Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and...

Area of Science:

• Biochemistry
• Glycomics
• Comparative Medicine

Background:

• Post-traumatic osteoarthritis (PTOA) is a prevalent joint disease following injury.
• Glycosylation changes in osteoarthritis (OA) are not well understood.
• Synovial fluid analysis offers a window into joint health and disease.

Purpose of the Study:

• To characterize glycosylation patterns in synovial fluid from healthy and OA-affected joints in horses, dogs, and humans.
• To identify conserved and distinct glycomic signatures associated with OA across species.
• To explore the utility of machine learning in distinguishing OA phenotypes based on glycosylation.

Main Methods:

• Lectin microarray analysis was employed for detailed glycan profiling of synovial fluid.
• Comparative analysis of glycosylation patterns was performed within and between species.
• Machine learning classification algorithms were utilized to differentiate between healthy and OA joints.

Main Results:

• Conserved and distinct glycomic signatures associated with OA were identified across horses, dogs, and humans.
• Machine learning models successfully distinguished OA from healthy joints.
• Key lectin markers and glycan epitopes crucial for OA prediction were identified.

Conclusions:

• Glycomic profiling combined with machine learning shows promise for understanding OA pathogenesis.
• Identified lectin markers may reflect glycosylation pathways and inflammatory mechanisms in OA.
• These findings support the development of novel diagnostic and therapeutic strategies for OA in both veterinary and human medicine.