Peripheral Transcriptomic Signatures Reveal Convergent Neuroinflammatory, Metabolic, and miRNA Dysregulation in Major Psychiatric Disorders.

Overview

1. School of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, Philippines; (R.J.B.A.); (J.L.O.D.L.C.)
2. School of Graduate Studies, Mapúa University, Manila 1002, Philippines
3. Department of Biology, School of Health Sciences, Mapúa University, Makati 1203, Philippines

Abstract

Background/Objectives: Although clinically distinct, bipolar disorder (BP), schizophrenia (SZ), major depressive disorder (MDD), and social anxiety disorder (SAD) share fundamental biology. We mapped these transdiagnostic systemic mechanisms. Methods: Weighted Gene Co-Expression Network Analysis (WGCNA) of peripheral blood RNA-Seq datasets evaluated module preservation, hub gene disruption, and microRNA (miRNA) networks. Results: Seven modules showed robust cross-disease preservation. Overall, 56 of 105 candidate hub genes exhibited altered expression, with 22 passing the false discovery rate (FDR) correction. Hubs like IL1B, TLR2, and MMP9 dominated networks linked to altered inflammatory signaling and structural remodeling. Downregulated ribosomal hubs characterized systemic metabolic stress. Discussion: These signatures capture extensive systemic dysregulation. Inflammation and metabolic shifts correlate strongly with pathways regulating chronic neuroinflammation, epigenetic control, and dendritic pruning. Computational models suggest these cascades evade miRNA controls, potentially compromising structural neural plasticity. Conclusions: This shared transcriptomic architecture challenges rigid diagnostic boundaries. Identifying systemic immune dysregulation and translational alterations as core pathogenic denominators provides a rationale for transdiagnostic therapies targeting upstream systemic networks to mitigate neural vulnerabilities.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Data links

• ncbi.nlm.nih.gov/geo, NCBI; found in “Data Availability Statement”

Data Availability Statement

The transcriptomic datasets analyzed in this study are publicly available from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 3 January 2026)). The datasets include SZ (GSE243841), BP (GSE124326), MDD (GSE251778), and SAD (GSE228702).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 194 references.

Cite

This paper

Avila, R. J. B., De La Cerna, J. L. O., & Tayo, L. L. (2026). Peripheral Transcriptomic Signatures Reveal Convergent Neuroinflammatory, Metabolic, and miRNA Dysregulation in Major Psychiatric Disorders. Biology, 15(9), 673. https://doi.org/10.3390/biology15090673

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