Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression

Cells use general stress response pathways to activate diverse target genes in response to a variety of stresses. However, general stress responses coexist with more specific pathways that are activated by individual stresses, provoking the fundamental question of whether and how cells control the generality or specificity of their response to a particular stress. Here we address this issue using quantitative time-lapse microscopy of the Bacillus subtilis environmental stress response, mediated by σ B . We analyzed σ B activation in response to stresses such as salt and ethanol imposed at varying rates of increase. Dynamically, σ B responded to these stresses with a single adaptive activity pulse, whose amplitude depended on the rate at which the stress increased. This rate-responsive behavior can be understood from mathematical modeling of a key negative feedback loop in the underlying regulatory circuit. Using RNAseq we analyzed the effects of both rapid and gradual increases of ethanol and salt stress across the genome. Because of the rate responsiveness of σ B activation, salt and ethanol regulons overlap under rapid, but not gradual, increases in stress. Thus, the cell responds specifically to individual stresses that appear gradually, while using σ B to broaden the cellular response under more rapidly deteriorating conditions. Such dynamic control of specificity could be a critical function of other general stress response pathways.systems biology | single-cell dynamics | computational biology C ells must respond to, and anticipate, a wide range of stresses that occur on multiple timescales. For this purpose, many species use general stress response pathways, which activate a diverse set of target regulons in response to a variety of stresses. For example, in mammals, p53 is activated by DNA damage (1-4) and hypoxia (5, 6), among others, and activates genes that impact cell cycle progression (7), DNA repair (8, 9), apoptosis, and angiogenesis (10, 11). In yeast, Msn2/4 responds to nutritional stress (12), as well as to salt (13), calcium (14), heat, and other stresses (15). Bacteria also contain general stress response pathways, including the alternative sigma factors RpoS in Escherichia coli (16) and σ B in Bacillus subtilis (17).It has been proposed that general stress response pathways enable cells to cross-protect, by anticipating stresses that may not be present at the moment, but are likely to occur soon (18). For example, preexposure to specific stresses is known to enhance bacterial resistance to different stresses applied subsequently (19)(20)(21). This raises a basic question: How do cells determine when to use the general stress response rather than activating more specific individual pathways?The σ B -mediated general stress response of B. subtilis provides an ideal model system to address these issues. σ B is activated by diverse stresses through a well-characterized and conserved transcriptional and posttranscriptional circuit mechanism (17). In response to stress, it activates ∼200 target ge...

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“…S6B). Both of these roles are consistent with previous analysis of negative feedback loops (37)(38)(39).…”

Section: Mathematical Modeling Shows That the Stressosome Can Enablesupporting

confidence: 92%

Rate of environmental change determines stress response specificity

Young

Locke

Elowitz

2013

Proc. Natl. Acad. Sci. U.S.A.

119

142

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“…Further, despite our increasing level of knowledge about the effect of feedback regulation 4,9,25,26 on gene network activity in single species and the effect of carbon metabolism gene regulation on fitness 27 , a systematic experimental and quantitative study characterizing the role of feedback-mediating promoters on the evolved activity of modular gene networks has been missing. At the cross section 28 of evolutionary biology and systems biology, our work provides an example to how gene network evolution could occur through tuning the strength of negative-feedback regulation.…”

Section: Discussionmentioning

confidence: 99%

Evolution of gene network activity by tuning the strength of negative-feedback regulation

et al. 2015

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Despite the examples of protein evolution via mutations in coding sequences, we have very limited understanding on gene network evolution via changes in cis-regulatory elements. Using the galactose network as a model, here we show how the regulatory promoters of the network contribute to the evolved network activity between two yeast species. In Saccharomyces cerevisiae, we combinatorially replace all regulatory network promoters by their counterparts from Saccharomyces paradoxus, measure the resulting network inducibility profiles, and model the results. Lowering relative strength of GAL80-mediated negative feedback by replacing GAL80 promoter is necessary and sufficient to have high network inducibility levels as in S. paradoxus. This is achieved by increasing OFF-to-ON phenotypic switching rates. Competitions performed among strains with or without the GAL80 promoter replacement show strong relationships between network inducibility and fitness. Our results support the hypothesis that gene network activity can evolve by optimizing the strength of negative-feedback regulation.

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“…Notably, these results held in several different contexts and for two distinct transcription factors. Previous studies showed that gene expression and noise can be decoupled by mutating the TATA-box (Murphy et al 2010) or by introducing negative autoregulation (Nevozhay et al 2009). Our work suggests that such decoupling can also be achieved through chromatin.…”

Section: Discussionmentioning

confidence: 99%

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

et al. 2013

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Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

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Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression

Cited by 321 publications

References 45 publications

Rate of environmental change determines stress response specificity

Rate of environmental change determines stress response specificity

Evolution of gene network activity by tuning the strength of negative-feedback regulation

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

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