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      Gene regulatory network architecture in different developmental contexts influences the genetic basis of morphological evolution

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          Abstract

          Convergent phenotypic evolution is often caused by recurrent changes at particular nodes in the underlying gene regulatory networks (GRNs). The genes at such evolutionary ‘hotspots’ are thought to maximally affect the phenotype with minimal pleiotropic consequences. This has led to the suggestion that if a GRN is understood in sufficient detail, the path of evolution may be predictable. The repeated evolutionary loss of larval trichomes among Drosophila species is caused by the loss of shavenbaby ( svb) expression. svb is also required for development of leg trichomes, but the evolutionary gain of trichomes in the ‘naked valley’ on T2 femurs in Drosophila melanogaster is caused by reduced microRNA-92a ( miR-92a) expression rather than changes in svb. We compared the expression and function of components between the larval and leg trichome GRNs to investigate why the genetic basis of trichome pattern evolution differs in these developmental contexts. We found key differences between the two networks in both the genes employed, and in the regulation and function of common genes. These differences in the GRNs reveal why mutations in svb are unlikely to contribute to leg trichome evolution and how instead miR-92a represents the key evolutionary switch in this context. Our work shows that variability in GRNs across different developmental contexts, as well as whether a morphological feature is lost versus gained, influence the nodes at which a GRN evolves to cause morphological change. Therefore, our findings have important implications for understanding the pathways and predictability of evolution.

          Author summary

          A major goal of biology is to identify the genetic causes of organismal diversity. Convergent evolution of traits is often caused by changes in the same genes–evolutionary ‘hotspots’. shavenbaby is a ‘hotspot’ for larval trichome loss in Drosophila, but microRNA-92a underlies the gain of leg trichomes. To understand this difference in the genetics of phenotypic evolution, we compared the expression and function of genes in the underlying regulatory networks. We found that the pathway of evolution is influenced by differences in gene regulatory network architecture in different developmental contexts, as well as by whether a trait is lost or gained. Therefore, hotspots in one context may not readily evolve in a different context. This has important implications for understanding the genetic basis of phenotypic change and the predictability of evolution.

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

            Heng Li (2011)
            Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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              The genetic causes of convergent evolution.

              The evolution of phenotypic similarities between species, known as convergence, illustrates that populations can respond predictably to ecological challenges. Convergence often results from similar genetic changes, which can emerge in two ways: the evolution of similar or identical mutations in independent lineages, which is termed parallel evolution; and the evolution in independent lineages of alleles that are shared among populations, which I call collateral genetic evolution. Evidence for parallel and collateral evolution has been found in many taxa, and an emerging hypothesis is that they result from the fact that mutations in some genetic targets minimize pleiotropic effects while simultaneously maximizing adaptation. If this proves correct, then the molecular changes underlying adaptation might be more predictable than has been appreciated previously.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: 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
                3 May 2018
                May 2018
                : 14
                : 5
                : e1007375
                Affiliations
                [1 ] Department of Biological and Medical Sciences, Oxford Brookes University, Gipsy Lane, Oxford, United Kingdom
                [2 ] Centro Andaluz de Biología del Desarrollo, CSIC/ Universidad Pablo de Olavide, Carretera de Utrera Km1, Sevilla, Spain
                [3 ] Departmento de Ecologia, Genetica y Evolucion, IEGEBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
                [4 ] Brighton and Sussex Medical School, University of Sussex, East Sussex, Falmer, Brighton, United Kingdom
                University of Illinois, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

                Author information
                http://orcid.org/0000-0002-2234-4539
                http://orcid.org/0000-0003-0811-8604
                http://orcid.org/0000-0002-2908-2420
                Article
                PGENETICS-D-17-02357
                10.1371/journal.pgen.1007375
                5953500
                29723190
                d73c83f5-cf92-41d5-84d1-b950eaed99a9
                © 2018 Kittelmann 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
                : 3 December 2017
                : 23 April 2018
                Page count
                Figures: 5, Tables: 0, Pages: 21
                Funding
                This work was funded by DFG Research Fellowships to SK (Ki 1831/1-1) and FAF (FR 3929/1-1), a BBSRC DTP studentship to ADB, grants from Ministerio de Economía y Competitividad (BFU2016-74961-P) and the Andalusian Government (BIO-396) to JLGS, and an Austrian Science Fund (FWF) Fellowship to APM (M1059-B09). RNA library preparation and sequencing were carried out by Edinburgh Genomics, The University of Edinburgh. Edinburgh Genomics is partly supported through core grants from NERC (R8/H10/56), MRC (MR/K001744/1) and BBSRC (BB/J004243/1). 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
                Plant Science
                Plant Anatomy
                Trichomes
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Legs
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Legs
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Developmental Biology
                Evolutionary Developmental Biology
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Developmental Biology
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Femur
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Femur
                Research and Analysis Methods
                Experimental Organism Systems
                Model Organisms
                Drosophila Melanogaster
                Research and Analysis Methods
                Model Organisms
                Drosophila Melanogaster
                Research and Analysis Methods
                Experimental Organism Systems
                Animal Models
                Drosophila Melanogaster
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Drosophila
                Drosophila Melanogaster
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Genetics
                Phenotypes
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-05-15
                All RNA-Seq and ATAC-Seq data files are available from the Gene Expression Omnibus database (accession number GSE113240).

                Genetics
                Genetics

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