The application of transcriptional benchmark dose modeling for deriving thresholds of effects associated with solar‐simulated ultraviolet radiation exposure

Abstract

Considerable data has been generated to elucidate the transcriptional response of cells to ultraviolet radiation (UVR) exposure providing a mechanistic understanding of UVR‐induced cellular responses. However, using these data to support standards development has been challenging. In this study, we apply benchmark dose (BMD) modeling of transcriptional data to derive thresholds of gene responsiveness following exposure to solar‐simulated UVR. Human epidermal keratinocytes were exposed to three doses (10, 20, 150 kJ/m2) of solar simulated UVR and assessed for gene expression changes 6 and 24 hr postexposure. The dose‐response curves for genes with p‐fit values (≥ 0.1) were used to derive BMD values for genes and pathways. Gene BMDs were bi‐modally distributed, with a peak at ∼16 kJ/m2 and ∼108 kJ/m2 UVR exposure. Genes/pathways within Mode 1 were involved in cell signaling and DNA damage response, while genes/pathways in the higher Mode 2 were associated with immune response and cancer development. The median value of each Mode coincides with the current human exposure limits for UVR and for the minimal erythemal dose, respectively. Such concordance implies that the use of transcriptional BMD data may represent a promising new approach for deriving thresholds of actinic effects. Environ. Mol. Mutagen. 59:502–515, 2018. © 2018 The Authors Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

“…Several previous studies used genes from the first mode of the BMD frequency distributions ( Qutob et al, 2018 ; Farmahin et al, 2019 ; Pagé-Larivière et al, 2019 ; Alcaraz et al, 2021 ). In the current study, density estimation was used with forward, backward, and centered differencing to estimate the first and second derivatives.…”

Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions

“…Different approaches for aggregating gene level BMDs to produce an overall POD tended to vary no more than one order of magnitude [28], demonstrating that different ways of summarizing BMD modeling results has comparatively small impact with regards to determining the threshold where significant perturbations in biology occur. In addition to POD determination, transcriptomic BMD modeling and pathway aggregation has been applied to more complex research questions that are applicable to chemical risk assessment such as investigating cross-species sensitivities to toxicants [30, 31, 54], characterizing the relative potency of structurally-related chemicals [32] and exploring dose-dependent transitions in toxicological responses [56, 57].…”

“…For example, if an estrogenic chemical is tested in an estrogen-responsive cell line and the CRGs then mapped to a gene set collection containing an estrogen signaling pathway, one would expect that this pathway would be included in the list of perturbed pathways and reasonably hypothesize that the chemical in question targets the estrogen receptor. As detailed above, there are numerous examples where mapping CRGs to gene set collections yielded biological pathways with plausible mechanistic linkage to the chemical of interest [21, 30, 31, 55–57]. Approaches such as Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) are designed to assess the coordinated responses of functionally related genes contained within a defined pathway/gene set/ontology structure using rank order statistical methods [64, 65].…”

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay

“…Multiple studies have now implemented transcriptional BMD modeling to derive points of departure relevant to setting safety standards for human health. , However, to date, few studies have utilized metabolomics data to estimate BMDs. , Metabolomics can provide a downstream signature of the biochemical status of a cell or organism, integrating both genetic and environmental factors, and hence could provide BMD values that are similar to traditional apical points of departure. This study applied BMD modeling to UHPLC–MS and NMR data sets to derive BMD estimates for endogenous metabolites after TPhP exposure.…”

“…In general, BMD modeling describes the process of fitting mathematical models to experimental toxicity data and deriving the BMD at a predefined benchmark response (BMR) level, often 10% deviation from the baseline in the case of dichotomous data or 1 standard deviation in the case of continuous data when a predefined level change associated with toxicity is not known . The lower 95% confidence limit of the BMD (BMDL) is commonly used to derive a more conservative limit for human risk guidance, offering several advantages over no-observed-adverse-effect levels (NOAELs), , including independence from the actual dose levels in a study, improved efficiency when investigating small sample sizes, and incorporation of the entire dose–response curve. , While often applied to apical end points (or adverse outcomes) such as organ weight, BMD modeling of transcriptomics data have also become routine. ,− In particular, transcriptional BMD values have been shown to be relatively consistent with apical end point BMDs, − opening the possibility of using a molecular BMD to derive a health-based guidance value for chemical exposure. Recently, the Division of Translational Toxicology (DTT), U.S. Department of Health and Human Services, evaluated the applicability of transcriptomics and BMD modeling to a 5 -day in vivo (rat) study to derive molecular BMDs and to contrast these with the responses of apical end points. , …”