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      Transcriptional signatures in prefrontal cortex confer vulnerability versus resilience to food and cocaine addiction-like behavior

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          Abstract

          Addiction is a chronic relapsing brain disease characterized by compulsive reward-seeking despite harmful consequences. The mechanisms underlying addiction are orchestrated by transcriptional reprogramming in the reward system of vulnerable subjects. This study aims at revealing gene expression alterations across different types of addiction. We analyzed publicly available transcriptome datasets of the prefrontal cortex (PFC) from a palatable food and a cocaine addiction study. We found 56 common genes upregulated in the PFC of addicted mice in these two studies, whereas most of the differentially expressed genes were exclusively linked to either palatable food or cocaine addiction. Gene ontology analysis of shared genes revealed that these genes contribute to learning and memory, dopaminergic synaptic transmission, and histone phosphorylation. Network analysis of shared genes revealed a protein–protein interaction node among the G protein-coupled receptors (Drd2, Drd1, Adora2a, Gpr6, Gpr88) and downstream targets of the cAMP signaling pathway (Ppp1rb1, Rgs9, Pde10a) as a core network in addiction. Upon extending the analysis to a cell-type specific level, some of these common molecular players were selectively expressed in excitatory neurons, oligodendrocytes, and endothelial cells. Overall, computational analysis of publicly available whole transcriptome datasets provides new insights into the molecular basis of addiction-like behaviors in PFC.

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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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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                Inigo.azua@lir-mainz.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 April 2021
                27 April 2021
                2021
                : 11
                : 9076
                Affiliations
                [1 ]GRID grid.410607.4, Institute for Human Genetics, , University Medical Center of the Johannes Gutenberg University Mainz, ; Mainz, Germany
                [2 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Laboratory of Neuropharmacology-Neurophar, Department of Experimental and Health Sciences, , Universitat Pompeu Fabra (UPF), ; Barcelona, Spain
                [3 ]GRID grid.20522.37, ISNI 0000 0004 1767 9005, Hospital del Mar Medical Research Institute (IMIM), ; Barcelona, Spain
                [4 ]GRID grid.410607.4, Institute of Physiological Chemistry, , University Medical Center of the Johannes Gutenberg University Mainz, ; Mainz, Germany
                [5 ]GRID grid.509458.5, ISNI 0000 0004 8087 0005, Leibniz Institute for Resilience Research (LIR), ; Mainz, Germany
                Article
                88363
                10.1038/s41598-021-88363-9
                8079697
                33907201
                16edf328-ecc0-4bf4-8b1c-cdf560a2242c
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 January 2021
                : 26 March 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008666, Fundació la Marató de TV3;
                Award ID: 2016/20-30
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003751, Ministerio de Sanidad, Servicios Sociales e Igualdad;
                Award ID: PNSD-2019I006
                Award ID: RD16/0017/0020
                Award ID: PNSD-2017I068
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100014440, Ministerio de Ciencia, Innovación y Universidades;
                Award ID: #AEI-SAF2017-84060-R FEDER
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004587, Instituto de Salud Carlos III;
                Award ID: RD16/0017/0020
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002809, Generalitat de Catalunya;
                Award ID: 2017-SGR-669
                Award ID: ICREA-Acadèmia 2015
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100008454, Boehringer Ingelheim Stiftung;
                Award ID: BIF01
                Award ID: BIF05
                Award ID: BIF09
                Award Recipient :
                Funded by: Carl-Zeiss-Stiftung
                Funded by: Mainz Institute of Multiscale Modeling- M3odel
                Funded by: Leibniz-Institut für Resilienzforschung (LIR) (9538)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                data processing,addiction
                Uncategorized
                data processing, addiction

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