Understanding the rheumatoid arthritis-gut axis by metabolic network analysis

Rheumatoid Arthritis (RA) is an autoimmune disease that mostly affects the joints. In addition to the genetic susceptibility, environmental factors including dietary intake are important parameters for the pathogenesis and understanding of the disease. In this study, genome-scale metabolic modeling was employed to understand the activities of the bacteria residing in the gut of RA patients. Changes in the bacterial growths, metabolite exchanges, cooperative or competitive behavior of the taxon, focal interactions, and elasticity values according to the dietary differences were examined by using the Python library, MICOM software and the CPLEX solver. Western, Mediterranean, and Vegan diets were selected as the nutritional media. Iron, ornithine, and phenylalanine supplementations were added to all three diets and their effects were determined. The study revealed that diet type did not have significant changes overall; however, some species were notably affected by the type of nutrient intake including Clostridium celatum, Fusobacterium varium and Haemophilus parainfluenzae. According to the bacterial growth results, MD and Vegan diets were not able to take actions against pathogens; however, depending on the bacterial species the exchange fluxes of some metabolites were altered with diet type. RA communities were found to have more cooperative interactions than the HC communities, however, in terms of the dual interactions considering short-chain-fatty acids species of the RA community were having less flux values compared to the HC community. After the dietary supplementations, Clostridium celatum displayed significant flux changes relative to the standard diets. At the end of the study, possible amino acid metabolites that could be listed as potential biomarkers for RA were alanine, proline and leucine. Among the gut microbiom, Clostridium Celatum, Enterococcus faecalis, Enterobacter aerogenes, Bacteroides faecis, Haemophilus parainfluenzae and Fusobacterium varium were highlighted as the possible biomarkers.