A Whole Transcriptome Analysis in Peripheral Blood Suggests That Energy Metabolism and Inflammation Are Involved in Major Depressive Disorder.

Abstract

Introduction:

Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in major depressive disorder (MDD). However, the interaction between different types of RNA was neglected. Therefore, integration of different RNA species in transcriptome analysis would be helpful for interpreting the functional readout of the transcriptome in MDD.

Methods:

A whole transcriptome sequencing were performed on the peripheral blood of 15 patients with MDD and 15 matched healthy controls (HCs). The differential expression of miRNAs, lncRNAs, circRNAs, and mRNAs was examined between MDD and HCs using empirical analysis of digital gene expression data in R (edgeR). Weighted correlation network analysis (WGCNA) was used to identify RNA co-expression modules associated with MDD. A ceRNA network was constructed for interpretation of interactions between different RNA species. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to explore potential biological mechanisms associated with MDD.

Results:

Multiple RNAs and co-expression modules were identified to be significantly dysregulated in MDD compared to HCs. Based on the differential RNAs, a ceRNA network that were dysregulated in MDD were constructed. The pathway networks that related to oxidative phosphorylation and the chemokine signaling were found to be associated with MDD.

Conclusion:

Our results suggested that the processes of energy metabolism and inflammation may be involved in the pathophysiology of MDD.

• Authors: Yu Wang, Jinxue Wei, Ting Chen, Xiao Yang, Liansheng Zhao, Min Wang, Yikai Dou, Yue Du, Rongjun Ni, Tao Li, Xiaohong Ma
• Journal: Frontiers in psychiatry (Front Psychiatry) Vol. 13 Pg. 907034 ( 2022) ISSN: 1664-0640 [Print] Switzerland
• PMID: 35633815 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Wang, Wei, Chen, Yang, Zhao, Wang, Dou, Du, Ni, Li and Ma.