Endocrine disrupting potential of environmental chemicals characterized by high-throughput screening

Over the past 20 years, an increased focus on detecting environmental chemicals that pose a risk of endocrine disruption and congressional legislation have driven the creation of the U.S. EPA Endocrine Disruptor Screening Program (EDSP). Several thousand chemicals are subject to the EDSP, which will require millions of dollars and decades to process using current test batteries. In order to identify opportunities for increased chemical throughput, we initially investigated how well EPA ToxCast in vitro high-throughput screening (HTS) assays relevant for estrogen, androgen, steroidogenic, and thyroid disrupting mechanisms could identify compounds relative to in vitro and in vivo data collected from studies related to the EDSP Tier 1 screen. An iterative, balanced optimization model was implemented and indicated that ToxCast HTS assays measuring estrogen receptor (ER) and androgen receptor (AR) activation classify compounds with estrogenic and androgenic activity in guideline studies with a high degree of accuracy, respectively. The ER signaling pathway involves a wide array of molecular initiating events and cellular processes. This dissertation examined whether active chemicals in ToxCast ER transactivation assays could indicate chemical-induced upregulation of the ER pathway through ligand binding leading to induced changes in T47D growth kinetics. Considering the complex set of ER in vitro assays in toto increased the overall sensitivity of detection for ER reference chemicals. In addition, this research highlighted important aspects of the biological response such as non-ER specificity in the cell growth assay. These nuances are likely important considerations for the construction of a predictive model. In effort to accurately predict the estrogenic potential of environmental chemicals in a high-throughput format, multiple orthogonal in vitro ER assays were used to develop a predictive model for ~2000 chemicals. Model results indicate a high degree of predictivity for both the uterotrophic in vivo assay and ER reference chemicals. The information provided by the model will aid in understanding how environmental chemicals contribute to endocrine-related human health consequences and predict the estrogenic potential of chemicals through the use of in vitro assays, limiting the need to run more costly and animal-intensive in vivo bioassays.Doctor of Philosoph

Incorporating Human Dosimetry and Exposure into the Utilization of ToxCast In Vitro Screening Data for Chemical Prioritization

Humans are frequently exposed to chemicals that have undergone limited safety testing. To reduce the number of untested chemicals and prioritize limited testing resources, multiple governmental programs are using high throughput in vitro toxicity screens for assessing effects across multiple cellular pathways. In this study, metabolic clearance and plasma protein binding were experimentally measured for 39 of the 309 ToxCast Phase I chemicals. The experimental data was modeled using pharmacokinetics for estimating human oral equivalent doses that would produce steady state in vivo concentrations equivalent to ToxCast in vitro AC[50] values. The range of oral equivalent doses for the ToxCast assays was compared with human oral exposure estimates to assess whether in vitro bioactivity could occur at human exposure levels. Two of the 39 chemicals had overlapping oral equivalent doses and human exposures and would not have been identified using the rank potencies of the AC50 values. These results demonstrate the importance of incorporating dosimetry and exposure when using high throughput in vitro data to identify the highest priorities for further testing and risk management.Master of Science in Public Healt

Genetic Variants in <i>CPA6</i> and <i>PRPF31</i> are Associated with Variation in Response to Metformin in Individuals with Type 2 Diabetes

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P &lt; 5 × 10-6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10-8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/-) knockout mice have increased total body fat (P = 1.78 × 10-6) and increased fasted circulating glucose (P = 5.73 × 10-6). Furthermore, rare variants in STAT3 associated with worse metformin response (q &lt;0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in PRPF31, CPA6, and STAT3 with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

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

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV ( p  < 1 × 10 −8 ) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2 , HPN-AS1 , and SIRPD appear to be novel ( q  < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant ( p  < 1 × 10 −8 ) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10−7 to p = 1.76 × 10−5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available

Comprehensive genomic characterization of five canine lymphoid tumor cell lines

Abstract Background Leukemia/lymphoma cell lines have been critical in the investigation of the pathogenesis and therapy of hematological malignancies. While human LL cell lines have generally been found to recapitulate the primary tumors from which they were derived, appropriate characterization including cytogenetic and transcriptional assessment is crucial for assessing their clinical predictive value. Results In the following study, five canine LL cell lines, CLBL-1, Ema, TL-1 (Nody-1), UL-1, and 3132, were characterized using extensive immunophenotyping, karyotypic analysis, oligonucleotide array comparative genomic hybridization (oaCGH), and gene expression profiling. Genome-wide DNA copy number data from the cell lines were also directly compared with 299 primary canine round cell tumors to determine whether the cell lines represent primary tumors, and, if so, what subtype each most closely resembled. Conclusions Based on integrated analyses, CLBL-1 was classified as B-cell lymphoma, Ema and TL-1 as T-cell lymphoma, and UL-1 as T-cell acute lymphoblastic leukemia. 3132, originally classified as a B-cell lymphoma, was reclassified as a histiocytic sarcoma based on characteristic cytogenomic properties. In combination, these data begin to elucidate the clinical predictive value of these cell lines which will enhance the appropriate selection of in vitro models for future studies of canine hematological malignancies

A genome-wide association study identifies genetic determinants of hemoglobin glycation index with implications across sex and ethnicity

IntroductionWe investigated the genetic determinants of variation in the hemoglobin glycation index (HGI), an emerging biomarker for the risk of diabetes complications.MethodsWe conducted a genome-wide association study (GWAS) for HGI in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 7,913) using linear regression and additive genotype encoding on variants with minor allele frequency greater than 3%. We conducted replication analyses of top findings in the Atherosclerosis Risk in Communities (ARIC) study with inverse variance-weighted meta-analysis. We followed up with stratified GWAS analyses by sex and self-reported race.ResultsIn ACCORD, we identified single nucleotide polymorphisms (SNPs) associated with HGI, including a peak with the strongest association at the intergenic SNP rs73407935 (7q11.22) (P = 5.8e−10) with a local replication in ARIC. In black individuals, the variant rs10739419 on chromosome 9 in the Whirlin (WHRN) gene formally replicated (meta-P = 2.2e−9). The SNP-based heritability of HGI was 0.39 (P&lt; 1e−10). HGI had significant sex-specific associations with SNPs in or near GALNT11 in women and HECW2 in men. Finally, in Hispanic participants, we observed genome-wide significant associations with variants near USF1 and NXNL2/SPIN1.DiscussionMany HGI-associated SNPs were distinct from those associated with fasting plasma glucose or HbA1c, lending further support for HGI as a distinct biomarker of diabetes complications. The results of this first evaluation of the genetic etiology of HGI indicate that it is highly heritable and point to heterogeneity by sex and race

Tumor exome sequencing and copy number alterations reveal potential predictors of intrinsic resistance to multi-targeted tyrosine kinase inhibitors

Multi-targeted tyrosine kinase inhibitors (TKIs) have broad efficacy and similar FDA-approved indications, suggesting shared molecular drug targets across cancer types. Irrespective of tumor type, 20-30% of patients treated with multi-targeted TKIs demonstrate intrinsic resistance, with progressive disease as a best response. We conducted a retrospective cohort study to identify tumor (somatic) point mutations, insertion/deletions, and copy number alterations (CNA) associated with intrinsic resistance to multi-targeted TKIs. Using a candidate gene approach (n=243), tumor next-generation sequencing and CNA data was associated with resistant and non-resistant outcomes. Resistant individuals (n=11) more commonly harbored somatic point mutations in NTRK1, KDR, TGFBR2, and PTPN11 and CNA in CDK4, CDKN2B, and ERBB2 compared to non-resistant (n=26, p<0.01). Using a random forest classification model for variable reduction and a decision tree classification model, we were able to differentiate intrinsically resistant from non-resistant patients. CNA in CDK4 and CDKN2B were the most important analytical features, implicating the cyclin D pathway as a potentially important factor in resistance to multi-targeted TKIs. Replication of these results in a larger, independent patient cohort has potential to inform personalized prescribing of these widely utilized agents

Genomic profiling reveals extensive heterogeneity in somatic DNA copy number aberrations of canine hemangiosarcoma

Canine hemangiosarcoma is a highly aggressive vascular neoplasm associated with extensive clinical and anatomical heterogeneity and a grave prognosis. Comprehensive molecular characterization of hemangiosarcoma may identify novel therapeutic targets and advanced clinical management strategies, but there are no published reports of tumor-associated genome instability and disrupted gene dosage in this cancer. We performed genome-wide microarray-based somatic DNA copy number profiling of 75 primary intra-abdominal hemangiosarcomas from five popular dog breeds that are highly predisposed to this disease. The cohort exhibited limited global genomic instability, compared to other canine sarcomas studied to date, and DNA copy number aberrations (CNAs) were predominantly of low amplitude. Recurrent imbalances of several key cancer-associated genes were evident; however the global penetrance of any single CNA was low and no distinct hallmark aberrations were evident. Copy number gains of dog chromosomes 13, 24 and 31, and loss of chromosome 16, were the most recurrent CNAs involving large chromosome regions, but their relative distribution within and between cases suggests they most likely represent passenger aberrations. CNAs involving CDKN2A, VEGFA and the SKI oncogene were identified as potential driver aberrations of hemangiosarcoma development, highlighting potential targets for therapeutic modulation. CNA profiles were broadly conserved between the five breeds, although subregional variation was evident, including a near two-fold lower incidence of VEGFA gain in Golden Retrievers versus other breeds (22% versus 40%). These observations support prior transcriptional studies suggesting that the clinical heterogeneity of this cancer may reflect the existence of multiple, molecularly-distinct subtypes of canine hemangiosarcoma