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      Creation of a gene expression portrait of depression and its application for identifying potential treatments

      research-article
      Scientific Reports
      Nature Publishing Group UK
      Neuroscience, Diseases of the nervous system

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          Abstract

          Depression is a complex mental health disorder and the goal here was to identify a consistent underlying portrait of expression that ranks all genes from most to least dysregulated and indicates direction of change relative to controls. Using large-scale neural gene expression depression datasets, a combined portrait (for men and women) was created along with one for men and one for women only. The depressed brain was characterized by a “hypo” state, that included downregulation of activity-related genes, including EGR1, FOS, and ARC, and indications of a lower brain temperature and sleep-like state. MAP kinase and BDNF pathways were enriched with overlapping genes. Expression patterns suggested decreased signaling for GABA and for neuropeptides, CRH, SST, and CCK. GWAS depression genes were among depression portrait genes and common genes of interest included SPRY2 and PSEN2. The portraits were used with the drug repurposing approach of signature matching to identify treatments that could reverse depression gene expression patterns. Exercise was identified as the top treatment for depression for the combined and male portraits. Other non-traditional treatments that scored well were: curcumin, creatine, and albiflorin. Fluoxetine scored best among typical antidepressants. The creation of the portraits of depression provides new insights into the complex landscape of depression and a novel platform for evaluating and identifying potential new treatments.

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          Most cited references79

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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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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                Author and article information

                Contributors
                scgammie@wisc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 February 2021
                15 February 2021
                2021
                : 11
                : 3829
                Affiliations
                GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Integrative Biology, , University of Wisconsin-Madison, ; Madison, USA
                Article
                83348
                10.1038/s41598-021-83348-0
                7884719
                33589676
                d48813d6-afe9-4610-a3de-adc0f5ce6e1e
                © 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
                : 10 December 2020
                : 1 February 2021
                Funding
                Funded by: University of Wisconsin-Madison Vilas Distinguished Achievement Professorship award
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                © The Author(s) 2021

                Uncategorized
                neuroscience,diseases of the nervous system
                Uncategorized
                neuroscience, diseases of the nervous system

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