CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The crucial role of small heat shock proteins in prostate cancer: mechanisms and new therapeutic perspectives

As resistance to new anti-androgen drugs occurs more frequently, increasing numbers of researchers are exploring alternative key molecular targets for prostate cancer treatment. The small heat shock protein (sHSP) family is a subclass of heat shock proteins (HSPs). Due to the smaller molecular size of their monomers, they often function as large oligomeric complexes with diverse biological roles, thus garnering increasing attention from urologists. Different members of the sHSP family exhibit distinct biological roles in prostate cancer, offering a new perspective for precision therapy. In this review, we summarize the specific roles of sHSP family members in prostate cancer and analyze their similarities and differences. Additionally, we discuss and review the drugs targeting various sHSPs in prostate cancer, providing new insights into the exploration and further application of sHSP-targeted therapies.

Bisphenol A enhanced cell migration through Kv3.4 in MCF7 cells

Bisphenol A (BPA) is a well-known endocrine-disrupting chemical (EDC) that has been implicated in cancer development. However, the specific mechanisms of EDCs, including BPA, remain unclear. Voltage-gated potassium (Kv) channels have been closely related to cancer. In particular, Kv3.4 plays a role in cancer malignancy, including cell migration via the ERK and AKT signaling pathways. In this study, we investigated the mechanism of BPA in relation to Kv3.4 expression in human breast cancer MCF7 cells. BPA treatment significantly increased Kv3.4 expression at both the mRNA and protein levels and induced cell migration. Further analysis demonstrated that Kv3.4 is closely related to integrin β and integrin-regulated FAK signaling. However, BPA-induced cell migration and integrin-regulated FAK signaling were significantly abolished by Kv3.4 silencing. Therefore, we concluded that BPA is closely associated with cancer cell migration mediated by Kv3.4 via integrin-regulated FAK signaling. These findings provide novel insights into the role of BPA in cancer progression and suggest Kv3.4 as a potential therapeutic target for BPA-associated cell migration.

Piperlongumine Inhibits Malignant Progression of Esophageal Squamous Cells Through the PI3K/AKT Signaling Pathway

To examine the impact of Piperlongumine (PL) on the proliferation, migration, invasion, cell cycle progression, and apoptosis in esophageal squamous cell carcinoma (ESCC) cells, as well as to elucidate the underlying molecular mechanisms. The suppressive effects of PL on the viability of ESCC cells were assessed using the CCK-8 assay, bright field imaging, and colony formation assays. Apoptosis induction and cell cycle disruption by PL were evaluated using flow cytometry. The impact of PL on ESCC cell migration and invasion was examined through scratch healing and Transwell assays. Differential gene expression analysis of ESCC tumor and normal tissues from the GSE29886 dataset, integrated with network pharmacology predictions, was conducted to identify core genes and molecular mechanisms involved in PL action. Key protein expression levels in the apoptosis, epithelial-mesenchymal transition (EMT), and PI3K/AKT signaling pathways were quantified by Western blotting. The CCK-8 and colony formation assays demonstrated that PL effectively suppressed cell viability and proliferation in ESCC. Flow cytometry revealed that PL down-regulated CDK1 expression, resulting in G2/M phase arrest, and promoted apoptosis by decreasing Bcl-2 levels and increasing cleaved caspase-3 and PARP. The scratch and Transwell assays indicated that PL inhibited ESCC cell migration and invasion, down-regulated the EMT-associated proteins Vimentin and N-cadherin, and up-regulated E-cadherin. Western blotting confirmed the down-regulation of P-PI3K and P-AKT, indicating the inhibition of the PI3K/AKT pathway by PL. These findings offer a pharmacological foundation for the development of PL as a potential phytotherapeutic agent for the clinical management of ESCC.

Integrative network and computational toxicology reveal the molecular mechanisms in PFOA-induced spermatogenic disorder

Perfluorooctanoic acid (PFOA), a widely used industrial chemical, poses significant environmental and biological toxicity, particularly affecting reproductive health. This study aimed to integrate network toxicology, machine learning, and molecular dynamics simulations (MDS) to uncover the molecular mechanisms of PFOA-induced spermatogenic toxicity. Toxicity profiling using admetSAR revealed that PFOA exhibited pronounced reproductive toxicity and a strong binding affinity to nuclear receptors, including estrogen, androgen, and PPAR gamma. By integrating PFOA targets derived from toxicology databases with differentially expressed genes associated with non-obstructive azoospermia, we pinpointed 256 differentially expressed spermatogenic toxicity targets from an initial pool of 4311 potential PFOA targets. Gene ontology (GO) and KEGG pathway enrichment analyses highlighted biological processes, such as spermatogenesis and cell cycle regulation, along with pathways related to cell division and intercellular communication. Protein-protein interaction networks and machine learning algorithms (LASSO, SVM-RFE, RF) pinpointed five core genes-RAD51, KIF15, PTTG1, BIRC5, and CDC25C-that serve as potential diagnostic biomarkers. Molecular docking revealed strong binding affinities between PFOA and these proteins, with RAD51 showing the highest binding stability (-8.467 kcal/mol). Furthermore, MDS confirmed stable interactions, with low RMSD, RMSF, and Rg values, indicating structural stability. In vivo studies showed that PFOA exposure (1 and 5 mg/kg) caused testicular damage in mice in a dose-dependent manner, with significant downregulation of core target proteins; in vitro experiments demonstrated a concentration-dependent reduction in GC1 cell viability and substantial alterations in its gene expression. This study highlights the critical roles of these mechanisms through which PFOA disrupts spermatogenesis, emphasizing core biomarkers that may serve as therapeutic targets. Our findings contribute insights into the reproductive toxicity of PFOA and similar environmental pollutants, offering a basis for developing strategies to protect male fertility.

Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study

To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regulating the glycolysis and phagocytosis of alveolar macrophages (AM). Datasets GSE130928 and GSE13896 were downloaded from the Gene Expression Omnibus (GEO) database. Genes related to glycolysis and phagocytosis phenotypes were obtained from the GeneCards and MSigDB databases, respectively. Weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted on GSE130928 to identify potential target genes for COPD (gene list 1). ISO target genes were gathered from the Traditional Chinese Medicine System Pharmacology (TCMSP) database, as well as the Comparative Toxicogenomic Database (CTD) and PubChem databases (gene list 2). COPD-related targets were gathered from the CTD and GeneCards databases, and the predicted targets of COPD were obtained by taking the intersection of these sources (gene list 3). From the three gene lists, key pathways were identified. The protein-protein interaction (PPI) network was created by extracting the common genes found in all key pathways and ISO targets. Candidate therapeutic targets were identified using the Minimum Common Oncology Data Element (MCODE) algorithm. These targets were then intersected with glycolysis and phagocytic phenotype-associated genes. The resulting intersection underwent further screening using eight distinct machine learning methods to identify phenotype-related key therapeutic targets. Clinical diagnostic evaluations-including nomogram analysis, receiver operating characteristic (ROC) analysis, correlation studies, and inter-group expression comparisons-were subsequently performed on these key targets. The research findings were validated using the independent dataset GSE13896. Additionally, gene set enrichment analysis (GSEA) was conducted to explore their functional relevance. Immune cell profiling was performed using mRNA expression data from AM in COPD patients. Molecular docking was then carried out to predict interactions between ISO and the identified key target genes. Differential expression analysis and WGCNA module analysis identified a total of 890 potential targets for COPD. Additionally, 3265 predicted targets for COPD were obtained through the intersection of two disease databases. Database searches also yielded 142 targets for ISO. Enrichment analysis identified 20 key pathways, from which three key targets (AKT1, IFNG, and JUN) were ultimately selected based on their overlap with enriched genes, ISO targets, and glycolysis- and phagocytosis-related genes. They were also validated using external datasets. Further analysis of signaling pathways and immune cell profiles highlighted the influence of immune infiltration in COPD and underscored the critical role of macrophages in disease pathology. Molecular docking simulations predicted the binding interactions between ISO and the three key targets. AKT1, IFNG, and JUN may be the key targets of ISO in regulating glycolysis and phagocytosis to affect COPD.

Oleanolic acid inhibits mesangial cell proliferation and inflammatory response in mesangial proliferative glomerulonephritis through IL-17/ERK/AKT pathway

BACKGROUND

Mesangial proliferative glomerulonephritis (MsPGN) is a common form of glomerulonephritis characterized by mesangial cell proliferation and inflammatory responses. However, current clinical treatment options for MsPGN are rather limited. Oleanolic acid (OA), a natural pentacyclic triterpenoid compound, exhibits anti-tumor and anti-inflammatory properties and has been proven to have renal protective effects. We speculate that OA could potentially serve as an alternative therapy for MsPGN.

OBJECTIVE

This study aimed to investigate the therapeutic efficacy and mechanism of OA against MsPGN.

METHODS

Tail vein injection of anti-Thy1 antibody was used to establish the MsPGN model, followed by a comprehensive assessment of the effects of OA on renal function, histopathological changes, and inflammatory responses in anti-Thy1 nephritis rats. Subsequently, network pharmacology was employed to predict the key targets and pathways of OA in treating MsPGN. Finally, in vivo and in vitro experiments were conducted to validate the results of network pharmacology.

RESULTS

OA significantly improved renal function, and attenuated mesangial cell proliferation and inflammatory reactions in anti-Thy1 nephritis rats. Network pharmacology analysis identified TNF-α, IL-6, IL-1β, MAPK3, and AKT1 as key targets of OA in the treatment of MsPGN, and involved the IL-17 signaling pathway. Additionally, we observed increased phosphorylation levels of ERK and AKT, as well as activation of downstream inflammatory responses, in both anti-Thy1 nephritis rats and mesangial cells stimulated with IL-17. In contrast, treatment with OA and the ERK inhibitor PD98059 reversed these effects. Furthermore, we identified IL17RA within this pathway as a potential target of OA.

CONCLUSIONS

Our study demonstrates that OA can modulate the IL-17/ERK/AKT signaling pathway, thereby improving anti-Thy1 antibody-induced MsPGN. This establishes a theoretical basis for OA to potentially serve as a therapeutic agent for treating MsPGN.

α-chaconine increases the sensitivity of HER2+ breast cancer cells to trastuzumab by targeting acetylcholinesterase

BACKGROUND

Trastuzumab (TRZ) is the first drug used to treat HER2-positive breast cancer, but some patients become resistant to it because of the PI3K/Akt pathway and other pathways that counteract it. TRZ, in conjunction with other therapeutic agents, is needed to overcome resistance. α-chaconine (CHA), a glycoalkaloid from the Solanaceae family, can suppress lung cancer cell proliferation in vitro by inhibiting PI3K/Akt signaling, one of the key regulatory pathways in TRZ resistance.

METHODS

This study used integrative bioinformatics analysis to screen for possible targets of CHA that can help fight breast cancer that is resistant to TRZ. In vitro experiments were used to confirm the target genes using TRZ-resistant HCC-1954 (HCC-TRZ) cells for cytotoxicity, gene expression studies, and enzymatic assay.

RESULTS

We identified several potential target genes of CHA, including EGFR, VEGF, ACHE, and ADORA. We generated HCC1954-TRZ cells, which showed an increase in cell viability after sequential treatment of the parental HCC1954 cells with TRZ. Further experiments showed the high sensitivity of HCC-TRZ toward TRZ when TRZ was combined with CHA. The combination of CHA and TRZ significantly increased the mRNA expression levels of various genes compared to a single TRZ treatment. Additionally, CHA alone and combined with CHA-TRZ inhibited acetylcholinesterase (AChE) activity in HCC-TRZ cells.

CONCLUSION

CHA increased the sensitivity of HCC-TRZ cells to TRZ by targeting several potential target genes and AChE activity. This study highlights the potential of using CHA in combination with TRZ to overcome TRZ resistance in HER2+ breast cancer cells.

Regulatory trends of organophosphate and pyrethroid pesticides in cannabis and applications of the Comparative Toxicogenomics Database and Caenorhabditis elegans

Organophosphate and pyrethroid pesticides are common contaminants in cannabis. Due to the status of cannabis as an illicit Schedule I substance at the federal level, there are no unified national guidelines in the United States to mitigate the health risk of pesticide exposure in cannabis. Here, we examined the change in the state-level regulations of organophosphate and pyrethroid pesticides in cannabis. The medians of pyrethroid and organophosphate pesticides specified by each state-level jurisdiction increased from zero pesticide in 2019 to 4.5 pyrethroid and 7 organophosphate pesticides in 2023, respectively. Next, we evaluated the potential connections between pyrethroids, organophosphates, cannabinoids, and Parkinson's disease using the Comparative Toxicogenomics Database (CTD). Eleven pyrethroids, 30 organophosphates, and 14 cannabinoids were associated with 95 genes to form 3,237 inferred and curated Chemical-Gene-Phenotype-Disease tetramers. Using a behavioral repulsion assay with the whole organism model Caenorhabditis elegans, we examined the effect of cannabinoids and insecticides on depleting dopamine synthesis. Exposure to chlorpyrifos and permethrin, but not Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), results in dose-dependent effects on 1-nonanol repulsive behaviors in C. elegans, indicating dopaminergic neurotoxicity (P < 0.01). Dose-dependent effects of chlorpyrifos are different in the presence of Δ9-THC and CBD (P < 0.001). As a proof of concept, this study demonstrated how to use new approach methodologies such as C. elegans and the CTD to inform further testing and pesticide regulations in cannabis by chemical class.

Integrative analysis of genes provides insights into the molecular and immune characteristics of mitochondria-related genes in atherosclerosis

Atherosclerosis is a chronic inflammatory disease characterized by lipid accumulation in arterial walls. The role of the interplay between mitochondrial dysfunction and immune inflammation in atherosclerosis is still unclear. This study aimed to investigate the molecular characteristics and immune landscape of mitochondrial hub genes involved in atherosclerosis. Based on bioinformatics analysis, three hub Mitochondria-related DEGs (MitoDEGs), including OXCT1, UCP2, and CPT1B, were screened out and showed good diagnostic performance in identifying atherosclerosis patients and controls. Immune analysis demonstrated strong correlations between hub MitoDEGs and immune cells, such as macrophages and T cells. Additionally, the predicted transcription factors of these hub MitoDEGs were significantly enriched in Th17, Th1 and Th2 cell differentiation signaling pathways. Both cell and animal experiments confirmed the expression trends of OXCT1 and CPT1B observed in the bioinformatics analysis. These hub MitoDEGs may play an important role in coordinating mitochondrial metabolism in the immune inflammation of atherosclerosis.

Assessment of the Cytotoxicity Mechanism of Diazinon on HFFF2 Cells: A Bioinformatic and Experimental Study

Pesticide exposure can cause many skin diseases such as hypopigmentation and contact dermatitis, but the underlying mechanisms remain unclear. Furthermore, Organophosphate pesticides (OPs) including Diazinon (DZN) can affect cellular pathways like ATPase, leading to mitochondrial energy deficit and even apoptosis in the cell's functions. Following cell exposure to the OP pesticide DZN through treatment, we evaluated alteration in gene expression and DNA damage. Bioinformatic analysis was performed based on the AutoDock, Protein Data Bank, STRING, Way2Drug, and Comparative Toxicogenomics databases and tools. The MTT assay, wound healing, DAPI staining, flow cytometry, and real-time PCR were applied in the current study. The results showed that the viability and migration capacity of HFFF2 cells decreased, and the apoptosis rate increased in the DZN-treated group. These findings revealed that DZN regulated the expression of the apoptotic genes in DZN cells.

Integrating AI-powered text mining from PubTator into the manual curation workflow at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD) is a manually curated knowledge- and discovery-base that seeks to advance understanding about the relationship between environmental exposures and human health. CTD's manual curation process extracts from the biomedical literature molecular relationships between chemicals/drugs, genes/proteins, phenotypes, diseases, anatomical terms, and species. These relationships are organized in a highly systematic way in order to make them not only informative but also scientifically computational, enabling inferential hypotheses to be formed to address gaps in understanding. Integral to CTD's functionality is the use of structured, hierarchical ontologies and controlled vocabularies to describe these molecular relationships. Normalizing text (i.e. translating raw text from the literature into these controlled vocabularies) can be a time-consuming process for biocurators. To facilitate the normalization process and improve the efficiency with which our scientists curate the literature, CTD evaluated and integrated into the curation process PubTator 3.0, a state-of-the-art, AI-powered resource which extracts and normalizes from the literature many of the key biomedical concepts CTD curates. Here, we describe CTD's long-standing history with Natural Language Processing (NLP), how this history helped form our objectives for NLP integration, the evaluation of PubTator against our objectives, and the integration of PubTator into CTD's curation workflow. Database URL: https://ctdbase.org.

Therapeutic Role of Quercetin in Prostate Cancer: A Study of Network Pharmacology, Molecular Docking, and Dynamics Simulation

Prostate cancer (PCa) is a major cause of cancer-related mortality in men. This study explores the anticancer potential of Quercetin, a polyphenolic compound with antioxidant and anti-inflammatory properties, by network pharmacology, molecular docking, and molecular dynamics simulation approaches. Target genes for Quercetin and PCa were identified from the bioinformatics databases MalaCards, Comparative Toxicogenomics Databases, SwissTargetPrediction, and Traditional Chinese Medicine Systems Pharmacology, and the obtained genes were matched using the Venny platform to find out the common genes. We obtained 11 preliminary genes and analyzed them in ShinyGO-0.77 databases to obtain genetic otology data. Then, we constructed a protein-protein interaction network in STRING, which enabled us to identify six hub genes AKT1, EGFR, MMP2, MMP9, PARP1, and ABCG2. Hub genes were analyzed in the TISIDB database for immune cell infiltration. Furthermore, a molecular docking study between the target proteins and Quercetin was performed in the SwissDock databases. Subsequently, we corroborated the docking with molecular dynamics studies using GROMACS software. Gene Ontology and KEGG pathway analyses revealed that Quercetin influences oxidative stress, mitochondrial function, and metalloproteinase activity. Immune cell infiltration analysis highlighted correlations between key genes and specific immune responses, suggesting a modulatory role of Quercetin in the tumor microenvironment. Finally, docking and molecular dynamics analysis showed that Quercetin has a stable interaction with the hub genes. In conclusion, these findings underline the potential of Quercetin to induce apoptosis, inhibit angiogenesis, and suppress metastasis, proposing it as a promising therapeutic tool for the treatment of PCa. However, additional experimental studies are required to translate these findings into clinical practice.

An integrative analysis reveals cancer risk associated with artificial sweeteners

BACKGROUND

Artificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.

METHODS

Targets associated with commonly used AS were screened and validated using databases such as CTD, STITCH, Super-PRED, Swiss Target Prediction, SEA, PharmMapper, and GalaxySagittarius. Cancer-related targets were sourced from GeneCards, OMIM, and TTD databases. AS-cancer targets were identified through the intersection of these datasets. A network visualization ('AS-targets-cancer') was constructed using Cytoscape 3.9.0. Protein-protein interaction analysis was conducted using the STRING database to identify significant AS-cancer targets. GO and KEGG enrichment analyses were performed using the DAVID database. Core targets were identified from significant targets and genes involved in the 'Pathways in cancer' (map05200). Molecular docking and dynamics simulations were employed to verify interactions between AS and target proteins. Pan-cancer and univariate Cox regression analyses of core targets across 33 cancer types were conducted using GEPIA 2 and SangerBox, respectively. Gene chip datasets (GSE53757 for KIRC, GSE21354 for LGG, GSE42568 for BRCA, and GSE46602 for PRAD) were retrieved from the GEO database, while transcriptome and overall survival data were obtained from TCGA. Data normalization and identification of differentially expressed genes (DEGs) were performed on these datasets using R (version 4.3.2). Gene Set Enrichment Analysis (GSEA) was employed to identify critical pathways in the gene expression profiles between normal and cancer groups. A cancer risk prognostic model was constructed for key targets to further elucidate their significance in cancer initiation and progression. Finally, the HPA database was utilized to investigate variations in the expression of key AS-cancer target proteins across KIRC, LGG, BRCA, PRAD, and normal tissues.

RESULTS

Seven commonly used AS (Aspartame, Acesulfame, Sucralose, NHDC, Cyclamate, Neotame, and Saccharin) were selected for study. A total of 368 AS-cancer intersection targets were identified, with 48 notable AS-cancer targets, including TP53, EGFR, SRC, PIK3R1, and EP300, retrieved. GO biological process analysis indicated that these targets are involved in the regulation of apoptosis, gene expression, and cell proliferation. Thirty-five core targets were identified from the intersection of the 48 significant AS-cancer targets and genes in the 'Pathways in cancer' (map05200). KEGG enrichment analysis of these core targets revealed associations with several cancer types and the PI3K-Akt signaling pathway. Molecular docking and dynamics simulations confirmed interactions between AS and these core targets. HSP90AA1 was found to be highly expressed across the 33 cancer types, while EGF showed the opposite trend. Univariate Cox regression analysis demonstrated strong associations of core targets with KIRC, LGG, BRCA, and PRAD. DEGs of AS-cancer core targets across these four cancers were analyzed. GSEA revealed upregulated and downregulated pathways enriched in KIRC, LGG, BRCA, and PRAD. Cancer risk prognostic models were constructed to elucidate the significant roles of key targets in cancer initiation and progression. Finally, the HPA database confirmed the crucial function of these targets in KIRC, LGG, BRCA, and PRAD.

CONCLUSION

This study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. This paper provides a valuable reference for the safety assessment and cancer risk evaluation of food additives. It urges food safety regulatory agencies to strengthen oversight and encourages the public to reduce consumption of foods and beverages containing artificial sweeteners and other additives.

Comparative Toxicogenomics Database's 20th anniversary: update 2025

For 20 years, the Comparative Toxicogenomics Database (CTD; https://ctdbase.org) has provided high-quality, literature-based curated content describing how environmental chemicals affect human health. Today, CTD includes over 94 million toxicogenomic connections relating chemicals, genes/proteins, phenotypes, anatomical terms, diseases, comparative species, pathways and exposures. In this 20th year anniversary update, we reflect on CTD's remarkable growth and provide an overview of the increased data content and new features, including enhancements to the curation workflow (e.g. new exposure curation tool and expanded use of natural language processing), added functionality (e.g. improvements to CTD Tetramers and Pathway View tools) and significant upgrades to software and infrastructure. Linking lab-based core curation with real-world human exposure curation via the use of controlled vocabularies facilitates analysis of content across the entire environmental health continuum, from molecular toxicological mechanisms to the population level, and vice versa. The 'prototype database' originally described in 2004 has evolved into a premier, sophisticated, highly cited and well-engineered knowledgebase and discoverybase that is utilized by scientists worldwide to design testable hypotheses about environmental health.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Study to Explore the Effect of Resveratrol on Type 2 Diabetes

This network pharmacology study represents a significant step in understanding the potential of Resveratrol as an antidiabetic agent and its molecular targets. Targets for Type 2 diabetes were obtained from the MalaCards and DisGeNET databases, while targets for Resveratrol were sourced from the STP and CTD databases. Subsequently, we performed matching to identify common disease-compound targets. The identified genes were analyzed using the ShinGO-0.76.3 database for functional enrichment analysis and KEGG pathway mapping. A protein-protein interaction network was then constructed using Cytoscape software, and hub genes were identified. These hub genes were subjected to molecular docking and dynamic simulations using AutoDock Vina and Gromacs software. According to functional enrichment and KEGG pathway analysis, Resveratrol influences insulin receptors, endoplasmic reticulum functions, and oxidoreductase activity and is involved in the estrogen and HIF-1 pathways. Ten hub genes were identified, including ESR1, PTGS2, SRC, NOS3, MMP9, IGF1R, CYP19A1, MTOR, MMP2, and PIK3CA. The proteins associated with these genes exhibited high interaction with Resveratrol in the molecular docking analysis, and molecular dynamics showed a stable interaction of Resveratrol with ESR1, MMP9, PIK3CA, and PTGS2. In conclusion, our work enhances the understanding of the antidiabetic activity of Resveratrol, which future studies should experimentally corroborate.

Unveiling esophageal cancer treatment mechanisms: network pharmacology and molecular docking of Physcion

This study investigates the effects of Physcion on esophageal cancer and its possible mechanisms of action. Potential Physcion targets were identified using databases. Transcriptomic data from 17 esophageal cancer and adjacent tissues were analyzed to find differentially expressed genes, intersecting with potential targets to select 16 key genes. Their expression and distribution were evaluated in patient sequencing data. Diagnostic potential was assessed through differential gene expression and ROC curves. Pathway enrichment analysis was performed using KEGG, and molecular docking simulations were conducted to assess Physcion's binding affinity to key genes. In vitro assays complemented these findings. A total of 161 drug targets were identified, narrowing down to 16 pivotal genes. Expression patterns were examined across cell populations, and enrichment analysis showed significant PI3K/AKT pathway involvement. Molecular docking indicated strong binding of Physcion to HSP90AA1 and MMP2. In vitro assays confirmed Physcion's dose- and time-dependent impact on esophageal cancer cells, with significant DAPI staining effects. Physcion shows promise as a therapeutic agent for esophageal cancer. The study supports its potential for clinical development and future research in esophageal cancer treatment.

Predicting the effects of drugs and unveiling their mechanisms of action using an interpretable pharmacodynamic mechanism knowledge graph (IPM-KG)

BACKGROUND

Multiple studies have aimed to consolidate drug-related data and predict drug effects. However, most of these studies have focused on integrating diverse data through correlation rather than representing them based on the pharmacodynamic mechanism of action (MOA). It is thus crucial to obtain interpretability to validate prediction results. In this study, we propose a novel framework to construct knowledge graphs that represent pharmacodynamic MOA, predict drug effects, and derive conceivable mechanistic pathways.

METHODS AND RESULTS

We constructed an interpretable pharmacodynamic mechanism knowledge graph (IPM-KG) by integrating various existing databases and combining them with the approach of this study to automatically fill in the missing data. This yielded a knowledge graph comprising 1455 drugs and 2547 diseases. Additionally, a graph neural network (GNN)-based approach was used to predict therapeutic medication and indication, which outperformed previous approaches that relied on correlation-based knowledge graphs lacking pharmacodynamic MOA representations. Furthermore, we proposed and assessed a method to interpret pharmacodynamic MOA using gene perturbation data. This feasibility study demonstrated the successful derivation of an accurate mechanism in approximately 50 % of cases. Additionally, it facilitated the identification of candidate drugs, which are currently unapproved but exhibit potential for drug repositioning, and their mechanisms of action.

CONCLUSIONS

This framework not only enables the derivation of highly accurate "drug-indication" predictions but also offers a basic mechanistic understanding, thereby facilitating future drug repositioning efforts.

Curcumin activates the Wnt/β-catenin signaling pathway to alleviate hippocampal neurogenesis abnormalities caused by intermittent hypoxia: A study based on network pharmacology and experimental verification

The purpose of this work was to investigate how curcumin (Cur) might enhance cognitive function and to gain a better understanding of the molecular mechanisms behind Cur's impacts on neurogenesis deficits brought on by intermittent hypoxia (IH). Using network pharmacology, we explored possible targets for Cur's obstructive sleep apnea (OSA) therapy. We established an IH model using C57BL/6 mice and c17.2 cells, and we assessed the influence of Cur on treatment outcomes as well as the effect of IH on cognitive function. Hippocampal damage and neurogenesis, as well as expression of core targets, were then examined. Network pharmacology analysis revealed that Cur has the potential for multi-target, multi-pathway therapy, with CTNNB1 and MYC as core target genes. The Morris water maze test showed that Cur (100 mg/kg, intragastrically) significantly improved cognitive dysfunction induced by IH. The hematoxylin and eosin (H&E) and Nissl staining indicated that Cur could alleviate damage to the hippocampus caused by IH. Immunohistochemistry, immunofluorescence, and western blotting results showed that Cur might promote neurogenesis and upregulate the expression of β-catenin and c-myc. In vitro, Cur (0.5 μM) has a protective effect on IH-induced neural stem cells (NSCs) injury and apoptosis and can restore the Wnt/β-catenin. Cur significantly increased the neurogenesis via the Wnt/β-catenin pathway, providing the scientific groundwork for the development of new treatment strategies for neurological damage linked to OSA.

Systematic pan-cancer analysis identifies ZBTB11 as a potential pan-cancer biomarker and immunotherapy target in multiple tumor types

BACKGROUND

ZBTB11 is a putative transcription factor with an N-terminal BTB domain and tandem C-terminal zinc finger motifs. Recent studies have suggested a potential role for ZBTB11 in tumorigenesis. However, the biological significance of ZBTB11 in different cancer types remains uncertain.

METHODS

The expression levels, prognostic values, genetic mutations, and DNA promoter methylation of ZBTB11 across tumor types were explored via various online websites and databases, including TIMER2.0, GEPIA2, cBioPortal, UALCAN, GSCA, CancerSEA, and others. Additionally, a competing lncRNA-miRNA network of ZBTB11 was constructed, and its interaction with chemicals and genes was investigated.

RESULTS

Our findings revealed that ZBTB11 was aberrantly expressed in a multitude of tumor types and exhibited variability across various tumor stages. A survival analysis revealed that ZBTB11 predicted a poor prognosis in BRCA, KIRP, LIHC, PCPG, PRAD, SARC, UCEC, and a good prognosis in CHOL, ESCA, GBM, KIRC, and READ. We also found that the most frequent genetic alterations type of ZBTB11 was mutation, and the DNA methylation level of ZBTB11 decreased in various cancers. Furthermore, ZBTB11 expression correlated with immune cells infiltration and genetic markers of immunodulators in cancers. Moreover, the results of single-cell sequencing demonstrated that ZBTB11 could regulate several tumor biological behaviors, including apoptosis, DNA damage, and angiogenesis. A lncRNA-miRNA network regulating ZBTB11 expression in tumor development and progression was constructed. It is of particular significance that ZBTB11 demonstrated a correlation with the CTRP and GDSC drug sensitivity, and that it served as a mediator between chemicals and cancers.

CONCLUSION

These findings demonstrate that ZBTB11 is associated with multiple tumor types and disease prognosis. ZBTB11 may represent a potential key biomarker and therapeutic target in cancers.

Exploring the protective effect and molecular mechanism of betulin in Alzheimer's disease based on network pharmacology, molecular docking and experimental validation

Alzheimer's disease (AD) is a neurodegenerative disorder that impairs learning and memory, with high rates of mortality. Birch bark has been traditionally used in the treatment of various skin ailments. Betulin (BT) is a key compound of birch bark that exhibits diverse pharmacological benefits and therapeutic potential in AD. However, the therapeutic effects and molecular mechanisms of BT in AD remain unclear. The present study aimed to predict the potential therapeutic targets of BT in the treatment of AD, and to determine the specific underlying molecular mechanisms through network pharmacology analysis and experimental validation. PharmMapper was used to predict the target genes of BT, and four disease databases were searched to screen for AD targets. The intersection targets were identified using the jveen website. Drug‑disease target protein‑protein interaction networks and hub genes were obtained and visualized using the Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape. The Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and AutoDock was used for molecular docking analysis of BT and hub genes. Subsequently, the network‑predicted mechanisms of BT in AD were verified in vitro. A total of 495 BT and 1,386 AD targets were identified, and 120 were identified as potential targets of BT in the treatment of AD. The results of the molecular docking analysis revealed a strong binding affinity between BT and the hub genes. In addition, enrichment analyses of GO and KEGG pathways indicated that the neuroprotective effects of BT mainly involved the 'PI3K‑Akt signaling pathway'. The results of in vitro experiments demonstrated that pretreatment with BT for 2 h may ameliorate formaldehyde (FA)‑induced cytotoxicity and morphological changes in HT22 cells, and decrease FA‑induced Tau hyperphosphorylation and reactive oxygen species levels. Furthermore, the PI3K/AKT signaling pathway was activated and the expression levels of downstream proteins, namely GSK3β, Bcl‑2 and Bax, were modified following pre‑treatment with BT. Overall, the results of network pharmacology and in vitro analyses revealed that BT may reduce FA‑induced AD‑like pathology by modulating the PI3K/AKT signaling pathway, highlighting it as a potential multi‑target drug for the treatment of AD.

Chen's peiyuan tang and premature ovarian failure: unveiling the mechanisms through network pharmacology

Background

Chen's Peiyuan Tang (CSPYT) is a compound herbal formula that has shown the potential to enhance ovarian function and reduce autophagy in ovarian granulosa cells, which plays a crucial role in follicular development and maturation. The application of Chinese herbal medicine offers a promising alternative to traditional hormone replacement therapy (HRT).

Methods

This study explores CSPYT's therapeutic mechanisms in treating POF, focusing on its modulation of autophagy through network pharmacology and transcriptomics-based analysis, predicting potential interactions and pathways. KGN cell line and rat ovarian granulosa cells were used for in vitro experiment. 4-Hydroperoxy cyclophosphamide(4-HC) stimulation was carried out for establishing the POF cell model. Q-PCR, Western Blot, Transmission electron microscopy to detect the results.

Results

According to the drug and disease database, the common targets of Chen's Peiyuan Tang and premature ovarian failure were screened, combined with autophagy gene targets and transcriptome analysis, and finally 8 intersection targets were obtained, namely CDKN1B, MAPK3, PRKCD, CDKN1A, MAPK1, RAF1, BIRC5, CTSB. Enrichment analysis of 8 genes found that they were closely related to the animal autophagy pathway. Construct PPI network diagram. CytoScape 3.9.1 builds CSPYT Drug Target-POF Disease Target-Autophagy Gene Network Diagram. Based on the PPI network diagram and CytoScape 3.9.1 analysis results, it is estimated that MAPK1 and MAPK3 are the key targets of CSPYT in the treatment of POF. The eight final intersection targets were docked with the corresponding active pharmaceutical ingredients. The one that docked most closely with the MAPK family was naringenin. In cell experiment verification, it was confirmed that Chen's Peiyuan Tang can inhibit the MAPK signaling pathway, significantly reduce the number of autophagosomes, and reduce autophagy damage in ovarian granulosa cells.

Discussion

CSPYT can inhibit the MAPK signaling pathway, prevent autophagy overexpression and restore ovarian granulosa cell function, effectively alleviating the disease pressure of POF.

Taxus chinensis var. mairei (Lemée et Lévl) Cheng et L.K. Fu overcomes the resistance to osimertinib in EGFR-mutant non-small-cell lung cancer via suppression of ERK1/2-related cholesterol biosynthesis

ETHNOPHARMACOLOGICAL RELEVANCE

Acquired resistance to osimertinib limits its clinical efficacy in non-small cell lung cancer (NSCLC) with EGFR mutations. The widespread recognition of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu (Chinese yew) as a natural anti-cancer medication is well-established. However, the specific contribution of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu in addressing resistance to osimertinib is still uncertain.

AIM OF THE STUDY

Based on the biological behaviors and lipid metabolism, we investigated whether aqueous extract of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu (AETC) could enhance the antitumor effect of osimertinib in NSCLC with an investigation on the precise mechanisms.

MATERIALS AND METHODS

The effect of AETC on enhancing osimertinib sensitivity was assessed via cell viability measurements, levels of reactive oxygen species (ROS), apoptosis, and lipid levels. Western blotting was used to verify the mechanisms of AETC responsible for overcoming the resistance to osimertinib via ERK1/2 overexpression and knockdown models. In vivo validation was conducted using subcutaneous xenografts from osimertinib-resistant cells in nude mice.

RESULTS

Osimertinib-resistant cells exhibited altered cholesterol biosynthesis, which was induced by ERK1/2 activation. The combination of AETC and osimertinib can synergistically decrease the levels of ROS in cells, enhance apoptosis, and inhibit the growth of osimertinib-resistant cells. Mechanistic experiments demonstrated that AETC can downregulate the key regulators of cholesterol biosynthesis by regulating ERK1/2, inhibiting the endogenous synthesis rate of cholesterol, and suppressing the level of lipids in osimertinib-resistant cells and xenograft tumors when combined with osimertinib, ultimately reversing resistance to osimertinib.

CONCLUSIONS

The resistance to osimertinib is significantly influenced by cholesterol biosynthesis, highlighting its pivotal role in this context. AETC can enhance osimertinib sensitivity via ERK/SREBP-2/HMGCR-mediated cholesterol biosynthesis. These results provide a promising therapeutic target and potential treatment option for resistance to osimertinib.

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.

Hyrtios sp.-associated Cladosporium sp. UR3 as a potential source of antiproliferative metabolites

BACKGROUND

Sponge-associated microorganisms are promising resources for the production of bioactive compounds with cytotoxic potential. The main goal of our study is to isolate the fungal endophytes from the Red Sea sponge Hyrtios sp. followed by investigating their cytotoxicity against number of cell lines.

RESULTS

The fungal strain UR3 was isolated from the Red Sea sponge using Sabouraud dextrose agar media. It was identified based on partial 18 S rRNA gene and ITS sequence analyses as Cladosporium sp. UR3. The in vitro cytotoxic potential of the ethyl acetate extract of the fungal isolate was evaluated using MTT assay against three cancer cell lines: CACO2, MCF7, and HEPG2. Metabolomics profiling of the obtained ethyl acetate extract using LC-HR-ESI-MS, along with molecular docking and pharmacological network studies for the dereplicated compounds were performed to explore its chemical profile and the possible cytotoxic mechanism of the sponge-associated fungi.

CONCLUSION

These results highlighted the role of sponge-associated fungi as a fruitful resource for the discovery of cytotoxic metabolites.

Enhancing insight into ferroptosis mechanisms in sepsis: A genomic and pharmacological approach integrating single-cell sequencing and Mendelian randomization

This research investigated the intricate relationship between ferroptosis and sepsis by utilizing advanced genomic and pharmacological methodologies. Specifically, we obtained expression quantitative trait loci (eQTLs) for 435 genes associated with ferroptosis from the eQTLGen Consortium and detected notable cis-eQTLs for 281 of these genes. Next, we conducted a detailed analysis to assess the impact of these eQTLs on susceptibility to sepsis using Mendelian randomization (MR) with data from a cohort of 10,154 sepsis patients and 452,764 controls sourced from the UK Biobank. MR analysis revealed 16 ferroptosis-related genes that exhibited significant associations with sepsis outcomes. To bolster the robustness of these findings, sensitivity analyses were performed to assess pleiotropy and heterogeneity, thus confirming the reliability of the causal inferences. Furthermore, single-cell RNA sequencing data from sepsis patients offered a detailed examination of gene expression profiles, demonstrating varying levels of ferroptosis marker expression across different cell types. Pathway enrichment analysis utilizing gene set enrichment analysis (GSEA) further revealed the key biological pathways involved in the progression of sepsis. Additionally, the use of computational molecular docking facilitated the prediction of interactions between identified genes and potential therapeutic compounds, highlighting novel drug targets. In conclusion, our integrated approach combining genomics and pharmacology offers valuable insights into the involvement of ferroptosis in sepsis, laying the groundwork for potential therapeutic strategies targeting this cell death pathway to enhance sepsis management.

Identification of molecular targets of Hypericum perforatum in blood for major depressive disorder: a machine-learning pharmacological study

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericum perforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD.

METHODS

We performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD.

RESULTS

Differential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking.

CONCLUSIONS

The active constituents of Hypericum perforatum can act on MDD and key targets and pathways of this action were identified.

Exploring the molecular targets of fingolimod and siponimod for treating the impaired cognition of schizophrenia using network pharmacology and molecular docking

The treatment of cognitive impairment in schizophrenia is an unaddressed need due to the absence of novel treatments. Recent studies demonstrated that fingolimod and siponimod have neuroprotective effects in several neuropsychiatric disorders; however, their pharmacological mechanisms are unclear. The objective of this study was to identify potential molecular mechanisms of fingolimod and siponimod for improving cognition of schizophrenia through network pharmacology and molecular docking. The putative target genes of ingredients, schizophrenia, and impaired cognition were obtained from online databases, including SwissTargetPrediction, PharmMapper, GeneCards, CTD, DisGeNET, and OMIM. A protein-protein interaction network was constructed to identify core targets. The DAVID database was used for GO and KEGG pathway enrichment analyses. An ingredient-target-pathway-disease network was constructed using Cytoscape. Finally, the interactions between ingredients and core targets were assessed with molecular docking. The analysis revealed 260 targets shared by fingolimod and siponimod, 257 unique targets for fingolimod, and 88 unique targets for siponimod. Two signaling pathways were involved in fingolimod-mediated improvements in the cognition of schizophrenia, including the PI3K-Akt and MAPK signaling pathways. The core targets that regulated these two pathways included IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF. The MAPK signaling pathway was involved in siponimod-mediated improvement in the cognition of schizophrenia. The MAPK pathway was regulated by three core targets, namely TNF, AKT1, and CASP3. Docking scores ranged from -5.0 to -10.4 kcal/mol. Our analysis revealed that fingolimod regulates the PI3K-Akt and MAPK signaling pathways via the core targets IL1B, AKT1, TNF, IL6, INS, BCL2, and BDNF, and siponimod regulates the MAPK signaling pathways via the core targets AKT1, TNF, and CASP3 to improve the cognition of schizophrenia. Our results provide potential targets and a theoretical basis for the design of new drugs to treat the impaired cognition of schizophrenia.

CPMKG: a condition-based knowledge graph for precision medicine

Personalized medicine tailors treatments and dosages based on a patient's unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information-such as dosage, effectiveness, and side effects-affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes. Database URL: https://www.biosino.org/cpmkg/.

Catalpol alleviates heat stroke-induced liver injury in mice by downregulating the JAK/STAT signaling pathway

BACKGROUND

Heat stroke (HS) generated liver injury is a lethal emergency that occurs when the body is exposed to temperatures up to 40 °C for a few hours.

PURPOSE

This study aimed to evaluate the therapeutic prospects of Catalpol (CA) from the blood-cooling herb Rehamanniae Radix on liver injury by HS.

STUDY DESIGN AND METHODS

A murine HS model (41 ± 0.5 °C, 60 ± 5 % relative humidity) and two cell lines (lipopolysaccharide + 42 °C) were used to assess the protective effects of CA on physiological, pathological, and biochemical features in silico, in vivo, and in vitro.

RESULTS

CA treatment significantly improved survival rates in vivo and cell viability in vitro over those of the untreated group. Additionally, CA treatment reduced core body temperature, enhanced survival time, and mitigated liver tissue damage. Furthermore, CA treatment also reduced the activities of AST and ALT enzymes in the serum samples of HS mice. Molecular docking analysis of the 28 overlapping targets between HS and CA revealed that CA has strong binding affinities for the top 15 targets. These targets are primarily involved in nine major signaling pathways, with the JAK-STAT pathway being highly associated with the other eight pathways. Our findings also indicate that CA treatment significantly downregulated the expression of proinflammatory cytokines both in vivo and in vitro while upregulating the expression of anti-inflammatory cytokines. Moreover, CA treatment reduced the levels of JAK2, phospho-STAT5, and phospho-STAT3 both in vivo and in vitro, which is consistent with its inhibition of the apoptotic markers p53, Bcl2, and Bax.

CONCLUSIONS

Heat stroke-induced liver injury was inhibited by CA through the downregulation of JAK/STAT signaling.

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish

Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-mediated adverse outcomes on female zebrafish fertility. This model started with chemical fingerprints as the input layer and was implemented through a five-layer virtual AR-induced adverse outcome pathway (AOP). Results indicated that the AR-DNN effectively and accurately screens new reproductive toxicants (AUC = 0.94, accuracy = 0.85), providing potential toxicity pathways. Furthermore, 1477 and 2448 chemicals that could lead to infertility were identified in the plastic additives list (PLASTICMAP, n = 7112) and the Inventory of Existing Chemical Substances in China (IECSC, n = 17741), respectively. Colourants containing steroid-like structures are the major active plastic additives that might lower female zebrafish fertility through AR binding, DNA binding, and transcriptional activation. While active IECSC chemicals primarily have the same fragments, such as benzonitrile, nitrobenzene, and quinolone. The predicted toxicity pathways were consistent with existing fish evidence, demonstrating the model's applicability. This knowledge-based approach offers a promising computational toxicology strategy for predicting and characterising the endocrine-disrupting effects and toxic mechanisms of organic chemicals, potentially leading to more efficient and cost-effective screening of EDCs.

BioBricks.ai: A Versioned Data Registry for Life Sciences Data Assets

Researchers in biomedical research, public health and the life sciences often spend weeks or months discovering, accessing, curating, and integrating data from disparate sources, significantly delaying the onset of actual analysis and innovation. Instead of countless developers creating redundant and inconsistent data pipelines, BioBricks.ai offers a centralized data repository and a suite of developer-friendly tools to simplify access to scientific data. Currently, BioBricks.ai delivers over ninety biological and chemical datasets. It provides a package manager-like system for installing and managing dependencies on data sources. Each 'brick' is a Data Version Control git repository that supports an updateable pipeline for extraction, transformation, and loading data into the BioBricks.ai backend at https://biobricks.ai. Use cases include accelerating data science workflows and facilitating the creation of novel data assets by integrating multiple datasets into unified, harmonized resources. In conclusion, BioBricks.ai offers an opportunity to accelerate access and use of public data through a single open platform.

VAIV bio-discovery service using transformer model and retrieval augmented generation

BACKGROUND

There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery.

MAIN BODY

We propose a novel biomedical neural search service called 'VAIV Bio-Discovery', which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25.

CONCLUSION

As a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as "CGPD-tetramers," wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum.

Hesperetin Alleviated Experimental Colitis via Regulating Ferroptosis and Gut Microbiota

Hesperetin (HT) is a type of citrus flavonoid with various pharmacological activities, including anti-tumor, anti-inflammation, antioxidant, and neuroprotective properties. However, the role and mechanism of HT in ulcerative colitis (UC) have been rarely studied. Our study aimed to uncover the beneficial effects of HT and its detailed mechanism in UC. Experimental colitis was induced by 2.5% dextran sodium sulfate (DSS) for seven days. HT ameliorated DSS-induced colitis in mice, showing marked improvement in weight loss, colon length, colonic pathological severity, and the levels of TNFα and IL6 in serum. A combination of informatics, network pharmacology, and molecular docking identified eight key targets and multi-pathways influenced by HT in UC. As a highlight, the experimental validation demonstrated that PTGS2, a marker of ferroptosis, along with other indicators of ferroptosis (such as ACSL4, Gpx4, and lipid peroxidation), were regulated by HT in vivo and in vitro. Additionally, the supplement of HT increased the diversity of gut microbiota, decreased the relative abundance of Proteobacteria and Gammaproteobacteria, and restored beneficial bacteria (Lachnospiraceae_NK4A136_group and Prevotellaceae_UCG-001). In conclusion, HT is an effective nutritional supplement against experimental colitis by suppressing ferroptosis and modulating gut microbiota.

Integrative investigation of hematotoxic effects induced by low doses of lead, cadmium, mercury and arsenic mixture: In vivo and in silico approach

The effect of the lead (Pb), cadmium (Cd), mercury (Hg) and arsenic (As) mixture (MIX) on hematotoxicity development was investigated trough combined approach. In vivo subacute study (28 days) was performed on rats (5 per group): a control group and five groups orally exposed to increasing metal(loid) mixture doses, MIX 1- MIX 5 (mg/kg bw./day) (Pb: 0.003, 0.01, 0.1, 0.3, 1; Cd: 0.01, 0.03, 0.3, 0.9, 3; Hg: 0.0002, 0.0006, 0.006, 0.018, 0.06; As: 0.002, 0.006, 0.06, 0.18, 0.6). Blood was taken for analysis of hematological parameters and serum iron (Fe) analysis. MIX treatment increased thrombocyte/platelet count and MCHC and decreased Hb, HCT, MCV and MCH values compared to control, indicating the development of anemia and thrombocytosis. BMDIs with the narrowest width were identified for MCH [pg] (6.030E-03 - 1.287E-01 mg Pb/kg bw./day; 2.010E-02 - 4.290E-01 mg Cd/kg bw./day; 4.020E-04 - 8.580E-03 mg Hg/kg bw./day; 4.020E-03 - 8.580E-02 mg As/kg bw./day). In silico analysis showed target genes connected with MIX and the development of: anemia - ACHE, GSR, PARP1, TNF; thrombocytosis - JAK2, CALR, MPL, THPO; hematological diseases - FAS and ALAD. The main extracted pathways for anemia were related to apoptosis and oxidative stress; for thrombocytosis were signaling pathways of Jak-STAT and TPO. Changes in miRNAs and transcription factors enabled the mode of action (MoA) development based on the obtained results, contributing to mechanistic understanding and hematological risk related to MIX exposure.

Transcriptomic analysis reveals molecular characterization and immune landscape of PANoptosis-related genes in atherosclerosis

BACKGROUND

Atherosclerosis is a chronic inflammatory disease characterized by abnormal lipid deposition in the arteries. Programmed cell death is involved in the inflammatory response of atherosclerosis, but PANoptosis, as a new form of programmed cell death, is still unclear in atherosclerosis. This study explored the key PANoptosis-related genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.

METHODS

We evaluated differentially expressed genes (DEGs) and immune infiltration landscape in atherosclerosis using microarray datasets and bioinformatics analysis. By intersecting PANoptosis-related genes from the GeneCards database with DEGs, we obtained a set of PANoptosis-related genes in atherosclerosis (PANoDEGs). Functional enrichment analysis of PANoDEGs was performed and protein-protein interaction (PPI) network of PANoDEGs was established. The machine learning algorithms were used to identify the key PANoDEGs closely linked to atherosclerosis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of key PANoDEGs. CIBERSORT was used to analyze the immune infiltration patterns in atherosclerosis, and the Spearman method was used to study the relationship between key PANoDEGs and immune infiltration abundance. The single gene enrichment analysis of key PANoDEGs was investigated by GSEA. The transcription factors and target miRNAs of key PANoDEGs were predicted by Cytoscape and online database, respectively. The expression of key PANoDEGs was validated through animal and cell experiments.

RESULTS

PANoDEGs in atherosclerosis were significantly enriched in apoptotic process, pyroptosis, necroptosis, cytosolic DNA-sensing pathway, NOD-like receptor signaling pathway, lipid and atherosclerosis. Four key PANoDEGs (ZBP1, SNHG6, DNM1L, and AIM2) were found to be closely related to atherosclerosis. The ROC curve analysis demonstrated that the key PANoDEGs had a strong diagnostic potential in distinguishing atherosclerotic samples from control samples. Immune cell infiltration analysis revealed that the proportion of initial B cells, plasma cells, CD4 memory resting T cells, and M1 macrophages was significantly higher in atherosclerotic tissues compared to normal tissues. Spearman analysis showed that key PANoDEGs showed strong correlations with immune cells such as T cells, macrophages, plasma cells, and mast cells. The regulatory networks of the four key PANoDEGs were established. The expression of key PANoDEGs was verified in further cell and animal experiments.

CONCLUSIONS

This study evaluated the expression changes of PANoptosis-related genes in atherosclerosis, providing a reference direction for the study of PANoptosis in atherosclerosis and offering potential new avenues for further understanding the pathogenesis and treatment strategies of atherosclerosis.

Upregulation of GOLPH3 mediated by Bisphenol a promotes colorectal cancer proliferation and migration: evidence based on integrated analysis

Background

The interaction between environmental endocrine-disrupting chemicals, such as Bisphenol A (BPA), and their influence on cancer progression, particularly regarding the GOLPH3 gene in colorectal cancer, remains unclear.

Methods

We performed an integrated analysis of transcriptional profiling, clinical data, and bioinformatics analyses utilizing data from the Comparative Toxicogenomics Database and The Cancer Genome Atlas. The study employed ClueGO, Gene Set Enrichment Analysis, and Gene Set Variation Analysis for functional enrichment analysis, alongside experimental assays to examine the effects of BPA exposure on colorectal cancer cell lines, focusing on GOLPH3 expression and its implications for cancer progression.

Results

Our findings demonstrated that BPA exposure significantly promoted the progression of colorectal cancer by upregulating GOLPH3, which in turn enhanced the malignant phenotype of colorectal cancer cells. Comparative analysis revealed elevated GOLPH3 protein levels in cancerous tissues versus normal tissues, with single-cell analysis indicating widespread GOLPH3 presence across various cell types in the cancer microenvironment. GOLPH3 was also associated with multiple carcinogenic pathways, including the G2M checkpoint. Furthermore, our investigation into the colorectal cancer microenvironment and genomic mutation signature underscored the oncogenic potential of GOLPH3, exacerbated by BPA exposure.

Conclusion

This study provides novel insights into the complex interactions between BPA exposure and GOLPH3 in the context of colorectal cancer, emphasizing the need for heightened awareness and measures to mitigate BPA exposure risks. Our findings advocate for further research to validate these observations in clinical and epidemiological settings and explore potential therapeutic targets within these pathways.

Integrating network pharmacology with microRNA microarray analysis to identify the role of miRNAs in thrombosis treated by the Dahuang Zhechong pill

BACKGROUND

Thrombotic diseases are the leading causes of death worldwide, urging for improvements in treatment strategies. Dahuang Zhechong pill (DHZCP) is a traditional Chinese medicine widely used for treating thrombotic diseases; however, the underlying mechanisms remain unclear. This study aimed to explore the potential mechanisms of DHZCP in treating thrombosis with a focus on bioinformatics and miRNAs.

METHODS

We used network pharmacology to explore the targets of thrombosis treated with DHZCP and performed microarray analysis to acquire miRNA profiles and predict the target genes in thrombin-stimulated MEG-01 cells treated with DHZCP. Based on the overlapping of targets, we carried out a component-target-miRNA network and enrichment analysis and validated the selected miRNAs and mRNAs using quantitative reverse transcription-polymerase chain reaction.

RESULTS

Our data showed 850 targets of 230 active ingredients of DHZCP and 1214 thrombosis-related genes; 235 targets were common. We identified 32 miRNAs that were regulated by thrombin stimulation but regulated reversely by DHZCP treatment in MEG-01 cells, and predicted 1846 targets with function annotation. We analyzed conjointly 23 integrating targets from network pharmacology and microarray. HIF1A, PIK3CA, MAPK1 and BCL2L1 emerged as key nodes in the network diagrams. We confirmed the differential expression of seven miRNAs, one mRNA (BCL2L1) and platelet surface protein.

CONCLUSIONS

This study showed that miRNAs and their targets, such as BCL2L1, played crucial roles in platelet activation during DHZCP intervention in thrombosis, highlighting their potential to alleviate platelet activation and increase cell apoptosis. The study's findings could help develop new strategies for improving thrombosis treatment.

Network pharmacology-based approach to elucidate the pharmacologic mechanisms of natural compounds from Dictyostelium discoideum for Alzheimer's disease treatment

Alzheimer's disease (AD) is increasingly becoming a major public health concern in our society. While many studies have explored the use of natural polyketides, alkaloids, and other chemical components in AD treatment, there is an urgent need to clarify the concept of multi-target treatment for AD. This study focuses on using network pharmacology approach to elucidate how secondary metabolites from Dictyostelium discoideum affect AD through multi-target or indirect mechanisms. The secondary metabolites produced by D. discoideum during their development were obtained from literature sources and PubChem. Disease targets were selected using GeneCards, DisGeNET, and CTD databases, while compound-based targets were identified through Swiss target prediction and Venn diagrams were used to find intersections between these targets. A network depicting the interplay among disease, drugs, active ingredients, and key target proteins (PPI network) was formed utilizing the STRING (Protein-Protein Interaction Networks Functional Enrichment Analysis) database. To anticipate the function and mechanism of the screened compounds, GO and KEGG enrichment analyses were conducted and visually presented using graphs and bubble charts. After the screening phase, the top interacting targets in the PPI network and the compound with the most active target were chosen for subsequent molecular docking and molecular dynamic simulation studies. This study identified nearly 50 potential targeting genes for each of the screened compounds and revealed multiple signaling pathways. Among these pathways, the inflammatory pathway stood out. COX-2, a receptor associated with neuroinflammation, showed differential expression in various stages of AD, particularly in pyramidal neurons during the early stages of the disease. This increase in COX-2 expression is likely induce by higher levels of IL-1, which is associated with neuritic plaques and microglial cells in AD. Molecular docking investigations demonstrated a strong binding interaction between the terpene compound PQA-11 and the neuroinflammatory receptor COX2, with a substantial binding affinity of -8.4 kcal/mol. Subsequently, a thorough analysis of the docked complex (COX2-PQA11) through Molecular Dynamics Simulation showed lower RMSD, minimal RMSF fluctuations, and a reduced total energy of -291.35 kJ/mol compared to the standard drug. These findings suggest that the therapeutic effect of PQA-11 operates through the inflammatory pathway, laying the groundwork for further in-depth research into the role of secondary metabolites in AD treatment.

Metabolomic Profiling of Leptadenia reticulata: Unveiling Therapeutic Potential for Inflammatory Diseases through Network Pharmacology and Docking Studies

Medicinal plants have been utilized since ancient times for their therapeutic properties, offering potential solutions for various ailments, including epidemics. Among these, Leptadenia reticulata, a member of the Asclepiadaceae family, has been traditionally employed to address numerous conditions such as diarrhea, cancer, and fever. In this study, employing HR-LCMS/MS(Q-TOF) analysis, we identified 113 compounds from the methanolic extract of L. reticulata. Utilizing Lipinski's rule of five, we evaluated the drug-likeness of these compounds using SwissADME and ProTox II. SwissTarget Prediction facilitated the identification of potential inflammatory targets, and these targets were discerned through the Genecard, TTD, and CTD databases. A network pharmacology analysis unveiled hub proteins including CCR2, ICAM1, KIT, MPO, NOS2, and STAT3. Molecular docking studies identified various constituents of L. reticulata, exhibiting high binding affinity scores. Further investigations involving in vivo testing and genomic analyses of metabolite-encoding genes will be pivotal in developing efficacious natural-source drugs. Additionally, the potential of molecular dynamics simulations warrants exploration, offering insights into the dynamic behavior of protein-compound interactions and guiding the design of novel therapeutics.

Assessing the ecological impacts of polycyclic aromatic hydrocarbons petroleum pollutants using a network toxicity model

Polycyclic aromatic hydrocarbons (PAHs) are significant petroleum pollutants that have long-term impacts on human health and ecosystems. However, assessing their toxicity presents challenges due to factors such as cost, time, and the need for comprehensive multi-component analysis methods. In this study, we utilized network toxicity models, enrichment analysis, and molecular docking to analyze the toxicity mechanisms of PAHs at different levels: compounds, target genes, pathways, and species. Additionally, we used the maximum acceptable concentration (MAC) value and risk quotient (RQ) as an indicator for the potential ecological risk assessment of PAHs. The results showed that higher molecular weight PAHs had increased lipophilicity and higher toxicity. Benzo[a]pyrene and Fluoranthene were identified as core compounds, which increased the risk of cancer by affecting core target genes such as CCND1 in the human body, thereby influencing signal transduction and the immune system. In terms of biological species, PAHs had a greater toxic impact on aquatic organisms compared to terrestrial organisms. High molecular weight PAHs had lower effective concentrations on biological species, and the ecological risk was higher in the Yellow River Delta region. This research highlights the potential application of network toxicity models in understanding the toxicity mechanisms and species toxicity of PAHs and provides valuable insights for monitoring, prevention, and ecological risk assessment of these pollutants.

Investigation of the shared gene signatures and molecular mechanisms between obstructive sleep apnea syndrome and asthma

BACKGROUND

Obstructive sleep apnea syndrome (OSAS) is highly related with asthma from the epidemiology to pathogenesis, while the underlying mechanism is still unclear. Herein, we aimed to reveal the shared gene signatures and molecular mechanisms underlying the coexistence of OSAS and asthma and verified relating pathway in mouse models. We downloaded GSE75097 of OSAS and GSE165934 of asthma from GEO database and performed differential expression analysis and functional enrichment analysis to screen differentially expressed genes (DEGs) and potential pathogenic pathway. PPI network was constructed with the STRING database. Hub genes were identified with cytoHubba and immune infiltration analysis was performed with cibersort for further verification. Potential drugs were screened with Comparative Toxicogenomics Database and miRNA-gene network was constructed. Besides, to test the pulmonary function and inflammatory cytokine, mouse models with OSAS and asthma were constructed, followed by validating the involvement of NOD1/NOD2-RIPK2-NF-κB-MCPIP-1 pathway in associated diseases.

RESULTS

In total, 104 DEGs were identified, in which PLAUR, RIPK2, PELI1, ZC3H12A, and TNFAIP8 are the hub genes, while NOD-like receptor signaling pathway and apoptosis signaling pathway were the potential influential pathways. Increased γδT cells and neutrophils were detected in asthma patients through immune infiltration analysis. Significant difference was detected among genders in OSAS, and acetaminophen is a potential drug in the comorbidity by screening the drugs in the Comparative Toxicogenomics Database. Mice with OSAS and asthma presented with worse pulmonary function and higher levels of inflammatory cytokines. The relative proteins, including NOD1, NOD2, RIPK2, NF-κB, and MCPIP-1, were up-regulated in mice with the OSAS and asthma.

CONCLUSIONS

This research firstly elucidates NOD1/NOD2-RIPK2-NF-κB-MCPIP-1 pathway as the shared pathway in the development of OSAS and asthma through bioinformatics and experimental methods. There is an interactive deterioration model between OSAS and asthma. This study may provide some potential biomarkers in the future research of the underlying pathogenesis and treatment of comorbidity of OSAS and asthma.

Mechanism of Erianin anti-triple negative breast cancer based on transcriptomics methods and network pharmacology

Triple negative breast cancer (TNBC) is a highly aggressive illness that lacks effective targeted treatments. Although Erianin has shown potential antitumor properties, its precise mechanism of action and target in TNBC remain unclear, hampering the development of drugs. The present study investigated the underlying mechanism of action of Erianin in treating TNBC by using transcriptomics and network pharmacology approaches. We evaluated Erianin's bioactivity in TNBC cell lines and xenograft tumor models. The results showed that Erianin significantly inhibited TNBC cell proliferation and impeded tumor growth. A subsequent analysis of transcriptomic and network pharmacological data identified 51 mutual targets. Analysis of protein-protein interactions identified eight hub targets. Furthermore, molecular docking indicated that the PPARA binding energy was the lowest for Erianin among the hub targets, followed by ROCK2, PDGFRB, CCND1, MUC1, and CDK1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis showed that the common targets were associated with multiple cancer-related signaling pathways, including focal adhesion, PI3K-Akt signaling pathway, Rap1 signaling pathway, microRNAs in cancer, and human papillomavirus infection. The results of the Western blot and immunohistochemistry experiment further showed that Erianin could suppress PI3K/Akt signaling pathway activation. After co-incubation with SC79, the cell inhibition rate of Erianin was decreased, which further confirmed that Erianin inhibits TNBC progression via the PI3K-AKT signaling pathway. In conclusion, our results indicated that Erianin has the potential to inhibit the proliferation of TNBC by downregulating the PI3K/AKT signaling pathway by transcriptomics and network pharmacology. Therefore, Erianin appears to be a promising compound for the effective treatment of TNBC.

Dual-channel hypergraph convolutional network for predicting herb-disease associations

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Integrated genomic network analysis revealed potential of a druggable target for hemorrhoid treatment

Hemorrhoids are a prevalent medical condition that necessitates effective treatment options. The current options for treatment consist of oral medications, topical applications, or surgery, yet a scarcity of highly effective drugs still exists. Genetic markers provide promising avenues for investigating the treatment of hemorrhoids, as they may reveal intricate biological mechanisms and targeted drug therapies, ultimately enhancing more precise treatment tailored to the patient. This study aims to identify new drug candidates for treating hemorrhoids through a meticulous bioinformatics approach and integrated with genomic network analysis. After extracting 21 druggable target genes using DrugBank from 293 genes connected to hemorrhoids, 87 possible drugs were selected. Three of these drugs (ketamine, methylene blue, and fulvestrant) hold potential in addressing issues associated with hemorrhoids and have been supported by clinical or preclinical studies. Eighty-four compounds present new therapeutic possibilities for managing hemorrhoids. We highlight that our findings indicate that NOX1 and NOS3 genes are promising biomarkers, with NOS3 gaining significance owing to its robust systemic functional annotations. Sapropterin, an existing drug, is closely associated with NOS3, providing a clear target for biomarker-driven interventions. This study illustrates the potential of combining genomic network analysis with bioinformatics to repurpose drugs for treating hemorrhoids. Subsequent research will explore the mechanisms for utilizing NOS3 targeting in the treatment of hemorrhoids.