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)

13 April 2010BioVisionAlexandria Conference 2010

Various types of Next Generation Sequencers

SOLiD

HeliScope

Solexa454

RIKEN OSC as the Japanese sequencing center

Base/day

Data production per day with DNA sequencers

Sequencing cost per informationis drastically decreasing every year.

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

Promoter

mRNAs

Tag sequences

Transcription Promoter

20-27 bp

CAGE: Cap Analysis of Gene Expression

Our original technology. CAGE analyzes 5’-end of the capped transcripts by DNA sequencing.1) Precise transcriptional starting sites (TSSs) are clarified.2) Expression profile of each promoter (not gene) could be analyzed.

DeepCAGE: deep sequencing application of CAGE　　

AGCTAGCTAGCTAGCTAGCTAGAGCTAGGTAGCTAGCTAGCTAGAGCTAGCTAGCTACCTAGCTAG

Large-scale sequencing

Mapping on Genome

Precise transcriptional starting sites (TSSs)

+ Expression profile of each promoter

+ Sequece-based mapping power

+ small RNA sequencing

Nat. Genet. 2009

Nat. Genet. 2009

Nat. Genet. 2009

Transcription Regulation Network Analysis

Cells are programmed in terms of transcription.

What’s the program in the cell? 　　

Genome

cytoplasm

TF gene 　 RNA TF gene 　

RNA

TF1TF1

TF gene 　

RNA

TF gene 　 RNA

TF3

TF4

TF gene 　

RNA TF0 　

• 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.

• There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation).

• The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes.

• These homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes“. They determine cellular traits.

TF1TF3

TF4

TF5

TF10

TF2

miRNA1

miRNA2

TF8

TF9TF7

TF6

Core regulation

Gene1

Gene5

Gene2

Gene3

Gene4

TF11

TF12

Gene8 Gene7

Gene6

Gene9

Gene10

TF1TF3

TF4

TF5

TF10

TF2

miRNA1

miRNA2

TF8

TF9TF7

TF6

Core regulation

Gene1

Gene5

Gene2

Gene3

Gene4

TF11

TF12

Gene8 Gene7

Gene6

Gene9

Gene10

Core Regulation 　

Peripheral genes 　

ncRNA 2

ncRNA 1

ncRNA 3

E

Attractor Basin

1

Attractor Basin

2

Cell Differentiation is a transition from basin to basin 　

Attractor Basin 1

Attractor Basin 2

Transition stateE

Time

Our goal

1. Development of a pipeline for systematic analysis of transcriptional regulation

2. Acquisition of new biological insights from the analysis

Timecourse data production

Monoblast Monocyte

PMAstimulation

0 h 1 h 2 h 4 h 6 h 12 h 48 h 96 h72 h

H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)

m mspmps ARe m mspmps ARe

Motif Activity

m1 m1m1 m2 m3

m1 m4

m1 m5

Reaction efficiency•　 Number of possible binding sites•　 Degree of conservation of the motif•　 Chromatin status

Effective concentration

Number of CAGE tags that mapped on the same site

CAGE tageps

H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)

PU.1 mRNA expressionSlightly up-regulated

Band shift in Western blot.

Nuclear translocation inImmunofluorescence

These changes are caused by protein

phosphrylation.

Band shift-down was observed by phosphatase treatment.

The drastic PU.1 motif activity change is considered to occur by both mRNA up-regulation and post-translational modification.

Motif Activity vs. mRNA expression profile

0

500

1000

1500

2000

2500

3000

1 2 3 4 5 6

3000

2000

1000

0 0 1 4 12 24 96

PU.1 motif activityStrongly induced during THP-1 cell differentiation

Motif activity: promoter regulation activity of TFs that bind the motif.

Check PU.1 protein level expression 　

Cell cycleMitosisMicrotubele cytoskele

Inflammatory response

Cell adhesion

Immune response

Transcription regulation network consisting of 30 core motifs

Edge supportGreen: siRNARed: literatureBlue: ChIP

Motif activity

Up

Down

Transient

： enriched GO for regulated genes

Enriched GO: from cell growth related to cell function related

Size of nodes ：Significance of motifs

55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!)

Monocyte

Monoblast

H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)

Promoter analysis

Timecourse data production

Promoter Analysis

Number of promoters per gene Number of genes1 36982 20873 12474 7525 4286 2637 1698 1109 96

10 5211 3212 2713 1314 1315 816 1017 418 819 120 221 122 223 024 225 026 027 028 029 1

Total 9026

H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)

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

Expression of retrotransposon elements

Mouse Human

RED: overrepresentedGreen: underrepresented

Tissues Tissues

Satellite

Simple

TE

More than 35% show strong tissue specificity (17% for other promoters).

Tissues Tissues P. Carninci et al.

J. Faulkner et al, Nature Genetics, 41:5, 563-571 (2009)

Transcription Factor Protein-protein Interactions(TF-PPIs)

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.

TF modulators

Basic TFs

TFs(Activators)

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

Human Transcription Factor Interaction Map

Natto (fermented beans: Japanese traditional food)Human TF PPIs

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

A TF PPI sub-network critical for cell fate

TF network associated with tissue origin

Development is not only regulated by TF expression level, but also TF-PPI!!

TF PPI sub-network between Human and Mouse

Human Frontal Cortex Mouse Frontal Cortex

A sub-network related to Neural Development

Spatio-temporal Similarity between human and mouse TF PPI network

Negative regulation

時間 (hour)

Exp

ress

ion

le

vel

Time (hour)0 hr 96 hrs

Differentiation

Newly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte.

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

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.

Acknowledgement

OSC head quarters Yoshihide Hayashizaki Jun Kawai Piero Carninci Carsten Daub

This work has been achieved in the FANTOM4 consortium with support of the Genome Network Project (MEXT).