Dataset Information

Ocular surface microbiome in diabetes mellitus.

ABSTRACT: This cross-sectional, age- and gender-matched study included 20 eyes of non-diabetic subjects (non-DM group) and 60 eyes of type 2 diabetes mellitus (DM group). Subgroups of DM were classified by diabetic retinopathy (DR) staging into no DR (DM-no DR), non-proliferative DR (DM-NPDR), proliferative DR (DM-PDR), and by glycemic control (well-controlled DM; HbA1c < 7%, poorly controlled DM; HbA1c ≥ 7%). Conjunctival swabs were performed for ocular surface microbiome analysis using conventional culture and next-generation sequencing analysis (NGS). A higher culture-positive rate was found in DM (15%) than in non-DM group (5%) (p value = 0.437). Pathogenic organisms and antibiotic-resistant strains were detected in the DR groups (DM-NPDR and DM-PDR). The NGS analysis showed that potentially pathogenic bacteria such as Enterobacteriaceae, Neisseriaceae, Escherichia-Shigella, and Pseudomonas predominated in DM, especially in DR. There was dissimilarity in the ocular surface microbiome between DM and non-DM groups. The subgroup analysis showed that the DR group had significantly different microbial community from DM-no DR and non-DM groups (p value < 0.05). The microbial community in the poorly controlled DM was also significantly different from well-controlled DM and non-DM groups (p < 0.001). Using the NGS method, our study is the first to signify the importance of DR and glycemic control status, which affect the changes in the ocular surface microbiome.

SUBMITTER: Suwajanakorn O

PROVIDER: S-EPMC9747965 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Ocular surface microbiome in diabetes mellitus.

Suwajanakorn Orathai O Puangsricharern Vilavun V Kittipibul Thanachaporn T Chatsuwan Tanittha T

Scientific reports 20221213 1

This cross-sectional, age- and gender-matched study included 20 eyes of non-diabetic subjects (non-DM group) and 60 eyes of type 2 diabetes mellitus (DM group). Subgroups of DM were classified by diabetic retinopathy (DR) staging into no DR (DM-no DR), non-proliferative DR (DM-NPDR), proliferative DR (DM-PDR), and by glycemic control (well-controlled DM; HbA1c < 7%, poorly controlled DM; HbA1c ≥ 7%). Conjunctival swabs were performed for ocular surface microbiome analysis using conventional cult ...[more]

PMID: 36513692

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Project description:BackgroundModifications in the gut microbiota may be a crucial factor in the efficacy of canagliflozin (Cana) in managing patients with type 2 diabetes mellitus (T2DM). However, the interplay between oral and ocular surface microbiota and this treatment remains poorly explored.AimThis study aimed to assess alterations in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment in patients with T2DM.MethodsIn this 30-day, controlled before-and-after study, 21 treatment-naïve patients with T2DM received sole treatment with Cana (100 mg/day), and were matched with 10 healthy controls based on gender and age. Using 16S rRNA sequencing, changes in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment were assessed and compared with those of healthy controls. Concurrently, diabetes-related clinical parameters were recorded over the study period. The trial was registered in the Chinese Clinical Trial Registry (ChiCTR200034878).ResultsA noticeable shift was observed in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment. The post-Cana treatment gut microbiota was more similar to that of the healthy controls. Network correlation analysis revealed that modifications in the gut, oral, and ocular surface microbiota were related to changes in clinical parameters, especially for the ocular surface microbiota.Clinical parametersA significant decrease in fasting plasma glucose (8.22 ± 2.19 vs 6.87 ± 1.09 mmol/L), glycated serum protein [291.00 (264.00, 353.00) vs 275.00 (251.00, 342.50) μmol/L], hemoglobin A1c (7.39 ± 1.18 vs 7.12 ± 1.33%), body mass index (25.32 ± 2.99 vs 24.83 ± 2.95 kg/m2), systolic blood pressure (129.05 ± 17.51 vs 123.43 ± 14.82 mmHg), and urinary creatinine [158.40 (74.75, 219.15) vs 79.70 (56.25, 138.10) μmmol/kg] levels was noted after 30-day Cana monotherapy (P < 0.05).Gut microbiomeTreatment with Cana resulted in an increase in the relative abundance of short-chain fatty acid (SCFA)-producing bacteria, particularly Lachnospiraceae UCG 004, Bacteroides, and Lachnospiraceae NK4A136 group.Oral microbiotaAfter Cana treatment, a significant increase of Prevotella and Veillonella, both of which are known to be closely associated with SCFAs, was observed.Ocular surface microbiotaPost-Cana administration, the ocular surface microbiota exhibited the most distinct changes in structure and composition. Remarkably, the majority of the increased ocular surface microbiota could produce SCFAs within the gut microbiota.ConclusionCana effectively improved the dysregulated glucose metabolism in patients with T2DM. This improvement can potentially be attributed to the restoration of balance among the gut, oral, and ocular surface microbial communities.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=56487, identifier ChiCTR2000034878.

Project description:PurposeThe 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.MethodsBoth 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.ResultsSamples 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.ConclusionHigh-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.

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Ocular surface microbiome in diabetes mellitus.

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Ocular surface microbiome in diabetes mellitus.

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