Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network

  • Gal Chechik
  • , Eugene Oh
  • , Oliver Rando
  • , Jonathan Weissman
  • , Aviv Regev
  • , Daphne Koller

Research output: Contribution to journalArticlepeer-review

141 Scopus citations

Abstract

Significant insight about biological networks arises from the study of network motifs - overly abundant network subgraphs - but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction. Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions.

Original languageEnglish
Pages (from-to)1251-1259
Number of pages9
JournalNature Biotechnology
Volume26
Issue number11
DOIs
StatePublished - Nov 2008
Externally publishedYes

Bibliographical note

Funding Information:
Work was supported by the National Science Foundation under grant BDI-0345474. A.R. was supported by a Career award at the Scientific Interface from the Burroughs Wellcome Fund and by NIGMS. J.W. was supported by the Howard Hughes Medical Institute. The authors thank Trey Ideker, Dwight Kuo, Craig Mak and Eran Segal for assistance in early stages of this project, and Dana Pe’er and especially Eric Lander for useful discussions.

Funding

Work was supported by the National Science Foundation under grant BDI-0345474. A.R. was supported by a Career award at the Scientific Interface from the Burroughs Wellcome Fund and by NIGMS. J.W. was supported by the Howard Hughes Medical Institute. The authors thank Trey Ideker, Dwight Kuo, Craig Mak and Eran Segal for assistance in early stages of this project, and Dana Pe’er and especially Eric Lander for useful discussions.

FundersFunder number
National Science FoundationBDI-0345474
Howard Hughes Medical Institute
National Institute of General Medical SciencesR01GM079205
Burroughs Wellcome Fund

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