Alterations in the gut bacterial microbiome in fungal Keratitis patients

Abstract

Dysbiosis in the gut microbiome has been implicated in several diseases including auto-immune diseases, inflammatory diseases, cancers and mental disorders. Keratitis is an inflammatory disease of the eye significantly contributing to corneal blindness in the developing world. It would be worthwhile to investigate the possibility of dysbiosis in the gut microbiome being associated with Keratitis. Here, we have analyzed fungal and bacterial populations in stool samples through high-throughput sequencing of the ITS2 region for fungi and V3-V4 region of 16S rRNA gene for bacteria in healthy controls (HC, n = 31) and patients with fungal keratitis (FK, n = 32). Candida albicans (2 OTUs), Aspergillus (1 OTU) and 3 other denovo-OTUs were enriched in FK samples and an unclassified denovo-OTU was enriched in HC samples. However, the overall abundances of these 'discriminatory' OTUs were very low (< 0.001%) and not indicative of significant dysbiosis in the fungal community inhabiting the gut of FK patients. In contrast, the gut bacterial richness and diversity in FK patients was significantly decreased when compared to HC. 52 OTUs were significantly enriched in HC samples whereas only 5 OTUs in FK. The OTUs prominently enriched in HC were identified as Faecalibacterium prausnitzii, Bifidobacterium adolescentis, Lachnospira, Mitsuokella multacida, Bacteroides plebeius, Megasphaera and Lachnospiraceae. In FK samples, 5 OTUs affiliated to Bacteroides fragilis, Dorea, Treponema, Fusobacteriaceae, and Acidimicrobiales were significantly higher in abundance. The functional implications are that Faecalibacterium prausnitzii, an anti-inflammatory bacterium and Megasphaera, Mitsuokella multacida and Lachnospira are butyrate producers, which were enriched in HC patients, whereas Treponema and Bacteroides fragilis, which are pathogenic were abundant in FK patients, playing a potential pro-inflammatory role. Heatmap, PCoA plots and functional profiles further confirm the distinct patterns of gut bacterial composition in FK and HC samples. Our study demonstrates dysbiosis in the gut bacterial microbiomes of FK patients compared to HC. Further, based on inferred functions, it appears that dysbiosis in the gut of FK subjects is strongly associated with the disease phenotype with decrease in abundance of beneficial bacteria and increase in abundance of pro-inflammatory and pathogenic bacteria.

Fig 1. Bar plot depicting abundance of different fungal phyla in HC and FK samples.

Fig 2. Box-plots illustrating alpha diversity indices (Shannon diversity, Simpson index and Observed OTUs) in fungal microbiomes of FK and HC samples.

Median values and interquartile ranges have been indicated in the plots.

Fig 3. Box-plots illustrating alpha diversity indices (Shannon diversity, Simpson index and Observed OTUs) in bacterial microbiomes of FK and HC samples.

Median values and interquartile ranges have been indicated in the plots. * indicates significant difference between HC and FK (p-value <0.05).

Fig 4. Taxonomic abundance of different bacterial phyla, across HC and FK samples.

Only those phyla with > 1% mean abundance are depicted in the plot.

Fig 5. Box plots indicating relative abundance of different bacterial OTUs which exhibit significant (BH corrected P < 0.05) differential abundance across HC and FK samples.

Differentially abundant OTUs having a median abundance > 0.1% in at least one group of samples has been depicted. The median abundances and the interquartile ranges have been indicated in the plots.

Fig 6. Box plots indicating relative abundance of different bacterial genera which exhibited significant (BH corrected P < 0.05) differential abundance across HC and FK samples.

Differentially abundant genera having a median abundance > 0.1% in at least one group of samples has been depicted. Median abundances and interquartile ranges have been indicated in the plots.

Fig 7. Two dimensional heatmap depicting rank normalized abundances (scaled between 0 and1) of 12 bacterial genera which were significantly enriched either in HC or FK samples.

The discriminating genera, as well as the samples (HC and FK) have been arranged along the two dimensions (axes) based on hierarchical clustering.

Fig 8. Principal Coordinate Analysis (PCoA) based on JSD distances between bacterial OTU abundance profiles of different FK (red) and HC (blue) microbiome samples.

Samples plotted along first two principal coordinates showed distinct clustering of HC and FK samples.

Fig 9. Principal Coordinate Analysis (PCoA) based on functional abundance profiles (KEGG modules) of FK (red) and HC (blue) microbiome samples.

Samples plotted along first two principal coordinates showed distinct clustering of HC and FK samples.

Fig 10. Random forest classifier for microbiome based detection of fungal Keratitis.

Fig 11. Bacteria-Fungi interaction network for the HC samples (based on correlation of genera-level abundance).

The node sizes in the network correspond to their degree. The bacterial genera have been highlighted as red nodes, whereas the fungal genera have been highlighted as green nodes. The positive and negative correlations / interactions have been indicated with green edges and red edges respectively.

Fig 12. Bacteria-Fungi interaction network for the FK samples (based on correlation of genera-level abundance).

The node sizes in the network correspond to their degree. The bacterial genera have been highlighted as red nodes, whereas the fungal genera have been highlighted as green nodes. The positive and negative correlations / interactions have been indicated with green edges and red edges respectively.