Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus

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Abstract

Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.

Abstract

Schizophrenia research has traditionally overlooked sex differences. Here, the authors show the importance of sex-based analysis across multi-brain regions by identifying sex-specific genes and genetic interactions in schizophrenia and sex-specific risk.

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

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

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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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Is Open Access

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

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

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

Kynon J. M. Benjamin: KynonJade.Benjamin@libd.org

Apuã C. M. Paquola: Apua.Paquola@libd.org

Jennifer A. Erwin:

ORCID: http://orcid.org/0000-0002-6784-9290

Jennifer.Erwin@libd.org

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 10 May 2024

Publication date PMC-release: 10 May 2024

Publication date Collection: 2024

Volume: 15

Electronic Location Identifier: 3980

Affiliations

[1 ]Lieber Institute for Brain Development, ( https://ror.org/04q36wn27) Baltimore, MD USA

[2 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Psychiatry and Behavioral Sciences, , Johns Hopkins University School of Medicine, ; Baltimore, MD USA

[3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Neurology, , Johns Hopkins University School of Medicine, ; Baltimore, MD USA

[4 ]Department of Biology, Johns Hopkins University Krieger School of Arts & Sciences, ( https://ror.org/00za53h95) Baltimore, MD USA

[5 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Genetic Medicine, , Johns Hopkins University School of Medicine, ; Baltimore, MD USA

[6 ]Center for Computational Biology, Johns Hopkins University, ( https://ror.org/00za53h95) Baltimore, MD USA

[7 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Neuroscience, , Johns Hopkins University School of Medicine, ; Baltimore, MD USA

Author information

Arthur S. Feltrin http://orcid.org/0000-0002-1810-2203

Geo Pertea http://orcid.org/0000-0002-3210-7182

Hunter H. Giles http://orcid.org/0000-0002-2340-5444

Joo Heon Shin http://orcid.org/0000-0002-5563-8605

Joel E. Kleinman http://orcid.org/0000-0002-4210-6052

Daniel R. Weinberger http://orcid.org/0000-0003-2409-2969

Jennifer A. Erwin http://orcid.org/0000-0002-6784-9290

Article

Publisher ID: 48048

DOI: 10.1038/s41467-024-48048-z

PMC ID: 11087501

PubMed ID: 38730231

SO-VID: 7bb8c850-0133-4d6a-bde6-d6ec874eb600

License:

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

Date received : 21 November 2022

Date accepted : 15 April 2024

Funding

Funded by: FundRef https://doi.org/10.13039/100000874, Brain and Behavior Research Foundation (Brain & Behavior Research Foundation);

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ScienceOpen disciplines: Uncategorized

Keywords: schizophrenia,development of the nervous system

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ScienceOpen disciplines: Uncategorized

Keywords: schizophrenia, development of the nervous system

Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus

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Most cited references 96

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

clusterProfiler: an R package for comparing biological themes among gene clusters.

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

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