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      Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation

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          Abstract

          Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother’s old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother’s old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington’s genetic assimilation. Our model is able to quantify the evolution of the assimilation because it characterizes the fitness consequences of variation.

          Author Summary

          The benefit of non-genetic variation in phenotypic traits is debated. We show that the partitioning of damage by a mother bacterium to its two daughters is a variable trait that provides an advantage by generating fitness variation. Present day bacteria partition asymmetrically more damage to the old daughter, the one receiving the mother’s old pole. By modeling damage partitioning in a population, we find that the asymmetry is advantageous because it increases fitness variation and hence the efficiency of natural selection. However, our model also shows that symmetrical but randomly variable partitioning can be similarly beneficial. To examine why bacteria evolved asymmetry, we modeled the effect of damage anchored to the mother’s old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variation, it has the opposite effect on symmetry. Thus, symmetrical but randomly variable partitioning may have been sufficient to protect early bacteria from damage. However, natural selection then supplanted it with a genetically controlled mechanism in the form of asymmetrical partitioning. The change from random to genetic control is an important evolutionary transition that is poorly characterized. We are able to quantify this transition because our model estimates the costs and benefits of fitness variation.

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          Stochasticity in gene expression: from theories to phenotypes.

          Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
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            Robust growth of Escherichia coli.

            The quantitative study of the cell growth has led to many fundamental insights in our understanding of a wide range of subjects, from the cell cycle to senescence. Of particular importance is the growth rate, whose constancy represents a physiological steady state of an organism. Recent studies, however, suggest that the rate of elongation during exponential growth of bacterial cells decreases cumulatively with replicative age for both asymmetrically and symmetrically dividing organisms, implying that a "steady-state" population consists of individual cells that are never in a steady state of growth. To resolve this seeming paradoxical observation, we studied the long-term growth and division patterns of Escherichia coli cells by employing a microfluidic device designed to follow steady-state growth and division of a large number of cells at a defined reproductive age. Our analysis of approximately 10(5) individual cells reveals a remarkable stability of growth whereby the mother cell inherits the same pole for hundreds of generations. We further show that death of E. coli is not purely stochastic but is the result of accumulating damages. We conclude that E. coli, unlike all other aging model systems studied to date, has a robust mechanism of growth that is decoupled from cell death. Copyright 2010 Elsevier Ltd. All rights reserved.
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              Gene regulation at the single-cell level.

              The quantitative relation between transcription factor concentrations and the rate of protein production from downstream genes is central to the function of genetic networks. Here we show that this relation, which we call the gene regulation function (GRF), fluctuates dynamically in individual living cells, thereby limiting the accuracy with which transcriptional genetic circuits can transfer signals. Using fluorescent reporter genes and fusion proteins, we characterized the bacteriophage lambda promoter P(R) in Escherichia coli. A novel technique based on binomial errors in protein partitioning enabled calibration of in vivo biochemical parameters in molecular units. We found that protein production rates fluctuate over a time scale of about one cell cycle, while intrinsic noise decays rapidly. Thus, biochemical parameters, noise, and slowly varying cellular states together determine the effective single-cell GRF. These results can form a basis for quantitative modeling of natural gene circuits and for design of synthetic ones.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                13 January 2016
                January 2016
                : 12
                : 1
                : e1004700
                Affiliations
                [1 ]Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California, San Diego, La Jolla, California, United States of America
                [2 ]CAPES Foundation, Ministry of Education of Brazil, Brasilia, Brazil
                University of New South Wales, AUSTRALIA
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: LC CUR AMP JUC. Performed the experiments: LC. Analyzed the data: LC CUR AMP JUC. Contributed reagents/materials/analysis tools: LC. Wrote the paper: LC CUR.

                Article
                PCOMPBIOL-D-15-01640
                10.1371/journal.pcbi.1004700
                4711911
                26761487
                806ab1b5-a104-4029-bf3e-b3bae5992747
                © 2016 Chao et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 September 2015
                : 10 December 2015
                Page count
                Figures: 5, Tables: 0, Pages: 17
                Funding
                This work was supported by National Science Foundation( http://www.nsf.gov/) research grant award DEB-1354253 to LC; and CAPES ( http://www.capes.gov.br/) fellowship award to AMP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper.

                Quantitative & Systems biology
                Quantitative & Systems biology

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