Environmental and Molecular Mutagenesis最新文献

Wildland-urban interface (WUI) firefighting involves exposure to burning vegetation, structures, and other human-made hazards, often without respiratory protection. Response activities can last for long periods of time, spanning multiple days or weeks. Epigenetic modifications, including microRNA (miRNA) expression and DNA methylation, are responsive to toxicant exposures and are part of the development of cancers and other diseases. Epigenetic modifications have not been studied in relation to WUI fires. Firefighters (n = 99) from southern California, including 79 firefighters who responded to at least one WUI fire, provided blood samples at baseline and approximately 10 months later. We quantified the relative abundance of 800 miRNAs in blood samples using the nCounter Human v3 miRNA expression panel and blood leukocyte DNA methylation throughout the genome via the Infinium EPIC array. We used linear mixed models to compare the expression of each miRNA across time and DNA methylation at each locus, adjusting for potential confounders. In the miRNA analysis among all firefighters, 65 miRNAs were significantly different at follow-up compared to baseline at a false discovery rate of 5%. Results were similar when restricted to firefighters with a recorded WUI fire exposure during the interim period, although only 50 were significant. Expression of miRNA hsa-miR-518c-3p, a tumor suppressor, was significantly associated with WUI fire response (fold change 0.77, 95% CI = [0.69, 0.87]). In the DNA methylation analysis, no statistically significant changes over time were identified. In summary, WUI fire exposures over a wildfire season altered miRNA expression but did not substantially impact DNA methylation.

There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight-of-evidence approach, providing opportunities to reduce reliance on the 2-year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various 'omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo. A total of 1341 papers were screened to identify those that were most relevant. While six published biomarkers with characterized accuracy were initially examined, four of the six were not considered further, because they had not been tested for classification accuracy using additional sets of chemicals or other transcript profiling platforms. Two independently derived biomarkers used in conjunction with standard computational techniques can identify genotoxic chemicals in vivo (rat liver or both rat and mouse liver) on different gene expression profiling platforms. The biomarkers have predictive accuracies of ≥92%. These biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies using short-term rodent exposures to identify genotoxic and nongenotoxic chemicals that cause cancer.

The bacterial reverse mutation test is essential for identifying the mutagenic potential of chemicals. The solubility of the test substance is vital for achieving the recommended assay concentration. Preferred solvents like dimethyl sulfoxide and water are chosen for their compatibility and historical data. Selecting a compatible solvent with Salmonella typhimurium and Escherichia coli WP2 uvrA strains, considering a maximum cytotoxic concentration or the limit of 5 mg/plate, can be challenging. This study assessed various solvents, including N,N-dimethylformamide, acetone, acetonitrile, ethyl acetate, 95% ethanol, diethylene glycol monomethyl ether, methanol, P-dioxane, tetrahydrofuran, and dimethylacetamide, as alternative solvents in the AMES test. Results showed all solvents, except tetrahydrofuran, were compatible at concentrations up to 100 μL/plate or more, as they did not inhibit S9 enzymes, bacterial growth, or alter bacterial revertant colony counts, making them suitable for the bacterial reverse mutation test.

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can enhance precision on classifying the MoA of genotoxicants. Nevertheless, these approaches need to be tailored to specific chemical spaces and lab conditions for accurate risk assessment. In this study, we applied various state-of-the-art ML algorithms available in an open-source R package (caret) to build MFA-ML models using data from Bryce et al. (2016). The best model achieved 95% accuracy on the training dataset and correctly predicted genotoxicity in 16 out of 17 cases in the test dataset. Incorporating molecular descriptors properties from established in silico models demonstrated further improved performance of the approach to cover challenging examples of pharmaceuticals exhibiting a pharmacological mode of action that could interfere with the biomarker response. Further model validation on an external test set with 49 non-overlapped compounds showed a high model accuracy at 92%. Additionally, a tailored graphical user interface was developed using a freely available R package (shiny) to support visual analysis of MFA data including MoA predictions, facilitating broad usage by laboratory scientists. Lastly, a perspective on the integration of MoA predictions as additional evidence into a genotoxicity assessment workflow is proposed.

Sevoflurane is an inhalation anesthetic widely used for general anesthesia, but its genotoxic potential is controversial in clinical studies. It is unknown whether the effects are due to surgery or the anesthetic. Thus, for the first time, the present study investigated genotoxicity in peripheral blood cells and in target organs (liver, lung, and kidney) and micronucleus (MN) in the bone marrow of a single exposure to sevoflurane at three different concentrations in monitored mice. Ninety Swiss mice were distributed into the following groups: exposure to sevoflurane at 3.3% (low), 4.5% (intermediate), and 6.0% (high) in 40% oxygen (O₂) for 2 h; negative control (no exposure); negative control with O₂; and positive control. The exposed animals were heated, monitored for vital signs (temperature, O₂ saturation, heart rate/pulse, and respiratory rate), and anesthetized via a modern low-flow digital system. Mice were euthanized 2 and 24 h after exposure for evaluation by the comet assay and MN test, respectively. No DNA damage occurred in the 3.3% group for any of the organs evaluated, and no genotoxic or mutagenic effects were observed at any sevoflurane concentration in the peripheral blood or liver cells. However, a significant increase in DNA damage was observed at higher concentrations in kidney (4.5%) and lung cells (6.0%) and in the MN frequency (groups 4.5% and 6.0%). No cytotoxicity or histological alterations were observed. In conclusion, high concentrations of sevoflurane induce DNA damage, but concentrations equivalent to those used in clinical practice do not demonstrate genotoxic or mutagenic effects.

As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high-throughput transcriptomics, which in turn is based on high-throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker-specific reporting module within the OECD Omics Reporting Framework.

In human health risk assessment of chemicals and pharmaceuticals, identification of genotoxicity hazard usually starts with a standard battery of in vitro genotoxicity tests, which is needed to cover all genotoxicity endpoints. The individual tests included in the battery are not designed to pick up all endpoints. This explains why resulting data can appear contradictory, thereby complicating accurate interpretation of the findings. Such interpretation could be improved through application of mathematical modeling. One of the advantages of mathematical modeling is that the strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and convey the associated uncertainties. This approach was explored by the working group "Predictivity of In Vitro Genotoxicity Testing," convened in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). Specifically, we applied mathematical modeling to a database with publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide strong predictive weight-of-evidence for evaluating genotoxic hazard of a substance, although they are better in predicting absence of genotoxic potential than in predicting presence of genotoxic potential. Remarkably, the bacterial reverse mutation (Ames) test did not significantly change these predictions when used in combination with in vitro mutagenicity and clastogenicity tests using cells of mammalian origin. However, in case only data from a bacterial reverse mutation test are available for the assessment of genotoxic potential, these do bear weight of evidence and thus can be used. Genotoxicity assays are generally executed in tiers, in which the bacterial reverse mutation test often is the starting point. Thus, it is reasonable to suspect that early in development test results from the bacterial reverse mutation test have influenced the composition of the database studied here. We performed several tests on the robustness of the database used for the analyses presented here, and the forthcoming results do not indicate a strong bias. Further research comparing in vitro genotoxicity data with in vivo data for additional compounds will provide more insights whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.

This work describes career “adventures” into applied research in environmental mutagenesis. Surprising and interesting results, as well as publications in Nature and Science, may counter an assumption that applied research is not as exciting or impactful as basic research. The narrative is described in terms of “mentors,” whose advice had a lasting impact and resonated in many ways. Adventures included forays into nitrosamine mutagenicity, nanomaterial assessment, photoactivated DNA damaging agents, p53 gene mutagenicity, germ cell mutagenic risk, bacterial mutagenicity assays, genotoxicity of cell phone radiation, Covid diagnostic PCR assays, personalized cancer prevention, and >25 years in regulatory safety assessment at FDA. FDA work included review of genotoxicity data, experiments in the lab, international standards generation, and collaboration with others to foster better analyses of DNA damaging agents, generally in relation to cancer risk. As demonstrated by accidental adventures that led to scientific as well as life-altering personal realizations, many of the most critical happenings in science and in life turn out to be “random,” unexpected, events. Finally, with this work and that of my lifelong tripmate William Lijinsky as models, it is suggested that a “non-hypothesis driven,” open-ended approach to research can be path-breaking and forefront.

The epigenome is a target for environmental exposures and a potential determinant of inter-individual differences in response. In genetically identical C57Bl/6 mice exposed from gestation to weaning to the endocrine-disrupting chemical (EDC) tributyltin (TBT), hepatic tumor development later in life varied across multiple cohorts over time and depending on sex and diet. In one cohort where approximately half of TBT-exposed male mice developed liver tumors at 10 months (Katz et al. Hepatic tumor formation in adult mice developmentally exposed to organotin, Environmental Health Perspectives, 128 (1), 17010, 2020), transcriptomic (RNA-seq) and epigenomic (ChIP-seq) profiling was performed on blood and liver tissue from mice that developed tumors (i.e., “high-risk”) and equivalently exposed mice did not (i.e., “low-risk”). Blood transcriptomic signatures separated TBT-exposed from vehicle controls but did not discriminate between animals that developed tumors versus those that did not. However, uninvolved liver tissue of mice with tumors exhibited transcriptomic and epigenomic signatures distinct from liver tissue of mice without tumors and had many features in common with tumors. These high-risk transcriptomic and epigenomic features were also found in 10/26 TBT-exposed mice at 5 months, indicating that this risk signature preceded tumor development. Thus, while early life exposure to TBT exhibits variable penetrance for hepatic tumor development, indicating TBT exposure is not sufficient for liver tumorigenesis, increased risk for hepatic tumor development is linked to epigenomic and transcriptomic reprogramming of the liver induced by this EDC.