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 apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators and role in key physiological responses

“…Using an extended ICA algorithm 7 , we decomposed the expression compendium into 29 'i-modulons'. An i-modulon contains a set of genes whose expression levels vary concurrently with each other, but independently of all other genes not in the given i-modulon.…”

“…aureus . Previously described in E. coli , this trade-off describes the allocation of resources towards optimal growth (greed) versus allocation towards bet-hedging strategies to mitigate the effect of stressors in the environment (fear) 7,42 . This balance is reflected in the transcriptome composition as an inverse correlation between the activities of the stress-responsive SigS i-modulon and the Translation i-modulon ( Figure 4d).…”

“…To address this challenge, we previously introduced an Independent Component Analysis (ICA)-based framework in Escherichia coli that decomposes a compendium of RNA-sequencing (RNA-seq) expression profiles to determine the underlying regulatory structure 7 . An extensive analysis of module detection methods demonstrated that ICA out-performed most other methods in consistently recovering known biological modules 8 .…”

“…ICA analysis of expression profiles in E. coli have been used to describe undefined regulons, link strain-specific mutations with changes in gene expression, and understand rewiring of TRN during Adaptive Laboratory Evolution (ALE) 7,9 . Given the deeper insights it provided into the TRN of E. coli , we sought to expand this approach to the human pathogen S. aureus .…”

Matrix factorization recovers consistent regulatory signals from disparate datasets

“…Each IC contains a coefficient for every gene, although most gene coefficients were near zero for a given IC. To understand the biological role of each IC, we selected genes with sufficiently non-zero coefficients and refer to this set of genes as an "i-modulon" 30 . ICs, and their corresponding i-modulons, represent the underlying structure of the transcriptome under any experimental condition in the database.…”