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Next Generation Sequencers and Progress on Omics Research

13 April 2010 BioVisionAlexandria Conference 2010. Next Generation Sequencers and Progress on Omics Research. Harukazu Suzuki PhD. Project Director, RIKEN Omics Science Center, Japan (Yoshihide Hayashizaki, M.D., Ph.D.) (Director, RIKEN Omics Science Center).

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Next Generation Sequencers and Progress on Omics Research

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  1. 13 April 2010 BioVisionAlexandria Conference 2010 Next Generation Sequencers and Progress on Omics Research Harukazu 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 center 454 Solexa SOLiD HeliScope

  3. Data production per day with DNA sequencers Base/day Sequencing cost per information is drastically decreasing every year.

  4. Use of Next Generation Sequencers onOmics research Apply to the CAGE (Cap Analysis of Gene Expression) technology Mammalian Transcriptome Analysis Transcription Regulation Network Promoter analysis Transcription Factor Protein-protein Interactions

  5. Promoter 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. Promoter Transcription 20-27 bp Tag sequences mRNAs

  6. DeepCAGE: deep sequencing application of CAGE Precise transcriptional starting sites (TSSs) + Expression profile of each promoter Nat. Genet. 2009 + Sequece-based mapping power Large-scale sequencing AGCTAGCTAGCTAGCTAGCTAG AGCTAGGTAGCTAGCTAGCTAG AGCTAGCTAGCTACCTAGCTAG Nat. Genet. 2009 + small RNA sequencing Mapping on Genome Nat. Genet. 2009

  7. Transcription Regulation Network Analysis Cells are programmed in terms of transcription.

  8. What’s the program in the cell? • The 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. Peripheral genes TF gene cytoplasm Core Regulation TF0 RNA ncRNA 3 Genome TF gene • There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation). RNA TF1 TF1 ncRNA 2 RNA • The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes. ncRNA 1 RNA TF gene TF gene TF3 • These homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes“. They determine cellular traits. RNA TF gene TF4 E

  9. Cell Differentiation is a transition from basin to basin Our goal 1. Development of a pipeline for systematic analysis of transcriptional regulation 2. Acquisition of new biological insights from the analysis Attractor Basin 1 Attractor Basin 2 Transition state E Time Attractor Basin 1 Attractor Basin 2

  10. H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Timecourse data production PMA stimulation 72 h 96 h 0 h 1 h 2 h 4 h 6 h 12 h 48 h Monoblast Monocyte

  11. H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Motif Activity eps CAGE tag m1 m1 m2 m3 m1 m1 m4 m1 m5 Number of CAGE tags that mapped on the same site • Reaction efficiency • Number of possible binding sites • Degree of conservation of the motif • Chromatin status Effective concentration

  12. 3000 2000 1000 0 0 1 4 12 24 96 Motif Activity vs. mRNA expression profile Motif activity: promoter regulation activity of TFs that bind the motif. PU.1 motif activity Strongly induced during THP-1 cell differentiation PU.1 mRNA expression Slightly up-regulated These changes are caused by protein phosphrylation. Check PU.1 protein level expression Band shift in Western blot. Band shift-down was observed by phosphatase treatment. Nuclear translocation in Immunofluorescence The drastic PU.1 motif activity change is considered to occur by both mRNA up-regulation and post-translational modification.

  13. H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Transcription regulation network consisting of 30 core motifs 55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!) Enriched GO: from cell growth related to cell function related Motif activity Immune response Inflammatory response Up Monocyte Cell adhesion Down Transient Size of nodes： Significance of motifs Edge support Green: siRNA Red: literature Blue: ChIP Microtubelecytoskele Monoblast Mitosis Cell cycle ：enriched GO for regulated genes

  14. Promoter analysis

  15. H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009) Timecourse data production Promoter Analysis 24.3M tags: detection level of 1 copy/10 cells at 99.9955% 29,857 active promoters (with novel promoters) 23,403 promoters linked to 9,026 genes Multiple-promoters in approximately 60% of genes

  16. J. Faulkner et al, Nature Genetics, 41:5, 563-571 (2009) Expression of retrotransposon elements Mouse Human Satellite Simple TE Tissues More than 35% show strong tissue specificity (17% for other promoters). Tissues RED: overrepresented Green: underrepresented Tissues Tissues P. Carninci et al.

  17. Transcription Factor Protein-protein Interactions(TF-PPIs)

  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 TFs TF modulators T. Ravasi, et al, Cell, 140, 744-752 (2010)

  19. Human Transcription Factor Interaction Map Natto (fermented beans: Japanese traditional food) Human TF PPIs T. Ravasi, et al, Cell, 140, 744-752 (2010)

  20. A TF PPI sub-network critical for cell fate

  21. TF network associated with tissue origin Development is not only regulated by TF expression level, but also TF-PPI!!

  22. TF PPI sub-network between Human and Mouse Spatio-temporal Similarity between human and mouse TF PPI network Human Frontal Cortex Mouse Frontal Cortex A sub-network related to Neural Development

  23. Expression level 時間(hour) Negative regulation Newly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte. Differentiation 0 hr Time (hour) 96 hrs T. Ravasi, et al, Cell, 140, 744-752 (2010)

  24. Summary & Future Perspective Power of the Next Generation Sequencers is rapidly changing a way for the Omics Research. Transcriptome Analysis: deepCAGE Transcriptional Regulation Network Analysis Promoter Analysis TF-PPI analysis Genome: 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. Acknowledgement This work has been achieved in the FANTOM4 consortium with support of the Genome Network Project (MEXT). OSC head quarters Yoshihide Hayashizaki Jun Kawai Piero Carninci Carsten Daub

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