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      PolyQ length co-evolution in neural proteins

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      NAR Genomics and Bioinformatics
      Oxford University Press

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          Abstract

          Intermolecular co-evolution optimizes physiological performance in functionally related proteins, ultimately increasing molecular co-adaptation and evolutionary fitness. Polyglutamine (polyQ) repeats, which are over-represented in nervous system-related proteins, are increasingly recognized as length-dependent regulators of protein function and interactions, and their length variation contributes to intraspecific phenotypic variability and interspecific divergence. However, it is unclear whether polyQ repeat lengths evolve independently in each protein or rather co-evolve across functionally related protein pairs and networks, as in an integrated regulatory system. To address this issue, we investigated here the length evolution and co-evolution of polyQ repeats in clusters of functionally related and physically interacting neural proteins in Primates. We observed function-/disease-related polyQ repeat enrichment and evolutionary hypervariability in specific neural protein clusters, particularly in the neurocognitive and neuropsychiatric domains. Notably, these analyses detected extensive patterns of intermolecular polyQ length co-evolution in pairs and clusters of functionally related, physically interacting proteins. Moreover, they revealed both direct and inverse polyQ length co-variation in protein pairs, together with complex patterns of coordinated repeat variation in entire polyQ protein sets. These findings uncover a whole system of co-evolving polyQ repeats in neural proteins with direct implications for understanding polyQ-dependent phenotypic variability, neurocognitive evolution and neuropsychiatric disease pathogenesis.

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          Most cited references88

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            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

              We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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                Author and article information

                Contributors
                Journal
                NAR Genom Bioinform
                NAR Genom Bioinform
                nargab
                NAR Genomics and Bioinformatics
                Oxford University Press
                2631-9268
                June 2021
                14 May 2021
                14 May 2021
                : 3
                : 2
                : lqab032
                Affiliations
                Rita Levi Montalcini Department of Neuroscience, University of Torino , Torino 10125, Italy
                Rita Levi Montalcini Department of Neuroscience, University of Torino , Torino 10125, Italy
                National Institute of Neuroscience (INN), University of Torino , Torino 10125, Italy
                Author notes
                To whom correspondence should be addressed. Tel: +39 0116708486; Fax: +39 0116708174; E-mail: ferdinando.fiumara@ 123456unito.it
                Author information
                https://orcid.org/0000-0003-0715-7863
                Article
                lqab032
                10.1093/nargab/lqab032
                8121095
                34017944
                7304c787-25ac-4f13-a3a8-a9379452b410
                © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://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@ 123456oup.com

                History
                : 30 December 2020
                : 10 February 2021
                : 31 March 2021
                Page count
                Pages: 17
                Funding
                Funded by: Ministero dell’Istruzione, dell’Università e della Ricerca, DOI 10.13039/501100003407;
                Award ID: FFABR-2017
                Categories
                AcademicSubjects/SCI00030
                AcademicSubjects/SCI00980
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI01180
                Standard Article

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