Association of the Intestinal Microbiome with the Development of Neovascular Age-Related Macular Degeneration

Abstract

Age-related macular degeneration (AMD) is the most frequent cause of blindness in the elderly. There is evidence that nutrition, inflammation and genetic risk factors play an important role in the development of AMD. Recent studies suggest that the composition of the intestinal microbiome is associated with metabolic diseases through modulation of inflammation and host metabolism. To investigate whether compositional and functional alterations of the intestinal microbiome are associated with AMD, we sequenced the gut metagenomes of patients with AMD and controls. The genera Anaerotruncus and Oscillibacter as well as Ruminococcus torques and Eubacterium ventriosum were relatively enriched in patients with AMD, whereas Bacteroides eggerthii was enriched in controls. Patient's intestinal microbiomes were enriched in genes of the L-alanine fermentation, glutamate degradation and arginine biosynthesis pathways and decreased in genes of the fatty acid elongation pathway. These findings suggest that modifications in the intestinal microbiome are associated with AMD, inferring that this common sight threatening disease may be targeted by microbiome-altering interventions.

Figure 1. Disease phenotype and bioinformatics pipeline.

(a) Representative color photographs and optical coherence tomography (OCT) images of a patient with neovascular age-related macular degeneration (AMD; left) and an age-matched control (right). (b) Illustration of our bioinformatics pipeline for analysing metagenomic data and its relation to neovascular AMD. Sequencing reads from stool probes were generated with high throughput sequencing and subjected to quality control. Species abundances were estimated by alignment of high quality reads to clade-specific marker sequences using the Metagenomic Phylogenetic Analysis (MetaPhlAn) tool. To describe the functional profile of the intestinal microbiome, the HMP (Human Microbiome Project) Unified Metabolic Analysis Network (HUMAnN) was used. The linear discriminant analysis (LDA) effect size (LEfSe) algorithm was applied to identify features significantly different between patients and controls. To provide a global analysis of microbial species and pathway abundance principal component analysis (PCA) using ade4 were performed.

Figure 2. Taxonomic characterization of the intestinal microbiome using MetaPhlAn.

Mean relative abundance of the major microbial classes among individuals of the cohort. Red are patients (age-related macular degeneration (AMD), n = 12), green are controls (n = 11).

Figure 3. Microbial composition associated with neovascular age-related macular degeneration (AMD).

(a) Principal component analysis of microbial species abundance using health status as grouping variable. The relation between microbial abundance and health status was assessed using Monte Carlo simulations with 10,000 iterations by which a p value was calculated. (b,c) Identification of taxonomic features relevant in neovascular AMD using LefSe. Cladogram for taxonomic representation of significant differences among AMD and controls. The diameter of each circle is proportional to its abundance (b). Histogram of the LDA scores for differentially abundant taxonomic features among groups. The threshold on the logarithmic LDA (linear discriminant analysis) score for discriminative features was set to 2.0 (C). (d) Box plots representing the mean abundance +/−SD of bacterial genera and species associated with AMD (Kruksal-Wallis test, p ≤ 0.05). Red are patients (AMD, n = 12), green are controls (C, n = 11).

Figure 4. Species-specific microbial pathways associated with neovascular age-related macular degeneration (AMD).

(a) Principal component analysis of microbial pathway abundance with health status as instrumental variable. The relation between pathway abundance and health status was assessed using Monte Carlo simulations with 10,000 iterations by which a p value was calculated. (b) Metabolic pathways that are differentially abundant among subjects with AMD and controls, ranked according to the effect size (LDA score, linear discriminant analysis score). (c) Box plots representing the mean abundance +/−SD of pathways associated with AMD (empiric p > 0.05). Red is patients (AMD, n = 12), green is controls (C, n = 11).