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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 E-mail: [email protected] microRNA Biogenesis and action Dicer miRNA-miRNA* pre-miRNA miRNA with RISC Messenger RNA AAAAA Exportin 5 Drosha/Pasha Transcript Degradation pri-miRNA Transcript RNAPol II P Bodies Polypeptide Scaria et al. Retrovirology 2006 Host-Pathogen Interactions: The role of functional noncoding RNAs Human (Host) Cell Host Pathogen Interaction Pathogen • 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 ? Model of microRNA mediated host-virus crosstalk MODEL-II DICER MODEL-I MODEL-III RISC MODEL-IV EXPORTIN DROSHA/PA SHA Host Transcript Host Transcript Viral Transcript Polypeptide Viral Transcript RNAPol II Scaria et al. Retrovirology 2006 Computational Pipeline for Prediction of HighConfidence microRNA Targets Sequence Datasets + microRNA Sequences Viral Genome Reference Sequence Computational Target Prediction High Confidence Target Prediction using Consensus of 3 Algorithms • miRanda • RNAhybrid • TargetScan Verification of Predictions Verification of thermodynamically feasible microRNA-Target pairs Secondary Structure Prediction of messenger RNA Calculation and Comparison of Thermodynamic Stabilities High Confidence miRNATarget Pairs miRNA + miRacle is a second generation microRNA prediction server incorporating target secondary structure and accessibility a Predicts the thermodynamically feasible microRNA-Target pairs High Accuracy, Significantly reducing on positives c b false Sequence Based Prediction of potential Target Sites on mRNA Case a: Binding Site in the Loop/Unstructured Region Secondary Structure Prediction of messenger RNA Calculation and Comparison of Thermodynamic Stabilities Case b: Binding Site in the Stem Case c: Binding Site in Stem-Loop http://miracle.igib.res.in 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 http://miracle.igib.res.in START SEARCH SELECT Developed in Collaboration with Dr. Beena Pillai’s Group ANALYZE microRNAs with putative targets in HIV are expressed variably in T-cell samples hsa-miR-29a hsa-miR-29b hsa-miR-149 hsa-miR-378 hsa-miR-324-5p Conservation of Targets * low average high Hariharan et al, Biochem Biophys Res Commun. 2005 Dec 2;337(4):1214-8. Detection of microRNAs in Human Cell Lines Methodology R T TTTTTTTT HEK293T HeLa 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 PBMC TTTTTTTT The extension product is labeled by introduction of alphaP32-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 Dr. Beena Pillai’s Group Product sizes (nucleotides) indicated in parentheses include length of T tails added to improve resolution Reporter Construct for Validation of microRNA targets Poly A site MCS microRNA Target region Reporter Gene Clone into the MCS Promoter Poly A site microRNA Target region Reporter Gene Transfected in cells along with the miRNA Promoter Transcript If the predicted gene IS actually the target for miRNA If the predicted gene NOT actually the target for miRNA microRNA Protein Protein Protein expression detected using Reporter assay Validation of the microRNA target using luciferase reporter gene constructs hsa-mir-29a 5’-UAGCACCAUCUGAAAUCGGUUA-3’ 3’-TAAACTTTAGCCAA-5’ hsa-mir-29b * * * * 5’-UAGCACCAUUUGAAAUCAGUGUU-3’ 3’-GGTAAACTTTAGTCAC-5’ * * * * * 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 SEM for 3 replicates 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 Dr. Beena Pillai’s Group hsa-miR-29a and b inhibit the expression of Nef and HIV-1 replication pEGFP-miRNA pCDNA-HA-Nef 29b + 29a + Control vector + HA-Nef Actin pEGFP-miRNA pNL4.3 Nef Control Vector + 29a + 29b + p24 pg/ml Expression of Nef analyzed by immunoblotting using HA antibody Tubulin pNL4.3 pEGFP-miRNA + + + Control vector 29a 29b 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 With Dr. Debasis Mitra’s Group(NCCS Pune) Human microRNAs target HA and PB2 genes in Influenza A/H5N1 genome Polymerase PB2 SEGMENT1 hsa-mir-507 responsible for RNA replication and transcription Hemagglutinin (HA) SEGMENT4 hsa-mir-136 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' hsa-mir-507 target site 5' -------tcaaaaggcaatagatggagt 3' |||||| ||| ||||||| 3' aggtagtagtttt--gtt---tacctca 5' 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 http://miracle.igib.res.in hsa-mir-507 target site hsa-mir-136 target site The Chicken Genome lacked both of the microRNAs I have my microRNAs Virus I’m doomed Virus Virus Mechanisms of microRNAs in viral oncogenesis Altered host gene expression Viral genome integration and mutations Viral encoded microRNAs Regulatory dysfunction Oncogenesis Virus induced epigenetic changes Viral suppression of RNAi Altered host microRNA expression Scaria and Jadhav, Retrovirology, 2007 Host-Pathogen Interaction: An integrative Model for microRNAs in viral oncogenesis Virus encoded proteins and cell signaling mediated by viral infections PROTEIN INTERACTION AND SIGNALLING Virus encoded microRNAs POST-TRANSCRIPTIONAL REGULATION Virus encoded suppressors of RNAi SPLICING AND RNA EDITING Viral encoded transcriptional regulators Viral Genome integration Chromosomal Instabilities Epigenetic Changes TRANSCRIPTIONAL REGULATION GENOME STRUCTURE AND CHROMATIN ORGANISATION GENOME SEQUENCE Scaria and Jadhav, Retrovirology. 2007 Nov 24;4(1):82 microRNA mediated regulation de novo prediction of microRNAs Structure ? Sequence ? or both ? AACCCGCCCCCCCCAGCGCUGCUUCAGCUUUCGUAGGCGCUGGCAUUGCCGGCGCGGCUGUUGGUAGCAUAGGUGUUGGGAAGGUGCUUG .....((((..(((((((((((((((((....((..((((((((...)))))))).)).))))).)))...)))))))))..)).))... AAACCAUUUCUCGCCAGGCUCAUAUGGUGGUUACAAUACUUUAUCACCAGGGCCGAGGCGCUAGUACAGGUGUGGAUCCCCCCCCUCAAC ...((((.(((((((.(((((...(((((((...........))))))))))))..)))).......))).))))............... Content of feature (i) = Total number of features of type (i) in the -sequence Total number of triplets in the sequence Support Vector Machine Datasets Training and Quality 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 INPUTSEQUENCE SEQUENCE Hairpin sequences Gene/Genome sequences HAIRPIN HAIRPIN SEQUENCES SEQUENCES SSEARCH SSEARCH CCAUCAGUGUUCAUAAGGAAUGU (((((..(((((.(((((((.(( BLAST BLAST SVM SVMModel Model libSVM Sequence Sequence Composition Composition RNAfold RNAfold OUTPU OUTPU TT http://miracle.igib.res.in Mir-abela Mir-abela BayesmiRNAfi BayesmiRNAfi nd nd Integrated tools/servers Data Exchange between servers RNAfold RNAfold Protocol for Prediction of Human targets for EBV encoded microRNAs Computational Analysis EBV encoded microRNAs (32) Human 3’UTRs of Transcripts (Ensembl 42) High Confidence Targets predicted by miRanda, RNAhybrid and TargetScan Functional Analysis of the Genes and their Interactomes Target Gene EBV encoded microRNA ST13 CCL22 SFRP1 DAP TUSC2 HEMK1 APC2 RNF2 VHL APC UQCR GLTSCR1 CD81 TSSC1 TP73L WDR39 LRP12 LOH11CR2A BAP1 ABR CTNNA1 HIC2 KIAA1967 MRVI1 BIN1 WT1 HYAL3 RASSF1 PIK3CG ebv-miR-BART14-5p ebv-miR-BART14-5p ebv-miR-BART6-3p ebv-miR-BART14-5p ebv-miR-BART6-3p ebv-miR-BART20-5p ebv-miR-BHRF1-1 ebv-miR-BART14-5p ebv-miR-BART8-3p ebv-miR-BART17-3p ebv-miR-BART3-3p ebv-miR-BART6-3p ebv-miR-BART20-3p ebv-miR-BART14-3p ebv-miR-BART17-5p ebv-miR-BART3-3p ebv-miR-BART11-5p ebv-miR-BART17-5p ebv-miR-BART14-5p ebv-miR-BART20-5p, ebv-miR-BART3-3p ebv-miR-BART12 ebv-miR-BART11-3p ebv-miR-BART17-5p ebv-miR-BART4 ebv-miR-BART17-5p ebv-miR-BART1-3p ebv-miR-BHRF1-1 ebv-miR-BART3-3p 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 microRNA DAP TNFSF14 HRK BCL2L14 TNFSF12 TNFRSF21 TNFRSF11B CASP3 CASP2 MADD TNFRSF10D TNFRSF12A PDCD1 TNFRSF10B BCL2L11 APAF1 ebv-miR-BART14-5p ebv-miR-BART3-3p ebv-miR-BHRF1-3 ebv-miR-BHRF1-2 ebv-miR-BART1-5p ebv-miR-BART8-3p ebv-miR-BART11-5p,ebv-miR-BART12 ebv-miR-BART13 ebv-miR-BART12 ebv-miR-BART17-5p ebv-miR-BART1-3p ebv-miR-BART14-3p ebv-miR-BART12 ebv-miR-BART12 ebv-miR-BART4 ebv-miR-BART11-3p Summary of the apoptosis related genes targeted by EBV encoded microRNAs. Specific Gene Ontology Classes are enriched in the target gene set. GO ID Level GO:0007154 3 GO:0007275 2 GO:0008219 4 GO:0012502 8,7 GO:0006917 9,8 GO:0008104 4 GO:0009605 4 GO:0043065 8,7 GO:0043068 7,6 GO Term cell communication development cell death induction of programmed cell death induction of apoptosis protein localization response to external stimulus positive regulation of apoptosis positive regulation of programmed cell death GO Terms Enriched in the Target Gene set (p values after correction for multiple testing) P-value 1.11E-09 3.00E-05 0.00017 0.00033 0.00033 0.00058 0.00063 0.0017 0.00189 Cellular Targets of EBV encoded microRNAs are enriched in genes involved in Apoptosis and Tumour Supression Scaria et al, Cell Microbiology 2007 Scaria and Jadhav, Retrovirology 2007 *Protein Interactions are from Human Protein Interaction map (HiMap) Computational pipeline for microRNA target prediction Human miRNAs Genome Sequences | 3’UTR sequences Large Scale Computation in 288 node 4 TeraFlop Supercomputer • miRanda • RNAhybrid •TargetScan hp XC 3000 Cluster 288 Nodes Infiniband Interconnect 4.7 Teraflops http://miracle.igib.res.in Consensus Targets TargetmiR: Features Interface http://miracle.igib.res.in Predicted Targets Validated Targets microRNA Details and Validation Methods Design of artificial antiviral microRNAs (amiRNAs) Computational Design HIV Genome Ultra-Conserved Regions SCORING MATRIX Human 3’UTRdb Seed Region Seed Counts db Artifical miRNA (amiRNA) Scaria et al, Cell Microbiol. 2007;9(12):2784-2794 3’UTR miracle rnahybrid mirtif HIV-1 transcripts targeted by amiRNAs GAG GAG ENV ENV POL POL POL POL POL rna22 Artificially designed microRNAs (amiRNAs) AMIRNA-001 AMIRNA-002 AMIRNA-003 AMIRNA-004 AMIRNA-005 AMIRNA-006 AMIRNA-007 AMIRNA-008 AMIRNA-009 miranda Computational Validation of Design X X X in vitro validation of artificial miRNA Construct map of the plasmids used for the luciferase assay pMir reporter. Target sequence. CM V SV 40 poly A signal. Luc SV 40 poly A signal. pSilencer. CM V Pre-miRNA The target sequence was cloned into the vector after the luciferase gene to form a fusion transcript (pmiRreporter) 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. Down-regulation of HIV target sequence by artificial miRNA. No miRNA miRNA+ Target Relative Luciferase values (%Control) NO miRNA Shuffled miRNA miRNA and its target 100 Target Shuffled miRNA Shuffle Taget Shuffle 80 60 40 * * * 20 0 Amir_01 Amir_04 Amir_06 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). In Collaboration with Dr. Souvik Maiti’s Group Summary Human microRNAs have conserved targets in viral genes Viral microRNAs may influence cellular biological processes resulting in oncogenesis miRNA-miRNA* Dicer pre-miRNA Exportin 5 miRNA with RISC miRNA levels in Human can be used as a molecular marker for disease susceptibility and prognosis. Drosha Transcript Transcript pri-miRNA P Bodies RNAPol II NUCLEUS Degradation Polypeptide CYTOPLASM Synthetic/Artifical miRNAs or miRNA analogs may be used as therapeutics RNA@IGIB Prof.Samir K. Brahmachari Vinod Scaria Manoj Hariharan Shiva Kumar Abhiranjan Prasad Beena Pillai Jasmine Ahluwalia Kartik Soni Souvik Maiti Vaibhav Jadhav Computational Biology Debasis Mitra (NCCS, Pune) Zohrab Zafar Khan Viral Assays Expression Studies microRNA Validation Artificial microRNA Validation Tools,databases, datasets & reprints http://miracle.igib.res.in RNA@IGIB http://miracle.igib.res.in