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Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

Abstract：

: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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Rychel K,Sastry AV,Palsson BO

doi

10.1038/s41467-020-20153-9

subject

Has Abstract

pub_date

2020-12-11 00:00:00

pages

6338

issue

1

issn

2041-1723

pii

10.1038/s41467-020-20153-9

journal_volume

11

pub_type

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