Pulcherrimin formation controls growth arrest of the
            <i>Bacillus subtilis</i>
            biofilm

Arnaouteli, Sofia; Matoz-Fernandez, D. A.; Porter, Michael; Kalamara, Margarita; Abbott, James; MacPhee, Cait E.; Davidson, Fordyce A.; Stanley‐Wall, Nicola R.

doi:10.1073/pnas.1903982116

Cited by 54 publications

(80 citation statements)

References 63 publications

(82 reference statements)

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“…We also see PchR activation in late-stage biofilm, glucose exhaustion, and phosphate starvation experiments. These results agree with a recent study that found pulcherrimin to be an important intercellular signal for the stationary phase that also helps exclude competing bacteria from established biofilms 47 . The regulation mechanisms of iModulons like this one can be the subject of future research.…”

Section: Resultssupporting

confidence: 93%

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

2020

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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.

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Section: Resultssupporting

confidence: 93%

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

2020

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“…We also see PchR activation in late stage biofilm, glucose exhaustion, and phosphate starvation experiments. These results agree with a recent study that found pulcherrimin to be an important intercellular signal for the stationary phase that also helps exclude competing bacteria from established biofilms 38 . The regulation mechanisms of i-modulons like this one can be the subject of future research.…”

Section: I-modulons Generate Novel Hypotheses I-modulon Activities Csupporting

confidence: 93%

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

Rychel

Sastry

Kim

2020

Preprint

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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 novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing.The TRN also facilitates quantification of population-level sporulation states, revealing a putative anaerobic metabolism role for SigG. 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.

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“…For instance, we observed that the iron chelator pulcherrimin was active in stationary phase signaling in B. subtilis ( 23 ). This observation was recently externally validated ( 24 ). More broadly, each decomposition explained 65–80% of the variance in the transcriptomic data sets, indicating that the functions and regulators identified captured most of the transcriptional functions of the TRNs under the conditions where the data was obtained.…”

Section: Introductionmentioning

confidence: 58%

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Rychel

Decker

Sastry

et al. 2020

Nucleic Acids Research

108

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Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

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Pulcherrimin formation controls growth arrest of the Bacillus subtilis biofilm

Cited by 54 publications

References 63 publications

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

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