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Adaptability of non-genetic diversity in bacterial chemotaxis

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      Abstract

      Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

      DOI: http://dx.doi.org/10.7554/eLife.03526.001

      eLife digest

      Bacterial colonies are generally made up of genetically identical cells. Despite this, a closer look at the members of a bacterial colony shows that these cells can have very different behaviors. For example, some cells may grow more quickly than others, or be more resistant to antibiotics. The mechanisms driving this diversity are only beginning to be identified and understood.

      Escherichia coli bacteria can move towards, or away from, certain chemicals in their surrounding environment to help them navigate toward favorable conditions. This behavior is known as chemotaxis. The signals from all of these chemicals are processed in E. coli by just one set of proteins, which control the different behaviors that are needed for the bacteria to follow them. Different numbers of these proteins are found in different—but genetically identical—bacteria, and the number of proteins is linked to how the bacteria perform these behaviors.

      It has been suggested that diversity can be beneficial to the overall bacterial population, as it helps the population survive environmental changes. This suggests that the level of diversity in the population should adapt to the level of diversity in the environment. However, it remains unknown how this adaptation occurs.

      Frankel et al. developed and combined several models and simulations to investigate whether differences in chemotaxis protein production help an E. coli colony to survive. The models show that in different environments, it can be beneficial for the population as a whole if different cells have different responses to the chemicals present. For example, if a lot of a useful chemical is present, bacteria are more likely to survive by heading straight to the source. If not much chemical is detected, the bacteria may need to move in a more exploratory manner.

      Frankel et al. find that different amounts of chemotaxis proteins produce these different behaviors. To survive in a changing environment, it is therefore best for the E. coli colony to contain cells that have different amounts of these proteins. Frankel et al. propose that the variability of chemotaxis protein levels between genetically identical cells can change through mutations in the genes that control how many of the proteins are produced, and predict that such mutations allow populations to adapt to environmental changes.

      The environments simulated in the model were much simpler than would be found in the real world, and Frankel et al. describe experiments that are now being performed to confirm and expand on their results. The model could be used in the future to shed light on the behavior of other cells that are genetically identical but exhibit diverse behaviors, from other bacterial species to more complex cancer cells.

      DOI: http://dx.doi.org/10.7554/eLife.03526.002

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      Most cited references 73

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      Stochastic gene expression in a single cell.

      Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
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        Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells.

        Protein and messenger RNA (mRNA) copy numbers vary from cell to cell in isogenic bacterial populations. However, these molecules often exist in low copy numbers and are difficult to detect in single cells. We carried out quantitative system-wide analyses of protein and mRNA expression in individual cells with single-molecule sensitivity using a newly constructed yellow fluorescent protein fusion library for Escherichia coli. We found that almost all protein number distributions can be described by the gamma distribution with two fitting parameters which, at low expression levels, have clear physical interpretations as the transcription rate and protein burst size. At high expression levels, the distributions are dominated by extrinsic noise. We found that a single cell's protein and mRNA copy numbers for any given gene are uncorrelated.
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          Phenotypic diversity, population growth, and information in fluctuating environments.

          Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
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            Author and article information

            Affiliations
            [1 ]Department of Molecular, Cellular and Developmental Biology, Yale University , New Haven, United States
            [2 ]Department of Physics, Yale University , New Haven, United States
            Brandeis University , United States
            Author notes
            [* ]For correspondence: thierry.emonet@ 123456yale.edu
            Contributors
            Role: Reviewing editor,
            Brandeis University , United States
            Journal
            eLife
            Elife
            eLife
            eLife
            eLife
            eLife Sciences Publications, Ltd
            2050-084X
            03 October 2014
            2014
            : 3
            25279698
            4210811
            03526
            10.7554/eLife.03526
            (Reviewing editor)
            Copyright © 2014, Frankel et al

            This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

            Product
            Funding
            Funded by: James S. McDonnell Foundation FundRef identification ID: http://dx.doi.org/10.13039/100000913
            Award ID: 220020224
            Award Recipient :
            Funded by: Paul G. Allen Family Foundation FundRef identification ID: http://dx.doi.org/10.13039/100000952
            Award ID: 11562
            Award Recipient :
            Funded by: National Institutes of Health FundRef identification ID: http://dx.doi.org/10.13039/100000002
            Award ID: 1R01GM106189
            Award Recipient :
            The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
            Categories
            Research Article
            Ecology
            Microbiology and Infectious Disease
            Custom metadata
            0.8
            An experimentally constrained model shows that Escherichia coli faces fitness trade-offs in chemotaxis behaviors, and that adaptation of phenotypic diversity through altered gene regulation permits populations to resolve these trade-offs.

            Life sciences

            chemotaxis, fitness trade-offs, phenotypic diversity, e. coli

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