Identification of a p-Coumarate Degradation Regulon in Rhodopseudomonas palustris by
Inferring transcription factor function through regulon-based expression analysis
description
Transcript of 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
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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/
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ATACACAAAGACTCGTTACAAAAGCCG
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PSAM
AffinityLandscape
FunctionalPredictor
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aaaaccacggcttat
tctactacgagcgata
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mRNA expression
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(C,F,w) = argminC ,F ,w
IpIP
Ipcontrol
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Lw
∏i=1
L p
∑p
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2
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