Large-Scale Genetic Perturbations Reveal Regulatory Networks and an Abundance of Gene-Specific Repressors
Author(s) -
Patrick Kemmeren,
Katrin Sameith,
Loes A. L. van de Pasch,
Joris J. Benschop,
Tineke L. Lenstra,
Thanasis Margaritis,
Eoghan O’Duibhir,
Eva Apweiler,
Sake van Wageningen,
Cheuk W. Ko,
Sebastiaan van Heesch,
Mehdi Kashani,
Giannis Ampatziadis-Michailidis,
Mariël Brok,
Nathalie Brabers,
Anthony J Miles,
Diane Bouwmeester,
Sander R. van Hooff,
Harm van Bakel,
Erik Sluiters,
Linda V. Bakker,
Berend Snel,
Philip Lijnzaad,
Dik van Leenen,
Marian J.A. Groot Koerkamp,
Frank C. P. Holstege
Publication year - 2014
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2014.02.054
Subject(s) - biology , genetics , gene , gene regulatory network , computational biology , regulation of gene expression , repressor , transcription factor , gene expression , chromatin , gene expression profiling , regulator gene , saccharomyces cerevisiae , systems biology
To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.
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