Archives of Toxicology

BACKGROUND Botanicals are widely consumed as herbal remedies and plant-derived products, but their complex and variable chemical composition presents major challenges for safety assessment. Although hepatotoxicity has been reported for several botanicals, the constituent compounds responsible and the underlying mechanisms often remain poorly characterised. OBJECTIVES We aimed to determine whether Cell Painting could identify candidate constituent drivers of botanical bioactivity and generate hypotheses about potential mechanisms of action in primary human hepatocytes. METHODS We profiled 13 botanical extracts and 85 known constituents across multiple concentrations using Cell Painting alongside assays of metabolic activity and membrane integrity. To interpret morphological phenotypes, we trained machine-learning models using a Cell Painting reference atlas of >1,000 chemicals linked to ToxCast/Tox21 molecular and cellular bioactivity annotations. RESULTS Cell Painting detected bioactivity in 11/13 botanical extracts and 46/85 constituents, compared with 9/13 extracts and 40/85 constituents in the cell-health assays, and generally detected activity at lower concentrations. For 9/13 extracts, at least one constituent was active at a lower concentration than the corresponding botanical extract, supporting the contribution of a subset of potent constituents to mixture-level responses. Aristolochia and goldenseal were the most potent extracts across Cell Painting and cell-health assays and contained highly active constituents. Reference-atlas models generated specific mechanistic hypotheses, including cytotoxicity-, endocrine-, and genotoxicity-associated responses for several botanicals and constituents. CONCLUSION These results support Cell Painting combined with machine learning as a sensitive approach for characterising cellular responses to botanicals, prioritising candidate constituent drivers of hepatotoxicity-associated phenotypes, and generating testable hypotheses about potential mechanisms of action.

Genetic variation impacts biological response to chemical and environmental exposures, contributing to differences in resistance or susceptibility. Forward genetic screens of genetically diverse cell populations can be used to identify the precise genetic variants that drive response variation. Cell lines from laboratory mouse genetic reference populations like the Diversity Outbred (DO) population are well powered for genetic mapping. Diverse stem cell panels are especially beneficial for studying early developmental effects of chemical exposures like triphenyl phosphate (TPHP), an organophosphate flame retardant linked to adverse developmental effects, including altered cell cycle in stem cells. Self-renewal and pluripotency are defining properties of stem cells during development; therefore, it is important to understand how chemicals like TPHP may alter these critical characteristics. To better understand the influence of genetic variation on TPHP exposure response, we utilized a genetically diverse panel of DO induced pluripotent stem cells (DO iPSCs). Our analyses characterize interline variation in gene expression through differential gene expression analysis and expression quantitative trait locus (eQTL) mapping. We identified genomic loci that contribute to variation in gene expression following TPHP exposure, including a regulatory hotspot on chromosome 15. Gene set enrichment highlighted several pathways affected by TPHP including lipid metabolism, steroid hormone signaling, and cell cycle. Our study shows that genetic variation modulates the effects of TPHP on gene expression and cell cycle in stem cells. Additionally, our work demonstrates that genetically diverse cell panels like the DO iPSC resource offer a tractable and scalable approach for identifying the genetic determinants of chemical toxicity.

Drug-induced liver injury (DILI) remains a major challenge in drug development because existing in vitro models poorly predict clinical hepatotoxicity. We coupled patient-derived human liver organoids (HLOs) with high-content imaging and automated machine learning (ML) to detect hepatotoxicity-associated phenotypes by morphological profiling. HLOs from six DILI patients and one healthy donor (n = 7) retained donor-specific baseline phenotypes in hepatocyte maturity, metabolism, and morphology. ML models trained per line on MAD-robustized, well-level-aggregated median features achieved compound-level AUROCs of 0.88 (95% CI 0.86-0.90) to 0.91 (95% CI 0.89-0.93) and balanced accuracies of 0.81-0.84 in distinguishing 54 hepatotoxicants from 20 non-hepatotoxic controls. Averaging predictions across all seven lines at an optimized threshold gave 94.4% sensitivity (95% CI 84.6-98.8%) and 85.0% specificity (95% CI 62.1-96.8%), substantially outperforming HLO cytotoxicity assays and cancer cell line profiling. Lines were moderately-to-highly concordant (Pearson r = 0.66-0.85), and benchmark concentration modeling revealed reproducible inter-line differences in phenotypic sensitivity (median 4.0-fold, several compounds exceeding 20-fold). Exposure anchoring of benchmark concentrations to human-equivalent plasma concentrations, using in vitro mass-balance dosimetry and curated plasma free fractions, placed organoid points of departure within 100-fold of clinical Cmax for 60 of 66 compounds (23-fold residual offset, 95% CI 12-45), and placed two of the three misclassified negative controls an order of magnitude above clinical Cmax. Sulindac injury signatures were significantly enhanced by TNF co-treatment, demonstrating capacity to detect pro-inflammatory-mediated DILI. This platform preserves inter-line variability while enabling reproducible, scalable multi-line consensus hazard classification; because compounds were tested over a common nominal concentration range, the readout defines hepatocellular hazard, which must be combined with exposure as an independent input to estimate clinical DILI risk.

Human metabolism converts inorganic arsenic to the pentavalent methylated species MMA(V) and DMA(V), the forms most people excrete, and the forms long read as the end of a detoxification pathway. Whether a transporter sets how much of these metabolites reaches the genome has not been tested in a mammalian cell. We expressed human AQP3, AQP7, AQP9 or AQP10 in HEK293T and MRC5-SV40 cells and measured gamma-H2AX by flow cytometry across dose series of As(V), MMA(V) and DMA(V), pairing every aquaporin with a GFP-Tubulin control and an untransfected mock acquired in the same replicate. As(V) was inactive in HEK293T cells and only weakly active in MRC5-SV40 cells to 20 micromolar, and both methylated species damaged DNA only in the millimolar range, DMA(V) being the more potent of the two in both cell lines. Against that weak baseline, AQP9 and AQP10 raised DMA(V)-induced gamma-H2AX in HEK293T cells by roughly 17 percentage points over the matched control, more than doubling the damage the same exposure produced in control cells, whereas AQP3 and AQP7 changed it not at all. AQP9 alone remained active with MMA(V). The ranking held in MRC5-SV40 fibroblasts at one-sixth the size, and within single wells the damage rose with the amount of AQP9 a cell carried while the control was flat. Aquaglyceroporins therefore discriminate among arsenic species, and AQP9 and AQP10 turn a weakly genotoxic metabolite into a substantially more genotoxic one.

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

Acute radiation syndrome remains a major consequence of high-dose ionizing radiation exposure, and effective pharmacological countermeasures are limited. Nitro-fatty acids possess anti-inflammatory and cytoprotective properties, suggesting potential effectiveness in radiation injury. Here, we investigated the radioprotective effects of 10-nitro-oleic acid (10-NO2OA) in mouse models of total body irradiation (TBI). C57BL/6 mice were exposed to either sublethal (4 Gy) or LD50-level (7.5 Gy) gamma irradiation and treated with 10-NO2OA according to pre-irradiation, post-irradiation, or combined treatment regimens. Survival, hematological responses, bone marrow cells (BMCs)/colony-forming units (CFU), plasma granulocyte colony-stimulating factor (G-CSF), NRF2-responsive signaling, and intestinal responses were evaluated. Administration of 10-NO2OA before and after irradiation significantly improved 30-day survival following 7.5 Gy TBI (57% vs. 20% in vehicle-treated controls). In contrast, post-irradiation treatment alone was ineffective, whereas pre-irradiation treatment alone produced a non-significant trend toward improved survival. The protective effect was absent in BALB/c mice, indicating strain dependence. Although 10-NO2OA did not prevent long-term radiation-induced cytopenia, it increased neutrophil and platelet counts, elevated plasma G-CSF levels shortly after irradiation, and transiently enhanced BMC counts. In irradiated BMCs, 10-NO2OA activated NRF2-responsive gene expression in vitro and in vivo. Exploratory intestinal analyses revealed improved macroscopic appearance and partial restoration of irradiation-suppressed cytokine and growth factor production. In conclusion, 10-NO2OA improves survival following LD50-level gamma irradiation and is associated with modulation of hematopoietic, cytoprotective, and intestinal responses. These findings identify 10-NO2OA as a promising candidate radioprotective agent and support a multifactorial mechanism of action.

Experimental assessment of developmental neurotoxicity (DNT) is costly, time-consuming, and difficult to scale across large chemical inventories. New approach methodologies (NAMs), including computational approaches based on machine learning, deep learning, and large language models (LLMs), offer new ways to screen and prioritize chemicals. However, these emerging computational approaches have largely focused on predicting toxicity endpoints, with less attention to the mechanisms underlying those predictions. For DNT assessment, understanding this mechanistic basis is equally important. In this study, we integrated LLM inference with the adverse outcome pathway (AOP) framework to connect DNT predictions with plausible mechanisms. We constructed an AOP-informed benchmark linking ChEMBL target-activity measurements for 5,861 compounds to 14 AOPs representing five neurodevelopmental outcomes and evaluated ten LLMs for the prediction of molecular initiating events (MIEs), key events (KEs), key event relationships (KERs), and adverse outcomes (AOs), as well as the generation of mechanistic explanations. Explanation quality was evaluated using both a naive LLM judge and a rubric-based LLM judge. Across the zero-shot models, the highest observed macro-F1 scores were 0.818 for MIEs and 0.463 for AOs, but only 0.056 for KEs and 0.034 for KERs, identifying downstream pathway reconstruction as the principal challenge. Few-shot prompting substantially improved performance, with Gemini 3.8 Flash under four-shot prompting achieving macro-F1 scores of 0.956 for AOs, 0.813 for MIEs, 0.492 for KEs, and 0.457 for KERs. Both LLM-judge configurations indicated improved mechanistic explanation quality with few-shot examples, although strong outcome prediction did not consistently ensure recovery of the recorded pathway. Our results demonstrate the potential of AOP-informed LLMs to support DNT assessment by linking predicted neurodevelopmental outcomes to plausible biological mechanisms and enabling predictions to be evaluated at both the outcome and pathway levels.

Pesticide use is ubiquitous. While pesticide resistance in target species such as insects is a well-known phenomenon, it is less known how pesticide exposures affect the genetics of non-target species. Here, we demonstrate the research value of integrating existing datasets to study the relationship between chemicals and biodiversity from the genetics perspective by analyzing spatial pesticide concentration data and genetic data from Europe that were not initially generated for toxicological purposes. Specifically, we found associations between environmental pesticide concentrations and genetic variation (i.e., single nucleotide polymorphisms, SNPs) in a non-target species, the wild boar. Gene Ontology (GO) terms and biological signaling pathways related to nervous system function were overrepresented for the impacted genes, especially for insecticides and fungicides. Further, we found that external studies characterizing the molecular mechanisms of these pesticides revealed the same pesticide - GO relationships as those observed in our analysis. Our results show that pesticide-induced genetic changes may not be constrained to target species but may also have an impact on the long-term viability of other wildlife populations. We therefore encourage further research into this area and collaboration across disciplines to develop more comprehensive designated datasets for such analyses. This finding also urges the consideration and inclusion of chemical exposure into conservation protection strategies and greater consideration of genetic impacts from chemicals in environmental risk assessment.

Current insecticide toxicology still lacks an integrated understanding of how lethal and sublethal exposure shape organismal performance across biologically and environmentally relevant contexts. In particular, insecticide susceptibility may vary with physiological state, environmental conditions, and recent exposure history, factors that are only partially captured by standardized toxicity assessments. Here, using Drosophila melanogaster, we systematically compared responses to three insecticides with distinct modes of action - acetamiprid, chlorantraniliprole, and deltamethrin - across developmental stages, sexes, adult ages, and ambient temperatures. Insecticide sensitivity varied substantially across these conditions, with temperature modulating toxicity in a compound-dependent manner. Notably, deltamethrin caused substantial lethality at concentrations more than 10,000-fold below approved field application rates. Beyond these context-dependent differences in acute toxicity, sublethal pre-exposure to each insecticide rapidly reduced lethality during a subsequent challenge with the same compound, with increased tolerance already detectable after 24 h. Importantly, these exposure conditions that enhanced insecticide tolerance were associated with reduced resistance to environmental challenges, including heat and nutrient starvation, revealing a potential trade-off in organismal stress resilience. Acetamiprid exposure further altered reproductive performance in a concentration-dependent manner, with higher exposure reducing egg production while increasing developmental success among the resulting offspring, such that the number of offspring reaching adulthood remained largely unchanged. Together, these findings demonstrate that insecticide susceptibility is highly context dependent and can be rapidly modified by recent exposure history. We identify short-term, compound-specific, and potentially costly tolerance as an underappreciated consequence of insecticide exposure and highlight the importance of incorporating biological context, environmental conditions, and sublethal exposure history into pesticide risk assessment.

Objective To systematically evaluate the predictability of drug chemical structures against the complete set of MedDRA Preferred Terms (PTs), construct a comprehensive structure adverse reaction landscape, validate the utility of molecular fingerprint representations in pan-screening tasks, and establish quantitative tools for drug safety pre-screening. Methods Drug adverse event association data were compiled from SIDER 4.1 and its derivative compilation Nsider, encompassing 2,005 approved drugs, of which 1,120 small-molecule drugs with successfully retrieved chemical structures were retained for analysis. For each Preferred Term (PT) meeting the minimum sample-size threshold ([&ge;]25 positive and [&ge;]30 negative drugs; n = 768), XGBoost classifiers were trained using MACCS 166-bit fingerprints, with model performance evaluated via 5-fold stratified cross-validation. A PT was designated as high-confidence if it achieved a validation AUC [&ge;] 0.75, a cross-validation AUC [&ge;] 0.70, and passed Benjamini Hochberg false discovery rate (FDR) correction (q < 0.05). The entire analytical pipeline was replicated using Morgan ECFP4 2048-bit fingerprints to assess the robustness of fingerprint selection. The robustness of significance screening was independently validated by permutation testing (500 random label permutations), and model generalizability was evaluated through Bemis-Murcko scaffold-based stratified split validation. SHAP (SHapley Additive exPlanations) analysis was employed to identify critical structural features, and mechanism-based case studies further demonstrated concordance between model-identified features and established pharmacophores. Results Among the 768 modelable PTs, 71 (9.2%) met the high-confidence significance criteria (FDR q < 0.05), predominantly comprising nervous system disorders (10 PTs) and psychiatric disorders (9 PTs). The toxicities exhibiting the strongest structural predictability included hyperprolactinaemia (AUC = 0.968), dermatitis acneiform (AUC = 0.966), tardive dyskinesia (AUC = 0.952), and breast tenderness (AUC = 0.979). Under a unified XGBoost hyperparameter configuration, the low-dimensional MACCS fingerprint (166-bit) demonstrated near-equivalent performance to the high-dimensional Morgan fingerprint (2048-bit) in pan-screening tasks ({Delta}AUC = -0.012, Cohen's d = 0.16), and achieved comparable performance at a substantially lower dimensionality across 18 of 22 organ systems. SHAP analysis identified key structural fragments corresponding to established pharmacophores (six-membered rings, tertiary amine cores, heteroatomic bonds, and bridged heteroatomic chains), and mechanism-based case studies further demonstrated that the model captured structural patterns concordant with known pharmacophores of D2 antagonists, EGFR inhibitors, and related agents. Bemis-Murcko scaffold-based stratified split validation revealed a median AUC decrease of merely 0.042, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions. Immune-mediated and multifactorial endpoints remained structurally opaque under the current data conditions. Conclusion This study constructed a comprehensive structure-adverse reaction predictability landscape encompassing 768 MedDRA PTs; seventy-one PTs achieved high-confidence criteria. The MACCS 166-bit fingerprint achieved near-equivalent performance to the Morgan 2048-bit fingerprint at a markedly lower dimensionality, and its chemically explicit features enable direct translation of model outputs into structural alerts, providing a computational tool for prospective screening of potential adverse reaction profiles for any novel compound. A tiered assessment strategy is proposed: machine learning ensemble models for high-confidence screening, substructure alert rules for high-specificity exclusion, and pharmacovigilance signal detection for complementary coverage of structurally unpredictable endpoints. Bemis-Murcko scaffold-based stratified split validation further demonstrated that the median generalization performance decrease was merely 0.042 AUC, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions, corroborating the existence of a genuine chemical basis underlying structure-adverse reaction mappings.

Carbaryl, a widely used carbamate pesticide, has been epidemiologically associated with genotoxic and carcinogenic outcomes in exposed human populations, including elevated cancer risk at several tissue sites and, in some agricultural communities, chronic tubulointerstitial kidney disease of unknown etiology (CKDu). However, the cellular mechanisms underlying these associations, and whether carbaryl acts uniformly across tissues, remain poorly defined. Screening a panel of cell lines of distinct species and lineage, we found that carbaryl elicits pronounced, dose-dependent cytotoxicity and cell cycle arrest selectively in a renal tubular epithelial line (IMCD3) and, to a lesser extent, a cervical epithelial line (HeLa), while a fibroblast line (NIH3T3) was unaffected. In IMCD3 cells, carbaryl induces S- and G2/M-phase arrest through direct binding to DNA and consequent DNA damage, triggers Bcl-2-mediated late apoptosis independently of reactive oxygen species, and drives irreversible mitotic arrest. Molecular dynamics simulations, cellular thermal shift assays, and kinase measurements converge on CDK1 as a direct target of carbaryl: carbaryl destabilises the CDK1 active site and inhibits its catalytic activity, whereas CDK2 is engaged through an opposing, rigidifying mode with no functional consequence. This asymmetry provides a coherent mechanistic basis for the combined S-phase and G2/M arrest observed. Together, these findings establish carbaryl as a genotoxic, cell-cycle-disrupting agent with tissue-selective potency acting directly on CDK1, and provide a cell-based mechanistic framework for interpreting its reported associations with renal and other tissue pathologies in exposed human populations.

Benzyl paraben (BzP) and butyl paraben (BuP) are used as preservatives and antimicrobial agents in cosmetics, pharmaceuticals, and foods. BzP and BuP have been detected in urine, placental tissue, and ovarian follicular fluid and are understudied with regards to their endocrine activity, raising concerns on their impact to the reproductive system. We tested the hypothesis that BzP and BuP are toxic to the ovary and behave similarly to other structurally similar endocrine disrupting chemicals, such as phthalates and terephthalates, by altering hormone levels and impacting cell growth and gene expression in vitro. Untreated mouse antral follicles were exposed to vehicle, 0.1, 1, 10, or 100 g/mL BzP, BuP, or the BuP analog monobutyl terephthalate (MBuTP) for 96 hours. The highest dose of BzP and BuP were cytotoxic to follicles, suggesting that medium chain length hydroxybenzoates are more cytotoxic to the ovary than corresponding terephthalic acids. BzP significantly increased levels of estradiol and testosterone compared to control. BuP decreased follicle growth and disrupted expression of cell cycle regulators, similar to phthalates. The peroxisome proliferator-activated receptor gamma (PPARg)-transcriptionally controlled gene Fabp4 was significantly upregulated by both BzP and BuP, whereas MBuTP upregulated all four PPAR target genes and decreased follicle growth. Overall, BzP, BuP, and MBuTP disrupted hormone levels, follicle growth, and cell cycle progression, suggesting similarities in toxicity to phthalates and potential endocrine disruption. Further study should be done on the reproductive toxicity and endocrine disrupting effects of chemicals similar in structure to phthalates to reduce exposure to endocrine disruptors and inform design of safer replacements.

BACKGROUND: Pesticides are extensively used in agriculture, raising concerns about their potential impact on human health through dietary and environmental exposure. OBJECTIVES: This study evaluated the in vitro cytotoxicity of ten commonly used pesticides and their mixtures (lambda-cyhalothrin, cypermethrin, deltamethrin, tebuconazole, glyphosate, acetamiprid, cyprodinil, piperonyl butoxide, fluopyram, and imazalil) on human intestinal Caco-2 cells. METHODS: Cytotoxicity was assessed using the MTT assay, as a measure of metabolic activity, and the trypan blue exclusion test, as an indicator of cell membrane integrity. FINDINGS: Results showed that high concentrations (100 mg/L) of all pesticides significantly reduced cell viability and vitality. Notably, glyphosate and tebuconazole exhibited significant toxicity even at lower concentrations, respectively 0.1 mg/L and 10 mg/L. Combination treatments (Top 3 and Top 8 pesticide mixtures) retained the cytotoxic effects observed for individual compounds, showing additive (non-synergistic) effects. CONCLUSIONS: Overall, these findings indicate that certain pesticides-based herbicides can exert cytotoxic effects on intestinal cells even at relatively low concentrations and highlight the importance of using the component-based approach in mixture risk assessment for humans. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

The pharmacokinetics concentration-time profile encodes ADME dynamics. Its optimization in drug discovery curbs late-stage attrition, yet forecasting human pharmacokinetics from chemical structure remains difficult. Machine learning models ignore continuous ADME dynamics, whereas physiologically based pharmacokinetics (PBPK) models demand unavailable parameters and impose rigid, misspecified compartmental structures. We present PK-MUSE (PharmacoKinetic hybrid Mechanistic Universal System Equation), augmenting a two-compartment model with bounded, state- and time-dependent neural corrections to elimination and intercompartmental distribution. Molecular structure parameterizes compound-specific behavior, while learned corrections refine dynamic equations. Across scaffold and temporal out-of-distribution evaluations, PK-MUSE achieved the lowest mean exposure-endpoint MdFE, reducing MdFE by 7.2%, 45.2%, and 26.2% versus state-of-the-art machine learning (HML- RF), deep learning compartmental (DeepCt), and PBPK (PK-Sim) models. Interrogating the corrections localized misspecification to distribution rather than elimination. By extending mechanistic simulators with data-driven constrained neural dynamics that preserve interpretability and generalize to unseen chemotypes, PK-MUSE advances digital twins for drug discovery.

Objectives: Social media trends such as the "Benadryl Challenge" have increased adolescent misuse of diphenhydramine (DPH), an over-the-counter antihistamine with anticholinergic activity. Whether co-ingested anticholinergic medications compound cardiac risk is untested. Using the Anticholinergic Cognitive Burden (ACB) scale, this study examined whether co-medication ACB burden is associated with outcome severity and cardiac toxicity in adolescent DPH cases reported to the FDA Adverse Event Monitoring System (AEMS). Methods: This study extracted all adolescent reports (age 13-19) mentioning DPH in any drug role from AEMS, yielding 3,168 cases from 217,507 deduplicated records. Given non-normality (Shapiro-Wilk, all p < 0.001), non-parametric tests were used. A multivariable logistic regression predicted cardiac toxicity; logistic and random-forest models were compared by cross-validated AUC. Results: Co-medication ACB burden correlated positively with case severity ({rho} = 0.142, p < 0.001) and differed by age group (H = 19.52, p < 0.001). However, burden and cardiac risk diverged: cardiac cases carried a lower burden (mean 0.55 vs 1.27) and were younger, and burden did not independently predict cardiac toxicity (OR = 0.927, 95% CI [0.841, 1.022], p = 0.128). Younger age was the strongest predictor (OR = 0.865 per year, p < 0.001); cardiac events occurred in 10.2% of 13-15-year-olds versus 4.9% of older adolescents. Conclusion: Anticholinergic co-medication burden and cardiac risk dissociate: burden tracks overall severity, but cardiac events concentrate in the youngest, least co-medicated adolescents, a profile consistent with deliberate single-agent overdose rather than polypharmacy. A low co-medication burden should not reassure clinicians triaging young adolescents.

Lead (Pb) exposure is strongly associated with cardiovascular diseases, but the effects of developmental Pb exposure on the electrophysiological properties of human cardiomyocytes have not yet been investigated. Using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes, we investigated the effects of human-relevant Pb exposure during ventricular cardiomyocyte differentiation on action potential properties and calcium dynamics in differentiated myocytes. We exposed 2 iPSC lines, from 1 male and 1 female donor, to Pb (0.5 or 5 micromolar) or control during only the first 8 days of differentiation, prior to functional maturation. Differentiation was carried out using an established protocol involving temporal modulation of Wnt signaling, followed by purification and maturation of contractile myocytes. We measured action potential properties and calcium dynamics using fluorescent dyes and optical mapping. We hypothesized that Pb exposure would adversely affect cardiomyocyte function, with effects persisting after cessation of exposure. In keeping with this hypothesis, we found significant Pb-induced changes in calcium (Ca) transients and action potential dynamics that were concentration and cell line specific and persisted well after exposure had ended. In the female cell line, beat frequency significantly increased with Pb exposure for both action potentials and Ca transients, while in the male cell line, beat frequency significantly decreased with Pb exposure for action potentials and increased for Ca transients. For voltage measurements, we observed significantly increased arrythmias with Pb exposure in both cell lines. Significant changes in repolarization and recovery time (for voltage and Ca, respectively) were present with Pb exposure in both cell lines. Gene expression of Ca transporters/channels and K channels were not affected, suggesting that persistent effects of Pb exposure on cardiomyocyte electrophysiology may occur through post-translational mechanisms. Overall, this study paves the way for future work to examine how exposure to Pb impacts long-term cardiac function through developmental programming.

Chemobrain, characterized by cognitive dysfunction, has emerged as a significant health concern following cancer treatment. Doxorubicin, an FDA-approved chemotherapeutic agent, is widely used to treat various cancers. However, its adverse effects raise concerns regarding toxicity in non-cancerous cell types. Microglia, the resident immune cells of the brain, play a central role in maintaining neural homeostasis. Despite this, the mechanisms by which microglia become dysfunctional following doxorubicin exposure remain poorly understood. Understanding the transcriptomic alterations induced by doxorubicin in microglia could provide critical insights into mechanisms underlying its neurotoxic effects. In this study, the transcriptomic response of human microglia to doxorubicin using mRNA sequencing combined with bioinformatic analysis was investigated. Exposure to doxorubicin significantly altered microglial morphology, promoting a shift toward a process-bearing phenotype in human HMC3 cells. This phenotypic change was accompanied by elevated levels of DNA damage and was associated with the induction of cellular senescence. At the gene regulation level, the Illumina-based transcriptomic analysis provided 25,834 gene expression profiling in which 5,669 were differentially expressed. The future analyses revealed that 3,431 (upregulated) and 2,238 (downregulated) were differentially expressed under the treatment of doxorubicin. Genes associated with the p53 signaling pathway and inflammatory responses were upregulated, whereas genes involved in translation, DNA replication, and cell-cycle progression were downregulated. This transcriptional dysregulation was accompanied by differential expression of key transcription factors. This study provides transcriptomic evidence elucidating the genotoxic response to doxorubicin in microglial cells, offering mechanistic insights into chemobrain and highlighting key molecular pathways as potential targets for therapeutic intervention.

Peroxisome proliferator-activated receptor (PPAR) is a nuclear receptor that plays a central role in metabolic adaptation by regulating gene expression programs involved in lipid utilization and energy homeostasis. Although the hepatic functions of PPAR and its target genes are well characterized, less is known about its transcriptional activity in extrahepatic tissues. Pemafibrate (K-877), a selective PPAR modulator (SPPARM), is a high-affinity PPAR ligand with improved potency and selectivity compared with conventional fibrates. Here, we investigated pemafibrate-induced tissue-specific transcriptional responses comparing wild type and whole-body PPAR-deficient littermate mice. Twelve-week-old male PPAR+/+ and PPAR-/- mice were treated daily with pemafibrate (0.1 mg/kg) or vehicle by oral gavage for 14 days. We performed genome-wide transcriptomic analyses in the liver, jejunum, kidney, soleus muscle, and BAT. Pemafibrate-induced transcriptional changes were entirely dependent on PPAR, as no differentially expressed genes were detected in PPAR-/- mice. The magnitude and nature of the response were tissue-specific, with the strongest transcriptional effects observed in the liver and jejunum, whereas the kidney, soleus muscle, and BAT showed limited responses. In the liver, pemafibrate primarily induced the expression of genes involved in fatty acid metabolism, mitochondrial function, and energy production. In the jejunum, pemafibrate activated lipid metabolic programs together with tissue-specific pathways related to cellular homeostasis. Collectively, our findings show that pemafibrate is a highly selective PPAR activator with a preferential transcriptional activity in the liver and intestine after oral administration. This study provides a resource for defining tissue-specific PPAR-responsive gene networks and expands our understanding of PPAR functions beyond the liver.

Environmental exposure involves pollutant mixtures. Building on evidence that individual bisphenols alter zebrafish larval physiology and transcriptomic profiles at ng/L concentrations, we assessed the effects of specific mixtures of BPS, BPF, and BPAF without (M1) or with (M2) BPA. Experimentally, M1 and M2 induced 4.16% and 6.54% mortality in zebrafish larvae, despite the concentration addition model predicting no toxicity. Both mixtures reduced swimming-locomotor activities. These behaviors were accompanied by organ-level functional defects, as in vivo optical mapping revealed bradycardia and increased high-frequency (30-80 Hz) brain oscillations for both mixtures, while M2 additionally altered low-frequency (0.2-4 Hz) brain activity. The neurophysiological changes were accompanied by neurovascular remodeling, with in vivo two-photon microscopy revealing decreased vascular length and branching. Finally, transcriptomic analysis revealed common M1- and M2-induced alterations linked to pathways of vascular function, neuronal communication, and metabolism. M2 altered additional pathways of cardiac and neuronal signaling, consistent with the reported organ-physiology effects. We discuss the environmental relevance of mixture exposures and assess whether BPA intensifies mixture-induced responses.

Chemotaxis assays are widely used to quantify sensory-driven behavioral responses in Caenorhabditis elegans. Sodium azide is commonly used to immobilize animals at assay endpoints, although concerns have been raised that diffusion beyond the endpoint region could affect behavior before a choice is completed. We compared sodium azide-mediated immobilization with post-assay cooling using the repellent 1-octanol and attractant diacetyl. Wild-type animals showed robust avoidance of 30% 1-octanol and attraction to 0.5% diacetyl with either method, across multiple assay durations, and with or without worm food Escherichia coli OP50. These results support post-assay cooling as an alternative endpoint-scoring method.