Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions

Choudhary, Divya; Lagage, Valentine; Foster, Kevin; Uphoff, Stephan

doi:10.1016/j.celrep.2023.112168

Cited by 29 publications

(28 citation statements)

References 68 publications

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“…This understanding leads us to predict that the presence of wild-type cells should restore DAMP in the peroxidase and catalase deficient hpx - strain. It has previously been shown that a wild-type population can provide protection against environmental H 2 O 2 to cocultured hpx - cells, or similarly H 2 O 2 sensitive Δ oxyR cells, through decreasing the peroxide concentration of the external environment (24, 40). To better distinguish hpx - and wild-type strains in a coculture, two nalidixic acid (Nal) resistant strains of hpx - were independently created with the resistance conferred by point mutations in gyrA (D87G & D87Y).…”

Section: Resultsmentioning

confidence: 99%

“…S6). As well as improving survival under extreme H 2 O 2 stress, previous work also finds mutation rates to decrease in cells protected by a higher density of neighbours able to detoxify the environment (40). Here we find that this mutation protection holds in the absence of external H 2 O 2 application with the presence of higher density wild-type rescuers able to modify mutation rates in catalase/peroxidase deficient cells (Fig.…”

Section: Discussionmentioning

confidence: 99%

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Working together to control mutation: how collective peroxide detoxification determines microbial mutation rate plasticity

Green,

Wang,

Botchey

et al. 2023

Preprint

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Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling andin vivomutation rate estimation we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity whenEscherichia colipopulations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for density-associated mutation rate plasticity, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Working together to control mutation: how collective peroxide detoxification determines microbial mutation rate plasticity

Green,

Wang,

Botchey

et al. 2023

Preprint

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“…Microfluidic setup and preparation of 'mother machine' devices were performed as described in Choudhary et al 28 .…”

Section: Microfluidic Chip Preparation and Setupmentioning

confidence: 99%

A simple regulatory network coordinates a bacterial stress response in space and time

Choudhary,

Foster,

Uphoff

2024

Preprint

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Bacteria employ diverse gene regulatory networks to protect themselves from stressful environments. While transcriptomics and proteomics show that the expression of different genes can shift strongly in response to stress, the underlying logic of large regulatory networks is difficult to understand from bulk measurements performed at discrete time points. As a result, it remains challenging to predict how these regulatory networks function at a system level. Here we use time-resolved single-cell imaging to explore the functioning of a key bacterial stress response: The Escherichia coli response to oxidative stress. Our work reveals a striking diversity in the expression dynamics of genes in the regulatory network, with differences in the timing, magnitude, and direction of expression changes. Nevertheless, we find that these patterns have a simple underlying logic. Firstly, all genes exhibit a transient increase in their protein levels simply due to the slowing down of cell growth under stress. Controlling for this effect reveals three classes of gene regulation driven by the transcription factor OxyR. Downregulated genes drop in expression level, while upregulated genes either show pulsatile expression that decays rapidly or gradual induction, dependent upon transcription factor binding dynamics. These classes appear to serve distinct functional roles in cell populations. Pulsatile genes are stress-sensitive and activate rapidly and transiently in a few cells, which provides an initial protection for cell groups. Gradually upregulated genes are less sensitive and induce more evenly generating a lasting protection that involves a larger number of cells. Our study shows how bacterial populations use simple regulatory principles to coordinate a stress response in space and time.

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“…In recent years, single-cell experiments have revealed extensive heterogeneity in the expression of stress responses in bacterial populations [17,18,19,20,21,22,23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Heterogeneity can arise for various reasons, including stochastic expression of genes involved in stress responses, especially where the corresponding proteins are initially present in small numbers [18,19,20], phenotypic variability in the stability of key regulators [23], or micro-environmental variation in cell-to-cell interactions [26]. Positive and negative feedback loops are common features of stress response regulatory networks, which can generate, amongst other features, cell-to-cell variation [27].…”

Section: Introductionmentioning

confidence: 99%

Estimating Mutation Rates Under Heterogeneous Stress Responses

Lansch-Justen,

El Karoui,

Alexander

2023

Preprint

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Exposure to environmental stressors, including certain antibiotics, induces stress responses in bacteria. Some of these responses increase mutagenesis and, this way, potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard lab approach of fluctuation assays to estimate the mutation rates. However, many stress responses are heterogeneously expressed in bacterial populations, which existing estimation methods have not yet addressed. We develop a population dynamic model that considers heterogeneous stress responses (subpopulations of cells with the response 'off' or 'on') and derive the mutant count distribution arising in fluctuation assays under this model. We then implement a computational method to estimate the mutation-rate increase specifically associated with the induction of the stress response. Using simulated mutant count data, we show that our inference method allows for accurate and precise estimation of the mutation-rate increase, provided induction of the response also reduces the division rate, as is the case for the SOS response. Interestingly, we find that existing methods assuming a homogeneous stress response can also accurately infer the increase in population mean mutation rate. Without additional experiments, it is not generally possible to distinguish between a model with heterogeneously expressed stress responses and a model in which mutants have a differential fitness compared to non-mutants.

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Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions

Cited by 29 publications

References 68 publications

Working together to control mutation: how collective peroxide detoxification determines microbial mutation rate plasticity

Working together to control mutation: how collective peroxide detoxification determines microbial mutation rate plasticity

A simple regulatory network coordinates a bacterial stress response in space and time

Estimating Mutation Rates Under Heterogeneous Stress Responses

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