HMGCS2 and AMACR as potential targets linking mitochondrial dysfunction and ulcerative colitis

Sci Rep. 2024 Dec 30;14(1):31783. doi: 10.1038/s41598-024-82900-y.

Abstract

Ulcerative colitis (UC) is characterised notably by an imbalance in intestinal mucosal homeostasis. Although mitochondrial dysfunction has been identified as a potential contributor to this imbalance, it remains an incomplete understanding. Consequently, further investigation into the role of mitochondria in UC is warranted. The study focusing on the GSE87466 dataset for differential gene expression analysis. Mitochondria-related genes were sourced from the MitoCart3.0 database. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify hub genes. The intersection of DEGs, hub genes, and mitochondria-related genes facilitated the identification of 14 mitochondria-related differentially expressed genes (MitoDEGs). Three machine learning algorithms were then applied to select signature MitoDEGs specific to UC: HMGCS2 and AMACR. They have decreased expression in UC patients and have a high diagnostic value for UC. In the inflammatory environment, knockout of both HMGCS2 and AMACR showed disruption of mitochondrial structure and function. Among them, the AMACR knockdown group had an increased number of damaged mitochondria and a significant reduction in the length, area and circumference of MAMs. Therefore, the study identified two new signature MitoDEGs in UC. HMGCS2 and AMACR provide insights into the interplay between mitochondrial dysfunction and UC intestinal mucosal homeostasis.

Keywords: Immune infiltration; Machine learning; Mitochondria; Ulcerative colitis; WGCNA.

MeSH terms

  • Animals
  • Colitis, Ulcerative* / genetics
  • Colitis, Ulcerative* / metabolism
  • Colitis, Ulcerative* / pathology
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Humans
  • Intestinal Mucosa / metabolism
  • Intestinal Mucosa / pathology
  • Machine Learning
  • Mice
  • Mitochondria* / genetics
  • Mitochondria* / metabolism