Gene expression profiling reveals candidate biomarkers and probable molecular mechanisms in chronic stress

Bioengineered. 2022 Mar;13(3):6048-6060. doi: 10.1080/21655979.2022.2040872.

Abstract

Chronic stress refers to nonspecific systemic reactions under the over-stimulation of different external and internal factors for a long time. Previous studies confirmed that chronic psychological stress had a negative effect on almost all tissues and organs. We intended to further identify potential gene targets related to the pathogenesis of chronic stress-induced consequences involved in different diseases. In our study, mice in the model group lived under the condition of chronic unpredictable mild stress (CUMS) until they expressed behaviors like depression which were supposed to undergo chronic stress. We applied high-throughput RNA sequencing to assess mRNA expression and obtained transcription profiles in lung tissue from CUMS mice and control mice for analysis. In view of the prediction of high-throughput RNA sequences and bioinformatics software, and mRNA regulatory network was constructed. First, we conducted differentially expressed genes (DEGs) and obtained 282 DEGs between CUMS (group A) and the control model (group B). Then, we conducted functional and pathway enrichment analyses. In general, the function of upregulated regulated DEGs is related to immune and inflammatory responses. PPI network identified several essential genes, of which ten hub genes were related to the T cell receptor signaling pathway. qRT-PCR results verified the regulatory network of mRNA. The expressions of CD28, CD3e, and CD247 increased in mice with CUMS compared with that in control. This illustrated immune pathways are related to the pathological molecular mechanism of chronic stress and may provide information for identifying potential biomarkers and early detection of chronic stress.

Keywords: Biomarkers; immune; lung; mRNA; stress.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Biomarkers
  • Computational Biology / methods
  • Gene Expression Profiling* / methods
  • Gene Regulatory Networks*
  • Mice
  • RNA, Messenger

Substances

  • Biomarkers
  • RNA, Messenger

Grants and funding

This work was supported by the National Natural Science Foundation of China [81772108].