Cross-species transcriptomics identifies obesity associated genes between human and mouse studies

Area of Science:

• Genomics and Molecular Biology
• Biomedical Engineering
• Metabolic Research

Background:

• Obesity is a major risk factor for osteoarthritis (OA), driven by increased adiposity and systemic inflammation.
• Adipose tissue releases inflammatory cytokines and adipokines, impacting bone remodeling and cartilage loss in OA.
• Obesity-associated metabolic changes enrich pro-inflammatory fibroblast subsets in joint synovial tissues.

Purpose of the Study:

• To identify obesity-associated genes within osteoarthritis joint tissues.
• To determine if these genes are conserved across species and conditions.
• To explore potential therapeutic targets for OA in obese individuals.

Main Methods:

• Utilized six public transcriptomic datasets (human and mouse) from the Gene Expression Omnibus (GEO).
• Employed machine learning models to identify conserved gene expression profiles.
• Validated identified genes in OA joint tissues from obese and healthy-weight individuals using quantitative PCR.

Main Results:

• Machine learning identified 12 differentially expressed genes (DEGs) associated with obesity across species.
• Pathway analysis indicated these genes are involved in lipid metabolism.
• Quantitative PCR validated three genes (IGFBP2, DOK6, CASP1) significantly upregulated in obese joint tissues.

Conclusions:

• Identified conserved obesity-associated genes in osteoarthritis joint tissues using machine learning.
• These genes may play a fundamental role in disease acceleration in obese individuals.
• Findings support patient stratification and targeted therapeutic interventions for obese OA subpopulations.