NetBioSIG2012 kostiidit
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A transcription-splicing integrated network reveals pervasive cross- regulation among regulatory proteins Idit Kosti Computational Biology Lab Technion, Haifa, Israel Network Biology SIG ISMB 2012 Long Beach CA
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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!
