Biology subjects

Mertens, B.

Publications and source records attributed to Mertens, B..

4 recordsLinked to original sources

bioRxiv2026

Hazard characterization of Alternaria toxins - filling data gaps on in vitro genotoxicity.

Alternaria toxins are naturally occurring food contaminants with limited and often inconsistent genotoxicity and mutagenicity data. Within the European Partnership for the Assessment of Risks from Chemicals (PARC), an OECD-aligned in vitro testing strategy was applied to fill existing data gaps and to characterize the genotoxic potential of major Alternaria toxins using high-purity test materials. Mutagenicity was assessed using bacterial reverse mutation test (OECD TG 471) and SOS/umu assay, while chromosomal damage was assessed using the in vitro micronucleus (MN) assay (OECD TG 487) in TK6 and HepG2 cells, complemented by fluorescence in situ hybridization (FISH) and {gamma}H2AX assay in HepaRG cells. Alternariol (AOH), alternariol monomethyl ether (AME), and altertoxin-I (ATX-I) showed clear mutagenicity in bacteria, whereas altenuene (ALT), tenuazonic acid (TeA), and tentoxin (TEN) were negative under the tested conditions. In mammalian cells, AOH, AME, and ATX-I induced MN formation in TK6 cells at concentrations [≥]5.5 {micro}M, [≥]2.5 {micro}M, and [≥]0.21 {micro}M, respectively, with FISH analysis supporting a clastogenic mode of action. In HepG2 cells, all tested toxins induced chromosomal damage, with effect threshold ranging from [≥]6.25 {micro}M (AOH) to [≥]50 {micro}M (TeA). {gamma}H2AX induction confirmed DNA damage for AOH and ATX-I, and at higher concentrations for TeA (1000 {micro}M). Overall, the data indicate clear in vitro genotoxic potential for AOH, AME, and ATX-I and provide evidence of chromosomal damage for ALT, TEN, and TeA, thereby reducing critical data gaps for hazard assessment.

pharmacology and toxicology↗

bioRxiv2026

Analytical Choices Drive Toxicogenomic Potency Estimates: A Systematic Evaluation of Transcriptomic Points of Departure

Omics technologies are increasingly integrated into next-generation risk assessment, yet quantitative toxicogenomics outcomes remain highly dependent on analytical choices, motivating a systematic evaluation of how bioinformatics workflows influence hazard characterization and transcriptomic Points of Departure (tPOD). Here, we applied five independent transcriptomics pipelines to a shared dataset of RPTEC-TERT1 kidney cells exposed to cisplatin across multiple concentrations and timepoints, comparing effects of pre-processing, benchmark concentration modeling, and pathway-based interpretation strategies. Across workflows, substantial variability was observed in gene-level benchmark concentrations (BMCs), primarily driven by differences in normalization, filtering, and especially the modeling software used. Despite this variability, convergence increased at later timepoints as transcriptional responses strengthened, with 24 h consistently identified as the most sensitive timepoint at the gene level. Aggregation of gene-level BMCs into pathway-based metrics reduced variability but did not eliminate it, with pathway definition emerging as a major determinant of sensitivity estimates. Notably, distinct pathway resources showed minimal gene overlap, and smaller, biologically coherent gene sets (e.g., co-expression modules and biomarker panels) produced lower and less dispersed BMCs compared with broader pathway annotations. Furthermore, direct modeling of pathway activity scores yielded systematically different sensitivity estimates relative to median-based aggregation, with method-dependent conservativeness influenced by pathway coverage and response strength. Overall, our findings demonstrate that both analytical workflow design and pathway selection critically shape toxicogenomic-derived potency estimates, highlighting the need for harmonized, transparent methodologies to enable robust application of transcriptomics in chemical safety assessment and regulatory decision-making.

bioinformatics↗

bioRxiv2025

A Data-Driven Approach for the Development of a Time-informed Adverse Outcome Pathway-network for Cardiotoxicity of Environmental Chemicals

We present a novel Adverse Outcome Pathway (AOP) network for environmental chemical-induced cardiotoxicity using a bottom-up, data-driven AOP development approach. Mechanistic endpoints were systematically extracted from 339 in vitro and in vivo studies, yielding 1,759 Key Event (KE) entries and 4,938 Key Event Relationship (KER) entries, including information on experimental methods, essentiality evidence (intervention experiments demonstrating upstream-downstream dependence), and study metadata. After quality filtering (high risk of bias, confounding cytotoxicity in vitro, excessive toxicity or animal well-being concerns in vivo, and low-frequency observations), 112 unique KEs and 829 unique KERs supported by at least three independent observations were retained for network construction. Network analysis identified oxidative stress and mitochondrial dysfunction as dominant hub processes linking diverse upstream perturbations to downstream cardiomyocyte injury, inflammation, cardiac remodelling (fibrosis and hypertrophy), decreased cardiac contractility, and reduced left ventricular function. Incorporating exposure duration at the KER level enabled time-resolved pathway interpretation and demonstrated that KE timing is relationship-dependent, revealing temporal patterns not apparent when analysing KEs in isolation. This evidence-weighted, time-resolved AOP network can support endpoint prioritisation and exposure-window selection for non-animal method (NAM) test batteries and mechanistically informed cardiotoxicity assessment. SynopsisEnvironmental chemicals converge on shared stress and injury pathways that drive cardiac remodelling and ventricular dysfunction. A time-resolved AOP network helps prioritise endpoints and exposure windows for non-animal cardiotoxicity testing.

pharmacology and toxicology↗

bioRxiv2025

Variability and uncertainty of data from genotoxicity Test Guidelines: What we know and why it matters.

This review comprehensively examines the variability and uncertainty associated with test guideline (TG)-conform genotoxicity data and explores the respective implications for the integration of non-animal-methods (NAMs) into regulatory frameworks. Historical amendments to OECD TGs are mapped to reveal the methods evolution that improves the scientific quality of the data but also explains data heterogeneity within available databases. An analysis of the major genotoxicity databases ECVAM, ISSMIC, and OASIS demonstrates substantial variability in genotoxicity calls. Using the EFSA genotoxicity database, which currently harbours the best-curated (meta-) data, we estimate that 22-77% of compounds exhibit similarity of replicate results below 85%, depending on the assay. The potentially most important variables statistically explaining variability and sensitivity were analysed. The practical limitations to identify them with high reliability and to define their optimum needs to be accepted as a qualitative baseline uncertainty. These findings underscore the necessity of contextualizing NAM performance evaluations within the intrinsic variability and uncertainty of animal and in vitro reference data. We propose that this variability is explicitly considered in the development and validation of NAM-based Integrated Approaches for Testing and Assessment (IATAs). This review provides a critical foundation for regulators and scientists aiming to enhance the acceptance and utility of NAMs in genotoxicity assessment.

pharmacology and toxicology↗