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      Relationship between sex biases in gene expression and sex biases in autism and Alzheimer’s disease

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          Abstract

          Background

          Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions.

          Methods

          In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-Seq datasets using both within-region and pan-regional frameworks.

          Results

          We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-nucleus data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case–control gene expression studies suggests a female molecular predisposition towards Alzheimer’s disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis.

          Conclusion

          Overall, these analyses highlight mechanisms by which sex differences may interact with sex-biased conditions in the brain. Furthermore, we provide region-specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.

          Graphical Abstract

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13293-024-00622-2.

          Highlights

          • Numerous but small autosomal sex differences in expression exist in all brain regions tested.

          • Autosomal genes with enriched expression in males are enriched in neuronal pathways.

          • Autosomal genes with enriched expression in females are enriched with immune system functions.

          • Integration with single-nucleus datasets suggest these differences are more likely related to cell state differences than cell number differences.

          • The female cortex shows an enrichment of genes expressed in Alzheimer’s disease brains.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13293-024-00622-2.

          Abstract

          We sought to understand why females have higher rates of Alzheimer’s disease, and males have higher rates of autism. One idea was that the female brain at baseline may be more similar to an Alzheimer’s brain, so it is easier for them to shift into that state (likewise, males may be more similar to autism). To test this, we examined gene expression differences between brains of biological males and biological females. While all people have the same ~ 25,000 genes, each gene can be on or off (‘expressed’) to different extents. Overall, we found that there were differences in gene expression between males and females in all brain regions tested but more differences in some brain regions than others. By looking at the role of these genes we estimate that female immune system processes might be more active in the brain. We also found female brain gene expression looked slightly more like people with Alzheimer’s compared to people without Alzheimer’s, which may explain why females get Alzheimer’s disease more easily. However, the male brain gene expression did not look more like autism, suggesting that the reason males have higher rates of autism is complex and needs further investigation.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13293-024-00622-2.

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          Most cited references77

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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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            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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              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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                Author and article information

                Contributors
                jdougherty@wustl.edu
                Journal
                Biol Sex Differ
                Biol Sex Differ
                Biology of Sex Differences
                BioMed Central (London )
                2042-6410
                7 June 2024
                7 June 2024
                2024
                : 15
                : 47
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Genetics, , Washington University School of Medicine, ; 660 S. Euclid Ave, Saint Louis, MO 63110 USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Psychiatry, , Washington University School of Medicine, ; 660 S. Euclid Ave, Saint Louis, MO 63110 USA
                [3 ]Lieber Institute for Brain Development, ( https://ror.org/04q36wn27) 855 North Wolfe St. Ste 300, Baltimore, MD 21205 USA
                [4 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, McDonnell Genome Institute, Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [5 ]Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, ( https://ror.org/03x3g5467) 660 S. Euclid Ave, Saint Louis, MO 63110 USA
                [6 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Institute for Human Genetics, , University of California, ; San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143 USA
                [7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Psychiatry and Behavioral Sciences, , University of California, ; San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143 USA
                [8 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Weill Institute for Neurosciences, , University of California, ; San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143 USA
                [9 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Genetics, ; 4566 Scott Ave., Campus Box 8232, St. Louis, MO 63110-1093 USA
                Author information
                http://orcid.org/0000-0002-6385-3997
                Article
                622
                10.1186/s13293-024-00622-2
                11157820
                38844994
                65e2e7ed-7895-49a9-b3a4-bbd907eec997
                © The Author(s) 2024

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 6 September 2023
                : 23 May 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 734069
                Award Recipient :
                Categories
                Research
                Custom metadata
                © Society for Women's Health Research and BioMed Central Ltd. 2024

                Human biology
                sex-bias,sex,expression,rna-seq,alzheimer’s,autism,neuronal,immune,brain,human
                Human biology
                sex-bias, sex, expression, rna-seq, alzheimer’s, autism, neuronal, immune, brain, human

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