PubChem in 2021: new data content and improved web interfaces

Exploring the osteogenic effects of simiao wan through activation of the PI3K/AKT pathway in osteoblasts

ETHNOPHARMACOLOGICAL RELEVANCE

Osteoporosis (OP) is a degenerative bone disease commonly associated with reduced bone density and increased fracture risk.

AIM OF THE STUDY

This study aimed to validate the therapeutic effects of Simiao wan (SMW) on OP and explore the underlying mechanism, particularly focusing on the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway.

MATERIALS AND METHODS

The chemical components of SMW were identified using UPLC-Q-TOF-MS/MS. The obtained compounds were then input into the TCMSP, TargetNet, and SwissTargetPrediction databases to predict potential targets. OP-related targets were collected from the GeneCards and DisGeNET databases, and intersecting targets were identified through a Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the intersecting targets using the Database for Annotation, Visualization and Integrated Discovery (DAVID). SMW extract was subsequently used to treat osteoblasts in vitro, and its toxicity on osteoblasts was assessed using Cell Counting Kit-8 (CCK-8) and lactate dehydrogenase (LDH) release assays. Osteoblast differentiation and activity were further evaluated using alizarin red staining, alkaline phosphatase staining, and Western blot analyses to validate the activation of network pharmacological signaling pathways.

RESULTS

A total of 121 potential targets were identified for SMW in the treatment of OP, with AKT1 as the primary target. The PI3K/AKT pathway emerged as a key signaling pathway potentially involved in SMW's therapeutic effects o OP. Toxicity assessments showed no significant toxicity of SMW on osteoblasts. Additionally, SMW promoted osteoblast proliferation, alkaline phosphatase activity, calcium nodule deposition, and the expression of osteogenic markers (osteocalcin (OCN), runt-related transcription factor 2 (RunX2), and collagen I), and activated the PI3K/AKT signaling pathway. The PI3K/AKT pathway inhibitor LY294002 partially reversed the SMW-induced mineral deposition and expression of OCN, RunX2, and collagen I.

CONCLUSION

SMW demonstrated effective multi-target and multi-pathway therapeutic potential in the treatment of OP, with a significant impact on the PI3K/AKT signaling pathway.

Network pharmacology, molecular docking and biological verification to explore the potential anti-prostate cancer mechanisms of Tripterygium wilfordii Hook. F

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii Hook. f. (TW) is extensively utilized in clinical practice for its effective anti-inflammatory and anti-cancer properties.

AIM OF THE STUDY

This study aims to elucidate the processes of TW in combating prostate cancer through a comprehensive strategy that integrates network pharmacology, molecular docking and molecular biology validation.

MATERIALS AND METHODS

A drug-target network and protein-protein interaction network were constructed established to predict the potential targets of TW for prostate cancer treatment. The interaction between active components and targets was confirmed using molecular docking. Moreover, prostate cancer cells were used to examine the anti-tumor effects of active ingredients in vitro. The xenograft animal model was constructed to evaluate the anti-tumor effect of triptonoterpene in vivo.

RESULTS

Twenty-nine active components interact with 226 corresponding targets, and 112 disease targets specifically related with prostate cancer were identified. The primary targets (AKT1, TP53, RELA) were chosen, and kaempferol, triptolide, and triptonoterpene exhibited probable binding affinity with these targets, respectively. Triptonoterpene was subsequently confirmed to inhibit the growth of prostate cancer cells and induce apoptosis in vitro and in vivo.

CONCLUSION

Overall, this study demonstrated that TW may serve as a viable therapeutic agent for prostate cancer. Triptonoterpene is a specific inhibitor of p-AKT1 and p65, making it an attractive contender for prostate cancer therapy.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Five ML algorithms including Bayesian, Learning Vector Quantization (LVQ), Wrapper (Boruta), Random Forest (RF), and Logistic Regression were employed to select immune-related genes associated with DR (DR.Sig). Seven ML algorithms including Naive Bayes (NB), RF, Support Vector Machine (SVM), AdaBoost Classification Trees (AdaBoost), Boosted Logistic Regressions (LogitBoost), K-Nearest Neighbors (KNN), and Cancerclass were utilized to construct a predictive model for DR. The relationship between DR.Sig genes and immune cells was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, drug sensitivity prediction of DR.Sig genes and molecular docking were performed. Through the utilization of 5 ML algorithms, 6 immune-related biomarkers closely related to the occurrence of DR were identified, including FCGR2B, CSRP1, EDNRA, SDC2, TEK, and CIITA. The DR predictive model constructed based on these 6 DR.Sig genes using the Cancerclass algorithm demonstrated superior predictive performance compared to 4 previously published DR-related biomarkers. In vivo and in vitro experiments also provided strong validation of the expression of the 6 genes in DR. Positive correlations were observed between these genes and 22 types of immune cells. Molecular docking results revealed that CSRP1, EDNRA, and TEK exhibited the highest affinities with the small molecule compounds etoposide, FR-139317, and camptothecin, respectively. The models constructed based on various ML algorithms can effectively predict the occurrence of DR events and hold potential for targeted drug therapies, providing a basis for the early diagnosis and targeted treatment of DR.

DPP4 promotes an immunoenhancing tumor microenvironment through exhausted CD8+ T cells with activating IL13-IL13RA2 axis in papillary thyroid cancer

BACKGROUND

Papillary thyroid cancer (PTC) is among the most prevalent forms of endocrine malignancy with a rapid rise in incidence rates worldwide; however, the composition and characteristics of its immune microenvironment is poorly understand. Here, this work investigated the precise function of Dipeptidyl peptidase 4 (DPP4) in tumor-infiltrated T cells within PTC by investigating its role in cytokine-mediated signaling pathways.

METHODS

TCGA and GEO data as well as human PTC specimens confirmed the expression of DPP4 in PTC. The CIBERSORT and TIMER tool were used to analyze the distribution of tumor-infiltrating immune cells in PTC. CD8+ T cells from PTC patient's peripheral blood were cultured and used in a three-dimensional model for direct co-culture with PTC tumors to investigate DPP4 function.

RESULTS

Bioinformatic analyses has uncovered a significant upregulation of DPP4, which enhances the survival and migration of PTC cells in vitro. DPP4 upregulation significantly correlated with advanced grades, stages, and poor progression-free survival. DPP4 influences immune function and the exhaustion of CD8+ T cells through the IL13-IL13RA2 axis. The inhibition of DPP4 reduces CD8+ T cell exhaustion and IL13 secretion, while also blocking the IL13-IL13RA2 axis, thereby promoting the mesenchymal-to-epithelial transition of PTC cells.

CONCLUSION

Blocking DPP4 leads to the conversion of exhausted CD8+ T cells with decreased IL13 level, resulting in downregulation of IL13RA2 to promote mesenchymal-to-epithelial transition of PTC cells. This highlights DPP4 as a potential therapeutic target, particularly between CD8+ T cells and PTC cells via IL13-IL13RA2 axis, and represents a novel avenue for combined immunotherapy in PTC.

Elucidating the mechanism of action of astragalus polysaccharide on ionizing radiation-induced myocardial damage based on network pharmacology and experimental research

Due to the unavoidable impact of ionizing radiation on the heart located near the mediastinum, varying degrees of myocardial damage may occur. As a result, the clinical application of radiotherapy in cancer treatment is significantly limited. However, the molecular mechanisms underlying radiation-induced heart disease (RIHD) are not yet fully understood, and there is a lack of disease-specific treatment strategies. Astragalus polysaccharide (APS), is an active compound abundant in the traditional Chinese herb Astragalus membranaceus (Fisch.) Bunge (AS), has been shown to have cardioprotective effects against various cardiovascular diseases. Thus, this study aims to investigate the potential cardioprotective effect of APS on RIHD and its underlying molecular mechanisms. The network pharmacology results indicated that 9 core genes were identified from the biological network of the effective components of AS acting on RIHD. The results of GO enrichment analysis showed that these hub genes were mainly involved in biological processes such as cell apoptosis, cell proliferation, inflammatory response, and response to external stimuli. The results of KEGG enrichment analysis showed that these hub genes mainly regulated the occurrence of RIHD through pathways such as the EGFR signaling pathway, PI3K/Akt signaling pathway, IL-17 signaling pathway, and so on. In molecular docking analysis, we found that AKT1 and mTOR had good and stable binding abilities with the three types of glucosides rich in AS. The results of in vitro and in vivo experiments all showed that APS could not only improve cardiac dysfunction, myocardial injury, inflammatory response, and myocardial fibrosis in RIHD rats, but also alleviated apoptosis and atrophy of H9C2 cells under ionizing radiation stimulation. In addition, we also found that APS improved the accumulation of autophagic flux induced by ionizing radiation, which could be confirmed by the reversal of Beclin1, p62, LC3B proteins and accelerated degradation of accumulated autophagic vesicles. Rapamycin (Rap) was a classic autophagy flux inducer that could attenuate the improvement effect of APS on H9C2 cell apoptosis under ionizing radiation stimulation. Finally, we found that APS could reverse the inhibition of PI3K/Akt/mTOR signaling pathway activity by ionizing radiation in vitro, thereby improving ionizing radiation-induced autophagy flux accumulation, cardiomyocyte apoptosis, and atrophy. All in all, this study provides important evidence for understanding the molecular mechanisms of the cross-talk between autophagy and apoptosis, and provides new directions and insights for APS combined with autophagy regulators as a therapeutic strategy for RIHD.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology

BACKGROUND

Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

OBJECTIVE

This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP.

METHODS

OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells.

RESULTS

A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast.

CONCLUSION

CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on GraphTransformer

BACKGROUND

Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication, but drug research and development have the disadvantage of taking a long time and having a high cost.

OBJECTIVE

Therefore, we developed a graph neural network with the help of artificial intelligence to provide new ideas for drug research and development for OP.

METHODS

In this study, we built a new osteoporosis graph (called OPGraph) and proposed a deep graph neural network (called DeepTransformer) to predict new drugs for OP. OPGraph is a graph data model established by gathering features and their interrelationships from a vast amount of OP data. DeepTransformer uses GraphTransformer as its foundational network and applies residual connections for deep layering.

RESULTS

The analysis and results showed that DeepTransformer outperformed numerous models on OPGraph, with area under the curve (AUC) and area under the precision-recall curve (AUPR) reaching 0.9916 and 0.9911, respectively. In addition, we conducted an in vitro validation experiment on two of the seven predicted compounds (Puerarin and Aucubin), and the results corroborated the predictions of our model.

CONCLUSION

The model we developed with the help of artificial intelligence can effectively reduce the time and cost of OP drug development and reduce the heavy economic burden brought to patient's family by complications caused by osteoporosis.

Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.

Reshaping the substrate-binding pocket of acyl-ACP reductase to enhance the production of sustainable aviation fuel in Escherichia coli

To reduce carbon emissions and address environmental concerns, the aviation industry is exploring the use of sustainable aviation fuel (SAF) as an alternative to traditional fossil fuels. In this context, bio-alkane is considered a potentially high-value solution. The present study focuses on the enzymes acyl-acyl carrier protein [ACP] reductase (AAR) and aldehyde-deformylating oxygenase (ADO), which are crucial enzymes for alka(e)ne biosynthesis. By using protein engineering techniques, including semi-rational design and site-directed mutagenesis, we aimed to enhance the substrate specificity of AAR and improve alkane production efficiency. The co-expression of a modified AAR (Y26G/Q40M mutant) with wild-type ADO in Escherichia coli significantly increased alka(e)ne production from 28.92 mg/L to 167.30 mg/L, thus notably demonstrating a 36-fold increase in alkane yield. This research highlights the potential of protein engineering in optimizing SAF production, thereby contributing to the development of more sustainable and efficient SAF production methods and promoting greener air travel.

Multivariate analysis of organic contaminants in groundwater of an endorheic basin draining to a salt lagoon - Fuente de Piedra (Southern Spain)

Forthcoming EU environmental requirements on water resources quality are likely to include concentration limits of certain contaminants of emerging concern, such as pharmaceuticals and personal care products. However, understanding the occurrence of organic contaminants, including contaminants of emerging concern, in hydro(geo)logical media remains challenging. This study is based on a comprehensive screening of OCs in groundwater of the unique and complex Fuente de Piedra Lagoon endorheic basin system using hydrochemistry and isotopic tools. The basin includes interconnected aquifers of various types (detrital, carbonate and evaporitic). Groundwater recharges the hypersaline lagoon, which holds significant ecological value, but is heavily impacted by anthropogenic activities such as water exploitation for urban supply and irrigation, soil fertilization for agricultural activities and urban wastewater discharges. Out of 185 analyzed compounds, 32 were detected, including 6 personal care products, 10 antibiotics and 11 pharmaceuticals. Concentrations ranged from 0.1 ng/L to 974 ng/L. Notably, the pesticide aldrin was detected with a maximum concentration of 668 ng/L. Two main processes are suggested as potentially affecting the occurrence of different groups of organic contaminants: (i) re-concentration of the more mobile compounds due to irrigation return flows in the unconfined detrital aquifer and (ii) accumulation of certain contaminants in the deep, saline underground media within the evaporitic aquifer, which forms the geologic basement of the detrital and carbonate aquifers of the basin. This study highlights the difficulty in understanding the occurrence of organic contaminants in complex systems and underscores the challenge of meeting the forthcoming environmental requirements.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

The effects and mechanisms of Chai Shao Jie Yu Granules on chronic unpredictable mild stress (CUMS)-induced depressive rats based on network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Chai Shao Jie Yu Granules (CSJY) is a renowned and time-honored formula employed in clinical practice for the management of various conditions, notably depression. Depression, a prevalent psychiatric disorder, poses challenges with limited effective treatment options. Traditional herbal medicines have garnered increasing attention in the realm of combating depression, being perceived as safer alternatives to pharmacotherapy.

AIM OF THE STUDY

To explore the effects and mechanisms of CSJY in chronic unpredictable mild stress (CUMS)-induced depression.

MATERIALS AND METHODS

Rat models of CUMS-induced depression were established, and the rats were randomly allocated into six groups: Control, CUMS, CUMS + Paroxetine (PX), CUMS + CSJY-L, CUMS + CSJY-M, and CUMS + CSJY-H. Throughout the study, the rats' body weight was monitored. Depression-related behaviors were assessed using the sucrose preference test (SPT) and open field test (OFT). High-performance liquid chromatography-mass spectrometry (HPLC-MS) measured monoamine neurotransmitters in the rat cortex and hippocampus. We measured adrenocorticotropic hormone (ACTH), corticosterone (CORT), and corticotropin-release hormone (CRH) levels in rat serum. Additionally, network pharmacology was employed to predict relevant molecular targets and potential mechanisms, followed by in vivo validation. Western blot analysis was conducted to evaluate the protein levels of 5-hydroxytryptamine/serotonin receptor 1A (5-HT1A) and Glutamate (Glu)-related proteins, such as p-GluA1, GluA1, p-GluN1, GluN1, p-GluN2A and GluN2A in the hippocampus.

RESULTS

In behavioral assessments, CUMS rats exhibited depressive behaviors, which were ameliorated by CSJY or PX treatment. Moreover, CSJY or PX treatment increased serotonin (5-HT) levels. It reduced the kynurenine/tryptophan (KYN/TRP) and gamma-aminobutyric acid/glutamate (GABA/Glu) in the hippocampus and cortex, as well as reduced serum levels of ACTH, CORT and CRH. Furthermore, CSJY or PX administration enhanced the decreased expression of p-GluN1/GluN1 while upregulating 5-HT1A and p-GluA1/GluA1 levels in the CUMS group.

CONCLUSION

CSJY demonstrated the ability to alleviate depressive behaviors in CUMS-induced depression rats, potentially through the inhibition of the hypothalamic-pituitary-adrenal (HPA) axis, modulation of monoamine neurotransmitters, and glutamatergic neurons. These findings suggest that CSJY could serve as a promising treatment option for depression.

Docking‑based virtual screening of BRD4 (BD1) inhibitors: assessment of docking methods, scoring functions and in silico molecular properties

To enhance the accuracy of virtual screening for bromodomain-containing protein 4 (BRD4) inhibitors, two docking protocols and seven scoring functions were compared. A total of 73 crystal structures of BRD4 (BD1) complexes were selected for analysis. Firstly, docking was carried out using both the LibDock and CDOCKER methods. The CDOCKER protocol was shown to be more effective based on the root mean square deviation (RMSD) values (in Å) between the docking positions and the co-crystal structures, achieving a docking accuracy rate of 86.3%. Then, among the various scoring functions (LigScore1, LigScore2, PLP1, PLP2, PMF, PMF04 and Ludi3), PMF showed the highest correlation with inhibition constants (r2 = 0.614), while Ludi3 scored lowest (r2 = 0.266). Finally, using ligand descriptors from PubChem, a strong correlation (r2 > 0.5) with inhibition constants for heavy atom count was found. Based on these comprehensive evaluations, the PMF scoring function emerged as the best tool for docking-based virtual screening of potential BRD4 (BD1) inhibitors. And the correlation between molecular properties and BRD4 (BD1) ligands also provided information for future design strategies.

Systematic insight into the dual COX-2/5-LOX inhibitory mechanism of Duhuo Jisheng decoction for treatment of osteoarthritis based on in silico and bioassay

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) is frequently used to treat osteoarthritis (OA). Duhuo Jisheng decoction (DHJSD), a Chinese patent medicine, was commonly used Chinese herbal formula for the treatment of OA. In Western medicine, dual inhibition of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) enzyme has been proved to be a promising strategy to treat inflammatory diseases with reduced side effects.

AIM OF THE STUDY

To elucidate the dual action mechanism of DHJSD targeting COX-2 and 5-LOX against OA.

MATERIALS AND METHODS

DHJSD, containing 1495 compounds was screened using a virtual screening approach based on molecular docking. The inhibitory effect of hit compounds against COX-2 and 5-LOX was validated using enzyme-based assays. In vitro, rat chondrocytes were treated with IL-1β (10 ng/mL) for 24 h to induce OA model in vitro. The chondrocyte viability was evaluated using an CCK-8 assay. ELISA was used to detect inflammatory factors expression. Immunofluorescence was used to assess the expression level of collagen II and MMP-13. In addition, a rat cartilage explants culture model was established, and safranin O and HE staining analysis were carried to assess cartilage matrix degradation and cartilage damage, respectively. In vivo, carrageenan-induced paw edema assay was used to examine anti-inflammatory activity, and the gastric ulcerogenic effect was further detected. Finally, Molecular dynamics simulations and binding free energy analysis were carried to explore the binding mechanism.

RESULTS

13 compounds from DHJSD were identified as promising candidates by a virtual screening approach. Among these candidates, three hits 7,4'-dimethoxyisoflavone, genistein, and fraxetin displayed dual inhibition of COX-2 and 5-LOX. Further in vitro assay indicated that 7,4'-dimethoxyisoflavone, genistein, and fraxetin could inhibit PGE2, LTB4, TNF-α, IL-6, or NO production in IL-1β-induced chondrocytes. In addition, the three compounds reduced IL-1β-induced degradation of collagen II and expression of MMP-13 in rat chondrocytes. The results of anti-inflammatory activity of the three compounds in vivo showed that the highest anti-inflammatory activity with edema inhibition percentages of 50.00%, 56.00%, and 51.00% after 3 h, respectively. Moreover, it was found that 7,4'-dimethoxyisoflavone, genistein, and fraxetin have a superior gastric safety profile comparable to indomethacin. Finally, molecular dynamics simulations, binding free energy analysis, and detailed interaction mode demonstrated that 7,4'-dimethoxyisoflavone, genistein, and fraxetin interacted well with both COX-2 and 5-LOX.

CONCLUSIONS

7,4'-dimethoxyisoflavone, genistein, and fraxetin from DHJSD with excellent anti-inflammatory effects and no gastric ulceration effects, which helps to explain the dual action mechanism and potential material basis of DHJSD in treating OA and provide evidence to support DHJSD's clinical use.

Computational insights in repurposing a cardiovascular drug for Alzheimer's disease: the role of aromatic amino acids in stabilizing the drug through π-π stacking interaction

Alzheimer's disease (AD) is a neurological condition that worsens over time and causes linguistic difficulties, cognitive decline, and memory loss. Since AD is a complicated, multifaceted illness, it is critical to identify drugs to combat this degenerative condition. Histone deacetylase 2 (HDAC2) represents a promising epigenetic target for neurodegenerative diseases. So, for this study, we chose HDAC2 as the targeted protein. Repurposing drugs has many advantages, including reduced costs and high profits. There is a lower probability of malfunction because the unique drug candidate has previously completed numerous investigations. In this study, we have taken 58 clinically approved food and drug administration (FDA) drugs utilized in clinical trials for AD. Molecular docking was carried out for the 58 compounds. The telmisartan drug has the highest binding score of -9.4 kcal mol-1. The angiotensin II receptor blocker (ARB) telmisartan has demonstrated some promise in AD research as of the last update in January 2022. However, its exact significance in treating or preventing AD is still being studied. Molecular dynamics (MD) and molecular mechanics with generalized born and surface area solvation (MM-GBSA)/interaction entropy (IE) calculations were carried out to study the structural stability of the complexes. Umbrella sampling (US) techniques are a cutting-edge drug development method to understand more about the interactions between protein and ligand. π-π stacking interactions play a major role in helping the ligand to bind in the zinc bounding domain of the protein. From these analyses, we conclude that telmisartan, which is a cardiovascular drug, is more potent than the other drugs to treat AD. The anti-inflammatory, neuroprotective, and blood-brain barrier-crossing qualities of telmisartan make it a promising therapeutic agent for AD; however, more research, including larger clinical trials, is needed to determine the drug's precise role in treating AD.

Rabeprazole inhibits lung cancer progression by triggering NLRP3/CASP-1/caspase-dependent pyroptosis

BACKGROUND

Gastric acid-related diseases could be treated using proton pump inhibitors (PPIs), which have been found to have anti-tumor ability. Rabeprazole is a type of PPI whose effect and mechanism in lung cancer remained to be clarified.

METHODS

Lung cancer cells and lung cancer mice were treated with different concentrations of Rabeprazole and then cell proliferation was detected by CCK-8 and colony formation assays. Pyroptosis was assessed by morphological observation and Lactate dehydrogenase (LDH) release assays. Western blot, immunofluorescence and immunohistochemistry were adopted to detect the expressions of GSDMD and NLRP3. Reactive oxygen species (ROS) level, lysosomal damage and autophagic flux were measured by flow cytometry.

RESULTS

Rabeprazole suppressed lung cancer cell proliferation and lung tumor growth in mice in a concentration-dependent manner. Lung cancer cells treated with Rabeprazole showed typical pyroptosis morphology and significantly increased LDH release. Rabeprazole upregulated the expression of GSDMD, NLRP3, and cleaved-Caspase 1, but such an effect was partially blocked by Z-LLSD-FMK. In lung cancer cells treated with Rabeprazole and lung cancer mice injected with Rabeprazole, the expressions of GSDMD, NLRP3 and caspase-1 were promoted, ROS-stained cells were increased significantly, lysosomal damage was aggravated, and autophagic flux was noticeably reduced.

CONCLUSIONS

Rabeprazole activated NLRP3/caspase 1/GSDMD cascade by promoting ROS accumulation and lysosomal destruction, thereby inducing pyroptosis to fulfill its anti-tumor effect on lung cancer.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-based, graph-based, and combinations of these models, accommodating both single- and multimodal model architectures. For validation, we performed a performance evaluation on the MatBench benchmarking dataset of five properties, targeting five types of regression architectures that can be designed using Matini-Net. When applied to each of the five material property datasets, the best model performance for the various architectures exhibited R2 > 0.84. This highlights the usefulness and flexibility of Matini-Net for accelerating materials discovery. Specifically, this framework was developed for researchers with limited experience in deep learning to easily apply it to research through automated feature engineering, hyperparameter tuning, and network construction. Moreover, Matini-Net improves the model interpretability by performing an importance analysis of the selected features. We believe that by employing Matini-Net, machine and deep learning can be applied more easily and effectively in various types of materials research.

Analyzing the potential targets and mechanisms of chronic kidney disease induced by common synthetic Endocrine Disrupting Compounds (EDCs) in Chinese surface water environment using network toxicology and molecular docking techniques

Long-term exposure to NP and OP, as common synthetic endocrine-disrupting chemicals (EDCs) in surface water environments in China, is closely associated with the development of chronic kidney disease (CKD). However, their potential targets and toxicological mechanisms for inducing CKD remain unknown. This study utilizes network toxicology and molecular docking techniques to explore the potential toxic targets and molecular mechanisms of CKD induction by NP and OP. We identify 49 core targets of NP and OP action in CKD using the Comparative Toxicogenomics Database (CTD) and GeneCards databases. Using the STRING database and Cytoscape software, we identify five hub genes: MAPK3, TNF, BCL2, ESR1, and FOS. We construct a nomogram model based on the CKD dataset GSE66494, utilizing these five hub genes. Calibration and ROC curves demonstrate that the model has good diagnostic value for CKD, and the DCA curve indicates that the model has high clinical utility. Single-gene GSEA enrichment analysis identifies five hub genes that influence the development of CKD through multiple biological pathways, revealing that several immune-regulatory signaling pathways are activated. The CIBERSORT algorithm identifies eight types of immune cell infiltration levels that change significantly during CKD development, and correlation analyses suggest that the five hub genes are strongly associated with multiple immune cell infiltrations. The molecular docking results suggested that ESR1, MAPK3, and TNF had the lowest binding energies and high binding affinities with NP and OP. The results of molecular dynamics simulations similarly confirmed the stability of the interactions between ESR1, MAPK3 and TNF proteins with NP and OP. The results of this study provide a theoretical basis for understanding the potential toxicity targets and mechanisms of NP- and OP-induced CKD and promote the application of network toxicology and molecular docking techniques in the study of environmental pollutants.

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking and machine learning strategies

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (ATBC), may adversely affect thyroid health, yet the precise mechanisms remain poorly understood. The objective of this study was to elucidate the complex effects of acetyl tributyl citrate (ATBC) on the thyroid gland and to clarify the potential molecular mechanisms by which environmental pollutants influence the disease process. Through an exhaustive exploration of databases such as ChEMBL, STITCH, and GEO, we identified a comprehensive list of 19 potential targets closely associated with ATBC and the thyroid gland. After rigorous screening using the STRING platform and Cytoscape software, we narrowed this list to 15 candidate targets, ultimately identifying five core targets: CBX5, HADHB, TRIM33, TP53, and CUL4A, utilizing three well-established machine learning methods. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses conducted in the DAVID database revealed that the primary pathways through which ATBC affects the thyroid gland involve key signaling cascades, including the FoxO signaling pathway and metabolic pathways such as fatty acid metabolism. Furthermore, molecular docking simulations using Molecular Operating Environment software confirmed strong binding interactions between ATBC and these core targets, enhancing our understanding of their interactions. Overall, our findings provide a theoretical framework for comprehending the intricate molecular mechanisms underlying ATBC's effects on thyroid damage and pave the way for the development of preventive and therapeutic strategies against thyroid disorders caused by exposure to ATBC-containing plastics or overexposure to ATBC.

From SMILES to Enhanced Molecular Property Prediction: A Unified Multimodal Framework with Predicted 3D Conformers and Contrastive Learning Techniques

We present a novel molecular property prediction framework that requires only the SMILES format as input but is designed to be multimodal by incorporating predicted 3D conformer representations. Our model captures comprehensive molecular features by leveraging both the sequential character structure of SMILES and the three-dimensional spatial structure of conformers. The framework employs contrastive learning techniques, utilizing InfoNCE loss to align SMILES and conformer embeddings, along with task-specific loss functions, such as ConR for regression and SupCon for classification. To address data imbalance, we incorporate feature distribution smoothing (FDS), a common challenge in drug discovery. We evaluated the framework through multiple case studies, including SARS-CoV-2 drug docking score prediction, molecular property prediction using MoleculeNet data sets, and kinase inhibitor prediction for JAK-1, JAK-2, and MAPK-14 using custom data sets curated from PubChem. The results consistently outperformed state-of-the-art methods, with ConR and FDS significantly improving regression tasks and SupCon enhancing classification performance. These findings highlight the flexibility and robustness of our multimodal model, demonstrating its effectiveness across diverse molecular property prediction tasks, with promising applications in drug discovery and molecular analysis.

Repurposing raltegravir for reducing inflammation and treating cancer: a bioinformatics analysis

Inflammation is a defensive mechanism that safeguards the human body against detrimental stimuli. Within this intricate process, ADAM17, a zinc-dependent metalloprotease, emerges as an indispensable element, fostering the activation of diverse inflammatory and growth factors within the organism. Nonetheless, ADAM17 malfunctions can augment the rate of growth, inflammatory factors, and subsequent damage. Thus, in this study, we examined and repurposed drugs to suppress the activity of ADAM17. To this end, we employed bioinformatics techniques such as molecular docking, molecular dynamics, and pharmacokinetic studies. Five FDA-approved drugs including Raltegravir, Conivaptan, Paclitaxel, Saquinavir, and Venetoclax with the ability to impede the activity of the ADAM17 metalloenzyme were identified. Moreover, these drugs did not include strong zinc-binding functional groups when verified by the ACE functional group finder. However, further in silico analysis has indicated that Raltegravir demonstrates a commendable interaction with the active site amino acids and exhibits the most favorable pharmacokinetic properties compared to others. Considering the results of bioinformatics tools, it can be concluded that Raltegravir as an antiviral drug could be repurposed to prevent severe inflammatory response and tumorigenesis resulting from ADAM17 malfunction.

Acidic sphingomyelinase interactions with lysosomal membranes and cation amphiphilic drugs: A molecular dynamics investigation

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of CAD representatives, i.e., ebastine, hydroxyebastine and loratadine, on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only shown with coarse-grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. CADs also interfere with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

Cardioprotective effects of polydatin against myocardial injury in HFD/stz and high glucose-induced diabetes via a Caveolin 1-dependent mechanism

BACKGROUND

Diabetic cardiomyopathy (DCM) is defined as cardiac dysfunction involving changes in structure, function, and metabolism in the absence of coronary artery disease, which eventually developed into heart failure. There is still a lack of effective drugs for the treatment of DCM, while the ameliorative effects of traditional herbs on DCM have been commonly reported. Polydatin (PD) is a glucoside derivative of traditional herbs of resveratrol, which has been shown to ameliorate the pathological development of DCM. However, the cardioprotective effect and mechanism of PD in the improvement of myocardial injury are still unclear.

AIM OF STUDY

This study aimed to investigate the cardio-protective role of PD on DCM and reveal the critical effect of Cav1 in PD' regulation of DCM.

MATERIALS AND METHODS

The Cav1-/- and Cav1+/+mice and H9C2 cells were used to induce DCM models and then given PD treatment (150 mg/kg) or not. The cardiac functions of all mice were checked via echocardiography, and myocardial histological changes were measured by H&E, periodic acid-schiff (PAS) and Masson staining. The markers expression of heart fibrosis and inflammation, and hypertrophic factors were detected using western blotting. The NF-κB signaling activation was performed by confocal, immunohistochemical, Electrophoretic mobility shift assay (EMSA) and western blotting.

RESULTS

Here, we found that PD significantly improved the cardiac function and injury of diabetic Cav1+/+ mice, and enhanced the expression of Cav1 in the cardiac tissues of diabetic Cav1+/+ mice and HG-induced H9C2 cells. Further investigation showed that when Cav1 was knocked down, PD no longer plays the cardioprotective effect and inhibits the NF-κB signaling pathway activation in HFD/stz-treated diabetic mice and HG-induced H9C2 cells.

CONCLUSION

These results demonstrated that PD inhibited the hyperglycemia-induced myocardial injury and inflammatory fibrosis of DCM models in vivo and in vitro, and targeting Cav1 may provide a novel understanding the mechanism of the treatment of PD in DCM.

Environmental triggers and future risk of developing autoimmune diseases: Molecular mechanism and network toxicology analysis of bisphenol A

Bisphenol A (BPA), a chemical compound in plastics and resins, widely exist in people's production and life which have great potential to damage human and animal health. It has been proved that BPA could affect human immune function and promote the occurrence and development of autoimmune diseases (ADs). However, the mechanism and pathophysiology remain unknown. Therefore, this study aims to advance network toxicology strategies to efficiently investigate the putative toxicity and underlying molecular mechanisms of environmental pollutants, focusing on ADs induced by BPA exposure. Leveraging databases including ChEMBL, STITCH, SwissTargetPrediction, GeneCards, and OMIM, we identified potential targets associated with BPA exposure and ADs, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), Hashimoto's thyroiditis (HT), inflammatory bowel disease (IBD), and type 1 diabetes (T1D). Subsequent refinement using STRING and Cytoscape software highlighted core targets respectively, and Metascape was utilized for enrichment analysis. Gene expression data from the GEO database revealed the upregulation or downregulation of these targets across these ADs. Molecular docking performed with Autodock confirmed robust binding between BPA and core targets, notably PPARG, CTNNB1, ESR1, EGFR, SRC, and CCND1. These findings suggest that BPA exposure may serve as an environmental trigger in the development of autoimmunity, underscoring potential environmental risk factors for the onset of autoimmune conditions.

Molecular docking and in silico analysis of the pharmacokinetics, toxicological profile and differential gene expression of bioactive compounds from Cyrtopodium glutiniferum

The genus Cyrtopodium, from the Orchidaceae family, is widely used for its therapeutic properties in the treatment of tuberculosis, abscesses, urinary infection, and colds. C. glutiniferum, one of the species of this genus, is endemic in Brazil and largely used in herbal medicine. Thus, it is of great interest to recognize its composition, the properties of the molecules found in it. This study aimed to perform the in silico analysis of the main compounds from C. glutiniferum, on the platforms pKCSM, SwissADME, LAZAR, CLC-pred, ToxTree, DIGEPred, STRING, and Cytoscape. Further than this, the molecular docking was performed. The compounds present in the aqueous extract of C. glutiniferum were identified by UHPLC-MS/MS, finding Arbutin, Caffeic acid 4-O-glucoside, and Dihydroformononetin as the three most abundant molecules. The evaluation of the gastrointestinal absorption of Dihydroformononetin is given as high, also managing to cross the blood-brain barrier, while Arbutin can only be absorbed by the gastrointestinal tract and Caffeic acid 4-O-glucoside had very low absorption. Further analysis showed that Arbutin and Dihydroformononetin are possible leading molecules for drug synthesis, according to the prediction. Toxicological aspects were analysed, and no adverse effects were noted, but there were divergences in the mutagenic prediction of Arbutin and Dihydroformononetin, having different results in the used platforms, demonstrating that a cautious analysis and data insertion is needed in these tools to optimize them. The analysis of the differentially expressed genes predicted that the compounds can regulate several genes, including some genes associated with carcinogenesis and inflammation. The Molecular docking analysis showed high binding affinities of the molecules with different proteins. Therefore, C. glutiniferum demonstrates the potential to be used as a phytotherapeutic. The same was given through the in silico analysis of the three compounds found in the orchid, that show good individual potential.

Active Targets and Potential Mechanisms of Erhuang Quzhi Formula in Treating NAFLD: Network Analysis and Experimental Assessment

The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out. Identification of components in traditional Chinese medicine compounds was conducted by LC-MS. NAFLD models were established and relevant pathologic indicators and Western blot were analyzed in vivo and ex vivo. Totally 8 herbs attributed to the liver meridian and 20 corresponding targets of NAFLD were obtained from EHQZF. Flavonoids and phenolic acids as the main components of EHQZF treated NAFLD through the MAPK/AKT signaling pathway. Pathway enrichment analysis focused on the MAPK/AKT signaling pathway and apoptosis signaling pathway. Molecular docking showed that Quercetin and Luteolin had stable binding structures with AKT1, STAT3, and other targets. Experiments showed that EHQZF reduced lipid accumulation, regulated changes in adipose tissue, inhibited the MAPK/AKT signaling pathway and exert multiple components, several targets, and multiple pathway interactions to treat NAFLD.

Discerning computational, in vitro and in vivo investigations of self-assembling empagliflozin polymeric micelles in type-2 diabetes

BACKGROUND

Empagliflozin (EMPA) is an SGLT2 inhibitor, a new class of anti-diabetic medication, indicated for treating type-2 diabetes. Its low permeability, poor solubility and bioavailability limits its use in management of diabetes. The study was aimed to formulate EMPA loaded polymeric micelles (PMs) to overcome these obstacles in oral absorption.

METHODOLOGY

In silico studies-molecular docking, molecular dynamic simulation (MDS), and quantum chemical calculation were employed to study the interaction of EMPA with different polymers. EMPA loaded TPGS polymeric micelles (EMPA-TPGS-PMs) were formulated by direct dissolution method and characterized in terms of surface morphology, entrapment, particle size, in vitro drug release, and in vitro cytotoxicity (HEK293 cells). In vivo pharmacokinetic and pharmacodynamic studies were also performed.

RESULTS

The results suggested a good interaction between TPGS and EMPA with lowest binding energy compared to other polymers. Further MDS results and DFT calculations validated the stable binding of the complex hence TPGS was selected for further wet lab experiments. The EMPA-TPGS complex displayed lower value of Total energy (T.E.) than its individual components, indicating the overall stability of the complex while, the energy band gap (∆E) value lied between the two individual molecules, signifying the better electron transfer between HOMO and LUMO of the complex. Based on the solubility, entrapment and cytotoxicity studies, 5% TPGS was selected for formulating drug loaded micelles. EMPA-TPGS5-PMs presented a size of 9.008 ± 1.25 nm, Polydispersity index (PDI) of 0.254 ± 0.100, a controlled release behaviour upto 24 h. SEM and AFM images of the nanoformulation suggested spherical particles whereas, DSC, and PXRD studies confirmed the loss of crystallinity of EMPA. A 3.12-folds higher AUC and a greater reduction in blood glucose levels was exhibited by EMPA-TPGS5-PMs in comparison to EMPA-SUSP in mice model.

CONCLUSION

EMPA-TPGS-PMs has exhibited better bio absorption and therapeutic effectiveness in diabetes treatment. This improved performance would open the possibility of dose reduction, reduced dosing frequency & dose-related side effects, improving pharmaco-economics and thereby improved overall compliance to the patient. However, this translation from bench to bedside would necessitate studies in higher animals and human volunteers.

Artificial intelligence and omics in malignant gliomas

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

Network pharmacology provides new insights into the mechanism of traditional Chinese medicine and natural products used to treat pulmonary hypertension

BACKGROUND

Pulmonary hypertension (PH) is a rare cardiovascular disease with high morbidity and mortality rates. It is characterized by increased pulmonary arterial pressure. Current research into relevant therapeutic drugs and targets for PH, however, is insufficient still. Traditional Chinese medicine (TCM) and natural products have a long history as therapeutics for PH. Network pharmacology is an approach that integrates drug-target interactions and signaling pathways based on biomarkers information obtained from drug and disease databases. The concept of network pharmacology shows many similarities with the TCM philosophy. Network pharmacology help elucidate the mechanisms of TCM in PH. This review presents representative applications of network pharmacology in the study of the mechanisms of TCM and natural products for the treatment of PH.

METHODS

In this review, we used ("pulmonary hypertension" OR "pulmonary arterial hypertension" OR "chronic thromboembolic pulmonary hypertension") AND ("network pharmacology" OR "systematic pharmacology") as keywords to search for reports from PubMed, Web of Science, and Google Scholar databases from ten years ago. The studies were screened and those chosen are summarized here. The TCM and natural products inPH and their corresponding targets and signaling pathways are described. Additionally, we discuss the application of network pharmacology in the study of TCM in PH to provide insights for future application strategies.

RESULTS

Network pharmacology have shown that AKT-related pathways, HIF-1 signaling pathway, MAPK signaling pathway, TGF-β-Smad pathway, cell cycle-related pathways and inflammation-related pathways are the main signaling pathways enriched in the PH targets of TCM. Reservatrol, curcumol, genistin, formononetin, wogonin, luteolin, baicalein, berberine, triptolide and tanshinone llA are active ingredients specific for PH treatment. A number of databases and tools specific for the treatment of PH are used in network pharmacology and natural product research.

CONCLUSION

Through the reasonable combination of molecular docking, omics technology and bioinformatics technology, the mechanism of multi-targets can be explained more comprehensively. Analyzing the complex mechanism of TCM from the clinical perspective may be a potential development trend of network pharmacology. Combination of predicted targets and traditional pharmacology improves efficiency of drug development.

1-Dehydro-6-Gingerdione Exerts Anticancer Effects on MDA-MB-231 Cells and in the Xenograft Mouse Model by Promoting the Ferroptosis Pathway

Breast cancer (BC) is the most prevalent malignancy among women, with millions of newly diagnosed cases emerging annually. Therefore, identifying novel pharmaceuticals for therapeutic purposes is imperative. Several natural compounds and their products have demonstrated potential in the treatment of cancer. This study examined the effects of the ginger derivative 1-dehydro-6-gingerdione (1-D-6-G) on BC and its mechanisms of action. MTT and colony formation assays were used to check the anticancer effect of 1-D-6-G. Then the anticancer mechanism of 1-D-6-G was predicted using proteomics analysis. The molecular pathway was verified by qRT-PCR and immunobloting analysis. Additionally, the anticancer properties of 1-D-6-G were investigated in vivo using xenograft mice model. Finally, an in silico study was conducted to examine the interaction of 1-D-6-G and pathway-related proteins. MTT and colony formation assay results indicated that 1-D-6-G has potent cytotoxic properties against BC cells. Proteomic analysis revealed that the anticancer mechanism of 1-D-6-G on MDA-MB-231 cells is associated with the ferroptosis signaling pathway. In addition, qRT-PCR and immunoblotting analyses revealed that the cytotoxic effects of 1-D-6-G on MDA-MB-231 cells were associated with ferroptosis signaling induction. Our in vivo results further confirmed the in vitro findings. The administration of 1-D-6-G for 14 days exhibited anticancer properties in xenograft mice by stimulating the ferroptosis pathway without causing damage to essential organs such as the liver and kidneys. Additionally, in silico results confirmed the structural stability of the molecular interaction between 1-D-6-G and ferroptosis target proteins. Our findings indicate that 1-D-6-G has the potential to serve as a novel therapeutic agent for inhibiting BC progression by targeting the ferroptosis pathway.

Integration of network pharmacology, metabolomics and lipidomics for clarifying the role of sphingolipid metabolism in the treatment of liver cancer by regorafenib

AIMS

Regorafenib, an FDA-approved drug for advanced primary liver cancer (PLC), could provide survival benefits for patients. However, markers for its therapeutic sensitivity are lacking. This study seeks to identify sensitive targets of regorafenib in PLC from the perspective of small molecular metabolites.

MATERIALS AND METHODS

Initiated with network pharmacology (NP) to map regorafenib's target landscape and metabolic regulatory network in liver cancer. Subsequently, regorafenib's impact on hepatoma cells was evaluated by flow cytometry, western blotting (WB) and cell viability assay. Advanced metabolomics and lipidomics were employed to elucidate regorafenib's metabolic reprogramming effects in liver cancer. Metabolic enzyme expression was assessed by WB, immunohistochemical and immunofluorescence assays. Ultimately, mendelian randomization (MR) analysis was utilized to investigate the potential causality of sphingolipid metabolism in hepatic cancer.

KEY FINDINGS

Regorafenib was observed to inhibit hepatoma cell proliferation and cell cycle progression at G0/G1 phase, resulting in significant alterations in sphingolipid levels. It promoted the significant accumulation of 16:0 dihydroceramide (16:0 dhCer) by upregulating ceramide synthase 6 (CERS6) expression and inhibiting dihydroceramide desaturase 1 (DEGS1) activity. The MR analysis revealed that DEGS1 was a risk factor for the development and progression of liver cancer, while cumulative 16:0 dhCer was a protective factor.

SIGNIFICANCE

Sphingolipids, particularly dhCer and regulatory enzymes, may be potential sensitive markers of regorafenib in the treatment of liver cancer, providing new insights for enhancing the treated efficacy of regorafenib in liver cancer.

Assessing male reproductive toxicity of environmental pollutant di-ethylhexyl phthalate with network toxicology and molecular docking strategy

Environmental pollutants, especially endocrine-disrupting chemicals (EDCs) like di-ethylhexyl phthalate (DEHP), pose serious threats to human health, with DEHP widely implicated in male reproductive toxicity. However, the complex molecular interactions remain unknown. We employed a network toxicology approach combined with molecular docking analysis to identify potential targets and mechanisms of DEHP's toxic effects. Databases such as ChEMBL, STITCH, OMIM, and GeneCards were utilized to gather data, and Cytoscape software was used to construct protein-protein interaction networks. A total of 51 potential targets were identified, with eight core targets, including PTGS2, CASP3, and ESR1, highlighted for their roles in oxidative stress, apoptosis, and hormonal dysregulation. KEGG pathway enrichment analysis revealed significant associations with pathways in cancer, cytokine-mediated signaling, and the hypothalamic-pituitary-gonadal axis. Additionally, gene expression datasets from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes overlapped with DEHP targets in testicular diseases. Molecular docking results confirmed strong binding affinities between DEHP and the core target proteins, suggesting a robust interaction mechanism. This study underscores the need for further investigation into DEHP's toxic mechanisms and its combined effects with other environmental pollutants, paving the way for comprehensive risk assessments and the development of targeted intervention strategies.

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

In silico profiling, docking analysis, and protein interactions of secondary metabolites in Musa spp. Against the SGE1 protein of Fusarium oxysporum f. sp. cubense

Banana Fusarium Wilt (BFW), caused by Fusarium oxysporum f. sp. cubense (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in Musa accuminata subsp. aalaccensis. Through in silico analyses, we explored interactions between Musa spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in Musa spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected Musa metabolites, such as 3-Phenylphenol, Catechin, and Epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7 kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in Musa spp. identified key domains like Chalcone synthase, Phenylalanine ammonia-lyase, Aminotran 1-2, and CoA-ligase, which are integral to phenylpropanoid production-a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for Musa spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.

Exploring micropollutants in polar environments based on non-target analysis using LC-HRMS

The routine use of chemicals in polar regions contributes to unexpected occurrence of micropollutants, with sewage discharge as a prominent pollution source. The aim of this study was to identify and quantify micropollutants in polar environments near potential point sources using non-target analysis (NTA) with liquid chromatography high-resolution mass spectrometry. Seawater samples were collected from Ny-Ålesund, Svalbard and Marian Cove, King George Island, in 2023. We tentatively identified 32 compounds with NTA, along with 105 homologous series substances. Of these, 18 substances were confirmed, and 13 were quantified using the internal standard method. Most quantified substances in the Ny-Ålesund, including caffeine, naproxen, and polyethylene glycols (PEGs), exhibited concentrations ranged from 0.9 to 770,000 ng/L. In Marian Cove, the analysis predominantly detected acetaminophen, with concentrations ranging from 13 to 35 ng/L. The findings underscore the presence and spatial distribution of emerging micropollutants resulting from wastewater discharge in polar regions.

Discovery of dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors using an artificial intelligence model and their effects on tau and tubulin dynamics

The dual-specificity tyrosine-phosphorylation-regulated kinase 1 A (DYRK1A) presents a promising therapeutic target for neurological diseases. However, current inhibitors lack selectivity, which can lead to unexpected side effects and increase the difficulty of studying DYRK1A. Therefore, identifying selective inhibitors targeting DYRK1A is essential for reducing side effects and facilitating neurological disease research. This study aimed to discover DYRK1A inhibitors through a screening pipeline incorporating a deep neural network (DNN) model. Herein, we report an optimized model with an accuracy of 0.93 on a testing set. The pipeline was then performed to identify potential DYRK1A inhibitors from the National Cancer Institute (NCI) library. Four novel DYRK1A inhibitors were identified, and compounds NSC657702 and NSC31059 were noteworthy for their potent inhibition, with IC50 values of 50.9 and 39.5 nM, respectively. NSC31059 exhibited exceptional selectivity across 70 kinases. The compounds also significantly reduced DYRK1A-induced tau phosphorylation at key sites associated with the pathology of neurodegenerative diseases. Moreover, they promoted tubulin polymerization, suggesting a role in microtubule stabilization. Cytotoxicity assessments further confirmed the neuronal safety of the compounds. Together, the results demonstrated a promising screening pipeline and novel DYRK1A inhibitors as candidates for further optimization and development.

Bioinformatics and validation reveal the potential target of curcumin in the treatment of diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is a common nerve-damaging complication of diabetes mellitus. Effective treatments are needed to alleviate and reverse diabetes-associated damage to the peripheral nerves. Curcumin is an effective neuroprotectant that plays a protective role in DPN promoted by Schwann cells (SCs) lesions. However, the potential molecular mechanism of curcumin remains unclear. Therefore, our aim is to study the detailed molecular mechanism of curcumin-mediated SCs repair in order to improve the efficacy of curcumin in the clinical treatment of DPN. First, candidate target genes of curcumin in rat SC line RSC96 cells stimulated by high glucose were identified by RNA sequencing and bioinformatic analyses. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out by Metascape, followed by 8 algorithms on Cytoscape to determine 4 hub genes, namly Hmox1, Pten, Vegfa and Myc. Next, gene set enrichment analysis (GSEA) and Pearson function showed that Hmox1 was significantly correlated with apoptosis. Subsequently, qRT-PCR, MTT assay, flow cytometry, caspase-3 activity detection and westernblot showed that curcumin treatment increased RSC96 cell viability, reduced cell apoptosis, increased Hmox1, Pten, Vegfa and Myc expression, and up-regulated Akt phosphorylation level under high glucose environment. Finally, molecular docking predicted the binding site of curcumin to Hmox1. These results suggest that curcumin can reduce the apoptosis of SCs induced by high glucose, and Hmox1 is a potential target for curcumin. Our findings provide new insights about the mechanism of action of curcumin on SC as a potential treatment in DPN.

Molecular mechanisms underlying Tao-Hong-Si-Wu decoction treating hyperpigmentation based on network pharmacology, Mendelian randomization analysis, and experimental verification

CONTEXT

Hyperpigmentation, a common skin condition marked by excessive melanin production, currently has limited effective treatment options.

OBJECTIVE

This study explores the effects of Tao-Hong-Si-Wu decoction (THSWD) on hyperpigmentation and to elucidate the underlying mechanisms.

MATERIALS AND METHODS

We employed network pharmacology, Mendelian randomization, and molecular docking to identify THSWD's hub targets and mechanisms against hyperpigmentation. The Cell Counting Kit-8 (CCK-8) assay determined suitable THSWD treatment concentrations for PIG1 cells. These cells were exposed to graded concentrations of THSWD-containing serum (2.5%, 5%, 10%, 15%, 20%, 30%, 40%, and 50%) and treated with α-MSH (100 nM) to induce an in vitro hyperpigmentation model. Assessments included melanin content, tyrosinase activity, and Western blotting.

RESULTS

ALB, IL6, and MAPK3 emerged as primary targets, while quercetin, apigenin, and luteolin were the core active ingredients. The CCK-8 assay indicated that concentrations between 2.5% and 20% were suitable for PIG1 cells, with a 50% cytotoxicity concentration (CC50) of 32.14%. THSWD treatment significantly reduced melanin content and tyrosinase activity in α-MSH-induced PIG1 cells, along with downregulating MC1R and MITF expression. THSWD increased ALB and p-MAPK3/MAPK3 levels and decreased IL6 expression in the model cells.

DISCUSSION AND CONCLUSION

THSWD mitigates hyperpigmentation by targeting ALB, IL6, and MAPK3. This study paves the way for clinical applications of THSWD as a novel treatment for hyperpigmentation and offers new targeted therapeutic strategies.

In vitro and in silico characterization of the transport of selected perfluoroalkyl carboxylic acids and perfluoroalkyl sulfonic acids by human organic anion transporter 1 (OAT1), OAT2 and OAT3

Perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs) belong to the group of poly- and perfluoroalkyl substances (PFASs), which may accumulate in humans due to their limited excretion. To provide more insights into the active renal excretion potential of PFASs in humans, this work investigated in vitro the transport of three PFCAs (PFHpA, PFOA, PFNA) and three PFSAs (PFBS, PFHxS and PFOS) using OAT1-, OAT2- or OAT3-transduced human embryonic kidney (HEK) cells. Only PFHpA and PFOA showed clear uptake in OAT1-transduced HEK cells, while no transport was observed for PFASs in OAT2-transduced HEK cells. In OAT3-transduced HEK cells only PFHpA, PFOA, PFNA, and PFHxS showed clear uptake. To study the interaction with the transporters, molecular docking and dynamics simulations were performed for PFHpA and PFHxS, for which a relatively short and long half-lives in humans has been reported, respectively. Docking analyses could not always distinguish the in vitro transported from the non-transported PFASs (PFHpA vs. PFHxS), whereas molecular dynamic simulations could, as only a stable interaction of the PFAS with the inner part of transporter mouth was detected for those that were transported in vitro (PFHpA with OAT1, none with OAT2, and PFHpA and PFHxS with OAT3). Altogether, this study presents in vitro and in silico insight with respect to the selected PFASs transport by the human renal secretory transporters OAT1, OAT2, and OAT3, which provides further understanding about the differences between the capability of PFAS congeners to accumulate in humans.

Study of Host-Guest Interaction and In Vitro Neuroprotective Potential of Cinnamic Acid/Randomly Methylated β-Cyclodextrin Inclusion Complex

Cinnamic acid (CA) has many beneficial effects on human health. However, its poor water solubility (0.23 g/L, at 25 °C) is responsible for its poor bioavailability. This drawback prevents its clinical use. To overcome the solubility limits of this extraordinary natural compound, in this study, we developed a highly water-soluble inclusion complex of CA with randomly methylated-β-cyclodextrin (RAMEB). The host-guest interaction was explored in liquid and solid states by UV-Vis titration, phase solubility analysis, FT-IR spectroscopy, and 1H-NMR. Additionally, molecular modeling studies were carried out. Both experimental and theoretical studies revealed a 1:1 CA/RAMEB inclusion complex, with a high apparent stability constant equal to 15,169.53 M-1. The inclusion complex increases the water solubility of CA by about 250-fold and dissolves within 5 min. Molecular modeling demonstrated that CA inserts its phenyl ring into the RAMEB cavity with its propyl-2-enoic acid tail leaning from the wide rim. Finally, a biological in vitro study of the inclusion complex, compared to the free components, was performed on the neuroblastoma SH-SY5Y cell line. None of them showed cytotoxic effects at the assayed concentrations. Of note, the pretreatment of SH-SY5Y cells with CA/RAMEB at 10, 30, and 125 µM doses significantly counteracted the effect of the neurotoxin MPP+, whilst CA and RAMEB alone did not show any neuroprotection. Overall, our data demonstrated that inclusion complexes overcome CA solubility problems, supporting their use for clinical applications.

Mechanism of atorvastatin in treating hepatocellular carcinoma: a study based on network pharmacology, molecular docking, and bioinformatics analysis

Hepatocellular carcinoma (HCC) is a tumor with high morbidity and mortality. Current research suggests that statins may aid in its prevention and treatment, while studies on the associated mechanisms remain limited. Therefore, we aim to reveal the mechanism of atorvastatin treatment for HCC by using network pharmacology and bioinformatics methods. The databases SwissTargetPrediction, PharmMapper, and DrugBank were utilized to obtain targets of atorvastatin, while GSE169289, GSE135631, and GSE207435 were used to identify differentially expressed genes (DEGs) for HCC. The overlap between the two groups was used to identify atorvastatin's target for treating HCC. Following protein-protein interaction (PPI) analysis, hub genes were identified using Cytoscape software and LASSO analysis. The hub genes were further validated using data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (HPA) databases. To evaluate the clinical significance of the hub genes, Kaplan-Meier (KM) survival analysis and Cox analysis were conducted. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to investigate potential mechanisms. Finally, molecular docking analysis was performed to validate the interaction between atorvastatin and the hub genes. A total of 1948 DEGs of HCC and 380 targets of atorvastatin were identified, respectively. After taking the intersection, 79 genes were identified as potential targets of atorvastatin for HCC treatment. After multiple screening methods, CYP2C9 was ultimately identified as the hub gene. Analysis of data from TCGA and HPA databases showed reduced expression of CYP2C9 in HCC tissues. KM and Cox analysis showed a favorable prognosis for HCC patients with high CYP2C9 expression. KEGG and GSEA indicated that metabolism of xenobiotics by cytochrome P450, and PPAR signaling pathway could be the potential mechanisms for atorvastatin in treating HCC. Molecular docking analysis revealed that atorvastatin binds to CYP2C9 with a binding energy of - 8.837, indicating highly stable binding. CYP2C9 is associated with the prognosis of HCC patients and could serve as a potential target for atorvastatin treatment in HCC.

Exploring the therapeutic potency of cryptotanshinone in cervical cancer: a multi-omics and network pharmacology approach

Introduction

Cervical cancer remains a significant challenge in oncology with an escalating demand for novel therapeutic strategies that can navigate the complexities of its pathophysiology. This study elucidated the antineoplastic effects of cryptotanshinone, a derivative of danshen (Salvia miltiorrhiza), a herb widely utilized in traditional Chinese medicine practices.

Methods

Employing a comprehensive multi-omics approach, including transcriptomic, proteomic, and bioinformatics analyses, we investigated the potential effects of cryptotanshinone on cervical cancer through data mining and computational analysis.

Results and Discussion

Our results demonstrated that the potential of cryptotanshinone to disrupted cancer cell proliferation and induced apoptosis may be ascribed to its modulation of gene expression and interaction with specific protein networks. Furthermore, network pharmacology and pathway enrichment analyses identified critical hubs and signaling pathways, suggesting a multi-targeted mechanism of action. Furthermore, the establishment of a prognostic model, which is founded upon differentially expressed genes linked to cryptotanshinone treatment, underscores its promising role as both a prognostic biomarker and a therapeutic agent. These insights pave the way for the integration of cryptotanshinone into therapeutic regimens, offering a promising avenue for enhancing the efficacy of cervical cancer treatment and patient outcomes.

Elucidation of the inhibitory effects of Jiedu Yizhi formula on neuronal apoptosis in the treatment of Alzheimer's disease based on network pharmacology and in vivo experiments

OBJECTIVE

This study aimed to investigate the mechanism of action of Jiedu Yizhi formula (JDYZF) in the treatment of Alzheimer's disease (AD) through network pharmacology, molecular docking technology, and in vivo experiments.

METHOD

 The main active ingredients of seven herbs in the Chinese Medicine compound JDYZF were identified by searching the TCMSP database, PubChem database, CNKI, and other sources. Disease targets of AD were obtained from databases such as OMIM, TDD, DisGeNET, and DrugBank. A protein‒protein interaction (PPI) network was constructed using the STRING platform, and core targets were identified through topological analysis using Cytoscape software. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the relevant targets were performed using the Metascape database. The main active ingredients of JDYZF and potential core targets were identified based on degree values. Molecular docking technology was used to verify the interactions between the main active ingredients and potential core targets. Furthermore, water maze tests and hematoxylin-eosin (HE) staining of brain and liver tissues were performed to evaluate the effects of JDYZF on cognitive dysfunction in AD mice and neuronal damage in hippocampal brain tissue and to assess drug toxicity. PCR was performed to determine the expression levels of the apoptosis-related genes Bcl-2, Bax, and caspase-3 and to investigate the effect of JDYZF on hippocampal apoptosis in AD mice. Results. One hundred twelve core PPI target proteins, including CASP3, TP53, and VEGFA, were found between JDYZF and AD. The KEGG pathway enrichment analysis showed significant enrichment of the MAPK signaling pathway, PI3K/AKT signaling pathway and so on. Water maze tests revealed that the high-dose JDYZF treatment significantly improved the escape latency of AD model mice. The HE staining results showed that JDYZF exerted a protective effect on neuronal damage in the hippocampus of AD mice. JDYZF could upregulate the expression of the anti-apoptotic factor Bcl-2 while downregulating the expression of the proapoptotic factors Bax and caspase-3. Conclusion. JDYZF can improve the cognitive function of AD mice by suppressing cell apoptosis.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Yiqi Liangxue Jiedu Prescription Inhibited the Canonical Wnt Pathway to Prevent Hepatocellular Precancerous Lesions

Purpose

Yiqi Liangxue Jiedu prescription (YLJP), a Chinese medicine that is commonly used to prevent liver cancer and is authorized by a national patent (patent No. ZL202110889980.5) has a therapeutic effect on precancerous lesions; however, the underlying mechanism remains unclear. This study is aimed at determining the clinical therapeutic efficacy of YLJP in patients with precancerous liver lesions and to explore and validate its possible effector mechanism.

Patients and Methods

The 1-year incidence of hepatocellular carcinoma (HCC) was retrospectively analyzed in 241 patients with cirrhosis complicated by abnormal alpha-fetoprotein precancer. Network pharmacological analysis, molecular docking, and molecular dynamics simulation were used to explore the key targets and compounds of YLJP in treating HCC. Immunohistochemical methods were used to detect the expression of key proteins in tumor and cirrhotic tissues. Finally, the mechanism underlying the effects of YLJP was verified in rats with precancerous lesions.

Results

The 1-year incidence of HCC was lower in the YLJP group than in the Western medicine group. The Wnt pathway protein, CTNNB1, is a key target of YLJP in preventing and treating HCC, and the canonical Wnt pathway is the key signaling pathway and is overexpressed in human liver tumors. In vivo experiments showed that YLJP significantly inhibited the canonical Wnt pathway and reduced the abnormal differentiation of hepatic oval cells. The binding of CTNNB1 to oleanolic acid, stigmasterol, and beta-sitosterol was found to be stable, indicating the action of these compounds in treating HCC.

Conclusion

YLJP reduces the 1-year incidence of HCC, with its mechanism likely due to oleanolic acid, beta-sitosterol, and stigmasterol inhibition of the CTNNB1 activation of the β-catenin protein, which in turn regulates the Wnt signaling pathway and prevents the abnormal differentiation of hepatic oval cells into cancer cells, thus delaying the occurrence and progression of the disease.

QSAR, molecular docking, MD simulations, and ADMET screening identify potential Heliotropium indicum leads against key targets in benign prostatic hyperplasia

Steroid 5α-reductase (5αR) converts testosterone into dihydrotestosterone (DHT), a potent androgen driving prostate cell proliferation via the androgen receptor (AR). Both 5αR and AR play crucial roles in androgen-mediated disorders, making them key therapeutic targets in drug development. Current treatments target these enzymes individually and often cause significant side effects, highlighting the need for safer alternatives. Through in silico screening, 13 pyrrolizidine alkaloids of Heliotropium indicum (HI) were assessed for their inhibitory potential against 5αR and AR. Using machine learning, six alkaloids showed promising pIC50 values. The accuracy of the models was assessed using key statistical parameters, including the score, correlation coefficient for training sets (R2), correlation coefficient for test sets (Q2), standard deviation (SD), and root mean square error (RMSE). For 5αR, the results were 0.763 (R2), 0.781 (Q2), 0.748 (score), 0.362 (SD), and 0.832 (RMSE), while for AR, the values were 0.817 (R2), 0.783 (Q2), 0.713 (score), 0.427 (SD), and 0.782 (RMSE), indicating reliability. Europine-N-oxide (-10.27 kcal/mol) and Heliotridine-N-oxide (-9.72 kcal/mol) displayed stronger 5αR binding than Finasteride, while Heliotrine (-10.09 kcal/mol) and Europine-N-oxide (-8.76 kcal/mol) outperformed Enzalutamide in AR binding. Key hydrogen bonds and MD simulations confirmed stable interactions. Pharmacokinetic screening revealed favorable drug-like profiles, including good solubility and absorption with minimal CYP enzyme inhibition. These findings suggest that HI alkaloids are promising multi-target inhibitors for BPH treatment, warranting further in vivo validation and optimization.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00280-7.

PubChem 2025 update

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

Hydrophobic forces at play: insights into AmelOBP4 and brood volatile interactions in Apis mellifera hygienic behavior

Understanding the intricate processes underlying olfaction necessitates unraveling the complexities of odorant binding protein's interactions with volatile compounds triggering hygienic behavior in Apis mellifera, This study delves into the intricate processes of olfaction by focusing on the interactions between Apis mellifera Odorant Binding Protein 4 (AmelOBP4) and volatile compounds associated with hygienic behavior, employing a comprehensive computational approach. Molecular docking analyses reveal detailed binding interactions, emphasizing the significance of hydrophobic interactions and specific amino acid residues in stabilizing AmelOBP4-volatile complexes, notably with 2-nonacosanone (-8.4 kcal/mol) and hexacosyl acetate (-8.4 kcal/mol). Molecular dynamics simulations demonstrate sustained stability and principal component analysis affirms structural integrity through restricted global motions. Binding free energy calculations underscore robust interactions, with per-residue free energy decomposition identifying key amino acids contributing significantly to binding affinity. These findings illuminate the pivotal role of hydrophobic interactions and specific residues (Phe 60, Leu 83, Ile 116, Leu 126, and Leu 130) in modulating AmelOBP4-volatile interactions, providing foundational insights into volatile-based applications and potential olfactory response modulation, contributing to our understanding of olfactory processes.

Nucleotide and Amino Acid Analyses of Unique Infectious Bronchitis Virus (IBV) Variants from Canadian Poultry Flocks with Drop in Egg Production

BACKGROUND/OBJECTIVES

Infectious bronchitis (IB) is a highly infectious avian disease caused by the infectious bronchitis virus (IBV). The disease causes lesions mainly in the respiratory, reproductive, and renal systems and has a significant economic impact on the poultry industry worldwide.

METHODS

We discovered two unique IBV isolates (T-62: PP737794.1 and CL-61: PP783617.1) circulating in Canada and molecularly characterized them.

RESULTS

The phylogenetic analysis revealed that the IBV isolates belong to genotype I and fall between lineages 25 and 7. Further analysis of the T-62 IBV isolate indicated that it is a potential recombinant of the Iowa state isolate (IA1162/2020-MW) and that the CL-61 strain of the IBV is also a recombinant IBV with the Connecticut (Conn) vaccine strain as its major parent. The S1 glycoprotein of the CL-61 and T-62 strains of the IBV had 85.7% and 73.2% amino acid (aa) identities respectively compared to the Conn vaccine strain. There were 67 and 129 aa substitutions among the S1 glycoprotein of the CL-61 and T-62 strains of the IBV compared to the Conn vaccine, respectively. Importantly, two and nineteen of these aa variations were in hypervariable regions 1 (HVR1) and HVR3. Finally, the two IBV isolates possessed a higher affinity for the sialic acid ligand compared to the DMV/1639 and Mass/SES IBV strains.

CONCLUSIONS

Genetic recombination in the IBV results in the continual emergence of new variants, posing challenges for the poultry industry. As indicated by our analyses, live attenuated vaccine strains play a role in the genetic recombination of the IBV, resulting in the emergence of variants.