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Journal of veterinary internal medicine2023; 37(2); 689-696; doi: 10.1111/jvim.16660

Cerebrospinal fluid and serum proteomic profiles accurately distinguish neuroaxonal dystrophy from cervical vertebral compressive myelopathy in horses.

Abstract: Cervical vertebral compressive myelopathy (CVCM) and equine neuroaxonal dystrophy/degenerative myeloencephalopathy (eNAD/EDM) are leading causes of spinal ataxia in horses. The conditions can be difficult to differentiate, and there is currently no diagnostic modality that offers a definitive antemortem diagnosis. Objective: Evaluate novel proteomic techniques and machine learning algorithms to predict biomarkers that can aid in the antemortem diagnosis of noninfectious spinal ataxia in horses. Methods: Banked serum and cerebrospinal fluid (CSF) samples from necropsy-confirmed adult eNAD/EDM (n = 47) and CVCM (n = 25) horses and neurologically normal adult horses (n = 45). Methods: . A subset of serum and CSF samples from eNAD/EDM (n = 5) and normal (n = 5) horses was used to evaluate the proximity extension assay (PEA). All samples were assayed by PEA for 368 neurologically relevant proteins. Data were analyzed using machine learning algorithms to define potential diagnostic biomarkers. Results: Of the 368 proteins, 84 were detected in CSF and 146 in serum. Eighteen of 84 proteins in CSF and 30/146 in serum were differentially abundant among the 3 groups, after correction for multiple testing. Modeling indicated that a 2-protein test using CSF had the highest accuracy for discriminating among all 3 groups. Cerebrospinal fluid R-spondin 1 (RSPO1) and neurofilament-light (NEFL), in parallel, predicted normal horses with an accuracy of 87.18%, CVCM with 84.62%, and eNAD/EDM with 73.5%. Conclusions: Cross-species platform. Uneven sample size. Conclusions: Proximity extension assay technology allows for rapid screening of equine biologic matrices for potential protein biomarkers. Machine learning analysis allows for unbiased selection of highly accurate biomarkers from high-dimensional data.
© 2023 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.
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Publication Date: 2023-03-16 PubMed ID: 36929645PubMed Central: PMC10061172DOI: 10.1111/jvim.16660Google Scholar: Lookup
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Summary

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The research article explores how proteomic profiles can accurately differentiate between two major causes of spinal ataxia in horses: cervical vertebral compressive myelopathy (CVCM) and equine neuroaxonal dystrophy/degenerative myeloencephalopathy (eNAD/EDM). The study uses the proximity extension assay (PEA) technique to study cerebrospinal fluid and serum samples.

Objective and Methods

The primary objective of this research was to effectively discern between CVCM and eNAD/EDM, two of the leading causes of noninfectious spinal ataxia in horses. This is significant because currently, there are no known diagnostic tools that can conclusively deliver a diagnosis for these conditions while the horse is still alive.

  • To tackle this challenge, the research employed innovative proteomic techniques and machine learning algorithms to predict potential biomarkers—a naturally occurring molecule, gene, or characteristic by which a particular pathological or physiological process, disease, etc. can be identified.
  • The researchers used already available (banked) serum and cerebrospinal fluid (CSF) samples from horses that were confirmed to have eNAD/EDM (47 samples) and CVCM (25 samples), as well as from normal, neurologically healthy horses (45 samples).
  • The study employed the proximity extension assay (PEA) to examine a subset of these samples for 368 different proteins that have neurological relevance. This is a multiplex immunoassay that allows the detection and quantification of proteins.
  • Machine learning, a subset of artificial intelligence (AI), was used to analyze the data and pinpoint potential diagnostic biomarkers from these proteins.

Results

  • The PEA identified a total of 84 proteins in the CSF and 146 in the serum samples.
  • From these identified proteins, 18 in CSF and 30 in serum had differing abundance levels between the sample groups, even after statistical correction for multiple testing was applied.
  • Two proteins, R-spondin 1 (RSPO1) and neurofilament-light (NEFL), were found in the CSF. They demonstrated the highest accuracy in distinguishing between the three groups: normal horses, those with CVCM, and those with eNAD/EDM.

Conclusions

  • The study showed that proximity extension assay (PEA) technology facilitates quick screening of potential protein biomarkers in equine biological samples. However, it must be noted that the study used unequal sample sizes and a cross-species platform.
  • Machine learning was found to be an effective tool for the unbiased selection of highly accurate biomarkers from vast sets of data.
  • The notable finding was the indication of a two-protein biomarker test using CSF for effectively differentiating between CVCM and eNAD/EDM in horses.

Cite This Article

APA
Donnelly CG, Johnson AL, Reed S, Finno CJ. (2023). Cerebrospinal fluid and serum proteomic profiles accurately distinguish neuroaxonal dystrophy from cervical vertebral compressive myelopathy in horses. J Vet Intern Med, 37(2), 689-696. https://doi.org/10.1111/jvim.16660

Publication

Journal of veterinary internal medicine
ISSN: 1939-1676
NlmUniqueID: 8708660
Country: United States
Language: English
Volume: 37
Issue: 2
Pages: 689-696

Researcher Affiliations

Donnelly, Callum G
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, California, USA.
Johnson, Amy L
  • Department of Clinical Studies, New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania, USA.
Reed, Steve
  • Rood and Riddle Equine Hospital, Lexington, Kentucky, USA.
Finno, Carrie J
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, California, USA.

MeSH Terms

  • Animals
  • Horses
  • Spinal Cord Compression / diagnosis
  • Spinal Cord Compression / veterinary
  • Proteomics
  • Spinal Cord Diseases / veterinary
  • Neuroaxonal Dystrophies / diagnosis
  • Neuroaxonal Dystrophies / veterinary
  • Ataxia / veterinary
  • Neurodegenerative Diseases / veterinary
  • Biomarkers
  • Horse Diseases / diagnosis

Grant Funding

  • Alamo Pintado Equine Health Foundation
  • American Association of Equine Practitioners Foundation

Conflict of Interest Statement

Authors declare no conflict of interest.

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Citations

This article has been cited 1 times.
  1. Akbarein H, Taaghi MH, Mohebbi M, Soufizadeh P. Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review. Vet Med Sci 2025 May;11(3):e70315.
    doi: 10.1002/vms3.70315pubmed: 40173266google scholar: lookup
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