Exploring the Microbial Changes in Meibomian Gland Dysfunction through 16S rDNA Sequencing

Meibomian gland dysfunction (MGD) is a common condition that occurs when the Meibomian glands, responsible for secreting tears in the eyes, do not produce enough oil or produce oil of poor quality. This can lead to various symptoms including dry eyes, discomfort, and poor ocular function. The study examines bacterial community diversity in patients with Meibomian Gland Dysfunction (MGD) using 16S rDNA sequencing, compared to healthy individuals. The goal is to understand microbial changes in MGD and provide insights into potential treatments.

27 16S rRNA gene sequences were obtained from the (EMBL-EBI) website, consisting of 3 sequences from healthy individuals, 7 sequences from individuals with mild Meibomian gland dysfunction, 6 sequences from individuals with moderate Meibomian gland dysfunction, and 11 sequences from individuals with severe Meibomian gland dysfunction. An algorithm utilizing machine learning was applied to identify the association with each sequence. A trained classifier was then used to create an OTU table.

Our results found that there were significant differences in alpha diversity among individuals with severe (MGD) and healthy individuals. Furthermore, the microbial composition was found to be similar across all groups, regardless of their MGD status.

This study highlights the correlation between meibomian gland dysfunction (MGD) and imbalances in the bacterial microbiota on the ocular surface. The results suggest a role for Staphylococcus, Corynebacterium, and Sphingomonas in the development of MGD, with a positive correlation between MGD severity and bacterial abundance. The findings provide a basis for considering antibiotics in MGD treatment, with insights into the microbiome’s role in the pathogenesis of the condition.