A systems-biology approach for finding TSS-specific transcriptional regulation in the same gene

Yishai Shimoni

Andrea Califano LabColumbia University

POST-TRANSLATIONAL INTERACTIONS

TRANSCRIPTIONAL INTERACTIONS

Zhao X et al. (2009) Dev Cell. 17(2):210-21.Mani KM et al. (2008) Mol Syst Biol. 4:169Palomero T et al., Proc Natl Acad Sci U S A 103, 18261 (Nov 28, 2006).Margolin AA et al., Nature Protocols; 1(2): 662-671 (2006)Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006).Basso K et al. (2005), Nat Genet.;37(4):382-90. (Apr. 2005)

Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39Zhao X et al. (2009) Dev Cell. 17(2):210-21.Wang K et al. (2009) Pac Symp Biocomput. 2009:264-75.Mani KM et al. (2008) Mol Syst Biol. 4:169Wang K et al. (2006) RECOMB

POST-TRANSCRIPTIONAL INTERACTIONS

Basso et al. Immunity. 2009 May;30(5):744-52Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40.

MASTER REGULATORS AND MECHANISM OF ACTION

Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377.Carro MS et al. (2010) Nature Jan 21;463(7279):318-25Mani K et al, (2008) Molecular Systems Biology, 4:169

ARACNe: Reverse Engineering Regulatory Networks

Computing Mutual Information Start with a large collection of Microarray Gene

Expression Profiles Select two genes, a TF and a candidate target t: Use expression across multiple experiments to measure

Target tTF

( ), ( ), and p TF p t p(TF,t)

,[ ; ] log

p TF tI TF t

p TF p t

ARACNe: Reverse Engineering Regulatory Networks

Computing Mutual Information Start with a large collection of transcription start site

(TSS) activity levels Select a TF and a candidate target TSS t: Use expression across multiple experiments to measure

Target tTF

( ), ( ), and p TF p t p(TF,t)

,[ ; ] log

p TF tI TF t

p TF p t

ARACNe

Target]};TF[ ],TF;TF[min{Target];TF[ 2211 III

TF2 TargetTF1

Filtering indirect interaction: applying Data Processing Inequality

TF1 TF2 Target

X

ARACNe

TF1

T1

TF2

TFN

TM

T2

Compute all pairwise Mutual Information(One of the two nodes must be a TF)

TF1

T1

TF2

TFN

TM

T2

Remove Non StatisticallySignificant Interactions

TF1

T1

TF2

TFN

TM

T2

Apply the DPI

Challenges in applying ARACNe to FANTOM5 data

•Normalization•Joining tag counts into TSSs•Using multiple tissues together

MA plot of original tag counts shows need for normalization

Top rank-invariant locations follow power-law relation

MA plot of normalized TSS activity shows good normalization

Note: samples were NOT normalized compared to each other, but each one compare to reference

Good normalization observed in multiple random pairs

Samples are separated by tissue

Correlation clustering shows significant negative correlations

Strong Negative

Correlation

Hematopoietic

Assorted samples

Possible solution to tissue driven mutual informationCalculate MI only form the subset of samples in which both the TF and the target TSS are expressed

In spite of caveats MI agrees with chip-seq

http://amp.pharm.mssm.edu/lib/chea.jsp

Interrogating Gene regulatory networks

Analyzing data using ARACNe network

MARINa: Master Regulator Inference analysis

RepressedTFx Targets

ActivatedTFx Targets

TFx

TF Regulon

Over-expressed in Ph2 vs. Ph1Under-expressed in Ph2 vs. Ph1

If TFx were a Master Regulator of Ph1→Ph2 transformation, then its regulated genes should distribute as follows:

A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event.

Phenotype 2Phenotype 1

TFx ?

Lefebvre C. et al (2010), Molecular Systems Biology, Jun 8;6:377.Carro MS et al. (2010) Nature Jan 21;463(7279):318-25

Gene Expression

RepressedTFx Targets

ActivatedTFx Targets

Differentially regulated same-gene TSS

TSS1 TSS2

Methylation data does not explain alternative TSS usage

CD34+

Adult kidney

Fetal lung

Pancreatic Islets

Smooth Muscles

http://www.genboree.org/epigenomeatlas

TSS1 TSS2

MARINA initial results for ARHGAP24 differential TSS activity

geneID symbol size NES p.value3659 IRF1 83 -3.721 0.00019823515 MORC3 129 -3.689 0.0002253720 JARID2 55 -3.634 0.00027910308 ZNF267 182 -3.32 0.0009017538 ZFP36 170 -3.179 0.001481316 KLF6 56 -2.946 0.003225914 RARA 66 -2.845 0.004443726 JUNB 78 -2.834 0.0045957688 ZSWIM6 38 -2.791 0.0052551621 KLF13 36 -2.766 0.005674790 NFKB1 107 -2.765 0.00569571 BACH1 114 -2.726 0.00642

Future directionsApply ARACNe to the whole dataset to find

differential regulation of TSS activity in the same gene

Apply MARINA to find master regulator of mutually exclusive TSS in the same gene

Use additional algorithms to analyze regulatory networks in developmental stages, and in the time-course data

Acknowledgments

FANTOM5

Andrea Califano Mukesh BansalMariano AlvarezMaria Rodriguez MartinezGonzalo Lopez Garcia