Geographic Difference Shaped Human Ocular Surface Metagenome of Young Han Chinese From Beijing, Wenzhou, and Guangzhou Cities

Abstract

Purpose: Microbial ecosystems interact with the human body and affect human health. The microbial community on the ocular surface remains an underexplored territory despite its importance as the first line of defense barrier that protects the eye and ultimately sight. We investigated how age and sex affected human ocular surface microbiome, and in the present study wanted to understand how geographic difference shaped the microbiome in the ocular surface.

Methods: We collected conjunctival specimens of 172 eyes from 86 healthy volunteers living in three Chinese cities, namely, Guangzhou, Wenzhou, and Beijing. Using the direct metagenomic shotgun sequencing approach, we characterized how geographic difference affected the human ocular microbiome.

Results: We surveyed the taxonomic composition and metabolic function of the microbiota on human ocular surface. We showed that the ocular surface microbiota was composed of bacteria, viruses, and fungi. A geographical difference in both composition and function of the conjunctival microbiome suggests that the environment people lived in shapes their conjunctival microbiome, especially the dominate species.

Conclusions: Our study provides a reference catalog of the healthy conjunctival metagenome and raises a concern for environmental influences on the ocular surface microbiome.

Figure 1.

The microbiota on healthy human conjunctiva (A). Relative composition of major kingdoms on human conjunctiva. (B) Relative abundance of the core bacterial species (average >1%) on conjunctiva. (C) Relative abundance plots of core bacterial taxa by sampling groups.

Figure 2.

Major bacterial species identified on the ocular surface are viable. Traditional culture of conjunctival swab was carried out in the clinical laboratory at ZOC. The percentage of individuals with positive culture results of varied numbers of bacteria (A) and the percentage of each species that was positively cultured among all species (B) showed the viability of ocular surface commensals. Aerobic and anaerobic bacteria containing in the conjunctival swab samples had been cultured respectively.

Figure 3.

The geographical diversity of the conjunctival microbiome. The microbial community diversity of Guangzhou (GZ), Wenzhou (WZ), and Beijing (BJ) samples measured by Shannon index (Mann-Whitney U test) (GZ vs WZ Z = −0.044 [P = 0.979]; GZ vs BJ Z = −3.836 [P < 0.001]; WZ vs BJ Z = −3.979 [P < 0.001]) (A) and principal component analysis of bacterial relative abundance (GZ vs WZ Z = −1.454 [P = 0.148]; GZ vs BJ Z = −4.230 [P < 0.001]; WZ vs BJ Z = −4.589 [P < 0.001]) (B). The hierarchical clustering of different P. acnes strains was shown (C).

Figure 4.

The characteristics of Guangzhou and Beijing conjunctival microbiome (A). The LDA effect size program was used to find the bacterial species which significantly distinguished Beijing (green) and Guangzhou (red) microbiomes (LDA score > 3). (B) Relative abundance of Propionibacteruim acnes (t = 3.450; P = 0.0007; left) and P aeruginosa (t = 5.209; P < 0.0001; right) in the samples from Guangzhou and Beijing cities.

Figure 5.

KEGG pathways differentially identified metagenomes from Beijing (green) and Guangzhou (red) samples. CoA, coenzyme A.

Figure 6.

Environmental changes cause divergence in the conjunctival microbiome. The comparison of conjunctival microbial diversity between samples collected within three weeks from volunteers who did not travel showed no significant differences as measured by alpha diversity (t = 0.009; P = 0.993) (A), principal component analysis of bacterial abundance (Z = –1.792; P = 0.08) (B), and functional pathway abundance (Z = –1.517; P = 0.14) (C). The comparison of conjunctival microbial diversity between samples collected within three weeks from volunteers who traveled to other cities showed significant changes as measured by alpha diversity (t = 2.964; P = 0.003) (D), principal component analysis of bacterial abundance (Z = –2.589; P = 0.01) (E) and functional pathway abundance (Z = –2.188; P = 0.03) (F). P values associated with principal component plots represent a Wilcoxson rank-sum test on samples’ projection onto PC1.