ABSTRACT
Gut microbiota plays an essential role in the development of rheumatoid arthritis (RA) and affects drug responses. However, the underlying mechanism remains elusive and urgent to elucidate to explore the pathology and clinical treatment of RA. Therefore, we selected methotrexate (MTX) as an example of RA drugs to explore the interactions between the gut microbiota and drug responses and obtain an in-depth understanding of their correlation from the perspective of the metabolic capability of gut microbiota on drug metabolism. We identified 2,654 proteins and the corresponding genes involved in MTX metabolism and then profiled their abundances in the gut microbiome datasets of four cohorts. We found that the gut microbiota harbored various genes involved in MTX metabolism in healthy individuals and RA patients. Interestingly, the number of genes involved in MTX metabolism was not significantly different between response (R) and non-response (NR) groups to MTX, but the gene composition in the microbial communities significantly differed between these two groups. Particularly, several models were built based on clinical information, as well as data on the gene, taxonomical, and functional biomarkers by using the random forest algorithm and then validated. Our findings provide bases for clinical management not only of RA but also other gut microbiome-related diseases. First, it suggests that the potential metabolic capability of gut microbiota on drug metabolism is important because they affect drug efficiency; as such, clinical treatment strategies should incorporate the gene compositions of gut microbial communities, in particular genes involved in drug metabolism. Second, a suitable model can be developed to determine hosts' responses to drugs before clinical treatment.
ABSTRACT
Faecalibacterium prausnitzii is a beneficial human gut microbe and a candidate for next-generation probiotics. With probiotics now being used in clinical treatments, concerns about their safety and side effects need to be considered. Therefore, it is essential to obtain a comprehensive understanding of the genetic diversity, functional characteristics, and potential risks of different F. prausnitzii strains. In this study, we collected the genetic information of 84 F . prausnitzii strains to conduct a pan-genome analysis with multiple perspectives. Based on single-copy genes and the sequences of 16S rRNA and the compositions of the pan-genome, different phylogenetic analyses of F. prausnitzii strains were performed, which showed the genetic diversity among them. Among the proteins of the pan-genome, we found that the accessory clusters made a greater contribution to the primary genetic functions of F. prausnitzii strains than the core and specific clusters. The functional annotations of F. prausnitzii showed that only a very small number of proteins were related to human diseases and there were no secondary metabolic gene clusters encoding harmful products. At the same time, complete fatty acid metabolism was detected in F. prausnitzii. In addition, we detected harmful elements, including antibiotic resistance genes, virulence factors, and pathogenic genes, and proposed the probiotic potential risk index (PPRI) and probiotic potential risk score (PPRS) to classify these 84 strains into low-, medium-, and high-risk groups. Finally, 15 strains were identified as low-risk strains and prioritized for clinical application. Undoubtedly, our results provide a comprehensive understanding and insight into F. prausnitzii, and PPRI and PPRS can be applied to evaluate the potential risks of probiotics in general and to guide the application of probiotics in clinical application.
