House Dust-Derived Mixtures of Organophosphate Esters Alter the Phenotype, Function, Transcriptome, and Lipidome of KGN Human Ovarian Granulosa Cells.

Organophosphate esters (OPEs), used as flame retardants and plasticizers, are present ubiquitously in the environment. Previous studies suggest that exposure to OPEs is detrimental to female fertility in humans. However, no experimental information is available on the effects of OPE mixtures on ovarian granulosa cells, which play essential roles in female reproduction. We used high-content imaging to investigate the effects of environmentally relevant OPE mixtures on KGN human granulosa cell phenotypes. Perturbations to steroidogenesis were assessed using ELISA and qRT-PCR. A high-throughput transcriptomic approach, TempO-Seq™, was used to identify transcriptional changes in a targeted panel of genes. Effects on lipid homeostasis were explored using a cholesterol assay and global lipidomic profiling. OPE mixtures altered multiple phenotypic features of KGN cells, with triaryl OPEs in the mixture showing higher potencies than other mixture components. The mixtures increased basal production of steroid hormones; this was mediated by significant changes in the expression of critical transcripts involved in steroidogenesis. Further, the total-OPE mixture disrupted cholesterol homeostasis and the composition of intracellular lipid droplets. Exposure to complex mixtures of OPEs, similar to those found in house dust, may adversely affect female reproductive health by altering a multitude of phenotypic and functional endpoints in granulosa cells. This study provides novel insights into the mechanisms of actions underlying the toxicity induced by OPEs and highlights the need to examine the effects of human relevant chemical mixtures.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium.

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

Transcriptome analysis in mouse skin after exposure to ultraviolet radiation from a canopy sunbed.

Exposure to ultraviolet radiation (UV-R), from both natural and artificial tanning, heightens the risk of skin cancer by inducing molecular changes in cells and tissues. Despite established transcriptional alterations at a molecular level due to UV-R exposure, uncertainties persist regarding UV radiation characterization and subsequent genomic changes. Our study aimed to mechanistically explore dose- and time-dependent gene expression changes, that may drive short-term (e.g., sunburn) and long-term actinic (e.g., skin cancer) consequences. Using C57BL/6N mouse skin, we analyzed transcriptomic expression following exposure to five erythemally weighted UV-R doses (0, 5, 10, 20, and 40 mJ/cm2 ) emitted by a UV-R tanning device. At 96 h post-exposure, 5 mJ/cm2 induced 116 statistically significant differentially expressed genes (DEGs) associated with structural changes from UV-R damage. The highest number of significant gene expression changes occurred at 6 and 48 h post-exposure in the 20 and 40 mJ/cm2 dose groups. Notably, at 40 mJ/cm2 , 13 DEGs related to skin barrier homeostasis were consistently perturbed across all timepoints. UV-R exposure activated pathways involving oxidative stress, P53 signaling, inflammation, biotransformation, skin barrier maintenance, and innate immunity. This in vivo study's transcriptional data offers mechanistic insights into both short-term and potential non-threshold-dependent long-term health effects of UV-R tanning.

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals.

To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

The functional mutational landscape of the lacZ gene.

The lacZ gene of Escherichia coli encodes ß-galactosidase (ß-gal), a lactose metabolism enzyme of the lactose operon. Previous chemical modification or site-directed mutagenesis experiments have identified 21 amino acids that are essential for ß-gal catalytic activity. We have assembled over 10,000 lacZ mutations from published studies that were collected using a positive selection assay to identify mutations in lacZ that disrupted ß-gal function. We analyzed 6,465 independent lacZ mutations that resulted in 2,732 missense mutations that impaired ß-gal function. Those mutations affected 492 of the 1,023 lacZ codons, including most of the 21 previously known residues critical for catalytic activity. Most missense mutations occurred near the catalytic site and in regions important for subunit tetramerization. Overall, our work provides a comprehensive and detailed map of the amino acid residues affecting the structure and catalytic activity of the ß-gal enzyme.

Toxicological Mechanisms and Potencies of Organophosphate Esters in KGN Human Ovarian Granulosa Cells as Revealed by High-throughput Transcriptomics.

Despite the growing number of studies reporting potential risks associated with exposure to organophosphate esters (OPEs), their molecular mechanisms of action remain poorly defined. We used the high-throughput TempO-Seq™ platform to investigate the effects of frequently detected OPEs on the expression of ∼3000 environmentally responsive genes in KGN human ovarian granulosa cells. Cells were exposed for 48 h to one of five OPEs (0.1 to 50 µM): tris(methylphenyl) phosphate (TMPP), isopropylated triphenyl phosphate (IPPP), tert-butylphenyl diphenyl phosphate (BPDP), triphenyl phosphate (TPHP), or tris(2-butoxyethyl) phosphate (TBOEP). The sequencing data indicate that four OPEs induced transcriptional changes, whereas TBOEP had no effect within the concentration range tested. Multiple pathway databases were used to predict alterations in biological processes based on differentially expressed genes. At lower concentrations, inhibition of the cholesterol biosynthetic pathway was the predominant effect of OPEs; this was likely a consequence of intracellular cholesterol accumulation. At higher concentrations, BPDP and TPHP had distinct effects, primarily affecting pathways involved in cell cycle progression and other stress responses. Benchmark concentration (BMC) modelling revealed that BPDP had the lowest transcriptomic point of departure. However, in vitro to in vivo extrapolation modeling indicated that TMPP was bioactive at lower concentrations than the other OPEs. We conclude that these new approach methodologies provide information on the mechanism(s) underlying the effects of data-poor compounds and assist in the derivation of protective points of departure for use in chemical read-across and decision-making.

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

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

Duplex sequencing provides detailed characterization of mutation frequencies and spectra in the bone marrow of MutaMouse males exposed to procarbazine hydrochloride.

Mutagenicity testing is an essential component of health safety assessment. Duplex Sequencing (DS), an emerging high-accuracy DNA sequencing technology, may provide substantial advantages over conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and BM sampled 42 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in mutation frequencies and changes to mutation spectra at all PRC doses. Low intra-group variability within DS samples allowed for detection of increases at lower doses than the lacZ assay. While the lacZ assay initially yielded a higher fold-change in mutant frequency than DS, inclusion of clonal mutations in DS mutation frequencies reduced this discrepancy. Power analyses suggested that three animals per dose group and 500 million duplex base pairs per sample is sufficient to detect a 1.5-fold increase in mutations with > 80% power. Overall, we demonstrate several advantages of DS over classical mutagenicity assays and provide data to support efforts to identify optimal study designs for the application of DS as a regulatory test.

Cerium oxide nanoparticles (nCeO2) exert minimal adverse effects on microbial communities in soils with and without biosolids amendment.

Increased use of nano-cerium oxide (nCeO2) in an array of industrial applications has raised environmental concerns due to potential increased loadings to the soil environment. This research investigated the potential adverse effects of nCeO2 (10-30 nm) on the soil microbial community in two exposure scenarios: direct application to soil, and indirect application to soil through chemical spiking of biosolids, followed by mixing into soil. Total Ce in test soils without, and with biosolids amendment, ranged from 44 to 770, and 73 to 664 mg Ce kg-1 soil, respectively. In order to help distinguish whether observed effects were elicited by the solid-phase colloids or the activity of dissolved Ce, a soluble Ce salt (Ce (NO3)3) treatment was included in select assays. A suite of tests was used to investigate effects on critical processes: microbial growth (heterotrophic plate count), microbial activity (organic matter (OM) decomposition, enzyme activity and, nitrification) and diversity (structural and functional). Although results showed significant inhibition on microbial growth in soil without biosolids amendment at ≥ 156 mg Ce kg-1 soil by week 5, these results were inconsistent and non-significant thereafter. In general, nCeO2 showed no evidence of consistent adverse effects on OM decomposition, nitrification, soil enzyme activities and functional diversity. Leucine aminopeptidase showed significant (p< 0.05) stimulatory effects over time at ≥ 44 mg Ce kg-1 in soils without biosolids, which was not observed in soils with biosolids amendment. The lack of inhibitory effects of nCeO2 may be attributed to its low solubility; Ce in soil extracts (0.01 M CaCl2) were all below detection (< 0.003 mg kg-1) in the nCeO2-spiked soils, but detectable in the Ce (NO3)3 samples. In contrast, soluble Ce at 359 mg Ce kg-1 showed a significant reduction in OM decomposition and effects on microbial genomic diversity based on the 16S rDNA data in soils with and without biosolids amendment (359 and 690 mg Ce kg-1). The nCeO2 behaviour and effects information described herein are expected to help fulfill data gaps for the characterization of this priority nanomaterial.

Per- and polyfluoroalkyl substances (PFAS) in mixtures show additive effects on transcriptomic points of departure in human liver spheroids.

Per- and polyfluoroalkyl substances (PFAS) are a wide range of chemicals that are used in a variety of consumer and industrial products leading to direct human exposure. Many PFAS are chemically nonreactive and persistent in the environment, resulting in additional exposure from water, soil, and dietary intake. While some PFAS have documented negative health effects, data on simultaneous exposures to multiple PFAS (PFAS mixtures) are inadequate for making informed decisions for risk assessment. The current study leverages data from previous work in our group using Templated Oligo-Sequencing (TempO-Seq) for high-throughput transcriptomic analysis of PFAS-exposed primary human liver cell spheroids; herein, we determine the transcriptomic potency of PFAS in mixtures. Gene expression data from single PFAS and mixture exposures of liver cell spheroids were subject to benchmark concentration (BMC) analysis. We used the 25th lowest gene BMC as the point of departure to compare the potencies of single PFAS to PFAS mixtures of varying complexity and composition. Specifically, the empirical potency of 8 PFAS mixtures were compared to predicted mixture potencies calculated using the principal of concentration addition (ie, dose addition) in which mixture component potencies are summed by proportion to predict mixture potency. In this study, for most mixtures, empirical mixture potencies were comparable to potencies calculated through concentration addition. This work supports that the effects of PFAS mixtures on gene expression largely follow the concentration addition predicted response and suggests that effects of these individual PFAS in mixtures are not strongly synergistic or antagonistic.

Dataset on DNA methylation and gene expression changes induced by 5-aza-2'-deoxycytidine in Syrian golden hamster fetal cell cultures.

The Syrian hamster (SH) is an animal model used in virology, toxicology, and carcinogenesis, where a better understanding of epigenetic mechanisms is required. Finding genetic loci regulated by DNA methylation may assist in the development of DNA methylation-based in vitro assays for the identification of carcinogens. This dataset informs on the regulation of gene expression by DNA methylation. Primary cultures of SH male fetal cells (sex determined by differences in kdm5 loci on the X and Y chromosome) were exposed for 7 days to the carcinogen benzo[a]pyrene (20 µM) from which a morphologically transformed colony was collected and reseeded. The colony bypassed senescence and sustained growth. After 210 days of culture, the cells were collected and divided in 16 aliquots to create 4 experimental groups to test the effects of the DNA methylation inhibitor 5-aza-2'-deoxycytidine (5adC). The experiment was initiated 24 h after cell seeding in 10 cm plates. The groups are naïve cells (N), cells exposed for 48 h to either 0.05% DMSO as vehicle (V), or to 5adC at 1 µM and 5 µM. DNA and RNA libraries were sequenced on an Illumina NextSeq 500. Gene expression was analysed by RNAseq and differentially methylated DNA regions (DMRs: clusters of 200 base pairs (bp), read depth >20, q< 0.05, methylation difference >|25%|) were identified by reduce representation bisulfite sequencing (RRBS). Global genome DNA methylation was similar between the N (mean±SD, 47.3%±0.02) and V groups (47.3%±0.01). Although 5adC reduced methylation, the reduction was larger in the 1 µM (39.2%±0.002) than in the 5 µM group (44.3%±0.01). 5adC induced a total of 612 and 190 DMRs by 1 µM and 5 µM, among which 79 and 23 were in the promoter regions (±3,000 bp from the transcription start site), respectively. 5adC induced a total of 1,170 and 1,797 differentially expressed genes (DEGs) by 1 µM and 5 µM, respectively. The 5 µM treatment induced statistically significant toxicity (% cell viability: group N 97%±8, V 98.8%±1.3, 1 µM 97.3%±0.5, 5 µM 93.8%±1.5), which perhaps reduced cell division and daughter cell numbers with inherited changes in methylation, but increased number of DEGs due to both toxicity and methylation changes. As usually observed in the literature, a small portion of DEGs (4% and 4% at 1 µM and 5 µM, respectively) are associated with DMRs in their promoters. These promoter DMRs by themselves are sufficient among other epigenetic marks to induce DEGs. The dataset provides the genomic coordinates of the DMRs and an opportunity to further examine their roles in distal putative promoters or enhancers (yet to be described in the SH) in contributing to gene expression changes, senescence bypass and sustained proliferation as essential carcinogenic events (see companion paper [1]). Finally, this experiment confirms the possibility in future experiments to use 5adC as a positive control for effects on DNA methylation in cells derived from SH.

Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds.

The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode of action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow® and MultiFlow® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada's New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-Seq™ assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow® and MultiFlow® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow®, and MultiFlow® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds.

DNA methylation changes from primary cultures through senescence-bypass in Syrian hamster fetal cells initially exposed to benzo[a]pyrene.

Current chemical testing strategies are limited in their ability to detect non-genotoxic carcinogens (NGTxC). Epigenetic anomalies develop during carcinogenesis regardless of whether the molecular initiating event is associated with genotoxic (GTxC) or NGTxC events; therefore, epigenetic markers may be harnessed to develop new approach methodologies that improve the detection of both types of carcinogens. This study used Syrian hamster fetal cells to establish the chronology of carcinogen-induced DNA methylation changes from primary cells until senescence-bypass as an essential carcinogenic step. Cells exposed to solvent control for 7 days were compared to naïve primary cultures, to cells exposed for 7 days to benzo[a]pyrene, and to cells at the subsequent transformation stages: normal colonies, morphologically transformed colonies, senescence, senescence-bypass, and sustained proliferation in vitro. DNA methylation changes identified by reduced representation bisulphite sequencing were minimal at day-7. Profound DNA methylation changes arose during cellular senescence and some of these early differentially methylated regions (DMRs) were preserved through the final sustained proliferation stage. A set of these DMRs (e.g., Pou4f1, Aifm3, B3galnt2, Bhlhe22, Gja8, Klf17, and L1l) were validated by pyrosequencing and their reproducibility was confirmed across multiple clones obtained from a different laboratory. These DNA methylation changes could serve as biomarkers to enhance objectivity and mechanistic understanding of cell transformation and could be used to predict senescence-bypass and chemical carcinogenicity.

R-ODAF: Omics data analysis framework for regulatory application.

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.

A Modern Genotoxicity Testing Paradigm: Integration of the High-Throughput CometChip® and the TGx-DDI Transcriptomic Biomarker in Human HepaRG™ Cell Cultures.

Higher-throughput, mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline DNA damage-sensing CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq®, as an integrated genotoxicity testing approach. We used metabolically competent differentiated human HepaRG™ cell cultures to enable the identification of chemicals that require bioactivation to cause genotoxicity. We studied 12 chemicals (nine DDI, three non-DDI) in increasing concentrations to measure and classify chemicals based on their ability to damage DNA. The CometChip® classified 10/12 test chemicals correctly, missing a positive DDI call for aflatoxin B1 and propyl gallate. The poor detection of aflatoxin B1 adducts is consistent with the insensitivity of the standard alkaline comet assay to bulky lesions (a shortcoming that can be overcome by trapping repair intermediates). The TGx-DDI biomarker accurately classified 10/12 agents. TGx-DDI correctly identified aflatoxin B1 as DDI, demonstrating efficacy for combined used of these complementary methodologies. Zidovudine, a known DDI chemical, was misclassified as it inhibits transcription, which prevents measurable changes in gene expression. Eugenol, a non-DDI chemical known to render misleading positive results at high concentrations, was classified as DDI at the highest concentration tested. When combined, the CometChip® assay and the TGx-DDI biomarker were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI biomarker were highly concordant (within 4-fold) and resulted in identical potency rankings. These results demonstrate that these two assays can be integrated for efficient identification and potency ranking of DNA damaging agents in HepaRG™ cell cultures.

High-Throughput Transcriptomic Analysis of Human Primary Hepatocyte Spheroids Exposed to Per- and Polyfluoroalkyl Substances as a Platform for Relative Potency Characterization.

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focused on 4 model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at 4 time points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS, (2) identify similarities in biological responses, (3) compare PFAS potency through benchmark concentration analysis, and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPARα activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (ie, was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

Low dose antibiotic ingestion potentiates systemic and microbiome changes induced by silver nanoparticles.

Changes in the mammalian gut microbiome are linked to the impairment of immunological function and numerous other pathologies. Antimicrobial silver nanoparticles (AgNPs) are incorporated into numerous consumer products (e.g., clothing, cosmetics, food packaging), which may directly impact the gut microbiome through ingestion. The human health impact of chronic AgNP ingestion is still uncertain, but evidence from exposure to other antimicrobials provides a strong rationale to assess AgNP effects on organ function, immunity, metabolism, and gut-associated microbiota. To investigate this, mice were gavaged daily for 5 weeks with saline, AgNPs, antibiotics (ciprofloxacin and metronidazole), or AgNPs combined with antibiotics. Animals were weighed daily, assessed for glucose tolerance, organ function, tissue and blood cytokine and leukocyte levels. At the end of the study, we used 16S rDNA amplicon and whole-metagenome shotgun sequencing to assess changes in the gut microbiome. In mice exposed to both AgNPs and antibiotics, silver was found in the stomach, and small and large intestines, but negligible amounts were present in other organs examined. Mice exposed to AgNPs alone showed minimal tissue silver levels. Antibiotics, but not AgNPs, altered glucose metabolism. Mice given AgNPs and antibiotics together demonstrated slower weight gain, reduced peripheral lymphocytes, and elevated splenic, but not circulatory markers of inflammation. 16S rDNA profiling of cecum and feces and metagenomic sequencing of fecal DNA demonstrated that combined AgNP-antibiotic treatment also significantly altered the structure and function of the gut microbiota, including depletion of the indicator species Akkermansia muciniphila. This study provides evidence for possible biological effects from repeated ingestion of AgNP-containing consumer products when antibiotics are also being used and raises concern that an impaired gut microbiome (e.g., through antibiotic use) can potentiate the harm from chemical exposures such as AgNPs.

Chemically induced mutations in a MutaMouse reporter gene inform mechanisms underlying human cancer mutational signatures.

Transgenic rodent (TGR) models use bacterial reporter genes to quantify in vivo mutagenesis. Pairing TGR assays with next-generation sequencing (NGS) enables comprehensive mutation pattern analysis to inform mutational mechanisms. We used this approach to identify 2751 independent lacZ mutations in the bone marrow of MutaMouse animals exposed to four chemical mutagens: benzo[a]pyrene, N-ethyl-N-nitrosourea, procarbazine, and triethylenemelamine. We also collected published data for 706 lacZ mutations from eight additional environmental mutagens. We report that lacZ gene sequencing generates chemical-specific mutation signatures observed in human cancers with established environmental causes. For example, the mutation signature of benzo[a]pyrene, a carcinogen present in tobacco smoke, matched the signature associated with tobacco-induced lung cancers. Our results suggest that the analysis of chemically induced mutations in the lacZ gene shortly after exposure provides an effective approach to characterize human-relevant mechanisms of carcinogenesis and propose novel environmental causes of mutation signatures observed in human cancers.

Soil exposed to silver nanoparticles reveals significant changes in community structure and altered microbial transcriptional profiles.

Anthropogenic activities can disrupt soil ecosystems, normally resulting in reduced soil microbial health. Regulatory agencies need to determine the effects of uncharacterized substances on soil microbial health to establish the safety of these chemicals if they end up in the environment. Previous work has focused on measuring traditional ecotoxicologial endpoints within the categories of microbial biomass, activity, and community structure/diversity. Because these tests can be labor intensive, lengthy to conduct, and cannot measure changes in individual gene functions, we wanted to establish whether metatranscriptomics could be used as a more sensitive endpoint and provide a perspective on community function that is more informative than taxonomic identification of microbes alone. We spiked a freshly collected sandy loam soil (Vulcan, Alberta, Canada) with 0, 60, 145, 347, 833, and 2000 mg kg-1 of silver nanoparticles (AgNPs), a known antagonist of microorganisms due to its propensity for dissolution of toxic silver ions. Assessments performed in our previous work using traditional tests demonstrated the toxicity of AgNPs on soil microbial processes. We expanded this analysis with genomics-based tests by measuring changes in community taxonomic structure and function using 16S rDNA profiling and metatranscriptomics. In addition to identifying bacterial taxa affected by AgNPs, we found that genes involved in heavy metal resistance (e.g., the CzcA efflux pump) and other toxicity response pathways were highly upregulated in the presence of silver. Dose-response analysis using BMDExpress2 software successfully modeled many physiologically relevant genes responding to low concentrations of AgNPs. We found that the transcriptomic point of departure (BMD50) was lower than the IC50s calculated using the traditional tests in our previous work. These results suggest that dose-response modeling of metatranscriptomic gene expression is a useful tool in soil microbial health assessment. SUMMARY: Genomics-based endpoints for the assessment of soil microbial health can be used to perform quantitative dose-response modeling, and soil-based RNAseq adds functional insights.

Whole Genome Sequencing of the Mutamouse Model Reveals Strain- and Colony-Level Variation, and Genomic Features of the Transgene Integration Site.

The MutaMouse transgenic rodent model is widely used for assessing in vivo mutagenicity. Here, we report the characterization of MutaMouse's whole genome sequence and its genetic variants compared to the C57BL/6 reference genome. High coverage (>50X) next-generation sequencing (NGS) of whole genomes from multiple MutaMouse animals from the Health Canada (HC) colony showed ~5 million SNVs per genome, ~20% of which are putatively novel. Sequencing of two animals from a geographically separated colony at Covance indicated that, over the course of 23 years, each colony accumulated 47,847 (HC) and 17,677 (Covance) non-parental homozygous single nucleotide variants. We found no novel nonsense or missense mutations that impair the MutaMouse response to genotoxic agents. Pairing sequencing data with array comparative genomic hybridization (aCGH) improved the accuracy and resolution of copy number variants (CNVs) calls and identified 300 genomic regions with CNVs. We also used long-read sequence technology (PacBio) to show that the transgene integration site involved a large deletion event with multiple inversions and rearrangements near a retrotransposon. The MutaMouse genome gives important genetic context to studies using this model, offers insight on the mechanisms of structural variant formation, and contributes a framework to analyze aCGH results alongside NGS data.