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      Transcriptome-wide association study for postpartum depression implicates altered B-cell activation and insulin resistance

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          Abstract

          Postpartum depression (PPD) affects 1 in 7 women and has negative mental health consequences for both mother and child. However, the precise biological mechanisms behind the disorder are unknown. Therefore, we performed the largest transcriptome-wide association study (TWAS) for PPD (482 cases, 859 controls) to date using RNA-sequencing in whole blood and deconvoluted cell types. No transcriptional changes were observed in whole blood. B-cells showed a majority of transcriptome-wide significant results (891 transcripts representing 789 genes) with pathway analyses implicating altered B-cell activation and insulin resistance. Integration of other data types revealed cell type-specific DNA methylation loci and disease-associated eQTLs (deQTLs), but not hormones/neuropeptides (estradiol, progesterone, oxytocin, BDNF), serve as regulators for part of the transcriptional differences between cases and controls. Further, deQTLs were enriched for several brain region-specific eQTLs, but no overlap with MDD risk loci was observed. Altogether, our results constitute a convergence of evidence for pathways most affected in PPD with data across different biological mechanisms.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Fast unfolding of communities in large networks

            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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              xCell: digitally portraying the tissue cellular heterogeneity landscape

              Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1349-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                guinti@email.unc.edu
                Journal
                Mol Psychiatry
                Mol Psychiatry
                Molecular Psychiatry
                Nature Publishing Group UK (London )
                1359-4184
                1476-5578
                1 April 2022
                1 April 2022
                2022
                : 27
                : 6
                : 2858-2867
                Affiliations
                [1 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Psychiatry, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [2 ]GRID grid.224260.0, ISNI 0000 0004 0458 8737, Center for Biomarker Research and Precision Medicine, , Virginia Commonwealth University, ; Richmond, VA USA
                [3 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Department of Psychiatry & Behavioral Sciences, , Texas A&M University, ; College Station, TX USA
                [4 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Genetics, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [5 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
                Author information
                http://orcid.org/0000-0003-3541-1101
                http://orcid.org/0000-0001-6103-5168
                Article
                1525
                10.1038/s41380-022-01525-7
                9156403
                35365803
                119d6141-dee7-4166-b786-a0211b398fb6
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 September 2021
                : 8 February 2022
                : 9 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: MH095992
                Award ID: MH093315
                Award ID: MH095992
                Award ID: MH095992
                Award ID: MH095992
                Award ID: MH095992
                Award ID: MH095992
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                Molecular medicine
                genetics,molecular biology,depression
                Molecular medicine
                genetics, molecular biology, depression

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