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An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

ABSTRACT: Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

SUBMITTER: Arrieta-Ortiz ML

PROVIDER: S-EPMC4670728 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

Arrieta-Ortiz Mario L ML Hafemeister Christoph C Bate Ashley Rose AR Chu Timothy T Greenfield Alex A Shuster Bentley B Barry Samantha N SN Gallitto Matthew M Liu Brian B Kacmarczyk Thadeous T Santoriello Francis F Chen Jie J Rodrigues Christopher D A CD Sato Tsutomu T Rudner David Z DZ Driks Adam A Bonneau Richard R Eichenberger Patrick P

Molecular systems biology 20151117 11

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of ...[more]

PMID: 26577401

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Project description:We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ?2500 of the ?4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same "ON" and "OFF" gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

Dataset Information

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

Publications

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

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