Stochastic Pulse Regulation in Bacterial Stress Response

Howard Hughes Medical Institute, Division of Biology and Bioengineering, Broad Center, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA.
Science (Impact Factor: 33.61). 10/2011; 334(6054):366-9. DOI: 10.1126/science.1208144
Source: PubMed


Gene regulatory circuits can use dynamic, and even stochastic, strategies to respond to environmental conditions. We examined activation of the general stress response mediated by the alternative sigma factor, σ(B), in individual Bacillus subtilis cells. We observed that energy stress activates σ(B) in discrete stochastic pulses, with increasing levels of stress leading to higher pulse frequencies. By perturbing and rewiring the endogenous system, we found that this behavior results from three key features of the σ(B) circuit: an ultrasensitive phosphorylation switch; stochasticity ("noise"), which activates that switch; and a mixed (positive and negative) transcriptional feedback, which can both amplify a pulse and switch it off. Together, these results show how prokaryotes encode signals using stochastic pulse frequency modulation through a compact regulatory architecture.

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    • "The combination of positive and negative feedback regulation provides a mechanism to regulate the strength and duration of a response [10] (Fig. 1E). For instance, the Bacillus subtilis energy stress response involving the alternative sigma factor B is temporally modulated by stochastic pulses of gene activation with the strength of the response controlled by the frequency, not the magnitude of the pulses [15]. Stochastic fluctuations in the concentration of a phosphatase serve as the pulse trigger. "
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    DNA repair 08/2014; 20(100). DOI:10.1016/j.dnarep.2014.02.015 · 3.11 Impact Factor
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    • "In our experimental system, the situation is the opposite: cell delineation is difficult, but the tracking is simple because cells grow slowly and minimally change position between two successive frames (Additional file 2). To examine if the developed algorithm is more widely applicable, we processed published time-lapse movies from three different bacterial genera: fast-growing rod-like Bacillus [28], crescent-shaped Caulobacter [7], and filamentous Streptomyces [29]. The most promising results were obtained by processing the Streptomyces time-lapse movie. "
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    ABSTRACT: Background The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing. Results Here we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree. Conclusions The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-251) contains supplementary material, which is available to authorized users.
    BMC Bioinformatics 07/2014; 15(1):251. DOI:10.1186/1471-2105-15-251 · 2.58 Impact Factor
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    • "We used standard methods for analyzing gene expression via RT-PCR and quantitative RT-PCR, with some modifications [26]. For qRT-PCR analysis of sinI, abbA, mstX, yqxM and sigA, we used the primers listed in Table S2. "
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