systems biology and genome informatics of m. tuberculosis and other infectious diseases october...
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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: [email protected]
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