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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.

SUBMITTER: Rychel K

PROVIDER: S-EPMC7732839 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Rychel Kevin K Sastry Anand V AV Palsson Bernhard O BO

Nature communications 20201211 1

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 ...[more]

PMID: 33311500

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Project description:BACKGROUND:Bacillus subtilis encounters a wide range of environmental pH. The bacteria maintain cytoplasmic pH within a narrow range. Response to acid stress is a poorly understood function of external pH and of permeant acids that conduct protons into the cytoplasm. METHODS AND PRINCIPAL FINDINGS:Cytoplasmic acidification and the benzoate transcriptome were observed in Bacillus subtilis. Cytoplasmic pH was measured with 4-s time resolution using GFPmut3b fluorimetry. Rapid external acidification (pH 7.5 to 6.0) acidified the B. subtilis cytoplasm, followed by partial recovery. Benzoate addition up to 60 mM at external pH 7 depressed cytoplasmic pH but left a transmembrane Delta pH permitting growth; this robust adaptation to benzoate exceeds that seen in E. coli. Cytoplasmic pH was depressed by 0.3 units during growth with 30 mM benzoate. The transcriptome of benzoate-adapted cells was determined by comparing 4,095 gene expression indices following growth at pH 7, +/- 30 mM benzoate. 164 ORFs showed > or = 2-fold up-regulation by benzoate (30 mM benzoate/0 mM), and 102 ORFs showed > or = 2-fold down-regulation. 42% of benzoate-dependent genes are regulated up or down, respectively, at pH 6 versus pH 7; they are candidates for cytoplasmic pH response. Acid-stress genes up-regulated by benzoate included drug resistance genes (yhbI, yhcA, yuxJ, ywoGH); an oligopeptide transporter (opp); glycine catabolism (gcvPA-PB); acetate degradation (acsA); dehydrogenases (ald, fdhD, serA, yrhEFG, yjgCD); the TCA cycle (citZ, icd, mdh, sucD); and oxidative stress (OYE-family yqjM, ohrB). Base-stress genes down-regulated by benzoate included malate metabolism (maeN), sporulation control (spo0M, spo0E), and the SigW alkali shock regulon. Cytoplasmic pH could mediate alkali-shock induction of SigW. CONCLUSIONS:B. subtilis maintains partial pH homeostasis during growth, and withstands high concentrations of permeant acid stress, higher than for gram-negative neutralophile E. coli. The benzoate adaptation transcriptome substantially overlaps that of external acid, contributing to a cytoplasmic pH transcriptome.

Dataset Information

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

Publications

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

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