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      Multiregional profiling of the brain transmembrane proteome uncovers novel regulators of depression

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          Abstract

          In-depth profiling of transmembrane proteins in the brain leads to the identification of GPCR regulators in a disease model.

          Abstract

          Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein–coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.

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          Most cited references73

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          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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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              SciPy 1.0: fundamental algorithms for scientific computing in Python

              SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                July 2021
                21 July 2021
                : 7
                : 30
                : eabf0634
                Affiliations
                [1 ]iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
                [2 ]School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
                [3 ]University of Chinese Academy of Sciences, Beijing 100049, China.
                [4 ]Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China.
                [5 ]CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
                Author notes
                [†]

                These authors contributed equally to this work.

                [‡]

                Lead contact.

                Author information
                http://orcid.org/0000-0001-5472-7772
                http://orcid.org/0000-0002-2948-6775
                http://orcid.org/0000-0001-5925-9779
                http://orcid.org/0000-0002-5613-7036
                http://orcid.org/0000-0003-1417-0329
                http://orcid.org/0000-0003-1576-8349
                http://orcid.org/0000-0002-7682-3498
                http://orcid.org/0000-0001-6377-1281
                http://orcid.org/0000-0003-0719-3901
                http://orcid.org/0000-0001-5363-9834
                http://orcid.org/0000-0003-0416-3774
                http://orcid.org/0000-0001-7930-2316
                http://orcid.org/0000-0002-5245-2477
                Article
                abf0634
                10.1126/sciadv.abf0634
                8294761
                34290087
                927489c2-e9c0-4894-997f-91bcbba913d5
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 03 October 2020
                : 03 June 2021
                Funding
                Funded by: National Program on Key Basic Research Project of China;
                Award ID: 2018YFA0507004
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Biochemistry
                Cellular Neuroscience
                Cellular Neuroscience
                Custom metadata
                Vivian Hernandez

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