Search

BackgroundPesticide exposure is associated with many long-term health outcomes; the potential underlying mechanisms are not well established for most associations. Epigenetic modifications, such as DNA methylation, may contribute. Individual pesticides may be associated with specific DNA methylation patterns but no epigenome-wide association study (EWAS) has evaluated methylation in relation to individual pesticides.ObjectivesWe conducted an EWAS of DNA methylation in relation to several pesticide active ingredients.MethodsThe Agricultural Lung Health Study is a case-control study of asthma, nested within the Agricultural Health Study. We analyzed blood DNA methylation measured using Illumina's EPIC array in 1,170 male farmers of European ancestry. For pesticides still on the market at blood collection (2009-2013), we evaluated nine active ingredients for which at least 30 participants reported past and current (within the last 12 months) use, as well as seven banned organochlorines with at least 30 participants reporting past use. We used robust linear regression to compare methylation at individual C-phosphate-G sites (CpGs) among users of a specific pesticide to never users.ResultsUsing family-wise error rate (p

With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose‐response function for each gene is assigned a prior via a discrete logistic stick‐breaking process (LSBP) whose weights depend on gene‐level characteristics (e.g., pathway membership) and atoms consist of different dose‐response functions subject to a shape constraint that ensures biological plausibility. The posterior distribution for the benchmark dose among genes within the same pathways can be estimated directly from the model, which is another advantage over current methods. The ability of COLDER to predict gene‐level dose‐response is evaluated in a simulation study and the method is illustrated with data from a National Toxicology Program study of Aflatoxin B1. [ABSTRACT FROM AUTHOR]

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research. [ABSTRACT FROM AUTHOR]

Use of molecular data in human and ecological health risk assessments of industrial chemicals and agrochemicals has been anticipated by the scientific community for many years; however, these data are rarely used for risk assessment. Here, a logic framework is proposed to explore the feasibility and future development of transcriptomic methods to refine and replace the current apical endpoint-based regulatory toxicity testing paradigm. Four foundational principles are outlined and discussed that would need to be accepted by stakeholders prior to this transformative vision being realized. Well-supported by current knowledge, the first principle is that transcriptomics is a reliable tool for detecting alterations in gene expression that result from endogenous or exogenous influences on the test organism. The second principle states that alterations in gene expression are indicators of adverse or adaptive biological responses to stressors in an organism. Principle 3 is that transcriptomics can be employed to establish a benchmark dose-based point of departure (POD) from short-term, in vivo studies at a dose level below which a concerted molecular change (CMC) is not expected. Finally, Principle 4 states that the use of a transcriptomic POD (set at the CMC dose level) will support a human health-protective risk assessment. If all four principles are substantiated, this vision is expected to transform aspects of the industrial chemical and agrochemical risk assessment process that are focused on establishing safe exposure levels for mammals across numerous toxicological contexts resulting in a significant reduction in animal use while providing equal or greater protection of human health. Importantly, these principles and approaches are also generally applicable for ecological safety assessment. [ABSTRACT FROM AUTHOR]