Composition and Diversity of the Ocular Surface Microbiota in Patients With Blepharitis in Northwestern China

Composition and diversity of meibum microbiota in meibomian gland dysfunction and the correlation with tear cytokine levels

Meibomian gland dysfunction (MGD) leads to meibum stasis and pathogenic bacteria proliferation. We determined meibum microbiota via next-generation sequencing (NGS) and examined their association with tear cytokine levels in patients with MGD. This cross-sectional study included 44 moderate-severe patients with MGD and 44 healthy controls (HCs). All volunteers underwent assessment with the ocular surface disease index questionnaire, Schirmer without anesthesia, tear break-up time, Oxford grading of ocular surface staining, and lid and meibum features. Sample collection included tears for cytokine detection and meibum for 16S rRNA NGS. No significant differences were observed in the α-diversity of patients with MGD compared with that in HCs. However, Simpson's index showed significantly decreased α-diversity for severe MGD than for moderate MGD (p = 0.045). Principal coordinate analysis showed no significant differences in β-diversity in meibum samples from patients with MGD and HCs. Patients with MGD had significantly higher relative abundances of Bacteroides (8.54% vs. 6.00%, p = 0.015) and Novosphingobium (0.14% vs. 0.004%, p = 0.012) than the HCs. Significantly higher interleukin (IL)-17A was detected in the MGD group than in the HC group, particularly for severe MGD (p = 0.008). Although Bacteroides was more abundant in the MGD group than in the HC group, it was not positively correlated with IL-17A. The relationship between core meibum microbiota and tear cytokine levels remains unclear. However, increased Bacteroides and Novosphingobium abundance may be critical in MGD pathophysiology.

Conjunctival microbiome changes in soft contact lens users and contact lens discomfort patients

INTRODUCTION

Contact lens discomfort (CLD) acts as a challenging problem, and the associated conjunctival microbiome changes were unclear.

MATERIAL AND METHODS

Conjunctival sac swab samples were collected from 12 eyes of nonwearers (NW), 12 eyes of asymptomatic contact lens (ACL) wearers, and 11 eyes of CLD. The V3-V4 region of the 16S rRNA gene sequencing was used to investigate differences among three groups.

RESULTS

No differences in alpha diversity were observed among the three groups. The beta diversity showed a distinct microbiome composition between ACL and CLD group (P = 0.018) with principal coordinate analysis. The relative abundance of Firmicutes was significantly higher in CLD (48.18%) than in ACL (13.21%) group (P = 0.018). The abundance of Bacillus in patients with ACL (0.05%) or with CLD (0.02%) were significantly lower than that in the NW (1.27%) group (P = 0.024, 0.028, respectively). Moreover, the abundance of Firmicutes was positively correlated with the OSDI scores in CLD patients (r = 0.817, P < 0. 01, Spearman).

DISCUSSIONS

Patients with CLD have various degrees of bacterial microbiota imbalance in the conjunctival sac, compared with NW and ACL groups.

CONCLUSION

Firmicutes may serve as a potential biomarker for the CLD patients.

Analysis of microbial communities of ocular prostheses and anophthalmic sockets using 16S rRNA gene sequencing

Ocular prostheses are liable to host microbial adhesion, which can favor conjunctival inflammation. Knowing the microbiota of the ocular prosthesis and anophthalmic socket is important for predicting infection-related pathogens. This study evaluated the microbiota of the prostheses and anophthalmic sockets of six individuals by 16S rRNA sequencing. The microbial abundance and diversity were analyzed using the Operational Taxonomic Units at the genus level. Forty-seven phyla, capturing 1,258 named genera, were recorded in the sample set. In both sites, the most frequent genera were Fusobacterium, Staphylococcus, Prevotella, and Streptococcus. The microbial abundance was higher for the anophthalmic socket than for the prosthesis. Alpha diversity showed no significant differences in bacterial richness or diversity between sites. Although the microbial abundance in the anophthalmic socket was higher, both sites had similar microbiota with high diversity and low dominance among the genera.

DNA extraction protocol impacts ocular surface microbiome profile

Purpose

The aim of this study is to provide a reference frame to allow the comparison and interpretation of currently published studies on 16S ribosomal ribonucleic acid amplicon sequencing of ocular microbiome samples using different DNA extraction protocols. Alongside, the quantitative and qualitative yield and the reproducibility of different protocols has been assessed.

Methods

Both eyes of 7 eligible volunteers were sampled. Five commercially available DNA extraction protocols were selected based on previous publications in the field of the ocular surface microbiome and 2 host DNA depletion protocols were added based on their reported effective host DNA depletion without significant reduction in bacterial DNA concentration. The V3-V4 region of the 16S rRNA gene was targeted using Illumina MiSeq sequencing. The DADA2 pipeline in R was used to perform the bio-informatic processing and taxonomical assignment was done using the SILVA v132 database. The Vegdist function was used to calculate Bray-Curtis distances and the Galaxy web application was used to identify potential metagenomic biomarkers via linear discriminant analysis Effect Size (LEfSe). The R package Decontam was applied to control for potential contaminants.

Results

Samples analysed with PowerSoil, RNeasy and NucleoSpin had the highest DNA yield. The host DNA depletion kits showed a very low microbial DNA yield; and these samples were pooled per kit before sequencing. Despite pooling, 1 of both failed to construct a library.Looking at the beta-diversity, clear microbial compositional differences - dependent on the extraction protocol used - were observed and remained present after decontamination. Eighteen genera were consistently retrieved from the ocular surface of every volunteer by all non-pooled extraction kits and a comprehensive list of differentially abundant bacteria per extraction method was generated using LefSe analysis.

Conclusion

High-quality papers have been published in the field of the ocular surface microbiome but consensus on the importance of the extraction protocol used are lacking. Potential contaminants and discriminative genera per extraction protocol used, were introduced and a reference frame was built to facilitate both the interpretation of currently published papers and to ease future choice - making based on the research question at hand.