The seamless integration of dietary plant-derived natural flavonoids and gut microbiota may ameliorate non-alcoholic fatty liver disease: a network pharmacology analysis

We comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.

New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study

We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.

Identification of Gut Microbiome Metabolites via Network Pharmacology Analysis in Treating Alcoholic Liver Disease

Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.