Presentation on theme: "Next Generation Sequencers and Progress on Omics Research"— Presentation transcript:
1 Next Generation Sequencers and Progress on Omics Research 13 April 2010BioVisionAlexandria Conference 2010Next Generation Sequencers and Progress on Omics ResearchHarukazu Suzuki PhD.Project Director, RIKEN Omics Science Center, Japan(Yoshihide Hayashizaki, M.D., Ph.D.)(Director, RIKEN Omics Science Center)
2 Various types of Next Generation Sequencers RIKEN OSC as the Japanese sequencing center454SolexaSOLiDHeliScope
3 Data production per day with DNA sequencers Base/daySequencing cost per informationis drastically decreasing every year.3
4 Use of Next Generation Sequencers on Omics research Apply to the CAGE (Cap Analysis of Gene Expression) technologyMammalian Transcriptome AnalysisTranscription Regulation NetworkPromoter analysisTranscription Factor Protein-protein Interactions
5 CAGE: Cap Analysis of Gene Expression Our original technology.CAGE analyzes 5’-end of the capped transcripts by DNA sequencing.Precise transcriptional starting sites (TSSs) are clarified.Expression profile of each promoter (not gene) could be analyzed.PromoterTranscription20-27 bpTag sequencesmRNAsPromoter55
6 DeepCAGE: deep sequencing application of CAGE Precise transcriptional starting sites (TSSs)+ Expression profile of each promoterNat. Genet. 2009+ Sequece-based mapping powerLarge-scale sequencingAGCTAGCTAGCTAGCTAGCTAGAGCTAGGTAGCTAGCTAGCTAGAGCTAGCTAGCTACCTAGCTAGNat. Genet. 2009+ small RNA sequencingMapping on GenomeNat. Genet. 2009
7 Transcription Regulation Network Analysis Cells are programmed in terms of transcription.
8 What’s the program in the cell? Core Regulation Peripheral genes ncRNA 2ncRNA 1ncRNA 3The stable transcriptional states are maintained by multiple factors and state transition requires the concerted actions of multiple transcription factors & microRNAs. The concentration of these factors is kept constant in each stable state.TF gene cytoplasmTF0 RNAGenomeTF gene There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation).RNATF1TF1The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes.RNARNATF gene TF gene TF3EThese homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes“. They determine cellular traits.RNATF gene TF48
9 Cell Differentiation is a transition from basin to basin Our goal1. Development of a pipeline for systematic analysis of transcriptional regulation2. Acquisition of new biological insights from the analysisAttractor Basin 1Attractor Basin 2Transition stateETimeAttractor Basin 1Attractor Basin 2As Yoshihide explained in this morning, there are programs of transcription in cells.The stable state is maintained by multiple TFs, and state transition needs their concerted actions.We call the stable cellular states as attractor basins.Therefore our goal of research isAnd99
10 Timecourse data production H. Suzuki et al. Nature Genetics, 41:5, (2009)Timecourse data productionMonoblastMonocytePMAstimulation0 h1 h2 h4 h6 h12 h48 h96 h72 h
11 Effective concentration H. Suzuki et al. Nature Genetics, 41:5, (2009)Motif ActivityepsCAGE tagm1m1m1m2m3m1m4m1m5Number of CAGE tags that mapped on the same siteReaction efficiency Number of possible binding sites Degree of conservation of the motif Chromatin statusEffective concentration
12 Motif Activity vs. mRNA expression profile Motif activity: promoter regulation activity of TFs that bind the motif.PU.1 motif activityStrongly induced during THP-1 cell differentiationPU.1 mRNA expressionSlightly up-regulatedThese changes are caused by protein phosphrylation.Check PU.1 protein level expression 300020001000Band shift in Western blot.Band shift-down was observed by phosphatase treatment.Nuclear translocation inImmunofluorescenceThe drastic PU.1 motif activity change is considered to occur by both mRNA up-regulation and post-translational modification.
13 Transcription regulation network consisting of 30 core motifs H. Suzuki et al. Nature Genetics, 41:5, (2009)Transcription regulation network consisting of 30 core motifs55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!)Enriched GO: from cell growth related to cell function relatedMotif activityCell cycleMitosisMicrotubele cytoskeleInflammatory responseCell adhesionImmune responseUpMonocyteDownTransientSize of nodes：Significance of motifsEdge supportGreen: siRNARed: literatureBlue: ChIPMonoblast：enriched GO for regulated genes
15 Timecourse data production H. Suzuki et al. Nature Genetics, 41:5, (2009)Timecourse data productionPromoter AnalysisNumber of promoters per geneNumber of genes1369822087312474752542862637169811099610521132122713141516171819202122232425262829Total902624.3M tags: detection level of 1 copy/10 cells at %29,857 active promoters(with novel promoters)23,403 promoters linked to 9,026 genesMultiple-promoters in approximately 60% of genes
16 Expression of retrotransposon elements J. Faulkner et al, Nature Genetics, 41:5, (2009)Expression of retrotransposon elementsMouseHumanSatelliteSimpleTETissuesMore than 35% show strong tissue specificity (17% for other promoters).TissuesRED: overrepresentedGreen: underrepresentedTissuesTissuesP. Carninci et al.
18 An atlas of combinatorial transcriptional regulation in mouse and man Typically, Transcription Factors (TFs) do not act independently, but form complexes with other TFs, chromatin modifiers, and co-factor proteins, which together assemble upon the regulatory regions of DNA to affect transcription.A clear and immediate challenge is to infer how larger combinations of TFs can act together to generate emergent behaviours that are not evident when each factor is considered in isolation.TFs(Activators)Basic TFsTF modulatorsT. Ravasi, et al, Cell, 140, (2010)
19 Human Transcription Factor Interaction Map Natto (fermented beans:Japanese traditional food)Human TF PPIsT. Ravasi, et al, Cell, 140, (2010)
21 TF network associated with tissue origin Development is not only regulated by TF expression level, but also TF-PPI!!Combinatorial interaction among transcription factors is critical for development of tissue (Davidson et al., 2002). To search for TF networks involved in tissue specification, we clustered the TF expression profiles across the 34 human tissues (see above) using two approaches: a basic clustering approach using expression levels only, and a “network-transformed” approach in which we clustered the differences in expression level across each TF-TF interaction, as suggested in a recent study (Taylor et al., 2009). We found that network transformation resulted in a substantially increased separation of tissues into four well-formed clusters (Figure 4a). These corresponded to well-defined tissue classes according to embryonic origin: ectoderm (including Central Nervous System or CNS), mesoderm, endoderm, and cell lines (Figure 4a and Supplementary Figure 1).
22 TF PPI sub-network between Human and Mouse Spatio-temporal Similarity between human and mouse TF PPI networkHuman Frontal CortexMouse Frontal CortexA sub-network related to Neural Development
23 Negative regulationNewly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte.時間(hour)Expression levelDifferentiationT. Ravasi, et al, Cell, 140, (2010)0 hrTime (hour)96 hrs
24 Summary & Future Perspective Power of the Next Generation Sequencers is rapidly changing a way for the Omics Research.Transcriptome Analysis: deepCAGETranscriptional Regulation Network AnalysisPromoter AnalysisTF-PPI analysisGenome: Now $20,000 per person --- $1,000-2,000 within a couple of years.Soon we will know own genome seq.Common events (Cell diffrentiation, Development) to Abnormality (diseases)Large Scale data needs powerful Bioinformatics and collaborations.
25 AcknowledgementThis work has been achieved in the FANTOM4 consortium with support of the Genome Network Project (MEXT).OSC head quartersYoshihide Hayashizaki Jun KawaiPiero Carninci Carsten Daub