTPUT

HAIRPIN SEQUENCES

HAIRPIN SEQUENCES

CCAUCAGUGUUCAUAAGGAAUGU(((((..(((((.(((((((.((

Mir-abelaMir-abela

BayesmiRNAfind

BayesmiRNAfind

Hairpin sequences

Gene/Genome sequences

Da

ta E

xch

an

ge

be

twee

n se

rvers

Integrated tools/servers

INPUT SEQUENCEINPUT SEQUENCE

RNAfoldRNAfold

Sequence Composition

Sequence Composition

BLASTBLAST

SVM ModelSVM Model

libSVM

SSEARCHSSEARCHRNAfoldRNAfold

OUTPUT

OUTPUT

HAIRPIN SEQUENCES

HAIRPIN SEQUENCES

CCAUCAGUGUUCAUAAGGAAUGU(((((..(((((.(((((((.((

Mir-abelaMir-abela

BayesmiRNAfind

BayesmiRNAfind

Hairpin sequences

Gene/Genome sequences

Da

ta E

xch

an

ge

be

twee

n se

rvers

Integrated tools/servershttp://miracle.igib.res.in

EBV encoded microRNAs (32)EBV encoded microRNAs (32)

Human 3’UTRs of Transcripts(Ensembl 42)

Human 3’UTRs of Transcripts(Ensembl 42)

Functional Analysis of the Genes and their Interactomes

Functional Analysis of the Genes and their Interactomes

High Confidence Targets predicted by miRanda, RNAhybrid and TargetScan

High Confidence Targets predicted by miRanda, RNAhybrid and TargetScan

Computational Analysis

Protocol for Prediction of Human targets for EBV encoded microRNAs

Target Gene EBV encoded microRNAST13 ebv-miR-BART14-5pCCL22 ebv-miR-BART14-5pSFRP1 ebv-miR-BART6-3pDAP ebv-miR-BART14-5pTUSC2 ebv-miR-BART6-3pHEMK1 ebv-miR-BART20-5pAPC2 ebv-miR-BHRF1-1RNF2 ebv-miR-BART14-5pVHL ebv-miR-BART8-3pAPC ebv-miR-BART17-3pUQCR ebv-miR-BART3-3pGLTSCR1 ebv-miR-BART6-3pCD81 ebv-miR-BART20-3pTSSC1 ebv-miR-BART14-3pTP73L ebv-miR-BART17-5pWDR39 ebv-miR-BART3-3pLRP12 ebv-miR-BART11-5pLOH11CR2A ebv-miR-BART17-5pBAP1 ebv-miR-BART14-5p

ABR ebv-miR-BART20-5p, ebv-miR-BART3-3pCTNNA1 ebv-miR-BART12HIC2 ebv-miR-BART11-3pKIAA1967 ebv-miR-BART17-5pMRVI1 ebv-miR-BART4BIN1 ebv-miR-BART17-5pWT1 ebv-miR-BART1-3pHYAL3 ebv-miR-BHRF1-1RASSF1 ebv-miR-BART3-3pPIK3CG ebv-miR-BART10

Summary of the tumor suppressor genes which are potential targets to EBV encoded microRNAs. The tumor suppressor genes derived from the Tumor Suppressor Gene Database (TSGdb).

Target Gene EBV encoded microRNADAP ebv-miR-BART14-5pTNFSF14 ebv-miR-BART3-3pHRK ebv-miR-BHRF1-3BCL2L14 ebv-miR-BHRF1-2TNFSF12 ebv-miR-BART1-5pTNFRSF21 ebv-miR-BART8-3pTNFRSF11B ebv-miR-BART11-5p,ebv-miR-BART12CASP3 ebv-miR-BART13CASP2 ebv-miR-BART12MADD ebv-miR-BART17-5pTNFRSF10D ebv-miR-BART1-3pTNFRSF12A ebv-miR-BART14-3pPDCD1 ebv-miR-BART12TNFRSF10B ebv-miR-BART12BCL2L11 ebv-miR-BART4APAF1 ebv-miR-BART11-3p

Summary of the apoptosis related genes targeted by EBV encoded microRNAs.

GO ID Level GO Term P-valueGO:0007154 3 cell communication 1.11E-09

GO:0007275 2 development 3.00E-05

GO:0008219 4 cell death 0.00017

GO:0012502 8,7 induction of programmed cell death 0.00033

GO:0006917 9,8 induction of apoptosis 0.00033

GO:0008104 4 protein localization 0.00058

GO:0009605 4 response to external stimulus 0.00063

GO:0043065 8,7 positive regulation of apoptosis 0.0017

GO:0043068 7,6 positive regulation of programmed cell death 0.00189

GO Terms Enriched in the Target Gene set (p values after correction for multiple testing)

Specific Gene Ontology Classes are enriched in the target gene set.

Cellular Targets of EBV encoded microRNAs are enriched in genes involved in Apoptosis and Tumour Supression

*Protein Interactions are from Human Protein Interaction map (HiMap)

Scaria et al, Cell Microbiology 2007Scaria and Jadhav, Retrovirology 2007

hp XC 3000 Cluster288 NodesInfiniband Interconnect4.7 Teraflops

hp XC 3000 Cluster288 NodesInfiniband Interconnect4.7 Teraflops

Human miRNAs

Human miRNAs Genome Sequences | 3’UTR

sequences

Genome Sequences | 3’UTR sequences

Consensus Targets

Consensus Targets

• miRanda• RNAhybrid•TargetScan

Large Scale Computation in 288 node 4 TeraFlop Supercomputer

Large Scale Computation in 288 node 4 TeraFlop Supercomputer

Computational pipeline for microRNA target prediction

http://miracle.igib.res.in

TargetmiR: Features

microRNA Details and Validation Methods

microRNA Details and Validation Methods

InterfaceInterface

Validated TargetsValidated Targets

Predicted TargetsPredicted Targets

http://miracle.igib.res.in

SCORING MATRIX

Human3’UTRdb

SeedCounts db

Seed Region

3’UTR

Artifical miRNA(amiRNA)

Computational Validation of Design

HIV Genome

Ultra-Conserved Regions

Computational Design

Scaria et al, Cell Microbiol. 2007;9(12):2784-2794

Design of artificial antiviral microRNAs (amiRNAs)

in vitro validation of artificial miRNA

CMV Luc

SV 40 poly A signal.Target sequence.

CMV Pre-miRNA

SV 40 poly A signal.

pMir reporter.

pSilencer.

The target sequence was cloned into the vector after the luciferase gene to form a fusion transcript (pmiR-reporter) and miRNA expression vector (pSilencer) where pre-miRNA were cloned. The luciferase activity would be decreased by binding of miRNAs to the 3 UTR of Firefly luciferase gene.

Construct map of the plasmids used for the luciferase assay

Luciferase activity of the reporter gene in the absence or presence of the amiR-01, amiR-04 and amiR-06 or either of reporter vector or miRNA expression vector shuffled measured. 293T cells were co-transfected with both the reporter gene and miRNA expression vector (pSiIencer). Data show the mean of five independent transfections (error bars indicate standard deviations; t-test used for statistical calculations;

*P < 0.001 (Significantly down regulated) and # p>0.001 (Significantly not down regulated) for each treatment compared with no miRNA control).

Amir_01 Amir_04 Amir_060

20

40

60

80

100

NO miRNA miRNA and its target miRNA Shuffle Taget Shuffle

* *

Re

lati

ve

Lu

cif

era

se

va

lue

s

(%C

on

tro

l)

*

No miRNA miRNA+ Target Shuffled miRNA Target Shuffled

Down-regulation of HIV target sequence by artificial miRNA.

In Collaboration with Dr. Souvik Maiti’s Group

Human microRNAs have conserved targets in viral genes

Synthetic/Artifical miRNAs or miRNA analogs may be used as therapeutics

miRNA levels in Human can be used as a molecular marker for disease susceptibility and prognosis.

Exportin 5

Drosha

Dicer

pre-miRNA

RNAPol II

pri-miRNA

miRNA withRISC

miRNA-miRNA*

Polypeptide

Transcript

DegradationP Bodies

NUCLEUS

CYTOPLASM

Transcript

Viral microRNAs may influence cellular biological processes resulting in oncogenesis

Summary

RNA@IGIB

Tools,databases, datasets & reprints

http://miracle.igib.res.in

Prof.Samir K. Brahmachari

Vinod ScariaManoj HariharanShiva KumarAbhiranjan Prasad

Beena PillaiJasmine AhluwaliaKartik Soni

Souvik MaitiVaibhav Jadhav

Computational Biology

Expression StudiesmicroRNA Validation

Artificial microRNAValidation

Debasis Mitra (NCCS, Pune)Zohrab Zafar KhanViral Assays

RNA@IGIB http://miracle.igib.res.in