Abstract

Simple Summary The microbiome of the ocular surface is composed of a large number of microorganisms dominated by bacteria and is poorly described in horses compared to other species, including humans. The objective of this study was to characterize and predict the abundance of metabolic genes of the ocular microbiome of a group of clinically healthy horses. Conjunctival swabs were obtained from both eyes of 14 horses, and DNA extraction was performed from the swabs, followed by next generation sequencing and bioinformatics analyses. The most abundant phylum was Pseudomonadota (Proteobacteria), followed by Actinomycetota (Actinobacteria) and Bacteroidota (Bacteroidetes). A total of 278 genera were identified, such as Massilia, Pedobacter, Pseudomonas, Sphingomonas, Suttonella and Verticia, among others. The inference of metabolic functions indicates that the microorganisms present in the ocular conjunctiva perform functions that point to cell growth and metabolism. The ocular microbiome in horses is poorly described compared to other species, and most of the information available in the literature is based on traditional techniques, which has limited the depth of the knowledge on the subject. The objective of this study was to characterize and predict the metabolic pathways of the ocular microbiome of a group of healthy horses. Conjunctival swabs were obtained from both eyes of 14 horses, and DNA extraction was performed from the swabs, followed by next generation sequencing and bioinformatics analyses employing DADA2 and PICRUSt2. A total of 17 phyla were identified, of which Pseudomonadota (Proteobacteria) was the most abundant (59.88%), followed by Actinomycetota (Actinobacteria) (22.44%) and Bacteroidota (Bacteroidetes) (16.39%), totaling an average of 98.72% of the communities. Similarly, of the 278 genera identified, Massilia, Pedobacter, Pseudomonas, Sphingomonas, Suttonella and Verticia were present in more than 5% of the samples analyzed. Both Actinobacteria and Bacteroides showed great heterogeneity within the samples. The most abundant inferred metabolic functions were related to vital functions for bacteria such as aerobic respiration, amino acid, and lipid biosynthesis.