Introduction: Mycobacterium abscessus (MAB) is severely resistant to available antibacterial agents. The current study aimed to find natural inhibitors against MAB to fight the resistant isolates.
Methodology: Ten lead compounds were selected against MAB VapC5 for Molecular Dynamics (MD) simulations for 200 ns. Root Mean Square Fluctuation (RMSF), Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), and Dynamic Cross Correlation Matrix (DCCM) of apo and VapC5-ligand complexes were analyzed.
Results: Among the ten lead compounds, eight compounds (deoxy artemisinin, glaucocalyxin A, (1R,4E,9E,11S)-4,12,12-trimethyl-8-oxobicyclo[9.1.0]dodeca-4,9-dien-2-yl acetate, isorhamnetin, Kissoone C, piperlongumine, tectorigenin, and wogonin) showed a good potential against MAB VapC5. The apo-VapC5 exhibits a stable RMSD of 0.154 nm and RMSF of 0.088 nm ± 0.14. At the same time, ligands including Deoxy Artemisinin, Ftaxilide, Glaucocalyxin-A, and others range in RMSF from 0.097 nm to 0.147 nm, with standard deviations varying between 0.12 and 0.22. The highest RMSF was observed with Kissoone C (0.147 nm ± 0.15), and the lowest with Tectorigenin (0.097 nm ± 0.12). The Apo-VapC5 exhibited an Rg of 3.064 nm, whereas in complexes with ligands, the Rg values ranged from 0.097 nm to 0.147 nm. The DCCM analysis of VapC5-ligand complexes also reveals a more pronounced negative correlated motion.
Discussion: The simulation outcomes indicate that ligand binding enhanced the structural stability of VapC5 compared to the apo form. Among the tested compounds, deoxy artemisinin, glaucocalyxin A, and tectorigenin showed the most stable interactions, highlighting their potential as promising VapC5 inhibitors.
Conclusion: The selected compounds exhibit good binding affinity and residue interaction patterns. The ligand binding influenced VapC5 flexibility and conformational changes observed in complexes with MABVapC5, which could be useful inhibitors after experimental validation.
Background: Qigesan (QGS) is a traditional Chinese herbal medicine used for the treatment of esophageal carcinoma (EC) and possesses anti-cancer properties. However, the mechanism of QGS in the treatment of EC remains unclear.
Objectives: This study aimed to investigate the molecular basis of QGS in the treatment of EC and establish a scientific foundation for its application.
Methods: This study employed a multifaceted approach-including network pharmacology, molecular docking, and molecular dynamics simulations-to investigate the therapeutic mechanisms of QGS in EC. By leveraging a comprehensive array of databases such as TCMSP, HERB, TTD, OMIM, GeneCards, and DrugBank, we systematically identified potential bioactive components and their corresponding targets related to QGS, as well as targets associated with EC.
Results: 271 overlapping targets of QGS and EC were obtained. Network pharmacology analysis identified eight hub targets (TP53, AKT1, IL6, STAT3, TNF, IL1B, EGFR, and CTNNB1) mediating the effects of QGS through dysregulated pathways, including PI3KAkt signaling, apoptosis regulation, AGE-RAGE, and IL-17 signaling. Molecular docking revealed that three QGS-derived compounds-peimisine, salvianolic acid J, and songbeinoneexhibited high binding affinities for multiple hub targets. These compounds concomitantly inhibit the MAPK/NF-κB pathways while activating cell cycle regulation, DNA repair, and apoptosis, suggesting a multi-target therapeutic mechanism against esophageal carcinoma.
Discussion: QGS, a TCM formulation, has been extensively applied in the clinical treatment of EC for a long time and has been demonstrated to relieve esophageal obstruction. Nevertheless, the exact active components within QGS and their underlying molecular mechanisms remain elusive. In this study, network pharmacology, molecular docking, and MD simulation were employed to investigate the potential molecular mechanisms by which QGS exerts its therapeutic effects in the treatment of EC.
Conclusion: These findings provide a comprehensive elucidation of the multi-component, multi-target therapeutic strategy employed by QGS in the treatment of EC, laying a solid theoretical foundation for subsequent pharmacological development and clinical validation.
Introduction: Hyperlipidemia is linked to multiple cardiovascular and cerebrovascular diseases. Traditional Chinese Medicine formulations show potential for managing this condition, but the underlying mechanisms remain unclear. This study investigates the therapeutic effects of the Fangji-Astragalus (FJ-HQ) on hyperlipidemia and explores its key components and molecular pathways.
Methods: Network pharmacology was applied to identify active ingredients in FJ-HQ and drugdisease co-targets. Transcriptomic analysis and HPLC-MS/MS were integrated to screen core components and associated targets. In vivo and in vitro experiments evaluated the effects of FJHQ in hyperlipidemic rat models and cell models.
Results: A total of 23 active ingredients and 109 drug.disease co-targets were identified, with enrichment in inflammatory and signaling pathways, notably the PI3K/AKT/mTOR and p53 pathways. Transcriptomic profiling revealed seven differentially expressed targets. Integrated chemical and serum analysis identified calycosin as the core component and highlighted CAMTA2 and RXRA as downstream targets. In hyperlipidemic rats, FJ-HQ lowered total cholesterol, triglycerides, and low-density lipoprotein cholesterol, and increased high-density lipoprotein cholesterol and apolipoprotein A1. FJ-HQ also modulated the expression of P53, AKT1, and IL6, as well as mRNA levels within the PI3K/AKT/mTOR pathway. In cell models, serum containing FJ-HQ inhibited lipid droplet formation.
Discussion: These findings demonstrate that FJ-HQ alleviates hyperlipidemia by modulating the PI3K/AKT/mTOR and p53 pathways, reducing lipid levels, and suppressing lipid droplet formation, with calycosin as a pivotal active component.
Conclusion: In summary, our study confirms the therapeutic effects of FJ-HQ on hyperlipidemia and identifies calycosin as a crucial component. Furthermore, we have experimentally validated the influence of FJ-HQ on the PI3K/AKT/mTOR signaling pathway. These findings highlight the potential of FJ-HQ as an effective lipid-lowering agent and provide preclinical evidence for future treatments of hyperlipidemia.
Introduction: There has been increasing interest in neuroimaging studies in recent years, and computer-aided approaches have gained prominence in improving diagnostic accuracy. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Traditional diagnostic approaches often rely on subjective assessments, highlighting the need for more objective, datadriven methods. This study aims to classify ADHD subtypes and assess medication effects by converting resting-state fMRI images into one-dimensional (1D) signals and extracting statistical features using Variational Mode Decomposition (VMD).
Methods: Resting-state fMRI data from the ADHD-200 dataset, including 41 healthy controls (HC), 41 medicated ADHD-Combined (ADHD-C) individuals, and 41 non-medicated ADHD-C individuals, were analyzed. The 1D fMRI signals were decomposed into nine sub-bands using VMD. Statistical features were extracted from each sub-band and classified using Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN).
Results: VMD-derived features substantially improved classification performance. The highest binary classification accuracy was achieved by LDA: 96.34% distinguishing non-medicated ADHD from controls and 88.41% for medicated ADHD versus controls. The classification between medicated and non-medicated ADHD yielded 79.63% accuracy. Ternary classification across all groups reached 69.51% accuracy.
Discussion: These findings show that the VMD-based approach improves the classification of ADHD subtypes and helps evaluate medication effects. However, the lower performance in multi-class classification reflects the complexity of ADHD neuroimaging data.
Conclusion: The VMD-based approach improves classification accuracy, especially in distinguishing ADHD subtypes and medication effects, supporting its potential as an objective tool for diagnosis and treatment planning.
Introduction: Paris Polyphylla var. Yunnanensis (PY) is an anti-liver cancer TCM used in clinical practice, but its core components and anti-liver cancer mechanism remain unclear. This study combines animal experiments, network pharmacology, molecular docking, and cell verification to explore the core components and mechanisms of PY in combating liver cancer.
Methods: The blood-entry components of PY were obtained through UPLC-QE-MS. Subsequently, network pharmacology was employed to predict the core components of anti-liver cancer and their potential targets. Molecular docking was then used to verify binding between the core components and the targets. Finally, by calculating the inhibitory rate and IC50 value of the core ingredient on HepG2 cells, the anti-liver cancer activity of the core ingredient was evaluated.
Results: A total of 103 compounds were identified in the drug-containing serum of rats. Seven ingredients were obtained after screening. The components, targets, and pathways of PY's antiliver cancer effect were predicted. 20-Hydroxyecdysone, parisyunnanoside B, paris saponin II, and dichotomin are considered the core components of PY's anti-liver cancer activity. The in vitro activity assay of the core components demonstrated that paris saponin II exhibited a high inhibitory effect on HepG2 cell proliferation in a concentration-dependent manner.
Discussion: This study reveals PY's anti-hepatocellular carcinoma mechanisms, informing clinical applications and future research on its constituents.
Conclusion: This study initially demonstrated that PY exerts therapeutic effects on liver cancer through multiple components, targets, and mechanisms, and elucidated its pharmacological basis.
Background: Hypertension, a major risk factor for cardiovascular disease morbidity and mortality, remains poorly controlled in many patients despite available treatments. There are many patients with poorly managed blood pressure despite the availability of treatments. We employed Mendelian Randomization (MR) and colocalization analyses of plasma proteins and hypertension to identify genetically supported drug targets.
Methods: We investigated genetic associations between plasma protein quantitative trait loci (pQTLs) and hypertension GWAS data from FinnGen using two-sample MR, enrichment analysis, and Protein-Protein Interaction (PPI) analysis. Colocalization verified shared causal variants between identified proteins and hypertension. Drug prediction and molecular docking were used to assess therapeutic potential.
Results: In the MR analysis, 12 plasma proteins were found to be associated with hypertension, three of which (ACE, AGT, and NPPA) were supported by colocalization. Among these, ACE and AGT are established drug targets, whereas NPPA remains relatively underexplored. Drug prediction and molecular docking results indicated that several candidate drugs exhibited highly stable interactions and strong binding affinities with the screened proteins.
Discussion: Our findings confirm the centrality of the renin-angiotensin system (ACE, AGT) and highlight NPPA as a novel, genetically supported protective target. While the study benefits from robust MR and colocalization methods, the focus on European ancestry warrants validation in diverse populations. Experimental and clinical studies are needed to translate these targets into therapies.
Conclusion: This proteome-wide MR analysis demonstrates a causal relationship between genetically determined levels of ACE, AGT, and NPPA and hypertension. These proteins represent promising targets for the development of novel hypertension therapeutics.
Introduction: Breast cancer is a leading cause of cancer-related mortality in women. Although the traditional Chinese medicine Codonopsis Pilosula (CP) is empirically used in its treatment, the underlying mechanisms of action remain elusive. This study aimed to apply a novel integrative network pharmacology and machine learning approach to identify bioactive compounds in CP and elucidate their anti-breast cancer mechanisms.
Methods: The analysis utilized a comprehensive and innovative workflow that combined network pharmacology, machine learning-based target prediction, bioinformatics analyses, and molecular docking and molecular dynamics simulations. Publicly available datasets were mined for CP constituents and putative targets, and integrated with breast cancer-associated gene profiles. Key compound-target interactions were prioritized via machine learning algorithms.
Results: Machine learning highlighted EGFR and PTGS2 as primary targets. Molecular docking and dynamics demonstrated stable binding of Taraxerol and Stigmasterol to these proteins, with EGFR-Taraxerol, EGFR-Spinasterol, PTGS2-Stigmasterol, and PTGS2-Taraxerol complexes exhibiting robust affinity and stability.
Discussion: The findings are significant as they reveal previously unreported interactions between CP's bioactive compounds and critical breast cancer targets. This provides a molecularlevel explanation for the traditional use of CP, bridging the gap between TCM and modern pharmacology. These results offer a solid foundation for further experimental validation.
Conclusion: This multidisciplinary, predictive strategy successfully identified key bioactive compounds in CP and their molecular targets in breast cancer. The study provides crucial mechanistic evidence for CP's therapeutic potential and highlights the power of this integrated approach for drug discovery from TCM (Traditional Chinese Medicine).
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