Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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., (© 2024 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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.