Interrogation of the Perturbed Gut Microbiota in Gouty Arthritis Patients Through in silico Metabolic Modeling

Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease in which an imbalance between uric acid production and excretion leads to the deposition of uric acid crystals in joints. To mechanistically investigate altered microbiota metabolism in gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic modeling. Patient-specific models were used to cluster samples according to their metabolic capabilities and to generate statistically significant partitioning of the samples into a Bacteroides-dominated, high gout cluster and a Faecalibacterium-elevated, low gout cluster. The high gout cluster samples were predicted to allow elevated synthesis of the amino acids D-alanine and L-alanine and byproducts of branched-chain amino acid catabolism, while the low gout cluster samples allowed higher production of butyrate, the sulfur-containing amino acids L-cysteine and L-methionine and the L-cysteine catabolic product H2S. The models predicted an important role for metabolite crossfeeding, including the exchange of acetate, D-lactate and succinate from Bacteroides to Faecalibacterium to allow higher butyrate production differences than would be expected based on taxa abundances in the two clusters. The surprising result that the high gout cluster could underproduce H2S despite having a higher abundance of H2S-synthesizing bacteria was rationalized by reduced L-cysteine production from Faecalibacterium in this cluster. Model predictions were not substantially altered by constraining uptake rates with different in silico diets, suggesting that sulfur-containing amino acid metabolism generally and H2S more specifically could be novel gout disease markers.