Understanding the Interactions Between the Ocular Surface Microbiome and the Tear Proteome

Abstract

Purpose: The purpose of this study was to explore the interplay between the ocular surface microbiome and the tear proteome in humans in order to better understand the pathogenesis of ocular surface-associated diseases.

Methods: Twenty eyes from 20 participants were included in the study. The ocular surface microbiome was sequenced by whole-metagenome shotgun sequencing using lid and conjunctival swabs. Furthermore, the tear proteome was identified using chromatography tandem mass spectrometry. After compositional and functional profiling of the metagenome and functional characterization of the proteome by gene ontology, association studies between the ocular microbiome and tear proteome were assessed.

Results: Two hundred twenty-nine taxa were identified with Actinobacteria and Proteobacteria being the most abundant phyla with significantly more Propionibacterium acnes and Staphylococcus epidermidis in lid compared to conjunctival swabs. The lid metagenomes were enriched in genes of the glycolysis lll and adenosine nucleotides de novo and L-isoleucine biosynthesis. Correlations between the phylum Firmicutes and fatty acid metabolism, between the genus Agrobacterium as well as vitamin B1 synthesis and antimicrobial activity, and between biosynthesis of heme, L-arginine, as well as L-citrulline and human vision were detected.

Conclusions: The ocular surface microbiome was found to be associated with the tear proteome with a role in human immune defense. This study has a potential impact on the development of treatment strategies for ocular surface-associated diseases.

Trial registration: ClinicalTrials.gov NCT04656197.

Figure 1.

Interactions between the ocular surface microbiome and the tear proteome. The tear film consists of an inner mucin-enriched glycocalyx layer, a middle aqueous layer secreted by lacrimal glands and an outer lipid layer composed of meibomian lipids. Sampling for this study was performed in the following manner: The ocular surface microbiome was sequenced using swabs from the conjunctiva and the eyelid and the tear proteome was characterized collecting tear fluid by Schirmer strips (A) (more details are found in section 2.2). Lacrimal constituents of tears may be influenced by the surface microbiome and vice versa through antimicrobial components found in the tear proteome such as lactoferrin, lipocalin-1, lysozyme, and immunoglobulin A (B).

Figure 2.

Composition of the ocular surface microbiome. Relative taxonomic composition of the ocular surface microbiome (A) and positive rate of species in participants (B). The top species with a positive rate ≥ 10% are shown.

Figure 3.

Diversity of the ocular surface microbiome in lid and conjunctival samples. Relative abundances of microbiota at phyla level in individual samples (A) and averaged for location (B). Relative abundances of microbiota at genus level in individual samples (C) and averaged for location (D). Conjunctiva n = 20 and lid n = 20.

Figure 4.

Distinct taxonomical and functional composition of the ocular microbiome between lid and conjunctival samples. Principal component analysis (PCA) of taxonomical feature abundance grouped lid and conjunctival samples separately, with PERMANOVA confirming a significant difference between the groups (A) (P = 0.032). PCA of functional feature abundance did not separate lid from conjunctival samples (B) (P = 0.63, PERMANOVA). Relative abundances of taxa (C) and pathways (D) associated with the location of sampling (mean value and standard deviation are shown, Wilcoxon rank sum test, P < 0.05). Red is lid (0 =20), green is conjunctiva n = 20.

Figure 5.

Median label-free quantification (LFQ) protein group intensity of all samples. LFQ intensities of all samples (y-axis) are represented as boxplots. The numbers on the y-axis represent the numbers of quantified proteins with a LFQ intensity. The lines within the boxes delineate median values, the left and right edge of the boxes the 25th and 75th percentiles, the whiskers extend to the most extreme data points, and outliers are represented individually by dots.

Figure 6.

Core proteome of human tears. Label-free quantification (LFQ) values of the most abundant proteins in the cohort (A) and the number of regulated proteins per sample (B) are represented. There are more downregulated proteins in the cohort (P = 0.013, unpaired t-test). Mean value and standard deviation are shown. IgA, immunoglobulin A; nb, number.

Figure 7.

Functional classification of the tear proteome. Classification based on Gene Ontology (GO) categories cellular components (A), biological processes (B), and molecular functions (C) using the DAVID Bioinformatics tool.

Figure 8.

Associations between the ocular surface microbiome and the tear proteome. Relative abundances of taxa (A, B) or pathways (C–F) associated with up- or downregulated Gene Ontology (GO) terms (q-values after adjusting for false discovery rate, MaAsLin). Mean values and standard deviation are shown.