The Escherichia coli Transcriptome Mostly Consists of Independently Regulated Modules

Abstract

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we applied unsupervised learning to a compendium of high-quality Escherichia coli RNA-seq datasets to identify 70 statistically independent signals that modulate the expression of specific gene sets. We show that 50 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals was validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provided: (1) a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations, (2) a guide to gene and regulator function discovery, and (3) a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation forms an underlying principle that describes the composition of a model prokaryotic transcriptome.