Kinetic modelling and meta-analysis of the B. subtilis SigA regulatory network during spore germination and outgrowth

Ramaniuk, Olga; Cerny, Marketa Cerna Josef; Krásný, Libor; Vohradský, Jiřı́

doi:10.1016/j.bbagrm.2017.06.003

Cited by 7 publications

(12 citation statements)

References 72 publications

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“…We did not detect a difference in the amount of the primary sigma factor mRNA, sigA Ramaniuk et al, 2017). We did not detect a difference in the amount of the primary sigma factor mRNA, sigA Ramaniuk et al, 2017).…”

Section: Transcription and Translation Machineriescontrasting

confidence: 72%

“…We detected lower amounts of mRNAs encoding the core RNAP subunits (a, b, b 0 ), which may be correlated with the slower growth rate of the ΔrnjA mutant. We did not detect a difference in the amount of the primary sigma factor mRNA, sigA Ramaniuk et al, 2017). However, we observed differences (in both directions) for most alternative sigma factors ( Fig 1C).…”

Section: Transcription and Translation Machineriesmentioning

confidence: 50%

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The torpedo effect inBacillus subtilis:RNase J1 resolves stalled transcription complexes

et al. 2019

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RNase J1 is the major 5 0 -to-3 0 bacterial exoribonuclease. We demonstrate that in its absence, RNA polymerases (RNAPs) are redistributed on DNA, with increased RNAP occupancy on some genes without a parallel increase in transcriptional output. This suggests that some of these RNAPs represent stalled, non-transcribing complexes. We show that RNase J1 is able to resolve these stalled RNAP complexes by a "torpedo" mechanism, whereby RNase J1 degrades the nascent RNA and causes the transcription complex to disassemble upon collision with RNAP. A heterologous enzyme, yeast Xrn1 (5 0 -to-3 0 exonuclease), is less efficient than RNase J1 in resolving stalled Bacillus subtilis RNAP, suggesting that the effect is RNase-specific. Our results thus reveal a novel general principle, whereby an RNase can participate in genome-wide surveillance of stalled RNAP complexes, preventing potentially deleterious transcription-replication collisions.

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Section: Transcription and Translation Machineriescontrasting

confidence: 72%

Section: Transcription and Translation Machineriesmentioning

confidence: 50%

The torpedo effect inBacillus subtilis:RNase J1 resolves stalled transcription complexes

et al. 2019

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“…We evaluated Genexpi in three ways: 1) direct biological testing of the suggested regulatory relationships, 2) comparing the ability of Genexpi and other tools to reconstruct two literature-derived regulons and 3) measuring computing time required to process the data. The first part of the evaluation is taken from our previous work [ 10 ], while the latter two are new contributions.…”

Section: Resultsmentioning

confidence: 99%

“…In this section we describe the model and fitting method of Genexpi – the implementation of the interfaces is straightforward. Initial part of this section is taken from [ 10 ] and its supplementary material where we describe first use of Genexpi in practice. In addition we provide details of regularization and parameter fitting as well as further developments made to make the method usable by non-expert users, especially the semi-automatic evaluation of good fits and the “no change” and “constant synthesis” models.…”

Section: Methodsmentioning

confidence: 99%

“…A preliminary version of the method presented in this paper has been applied in our previous work [ 10 ]. The additional contribution of this paper is a) a polished and documented publicly available implementation of the method with well-defined API, b) improved workflow and software support for the workflow c) interfacing the method with Cytoscape and R and d) evaluation of the method on additional datasets.…”

Section: Introductionmentioning

confidence: 99%

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Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data

Modrák

Vohradský

2018

BMC Bioinformatics

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BackgroundIdentifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks.ResultsWe introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package.ConclusionsGenexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2138-x) contains supplementary material, which is available to authorized users.

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Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference

Modrák

2018

Computational Methods in Systems Biology

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Kinetic modelling and meta-analysis of the B. subtilis SigA regulatory network during spore germination and outgrowth

Cited by 7 publications

References 72 publications

The torpedo effect inBacillus subtilis:RNase J1 resolves stalled transcription complexes

The torpedo effect inBacillus subtilis:RNase J1 resolves stalled transcription complexes

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data

Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference

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