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BMDx2: A Tool for Integrating Toxicogenomics-Based Dose-Dependency Analysis and AOP-Based Mechanistic Insights.

Despite the advent of mechanistic toxicology using omics data to link molecular perturbations with systemic outcomes, regulatory toxicology still lacks the application of mechanism-anchored metrics from such data. This is partially because traditional gene-centric analysis often falls short of linking molecular changes to adverse outcomes. To address this gap, BMDx2, an open-source tool that transforms multi-dose toxicogenomics datasets into quantitative, mechanistic evidence for human chemical safety assessment is developed. BMDx2 couples benchmark-dose modeling with Adverse Outcome Pathway (AOP) enrichment to derive transcriptomic-based points of departure, enabling potency ranking, chemical prioritization, and mechanistically anchored explanations of the effect of chemical exposures. BMDx2 can process a broad range of data, including DNA microarray and RNA sequencing studies. Here, case studies are used to illustrate the versatility of BMDx2 in characterizing the mechanism of action of chemicals. An initial case study on carbon nanotubes exposure applies integrative analysis of transcriptomics and genome-wide DNA methylation data, uncovering cellular reprogramming processes underlying fibrosis. A second case study on bleomycin exposure demonstrate how transcriptomic data alone can be mapped to fibrosis-related AOPs in a standardized, regulatory appropriate manner. Together, these examples show how BMDx2 supports the regulatory application of toxicogenomics and accelerates mechanism-based chemical safety evaluation.

Toxicogenetics

ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery.

MOTIVATION: Understanding the genetic basis of drug-induced toxicity is crucial for drug development. In-silico analysis of toxicogenomics datasets facilitates early detection of toxicity biomarkers. However, existing tools struggle with the complex interdependencies among hierarchically structured variables, leading to inaccurate biomarker identification. To address this limitation, we developed a Hierarchical Linear Model (HLM) and implemented it in the R package ToxAssay, offering extensive functionality for comprehensive toxicity assessment. RESULTS: ToxAssay outperforms existing methods by improving biomarker detection and computational efficiency. Applied to glutathione depletion-induced toxicity, it prioritized 71 key genes and identified 26 core genes with high discriminative accuracy (AUC = 0.97) and strong cross-correlation (Pearson's r = 0.88) with external datasets. Additionally, our advance outcome pathway (AOP) analysis algorithm uncovered disease outcomes linked to glutathione depletion. These findings provide precise insights into the molecular mechanisms driving drug-induced toxicity. AVAILABILITY AND IMPLEMENTATION: ToxAssay is available as an open-source R package at https://github.com/Fun-Gene/toxassay.

Biomarkers

Exploring potential targets and molecular mechanisms of traumatic brain injury exacerbated by Benzo(a)pyrene via network toxicology and molecular dynamics simulation.

Benzo(a)pyrene (BaP) is a common environmental pollutant from combustion sources that promotes oxidative stress, neuroinflammation and disruption of blood-brain barrier (BBB). However, its contribution to worsening traumatic brain injury (TBI) remains unclear. In this study, we aimed to assess the contribution of BaP to secondary injury in TBI. By integrating data from e.g., the Comparative Toxicogenomics Database, GeneCards, and Online Mendelian Inheritance in Man, 121 overlapping core targets were identified between BaP and TBI. Enrichment analyses via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, combined with protein-protein interaction networks and topological algorithms (degree, closeness centrality, betweenness centrality, average shortest path length, topological coefficient and partner of multi-edged node pairs), highlighted five hub genes (TP53, EGFR, AKT1, ACTB, and TNF) implicated in mitogen-activated protein kinase signaling, oxidative stress, and neuroinflammation. Molecular docking showed strong binding affinities of BaP to these hub proteins, with energies from -9.3 to -12.1&#xa0;kcal/mol, tighter than co-crystal ligands and existing protein-binding drugs. Molecular dynamics simulations confirmed interaction stability through low root-mean-square deviation (<&#x2009;0.5&#xa0;nm), fluctuation, and radius of gyration values. Calculation of binding free energies using MM-PBSA validated the strong binding affinity between BaP and binding pockets of each hub genes. Toxicity prediction analysis revealed an oral LD50 of 316&#xa0;mg/kg for BaP, with high probabilities for neurotoxicity, BBB permeability, carcinogenicity, and mutagenicity, associated with aryl hydrocarbon receptor activation. These findings reveal a "neurovascular homeostasis disruption" network underlying BaP-exacerbated TBI pathology and highlight potential targets to reduce pollution-related risks in TBI management.

Benzo(a)pyrene

Expression and prognosis of CXCL13 in uterine corpus endometrial carcinoma based on bioinformatics analysis.

OBJECTIVE: The biological significance of the chemokine ligand C-X-C motif chemokine ligand 13 (CXCL13) may play a significant role in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). This study aims to identify and verify CXCL13 with predictive value for prognosis in UCEC. METHODS: CXCL13 mRNA expression differences were analyzed using R software in three independent datasets: one each from The Cancer Genome Atlas (TCGA) and two from the Gene Expression Omnibus (GEO), namely GSE17025 and GSE106191. The correlation between CXCL13 expression and prognosis was evaluated by Kaplan-Meier analysis. Univariate and multivariate Cox analyses were utilized to construct a prognostic nomogram. Tumor Immune Estimation Resource (TIMER) and the Tumor and Immune System Interaction Database (TISIDB) were employed to assess the relationship between CXCL13 and tumor immune infiltration. Coexpressed genes with CXCL13 were identified by the Spearman correlation analysis. A CXCL13 protein-protein interaction (PPI) network was constructed with the STRING website tool and hub genes were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses were performed with the "clusterProfiler" R package. Gene set enrichment analysis (GSEA) was used to identify underlying biological mechanisms. A drug-gene interaction network was constructed in the Comparative Toxicogenomics Database (CTD). RESULTS: High CXCL13 mRNA expression were validated in UCEC in the above three independent datasets. High CXCL13 expression was associated with favorable prognosis in UCEC. A nomogram for predicting the 1-, 3-, and 5-year survival probability in UCEC was construct based on CXCL13 expression and other clinical parameters. The use of Spearman correlation indicated certain correlation between CXCL13 and immune cells and immune checkpoint (ICP) genes. Seven hub genes were upregulated in UCEC, namely CXCL9, IFNG, CXCL10, CXCL11, GBP5, CCL18, and GZMB. The expression and prognostic relevance of CXCL9, IFNG, GBP5, and GZMB were in accordance with CXCL13. The main biological processes enriched were cytokine-cytokine receptor interaction and chemokine signaling pathway. CONCLUSIONS: The above comprehensive analyses suggest that CXCL13 may serve as a potential prognostic biomarker for UCEC, specifically for early-stage UCEC.

CXCL13

An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics.

Adjuvants are immunostimulators used to enhance vaccine efficacy against infectious diseases. However, current methods for evaluating their efficacy and safety are limited, hindering large-scale screening. To address this, we developed a prototype Adjuvant Database (ADB) containing transcriptome data, generated using the same protocols as the widely used Open TG-GATEs (OTG) toxicogenomics database, covering 25 adjuvants across multiple species, organs, time points, and doses. This enabled cross-database integration of ADB and OTG. Transcriptomic patterns successfully distinguished each adjuvant regardless of organs or species. Using both databases, we built machine learning models to predict adjuvanticity and hepatotoxicity. Notably, we identified colchicine's adjuvant activity and FK565's liver toxicity through data-driven analysis. Overall, ADB combined with OTG offers a framework for transcriptomics-based, data-driven screening of adjuvant candidates.

Animals

Identification of CD55 as a downstream factor of EP4 receptor signaling in colorectal cancer cells.

Prostaglandin E2 (PGE2) signaling through the E-type prostanoid 4 (EP4) receptor has been implicated in the pathophysiology of colorectal cancer (CRC). We herein identified decay-accelerating factor, also known as CD55, as a novel CRC-associated downstream factor of the EP4 receptor. The integration of transcriptomic profiling of PGE2-stimulated HCA-7 human colon cancer cells with analyses of cancer genomic databases predicted CD55 as a potential EP4 receptor-regulated target. Inhibitor-based experiments showed the induction of CD55 after a PGE2 stimulation required the EP4 receptor and Gi protein in HCA-7 cells, whereas protein kinase A signaling was dispensable. In combination with a toxicogenomic database analysis, p38 mitogen-activated protein kinase (MAPK) was identified as the predominant effector connecting the EP4 receptor to CD55 upregulation. A single-cell RNA-seq re-analysis of human CRC tissues revealed CD55 upregulation and p38 MAPK-related gene set enrichment in epithelial cells expressing the EP4 receptor, suggesting that this induction mechanism may operate in a subset of epithelial cells in clinical specimens. Collectively, these results delineate a PGE2/EP4 receptor/Gi protein/p38 MAPK signaling axis that induces CD55 expression in HCA-7 cells and epithelial tumor cells, provide new mechanistic clues for understanding the regulation of complement regulatory molecule CD55 expression by prostaglandin signaling.

Humans

Identification of mitochondrial energy metabolism-related candidate genes UQCR10 and NDUFA6 in pediatric tetralogy of fallot: an exploratory bioinformatics study.

BACKGROUND: Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases in infants and young children. Its molecular basis remains incompletely understood. This study aimed to identify mitochondrial energy metabolism-related candidate genes associated with pediatric TOF using public heart tissue transcriptomic datasets from the GEO database. METHODS: Datasets GSE146218 and GSE217772 were downloaded and merged, followed by batch-effect correction. Differential expression analysis was performed to identify differentially expressed genes (DEGs). Functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were used to prioritize candidate genes. The Comparative Toxicogenomics Database (CTD) was used as an exploratory literature-based tool to summarize gene-disease associations. RESULTS: A total of 960 DEGs were identified. Functional enrichment analyses showed that these genes were mainly enriched in mitochondrial energy metabolism-related pathways, including oxidative phosphorylation and the mitochondrial respiratory chain. WGCNA and PPI network analyses further prioritized UQCR10 and NDUFA6 as candidate genes, and both genes showed increased expression in TOF heart tissue samples. CTD analysis suggested literature-based associations between these genes and cardiovascular or developmental disease-related terms. CONCLUSION: This exploratory bioinformatics study identified UQCR10 and NDUFA6 as mitochondrial energy metabolism-related candidate genes upregulated in pediatric TOF heart tissue. These findings suggest that mitochondrial respiratory chain-related transcriptional alterations may be involved in TOF-associated myocardial remodeling or stress responses. Further experimental and clinical validation is required to confirm their biological relevance.

Humans

Systematic understanding of mechanism of Shenfu decoction improve the prognosis of ischemic stroke using a network pharmacology and animal experiment approach.

OBJECTIVE: To explore the active compounds and the mechanism of Shenfu decoction (, SFD) against ischemic stroke (IS) through network pharmacology and animal experiments. METHODS: SFD components were retrieved from the Traditional Chinese Medicine (TCM) database. The Online Mendelian Inheritance in Man (OMIM), Comparative Toxicogenomics Database (CTD) and Therapeutic Target Database (TTD) database were used to retrieve the IS-related disease targets. The herb-compound-target network was built by Cytoscape 3.7.1 software. The core targets were obtained using protein-protein interaction (PPI) network. The core targets of SFD were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then performed molecular docking between the hub proteins and key active compounds. Finally, we conducted animal experiments to verify the regulation of SFD on apoptosis following IS. RESULTS: There were 221 corresponding targets and 25 components related to Chinese medicine throughout the compound-target network. The core targets of SFD in the treatment of IS was tumor protein P53 (Tp53), mitogen-activated protein kinase 3 (MAPK3), MAPK1, heat shock proteins 90AA1 and alpha serine/threonine-protein kinase1. There were 221 GO items in GO function enrichment analysis and 106 signaling pathways in KEGG, mainly including negative regulation of the apoptosis process, vascular endothelial growth factor signaling pathways, NOD-like receptor signaling pathway, etc. Among them, Tp53, MAPK3, and MAPK1 were docked with small molecule compounds. Through animal research, we confirmed the effect of SFD on apoptosis following stroke. CONCLUSION: This study demonstrates that SFD can treat IS through multiple targets and pathways, and provides new perspectives for exploring the core targets and mechanisms of SFD against IS.

Drugs, Chinese Herbal

Divergent PXR function in seals: Endocrine adaptation or functional loss?

Seals accumulate xenobiotics through dietary biomagnification and exposure to polluted marine environments, with contaminants concentrating in their blubber. Biotransformation mitigates xenobiotic toxicity by converting lipophilic compounds into excretable hydrophilic metabolites, a process coordinated by nuclear receptors including the Pregnane X Receptor (PXR), whose plastic ligand-binding domain enables broad xenobiotic sensing. By examining PXR in pinnipeds, we investigated the evolutionary conservation and functional characterization of PXR using genomic sequence analysis, protein structural prediction, and transactivation assays, revealing broadly conserved structural features alongside species-specific functional divergence in receptor responsiveness to environmental stressors. Specifically, the obtained results highlight divergent gene and functional landscapes with ORF-disrupting mutations identified in Monachus monachus and Neomonachus schauinslandi that abolish receptor activation toward known PXR ligands. In contrast, Leptonychotes weddelli retained an intact PXR ORF but showed reduced receptor activity, revealing functional divergence in PXR among pinnipeds.

Biotransformation