Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls
Open accessAbstract
Background: Gene expression profiling of the postmortem human brain is part of the effort to understand the neuropathological underpinnings of schizophrenia. Existing microarray studies have identified a large number of genes as candidates, but efforts to generate an integrated view of molecular and cellular changes underlying the illness are few. Here, we have applied a novel approach to combining coexpression data across seven postmortem human brain studies of schizophrenia. Results: We generated separate coexpression networks for the control and schizophrenia prefrontal cortex and found that differences in global network properties were small. We analyzed gene coexpression relationships of previously identified differentially expressed ‘schizophrenia genes’. Evaluation of network properties revealed differences for the up- and down-regulated ‘schizophrenia genes’, with clustering coefficient displaying particularly interesting trends. We identified modules of coexpressed genes in each network and characterized them according to disease association and cell type specificity. Functional enrichment analysis of modules in each network revealed that genes with altered expression in schizophrenia associate with modules representing biological processes such as oxidative phosphorylation, myelination, synaptic transmission and immune function. Although a immune-function enriched module was found in both networks, many of the genes in the modules were different. Specifically, a decrease in clustering of immune activation genes in the schizophrenia network was coupled with the loss of various astrocyte marker genes and the schizophrenia candidate genes. Conclusion: Our novel network-based approach for evaluating gene coexpression provides results that converge with existing evidence from genetic and genomic studies to support an immunological link to the pathophysiology of schizophrenia.
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Existing expression studies are probably underpowered to detect the subtle effects expected from a complex disease in tightly regulated brain tissue.
“These studies are probably underpowered to detect subtle effects that might be expected to arise as a result of this complex disease and within tightly regulated brain tissue9, among other limitations of existing microarray-based gene expression studies10.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [9]
Prior gene expression studies are probably underpowered to detect subtle effects expected from schizophrenia and tightly regulated brain tissue.
“These studies are probably underpowered to detect subtle effects that might be expected to arise as a result of this complex disease and within tightly regulated brain tissue9, among other limitations of existing microarray-based gene expression studies10.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [9]
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Increased cell-free DNA is associated with oxidative damage in patients with schizophrenia
Li, S., Jiang, J., Zhu, W., Wang, D., Dong, C., Bu, Y., Zhang, J., Gao, D., Hu, X., & Wan, C. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.04.047 · Cited by 10 · see citing works
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Using protein turnover to expand the applications of transcriptomics
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- Authors
- Mistry, M., Gillis, J., & Pavlidis, P.
- Affiliations
- University of British Columbia (CA)
- Cold Spring Harbor Laboratory (US)
- Corresponding author
- Pavlidis, Paul
- Venue
- BMC Neuroscience
- Published
- 2013-09-26
- Volume
- 14
- Issue
- 1
- Pages
- 105-105
- ISSN
- 1471-2202 (ISSN-L)
- PMC
- PMC3849476
- OpenAlex
- W2009170866
- Cited by
- 59
- References
- 59
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 2 · 2022: 5 · 2021: 8 · 2020: 2 · 2019: 1 · 2018: 4 · 2017: 5 · 2016: 11 · 2015: 8 · 2014: 4
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- cIRcle (University of British Columbia) (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (R01GM076990)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- schizophrenia, gene co-expression network, schizophrenia candidate genes, immune function, astrocytes, oxidative phosphorylation, myelination, synaptic transmission, clustering coefficient, gene modules, neuropathology
- MeSH terms
- Autopsy, Female, Humans, Male, Middle Aged, Schizophrenia, Cluster Analysis, Prefrontal Cortex, Gene Expression Profiling, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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