1. 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)

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. 3

4. Use of Next Generation Sequencers on Omics 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. 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
Promoter 5 5

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?
Core Regulation　
Peripheral genes　
ncRNA 2
ncRNA 1
ncRNA 3
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.
TF gene　
cytoplasm
TF0　
RNA
Genome
TF gene　
There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation).
RNA
TF1
TF1
The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes.
RNA
RNA
TF gene　
TF gene　
TF3 E
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 8

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
As 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 is
And 9 9

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

11. Effective concentration
H. Suzuki et al. Nature Genetics, 41:5, (2009)
Motif Activity
eps
CAGE tag m1 m1 m1 m2 m3 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. 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　
3000
2000
1000
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. Transcription regulation network consisting of 30 core motifs
H. Suzuki et al. Nature Genetics, 41:5, (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
Cell cycle
Mitosis
Microtubele cytoskele
Inflammatory response
Cell adhesion
Immune response Up
Monocyte
Down
Transient
Size of nodes：
Significance of motifs
Edge support
Green: siRNA
Red: literature
Blue: ChIP
Monoblast
：enriched GO for regulated genes

14. Promoter analysis

15. Timecourse data production
H. Suzuki et al. Nature Genetics, 41:5, (2009)
Timecourse data production
Promoter Analysis
Number of promoters per gene
Number of genes 1
3698 2
2087 3
1247 4
752 5
428 6
263 7
169 8
110 9 96 10 52 11 32 12 27 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29
Total
9026
24.3M tags: detection level of 1 copy/10 cells at %
29,857 active promoters
(with novel promoters)
23,403 promoters linked to 9,026 genes
Multiple-promoters in approximately 60% of genes

16. Expression of retrotransposon elements
J. Faulkner et al, Nature Genetics, 41:5, (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, (2010)

19. Human Transcription Factor Interaction Map
Natto (fermented beans:
Japanese traditional food)
Human TF PPIs
T. Ravasi, et al, Cell, 140, (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!!
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 network
Human Frontal Cortex
Mouse Frontal Cortex
A sub-network related to Neural Development

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

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