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      A spontaneous complex structural variant in rcan-1 increases exploratory behavior and laboratory fitness of Caenorhabditis elegans

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          Abstract

          Over long evolutionary timescales, major changes to the copy number, function, and genomic organization of genes occur, however, our understanding of the individual mutational events responsible for these changes is lacking. In this report, we study the genetic basis of adaptation of two strains of C. elegans to laboratory food sources using competition experiments on a panel of 89 recombinant inbred lines (RIL). Unexpectedly, we identified a single RIL with higher relative fitness than either of the parental strains. This strain also displayed a novel behavioral phenotype, resulting in higher propensity to explore bacterial lawns. Using bulk-segregant analysis and short-read resequencing of this RIL, we mapped the change in exploration behavior to a spontaneous, complex rearrangement of the rcan-1 gene that occurred during construction of the RIL panel. We resolved this rearrangement into five unique tandem inversion/duplications using Oxford Nanopore long-read sequencing. rcan-1 encodes an ortholog to human RCAN1/DSCR1 calcipressin gene, which has been implicated as a causal gene for Down syndrome. The genomic rearrangement in rcan-1 creates two complete and two truncated versions of the rcan-1 coding region, with a variety of modified 5’ and 3’ non-coding regions. While most copy-number variations (CNVs) are thought to act by increasing expression of duplicated genes, these changes to rcan-1 ultimately result in the reduction of its whole-body expression due to changes in the upstream regions. By backcrossing this rearrangement into a common genetic background to create a near isogenic line (NIL), we demonstrate that both the competitive advantage and exploration behavioral changes are linked to this complex genetic variant. This NIL strain does not phenocopy a strain containing an rcan-1 loss-of-function allele, which suggests that the residual expression of rcan-1 is necessary for its fitness effects. Our results demonstrate how colonization of new environments, such as those encountered in the laboratory, can create evolutionary pressure to modify gene function. This evolutionary mismatch can be resolved by an unexpectedly complex genetic change that simultaneously duplicates and diversifies a gene into two uniquely regulated genes. Our work shows how complex rearrangements can act to modify gene expression in ways besides increased gene dosage.

          Author summary

          Evolution acts on genetic variants that modify phenotypes that increase the likelihood of staying alive and passing on these genetic changes to subsequent generations (i.e. fitness). There is general interest in understanding the types of genetic variants that can increase fitness in specific environments. One route that fitness can be increased is through changes in behavior, such as finding new food sources. Here, we identify a spontaneous genetic change that increases exploration behavior and fitness of animals in laboratory environments. Interestingly, this genetic change is not a simple genetic change that deletes or changes the sequence of a protein product, but rather a complex structural variant that simultaneously duplicates the rcan-1 gene and also modifies its expression in a number of tissues. Our work demonstrates how a complex structural change can duplicate a gene, modify the DNA control regions that determine its cellular sites of action, and confer a fitness advantage that could lead to its spread in a population.

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          Most cited references60

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          Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

          We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
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            SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

            Background Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. Results SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used. Conclusions SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.
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              The probability of duplicate gene preservation by subfunctionalization.

              It has often been argued that gene-duplication events are most commonly followed by a mutational event that silences one member of the pair, while on rare occasions both members of the pair are preserved as one acquires a mutation with a beneficial function and the other retains the original function. However, empirical evidence from genome duplication events suggests that gene duplicates are preserved in genomes far more commonly and for periods far in excess of the expectations under this model, and whereas some gene duplicates clearly evolve new functions, there is little evidence that this is the most common mechanism of duplicate-gene preservation. An alternative hypothesis is that gene duplicates are frequently preserved by subfunctionalization, whereby both members of a pair experience degenerative mutations that reduce their joint levels and patterns of activity to that of the single ancestral gene. We consider the ways in which the probability of duplicate-gene preservation by such complementary mutations is modified by aspects of gene structure, degree of linkage, mutation rates and effects, and population size. Even if most mutations cause complete loss-of-subfunction, the probability of duplicate-gene preservation can be appreciable if the long-term effective population size is on the order of 10(5) or smaller, especially if there are more than two independently mutable subfunctions per locus. Even a moderate incidence of partial loss-of-function mutations greatly elevates the probability of preservation. The model proposed herein leads to quantitative predictions that are consistent with observations on the frequency of long-term duplicate gene preservation and with observations that indicate that a common fate of the members of duplicate-gene pairs is the partitioning of tissue-specific patterns of expression of the ancestral gene.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Visualization
                Role: Data curationRole: MethodologyRole: Visualization
                Role: Formal analysisRole: Investigation
                Role: Data curationRole: Visualization
                Role: Data curationRole: Visualization
                Role: Data curation
                Role: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Resources
                Role: Data curationRole: MethodologyRole: Supervision
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ValidationRole: VisualizationRole: 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
                24 February 2020
                February 2020
                : 16
                : 2
                : e1008606
                Affiliations
                [1 ] School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                [2 ] Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                [3 ] The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
                [4 ] Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, United States of America
                [5 ] Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                [6 ] School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                [7 ] School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States of America
                Fred Hutchinson Cancer Research Center, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-9496-0023
                http://orcid.org/0000-0002-9897-5900
                http://orcid.org/0000-0002-4434-9357
                http://orcid.org/0000-0002-3043-1544
                http://orcid.org/0000-0002-0546-8484
                http://orcid.org/0000-0002-2814-8950
                http://orcid.org/0000-0003-0229-9651
                http://orcid.org/0000-0002-6881-660X
                http://orcid.org/0000-0002-1598-3746
                Article
                PGENETICS-D-19-01356
                10.1371/journal.pgen.1008606
                7058356
                32092052
                e4814824-53df-4e10-99eb-b46a390b8673
                © 2020 Zhao 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
                : 12 August 2019
                : 11 January 2020
                Page count
                Figures: 7, Tables: 0, Pages: 29
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: GM114170
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: NS096581
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: GM088333
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: AG056436
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: CAREER
                Award Recipient :
                This work was supported by NIH GM114170 (to P.T.M), a John N. Nicholson fellowship (to S.C.B), an NSF CAREER Award (to E.C.A.), and NIH NS096581, GM088333, AG056436 (to H.L.). The John Nicholson Fellowship URL is: https://www.tgs.northwestern.edu/funding/fellowships-and-grants/internal-fellowships/nicholson-fellowship.html. 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
                Evolutionary Biology
                Evolutionary Genetics
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Caenorhabditis Elegans
                Research and Analysis Methods
                Model Organisms
                Caenorhabditis Elegans
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Caenorhabditis Elegans
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Nematoda
                Caenorhabditis
                Caenorhabditis Elegans
                Biology and Life Sciences
                Computational Biology
                Genome Complexity
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Complexity
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Alignment
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Inbred Strains
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Psychology
                Behavior
                Social Sciences
                Psychology
                Behavior
                Custom metadata
                vor-update-to-uncorrected-proof
                2020-03-05
                All RNA-seq and resequencing files are available from the SRA database NIH BioProject PRJNA526525.

                Genetics
                Genetics

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