The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology.

Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Gut microbiome and metabolome signatures in liver cirrhosis-related complications.

Background/Aims: Shifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis. Methods: Microbiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to time-of-flight-mass spectrometry. Correlation analyses were performed targeting the gut- microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased). Results: Veillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC > 0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites. Conclusion: Cirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.

The synchronized feature of Saururus chinensis and gut microbiota against T2DM, NAFLD, obesity and hypertension via integrated pharmacology.

Type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease (NAFLD), obesity (OB) and hypertension (HT) are categorized as metabolic disorders (MDs), which develop independently without distinct borders. Herein, we examined the gut microbiota (GM) and Saururus chinensis (SC) to confirm their therapeutic effects via integrated pharmacology. The overlapping targets from the four diseases were determined to be key protein coding genes. The protein-protein interaction (PPI) networks, and the SC, GM, signalling pathway, target and metabolite (SGSTM) networks were analysed via RPackage. Additionally, molecular docking tests (MDTs) and density functional theory (DFT) analysis were conducted to determine the affinity and stability of the conformer(s). TNF was the main target in the PPI analysis, and equol derived from Lactobacillus paracasei JS1 was the most effective agent for the formation of the TNF complex. The SC agonism (PPAR signalling pathway), and antagonism (neurotrophin signalling pathway) by SC were identified as agonistic bioactives (aromadendrane, stigmasta-5,22-dien-3-ol, 3,6,6-trimethyl-3,4,5,7,8,9-hexahydro-1H-2-benzoxepine, 4α-5α-epoxycholestane and kinic acid), and antagonistic bioactives (STK734327 and piclamilast), respectively, via MDT. Finally, STK734327-MAPK1 was the most favourable conformer according to DFT. Overall, the seven bioactives from SC and equol that can be produced by Lactobacillus paracasei JS1 can exert synergistic effects on these four diseases.

A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis.

Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley - signalling pathways - targets - metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.

New insight of chemical constituents in Persea americana fruit against obesity via integrated pharmacology.

Persea americana fruit (PAF) is a favorable nutraceutical resource that comprises diverse unsaturated fatty acids (UFAs). UFAs are significant dietary supplementation, as they relieve metabolic disorders, including obesity (OB). In another aspect, this study was focused on the anti-OB efficacy of the non-fatty acids (NFAs) in PAF through network pharmacology (NP). Natural product activity & species source (NPASS), SwissADME, similarity ensemble approach (SEA), Swiss target prediction (STP), DisGeNET, and online Mendelian inheritance in man (OMIM) were utilized to gather significant molecules and its targets. The crucial targets were adopted to construct certain networks: protein-protein interaction (PPI), PAF-signaling pathways-targets-compounds (PSTC) networks, a bubble chart, molecular docking assay (MDA), and density function theory (DFT). Finally, the toxicities of the key compounds were validated by ADMETlab 2.0 platform. All 41 compounds in PAF conformed to Lipinski's rule, and the key 31 targets were identified between OB and PAF. On the bubble chart, PPAR signaling pathway had the highest rich factor, suggesting that the pathway might be an agonism for anti-OB. Conversely, estrogen signaling pathway had the lowest rich factor, indicating that the mechanism might be antagonism against OB. Likewise, the PSTC network represented that AKT1 had the greatest degree value. The MDA results showed that AKT1-gamma-tocopherol, PPARA-fucosterol, PPARD-stigmasterol, (PPARG)-fucosterol, (NR1H3)-campesterol, and ILK-alpha-tocopherol formed the most stable conformers. The DFT represented that the five molecules might be promising agents via multicomponent targeting. Overall, this study suggests that the NFAs in PAF might play important roles against OB.

Gut microbiota-derived indole compounds attenuate metabolic dysfunction-associated steatotic liver disease by improving fat metabolism and inflammation.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease, and its prevalence has increased worldwide in recent years. Additionally, there is a close relationship between MASLD and gut microbiota-derived metabolites. However, the mechanisms of MASLD and its metabolites are still unclear. We demonstrated decreased indole-3-propionic acid (IPA) and indole-3-acetic acid (IAA) in the feces of patients with hepatic steatosis compared to healthy controls. Here, IPA and IAA administration ameliorated hepatic steatosis and inflammation in an animal model of WD-induced MASLD by suppressing the NF-κB signaling pathway through a reduction in endotoxin levels and inactivation of macrophages. Bifidobacterium bifidum metabolizes tryptophan to produce IAA, and B. bifidum effectively prevents hepatic steatosis and inflammation through the production of IAA. Our study demonstrates that IPA and IAA derived from the gut microbiota have novel preventive or therapeutic potential for MASLD treatment.