NetBioSIG2012 kostiidit

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Traditionally the gene expression pathway was regarded as being composed of independent steps, from RNA transcription to protein translation. To-date there is increasing evidence for coupling between the different processes of the pathway, specifically between transcription and splicing. Given the extensive cross-talk between these processes, we derived a transcription-splicing integrated network. The nodes of the network included experimentally verified human proteins belonging to three groups of regulators: Transcription factors (TFs), splicing factors (SFs) and kinases. The nodes were wired by instances of predicted transcriptional and alternative splicing regulation. Analysis of the network indicated a pervasive cross-regulation among the nodes, specifically; SFs were significantly more often regulated by alternative splicing relative to the two other subgroups, while TFs were more extensively controlled by transcriptional regulation. In particular, we found a significant preference of specific pairs of TF-TF and SF-SF to regulate their target genes, SFs being the most regulated group via independent and combinatorial binding of SFs. Consistent with the extensive cross-regulation among the splicing and transcription factors, the subgroup of kinases within the network had the highest density of predicted phosphorylation sites. The prevalent regulation of the regulatory proteins was further supported by computational analysis of the protein sequences, demonstrating the propensity of these proteins to be highly disordered relative to other proteins in the human proteome. Overall, our systematic study reveals that an organizing principle in the logic of integrated networks favor the regulation of regulatory proteins by the specific regulation they conduct. Based on these results we propose a new regulatory paradigm, postulating that fine-tuned gene expression regulation of the master regulators in the cell is commonly achieved by cross-regulation.

Transcript of NetBioSIG2012 kostiidit

A transcription-splicing integrated network reveals pervasive cross-regulation among

regulatory proteins

Idit KostiComputational Biology Lab

Technion, Haifa, Israel

Network Biology SIG ISMB 2012

Long Beach CA

Regulation of the gene expression pathwayDNA

RNA

Protein

Promoter intron exon

Splicing

Translation

Posttranslational modification

Transcription

Alternative Splicing

Transcription

PhosphorylationP

Transcription

• The first step leading to gene expression. • Transcription is regulated by transcription

factors (TFs) that are bound to the promoter region of the gene.

DNA Promoter intron exon

Transcription

Alternative splicing

• Alternative splicing (AS) creates a huge protein variety from a small number of genes.

• 95% of the genes have at least one AS event.• AS is regulated by splicing factors (SFs).

RNA

Alternative Splicing

Phosphorylation

• Protein phosphorylation plays a significant role in a wide range of cellular processes

• Phosphorylation occurs at phosphorylation sites and facilitated by kinases.

Protein Translation

PhosphorylationP

In our network we focus on transcription and alternative splicing regulation in human

The splicing-transcription co-regulatory network

SF

TF

K

SF

TF

SF

20 Splicing Factors (gene/protein)

90 Transcription Factors(gene/protein)

K

147 Kinases (gene only)

Kosti I., Radivojac P., Mandel-Gutfreund Y., An integrated regulatory network reveals pervasive cross-regulation among transcription and splicing factors, PLoS Computational Biology, in press.

Transcriptionregulation

Predicting TF binding sites using TRANSFAC

TRANSFAC PSSMs

Predication of TFBS using PSSM and conservation

Significant hits in promoter region

A [13 13 3 1]C [13 39 5 53]G [17 2 37 0]T [11 0 9 0]

TCGACGCCTCACGTGTTCCTCCTGGATCGCACTGCACGTGGGATCTGATC

Human

Mouse

TFBS table, UCSC genone broswer

The splicing-transcription co-regulatory network

SF

TF

K

SF

TF

SF

20 Splicing Factors (gene/protein)

90 Transcription Factors(gene/protein)

K

147 Kinases (gene only)

Transcriptionregulation

Alternative Splicingregulation

Predicting SF binding sites using SFmap

Experimentally defined binding motifs

Predication of SFBS using motif, conservation and multiplicity

Significant hits in AS region

UCUUYCAY

YGCUKYGAAGAA

UCGACGCCUUCCUUCUCUUUCCUCCUAUCGCACUGUCUUAUCGGAUCUGAUC

Human

Mouse

sfmap.technion.ac.il

Paz I. et al., Nucleic Acids Res. 2010

Regulation on AS events changes according to event types

Cassette exon

Alternative 5’ splice site

Alternative 3’ splice site

X

How do we wire the network?2 XA1 3

2

A1

3

X

Our network behaves like a regulatory network

Highly clustered Sparse

p-value =1.09e-61

Sparseness=0.046

0

0.1

0.2

Outdegree

Out

degr

ee F

requ

ency

Power law outdegree distribution

TF

K

Cross-regulation vs. Cross-talk regulation

TF

cross-talk regulation (regulation across functional group)cross-regulation (regulation within the functional group)

SF

Transcription RegulationN

umbe

r of i

nedg

es pv= 1.2E-3 pv = 3.8E-7

SF TF Kinase

Transcription regulation is highest among TFs

3654

pv < 0.05

Num

ber o

f ine

dges

Splicing Regulation

SF TF Kinase

pv= 2.7E-3

Splicing regulation is highest among SFs

pv= 2.3E-4

97

P-value < 2.2e-16

Similar gene length, number of exons and number of AS events

0 1 2 3 4 5 60

0.2

0.4

0.6

0.8

1

Number of alternative splicing events

Fre

qu

en

cy

fro

m t

arg

et

gro

up

SFs

TFs

Kinases

Ge

ne

len

gth

)n

t(

0

1000

0

20

000

3000

0

40

000

SF TF Kinase

Nu

mb

er

of

exo

ns

pe

r fa

cto

r

0

5

10

15

20

25

30

SF TF Kinase

Number of alternative splicing events per factor

Gene length Number of exons per factor

Random networks showed insignificant inedges density

1 2 30

1

2

3

4

5

6

7

8

9

1 2 30

1

2

3

4

5

6

7

8

9

Splicing regulation Transcription regulation

Ined

ge a

vera

ge

Ined

ge a

vera

ge

SF TF Kinase SF TF Kinase

Experimental binding data supports splicing regulation trend

-Log10)pvalue(

0 2 4 6 8 10 12

SF

2/A

SF

F

OX

2

P

TB

QK

I

RNA splicing

Transcriptionactivity

Cross regulation vs. cross-talk regulation

TF

K

TF

SF

Transcription regulation

Same trend, different organisms

YeastHuman Drosophila

SF TF SF TF SF TF

Marbach et al. Genome Res. 2011

pv= 1.2e-3 pv= 9.2e-10

Pelechano et al., PLoS Genetics 2009

Same trend, different organisms

Splicing regulation

Drosophila

Num

ber o

f ine

dges

Num

ber o

f ine

dges

Human

SF TF SF TF

pv= 2.3e-4 pv= 1.7e-11

Guy Plaut

Screening using expression data for muscle and heart tissues

Tissue specific networks show the same regulatory behavior

Tissue specific networks show the same regulatory behavior

Num

ber o

f ine

dges

SF TF SF TF

Splicing Regulation

Num

ber o

f ine

dges

SF TF SF TF

Transcription Regulation

33 TFs 14 SFs40 TFs 11 SFs

The splicing-transcription co-regulatory network

SF

TF

K

SF

TF

SF

20 Splicing Factors (gene/protein)

90 Transcription Factors(gene/protein)

K

147 Kinases (gene only)

Transcriptionregulation

Alternative Splicingregulation

Phosphorylation Regulation

Phosphorylation regulation is highest among Kinases

Predrag Radivojac

SF TF Kinase0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Frac

tion

of p

rote

in w

ith p

redi

cted

ph

osph

oryl

ation

site

Cross-regulation vs. cross-talk regulation

TF

K

TF

SF

The role of cross talk between splicing and transcription regulation

PAX 6

SRP55

SF2ASF9G8

SC35

SRP20

Regulatory proteins tend to be highly regulated by the specific regulation they

carry out.

TF

K

TF

SF

TechnionYael Mandel Gutfreund

Guy PlautInbal PazIris DrorMartin AkermanAnd all lab members

Indiana University Predrag Radivojac

And you for your attention!

Thanks!