Collagen 24 α1 Is Increased in Insulin-Resistant Skeletal Muscle and Adipose Tissue

Xiong Weng, De Lin, Jeffrey T. J. Huang, Roland H. Stimson, David H. Wasserman, Li Kang (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
55 Downloads (Pure)


Aberrant extracellular matrix (ECM) remodelling in muscle, liver and adipose tissue is a key characteristic of obesity and insulin resistance. Despite its emerging importance, the effective ECM targets remain largely undefined due to limitations of current approaches. Here, we developed a novel ECM-specific mass spectrometry-based proteomics technique to characterise the global view of the ECM changes in the skeletal muscle and liver of mice after high fat (HF) diet feeding. We identified distinct signatures of HF-induced protein changes between skeletal muscle and liver where the ECM remodelling was more prominent in the muscle than liver. In particular, most muscle collagen isoforms were increased by HF diet feeding whereas the liver collagens were differentially but moderately affected highlighting a different role of the ECM remodelling in different tissues of obesity. Moreover, we identified a novel association between collagen 24α1 and insulin resistance in the skeletal muscle. Using quantitative gene expression analysis, we extended this association to the white adipose tissue. Importantly, collagen 24α1 mRNA was increased in the visceral adipose tissue, but not the subcutaneous adipose tissue of obese diabetic subjects compared to lean controls, implying a potential pathogenic role of collagen 24α1 in obesity and type 2 diabetes.

Original languageEnglish
Article number5738
Number of pages13
JournalInternational Journal of Molecular Sciences
Issue number16
Publication statusPublished - 10 Aug 2020


  • Extracellular matrix
  • Collagen
  • obesity
  • diabetes
  • collagen
  • extracellular matrix

ASJC Scopus subject areas

  • Molecular Biology
  • Spectroscopy
  • Catalysis
  • Inorganic Chemistry
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry


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