Characterization of Ocular Surface Microbial Profiles Revealed Discrepancies between Conjunctival and Corneal Microbiota

Abstract

The ocular microbiome composition has only been partially characterized. Here, we used RNA-sequencing (RNA-Seq) data to assess microbial diversity in human corneal tissue. Additionally, conjunctival swab samples were examined to characterize ocular surface microbiota. Short RNA-Seq reads, obtained from a previous transcriptome study of 50 corneal tissues, were mapped to the human reference genome GRCh38 to remove sequences of human origin. The unmapped reads were then used for taxonomic classification by comparing them with known bacterial, archaeal, and viral sequences from public databases. The components of microbial communities were identified and characterized using both conventional microbiology and polymerase chain reaction (PCR) techniques in 36 conjunctival swabs. The majority of ocular samples examined by conventional and molecular techniques showed very similar microbial taxonomic profiles, with most of the microorganisms being classified into Proteobacteria, Firmicutes, and Actinobacteria phyla. Only 50% of conjunctival samples exhibited bacterial growth. The PCR detection provided a broader overview of positive results for conjunctival materials. The RNA-Seq assessment revealed significant variability of the corneal microbial communities, including fastidious bacteria and viruses. The use of the combined techniques allowed for a comprehensive characterization of the eye microbiome’s elements, especially in aspects of microbiota diversity.

Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network

“…( Huang et al., 2016 ; Wang et al., 2019 ; Ren et al., 2020 ) In addition, conjunctival and corneal microbiome have very similar microbial taxonomic profiles with different proportion. ( Matysiak et al., 2021 ) Besides, previous studies have confirmed that different types of pathogens can invoke various ocular pathophysiological responses. ( Suzuki et al., 2010 ; Lobo et al., 2019 ) Our present work is thus aimed to extend the clinical application of high-throughput 16S rRNA gene sequencing, especially for the tissue diagnosis associated with a microbiota.…”

A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

“…In this sense, applications of heatmaps to represent the presence or absence of microbial taxa and genes have been extensively reported in the literature. Some recent examples include the usage of heatmaps to elucidate the presence of coincident genes across microbial genomes [ 34 ] and coincident genera in human microbiota samples [ 35 ]. In the present study, most species belonging to the same genus were clustered together, highlighting common metabolic patterns.…”

Conjunctival Sac Microbiome in Infectious Conjunctivitis

“…Alteration of the microbiota due to antibiotics usage was characterized with the reduction of some susceptible bacteria and the induction of microbial substitution. In addition, it was confirmed that antibiotic administered prophylactically before ophthalmic procedures may further reduce the ocular surface's amount and variety of microbiota [21,22]. Reasonably, we can accept that bacterial composition of the microbiota in bilateral cases was different from unilateral cases.…”