Validating regulatory predictions from diverse bacteria with mutant fitness data
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 test 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.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1358532
- Alternate ID(s):
- OSTI ID: 1393228
- Journal Information:
- PLoS ONE, Journal Name: PLoS ONE Vol. 12 Journal Issue: 5; ISSN 1932-6203
- Publisher:
- Public Library of ScienceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria
Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff