Estimation of the Half-Lives of Recently Detected Per- and Polyfluorinated Alkyl Ethers in an Exposed Community.

To estimate half-lives for novel fluoroethers, the GenX Exposure Study obtained two serum measurements for per- and polyfluoroalkyl substances (PFAS) for 44 participants of age 12-86 years from North Carolina, collected 5 and 11 months after fluoroether discharges into the drinking water source were controlled. The estimated half-lives for these compounds were 127 days (95% confidence interval (95% CI) = 86, 243 days) for perfluorotetraoxadecanoic acid (PFO4DA), 296 days for Nafion byproduct 2 (95% CI = 176, 924 days), and 379 days (95% CI = 199, 3870 days) for perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoA). Using these estimates and the literature values, a model was built that predicted PFAS half-lives using structural properties. Three chemical properties predicted 55% of the variance of PFAS half-lives based on 15 PFAS. A model with only molecular weight predicted 69% of the variance. Some properties can predict the half-lives of PFAS, but a deeper understanding is needed. These fluoroethers had biological half-lives longer than published half-lives for PFHxA and PFHpA (30-60 days) but shorter than those for PFOA and PFOS (800-1200 days). These are the first and possibly only estimates of human elimination half-lives of these fluoroethers.

Source apportionment of serum PFASs in two highly exposed communities.

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals that are ubiquitous in environmental and biological systems, including human serum. PFASs are used in many products and industrial processes and are tied to numerous health effects. Due to multiple sources and exposure pathways, methods are needed to identify PFAS sources in communities to develop targeted interventions. We assessed effectiveness of three source apportionment methods (UNMIX, positive matrix factorization [PMF], and principal component analysis - multiple linear regression [PCA-MLR]) for identifying contributors to human serum PFAS concentrations in two highly exposed populations in Colorado and North Carolina where drinking water was contaminated via upstream sources, including a Space Force base and a fluorochemical manufacturing plant. UNMIX and PMF models extracted three to four potential PFAS exposure sources in the Colorado and North Carolina cohorts while PCA-MLR classified two in each cohort. No sources were characterized in NHANES (National Health and Nutrition Examination Study). Results suggest that these three methods can successfully identify sources in highly exposed populations. Future PFAS exposure research should focus on analyzing serum for an expanded PFAS panel, identifying cohorts with other distinct point source exposures, and combining biological and environmental data to better understand source apportionment results in the context of PFAS toxicokinetic behavior.

Leveraging a High-Throughput Screening Method to Identify Mechanisms of Individual Susceptibility Differences in a Genetically Diverse Zebrafish Model.

Understanding the mechanisms behind chemical susceptibility differences is key to protecting sensitive populations. However, elucidating gene-environment interactions (GxE) presents a daunting challenge. While mammalian models have proven useful, problems with scalability to an enormous chemical exposome and clinical translation faced by all models remain; therefore, alternatives are needed. Zebrafish (Danio rerio) have emerged as an excellent model for investigating GxE. This study used a combined bioinformatic and experimental approach to probe the mechanisms underlying chemical susceptibility differences in a genetically diverse zebrafish population. Starting from high-throughput screening (HTS) data, a genome-wide association study (GWAS) using embryonic fish exposed to 0.6 µM Abamectin revealed significantly different effects between individuals. A hypervariable region with two distinct alleles-one with G at the SNP locus (GG) and one with a T and the 16 bp deletion (TT)-associated with differential susceptibility was found. Sensitive fish had significantly lower sox7 expression. Due to their location and the observed expression differences, we hypothesized that these sequences differentially regulate sox7. A luciferase reporter gene assay was used to test if these sequences, alone, could lead to expression differences. The TT allele showed significantly lower expression than the GG allele in MCF-7 cells. To better understand the mechanism behind these expression differences, predicted transcription factor binding differences between individuals were compared in silico, and several putative binding differences were identified. EMSA was used to test for binding differences in whole embryo protein lysate to investigate these TF binding predictions. We confirmed that the GG sequence is bound to protein in zebrafish. Through a competition EMSA using an untagged oligo titration, we confirmed that the GG oligo had a higher binding affinity than the TT oligo, explaining the observed expression differences. This study identified differential susceptibility to chemical exposure in a genetically diverse population, then identified a plausible mechanism behind those differences from a genetic to molecular level. Thus, an HTS-compatible zebrafish model is valuable and adaptable in identifying GxE mechanisms behind susceptibility differences to chemical exposure.

Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions.

Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.

Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function.

The US Environmental Protection Agency's ToxCast program has generated toxicity data for thousands of chemicals but does not adequately assess potential neurotoxicity. Networks of neurons grown on microelectrode arrays (MEAs) offer an efficient approach to screen compounds for neuroactivity and distinguish between compound effects on firing, bursting, and connectivity patterns. Previously, single concentrations of the ToxCast Phase II library were screened for effects on mean firing rate (MFR) in rat primary cortical networks. Here, we expand this approach by retesting 384 of those compounds (including 222 active in the previous screen) in concentration-response across 43 network activity parameters to evaluate neural network function. Using hierarchical clustering and machine learning methods on the full suite of chemical-parameter response data, we identified 15 network activity parameters crucial in characterizing activity of 237 compounds that were response actives ("hits"). Recognized neurotoxic compounds in this network function assay were often more potent compared to other ToxCast assays. Of these chemical-parameter responses, we identified three k-means clusters of chemical-parameter activity (i.e., multivariate MEA response patterns). Next, we evaluated the MEA clusters for enrichment of chemical features using a subset of ToxPrint chemotypes, revealing chemical structural features that distinguished the MEA clusters. Finally, we assessed distribution of neurotoxicants with known pharmacology within the clusters and found that compounds segregated differentially. Collectively, these results demonstrate that multivariate MEA activity patterns can efficiently screen for diverse chemical activities relevant to neurotoxicity, and that response patterns may have predictive value related to chemical structural features.

Knockdown of arylalkylamine N-acetyltransferase-like 2 in Drosophila melanogaster.

Drosophila melanogaster produces fatty acid amides, and thus, provides a model to unravel the pathways for their biosynthesis. We previously demonstrated that arylalkylamine N-acetyltransferase-like 2 (AANATL2) from D. melanogaster will catalyze the formation of long-chain N-acylserotonins and N-acyldopamines in vitro. Generating silencing RNA via the UAS/GAL4 bipartite approach for targeted gene expression effectively decreased the endogenous levels of the AANATL2 transcripts in D. melanogaster, as shown by reverse transcription quantitative polymerase chain reaction. Consistent with these data, western blot analysis of the offspring of the AANATL2 knockdown flies using an anti-AANATL2 antibody revealed a significant reduction in the expression of the AANATL2 protein. Reduced expression of AANATL2 decreased the cellular levels of N-palmitoyldopamine (PALDA), providing strong evidence that AANATL2 is responsible for the biosynthesis of PALDA in vivo. This is the first time that the expression of an AANAT has been reduced in D. melanogaster to link one of these enzymes to the in vivo production of an N-acylarylalkylamide.

Bm-iAANAT and its potential role in fatty acid amide biosynthesis in Bombyx mori.

The purpose of this research is to unravel the substrate specificity and kinetic properties of an insect arylalkylamine N-acyltransferase from Bombyx mori (Bm-iAANAT) and to determine if this enzyme will catalyze the formation of long chain N-acylarylalkylamides in vitro. However, the determination of substrates and products for Bm-iAANAT in vitro is no guarantee that these same molecules are substrates and products for the enzyme in the organism. Therefore, RT-PCR was performed to detect the Bm-iAANAT transcripts and liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS) analysis was performed on purified lipid extracts from B. mori larvae (fourth instar, Bmi4) to determine if long chain fatty acid amides are produced in B. mori. Ultimately, we found that recombinant Bm-iAANAT will utilize long-chain acyl-CoA thioesters as substrates and identified Bm-iAANAT transcripts and long-chain fatty acid amides in Bmi4. Together, these data show Bm-iAANAT will catalyze the formation of long-chain N-acylarylalkylamides in vitro and provide evidence demonstrating that Bm-iAANAT has a role in fatty acid amide biosynthesis in B. mori, as well.