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      Acute and Long-Term Consequences of Co-opted doublesex on the Development of Mimetic Butterfly Color Patterns

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          Abstract

          Novel phenotypes are increasingly recognized to have evolved by co-option of conserved genes into new developmental contexts, yet the process by which co-opted genes modify existing developmental programs remains obscure. Here, we provide insight into this process by characterizing the role of co-opted doublesex in butterfly wing color pattern development. dsx is the master regulator of insect sex differentiation but has been co-opted to control the switch between discrete nonmimetic and mimetic patterns in Papilio alphenor and its relatives through the evolution of novel mimetic alleles. We found dynamic spatial and temporal expression pattern differences between mimetic and nonmimetic butterflies throughout wing development. A mimetic color pattern program is switched on by a pulse of dsx expression in early pupal development that causes acute and long-term differential gene expression, particularly in Wnt and Hedgehog signaling pathways. RNAi suggested opposing, novel roles for these pathways in mimetic pattern development. Importantly, Dsx co-option caused Engrailed, a primary target of Hedgehog signaling, to gain a novel expression domain early in pupal wing development that is propagated through mid-pupal development to specify novel mimetic patterns despite becoming decoupled from Dsx expression itself. Altogether, our findings provide multiple views into how co-opted genes can both cause and elicit changes to conserved networks and pathways to result in development of novel, adaptive phenotypes.

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          Most cited references51

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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              A general framework for weighted gene co-expression network analysis.

              Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press (US )
                0737-4038
                1537-1719
                September 2023
                05 September 2023
                05 September 2023
                : 40
                : 9
                : msad196
                Affiliations
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Department of Ecology & Evolution, The University of Chicago , Chicago, IL, USA
                Author notes
                Present address: Department of Biology, The University of Texas at Arlington, Arlington, TX, USA
                Corresponding author: E-mail: nvankuren@ 123456uchicago.edu .
                Author information
                https://orcid.org/0000-0001-8633-8851
                Article
                msad196
                10.1093/molbev/msad196
                10498343
                37668300
                cf287155-d27e-4cc8-ba8f-d1e40970b10a
                © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 13
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                mimicry,development,evolution,sexual dimorphism,polymorphism,gene regulation,co-option
                Molecular biology
                mimicry, development, evolution, sexual dimorphism, polymorphism, gene regulation, co-option

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