Selecting potential biomarkers of plasma proteins in mares with endometritis.

Abstract

Endometritis is a common condition in mares that causes significant economic loss. Lacking obvious clinical signs, the clinical diagnosis of endometritis in mares relies on case-by-case clinical examinations, which can be particularly inefficient in large-scale farms. Therefore, the identification of potential biomarkers can serve as a non-invasive and efficient screening technique for endometritis in mares. To compare the blood proteome between fertile mares and mares with endometritis to identify biomarkers potentially associated with the development of endometritis and validate their predictive potential. Observational and experimental study. Differentially expressed proteins were identified via Data Independent Acquisition (DIA) proteomic profiling in a screening cohort composed of eight healthy mares and eight mares with endometritis. Subsequently, enzyme-linked immunosorbent assay was employed that included a validation cohort of 40 healthy mares and 40 mares with endometritis to verify the accuracy and sensitivity of the identified proteins, thereby establishing a diagnostic threshold. In the screening cohort, 12 proteins were significantly differentially expressed between endometritis mares and healthy controls (p < 0.05, outside the 1/1.2 to 1.2-fold). In the validation experiment, all six screened proteins were assessed with area under the curve (AUC) >0.8. The samples displayed certain levels of individual heterogeneity, and the number of samples analysed was limited. Additionally, the identified biomarkers were primarily associated with generalised inflammation, which potentially limited their specificity for endometritis. Levels of plasma proteins are sensitive indicators of equine endometritis and potential tools for endometritis screening. In plasma, fetuin B, von Willebrand factor, vitamin K-dependent protein C, insulin-like growth factor binding protein 3, interleukin 1 receptor accessory protein, and type II cell cytoskeleton showed great predictive ability, with fetuin B being the best predictor (AUC = 0.93, 95% CI: 0.89-0.98), which performs better when combined with all six detected proteins (AUC = 1, 95% CI: 0.99-1.00).