Plain language summary
The prevalence of obesity and associated comorbidities, such as diabetes, heart attack, hypertension, and cancer, is increasing worldwide. Microbes in the gut may play a significant role in the management of obesity by fermenting dietary fibres and producing metabolites such as short-chain fatty acids and flavonoids. In this meta-analysis, data were retrieved about gut microbial metabolites from the gutMGene database to evaluate the beneficial effects of prebiotics, probiotics, and postbiotics on key targets of obesity. Tryptophan was converted into beneficial metabolites such as indole by Escherichia coli, and isoflavones were converted into equol by Lactobacillus paracasei JS1. A positive effect may be exerted by these metabolites on the treatment of obesity. According to this meta-analysis, equol can reduce the levels of Interleukin-6, one of the inflammatory cytokines associated with obesity. Prebiotic isoflavone is fermented by probiotic Lactobacillus paracasei JS1 to produce equol, a postbiotic that inhibits the action of interleukin-6 and exerts a beneficial effect on obesity. In addition to understanding the relationship between prebiotics, probiotics, and postbiotics, healthcare professionals can use the results of this study to modulate the pathophysiology of obesity. It is necessary to conduct further rigorous research in order to evaluate the pharmacological value of the elements.
Abstract
The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota-substrate-metabolite-target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein-protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via Lactobacillus paracasei JS1 (probiotics) can bind the most stably on IL6 (target) compared with the other four metabolites (3-indolepropionic acid, trimethylamine oxide, butyrate, and acetate). In this study, we demonstrated that the promising substate (prebiotics), microbe (probiotics), metabolite (postbiotics), and target are suitable for obsesity treatment, providing a microbiome basis for further research.
Methodological quality
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