Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan 5;9(1):112.
doi: 10.3390/microorganisms9010112.

Kinetic Modeling and Meta-Analysis of the Bacillus subtilis SigB Regulon during Spore Germination and Outgrowth

Affiliations

Kinetic Modeling and Meta-Analysis of the Bacillus subtilis SigB Regulon during Spore Germination and Outgrowth

Jiri Vohradsky et al. Microorganisms. .

Abstract

The exponential increase in the number of conducted studies combined with the development of sequencing methods have led to an enormous accumulation of partially processed experimental data in the past two decades. Here, we present an approach using literature-mined data complemented with gene expression kinetic modeling and promoter sequence analysis. This approach allowed us to identify the regulon of Bacillus subtilis sigma factor SigB of RNA polymerase (RNAP) specifically expressed during germination and outgrowth. SigB is critical for the cell's response to general stress but is also expressed during spore germination and outgrowth, and this specific regulon is not known. This approach allowed us to (i) define a subset of the known SigB regulon controlled by SigB specifically during spore germination and outgrowth, (ii) identify the influence of the promoter sequence binding motif organization on the expression of the SigB-regulated genes, and (iii) suggest additional sigma factors co-controlling other SigB-dependent genes. Experiments then validated promoter sequence characteristics necessary for direct RNAP-SigB binding. In summary, this work documents the potential of computational approaches to unravel new information even for a well-studied system; moreover, the study specifically identifies the subset of the SigB regulon, which is activated during germination and outgrowth.

Keywords: Bacillus subtilis; SigB; computational modeling; gene regulatory networks; promoter sequence analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Binding motifs found in genes of SigB regulon. The motifs are ordered from −35 to −10 with spacers of 5–20 nucleotides.
Figure 2
Figure 2
Relative occurrence of −35 and −10 binding motifs and the distance between them in the different SigB regulatory groups. Excluded (type 3)—regulator not found or the genes excluded during preprocessing; SigB and/or other (type 2)—genes for which besides SigB, also another regulator was found; SigB only (type 1)—genes for which SigB was found as the only regulator. The figure shows that the best characteristics were found for type 1 genes where the modeling results were consistent with the binding motifs analysis.
Figure 3
Figure 3
Experimental verification of SigB dependence of selected promoters. The validation was done by in vitro transcription with B. subtilis RNAP complexed with SigB (σB). (A)–Class I promoters [contain canonically spaced (by 13–15 bp) −35 and −10 elements]. (B)–A known Class II promoter contains only the −10 element, highly resembling the consensus sequence. (C)–genes of the Class II promoter. Curves represent modeled (red dashed line) and experimental (red solid line) expression profiles of the given gene; blue line is the expression profile of SigB. SHORT and LONG refer to template length (two sizes for each promoter region) to distinguish the orientation of the promoter within the template. −/+ indicate the absence/presence of SigB. In the absence of SigB, only the RNAP core was used. trxA was used as a standard.

Similar articles

Cited by

References

    1. Paget M.S. Bacterial Sigma Factors and Anti-Sigma Factors: Structure, Function and Distribution. Biomolecules. 2015;5:1245. doi: 10.3390/biom5031245. - DOI - PMC - PubMed
    1. Loskot P., Atitey K., Mihaylova L. Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks. Front. Genet. 2019;10:549. doi: 10.3389/fgene.2019.00549. - DOI - PMC - PubMed
    1. Wang Y.R., Huang H. Review on statistical methods for gene network reconstruction using expression data. J. Theor. Biol. 2014;362:53–61. doi: 10.1016/j.jtbi.2014.03.040. - DOI - PubMed
    1. Margolin A.A., Nemenman I., Basso K., Wiggins C., Stolovitzky G., Dalla-Favera R., Califano A. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinform. 2006;7:1. doi: 10.1186/1471-2105-7-S1-S7. - DOI - PMC - PubMed
    1. Yeung K.Y., Dombek K.M., Lo K., Mittler J.E., Zhu J., Schadt E.E., Bumgarner R., Raftery A.E. Construction of regulatory networks using expression time-series data of a genotyped population. Proc. Natl. Acad. Sci. USA. 2011;108:19436–19441. doi: 10.1073/pnas.1116442108. - DOI - PMC - PubMed

LinkOut - more resources