Validating regulatory predictions from diverse bacteria with mutant fitness data

Area of Science:

• Microbiology
• Genomics
• Systems Biology

Background:

• Transcriptional regulation is crucial for bacterial physiology, but identifying transcription factor (TF) targets remains a challenge.
• Comparative genomics predicts TF targets, yet experimental validation is often lacking.

Purpose of the Study:

• To experimentally validate sequence-based predictions of bacterial transcription factor targets using high-throughput mutant fitness data.
• To assess the reliability of comparative genomics predictions for TF-target interactions.

Main Methods:

• Utilized mutant fitness data, measuring gene essentiality across diverse conditions, to analyze correlations with transcription factor activity.
• Tested regulatory predictions from the RegPrecise database, a curated collection of comparative genomics predictions.
• Applied a false discovery rate of 3% to identify statistically significant co-fitness patterns between TFs and their predicted targets.

Main Results:

• Identified significant co-fitness between at least one predicted target and 158 transcription factors across 107 ortholog groups and 24 bacterial species.
• Demonstrated that correlated fitness patterns between TFs and their targets provide robust support for comparative genomics predictions.
• Established a high-confidence subset of sequence-based regulatory predictions through high-throughput genetic analysis.

Conclusions:

• High-throughput genetics, specifically mutant fitness profiling, is an effective method for validating computational predictions of transcriptional regulatory networks.
• This approach significantly enhances the confidence in identified TF-target relationships, contributing to a deeper understanding of bacterial gene regulation.