Network pharmacology‐based exploration of gut microbiota‐derived metabolites for type‐2 diabetes

JOURNAL/agad/04.03/02274129-990000000-00014/figure1/v/2025-07-29T163611Z/r/image-tiff The relationship between gut microbiota and osteoporosis has garnered significant attention in recent years. However, comprehensive evidence supporting this relationship remains elusive. This study included a total of 211 species of gut microbiota to explore associations with osteoporosis and bone mineral density at various ages. A two-sample Mendelian randomization (MR) study was conducted to assess the associations between gut microbiota and osteoporosis. Data for 211 gut microbiota and osteoporosis cases were sourced from Genome-Wide Association Studies (GWAS). The inverse-variance weighted method with fixed effects, a commonly used Mendelian randomization approach, served as the primary verification method and was applied in the two-sample Mendelian randomization analysis. We employed mean pleiotropy of genetic variation and sensitivity analyses, with Mendelian randomization-Egger tests and the leave-one-out method as core evaluation techniques. Our study showed that 33 species of the gut microbiota were associated with the development of osteoporosis; among them, 6 species of the gut microbiota were directly related to the occurrence of osteoporosis. Additionally, over 33 species of gut microbiota were found to be related to bone mineral density. Our findings revealed that 33 species of the gut microbiota had varying degrees of impact on our research objectives, among which 6 species of the gut microbiota were directly related to the occurrence of osteoporosis. Most of these microbiota may influence bone health through mechanisms such as lipid metabolism, calcium level regulation, and mental state adjustment. Therefore, we believe that the gut microbiota is closely related to osteoporosis.

Background Although the combination of Zingiberis rhizoma (ZR) and Coptidis rhizoma (CR) is a classic traditional Chinese medicine-based herbal pair used for its antitumor effect, the material basis and underlying mechanisms are unclear. Here, a network pharmacology approach was used to elucidate the antitumor mechanisms of ZR-CR. Materials and Methods To predict the targets of ZR-CR in treating tumors, we constructed protein–protein interactions and hub component-target networks and performed pathway and process enrichment and molecular docking analysis. We used a surface plasmon resonance (SPR) assay to validate the predicted component-target affinities. Hub gene expression and survival analysis in patients with tumors were used to predict the clinical significance. Results The active components of ZR-CR—shogaol, daucosterol, ginkgetin, berberine, quercetin, chlorogenic acid, and vanillic acid—exhibited antitumor activities via the MAPK, PI3K-AKT, TNF, FOXO, HIF-1, and VEGF signaling pathways. Molecular docking and SPR analyses suggested direct binding of berberine with AKT1 and TP53; quercetin with EGFR and VEGF165; and ginkgetin, isoginkgetin, and daucosterol with VEGF165 with weak affinities. Gene expression levels of the hub targets of ZR-CR were associated with overall survival and disease-free survival in patients with various tumor types. Conclusions The antitumor components of the ZR-CR herbal pair and the mechanisms underlying their antitumor effects were identified. These antitumor components deserve to be explored further in experimental and clinical studies.

Therapeutic mechanism of Convolvulus pluricaulis against Alzheimer's disease: Network pharmacology and molecular dynamics approach

Type 2 diabetes (T2D), typically characterized by insulin resistance, is a metabolic disorder that occurs when the body cannot use insulin effectively or does not produce enough insulin. In the treatment of T2D, insulin, metformin, and sulfonylureas are commonly used. Given the limitations of current treatment options, there is a strong need for intensive efforts in the discovery of new drugs. Berberine exhibits antidiabetic effects and possesses anti-inflammatory and antioxidant properties. Resveratrol is another natural compound that has been extensively researched due to its antioxidant and anti-inflammatory characteristics. This study aimed to investigate the interactions between berberine and resveratrol with proteins related to or causing T2D, including ADIPOR1 (PDB-ID: 6ks1), ADIPOR2 (PDB-ID: 5lxg), TNF-α (PDB-ID: 7kpb), PTP1B (PDB-ID: 4i8n), GLUT1 (PDB-ID: 4pyp), IGF-IR (PDB-ID: 8eyr), IGF1 (PDB-ID: 6pyh), ADAMTS9 (PDB-ID: 3ppv), and SPHK2 (PDB ID: 4v24). SwissADME was used to assess the pharmacokinetic properties of berberine and resveratrol. Molecular docking was performed to analyze the interactions between these ligands and the specified proteins. Additionally, the potential bioactivity features of compounds were determined. Protein-protein interactions were obtained from the STRING database. The study data indicated that both compounds have high blood-brain barrier (BBB) penetration and gastrointestinal absorption ability (HIA). Besides, berberine exhibited the highest binding affinity with GLUT4 (-10.1 Kcal/mol), GLUT1 (-9.3 Kcal/mol), and SPHK2 (-9.3 Kcal/mol), while resveratrol showed strong binding with SPHK2 (-9.0 Kcal/mol) and TNF-α (-8.7 Kcal/mol) and. All proteins displayed binding energies of more than -7 Kcal/mol, suggesting that both berberine and resveratrol hold promise as potential drug candidates for T2D.

It has been proposed that mitochondrial dysfunction is involved in the pathogenesis of type 2 diabetes (T2D). To dissect the underlying mechanisms, we performed a multiplexed proteomics study on liver mitochondria isolated from a spontaneous diabetic rat model before/after they were rendered diabetic. Altogether, we identified 1091 mitochondrial proteins, 228 phosphoproteins, and 355 hydroxyproteins. Mitochondrial proteins were found to undergo expression changes in a highly correlated fashion during T2D development. For example, proteins involved in beta-oxidation, the tricarboxylic acid cycle, oxidative phosphorylation, and other bioenergetic processes were coordinately up-regulated, indicating that liver cells confronted T2D by increasing energy expenditure and activating pathways that rid themselves of the constitutively increased flux of glucose and lipid. Notably, activation of oxidative phosphorylation was immediately related to the overproduction of reactive oxygen species, which caused oxidative stress within the cells. Increased oxidative stress was also evidenced by our post-translational modification profiles such that mitochondrial proteins were more heavily hydroxylated during T2D development. Moreover, we observed a distinct depression of antiapoptosis and antioxidative stress proteins that might reflect a higher apoptotic index under the diabetic stage. We suggest that such changes in systematic metabolism were causally linked to the development of T2D. Comparing proteomics data against microarray data, we demonstrated that many T2D-related alterations were unidentifiable by either proteomics or genomics approaches alone, underscoring the importance of integrating different approaches. Our compendium could help to unveil pathogenic events in mitochondria leading to T2D and be useful for the discovery of diagnosis biomarker and therapeutic targets of T2D.

Background To examine the potential therapeutic targets of Chinese medicine formula San-Miao-San (SMS) in the treatment of osteoarthritis (OA), we analyzed the active compounds of SMS and key targets of OA and investigated the interacting pathways using network pharmacological approaches and molecular docking analysis. Methods The active compounds of SMS and OA-related targets were searched and screened by TCMSP, DrugBank, Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. Venn analysis and PPI were performed for evaluating the interaction of the targets. The topological analysis and molecular docking were used to confirm the subnetworks and binding affinity between active compounds and key targets, respectively. The GO and KEGG functional enrichment analysis for all targets of each subnetwork were conducted. Results A total of 57 active compounds and 203 targets of SMS were identified by the TCMSP and DrugBank database, while 1791 OA-related targets were collected from the Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. By Venn analysis, 108 intersection targets between SMS targets and OA targets were obtained. Most of these intersecting targets involve quercetin, kaempferol, and wogonin. Moreover, intersecting targets identified by PPI analysis were introduced into Cytoscape plug-in CytoNCA for topological analysis. Hence, nine key targets of SMS for OA treatment were obtained. Furthermore, the potential binding conformations between active compounds and key targets were found through molecular docking analysis. According to the DAVID enrichment analysis, the main biological processes of SMS in the treatment of OA include oxidative stress, response to reactive oxygen species, and apoptotic signaling pathways. Finally, we found wogonin, the key compound in SMS, might play a pivotal role on Toll-like receptor, IL-17, TNF, osteoclast differentiation, and apoptosis signaling pathways through interacting with four key targets. Conclusions Therefore, this study elucidated the potential active compounds and key targets of SMS in the treatment of OA based on network pharmacology.

Absence of Cholic Acid 7α-Dehydroxylase Activity in the Strains of Lactobacillus and Bifidobacterium

Investigation of the effect and mechanism of Fei Re Pu Qing powder in treating acute lung injury (ALI) by modulating macrophage polarization via serum pharmacology and network pharmacology.

Network pharmacology and molecular docking approaches to elucidate the potential compounds and targets of Saeng-Ji-Hwang-Ko for treatment of type 2 diabetes mellitus

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic β- cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic β-cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2 and SRC) QQPPI networks. Thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol-ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.

The end or the start of understanding the genetics of type 2 diabetes.

Targeting the Gut Microbiota in Coronavirus Disease 2019: Hype or Hope?

Objective Estrogen is a critical hormone that is mainly produced by the ovary in females. Estrogen deficiency leads to various syndromes and diseases, partly due to gut microbiota alterations. Previous studies have shown that estrogen deficiency affects the gut microbiota at 6–8 weeks after ovariectomy, but the immediate effect of estrogen deficiency on the gut microbiota remains poorly understood. Methods To investigate the short time and dynamic effects of decreased estrogen levels on the gut microbiota and their potential impact on estrogen deficiency-related diseases, we performed metagenomic sequencing of 260 fecal samples from 50 ovariectomy (OVX) and 15 control C57BL/6 female mice at four time points after surgery. Results We found that seven gut microbiota species, including E. coli, Parabacteroides unclassified, Lachnospiraceae bacterium 8_1_57FAA, Bacteroides uniformis, Veillonella unclassified, Bacteroides xylanisolvens, and Firmicutes bacterium M10_2, were abundant in OVX mice. The abundance of these species increased with time after OVX surgery. The relative abundance of the opportunistic pathogen E. coli and the Crohn's disease-related Veillonella spp. was significantly correlated with mouse weight gain in the OVX group. Butyrate production and the Entner–Doudoroff pathway were significantly enriched in the control mouse group, while the degradation of glutamic acid and aspartic acid was enriched in the OVX mouse group. As the time after OVX surgery increased, the bacterial species and metabolic pathways significantly changed and tended to suggest an inflammatory environment, indicating a subhealthy state of the gut microbiota in the OVX mouse group. Conclusions Taken together, our results show that the dynamic gut microbiota profile alteration caused by estrogen deficiency is related to obesity and inflammation, which may lead to immune and metabolic disorders. This study provides new clues for the treatment of estrogen deficiency-related diseases.

Analysis of intestinal flora of a patient with congenital absence of the portal vein.

Analysis of intestinal flora of a patient with congenital absence of the portal vein