Comparative Toxicogenomic Responses to the Flame Retardant mITP in Developing Zebrafish

Monosubstituted isopropylated triaryl phosphate (mITP) is a major component of Firemaster 550, an additive flame retardant mixture commonly used in polyurethane foams. Developmental toxicity studies in zebrafish established mITP as the most toxic component of FM 550, which causes pericardial edema and heart looping failure. Mechanistic studies showed that mITP is an aryl hydrocarbon receptor (AhR) ligand; however, the cardiotoxic effects of mITP were independent of the AhR. We performed comparative whole genome transcriptomics in wild-type and <i>ahr2</i>^{<i>hu3335</i>} zebrafish, which lack functional <i>ahr2</i>, to identify transcriptional signatures causally involved in the mechanism of mITP-induced cardiotoxicity. Regardless of <i>ahr2</i> status, mITP exposure resulted in decreased expression of transcripts related to the synthesis of all-<i>trans</i>-retinoic acid and a host of Hox genes. Clustered gene ontology enrichment analysis showed unique enrichment in biological processes related to xenobiotic metabolism and response to external stimuli in wild-type samples. Transcript enrichments overlapping both genotypes involved the retinoid metabolic process and sensory/visual perception biological processes. Examination of the gene–gene interaction network of the differentially expressed transcripts in both genetic backgrounds demonstrated a strong AhR interaction network specific to wild-type samples, with overlapping genes regulated by retinoic acid receptors (RARs). A transcriptome analysis of control <i>ahr2-</i>null zebrafish identified potential cross-talk among AhR, Nrf2, and Hif1α. Collectively, we confirmed that mITP is an AhR ligand and present evidence in support of our hypothesis that mITP’s developmental cardiotoxic effects are mediated by inhibition at the RAR level

A New Statistical Approach to Characterize Chemical-Elicited Behavioral Effects in High-Throughput Studies Using Zebrafish

<div><p>Zebrafish have become an important alternative model for characterizing chemical bioactivity, partly due to the efficiency at which systematic, high-dimensional data can be generated. However, these new data present analytical challenges associated with scale and diversity. We developed a novel, robust statistical approach to characterize chemical-elicited effects in behavioral data from high-throughput screening (HTS) of all 1,060 Toxicity Forecaster (ToxCast™) chemicals across 5 concentrations at 120 hours post-fertilization (hpf). Taking advantage of the immense scale of data for a global view, we show that this new approach reduces bias introduced by extreme values yet allows for diverse response patterns that confound the application of traditional statistics. We have also shown that, as a summary measure of response for local tests of chemical-associated behavioral effects, it achieves a significant reduction in coefficient of variation compared to many traditional statistical modeling methods. This effective increase in signal-to-noise ratio augments statistical power and is observed across experimental periods (light/dark conditions) that display varied distributional response patterns. Finally, we integrated results with data from concomitant developmental endpoint measurements to show that appropriate statistical handling of HTS behavioral data can add important biological context that informs mechanistic hypotheses.</p></div

Synergistic Toxicity Produced by Mixtures of Biocompatible Gold Nanoparticles and Widely Used Surfactants

Nanoparticle safety is usually determined using solutions of individual particles that are free of additives. However, the size-dependent properties of nanoparticles can be readily altered through interactions with other components in a mixture. In applications, nanoparticles are commonly combined with surfactants or other additives to increase dispersion or to enhance product performance. Surfactants might also influence the biological activity of nanoparticles; however, little is known about such effects. We investigated the influence of surfactants on nanoparticle biocompatibility by studying mixtures of ligand-stabilized gold nanoparticles and Polysorbate 20 in embryonic zebrafish. These mixtures produced synergistic toxicity at concentrations where the individual components were benign. We examined the structural basis for this synergy using solution-phase analytical techniques. Spectroscopic and X-ray scattering studies suggest that the Polysorbate 20 does not affect the nanoparticle core structure. DOSY NMR showed that the hydrodynamic size of the nanoparticles increased, suggesting that Polysorbate 20 assembles on the nanoparticle surfaces. Mass spectrometry showed that these assemblies have both increased uptake and increased toxicity in zebrafish, as compared to the gold nanoparticles alone. We probed the generality of this synergy by performing toxicity assays with two other common surfactants, Polysorbate 80 and sodium dodecyl sulfate. These surfactants also caused synergistic toxicity, although the extent and time frame of the response depends upon the surfactant structure. These results demonstrate a need for additional, foundational studies to understand the effects of surfactants on nanoparticle biocompatibility and challenge traditional models of nanoparticle safety where the matrix is assumed to have only additive effects on nanoparticle toxicity

Experimental Design.

<p>Experimental timeline for chemical exposures at concentrations {0uM, 0.0064uM, 0.064uM, 0.64uM, 6.4uM, 64uM} added at 6hpf, with n = 32 embryos per concentration. At 24hpf, a nondestructive assay including two one-second light perturbations at 30s and 40s was performed (Details about this 24hpf behavioral assessment can be found at Reif et al. 2015). At 120hpf, behavior was measured under environmental conditions of 7 minutes continuous light exposure followed by an 8 minutes of dark. Behavioral and developmental (morphological) assessments were then recorded.</p

Behavioral response patterns.

<p>Differential entropy of each concentration was plotted across experimental time. Color key is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169408#pone.0169408.g001" target="_blank">Fig 1</a>. Y axis: Differential entropy (Nats); X axis: Time (m). Red segments represent light condition from 3m to 9m. <b>A: Inactive:</b> TX000888 (Terbacil) was inactive at all concentrations. <b>B: Hypoactivity (L) and Hypoactivity (D):</b> TX001406 (Cyclanilide) shows significant hypoactivities at 64uM for both light and dark intervals. <b>C: Inactive (L) and Hypoactivity (D):</b> TX001412 (Fipronil) is inactive at light interval and shows significant hypoactivity at dark interval at 0.064uM, 0.64uM, 6.4uM, and 64uM. <b>D: Hyperactivity (L) and Inactive (D):</b> TX007214 (Dieldrin) shows significant hyperactivity at light interval but it is inactive at dark at 64uM. <b>E: Hypoactivity (L) and Inactive (D):</b> TX003357 (44’-Oxydianiline) shows significant hypoactivity at light interval and inactive pattern at dark interval at 0.064uM, 6.4uM, and 64uM. <b>F: Hyperactivity (L) and Hypoactivity (D):</b> TX006644 (Haloperidol) shows significant hyperactivity at light interval and significant hypoactivity at dark interval at both 6.4uM and 64uM. In addition, at 0.64uM, it shows significant hyperactivity at light interval. <b>G: Hyperactivity (L) and Hyperactivity (D):</b> TX005098 (4-Pentylaniline) shows significant hyperactivity at 64uM for both light and dark conditions. <b>H: Inactive (L) and Hyperactivity (D):</b> TX005080 (44’4”-Ethane-111-triyltriphenol) is inactive at light and shows significant hyperactivity at dark at 6.4uM and 64uM.</p

Performance of Our Novel Statistical Modeling Method.

<p><b>A:</b> Example controls (having similar survival rates) illustrate the transformation. These two separate controls were plotted by different colors. Blue: TX000769 (Propoxycarbazone-sodium); Black: TX000900 (Methamidophos). Top: Movement index of each time point, and the line was drawn by connecting the mean movement indexes. Y axis: Movement index; X axis: Time. Bottom: Lines were drawn using our method, which connects the differential entropy of each time point. Y axis: Differential entropy (Nats); X axis: Time. <b>B:</b> All 1,060 control groups were plotted. Top: Each line represents a chemical. Line was drawn by connecting mean movement indexes. Y axis: Movement index; X axis: Time. Bottom: Each line represents a chemical. Line was drawn by connecting differential entropy across time. Y axis: Differential entropy (Nats); X axis: Time. <b>C:</b> Coefficient variation of each time point using all control groups and various statistical modeling methods. Y axis: Coefficient of Variation; X axis: Time.</p

Statistics for checking artifacts.

<p>P value and Cohen’s d from each permutation was plotted (Black represents plate; Blue represents position). Y axis: Student’s t test p value; X axis: Cohen’s d. Horizontal red line was drawn at a significance level of 0.05. Vertical red line was drawn at 0.2 to represent the general rule of thumb of effect size.</p

Summary of results by the whole experimental system.

<p>This Venn diagram provides the summary of the total number of significant chemicals detected by each assay. It also provides statistics regarding the benefits of including all assays. Missing rate: the number of chemicals that would have been missed using a subset of these assays.</p

Statistical Workflow.

<p>Step 1: Visualize movement index; Step 2: Remove annotated dead fish for every concentration of a chemical; Step 3: Propose a statistical modeling method; Step 4: Check for artifacts, such as technical issues, global plate and position effect, and remove any bad plates; P1: Plate 1; P2: Plate 2; C1: Column 1; C12: Column 12; Step 5: Apply statistical modeling method to provide dose-response patterns for analysis; Step 6: Statistical analysis pipeline; Step 7: Assess reproducibility of our computational framework.</p

Proteome-Driven Elucidation of Adaptive Responses to Combined Vitamin E and C Deficiency in Zebrafish

The purpose of this study was to determine the system-wide consequences of deficiencies in two essential micronutrients, vitamins E and C, on the proteome using zebrafish (<i>Danio rerio</i>) as one of the few vertebrate models that similar to humans cannot synthesize vitamin C. We describe a label-free proteomics workflow to detect changes in protein abundance estimates dependent on vitamin regimes. We used ion-mobility-enhanced data-independent tandem mass spectrometry to determine differential regulation of proteins in response to low dietary levels of vitamin C with or without vitamin E. The detection limit of the method was as low as 20 amol, and the dynamic range was five orders of magnitude for the protein-level estimates. On the basis of the quantitative changes obtained, we built a network of protein interactions that reflect the whole organism’s response to vitamin C deficiency. The proteomics-driven study revealed that in vitamin-E-deficient fish, vitamin C deficiency is associated with induction of stress response, astrogliosis, and a shift from glycolysis to glutaminolysis as an alternative mechanism to satisfy cellular energy requirements