Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.

https://science.sciencemag.org/content/sci/335/6072/1103.full.pdf

Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in Bacillus subtilis

Staphylococcus aureus is a major pathogen that colonizes about 20% of the human population. Intriguingly, this Gram-positive bacterium can survive and thrive under a wide range of different conditions, both inside and outside the human body. Here, we investigated the transcriptional adaptation of S. aureus HG001, a derivative of strain NCTC 8325, across experimental conditions ranging from optimal growth in vitro to intracellular growth in host cells. These data establish an extensive repertoire of transcription units and non-coding RNAs, a classification of 1412 promoters according to their dependence on the RNA polymerase sigma factors SigA or SigB, and allow identification of new potential targets for several known transcription factors. In particular, this study revealed a relatively low abundance of antisense RNAs in S. aureus, where they overlap only 6% of the coding genes, and only 19 antisense RNAs not co-transcribed with other genes were found. Promoter analysis and comparison with Bacillus subtilis links the small number of antisense RNAs to a less profound impact of alternative sigma factors in S. aureus. Furthermore, we revealed that Rho-dependent transcription termination suppresses pervasive antisense transcription, presumably originating from abundant spurious transcription initiation in this A+T-rich genome, which would otherwise affect expression of the overlapped genes. In summary, our study provides genome-wide information on transcriptional regulation and non-coding RNAs in S. aureus as well as new insights into the biological function of Rho and the implications of spurious transcription in bacteria.

/pdf/staphylococcus-aureus-transcriptome-architecture-from-503sz4e89x.pdf

Staphylococcus aureus Transcriptome Architecture: From Laboratory to Infection-Mimicking Conditions

Bacillus subtilis possesses three essential enzymes thought to be involved in mRNA decay to varying degrees, namely RNase Y, RNase J1, and RNase III. Using recently developed high-resolution tiling arrays, we examined the effect of depletion of each of these enzymes on RNA abundance over the whole genome. The data are consistent with a model in which the degradation of a significant number of transcripts is dependent on endonucleolytic cleavage by RNase Y, followed by degradation of the downstream fragment by the 5′–3′ exoribonuclease RNase J1. However, many full-size transcripts also accumulate under conditions of RNase J1 insufficiency, compatible with a model whereby RNase J1 degrades transcripts either directly from the 5′ end or very close to it. Although the abundance of a large number of transcripts was altered by depletion of RNase III, this appears to result primarily from indirect transcriptional effects. Lastly, RNase depletion led to the stabilization of many low-abundance potential regulatory RNAs, both in intergenic regions and in the antisense orientation to known transcripts.

/pdf/three-essential-ribonucleases-rnase-y-j1-and-iii-control-the-1iya4miwp3.pdf

Three essential ribonucleases-RNase Y, J1, and III-control the abundance of a majority of Bacillus subtilis mRNAs.

The transcriptional regulator Spx plays a key role in maintaining the redox homeostasis of Bacillus subtilis cells exposed to disulfide stress. Defects in Spx were previously shown to lead to differential expression of numerous genes but direct and indirect regulatory effects could not be distinguished. Here we identified 283 discrete chromosomal sites potentially bound by the Spx–RNA polymerase (Spx–RNAP) complex using chromatin immunoprecipitation of Spx. Three quarters of these sites were located near Sigma(A)-dependent promoters, and upon diamide treatment, the fraction of the Spx–RNAP complex increased in parallel with the number and occupancy of DNA sites. Correlation of Spx–RNAP-binding sites with gene differential expression in wild-type and Δspx strains exposed or not to diamide revealed that 144 transcription units comprising 275 genes were potentially under direct Spx regulation. Spx-controlled promoters exhibited an extended −35 box in which nucleotide composition at the −43/−44 positions strongly correlated with observed activation. In vitro transcription confirmed activation by oxidized Spx of seven newly identified promoters, of which one was also activated by reduced Spx. Our study globally characterized the Spx regulatory network, revealing its role in the basal expression of some genes and its complex interplay with other stress responses.

Genome-wide identification of genes directly regulated by the pleiotropic transcription factor Spx in Bacillus subtilis

Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or where the mean fluctuates locally between the abrupt changes that one wishes to detect. In these cases, default implementations, which are often based on assumptions of a constant mean between changes and independent noise, can lead to substantial over-estimation of the number of changes. We propose a principled approach to detect such abrupt changes that models local fluctuations as a random walk process and autocorrelated noise via an AR(1) process. We then estimate the number and location of changepoints by minimising a penalised cost based on this model. We develop a novel and efficient dynamic programming algorithm, DeCAFS, that can solve this minimisation problem; despite the additional challenge of dependence across segments, due to the autocorrelated noise, which makes existing algorithms inapplicable. Theory and empirical results show that our approach has greater power at detecting abrupt changes than existing approaches. We apply our method to measuring gene expression levels in bacteria.

pdf/detecting-abrupt-changes-in-the-presence-of-local-1hpolqvv1j.pdf

Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise

Motivation: High-density oligonucleotide tiling array technology holds the promise of a better description of the complexity and the dynamics of transcriptional landscapes. In organisms such as bacteria and yeasts, transcription can be measured on a genome-wide scale with a resolution >25 bp. The statistical models currently used to handle these data remain however very simple, the most popular being the piecewise constant Gaussian model with a fixed number of breakpoints.

Results: This article describes a new methodology based on a hidden Markov model that embeds the segmentation of a continuous-valued signal in a probabilistic setting. For a computationally affordable cost, this framework (i) alleviates the difficulty of choosing a fixed number of breakpoints, and (ii) permits retrieving more information than a unique segmentation by giving access to the whole probability distribution of the transcription profile. Importantly, the model is also enriched and accounts for subtle effects such as signal ‘drift’ and covariates. Relevance of this framework is demonstrated on a Bacillus subtilis dataset.

Availability: A software is distributed under the GPL.

Contact: pierre.nicolas@jouy.inra.fr

Supplementary information: Supplementary data is available at Bioinformatics online.

/pdf/transcriptional-landscape-estimation-from-tiling-array-data-30junk3cfp.pdf

Transcriptional landscape estimation from tiling array data using a model of signal shift and drift

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Transcriptional landscape estimation from tiling array data using a model of signal shift and drift