Exploring the Influence of Experimental Design on Toxicity Outcomes in Zebrafish Embryo Tests.

Compound toxicity data obtained from independent zebrafish laboratories can vary vastly, complicating the use of zebrafish screening for regulatory decisions. Differences in the assay protocol parameters are the primary source of variability. We investigated this issue by utilizing data from the NTP DNT-DIVER database (https://doi.org/10.22427/NTP-DATA-002-00062-0001-0000-1, last accessed June 2, 2022), which consists of data from zebrafish developmental toxicity (devtox) and locomotor response (designated as "neurotox") screens from 3 independent laboratories, using the same set of 87 compounds. The data were analyzed using the benchmark concentration (BMC) modeling approach, which estimates the concentration of interest based on a predetermined response threshold. We compared the BMC results from 3 laboratories (A, B, C) in 3 toxicity outcome categories: mortality, cumulative devtox, and neurotox, in terms of activity calls and potency values. We found that for devtox screening, laboratories with similar/same protocol parameters (B vs C) had an active call concordance as high as 86% with negligible potency difference. For neurotox screening, active call concordances between paired laboratories are lower than devtox screening (highest 68%). When protocols with different protocol parameters were compared, the concordance dropped, and the potency shift was on average about 3.8-fold for the cumulative devtox outcome and 5.8-fold for the neurotox outcome. The potential contributing protocol parameters for potency shift are listed or ranked. This study provides a quantitative assessment of the source of variability in zebrafish screening protocols and sets the groundwork for the ongoing Systematic Evaluation of the Application of Zebrafish in Toxicology effort at the National Toxicology Program.

Application of Benchmark Concentration (BMC) Analysis on Zebrafish Data: A New Perspective for Quantifying Toxicity in Alternative Animal Models.

Over the past decade, the zebrafish is increasingly being used as a model to screen for chemical-mediated toxicities including developmental toxicity (DT) and neurotoxicity (NT). One of the major challenges is lack of harmonization in data analysis approaches, thereby posing difficulty in comparing findings across laboratories. To address this, we sought to establish a unified data analysis strategy for both DT and NT data, by adopting the benchmark concentration (BMC) analysis. There are two critical aspects in the BMC analysis: having a toxicity endpoint amenable for BMC and selecting a proper benchmark response (BMR) for the endpoint. For the former, in addition to the typical endpoints in NT assay (eg, hyper/hypo- response quantified by distance moved), we also used endpoints that assess the differences in movement patterns between chemical-treated embryos and control embryos. For the latter, we standardized the selection of BMR, which is analogous to minimum activity threshold, based on intrinsic response variations in the endpoint. When comparing our BMC results with a traditionally used LOAEL method (lowest-observed-adverse-effect level), we found high active compound concordance (100% for DT vs 74% for NT); generally, the BMC was more sensitive than LOAEL (no. of BMC more sensitive/no. of concordant active compounds, 43/50 for DT vs 16/26 for NT). Using the BMC with standardized toxicity endpoints and an appropriate BMR, we may now have a unified data-analysis approach to comparing results across different zebrafish datasets, for a better understanding of strengths and challenges when using the zebrafish as a screening tool.

Functional and Mechanistic Neurotoxicity Profiling Using Human iPSC-Derived Neural 3D Cultures.

Neurological disorders affect millions of people worldwide and appear to be on the rise. Whereas the reason for this increase remains unknown, environmental factors are a suspected contributor. Hence, there is an urgent need to develop more complex, biologically relevant, and predictive in vitro assays to screen larger sets of compounds with the potential for neurotoxicity. Here, we employed a human induced pluripotent stem cell (iPSC)-based 3D neural platform composed of mature cortical neurons and astrocytes as a model for this purpose. The iPSC-derived human 3D cortical neuron/astrocyte co-cultures (3D neural cultures) present spontaneous synchronized, readily detectable calcium oscillations. This advanced neural platform was optimized for high-throughput screening in 384-well plates and displays highly consistent, functional performance across different wells and plates. Characterization of oscillation profiles in 3D neural cultures was performed through multi-parametric analysis that included the calcium oscillation rate and peak width, amplitude, and waveform irregularities. Cellular and mitochondrial toxicity were assessed by high-content imaging. For assay characterization, we used a set of neuromodulators with known mechanisms of action. We then explored the neurotoxic profile of a library of 87 compounds that included pharmaceutical drugs, pesticides, flame retardants, and other chemicals. Our results demonstrated that 57% of the tested compounds exhibited effects in the assay. The compounds were then ranked according to their effective concentrations based on in vitro activity. Our results show that a human iPSC-derived 3D neural culture assay platform is a promising biologically relevant tool to assess the neurotoxic potential of drugs and environmental toxicants.

Screening for Developmental Neurotoxicity at the National Toxicology Program: The Future Is Here.

The National Toxicology Program (NTP) receives requests to evaluate chemicals with potential to cause adverse health effects, including developmental neurotoxicity (DNT). Some recent requests have included classes of chemicals such as flame retardants, polycyclic aromatic compounds, perfluoroalkyl substances, and bisphenol A analogs with approximately 20-50 compounds per class, many of which include commercial mixtures. However, all the compounds within a class cannot be tested using traditional DNT animal testing guideline studies due to resource and time limitations. Hence, a rapid and biologically relevant screening approach is required to prioritize compounds for further in vivo testing. Because neurodevelopment is a complex process involving multiple distinct cellular processes, one assay will unlikely address the complexity. Hence, the NTP sought to characterize a battery of in vitro and alternative animal assays to quantify chemical effects on a variety of neurodevelopmental processes. A culmination of this effort resulted in a NTP-hosted collaborative project with approximately 40 participants spanning across domains of academia, industry, government, and regulatory agencies; collaborators presented data on cell-based assays and alternative animal models that was generated using a targeted set of compounds provided by the NTP. The NTP analyzed the assay results using benchmark concentration (BMC) modeling to be able to compare results across the divergent assays. The results were shared with the contributing researchers on a private web application during the workshop, and are now publicly available. This article highlights the overview and goals of the project, and describes the NTP's approach in creating the chemical library, development of NTPs data analysis strategy, and the structure of the web application. Finally, we discuss key issues with emphasis on the utility of this approach, and knowledge gaps that need to be addressed for its use in regulatory decision making.

In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell-derived model.

An important target area for addressing data gaps through in vitro screening is the detection of potential cardiotoxicants. Despite the fact that current conservative estimates relate at least 23% of all cardiovascular disease cases to environmental exposures, the identities of the causative agents remain largely uncharacterized. Here, we evaluate the feasibility of a combinatorial in vitro/in silico screening approach for functional and mechanistic cardiotoxicity profiling of environmental hazards using a library of 69 representative environmental chemicals and drugs. Human induced pluripotent stem cell-derived cardiomyocytes were exposed in concentration-response for 30min or 24h and effects on cardiomyocyte beating and cellular and mitochondrial toxicity were assessed by kinetic measurements of intracellular Ca2+ flux and high-content imaging using the nuclear dye Hoechst 33342, the cell viability marker Calcein AM, and the mitochondrial depolarization probe JC-10. More than half of the tested chemicals exhibited effects on cardiomyocyte beating after 30min of exposure. In contrast, after 24h, effects on cell beating without concomitant cytotoxicity were observed in about one third of the compounds. Concentration-response data for in vitro bioactivity phenotypes visualized using the Toxicological Prioritization Index (ToxPi) showed chemical class-specific clustering of environmental chemicals, including pesticides, flame retardants, and polycyclic aromatic hydrocarbons. For environmental chemicals with human exposure predictions, the activity-to-exposure ratios between modeled blood concentrations and in vitro bioactivity were between one and five orders of magnitude. These findings not only demonstrate that some ubiquitous environmental pollutants might have the potential at high exposure levels to alter cardiomyocyte function, but also indicate similarities in the mechanism of these effects both within and among chemicals and classes.

Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure.

A method is proposed that finds enriched pathways relevant to a studied condition using the measured molecular data and also the structural information of the pathway viewed as a network of nodes and edges. Tests are performed using simulated data and genomic data sets and the method is compared to two existing approaches. The analysis provided demonstrates the method proposed is very competitive with the current approaches and also provides biologically relevant results.

Characterization of the proneural gene regulatory network during mouse telencephalon development.

BACKGROUND: The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors. RESULTS: Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets. CONCLUSION: Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation.

Gene interaction network analysis suggests differences between high and low doses of acetaminophen.

Bayesian networks for quantifying linkages between genes were applied to detect differences in gene expression interaction networks between multiple doses of acetaminophen at multiple time points. Seventeen (17) genes were selected from the gene expression profiles from livers of rats orally exposed to 50, 150 and 1500 mg/kg acetaminophen (APAP) at 6, 24 and 48 h after exposure using a variety of statistical and bioinformatics approaches. The selected genes are related to three biological categories: apoptosis, oxidative stress and other. Gene interaction networks between all 17 genes were identified for the nine dose-time observation points by the TAO-Gen algorithm. Using k-means clustering analysis, the estimated nine networks could be clustered into two consensus networks, the first consisting of the low and middle dose groups, and the second consisting of the high dose. The analysis suggests that the networks could be segregated by doses and were consistent in structure over time of observation within grouped doses. The consensus networks were quantified to calculate the probability distribution for the strength of the linkage between genes connected in the networks. The quantifying analysis showed that, at lower doses, the genes related to the oxidative stress signaling pathway did not interact with the apoptosis-related genes. In contrast, the high-dose network demonstrated significant interactions between the oxidative stress genes and the apoptosis genes and also demonstrated a different network between genes in the oxidative stress pathway. The approaches shown here could provide predictive information to understand high- versus low-dose mechanisms of toxicity.

The TAO-Gen algorithm for identifying gene interaction networks with application to SOS repair in E. coli.

One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.

Gene interaction network suggests dioxin induces a significant linkage between aryl hydrocarbon receptor and retinoic acid receptor beta.

Gene expression arrays (gene chips) have enabled researchers to roughly quantify the level of mRNA expression for a large number of genes in a single sample. Several methods have been developed for the analysis of gene array data including clustering, outlier detection, and correlation studies. Most of these analyses are aimed at a qualitative identification of what is different between two samples and/or the relationship between two genes. We propose a quantitative, statistically sound methodology for the analysis of gene regulatory networks using gene expression data sets. The method is based on Bayesian networks for direct quantification of gene expression networks. Using the gene expression changes in HPL1A lung airway epithelial cells after exposure to 2,3,7,8-tetrachlorodibenzo-(Italic)p(/Italic)-dioxin at levels of 0.1, 1.0, and 10.0 nM for 24 hr, a gene expression network was hypothesized and analyzed. The method clearly demonstrates support for the assumed network and the hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system. Simulation studies demonstrated the method works well, even for small samples.

Carbon monoxide neurotoxicity: transient inhibition of avoidance response and delayed microglia reaction in the absence of neuronal death.

Carbon monoxide exposure produces neurobehavioral effects associated with the level of carboxyhemoglobin (COHb) in the blood. A threshold has been proposed of approximately 35% COHb for the manifestation of disruption in neurobehavioral tasks. The effects of CO exposure producing 30-40% carboxyhemoglobin (COHb) levels in young adult male Fischer 344 rats were examined with regard to clinical signs of toxicity, performance on a previously learned avoidance procedure, and neuronal and glia histopathology. High levels of exposure (4000 ppm) for 15 min were imposed on either a background blood COHb level of 5% produced by a 2 h exposure to 50 ppm CO or a control background from conditioned-air exposure. Upon removal from the nose-only inhalation holder, signs of mild lethargy and decreased activity were evident for 2 min for conditioned-air controls and 50 ppm CO exposure groups and 3-4 min following 4000 ppm CO. Performance on a two-way shuttle box active avoidance task showed no differences between 50 ppm CO rats and conditioned-air controls while the 4000 ppm CO exposed groups showed a significant decrease in avoidance and escape responses. Histological examination showed no evidence of delayed neuronal death or astrocyte reactivity in the hippocampus or cerebellum; however, a distinct focal staining of reactive microglia in both regions was evident in animals exposed to 4000 ppm CO. While 50 ppm CO (5% COHb) alone produced no disruption in avoidance performance, microglia staining in the cerebellum was significantly increased over conditioned-air controls. This regional and focal response of microglia suggests the need for further study regarding such subtle cellular changes and their relationship with COHb levels.

The association between biomarker-based exposure estimates for phthalates and demographic factors in a human reference population.

Population-based estimates of environmental exposures using biomarkers can be difficult to obtain for a variety of reasons, including problems with limits of detection, undersampling of key strata, time between exposure and sampling, variation across individuals, variation within individuals, and the ability to find and interpret a given biomarker. In this article, we apply statistical likelihoods, weighted sampling, and regression methods for censored data to the analysis of biomarker data. Urinary metabolites for seven phthalates, reported by Blount et al., are analyzed using these methods. In the case of the phthalates data, we assumed the underlying model to be a log-normal distribution with the mean of the distribution defined as a function of a number of demographic variables that might affect phthalate levels in individuals. Included as demographic variables were age, sex, ethnicity, residency, family income, and education level. We conducted two analyses: an unweighted analysis where phthalate distributions were estimated with changes in the means of these distributions as a function of demographic variables, and a weighted prediction for the general population in which weights were assigned for a subset of the population depending on the frequency of their demographic variables in the general U.S. population. We used statistical tests to determine whether any of the demographic variables affected mean phthalate levels. Individuals with only a high school education had higher levels of di-n-butyl phthalate than individuals with education beyond high school. Subjects who had family income less than $1,500 in the month before sampling and/or only high school education had higher levels of n-butyl benzyl phthalate levels than other groupings. Di(2-ethylhexyl) phthalate was higher in males and/or in urban populations and/or in people who had family income less than $1,500 per month. Our findings suggest that there may be significant demographic variations in exposure and/or metabolism of phthalates and that health-risk assessments for phthalate exposure in humans should consider different potential risk groups.