Distinct Ocular Surface Microbiome in Keratoconus Patients Correlate With Local Immune Dysregulation

Abstract

Purpose: Keratoconus (KC) is characterized by irregular astigmatism along with corneal stromal weakness and is associated with altered immune status. Tissue resident microbiomes are known to influence the immune status in other organs, but such a nexus has not been described in ocular conditions. Therefore, we examined the ocular surface microbiome of patients with KC and correlated it to the immune cell and tear molecular factor profiles.

Methods: Sixty-two patients with KC and 21 healthy controls underwent corneal topography analysis and eye examination followed by a collection of Schirmer's strip, ocular surface wash, and ocular surface swabs. Microbiomes were analyzed by extracting DNA from the swabs followed by 16S rRNA gene V3-V4 amplicon sequencing and analyzed using QIIME. Fifty-two molecular factors from Schirmer's strip tear extracts and 11 immune cells from ocular wash were measured using multiplex ELISA and flow cytometry. Alpha diversity, linear discriminant analysis effect size (LEfSe), relative abundance and receiver operating characteristic - area under the curve (ROC-AUC) analysis were performed. Unsupervised clustering at the genus level with clinical parameters, soluble factors, and immune cells was performed.

Results: Fifty-two phyla/class, 132 order, 283 family, and 718 genera were identified in our cohort. Alpha diversity indices were comparable between patients with KC and the healthy controls. Dominant phyla across groups were Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes. Alphaproteobacteria increased in KC eyes whereas Actinobacteria, Firmicutes_Bacilli reduced compared with the healthy controls. We found a significant positive correlation of Microbacterium, Cutibacterium, and Brevundimonas genera abundance with keratometry and corneal thickness. Levels of IL-21, IL-9, Fractalkine, and VEGF positively correlated with Tetrasphaera (P < 0.05). β2-microglobulin and CD66bhigh cells correlated with Bacteroides (P < 0.05). CD45+ cells correlated with Escherichia_Shigella (P < 0.02).

Conclusions: We discovered a unique microbiome signature of KC which correlated to disease grades and secreted molecular factors and immune cells. Therefore, the altered microbiome on the ocular surface may drive immune dysregulation in KC and provide scope for potential interventions in the future.

Figure 1.

Schematic representation of our study cohort. (A) Microbiomes from ocular swabs from healthy controls and patients with KC were assessed. Matched (B) ocular wash and (C) tears were isolated for flow cytometric based analysis.

Figure 2.

Descriptive status of ocular microbiome. (A) Identified OTUs across phyla, order, class, and genus in patients with KC and healthy controls. Alpha diversity measures are estimated by (B) biodiversity and (C) richness comparison between healthy controls (n = 21) and patients with KC (n = 62). Violin plot indicates the data distribution observed in the present study cohort. (D) Cladogram representation of differences between patients with KC (green) and healthy controls (red). Nodes represent taxa ranging from phylum_class, order, family, and genus levels from the inner to the outer circle. The size of the node represents the taxa abundance. (E) Linear discriminant analysis (LDA) score (log 10) plot derived from LEfSE analysis showing the biomarker taxa with LDA score > 3.

Figure 3.

Ocular microbiota composition in patients with KC and healthy control subjects. Venn diagram comparing the operational taxonomical unit (after stringent filter of < 50%) at (A) phylum, (D) order, and (E) family level between patients with KC (green) and healthy controls (red). (B) Bar graph showing the percentage of abundant phylum in patients with KC and healthy controls. Mann Whitney U test was performed for statistical analysis (*P = 0.05). (C) Heat map represents the individuals contributing the absolute abundance at phylum level in the healthy control subjects (n = 21) and patients with KC (n = 62).

Figure 4.

Validation and independent diagnosis of microbial markers for KC at the genus level. (A) Volcano plot showing the degree of differential abundant genus in patients with KC compared with the healthy controls (x-axis, log₂ fold change, and y-axis, minus log₁₀ of P value). The dashed vertical and horizontal lines at fold change ± 1.0 and minus log 1.3 corresponds to P value of 0.05. The red dots denote the higher abundant genus in patients with KC and the green dots are the significantly reduced taxa in patients with KC. (B) Venn diagram (after stringent filter of < 50%) at the genus level. (C) Alluvial plot illustrating the absolute abundance of genera observed uniquely, significantly more or less in the healthy controls and the patients with KC. Area under the curve receiver operator characteristics (AUC-ROC ≥ 0.7) achieved for genera (D) Microbacterium, (E) Sphingomonas, (F) Pseudomonas, (G) Pantoea with the significant P value and 95% confidence interval (CI) demonstrating the significantly altered microbial genera in patients with KC.

Figure 5.

Soluble factor and immune cells profile of KC. Volcano plot of (A) soluble factors and (H) immune cells subset in patients with KC versus healthy controls. X-axis denotes fold change (log2) and Y-axis denotes P value (−log10). Cutoff is set as ≥ 2-fold change. Red dots represent significantly upregulated levels and the blue dots represent significantly reduced levels in patients with KC. Area under the curve receiver operator characteristics (AUC-ROC) analysis of patients with KC with the signification soluble factors (B) IL-6, (C) IL-21, (D) IL-2, (E) MMP-2, (F) β2-microglobulin (G) EPO and immune cells, such as (I) CD45+ (J) Ratio of CD66b_High/CD66b_Low and (K) CD66b_High cells.

Figure 6.

The relationship among ocular surface microbiota, clinical parameters, and immune profile for KC. Heatmap showing the (A) Spearman correlation between the 17 shortlisted genera and (B) unsupervised clustering of the genus with other parameters reveals 6 distinct clusters. The heatmap represents the positive and negative correlation between parameters in each cluster. The magnitude of the boxes in the plot is proportional to the R-value and the color scale bar shows the interpretation of correlation coefficient based on the standard guidelines. The red color represents the positive correlation and the blue color represents the negative correlation. ***P values = 0.001; **P values = 0.01; *P values = 0.05.

Figure 7.

Schematic summary of the altered ocular microbiome with changes in the soluble factors and the immune cells leading to clinical pathology in KC. Ocular microbiome with KC disease presentation and its immune status. Microbes higher in abundance (Brevundimonas, Microbacterium) correlates with keratometry indices of KC. Lesser abundance of microbes (Cutibacterium, Pseudomonas, Sphingomonas, Staphylococcus) correlates with corneal thinning. Ocular surface microbiome influences immune cells activation and inflammatory milieu in tear film.