Search Results (45)

This study aims to elucidate the neurodevelopmental toxicity and molecular mechanisms of endocrine-disrupting chemicals (EDCs) in neurodevelopmental disorders (NDDs) through a network toxicology approach, using triclosan exposure as a case example. Potential targets of triclosan were identified via comparative analysis of toxicogenomics databases such as the Comparative Toxicogenomics Database (CTD), Similarity Ensemble Approach (SEA), SwissTargetPrediction, and TargetNet. NDD-related targets were retrieved from GeneCards, Disease Gene Network (DisGeNET), and Online Mendelian Inheritance in Man (OMIM), resulting in 633 overlapping genes associated with disease pathology and triclosan effectors. Protein–protein interaction networks were constructed using STRING and Cytoscape, applying median-based algorithms to identify six core genes: AKT1, TP53, EGFR, FN1, SRC, and ESR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses via Metascape revealed that triclosan-induced NDDs are primarily associated with endocrine signaling disruption and activation of the PI3K-Akt pathway. Molecular docking with CB-Dock2 demonstrated strong binding affinities between triclosan and the core targets, while YASARA molecular dynamics simulations confirmed stable interactions, notably with EGFR, exhibiting high binding stability. Collectively, these findings delineate the potential molecular mechanisms underlying triclosan-induced NDDs and underscore the utility of network toxicology, molecular docking, and molecular dynamics simulations in assessing neurotoxicity and related molecular pathways. This research provides novel insights for future investigations, enhances understanding of the potential impact of neurodevelopmental disorders on health, and lays a scientific foundation for the development of preventive and therapeutic strategies. Full article

Viral hepatitis B and C (HBV and HCV) and metabolic dysfunction-associated steatotic liver disease (MASLD) are major public health concerns in Mexico, driving liver cirrhosis and hepatocellular carcinoma. The Genome-based Mexican (GENOMEX) diet, rich in bioactive compounds, may provide a nutritional strategy for preventing and managing liver disease. This study combines a literature review with integrative bioinformatic analyses to map the antiviral and hepatoprotective mechanisms activated by GENOMEX-derived bioactives and assess their therapeutic potential for preventing and managing liver disease. A literature-based review integrated with bioinformatics to identify the pathways activated by nutrients and bioactive compounds of the GENOMEX diet against HBV, HCV, and MASLD, incorporating data from in silico, in vitro, in vivo, and clinical studies, was conducted. An integrative bioinformatic approach, incorporating the Comparative Toxicogenomic Database and Functional Enrichment Analysis (STRING, DAVID, and Enrichr), was used to identify links between genes, nutrients, and bioactive compounds, with a subset of Mexican food staples included in the GENOMEX diet. The GENOMEX diet includes bioactive nutrients that may modulate molecular pathways related to immune response, oxidative stress, nutrient metabolism, and inflammation. Through integrative analysis, we identified key molecular targets—including TNF, PPARA, TP53, and IL6—that are implicated in viral replication, MASLD progression, and hepatocarcinogenesis. Functional enrichment revealed that these traditional Mexican foods and their nutrients are associated with genes and pathways involved in viral infection, metabolic dysfunction, fibrosis, and liver cancer. These findings highlight that the gene–nutrient interactions of the Mexican staple food in the GENOMEX diet can be integrated into nutritional strategies to prevent and manage HBV, HCV, and MASLD, while reducing fibrosis and HCC progression. These strategies are especially relevant in regions where antiviral treatments are limited due to high costs, antiviral resistance, and an escalating mismatch between the population’s evolutionary genetics and modern environment. Full article

Background: Ulcerative colitis (UC) is a chronic inflammatory disease of the colon with a rising global incidence. Natural conjugated taurocholic acid (TCA) possesses anti-inflammatory properties and shows potential therapeutic effects against UC, although the underlying mechanisms remain unclear. Methods: This study employed an integrative approach—combining network pharmacology, bioinformatics, machine learning, immune infiltration analysis, and molecular docking—to investigate the therapeutic mechanisms of TCA in UC. UC-related gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database, and potential TCA targets were predicted using the Comparative Toxicogenomics Database (CTD) and TargetNet platforms. Differentially expressed genes (DEGs) were identified and analyzed via GO and KEGG enrichment analyses. Results: Four machine learning algorithms (XGBoost, RF, SVM, and NNet) were used to identify six hub genes (TGM2, MMP9, ABCB1, NOS2, ABCG2, CASP1), which were further validated using an artificial neural network. Immune infiltration analysis with CIBERSORT revealed significant alterations in immune cell populations in UC tissues. Further validation through an artificial neural network model confirmed their predictive ability. The enrichment analysis of the hub genes highlighted their roles in immune-related pathways, while the immune infiltration analysis indicated significant differences in immune cell populations between ulcerative colitis tissues and control tissues. The molecular docking results showed that the binding energies of these six proteins to TCA were lower than −5 kcal/mol, with TGM2 having the strongest binding affinity (−10 kcal/mol). The intervention of TCA on colitis mice could improve the inflammatory response by regulating the expression of the TGM2 gene. Conclusions: In conclusion, this study suggests that taurocholate alleviates ulcerative colitis by targeting key genes such as TGM2 and modulating immune-related pathways, providing a novel basis for future therapeutic exploration. Full article

Per- and polyfluoroalkyl substances (PFASs) are widespread in the environment, bioaccumulate in humans, and lead to disease and organ injury, such as liver steatosis. However, we lack a clear understanding of how these chemicals cause organ-level toxicity. Here, we aimed to analyze PFAS-induced metabolic perturbations in male and female rat livers by combining a genome-scale metabolic model (GEM) and toxicogenomics. The combined approach overcomes the limitations of the individual methods by taking into account the interaction between multiple genes for metabolic reactions and using gene expression to constrain the predicted mechanistic possibilities. We obtained transcriptomic data from an acute exposure study, where male and female rats received a daily PFAS dose for five consecutive days, followed by liver transcriptome measurement. We integrated the transcriptome expression data with a rat GEM to computationally predict the metabolic activity in each rat’s liver, compare it between the control and PFAS-exposed rats, and predict the benchmark dose (BMD) at which each chemical induced metabolic changes. Overall, our results suggest that PFAS-induced metabolic changes occurred primarily within the lipid and amino acid pathways and were similar between the sexes but varied in the extent of change per dose based on sex and PFAS type. Specifically, we identified that PFASs affect fatty acid-related pathways (biosynthesis, oxidation, and sphingolipid metabolism), energy metabolism, protein metabolism, and inflammatory and inositol metabolite pools, which have been associated with fatty liver and/or insulin resistance. Based on these results, we hypothesize that PFAS exposure induces changes in liver metabolism and makes the organ sensitive to metabolic diseases in both sexes. Furthermore, we conclude that male rats are more sensitive to PFAS-induced metabolic aberrations in the liver than female rats. This combined approach using GEM-based predictions and BMD analysis can help develop mechanistic hypotheses regarding how toxicant exposure leads to metabolic disruptions and how these effects may differ between the sexes, thereby assisting in the metabolic risk assessment of toxicants. Full article

(1) Background: Epidemiological studies link ozone (O₃) exposure to diabetes risk, but mechanisms and early biomarkers remain unclear. (2) Methods: Female mice exposed to 0.5/1.0 ppm O₃ were assessed for glucose tolerance and HOMA (homeostasis model assessment) index. Genes related to impaired glucose tolerance and insulin resistance were screened through the Comparative Toxicogenomics Database (CTD), and verified using quantitative real-time PCR. In addition, liver histopathological observations and the determination of basic biochemical indicators were conducted, and targeted metabolomics analysis was performed on the liver to verify glycogen levels and gene expression. In vitro validation was conducted with HepG2 and Min6 cell lines. (3) Results: Fasting blood glucose and insulin resistance were elevated following O₃ exposure. Given that the liver plays a critical role in glucose metabolism, we further investigated hepatocyte apoptosis and alterations in glycogen metabolism, including reduced glycogen levels and genetic dysregulation. Metabolomics analysis revealed abnormalities in fructose metabolism and glycogen synthesis in the livers of the O₃-exposed group. In vitro studies demonstrated that oxidative stress enhances both liver cell apoptosis and insulin resistance in pancreatic islet β cells. (4) Conclusions: O₃ triggers prediabetes symptoms via hepatic metabolic dysfunction and hepatocyte apoptosis. The identified metabolites and genes offer potential as early biomarkers and therapeutic targets. Full article

Environmental contaminants exhibit heterogeneous neurotoxicity profiles, yet systematic comparisons between legacy neurotoxicants and emerging pollutants remain scarce. To address this gap, we implemented an integrative transcriptome meta-analysis framework that harmonized eight transcriptomic datasets spanning in vivo and in vitro neural models exposed to two legacy neurotoxicants (bisphenol A [BPA], 2, 2′, 4, 4′-tetrabromodiphenyl ether [BDE-47]) and polystyrene nanoplastics (PSNPs) as an emerging contaminant. Our analysis revealed a substantial overlap (68% consistency) in differentially expressed genes (DEGs) between BPA and PSNPs, with shared enrichment in extracellular matrix disruption pathways (e.g., “fibronectin binding” and “collagen binding”, p < 0.05). Network-based toxicogenomic mapping linked all three contaminants to six neurological disorders, with BPA showing the strongest associations with Hepatolenticular Degeneration. Crucially, a sex-stratified analysis uncovered male-specific transcriptional responses to BPA (e.g., lipid metabolism and immune response dysregulation), whereas female models showed no equivalent enrichment. This highlights the sex-specific transcriptional characteristics of BPA exposure. This study establishes a novel computational toxicology workflow that bridges legacy and emerging contaminant research, providing mechanistic insights for chemical prioritization and gender-specific risk assessment. Full article

The metalloid arsenic (As) and the metals lead (Pb) and mercury (Hg), which together we call the “Toxic Triad”, are among the pollutants of greatest global concern, harming the health of millions of people and contributing to biodiversity loss. The widespread distribution of As, Pb and Hg facilitates the exposure of humans and other species to these elements simultaneously, potentially amplifying their individual toxic effects. While As, Pb and Hg are well established as toxic elements, the mechanisms by which they interact with genetic material and impact the health of various species remain incompletely understood. This is particularly true regarding the combined effects of these three elements. In this context, the objective of this work was to perform a toxicogenomic analysis of As, Pb and Hg to highlight multiple aspects of element-gene interactions, in addition to revisiting information on the genotoxicity and carcinogenicity of the Toxic Triad. By using The Comparative Toxicogenomics Database, it was possible to identify that As interacts with 7666 genes across various species, while Pb influences 3525 genes, and Hg affects 692 genes. Removing duplicate gene names, the three elements interact with 9763 genes across multiple species. Considering the top-20 As/Pb/Hg-interacting genes, catalase (CAT), NFE2 like bZIP transcription factor 2 (NFE2L2), caspase 3 (CASP3), heme oxygenase (HMOX1), tumor necrosis factor (TNF), NAD(P)H quinone dehydrogenase 1 (NQO1) and interleukin 6 (IL6) were the most frequently observed. In total, 172 genes have the potential to interact with the three elements. Gene ontology analysis based on those genes evidenced that the Toxic Triad affects several cellular compartments and molecular functions, highlighting its effect on stimulation of toxic stress mechanisms. These 172 genes are also associated with various diseases, especially those of the urogenital tract, as well as being related to biological pathways involved in infectious diseases caused by viruses, bacteria and parasites. Arsenic was the element with the best-substantiated genotoxic and carcinogenic activity. This article details, through a toxicogenomic approach, the genetic bases that underlie the toxic effects of As, Pb and Hg. Full article

Rectal carcinoma (RC) represents approximately 30% of all colorectal carcinomas (CRC) and is considered a distinct clinical entity. Vascular invasion (VI) is recognized as an independent predictor of poor outcomes in RC. In this study, we applied bioinformatics methods to identify gene pathways most likely associated with VI in rectal carcinoma. As ADAMTS8 showed statistically significant negative relations with the VI in RC patients, we further analyzed its top co-dependent genes—DNAL4, EVI2B, PPP1R35, PTGR3, RPL21, SOX4, and ZNF3—for the experimentally proven molecular modulators. We identified a total of 23 compounds from the Comparative Toxicogenomics Database based on previously reported data for all eight target genes. The search was expanded to include additional chemical agents by structure similarity using the PubChem database, which revealed 9661 additional compounds. These were subsequently used for molecular interaction analysis against target proteins co-expressed with, or associated with, ADAMTS8 in RC with VI. Ultimately, we identified four high-affinity compounds—cyanoginosin LR, doxorubicin, benzo[a]pyrene, and dibenzo(a,e)pyrene—that interacted with all target proteins. These compounds show potential for further assessment of their role in modulating processes related to vascular invasion, which is a strong negative predictor of RC outcomes. Full article

Bifenthrin (BF) is a widely used pyrethroid pesticide recognized as an endocrine-disrupting chemical (EDC). Previous studies have confirmed that chronic exposure to BF is associated with various health risks. However, its potential association with recurrent implantation failure (RIF) and recurrent pregnancy loss (RPL) remains unclear. In this study, the potential targets of BF were identified using several databases, including the Comparative Toxicogenomics Database (CTD), TargetNet, GeneCards, SwissTargetPrediction, and STITCH. Differentially expressed genes (DEGs) associated with RIF were obtained from bulk RNA-seq datasets in the GEO database. Candidate targets were identified by intersecting the predicted BF-related targets with the RIF-associated DEGs, followed by functional enrichment analysis using the DAVID and g:Profiler platforms. Subsequently, hub genes were identified based on the STRING database and Cytoscape. A diagnostic model was then constructed based on these hub genes in the RIF cohort and validated in an independent recurrent pregnancy loss (RPL) cohort. Additionally, we performed single-cell type distribution analysis and immune infiltration profiling based on single-cell RNA-seq and bulk RNA-seq data, respectively. Molecular docking analysis using AutoDock Vina was conducted to evaluate the binding affinity between BF and the four hub proteins, as well as several hormone-related receptors. Functional enrichment results indicated that the candidate genes were mainly involved in apoptotic and oxidative stress-related pathways. Ultimately, four hub genes—BCL2, HMOX1, CYCS, and PTGS2—were identified. The diagnostic model based on these genes exhibited good predictive performance in the RIF cohort and was successfully validated in the RPL cohort. Single-cell transcriptomic analysis revealed a significant increase in the proportion of myeloid cells in RPL patients, while immune infiltration analysis showed a consistent downregulation of M2 macrophages in both RIF and RPL. Moreover, molecular docking analysis revealed that BF exhibited high binding affinity to all four hub proteins and demonstrated strong binding potential with multiple hormone receptors, particularly pregnane X receptor (PXR), estrogen receptor α (ESRα), and thyroid hormone receptors (TR). In conclusion, the association of BF with four hub genes and multiple hormone receptors suggests a potential link to immune and endocrine dysregulation observed in RIF and RPL. However, in vivo and in vitro experimental evidence is currently lacking, and further studies are needed to elucidate the mechanisms by which BF may contribute to RIF and RPL. Full article

Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance and precise mechanism remain unknown, it is considered an adaptive response resulting from the induction of drug-metabolizing enzymes (DMEs). This study aimed to elucidate the association between centrilobular hepatocyte hypertrophy and DME induction using machine learning on toxicogenomic data. Utilizing publicly available gene expression data and pathological findings from rat livers of 134 compounds, we developed six different types of machine learning models to predict the occurrence of centrilobular hepatocyte hypertrophy based on gene expression data as explanatory variables. Among these, a LightGBM-based model demonstrated the best performance with an accuracy of approximately 0.9. With this model, we assessed each gene’s contribution to predicting centrilobular hepatocyte hypertrophy using mean absolute SHAP values. The results revealed that Cyp2b1 had an extremely significant contribution, while other DME genes also displayed positive contributions. Additionally, enrichment analysis of the top 100 genes based on mean absolute SHAP values identified “Metabolism of xenobiotics by cytochrome P450” as the most significantly enriched term. In conclusion, the current results suggest that the induction of multiple DMEs, including CYP2B1, is crucial for the development of centrilobular hepatocyte hypertrophy. Full article

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. Full article

Rapid industrialization has introduced a range of chemicals into the environment, posing significant risks to fetal and child brain development. Using the Comparative Toxicogenomics Database (CTD), we constructed chemical exposome frameworks for seven neurodevelopmental disorders (NDDs) and identified chemical pollutants of epidemiological concern, including air pollutants (n = 8), toxic elements (n = 14), pesticides and related compounds (n = 18), synthetic organic chemicals (n = 16), and solvents (n = 5). Gene set enrichment analysis validated and revealed significant toxicogenomic associations between these chemical pollutants and NDDs, including autism spectrum disorder (ASD) (12 pollutants, proportional reporting ratio (PRR) 3.56–7.21) and intellectual disability (ID) (9 pollutants, PRR 3.13–5.59). Functional annotation of pollutant-specific gene sets highlighted shared biological processes, such as metabolic processes (e.g., xenobiotic metabolic process, xenobiotic catabolic process, and cytochrome P450 pathway) for ASD and cognitive processes (e.g., cognition, social behavior, and synapse assembly) for ID (Bonferroni-corrected p-values < 0.05). Time trajectory analysis of developmental transcriptomic data from the BrainSpan database for ASD (275 genes) and ID (93 genes) revealed three distinct expression patterns of chemical-pollutant-associated genes—higher prenatal, postnatal, and perinatal expression—indicating common and divergent underlying mechanisms across critical windows of chemical pollutant exposure. Full article

Per- and polyfluoroalkyl substances (PFAS) are a group of environmental contaminants associated with various health risks; however, their relationship with all-cause mortality in individuals with diabetes remains unclear. A total of 1256 participants from the National Health and Nutrition Examination Survey (NHANES) were included to explore the association between seven PFAS compounds and all-cause mortality in diabetic patients. Preliminary logistic regression identified three PFAS compounds (perfluorooctanoic acid [PFOA], perfluorooctane sulfonic acid [PFOS], and 2-(N-methyl-PFOSA) acetate acid [MPAH]) as significantly associated with mortality in the diabetic population. The optimal cut-off values for PFOS, PFOA, and MPAH were determined using the X-tile algorithm, and participants were categorized into high- and low-exposure groups. Kaplan–Meier survival curves and multivariable Cox proportional hazards regression models were used to assess the relationship between PFAS levels and mortality risk. The results showed that high levels of PFOS were significantly associated with increased all-cause mortality risk in diabetic patients (hazard ratio [HR]: 1.55, 95% confidence interval [CI]: 1.06–2.29), while PFOA and MPAH showed no significant associations. To explore mechanisms underlying the PFOS–mortality link, toxicogenomic analysis identified 95 overlapping genes associated with PFOS exposure and diabetes-related mortality using the Comparative Toxicogenomics Database (CTD) and GeneCards. Functional enrichment analysis revealed key biological processes, such as glucose homeostasis and response to peptide hormone, with pathways including the longevity regulating pathway, apoptosis, and p53 signaling pathway. Protein–protein interaction network analysis identified 10 hub genes, and PFOS was found to upregulate or downregulate their mRNA expression, protein activity, or protein expression, with notable effects on mRNA levels. These findings suggest that PFOS exposure contributes to increased mortality risk in diabetic patients through pathways related to glucose metabolism, apoptosis, and cellular signaling. Our study provides new insights into the association between PFAS and all-cause mortality in diabetes, highlighting the need for large-scale cohort studies and further in vivo and in vitro experiments to validate these findings. Full article

This study aimed to explore the health impacts, mechanisms of toxicity, and key gene biomarkers of a mixture of the most prominent perfluoroalkyl/polyfluoroalkyl substances (PFAS) through in silico ADMET and toxicogenomic analysis. The following databases and tools were used: AdmetSAR (2.0), ADMETlab (2.0), Comparative Toxicogenomic Database, ToppGene Suite portal, Metascape (3.5), GeneMANIA server, and CytoHubba and CytoNCA Cytoscape (3.10.3) plug-ins. ADMET analysis showed that PFAS compounds pose risks of organ-specific toxicity, prolonged retention, and metabolic disruptions. Forty mutual genes were identified for all the tested PFAS. The mutual gene set was linked to disruption of lipid metabolism, particularly through nuclear receptors. The most important gene clusters identified were nuclear receptor signaling and PPAR signaling pathways, with kidney and liver diseases, diabetes, and obesity as the most significant related diseases. Phenotype data showed that PFAS compounds impact cell death, growth, inflammation, steroid biosynthesis, and thyroid hormone metabolism. Gene network analysis revealed that 52% of the 40 mutual genes showed co-expression, with co-localization as the next major interaction (18.23%). Eight key genes were extracted from the network: EHHADH, APOA2, MBL2, SULT2A1, FABP1, PPARA, PCK2, and PLIN2. These results highlight the need for further research to fully understand the health risks of PFAS mixtures. Full article

In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical–protein–disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical–protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical–protein–disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical–disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical–disease inference and can be implemented for other in silico toxicogenomics tools. Full article