Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

Kevin Rychel, Anand V. Sastry, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

Original languageEnglish
Article number6338
JournalNature Communications
Issue number1
Publication statusPublished - Dec 2020

Bibliographical note

Funding Information:
We thank Dr. Joe Pogliano, Saugat Poudel, and Eammon Riley for helpful discussions and biological insights. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work was funded by the Novo Nordisk Foundation Center for Bio-sustainability (Grant Number NNF10CC1016517).

Publisher Copyright:
© 2020, The Author(s).


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