Dataset Information

Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling.

ABSTRACT: Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease characterized by an imbalance between uric acid production and excretion. To mechanistically investigate altered microbiota metabolism associated with gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic models. Patient-specific community models constructed with the metagenomics modeling pipeline, mgPipe, were used to perform k-means clustering of samples according to their metabolic capabilities. The clustering analysis generated statistically significant partitioning of samples into a Bacteroides-dominated, high gout cluster and a Faecalibacterium-elevated, low gout cluster. The high gout cluster was 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 allowed higher production of butyrate, the sulfur-containing amino acids L-cysteine and L-methionine, and the L-cysteine catabolic product H₂S. By expanding the capabilities of mgPipe to provide taxa-level resolution of metabolite exchange rates, acetate, D-lactate and succinate exchanged from Bacteroides to Faecalibacterium were predicted to enhance butyrate production in the low gout cluster. Model predictions suggested that sulfur-containing amino acid metabolism generally and H₂S more specifically could be novel gout disease markers.

SUBMITTER: Henson MA

PROVIDER: S-EPMC8257998 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Acute gouty arthritis (AGA) is the most common first symptom of gout, and the development of gout as a metabolic and immune inflammatory disease is also correlated with the gut microbiota. However, the mechanism of the effect of changes in the gut microbiota on AGA remains unclear. The intestinal flora can not only affect purine metabolism or regulate inflammation, but also influence the therapeutic effect of drugs on AGA. The aim of this study was to investigate the exact mechanism of modified Baihu decoction (MBD) in the treatment of AGA and whether it is related to the regulation of the structure of the intestinal flora. Methods: On the 21st day of MBD administration by continuous gavage, a rat acute gouty arthritis model was constructed using sodium urate (0.1 mL/rat, 50 mg/mL), and the ankle joint swelling was measured before and 4 h, 8 h, 24 h, and 48 h after the injection of sodium urate. After 48 h of sodium urate injection, serum, liver, kidney, ankle synovial tissue and feces were collected from rats. The collected samples were examined and analyzed using H&E, Elisa, Immunohistochemistry, Histopathology, 16S rDNA, and Biochemical analysis. To investigate the mechanism of MBD to alleviate AGA using pro-inflammatory factors and intestinal flora. Results: MBD (5.84, 35 g/kg) was administered orally to AGA rats and diclofenac sodium tablets (DS-tablets) were used as standard treatment control. Serum biochemical assessment confirmed that MBD is a safe drug for the treatment of AGA. In addition, our findings confirmed that MBD relieved AGA-related symptoms, such as toe swelling. Lowering serum levels of uric acid, IL-1β, and TGF-β1 immunohistochemical results also confirmed that MBD reduced the expression of inflammatory elements such as IL-1β, NLRP3, ASC, and Caspase-1 in synovial tissue.Furthermore, compared with control group, the 16s rDNA sequencing of AGA rat faeces revealed an increase in the relative abundance of Lachnospiraceae, Muribaculaceae, and Bifidobacteriaceae species. While the relative abundance of Lactobacillaceae, Erysipelotrichaceae, Ruminococcaceae, Prevotellaceae and Enterobacteriaceae showed a relative decrease in species abundance. Of these, the reduction in species abundance of Enterobacteriaceae was associated with a reduction in amino acid metabolism and environmental perception. After MBD therapeutic intervention, the disturbance of the intestinal flora caused by AGA was restored. Conclusion: In summary, MBD is an effective agent for the treatment of AGA, with the potential mechanism being the regulation of intestinal flora to control inflammation. This would help to promote the therapeutic effect of MBD on AGA.

Project description:Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn's disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micomIMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.

Dataset Information

Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets