FastBMD: an online tool for rapid benchmark dose-response analysis of transcriptomics data

Benchmark Dose Estimation from Transcriptomics Data for Methylimidazolium Ionic Liquid Hepatotoxicity: Implications for Health Risk Assessment of Green Solvents

Ionic liquids (ILs), traditionally considered environmentally benign solvents, have shown potential toxicity to organisms, raising concerns about their safety. Among them, 1-octyl-3-methylimidazolium (M8OI) has been detected at high concentrations in soils and exhibits hepatotoxic properties. To uncover the molecular mechanisms underlying this toxicity, whole-transcriptome sequencing was performed, coupled with benchmark dose (BMD) modeling, to derive transcriptomic points-of-departure (tPOD) through dose-response analysis. The transcriptomic analysis identified 425, 667, and 567 differentially expressed genes (DEGs) following low (10 μmol/L), medium (50 μmol/L), and high (200 μmol/L) doses of M8OI exposure, respectively. Enrichment analysis revealed significant perturbations in pathways related to cytokine-cytokine receptor interaction and IL-17 signaling. BMD modeling yielded tPOD values of 1.51 μmol/L (median of the 20 most sensitive genes, omicBMD20), 2.98 μmol/L (tenth percentile of all genes, omicBMD10th), 6.83 μmol/L (mode of the first peak of all gene BMDs, omicBMDmode), and 5.9 μmol/L for pathway-level analysis. These transcriptomics-derived tPODs were at least 105-fold lower than M8OI's hepatotoxic concentration, as indicated by its EC50 of 723.6 μmol/L in HepG2 cells. Functional analysis of the transcriptomic data identified legionellosis, rheumatoid arthritis, and transcriptional misregulation in cancer as the most sensitive pathways affected by M8OI. These findings highlight the molecular mechanisms driving M8OI-induced hepatotoxicity and underscore the utility of transcriptomics in deriving sensitive and quantitative toxicity thresholds. The results provide critical insights for guideline-driven toxicological evaluations and regulatory decision-making, supporting a more comprehensive assessment of IL safety.

Transcriptomics Points-of-Departure (tPODs) to Support Hazard Assessment of Benzo[a]pyrene in Early-Life-Stage Rainbow Trout

New approach methods (NAMs) are urgently needed to address the significant ethical and economic concerns associated with live animal testing as well as the low throughput associated with current toxicity testing frameworks. NAMs such as rapid mechanistic early-life-stage fish assays are promising alternatives to current hazard assessment approaches, as they can be used to derive toxicity thresholds and guide decision-makers on identifying or prioritizing chemicals of concern. This study aimed to derive benchmark concentrations from RNaseq data (transcriptomic points-of-departure; tPOD) from a short-term exposure study with early life stages of rainbow trout (RBT; Oncorhynchus mykiss) using benzo[a]pyrene (B[a]P) as the model compound. tPODs were then calibrated with higher organizational-level responses observed during an extended 28 day exposure period. RBT were exposed from 1 to 28 days post-hatch (dph) to 0.079, 0.35, 1.5, 7.4, and 29 μg/L (28 d time weighted average measured) B[a]P, as well as 0.05% dimethyl sulfoxide and water only controls. Benchmark concentration analysis of transcriptomic responses at 4 dph, based on the most sensitive transcriptomic features, yielded tPODs between 0.028 and 0.47 μg/L B[a]P. At 28 dph, Cyp1a1 exhibited significantly increased catalytic activity, with biochemical POD, bPODEROD,28dph of 0.599 μg/L B[a]P, while morphometric analysis showed significant growth inhibition in terms of length, with apical POD, aPODlength,28dph of 1.77 μg/L B[a]P, with a notable decreasing trend in body weight. A toxicity pathway model constructed from genes and apical end points exhibiting concentration-dependent responses provided further evidence supporting the utility of tPODs from short-term RBT early-life-stage assay to support chemical risk assessment to guide decision-makers in chemical testing prioritization.

Modeling omics dose-response at the pathway level with DoseRider

The generation of omics data sets has become an important approach in modern pharmacological and toxicological research as it can provide mechanistic and quantitative information on a large scale. Analyses of these data frequently revealed a non-linear dose-response relationship underscoring the importance of the modeling process to infer biological exposure limits. A number of tools have been developed for dose-response modeling and various thresholds have been defined as a quantitative representation of the effect of a substance, such as effective concentrations or benchmark doses (BMD). Here we present DoseRider an easy-to-use web application and a companion R package for linear and non-linear dose-response modeling and assessment of BMD at the level of biological pathways or signatures using generalized mixed effect models. This approach allows to analyze custom or provided multi-omics data such as RNA sequencing or metabolomics data and its annotation of a collection of pathways and gene sets from various species. Moreover, we introduce the concept of the trend change doses (TCDs) as a numerical descriptor of effects derived from complex dose-response curves. The usability of DoseRider was demonstrated by analyses of RNA sequencing data of bisphenol AF (BPAF) treatment of a human breast cancer cell line (MCF-7) at 8 different concentrations using gene sets for chemical and genetic perturbations (MSigDB). The BMD for BPAF and a set of genes upregulated by estrogen in breast cancer was 0.2 µM (95 %-CI 0.1-0.5 µM) and the lowest TCD (TCD1) was 0.003 µM (95 %-CI 0.0006-0.01 µM). The comprehensive presentation of the results underlines the suitability of the system for pharmacogenomics, toxicogenomics, and applications beyond.

TPD-seq: A high throughput RNA-seq method to derive transcriptomic points of departure from cell lines

There is growing scientific and regulatory interest in transcriptomic points of departure (tPOD) values from high-throughput in vitro experiments. To further help democratize tPOD research, here we outline 'TPD-seq' which links microplate-based exposure methods involving cell lines for human (Caco-2, Hep G2) and environmental (rainbow trout RTgill-W1) health, with a commercially available RNA-seq kit, with a cloud-based bioinformatics tool (ExpressAnalyst.ca). We applied the TPD-seq workflow to derive tPODs for solvents (dimethyl sulfoxide, DMSO; methanol) and positive controls (3,4-dichloroaniline, DCA; hydrogen peroxide, H2O2) commonly used in toxicity testing. The majority of reads mapped to protein coding genes (∼9 k for fish cells; ∼6 k for human cells), and about 50 % of differentially expressed genes were curve-fitted from which 90 % yielded gene benchmark doses. The most robust transcriptomic responses were caused by DMSO exposure, and tPOD values were 31-155 mM across the cell lines. OECD test guideline 249 (RTgill-W1 cells) recommends the use of DCA and here we calculated a tPOD of ∼5 to 76 μM. Finally, exposure of the two human cell lines to H2O2 resulted in tPOD values that ranged from 0.7 to 1.1 mM in Caco-2 cells and 5-30 μM in Hep G2 cells. The methods outlined here are designed to be performed in laboratories with basic molecular and cell culture facilities, and the performance and scalability of the TPD-seq workflow can be determined with additional case studies.

Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

High-throughput, human-relevant approaches for predicting chemical toxicity are urgently needed for better decision-making in human health. Here, we apply image-based profiling (the Cell Painting assay) and two cytotoxicity assays (metabolic and membrane damage readouts) to primary human hepatocytes after exposure to eight concentrations of 1085 compounds that include pharmaceuticals, pesticides, and industrial chemicals with known liver toxicity-related outcomes. Three computational methods (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) were compared to extract morphology features from single cells or entire images. We used these morphology features to predict activity in the measured cytotoxicity assays, as well as in 412 curated ToxCast assays that span cytotoxicity, cell-based, and cell-free categories. We found that the morphological profiles detect compound bioactivity at lower concentrations than standard cytotoxicity assays. In supervised analyses, they predict cytotoxicity and targeted cell-based assay readouts, but not cell-free assay readouts. We also found that the various feature extraction methods performed relatively similarly and that filtering out non-bioactive or cytotoxic concentrations did not boost supervised assay prediction performance for any assay endpoint category, although it did have a large influence on unsupervised cluster analysis. We envision that image-based profiling could serve as a key component of modern safety assessment.

Clearing the path: Unraveling bisphenol a removal and degradation mechanisms for a cleaner future

Bisphenol A (BPA) is a prevalent chemical found in a range of consumer goods, which has raised worries about its possible health hazards. Comprehending the breakdown pathways of BPA is essential for evaluating its environmental consequences and addressing associated concerns. This review emphasizes the significance of studying the degradation/removal of BPA, with a specific focus on both natural and artificial routes. It explores natural processes such as photolysis, hydrolysis, and biodegradation, as well as manmade methods including advanced oxidation processes (AOPs) and enzymatic degradation. Examining the decomposition of BPA helps to understand how it behaves in the environment, providing valuable information for managing risks and addressing pollution. Furthermore, comprehending degradation mechanisms aids in the creation of more secure substitutes and regulatory actions to reduce BPA exposure and safeguard human health. This review emphasizes the need of promptly addressing this environmental and public health concern through the research of BPA degradation.

Far from Their Origins: A Transcriptomic Investigation on How 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl) Phenol Affects Rainbow Trout Alevins

Benzotriazole ultraviolet stabilizers (BUVSs) are a group of widely used chemicals added to a variety of consumer (e.g., plastics) and industrial (e.g., metal coating) goods. Although detected globally as an environmentally persistent pollutant, BUVSs have received relatively little toxicological attention and only recently have been acknowledged to affect development and the endocrine system in vivo. In our previous study, altered behavior, indicative of potential neurotoxicity, was observed among rainbow trout alevins (day 14 posthatching) that were microinjected as embryos with a single environmentally relevant dose of 2,4-di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl) phenol (UV-327). In the present follow-up study, we performed whole-transcriptome profiling (RNA sequencing) of newly hatched alevins from the same batch. The primary aim was to identify biomarkers related to behavior and neurology. Dose-specifically, 1 to 176 differentially expressed genes (DEGs) were identified. In the group presenting altered behavior (273.4 ng g-1), 176 DEGs were identified, yet only a fraction was related to neurological functions, including water, calcium, and potassium homeostasis; acetylcholine transmission and signaling; as well insulin and energy metabolism. The second objective was to estimate the transcriptomic point of departure (tPOD) and assess if point estimate(s) are protective of altered behavior. A tPOD was established at 35 to 94 ng UV-327 g-1 egg, making this tPOD protective of behavioral alterations. Holistically, these transcriptomic alterations provide a foundation for future research on how BUVSs can influence rainbow trout alevin development, while providing support to the hypothesis that UV-327 can influence neurogenesis and subsequent behavioral endpoints. The exact structural and functional changes caused by embryonic exposure to UV-327 remain enigmatic and will require extensive investigation before being deciphered and understood toxicologically. Environ Toxicol Chem 2024;43:2026-2038. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The dose disrupts the pathway: application of Paracelsus principle to mechanistic toxicology

Arguably the most famous principle of toxicology is "The dose makes the poison" formulated by Paracelsus in the 16th century. Application of the Paracelsus's principle to mechanistic toxicology may be challenging as one compound may affect many molecular pathways at different doses with different and often nonlinear dose-response relationships. As a result, many mechanistic studies of environmental and occupational compounds use high doses of xenobiotics motivated by the need to see a clear signal indicating disruption of a particular molecular pathway. This approach ignores the possibility that the same xenobiotic may affect different molecular mechanism(s) at much lower doses relevant to human exposures. To amend mechanistic toxicology with a simple and concise guiding principle, I suggest recontextualization of Paracelsus's following its letter and spirit: "The dose disrupts the pathway". Justification of this statement includes observations that many environmental and occupational xenobiotics affect a broad range of molecular cascades, that most molecular pathways are sensitive to chemical exposures, and that different molecular pathways are sensitive to different doses of a chemical compound. I suggest that this statement may become a useful guidance and educational tool in a range of toxicological applications, including experimental design, comparative analysis of mechanistic hypotheses, evaluation of the quality of toxicological studies, and risk assessment.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Examining effects of a novel estrogenic perfluoro-alcohol, 1H,1H,8H,8H-Perfluorooctane-1,8-diol (FC8-diol), using the fathead minnow EcoToxChip

In a previous in vivo study, adult male fathead minnows (Pimephales promelas) were exposed via water for 4 days to 1H,1H,8H,8H-perfluorooctane-1,8-diol (FC8-diol). The present study expands on the evaluation of molecular responses to this perfluoro-alcohol by analyzing 26 male fathead minnow liver RNA samples from that study (five from each test concentration: 0, 0.018, 0.051, 0.171, and 0.463 mg FC8-diol/L) using fathead minnow EcoToxChips Ver. 1.0. EcoToxChips are a quantitative polymerase chain reaction array that allows for simultaneous measurement of >375 species-specific genes of toxicological interest. Data were analyzed with the online tool EcoToxXplorer. Among the genes analyzed, 62 and 96 were significantly up- and downregulated, respectively, by one or more FC8-diol treatments. Gene expression results from the previous study were validated, showing an upregulation of vitellogenin mRNA (vtg) and downregulation of insulin-like growth factor 1 mRNA (igf1). Additional genes related to estrogen receptor activation including esr2a (estrogen receptor 2a) and esrrb (estrogen related receptor beta) were also affected, providing further confirmation of the estrogenic nature of FC8-diol. Furthermore, genes involved in biological pathways related to lipid and carbohydrate metabolism, innate immune response, endocrine reproduction, and endocrine thyroid were significantly affected. These results both add confidence in the use of the EcoToxChip tool for inferring chemical mode(s) of action and provide further insights into the possible biological effects of FC8-diol. Environ Toxicol Chem 2024;00:1-9. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

Effects of low doses of methylmercury (MeHg) exposure on definitive endoderm cell differentiation in human embryonic stem cells

Fetal development is one of the most sensitive windows to methylmercury (MeHg) toxicity. Laboratory and epidemiological studies have shown a dose-response relationship between fetal MeHg exposure and neuro performance in different life stages from infants to adults. In addition, MeHg exposure has been reported to be associated with disorders in endoderm-derived organs, such as morphological changes in liver cells and pancreatic cell dysfunctions. However, the mechanisms of the effects of MeHg on non-neuronal organs or systems, especially during the early development of endoderm-derived organs, remain unclear. Here we determined the effects of low concentrations of MeHg exposure during the differentiation of definitive endoderm (DE) cells from human embryonic stem cells (hESCs). hESCs were exposed to MeHg (0, 10, 100, and 200 nM) that covers the range of Hg concentrations typically found in human maternal blood during DE cell induction. Transcriptomic analysis showed that sub-lethal doses of MeHg exposure could alter global gene expression patterns during hESC to DE cell differentiation, leading to increased expression of endodermal genes/proteins and the over-promotion of endodermal fate, mainly through disrupting calcium homeostasis and generating ROS. Bioinformatic analysis results suggested that MeHg exerts its developmental toxicity mainly by disrupting ribosome biogenesis during early cell lineage differentiation. This disruption could lead to aberrant growth or dysfunctions of the developing endoderm-derived organs, and it may be the underlying mechanism for the observed congenital diseases later in life. Based on the results, we proposed an adverse outcome pathway for the effects of MeHg exposure during human embryonic stem cells to definitive endoderm differentiation.

The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics

High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights

BACKGROUND

Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples.

CONCLUSIONS

Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

ToxicR: A computational platform in R for computational toxicology and dose-response analyses

The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose-response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA's Benchmark Dose software and NTP's BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.

Multi-omics approaches for remediation of bisphenol A: Toxicity, risk analysis, road blocks and research perspectives

In this "plastic era" with the increased use of plastic in day today's life the accumulation of its degraded products like microplastics or plastic additives such as Bisphenol A(BPA) is also increasing. BPA is an endocrine-disrupting chemical used as a plasticizing agent in clear plastic, building materials, coatings, and epoxy resin. Several enzymes including laccases and lipases have been studied for the reduction of BPA toxicity. Over the decades of encountering these toxicants, microorganisms have evolved to degrade different classes of plastic additives. Since the degradation of BPA is a long process thus meta-omics approaches have been employed to identify the active microbiota and microbial dynamics involved in the mitigation of BPA. It is also necessary to investigate the impact of processing activities on transit of BPA in food items and to limit its entrance in food world. This review summarizes a comprehensive overview on BPA sources, toxicity, bio-based mitigation approaches along with a deeper understanding of multi-omics approaches for its reduction and risk analysis. Knowledge gaps and opportunities have been comprehensively compiled that would aid the state-of-the-art information in the available literature for the researchers to further address this issue.

Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling

Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 μg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.

Transcriptomic Points of Departure Calculated from Rainbow Trout Gill, Liver, and Gut Cell Lines Exposed to Methylmercury and Fluoxetine

Ethical and resource limitation concerns are pushing chemicals management to develop alternatives to animal testing strategies. The objective of our study was to determine whether transcriptomic point of departure (tPOD) values could be derived from studies that followed Organisation for Economic Co-operation and Development (OECD) Test No. 249 (rainbow trout gill cell line), as well as from studies on trout liver and gut cells. Gill, liver, and gut cell lines were exposed to methylmercury and fluoxetine. Concentrations causing 50% cytotoxicity (LC50) were derived, the whole transcriptome was sequenced, and gene tPOD and pathway benchmark dose (BMD) values were derived from transcriptomic dose-response analysis. Differences in LC50 and transcriptomic responses across the cell lines were noted. For methylmercury, the tPOD_mode values were 14.5, 20.5, and 17.8 ppb for the gill, liver, and gut cells, respectively. The most sensitive pathway (pathway BMDs in parentheses) was ferroptosis in the gill (3.1 ppb) and liver (3.5 ppb), and glutathione metabolism in the gut (6.6 ppb). For fluoxetine, the tPOD_mode values were 109.4, 108.4, and 97.4 ppb for the gill, liver, and gut cells, respectively. The most sensitive pathway was neurotrophin signaling in the gill (147 ppb) and dopaminergic signaling in the gut (86.3 ppb). For both chemicals, the gene tPOD and pathway BMD values were lower than cytotoxic concentrations in vitro, and within 10-fold below the in vivo LC50s. By bringing together transcriptomics and dose-response analysis with an OECD test method in three cell lines, the results help to establish an in vitro method yielding tPOD values that are hypothesized to be protective of in vivo concentrations associated with adverse outcomes, and also give insights into mechanisms of action. Environ Toxicol Chem 2022;41:1982-1992. © 2022 SETAC.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

A computational system for Bayesian benchmark dose estimation of genomic data in BBMD

BACKGROUND

Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision.

OBJECTIVE

By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose-response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress.

METHODS

The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties.

RESULTS

This system was tested and validated versus 24 previously published in-vivo microarray dose-response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78-0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established.

DISCUSSION

Overall, this study demonstrates that dose-response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose-response data analysis with outstanding functionalities to quantify uncertainty from various sources.

Comparative analysis of transcriptomic points-of-departure (tPODs) and apical responses in embryo-larval fathead minnows exposed to fluoxetine

Current approaches in chemical hazard assessment face significant challenges because they rely on live animal testing, which is time-consuming, expensive, and ethically questionable. These concerns serve as an impetus to develop new approach methodologies (NAMs) that do not rely on live animal tests. This study explored a molecular benchmark dose (BMD) approach using a 7-day embryo-larval fathead minnow (FHM) assay to derive transcriptomic points-of-departure (tPODs) to predict apical BMDs of fluoxetine (FLX), a highly prescribed and potent selective serotonin reuptake inhibitor frequently detected in surface waters. Fertilized FHM embryos were exposed to graded concentrations of FLX (confirmed at < LOD, 0.19, 0.74, 3.38, 10.2, 47.5 μg/L) for 32 days. Subsets of fish were subjected to omics and locomotor analyses at 7 days post-fertilization (dpf) and to histological and biometric measurements at 32 dpf. Enrichment analyses of transcriptomics and proteomics data revealed significant perturbations in gene sets associated with serotonergic and axonal functions. BMD analysis resulted in tPOD values of 0.56 μg/L (median of the 20 most sensitive gene-level BMDs), 5.0 μg/L (tenth percentile of all gene-level BMDs), 7.51 μg/L (mode of the first peak of all gene-level BMDs), and 5.66 μg/L (pathway-level BMD). These tPODs were protective of locomotor and reduced body weight effects (LOEC of 10.2 μg/L) observed in this study and were reflective of chronic apical BMDs of FLX reported in the literature. Furthermore, the distribution of gene-level BMDs followed a bimodal pattern, revealing disruption of sensitive neurotoxic pathways at low concentrations and metabolic pathway perturbations at higher concentrations. This is one of the first studies to derive protective tPODs for FLX using a short-term embryo assay at a life stage not considered to be a live animal under current legislations.

Dose-response metabolomics and pathway sensitivity to map molecular cartography of bisphenol A exposure

In the toxicological regime, the toxicological endpoint and its dose-response relationship are two of the most prominent characters in conducting a risk assessment for chemical exposure. Systems biological methods have been used to comprehensively characterize the impact of toxicants on the biochemical pathways. However, the majority of the current studies are only based on single-dose, and limited information can be extrapolated to other doses from these experiments, regardless of the sensitivity of each endpoint. This study aims to understand the dose-response metabolite dysregulation pattern and metabolite sensitivity at the system-biological level. Here, we applied bisphenol A (BPA), an endocrine-disrupting chemical (EDC), as the model chemical. We first employed the global metabolomics method to characterize the metabolome of breast cancer cells (MCF-7) upon exposure to different doses (0, 20, 50, and 100 µM) of BPA. The dysregulated features with a clear dose-response relationship were also effectively picked up with an R-package named TOXcms. Overall, most metabolites were dysregulated by showing a significant dose-dependent behaviour. The results suggested that BPA exposure greatly perturbed purine metabolism and pyrimidine metabolism. Interestingly, most metabolites within the purine metabolism were described as a biphasic dose-response relationship. With the established dose-response relationship, we were able to fully map the metabolite cartography of BPA exposure within a wide range of concentrations and observe some unique patterns. Furthermore, an effective concentration of certain fold changes (e.g., EC₊₁₀ means the dose at which metabolite is 10% upregulated) and metabolite sensitivity were defined and introduced to this dose-response omics information. The result showed that the purine metabolism pathway is the most venerable target of BPA, which can be a potential endogenous biomarker for its exposure. Overall, this study applied the dose-response metabolomics method to fully understand the biochemical pathway disruption of BPA treatment at different doses. Both dose-response omics strategy and metabolite sensitivity analysis can be further considered and emphasized in future chemical risk assessments.

Assessing the Toxicity of 17α-Ethinylestradiol in Rainbow Trout Using a 4-Day Transcriptomics Benchmark Dose (BMD) Embryo Assay

There is an urgent demand for more efficient and ethical approaches in ecological risk assessment. Using 17α-ethinylestradiol (EE2) as a model compound, this study established an embryo benchmark dose (BMD) assay for rainbow trout (RBT; Oncorhynchus mykiss) to derive transcriptomic points-of-departure (tPODs) as an alternative to live-animal tests. Embryos were exposed to graded concentrations of EE2 (measured: 0, 1.13, 1.57, 6.22, 16.3, 55.1, and 169 ng/L) from hatch to 4 and up to 60 days post-hatch (dph) to assess molecular and apical responses, respectively. Whole proteome analyses of alevins did not show clear estrogenic effects. In contrast, transcriptomics revealed responses that were in agreement with apical effects, including excessive accumulation of intravascular and hepatic proteinaceous fluid and significant increases in mortality at 55.1 and 169 ng/L EE2 at later time points. Transcriptomic BMD analysis estimated the median of the 20th lowest geneBMD to be 0.18 ng/L, the most sensitive tPOD. Other estimates (0.78, 3.64, and 1.63 ng/L for the 10th percentile geneBMD, first peak geneBMD distribution, and median geneBMD of the most sensitive over-represented pathway, respectively) were within the same order of magnitude as empirically derived apical PODs for EE2 in the literature. This 4-day alternative RBT embryonic assay was effective in deriving tPODs that are protective of chronic effects of EE2.