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OBJECTIVE: Sequencing cell-free DNA now allows detection of large chromosomal abnormalities and dominant Mendelian disorders in the prenatal period. Improving upon these methods would allow newborn screening programs to begin with prenatal genetics, ultimately improving the management of rare genetic disorders. METHODS: As a pilot study, we performed exome sequencing on the cell-free DNA from three mothers with singleton pregnancies to assess the viability of broad sequencing modalities in a noninvasive prenatal setting. RESULTS: We found poor resolution of maternal and fetal genotypes due to both sampling and technical issues. CONCLUSION: We find broad sequencing modalities inefficient for noninvasive prenatal applications. Alternatively, we suggest a more targeted path forward for noninvasive prenatal genotyping.
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Ácidos Nucleicos Libres de Células , Exoma , Femenino , Feto , Humanos , Recién Nacido , Proyectos Piloto , Embarazo , Diagnóstico Prenatal/métodos , Secuenciación del Exoma/métodosRESUMEN
BACKGROUND: As exome sequencing (ES) integrates into clinical practice, we should make every effort to utilize all information generated. Copy-number variation can lead to Mendelian disorders, but small copy-number variants (CNVs) often get overlooked or obscured by under-powered data collection. Many groups have developed methodology for detecting CNVs from ES, but existing methods often perform poorly for small CNVs and rely on large numbers of samples not always available to clinical laboratories. Furthermore, methods often rely on Bayesian approaches requiring user-defined priors in the setting of insufficient prior knowledge. This report first demonstrates the benefit of multiplexed exome capture (pooling samples prior to capture), then presents a novel detection algorithm, mcCNV ("multiplexed capture CNV"), built around multiplexed capture. RESULTS: We demonstrate: (1) multiplexed capture reduces inter-sample variance; (2) our mcCNV method, a novel depth-based algorithm for detecting CNVs from multiplexed capture ES data, improves the detection of small CNVs. We contrast our novel approach, agnostic to prior information, with the the commonly-used ExomeDepth. In a simulation study mcCNV demonstrated a favorable false discovery rate (FDR). When compared to calls made from matched genome sequencing, we find the mcCNV algorithm performs comparably to ExomeDepth. CONCLUSION: Implementing multiplexed capture increases power to detect single-exon CNVs. The novel mcCNV algorithm may provide a more favorable FDR than ExomeDepth. The greatest benefits of our approach derive from (1) not requiring a database of reference samples and (2) not requiring prior information about the prevalance or size of variants.
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Variaciones en el Número de Copia de ADN , Exoma , Algoritmos , Teorema de Bayes , Exoma/genética , Secuenciación de Nucleótidos de Alto Rendimiento , Secuenciación del ExomaRESUMEN
MOTIVATION: Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data. RESULTS: The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours. AVAILABILITY AND IMPLEMENTATION: Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies. CONTACT: aliverti@stat.unipd.it.
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Células Endoteliales , Programas Informáticos , Algoritmos , Animales , Expresión Génica , Perfilación de la Expresión Génica , RatonesRESUMEN
BACKGROUND: Tanzania's Zanzibar archipelago has made significant gains in malaria control over the last decade and is a target for malaria elimination. Despite consistent implementation of effective tools since 2002, elimination has not been achieved. Importation of parasites from outside of the archipelago is thought to be an important cause of malaria's persistence, but this paradigm has not been studied using modern genetic tools. METHODS: Whole-genome sequencing (WGS) was used to investigate the impact of importation, employing population genetic analyses of Plasmodium falciparum isolates from both the archipelago and mainland Tanzania. Ancestry, levels of genetic diversity and differentiation, patterns of relatedness, and patterns of selection between these two populations were assessed by leveraging recent advances in deconvolution of genomes from polyclonal malaria infections. RESULTS: Significant decreases in the effective population sizes were inferred in both populations that coincide with a period of decreasing malaria transmission in Tanzania. Identity by descent analysis showed that parasites in the two populations shared long segments of their genomes, on the order of 5 cM, suggesting shared ancestry within the last 10 generations. Even with limited sampling, two of isolates between the mainland and Zanzibar were identified that are related at the expected level of half-siblings, consistent with recent importation. CONCLUSIONS: These findings suggest that importation plays an important role for malaria incidence on Zanzibar and demonstrate the value of genomic approaches for identifying corridors of parasite movement to the island.
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Malaria Falciparum/prevención & control , Malaria Falciparum/parasitología , Plasmodium falciparum/genética , Estudios de Cohortes , Demografía , Biblioteca de Genes , Variación Genética , Haploidia , Haplotipos , Humanos , Incidencia , Islas/epidemiología , Malaria Falciparum/epidemiología , Malaria Falciparum/transmisión , Mutación , Plasmodium falciparum/clasificación , Tanzanía/epidemiología , Viaje , Secuenciación Completa del GenomaRESUMEN
Motivation: Large high-throughput screening (HTS) efforts are widely used in drug development and chemical toxicity screening. Wide use and integration of these data can benefit from an efficient, transparent and reproducible data pipeline. Summary: The tcpl R package and its associated MySQL database provide a generalized platform for efficiently storing, normalizing and dose-response modeling of large high-throughput and high-content chemical screening data. The novel dose-response modeling algorithm has been tested against millions of diverse dose-response series, and robustly fits data with outliers and cytotoxicity-related signal loss. Availability and Implementation: tcpl is freely available on the Comprehensive R Archive Network under the GPL-2 license. Contact: martin.matt@epa.gov.
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Evaluación Preclínica de Medicamentos/métodos , Ensayos Analíticos de Alto Rendimiento/métodos , Modelos Biológicos , Programas Informáticos , Pruebas de Toxicidad/métodos , Algoritmos , Simulación por Computador , Relación Dosis-Respuesta a DrogaRESUMEN
Thousands of environmental chemicals are subject to regulatory review for their potential to be endocrine disruptors (ED). In vitro high-throughput screening (HTS) assays have emerged as a potential tool for prioritizing chemicals for ED-related whole-animal tests. In this study, 1814 chemicals including pesticide active and inert ingredients, industrial chemicals, food additives, and pharmaceuticals were evaluated in a panel of 13 in vitro HTS assays. The panel of in vitro assays interrogated multiple end points related to estrogen receptor (ER) signaling, namely binding, agonist, antagonist, and cell growth responses. The results from the in vitro assays were used to create an ER Interaction Score. For 36 reference chemicals, an ER Interaction Score >0 showed 100% sensitivity and 87.5% specificity for classifying potential ER activity. The magnitude of the ER Interaction Score was significantly related to the potency classification of the reference chemicals (p < 0.0001). ERα/ERß selectivity was also evaluated, but relatively few chemicals showed significant selectivity for a specific isoform. When applied to a broader set of chemicals with in vivo uterotrophic data, the ER Interaction Scores showed 91% sensitivity and 65% specificity. Overall, this study provides a novel method for combining in vitro concentration response data from multiple assays and, when applied to a large set of ER data, accurately predicted estrogenic responses and demonstrated its utility for chemical prioritization.
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Disruptores Endocrinos/análisis , Receptor alfa de Estrógeno/agonistas , Receptor beta de Estrógeno/agonistas , Ensayos Analíticos de Alto Rendimiento , Modelos Químicos , Algoritmos , Animales , Bioensayo , Antagonistas de Estrógenos/análisis , Receptor alfa de Estrógeno/antagonistas & inhibidores , Receptor beta de Estrógeno/antagonistas & inhibidores , Estrógenos/análisis , Humanos , Células MCF-7 , Plaguicidas , Transducción de SeñalRESUMEN
BACKGROUND: Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing. OBJECTIVES: We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals. METHODS: Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells. RESULTS: The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model. DISCUSSION: Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779.
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Adipogénesis , Ensayos Analíticos de Alto Rendimiento , Células 3T3-L1 , Animales , Ensayos Analíticos de Alto Rendimiento/métodos , Técnicas In Vitro , RatonesRESUMEN
Introduction: Genome-wide association studies (GWAS) have played a critical role in identifying many thousands of loci associated with complex phenotypes and diseases. This has led to several translations of novel disease susceptibility genes into drug targets and care. This however has not been the case for analyses where sample sizes are small, which suffer from multiple comparisons testing. The present study examined the statistical impact of combining a burden test methodology, PrediXcan, with a multimodel meta-analysis, cross phenotype association (CPASSOC). Methods: The analysis was conducted on 5 addiction traits: family alcoholism, cannabis craving, alcohol, nicotine, and cannabis dependence and 10 brain tissues: anterior cingulate cortex BA24, cerebellar hemisphere, cortex, hippocampus, nucleus accumbens basal ganglia, caudate basal ganglia, cerebellum, frontal cortex BA9, hypothalamus, and putamen basal ganglia. Our sample consisted of 1,640 participants from the University of California, San Francisco (UCSF) Family Alcoholism Study. Genotypes were obtained through low pass whole genome sequencing and the use of Thunder, a linkage disequilibrium variant caller. Results: The post-PrediXcan, gene-phenotype association without aggregation resulted in 2 significant results, HCG27 and SPPL2B. Aggregating across phenotypes resulted no significant findings. Aggregating across tissues resulted in 15 significant and 5 suggestive associations: PPIE, RPL36AL, FOXN2, MTERF4, SEPTIN2, CIAO3, RPL36AL, ZNF304, CCDC66, SSPOP, SLC7A9, LY75, MTRF1L, COA5, and RRP7A; RPS23, GNMT, ERV3-1, APIP, and HLA-B, respectively. Discussion: Given the relatively small size of the cohort, this multimodel approach was able to find over a dozen significant associations between predicted gene expression and addiction traits. Of our findings, 8 had prior associations with similar phenotypes through investigation of the GWAS Atlas. With the onset of improved transcriptome data, this approach should increase in efficacy.
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Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or - 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model.
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Algoritmos , Área Bajo la Curva , Humanos , Modelos Lineales , Curva ROCRESUMEN
High-throughput in vitro assays and exposure prediction efforts are paving the way for modeling chemical risk; however, the utility of such extensive datasets can be limited or misleading when annotation fails to capture current chemical usage. To address this data gap and provide context for food-use in the United States (US), manual curation of food-relevant chemicals in ToxCast was conducted. Chemicals were categorized into three food-use categories: (1) direct food additives, (2) indirect food additives, or (3) pesticide residues. Manual curation resulted in 30% of chemicals having new annotation as well as the removal of 319 chemicals, most due to cancellation or only foreign usage. These results highlight that manual curation of chemical use information provided significant insight affecting the overall inventory and chemical categorization. In total, 1211 chemicals were confirmed as current day food-use in the US by manual curation; 1154 of these chemicals were also identified as food-related in the globally sourced chemical use information from Chemical/Product Categories database (CPCat). The refined list of food-use chemicals and the sources highlighted for compiling annotated information required to confirm food-use are valuable resources for providing needed context when evaluating large-scale inventories such as ToxCast.
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Bases de Datos Factuales , Aditivos Alimentarios , Residuos de Plaguicidas , Aditivos Alimentarios/química , Análisis de los Alimentos , Contaminación de Alimentos/análisis , Residuos de Plaguicidas/química , Pruebas de Toxicidad , Estados UnidosRESUMEN
Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity.
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Supervivencia Celular/efectos de los fármacos , Contaminación de Alimentos/análisis , Ensayos Analíticos de Alto Rendimiento/métodos , Preparaciones Farmacéuticas/análisis , Pruebas de Toxicidad/métodos , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Humanos , Medición de Riesgo , Estados Unidos , United States Environmental Protection AgencyRESUMEN
Disruption of steroidogenesis by environmental chemicals can result in altered hormone levels causing adverse reproductive and developmental effects. A high-throughput assay using H295R human adrenocortical carcinoma cells was used to evaluate the effect of 2060 chemical samples on steroidogenesis via high-performance liquid chromatography followed by tandem mass spectrometry quantification of 10 steroid hormones, including progestagens, glucocorticoids, androgens, and estrogens. The study employed a 3 stage screening strategy. The first stage established the maximum tolerated concentration (MTC; ≥ 70% viability) per sample. The second stage quantified changes in hormone levels at the MTC whereas the third stage performed concentration-response (CR) on a subset of samples. At all stages, cells were prestimulated with 10 µM forskolin for 48 h to induce steroidogenesis followed by chemical treatment for 48 h. Of the 2060 chemical samples evaluated, 524 samples were selected for 6-point CR screening, based in part on significantly altering at least 4 hormones at the MTC. CR screening identified 232 chemical samples with concentration-dependent effects on 17ß-estradiol and/or testosterone, with 411 chemical samples showing an effect on at least one hormone across the steroidogenesis pathway. Clustering of the concentration-dependent chemical-mediated steroid hormone effects grouped chemical samples into 5 distinct profiles generally representing putative mechanisms of action, including CYP17A1 and HSD3B inhibition. A distinct pattern was observed between imidazole and triazole fungicides suggesting potentially distinct mechanisms of action. From a chemical testing and prioritization perspective, this assay platform provides a robust model for high-throughput screening of chemicals for effects on steroidogenesis.
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Disruptores Endocrinos/toxicidad , Hormonas/biosíntesis , Esteroides/biosíntesis , Carcinoma Corticosuprarrenal/metabolismo , Carcinoma Corticosuprarrenal/patología , Técnicas de Cultivo de Célula , Línea Celular Tumoral , Supervivencia Celular/efectos de los fármacos , Cromatografía Líquida de Alta Presión , Relación Dosis-Respuesta a Droga , Ensayos Analíticos de Alto Rendimiento , Humanos , Dosis Máxima Tolerada , Espectrometría de Masas en TándemRESUMEN
BACKGROUND: Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is challenging. This review is intended to help researchers generate hypotheses about chemicals that may contribute to diabetes and to obesity-related health outcomes by summarizing relevant findings from the U.S. Environmental Protection Agency (EPA) ToxCast™ high-throughput screening (HTS) program. OBJECTIVES: Our aim was to develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high-throughput screening data. METHODS: We identified ToxCast™ assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and ß cell function, adipocyte differentiation, and feeding behavior) and presented chemical screening data against those assay targets to identify chemicals of potential interest. DISCUSSION: The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature. Testing hypotheses based on ToxCast™ data will also help assess the predictive utility of this HTS platform. CONCLUSIONS: More research is required to put these screening-level analyses into context, but the information presented in this review should facilitate the development of new hypotheses. CITATION: Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. 2016. Prioritizing environmental chemicals for obesity and diabetes outcomes research: a screening approach using ToxCast™ high-throughput data. Environ Health Perspect 124:1141-1154; http://dx.doi.org/10.1289/ehp.1510456.
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Diabetes Mellitus/inducido químicamente , Exposición a Riesgos Ambientales/estadística & datos numéricos , Contaminantes Ambientales/toxicidad , Obesidad/inducido químicamente , Bases de Datos Factuales , Diabetes Mellitus/epidemiología , Ambiente , Contaminantes Ambientales/normas , Ensayos Analíticos de Alto Rendimiento , Humanos , Obesidad/epidemiología , Evaluación de Resultado en la Atención de Salud , Medición de Riesgo , Estados Unidos , United States Environmental Protection AgencyRESUMEN
Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 µM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.
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Bioensayo/métodos , Estrés Fisiológico , Pruebas de Toxicidad/métodos , Humanos , Técnicas In VitroRESUMEN
We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.
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Contaminantes Ambientales/toxicidad , Antagonistas de Estrógenos/toxicidad , Receptor alfa de Estrógeno/metabolismo , Receptor beta de Estrógeno/metabolismo , Estrógenos no Esteroides/toxicidad , Modelos Biológicos , Receptores de Estrógenos/metabolismo , Animales , Bovinos , Línea Celular , Biología Computacional , Receptor alfa de Estrógeno/agonistas , Receptor alfa de Estrógeno/antagonistas & inhibidores , Receptor alfa de Estrógeno/genética , Receptor beta de Estrógeno/agonistas , Receptor beta de Estrógeno/antagonistas & inhibidores , Receptor beta de Estrógeno/genética , Genes Reporteros/efectos de los fármacos , Proteínas Fluorescentes Verdes/genética , Proteínas Fluorescentes Verdes/metabolismo , Ensayos Analíticos de Alto Rendimiento , Humanos , Ratones , Proteínas Recombinantes de Fusión/química , Proteínas Recombinantes de Fusión/metabolismo , Bibliotecas de Moléculas Pequeñas , Estados Unidos , United States Environmental Protection AgencyRESUMEN
Identifying chemicals, beyond those already implicated, to test for potential endocrine disruption is a challenge and high throughput approaches have emerged as a potential tool for this type of screening. This review focused the Environmental Protection Agency's (EPA) ToxCast(TM) high throughput in vitro screening (HTS) program. Utility for identifying compounds was assessed and reviewed by using it to run the recently expanded chemical library (from 309 compounds to 1858) through the ToxPi(TM) prioritization scheme for endocrine disruption. The analysis included metabolic and neuroendocrine targets. This investigative approach simultaneously assessed the utility of ToxCast, and helped identify novel chemicals which may have endocrine activity. Results from this exercise suggest the spectrum of environmental chemicals with potential endocrine activity is much broader than indicated, and that some aspects of endocrine disruption are not fully covered in ToxCast.
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Disruptores Endocrinos/toxicidad , Ensayos Analíticos de Alto Rendimiento , Animales , Bases de Datos Factuales , Humanos , Modelos Teóricos , Medición de Riesgo , Estados Unidos , United States Environmental Protection AgencyRESUMEN
Thyroperoxidase (TPO), the enzyme that catalyzes the synthesis of thyroid hormone, is a known target for thyroid-disrupting chemicals. In vivo toxicological evidence supporting TPO-inhibition as one molecular-initiating event that leads to thyroid disruption is derived largely from rat models; however, a significant fraction of research on the inhibition of TPO by xenobiotics has been conducted using porcine TPO. The current work tested the hypothesis that porcine and rat thyroid microsomes exposed to TPO-inhibiting chemicals would demonstrate different responses in a guaiacol oxidation assay. A primary objective of this work is to establish the degree of concordance between rat and porcine TPO inhibition data. Microsomes were isolated from both rat and pig thyroid glands, and the guaiacol oxidation assay was performed for a training set of 12 chemicals, including previously reported TPO inhibitors, thyroid-disrupting chemicals thought to perturb other targets, and several previously untested chemicals, to determine the relative TPO inhibition responses across species. Concentration-response curves were derived for methimazole (MMI), dibutylphthalate (DBP), diethylhexylphthalate (DEHP), diethylphthalate (DEP), 3,5-dimethylpyrazole-1-methanol (DPM), iopanoic acid (IOA), 2-mercaptobenzothiazole (MBT), sodium perchlorate (PERC), p-nonylphenol (PNP), 4-propoxyphenol (4POP), 6-propylthiouracil (PTU), and triclosan (TCS). MMI, PTU, MBT, DPM, 4POP, and at extremely high concentrations, PERC, inhibited TPO activity. Results demonstrated a strong qualitative concordance of response between the two species. All chemicals that inhibited TPO in porcine microsomes also inhibited TPO in rat microsomes. Hill model-derived IC50 values revealed approximate 1.5- to 50-fold differences in relative potency to MMI between species for positive chemicals. DPM, MBT, 4POP, and PTU exhibited greater relative potency to MMI using rat TPO versus porcine TPO, but rank order potency for inhibition was similar for the other test chemicals, with: PTU>MBT>DPM>4POP>PERC for rat TPO and MBT>PTU>DPM>4POP>PERC for porcine TPO. These data support the extrapolation of porcine TPO data to potential thyroid-disrupting activity in rodent models to evaluate TPO-inhibiting chemicals.