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      Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli

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          Abstract

          Adaptation to novel environments is often associated with changes in gene regulation. Nevertheless, few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial. To this end, we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of Escherichia coli. We found that lac operon regulation became strikingly variable, including changes in the mode of environmental response (bimodal, graded, and constitutive), sensitivity to inducer concentration, and maximum expression level. In addition, some classes of regulatory change were enriched in specific selective environments. Sequencing of evolved clones, combined with reconstruction of individual mutations in the ancestral background, identified mutations within the lac operon that recapitulate many of the evolved regulatory changes. These mutations conferred fitness benefits in environments containing lactose, indicating that the regulatory changes are adaptive. The same mutations conferred different fitness effects when present in an evolved clone, indicating that interactions between the lac operon and other evolved mutations also contribute to fitness. Similarly, changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations. Together these results underline how dynamic regulatory interactions can be, in this case evolving through mutations both within and external to the canonical lactose utilization network.

          Author Summary

          Differences in gene regulation underlie many important biological processes and are thought to be important for the adaption of organisms to novel environments. Here we focus on the regulation of a group of well-studied genes, the lac operon, that control the utilization of lactose sugar, and we examine how their regulation changes during the adaptation of populations of Escherichia coli bacteria to environments that differ only in the presence of lactose. We find that lac operon regulation is altered in almost all populations that evolve in the presence of lactose and identify two classes of mutations that explain a large part of this change and that confer significant fitness benefits. Interestingly, our study indicates that other mutations, lying outside of the commonly recognized control region, cause new regulation of the lac operon. Together these findings reinforce the importance of changes in gene regulation during evolution and suggest that the biological basis of these changes can be complex and involve novel interactions between genes.

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          Most cited references51

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          Optimality and evolutionary tuning of the expression level of a protein.

          Different proteins have different expression levels. It is unclear to what extent these expression levels are optimized to their environment. Evolutionary theories suggest that protein expression levels maximize fitness, but the fitness as a function of protein level has seldom been directly measured. To address this, we studied the lac system of Escherichia coli, which allows the cell to use the sugar lactose for growth. We experimentally measured the growth burden due to production and maintenance of the Lac proteins (cost), as well as the growth advantage (benefit) conferred by the Lac proteins when lactose is present. The fitness function, given by the difference between the benefit and the cost, predicts that for each lactose environment there exists an optimal Lac expression level that maximizes growth rate. We then performed serial dilution evolution experiments at different lactose concentrations. In a few hundred generations, cells evolved to reach the predicted optimal expression levels. Thus, protein expression from the lac operon seems to be a solution of a cost-benefit optimization problem, and can be rapidly tuned by evolution to function optimally in new environments.
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            flowCore: a Bioconductor package for high throughput flow cytometry

            Background Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. Results We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. Conclusion The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.
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              A constant rate of spontaneous mutation in DNA-based microbes.

              J DRAKE (1991)
              In terms of evolution and fitness, the most significant spontaneous mutation rate is likely to be that for the entire genome (or its nonfrivolous fraction). Information is now available to calculate this rate for several DNA-based haploid microbes, including bacteriophages with single- or double-stranded DNA, a bacterium, a yeast, and a filamentous fungus. Their genome sizes vary by approximately 6500-fold. Their average mutation rates per base pair vary by approximately 16,000-fold, whereas their mutation rates per genome vary by only approximately 2.5-fold, apparently randomly, around a mean value of 0.0033 per DNA replication. The average mutation rate per base pair is inversely proportional to genome size. Therefore, a nearly invariant microbial mutation rate appears to have evolved. Because this rate is uniform in such diverse organisms, it is likely to be determined by deep general forces, perhaps by a balance between the usually deleterious effects of mutation and the physiological costs of further reducing mutation rates.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                January 2012
                January 2012
                12 January 2012
                : 8
                : 1
                : e1002444
                Affiliations
                [1 ]Bio-X Program, Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America
                [2 ]Department of Systems Biology–Unit 950, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
                [3 ]Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
                University of Toronto, Canada
                Author notes

                Conceived and designed the experiments: RDM TFC SQ. Performed the experiments: RDM SQ ZK TD. Analyzed the data: JCJR GB TFC RDM SQ. Contributed reagents/materials/analysis tools: GB JCJR. Wrote the paper: RDM TFC GB.

                Article
                PGENETICS-D-11-01811
                10.1371/journal.pgen.1002444
                3257284
                22253602
                d5c0e97a-52b3-4682-a569-4429f564acc8
                Quan 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
                : 23 August 2011
                : 16 November 2011
                Page count
                Pages: 18
                Categories
                Research Article
                Biology
                Evolutionary Biology
                Genetics
                Microbiology
                Model Organisms
                Molecular Cell Biology
                Systems Biology

                Genetics
                Genetics

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