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bioRxiv : the preprint server for biology2025; 2025.10.16.682971; doi: 10.1101/2025.10.16.682971

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

Abstract: Post-traumatic osteoarthritis (PTOA) is a common sequela to joint injury in both humans and companion animal species such as horses and dogs. Despite the increasing prevalence of osteoarthritis (OA) in humans, investigation of glycosylation changes associated with OA remains in its infancy. Recent advances, such as lectin microarray analysis, now enable detailed glycan profiling in complex biofluids such as synovial fluid. Using lectin microarray technology, this study characterized glycosylation patterns in synovial fluid samples from healthy and OA-affected joints in horses, dogs, and humans. Comparative glycan-binding profiles within and between species revealed conserved and distinct glycomic signatures associated with OA. Machine learning models, including classification algorithms, effectively distinguished OA from healthy joints, identifying key lectins and glycan epitopes crucial to these predictions. The identified lectin markers reflect specific glycosylation pathways and potential inflammatory mechanisms, demonstrating their value in differentiating between healthy and OA phenotypes. Our findings underscore the promise of integrated glycomic profiling and machine learning to enhance our understanding of glycan involvement in the pathogenesis of OA and to facilitate the development of diagnostic and therapeutic strategies applicable to both veterinary and human medicine.
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Publication Date: 2025-10-17 PubMed ID: 41279656PubMed Central: PMC12632833DOI: 10.1101/2025.10.16.682971Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study uses lectin microarray technology and machine learning to identify shared biomarkers of osteoarthritis (OA) in synovial fluid from humans, dogs, and horses.
  • It highlights conserved changes in glycosylation associated with OA across these species, offering insights for diagnosis and treatment.

Background

  • Osteoarthritis (OA): A degenerative joint disease often following injury, known as post-traumatic osteoarthritis (PTOA), affecting humans and companion animals such as horses and dogs.
  • Current challenges: Although OA prevalence is increasing, investigations into glycosylation changes (modifications in sugar chains on proteins) associated with OA are limited.
  • Glycosylation: Plays a critical role in cell signaling, inflammation, and joint health, making it a potential biomarker source.

Technology and Methods

  • Lectin microarray: A tool that uses multiple lectins—proteins that bind specific sugar motifs—to profile glycans in complex biological samples like synovial fluid.
  • Sample analysis: Synovial fluid collected from healthy and OA-affected joints of humans, dogs, and horses was analyzed via lectin microarrays to detect glycosylation patterns.
  • Comparative approach: Glycan-binding profiles were compared within each species and across the three species to identify conserved (shared) and distinct glycomic signatures linked to OA.
  • Machine learning integration: Predictive models, including classification algorithms, were employed to distinguish OA samples from healthy ones, helping to validate and identify important glycan markers.

Key Findings

  • Conserved glycomic signatures: Certain glycosylation changes related to OA were conserved across humans, dogs, and horses, suggesting common underlying pathological mechanisms.
  • Differential glycosylation: Specific lectins showed different binding patterns between healthy and OA joints, pointing to altered glycosylation pathways during disease.
  • Machine learning success: Algorithms effectively classified OA versus healthy samples, pinpointing key lectins and glycan epitopes crucial for this discrimination.
  • Biological implications: Identified markers relate to glycosylation and inflammatory pathways, implicating these processes in OA pathogenesis.

Significance and Applications

  • Cross-species insights: Understanding shared biomarkers supports the use of animal models for studying human OA, facilitating translational research.
  • Diagnostic potential: Glycomic markers identified could lead to improved, non-invasive diagnostic tests to detect early or progressing OA.
  • Therapeutic development: Revealing glycosylation changes involved in OA offers targets for developing novel treatments aimed at modulating these pathways.
  • Integrative approach: Combining glycomic profiling with advanced analytics like machine learning offers a powerful framework for biomarker discovery in complex diseases.

Cite This Article

APA
Peralta AG, Raeisimakiani P, Hayashi K, Mahal LK, Reesink HL. (2025). Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses. bioRxiv, 2025.10.16.682971. https://doi.org/10.1101/2025.10.16.682971

Publication

bioRxiv : the preprint server for biology
ISSN: 2692-8205
NlmUniqueID: 101680187
Country: United States
Language: English
PII: 2025.10.16.682971

Researcher Affiliations

Peralta, Angelo G
  • Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis CA 95616 USA.
Raeisimakiani, Parisa
  • Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2 Canada.
Hayashi, Kei
  • Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca NY 14853 USA.
Mahal, Lara K
  • Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2 Canada.
Reesink, Heidi L
  • Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis CA 95616 USA.
  • Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca NY 14853 USA.

Grant Funding

  • R24 GM082910 / NIGMS NIH HHS
  • UL1 TR002384 / NCATS NIH HHS

Conflict of Interest Statement

Conflicts of Interest The authors declare that they have no conflicts of interests with the contents of this article.

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