inferring transcription factor function through regulon-based expression analysis

Inferring transcription factor function through regulon-based expression

analysis

Harmen BussemakerBiological Sciences & C2B2

Columbia University

TF1 TF2 TF3

Gene1 Gene2 Gene3

Hidden, protein-level TF activities

Measured mRNA abundances

Regulatory Connectivities

“T-profiler”(Lascaris, 2003; Boorsma, 2005)

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SE =1

n1

+1

n2

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SD =(n1 −1) SD1( )

2+ (n2 −1) SD2( )

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n1 + n2 − 2€

t =x( )S

− x( )S

SE

Quantify the difference in mean expression between a gene set and its complement:

Score condition-specific differential activity of regulon using t-test

Two types of yeast regulons:

• Based on ChIP-chip data (Harbison, 2004)

• Based on consensus motif matches (SCPD)

Large number (~1000) of conditions

Validation:

• Overexpression/deletion of TF

• Activator (Yap1p) and repressor (Rox1p)

T-values consistent with expectation

GFP-labeled Crz1p

mRNA level is a poor predictor of TF

activity

mRNA level is a good predictor of TF

activity

How good a proxy is mRNA level for TF activity?

[mRNA] vs. inferred TF activity correlation

The mRNA levels arepoorly correlated

Inferred TF activities are

highly correlated

Detecting “co-modulation” of pairs of TFs

Better performance observed for all pairs of TFs

Network of co-modulated TF pairs (r > 0.5)

What do these TFs have in common?

tup1 /wt cyc8 /wt

TF (condition) t-value TF (condition) t-value NRG1 (YPD) 14.8 SOK2 (BUT 14) 9.6 RIM101 (H2O2 low) 14.5 NRG1 (YPD) 9.6 CIN5 (H2O2 low) 13.9 YAP6 (YPD) 8.6 NRG1 (H2O2 low) 13.6 NRG1 (H2O2 low) 8.6 YAP6 (H2O2 low) 12.2 PHD1 (BUT 90) 8.5 SOK2 (BUT 14) 11.6 CIN5 (H2O2 low) 8.4 YAP6 (YPD) 11.0 RIM101 (H2O2 Low) 8.1 PHD1 (BUT 90) 10.6 NRG1 (H2O2 high) 8.1 MIG1 (YPD) 10.6 CIN5 (YPD) 8.0 PHD1 (YPD) 10.6 YAP6 (H2O2 low) 7.9 NRG1 (H2O2 high) 9.7 SUT1 (YPD) 7.5 SUT1 (YPD) 9.6 PHD1 (YPD) 7.5 CIN5 (H2O2 high) 9.3 CIN5 (H2O2 high) 6.8 YAP6 (H2O2 high) 8.6 MIG1 (YPD) 6.7 CIN5 (YPD) 8.5 AFT2 (H2O2 low) 6.5 YJL206C (H2O2 low) 7.5 SKN7 (H2O2 low) 6.4 SKN7 (H2O2 low) 7.2 XBP1 (H2O2 low) 5.6 AFT2 (H2O2 low) 7.0 SKN7 (H2O2 high) 5.5 XBP1 (H2O2 low) 6.5 YAP6 (H2O2 high) 5.4 CUP9 (YPD) 5.9 SKN7 (YPD) 5.3 SKN7 (YPD) 5.7 RCS1 (H2O2 high) 4.6 SKO1 (YPD) 5.7 PUT3 (H2O2 low) 4.5 SKN7 (H2O2 high) 5.6 ROX1 (YPD) 3.9 YJL206C (YPD) 5.6 YJL206C (YPD) 3.8 ROX1 (YPD) 4.8 YAP1 (H2O2 low) 4.1

RED: Part of network / BOLD: Significant for both

Dissecting the Environmental Stress Response

Conclusion

Regulon-based analysis of genomewide expression profiles using the unpaired t-test is a simple but effective tool for analyzing the condition-specific modulation of TF activity

http://www.t-profiler.org http://bussemakerlab.org/T-base/

ATACACAAAGACTCGTTACAAAAGCCG

ATACACAAAGACTCGTTACAAAAGCCG

+Genome

PSAM

AffinityLandscape

FunctionalPredictor

acgacgcagcagca

cccctcttcatcactca

aaaaccacggcttat

tctactacgagcgata

ggactatactacaac

mRNA expression

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(C,F,w) = argminC ,F ,w

IpIP

Ipcontrol

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Target of Rapamycin (TOR)Signaling Pathway

Nutrients Rapamycin

Ribosomes Mitochondria

Puf4p Puf3p

Foat et al, PNAS, 2005

Discovering Regulators of Human B-cell Maturation

E2F1

NF-Y ZNF42_1-4

bZIP910 GAMYB

ZNF42_5-13

Inferred TF Activity Time Course during GC Reaction

Acknowledgements

Mina Fazlollahi

Barrett Foat

Pilar Gomez-Alcala

Gabor Halasz

Eunjee Lee

Xiang-Jun Lu

Ben Snyder

Ron Tepper

Luke Ward

Sean HousmandiWendy Olivas

Kevin WhiteBas van Steensel

Alexandre Morozov

Andre BoorsmaFrans Klis

NIH, HFSP