Systems Biology and Genome Informatics of M. tuberculosis and other infectious diseases October 12-14, 2008 RUSSIA Molecular Players in Host-Pathogen Interaction: Novel roles for noncoding RNAs Dr. Vinod Scaria Scientist GN Ramachandran Knowledge Center for Genome Informatics Institute of Genomics and Integrative Biology (IGIB-CSIR) Delhi, INDIA

Exportin 5 miRNA with RISC Messenger RNA pre-miRNA miRNA-miRNA* Drosha/Pasha Dicer RNAPol II Polypeptide AAAAA pri-miRNA 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 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 Transcript Polypeptide MODEL-III MODEL-IV MODEL-II MODEL-I DROSHA/PA SHA 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 b c 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

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

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

Hariharan et al, Biochem Biophys Res Commun Dec 2;337(4): hsa-miR-29a hsa-miR-29b hsa-miR-149 hsa-miR-378 hsa-miR-324-5p Conservation of Targets lowaveragehigh *

PBMC HeLa HEK293T Marker hsa-miR-29a hsa-miR-29b hsa-miR-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- P 32 -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 RTRT 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 Protein 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-Nef pEGFP-miRNA +++ Control vector 29b 29a 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 and for inhibition by 29a and 29b respectively, as compared to control vector Nef Tubulin pNL4.3 pEGFP-miRNA a 29b Control Vector pEGFP-miRNA pNL b 29a Control vector p24 pg/ml 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 SEGMENT4 Hemagglutinin (HA) facilitates entry of the virus into the cell

The target site sequences of the human microRNAs in the Influenza genome are highly conserved hsa-mir-507 target sitehsa-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 sitehsa-mir-136 target site

The Chicken Genome lacked both of the microRNAs Virus I have my microRNAs Virus I’m doomed

Oncogenesis 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

Scaria and Jadhav, Retrovirology 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 Machine Datasets Training and Quality Measures Model NameSensitivitySpecificity Model %87% Model Model Model %78% Model %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

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 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 microRNA ST13ebv-miR-BART14-5p CCL22ebv-miR-BART14-5p SFRP1ebv-miR-BART6-3p DAPebv-miR-BART14-5p TUSC2ebv-miR-BART6-3p HEMK1ebv-miR-BART20-5p APC2ebv-miR-BHRF1-1 RNF2ebv-miR-BART14-5p VHLebv-miR-BART8-3p APCebv-miR-BART17-3p UQCRebv-miR-BART3-3p GLTSCR1ebv-miR-BART6-3p CD81ebv-miR-BART20-3p TSSC1ebv-miR-BART14-3p TP73Lebv-miR-BART17-5p WDR39ebv-miR-BART3-3p LRP12ebv-miR-BART11-5p LOH11CR2Aebv-miR-BART17-5p BAP1ebv-miR-BART14-5p ABRebv-miR-BART20-5p, ebv-miR-BART3-3p CTNNA1ebv-miR-BART12 HIC2ebv-miR-BART11-3p KIAA1967ebv-miR-BART17-5p MRVI1ebv-miR-BART4 BIN1ebv-miR-BART17-5p WT1ebv-miR-BART1-3p HYAL3ebv-miR-BHRF1-1 RASSF1ebv-miR-BART3-3p PIK3CGebv-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 microRNA DAPebv-miR-BART14-5p TNFSF14ebv-miR-BART3-3p HRKebv-miR-BHRF1-3 BCL2L14ebv-miR-BHRF1-2 TNFSF12ebv-miR-BART1-5p TNFRSF21ebv-miR-BART8-3p TNFRSF11Bebv-miR-BART11-5p,ebv-miR-BART12 CASP3ebv-miR-BART13 CASP2ebv-miR-BART12 MADDebv-miR-BART17-5p TNFRSF10Debv-miR-BART1-3p TNFRSF12Aebv-miR-BART14-3p PDCD1ebv-miR-BART12 TNFRSF10Bebv-miR-BART12 BCL2L11ebv-miR-BART4 APAF1ebv-miR-BART11-3p Summary of the apoptosis related genes targeted by EBV encoded microRNAs.

GO IDLevelGO TermP-value GO: cell communication 1.11E-09 GO: development 3.00E-05 GO: cell death GO: ,7 induction of programmed cell death GO: ,8 induction of apoptosis GO: protein localization GO: response to external stimulus GO: ,7 positive regulation of apoptosis GO: ,6 positive regulation of programmed cell death 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 2007 Scaria and Jadhav, Retrovirology 2007

hp XC 3000 Cluster 288 Nodes Infiniband Interconnect 4.7 Teraflops hp XC 3000 Cluster 288 Nodes Infiniband Interconnect 4.7 Teraflops Human miRNAs Genome Sequences | 3’UTR sequences Consensus Targets miRanda RNAhybrid TargetScan Large Scale Computation in 288 node 4 TeraFlop Supercomputer Computational pipeline for microRNA target prediction

TargetmiR: Features microRNA Details and Validation Methods Interface Validated Targets Predicted Targets

SCORING MATRIX Human 3’UTRdb Seed Counts db Seed Region 3’UTR Artifical miRNA (amiRNA) Artificially designed microRNAs (amiRNAs) HIV-1 transcripts targeted by amiRNAs miranda rna22 mirtif rnahybrid miracle AMIRNA-001 GAGX AMIRNA-002 GAGX AMIRNA-003 ENV AMIRNA-004 ENV AMIRNA-005 POL AMIRNA-006 POL AMIRNA-007 POLX AMIRNA-008 POL AMIRNA-009 POL Computational Validation of Design HIV Genome Ultra-Conserved Regions Computational Design Scaria et al, Cell Microbiol. 2007;9(12): Design of artificial antiviral microRNAs (amiRNAs)

in vitro validation of artificial miRNA CM V Luc SV 40 poly A signal. Target sequence. CM V 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 (Significantly not down regulated) for each treatment compared with no miRNA control). No miRNA miRNA+ TargetShuffled miRNATarget 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 with RISC miRNA-miRNA* Polypeptide Transcript Degradation P Bodies NUCLEUS CYTOPLASM Transcript Viral microRNAs may influence cellular biological processes resulting in oncogenesis Summary

Tools,databases, datasets & reprints Prof.Samir K. Brahmachari Vinod Scaria Manoj Hariharan Shiva Kumar Abhiranjan Prasad Beena Pillai Jasmine Ahluwalia Kartik Soni Souvik Maiti Vaibhav Jadhav Computational Biology Expression Studies microRNA Validation Artificial microRNA Validation Debasis Mitra (NCCS, Pune) Zohrab Zafar Khan Viral Assays