Validating Regulatory Predictions with Mutant Fitness Data

Shiori Sagawa, Morgan N. Price, Adam M. Deutschbauer, and Adam P. Arkin
November 2016

Abstract

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium’s growth across many conditions, to validate regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.

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