Changes in the ocular surface microbiome of patients with coronavirus disease 2019 (COVID-19)

Abstract

Purpose: To elucidate the reasons behind the increased incidence of ocular disease in patients with coronavirus disease 2019 (COVID-19), this study delved deeper into the specific effects of COVID-19 on patients' ocular surface microbiome (OSM) and investigated its relationship with the increased incidence of ocular disease.

Methods: In this study, conjunctival sac swabs were collected from 43 participants for 16S rRNA amplicon sequencing. The participants were categorized into three groups based on their COVID-19 status: the control group (C group) consisted of 15 participants who showed no evidence of COVID-19, the experimental group (E group) included 15 participants who tested positive for COVID-19, and the COVID-19 recovery period group (R group) comprised 13 participants.

Results: In the comparison of alpha diversity, group E had a higher Shannon, Chao1 and Goods coverage index. When comparing beta diversity, groups E and R were more similar to each other. At the phylum level, although the OSM of the three groups was dominated by Proteobacteria, Actinobacteriota, Bacteroidota and Firmicutes, the compositional proportions were significantly different. At the genus level, the dominant species in the three OSM groups were significantly different, with Pseudomonas becoming the dominant genus in groups E and R compared to group C, and the abundance of Ralstonia decreasing significantly.

Conclusion: This study provides additional evidence supporting the association between the OSM and COVID-19, which contributes to our understanding of the potential mechanisms underlying ocular symptoms and complications associated with COVID-19 in the future.

Keywords: 16S rRNA amplicon sequencing; COVID-19; COVID-19 recovery period; ocular surface; ocular surface microbiome.

Figure 1

Venn diagram and alpha diversity analysis. (A) Venn diagram showing how the AVAs of the Group Members relate to each other. (B) Species Accumulation Boxplot shows an increase in species diversity as sample size increases. (C) Shannon index box diagram showing significance differences in species diversity between groups, ^*p < 0.05, ^**p < 0.01, ^***p < 0.001. (D) Shannon index Rarefaction Curve suggests that the amount of sequencing data is asymptotically reasonable and that more data does not have a significant effect on the alpha diversity index.

Figure 2

Beta diversity analysis. (A) UPGMA cluster analysis. (B) NMDS statistics based on the weighted unifrac distance. (C) NMDS statistics based on the unweighted unifrac distance, stress values less than 0.2 indicates that the NMDS accurately reflects the degree of variation between samples.

Figure 3

Relative abundances of dominant phyla in samples. (A) Accumulative histogram of species abundance at the phylum level, other bacterial abundances in groups C, E and R were 8.84, 15.89 and 16.42%. (B) Accumulative histogram of species abundance at the genus level, other bacterial abundances in groups C, E and R were 39.41, 62.42, 47.78%. (C) Heatmap of microbial composition cluster analysis at genus level in each group. (D-F) Composition and proportions of the top 10 species ranked by relative abundance at genus level in groups C, E and R.

Figure 4

Biomarkers with statistically significant differences between the three groups. (A) The histogram of LDA value distribution. (B) The evolutionary branching diagram. LEfSe plot of taxonomic biomarkers identified in the ocular surface microbiome of patients and controls. LDA scores are effect size estimates for a particular taxonomic marker in a specific group, with values interpreted as the relative magnitude of abundance compared to the other group.