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      Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

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      American Association for the Advancement of Science (AAAS)

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          Abstract

          The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

          Abstract

          INTRODUCTION

          Stress-related disorders arise from the interplay between genetic susceptibility and stress exposure, occurring throughout the lifespan. Progressively, these interactions lead to epigenetic modifications in the human genome, shaping the expression of genes and proteins. Prior postmortem brain studies have attempted to elucidate the molecular pathology of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) compared with neurotypical controls (NCs) in a single-omic manner, revealing genomic overlap, sex differences, and immune and interneuron signaling involvement. However, without integrative systems approaches, progress in understanding the molecular underpinnings of these prevalent and debilitating disorders is hindered.

          RATIONALE

          To tackle this roadblock, we have created a brain multiregion, multiomic database of individuals with PTSD and MDD and NCs (77 per group, n = 231) to describe molecular alterations across three brain regions: the central nucleus of the amygdala (CeA), medial prefrontal cortex (mPFC), and hippocampal dentate gyrus (DG) at the transcriptomic, methylomic, and proteomic levels. By using this multiomic strategy that merges information across biological layers and organizational strata and complementing it with single-nucleus RNA sequencing (snRNA-seq), genetics, and blood plasma proteomics analyses, we sought to reveal an integrated-systems perspective of PTSD and MDD.

          RESULTS

          We found molecular differences primarily in the mPFC, with differentially expressed genes (DEGs) and exons carrying the most disease signals. However, altered methylation was seen mainly in the DG in PTSD subjects, in contrast to the CeA in MDD subjects. Replication analysis substantiated these findings with multiomic data from two cohorts ( n = 114). Moreover, we found a moderate overlap between the disorders, with childhood trauma and suicide being primary drivers of molecular variations in both disorders, and sex specificity being more notable in MDD. Pathway analyses linked disease-associated molecular signatures to immune mechanisms, metabolism, mitochondria function, neuronal or synaptic regulation, and stress hormone signaling with low concordance across omics. Top upstream regulators and transcription factors included IL1B, GR, STAT3, and TNF. Multiomic factor and gene network analyses provided an underlying genomic structure of the disorders, suggesting latent factors and modules related to aging, inflammation, vascular processes, and stress.

          To complement the multiomics analyses, our snRNA-seq analyses in the dorsolateral PFC ( n = 118) revealed DEGs, dysregulated pathways, and upstream regulators in neuronal and non-neuronal cell-types, including stress-related gene signals. Examining the intersection of brain multiomics with blood proteins (in >50,000 UK Biobank participants) revealed significant correlation, overlap, and directional similarity between brain-to-blood markers. Fine-mapping of PTSD and MDD genome-wide association studies’ (GWASs’) results showed a limited overlap between risk and disease processes at the gene and pathway levels .

          Ultimately, prioritized genes with multiregion, multiomic, or multitrait disease associations were members of pathways or networks, showed cell-type specificity, had blood biomarker potential, or were involved in genetic risk for PTSD and MDD.

          CONCLUSION

          Our findings unveil shared and distinct brain multiomic molecular dysregulations in PTSD and MDD, elucidate the involvement of specific cell types, pave the way for the development of blood-based biomarkers, and distinguish risk from disease processes. These insights not only implicate established stress-related pathways but also reveal potential therapeutic avenues.

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          Most cited references142

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                May 24 2024
                May 24 2024
                : 384
                : 6698
                Affiliations
                [1 ]McLean Hospital, Belmont, MA 02478, USA.
                [2 ]Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA.
                [3 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [4 ]Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA.
                [5 ]VA New York Harbor Healthcare System, Brooklyn, NY 11209, USA.
                [6 ]Center for Biomedical Research Support, The University of Texas at Austin, Austin, TX 78712, USA.
                [7 ]Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, Netherlands.
                [8 ]Biogen Inc., Cambridge, MA 02142, USA.
                [9 ]Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA.
                [10 ]Departments of Computer Science, University of Miami, Miami, FL 33146, USA.
                [11 ]Department of Biology, University of Miami, Miami, FL 33146, USA.
                [12 ]Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA.
                [13 ]VA Bedford Healthcare System, Bedford, MA 01730, USA.
                [14 ]National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA 02130, USA.
                [15 ]Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine, Atlanta GA 30329, USA.
                [16 ]National Center for PTSD, VA Boston Healthcare System, Boston, MA 02130, USA.
                [17 ]Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA.
                [18 ]Department of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA.
                [19 ]Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
                [20 ]Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA.
                [21 ]Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA.
                [22 ]Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA.
                [23 ]Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA.
                [24 ]Department of Psychiatry & Behavioral Sciences, Center for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
                [25 ]Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
                [26 ]Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
                [27 ]Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
                [28 ]Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
                [29 ]Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX 78712, USA.
                Article
                10.1126/science.adh3707
                4fd3d0b1-38fa-46f3-9a8b-5a6397fc2dee
                © 2024

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