Veterinary ocular microbiome: Lessons learned beyond the culture

Abstract

Ocular pathogens cause many painful and vision‐threatening diseases such as infectious keratitis, uveitis, and endophthalmitis. While virulent pathogens and pathobionts play important roles in disease pathogenesis, the scientific community has long assumed disruption of the ocular surface occurs prior to microbial colonization and subsequent infection. While nonpathogenic bacteria are often detected in corneal and conjunctival cultures from healthy eyes, cultures also frequently fail to yield growth of common ocular pathogens or nonpathogenic bacteria. This prompts the following question: Is the ocular surface populated by a stable microbial population that cannot be detected using standard culture techniques? The study of the microbiome has recently become a widespread focus in physician and veterinary medicine. Research suggests a pivotal symbiotic relationship with these microbes to maintain healthy host tissues, and when altered is associated with various disease states (“dysbiosis”). The microbiota that lives within and on mammalian bodies have long been known to influence health and susceptibility to infection. However, limitations of traditional culture methods have resulted in an incomplete understanding of what many now call the “forgotten organ,” that is, the microbiome. With the introduction of high‐throughput sequencing, physician ophthalmology has recognized an ocular surface with much more diverse microbial communities than suspected based on traditional culture. This article reviews the salient features of the ocular surface microbiome and highlights important future applications following the advent of molecular techniques for microbial identification, including characterizing ocular surface microbiomes in our veterinary species and their potential role in management of infectious and inflammatory ocular diseases.

Current ocular microbiome investigations limit reproducibility and reliability: Critical review and opportunities

“…NGS can detect and identify virtually all bacteria in a given sample, providing an unprecedented level of information. Conversely, culture‐dependent methods failed to detect a large proportion of the microorganisms in a given community, which was sometimes referred to as the “great plate count anomaly.” 1 Metagenomics is a rapidly evolving area of research that offers great promise in advancing both human and veterinary medicine, and clinical practitioners will increasingly require a working knowledge of the ocular surface microbiota 2 . However, enthusiasm for microbiome research has outpaced agreement upon experimental practices causing a lack of standardized methodologies 3 .…”

Feline conjunctival microbiota in a shelter: effects of time, upper respiratory disease and famciclovir administration

“…The community of living microorganisms in a particular environment, including its inhabitant bacteria, viruses, fungi and protozoa, is termed the microbiota. [1][2][3][4] By contrast, the microbiome describes all genetic material within the microbiota. 4,5 Initial investigation of the microbiota and microbiome was limited to culture-based and similar techniques.…”

“…The advent of metagenomic techniques, such as 16S ribosomal RNA (16S rRNA) gene sequencing, as well as bioinformatics systems, have allowed broader and more detailed investigation of the microbiome and microbiota, and their associations with various host factors and pharmacologic interventions. 2,3,[5][6][7][8][9] Evaluation of the bacterial microbiota can involve various approaches, including comparison of specific members (eg, different phyla and genera), alpha diversity indices (ecological indicators of the bacterial community in a sample) and assessment of beta diversity (evaluation of differences in overall bacterial communities between groups). Additional approaches can be used to provide different insights, such as the use of linear discriminate analysis effect size (LEfSe) to identify operational taxonomic units (OTUs) that are differentially abundant in species groups.…”

“…Microbiota analysis frequently begins following assembly of sequences with greater than 97% nucleotide identity into groups called OTUs. 3,10 Alpha diversity metrics indicate the taxonomic diversity (inverse Simpson's index), evenness (Shannon's evenness index) and richness (number of species observed [observed species richness: SOBS] and Chao1 index). 3,[10][11][12] Beta diversity metrics include measurements of microbial community membership (number of shared genera; Jaccard's index) and structure (number of shared genera and their relative abundances; Yue and Clayton index) between samples.…”

“…3,10 Alpha diversity metrics indicate the taxonomic diversity (inverse Simpson's index), evenness (Shannon's evenness index) and richness (number of species observed [observed species richness: SOBS] and Chao1 index). 3,[10][11][12] Beta diversity metrics include measurements of microbial community membership (number of shared genera; Jaccard's index) and structure (number of shared genera and their relative abundances; Yue and Clayton index) between samples. 10 A wide range of methods can be used to compare statistically the composition of microbiota communities, such as analysis of molecular variance (AMOVA) and unweighted UniFrac.…”