Characterization and Comparison of Ocular Surface Microbiome in Newborns

Abstract

The ocular microbiome is of fundamental importance for immune eye homeostasis, and its alteration would lead to an impairment of ocular functionality. Little evidence is reported on the composition of the ocular microbiota of term infants and on the impact of antibiotic prophylaxis.

Methods: A total of 20 conjunctival swabs were collected from newborns at birth and after antibiotic treatment. Samples were subjected to 16S rRNA sequencing via system MiSeq Illumina. The data were processed with the MicrobAT software and statistical analysis were performed using two-way ANOVA.

Results: Antibiotic prophylaxis with gentamicin altered the composition of the microbiota. In detail, a 1.5- and 2.01-fold reduction was recorded for Cutibacterium acnes (C. acnes) and Massilia timonae (M. timonae), respectively, whereas an increase in Staphylococcus spp. of 6.5 times occurred after antibiotic exposure.

Conclusions: Antibiotic prophylaxis altered the ocular microbiota whose understanding could avoid adverse effects on eye health.

Keywords: 16S rRNA sequencing; bacteria; newborn; ocular surface microbiota.

Figure 1

(A) Phylum of conjunctival samples performed at birth and (B) after antibiotic prophylaxis (p-value < 0.0001).

Figure 2

Distribution of relative genera abundance in conjunctival swabs before (A) and after (B) prophylaxis with gentamicin (p-value < 0.0001).

Figure 3

Distribution of relative species abundance in conjunctival swabs before (A) and after (B) prophylaxis with gentamicin (p-value < 0.0001).

Figure 4

Determinations of alpha (A) and beta diversity PCoA (B). Alpha-diversity measure using Shannon index at Species level is represented as boxplot; each boxplot represents the diversity distribution of a group (Shannon index values are reported in Figure S2). Beta diversity is represented as a 2-D PCoA plot using Bray–Curtis distance. The statistical significance of the clustering pattern in ordination plots is evaluated using Permutational ANOVA (PERMANOVA). Each axis reflects the percent of the variation between the samples with the X-axis representing the highest dimension of variation and the Y-axis representing the second highest dimension of variation. The explained variances are shown in brackets.