1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Modular epistasis and the compensatory evolution of gene deletion mutants

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes, metabolic pathways, and other functional modules. Although epistasis between adaptive mutations is also common in laboratory evolution experiments, the functional basis for these interactions is less well characterized. Here, we quantify the extent to which gene function (as determined by a genome-wide screen for epistasis among deletion mutants) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules. We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly, on average, than those with deletions in different modules. At the same time, initial fitness also plays a role: independent of the specific functional modules involved, adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds, consistent with a general pattern of diminishing returns epistasis. We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis (i.e. mutations tend to be more beneficial in the background in which they arose). In two functional modules, epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment, indicating an intimate functional relationship. Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations, meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics.

          Author summary

          The effects of mutations often depend on the presence or absence of other mutations. This phenomenon, known as epistasis, has been used extensively to infer functional associations between genes. For example, genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes. In the context of adaptation, epistasis can cause the outcomes of evolution to depend strongly on the initial genotype. Although studies have found that epistasis is common in laboratory populations, we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution. Here, by comparing evolution in strains with gene deletions in different functional modules, we quantify the effect of functional epistasis on evolutionary outcomes. We find that mutants with deletions in the same module have more similar evolutionary outcomes, on average, than mutants with deletions in different modules. This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          A global genetic interaction network maps a wiring diagram of cellular function.

          We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Inexpensive Multiplexed Library Preparation for Megabase-Sized Genomes

            Whole-genome sequencing has become an indispensible tool of modern biology. However, the cost of sample preparation relative to the cost of sequencing remains high, especially for small genomes where the former is dominant. Here we present a protocol for rapid and inexpensive preparation of hundreds of multiplexed genomic libraries for Illumina sequencing. By carrying out the Nextera tagmentation reaction in small volumes, replacing costly reagents with cheaper equivalents, and omitting unnecessary steps, we achieve a cost of library preparation of $8 per sample, approximately 6 times cheaper than the standard Nextera XT protocol. Furthermore, our procedure takes less than 5 hours for 96 samples. Several hundred samples can then be pooled on the same HiSeq lane via custom barcodes. Our method will be useful for re-sequencing of microbial or viral genomes, including those from evolution experiments, genetic screens, and environmental samples, as well as for other sequencing applications including large amplicon, open chromosome, artificial chromosomes, and RNA sequencing.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The molecular diversity of adaptive convergence.

              To estimate the number and diversity of beneficial mutations, we experimentally evolved 115 populations of Escherichia coli to 42.2°C for 2000 generations and sequenced one genome from each population. We identified 1331 total mutations, affecting more than 600 different sites. Few mutations were shared among replicates, but a strong pattern of convergence emerged at the level of genes, operons, and functional complexes. Our experiment uncovered a set of primary functional targets of high temperature, but we estimate that many other beneficial mutations could contribute to similar adaptive outcomes. We inferred the pervasive presence of epistasis among beneficial mutations, which shaped adaptive trajectories into at least two distinct pathways involving mutations either in the RNA polymerase complex or the termination factor rho.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                February 2019
                15 February 2019
                : 15
                : 2
                : e1007958
                Affiliations
                [1 ] Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
                [2 ] Section of Ecology, Behavior and Evolution, Division of Biological Sciences, University of California at San Diego, San Diego, California, United States of America
                [3 ] Department of Physics, Harvard University, Cambridge, Massachusetts, United States of America
                [4 ] NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, Massachusetts, United States of America
                [5 ] Quantitative Biology Initiative, Harvard University, Cambridge, Massachusetts, United States of America
                University College Dublin, IRELAND
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-9347-0647
                http://orcid.org/0000-0003-1357-6386
                http://orcid.org/0000-0002-9581-1150
                Article
                PGENETICS-D-18-01919
                10.1371/journal.pgen.1007958
                6395002
                30768593
                673f2047-91fa-4d11-95e0-0bdb4384d3e9
                © 2019 Rojas Echenique 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
                : 28 September 2018
                : 11 January 2019
                Page count
                Figures: 5, Tables: 4, Pages: 23
                Funding
                S.K. acknowledges support from the Burroughs Wellcome Fund Career Award at Scientific Interface (Grant 1010719.01), the Alfred P. Sloan Foundation (Grant FG-2017-9227) and the Hellman Foundation. M.M.D. acknowledges support from the Simons Foundation (Grant 376196), grant DEB-1655960 from the NSF, and grant GM104239 from the NIH. Computational work was performed on the Odyssey cluster supported by the Research Computing Group at Harvard University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Mutation
                Deletion Mutation
                Biology and Life Sciences
                Genetics
                Heredity
                Epistasis
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Evolutionary Adaptation
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Genetics
                Heredity
                Epistasis
                Fitness Epistasis
                Biology and Life Sciences
                Mycology
                Fungal Evolution
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Cloning
                Research and Analysis Methods
                Molecular Biology Techniques
                Cloning
                Biology and Life Sciences
                Organisms
                Eukaryota
                Fungi
                Yeast
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-02-28
                Raw sequencing reads have been deposited with the NIH SRA (Sequence Read Archive), under accession number PRJNA516029. All other relevant data are within the manuscript and its Supporting Information files.

                Genetics
                Genetics

                Comments

                Comment on this article