Skylar W. Marvel

High-throughput characterization of chemical-associated embryonic behavioral changes predicts teratogenic outcomes

New strategies are needed to address the data gap between the bioactivity of chemicals in the environment versus existing hazard information. We address whether a high-throughput screening (HTS) system using a vertebrate organism (embryonic zebrafish) can characterize chemical-elicited behavioral responses at an early, 24 hours post-fertilization (hpf) stage that predict teratogenic consequences at a later developmental stage. The system was used to generate full concentration–response behavioral profiles at 24 hpf across 1060 ToxCast™ chemicals. Detailed, morphological evaluation of all individuals was performed as experimental follow-up at 5 days post-fertilization (dpf). Chemicals eliciting behavioral responses were also mapped against external HTS in vitro results to identify specific molecular targets and neurosignalling pathways. We found that, as an integrative measure of normal development, significant alterations in movement highlighted active chemicals representing several modes of action. These early behavioral responses were predictive for 17 specific developmental abnormalities and mortality measured at 5 dpf, often at lower (i.e., more potent) concentrations than those at which morphological effects were observed. Therefore, this system can provide rapid characterization of chemical-elicited behavioral responses at an early developmental stage that are predictive of observable adverse effects later in life.

Transgenerational inheritance of neurobehavioral and physiological deficits from developmental exposure to benzo[a]pyrene in zebrafish

&NA; Benzo[a]pyrene (B[a]P) is a well‐known genotoxic polycylic aromatic compound whose toxicity is dependent on signaling via the aryl hydrocarbon receptor (AHR). It is unclear to what extent detrimental effects of B[a]P exposures might impact future generations and whether transgenerational effects might be AHR‐dependent. This study examined the effects of developmental B[a]P exposure on 3 generations of zebrafish. Zebrafish embryos were exposed from 6 to 120 h post fertilization (hpf) to 5 and 10 &mgr;M B[a]P and raised in chemical‐free water until adulthood (F0). Two generations were raised from F0 fish to evaluate transgenerational inheritance. Morphological, physiological and neurobehavioral parameters were measured at two life stages. Juveniles of the F0 and F2 exhibited hyper locomotor activity, decreased heartbeat and mitochondrial function. B[a]P exposure during development resulted in decreased global DNA methylation levels and generally reduced expression of DNA methyltransferases in wild type zebrafish, with the latter effect largely reversed in an AHR2‐null background. Adults from the F0 B[a]P exposed lineage displayed social anxiety‐like behavior. Adults in the F2 transgeneration manifested gender‐specific increased body mass index (BMI), increased oxygen consumption and hyper‐avoidance behavior. Exposure to benzo[a]pyrene during development resulted in transgenerational inheritance of neurobehavioral and physiological deficiencies. Indirect evidence suggested the potential for an AHR2‐dependent epigenetic route. Graphical abstract Figure. No caption available. HighlightsDevelopmental exposure to benzo[a]pyrene results in transgenerational effects.Neurobehavioral and physiological functions impacted across multiple generations.B[a]P decreases global DNA methylation and DNA methyltransferase expression.Behavioral methylation and gene changes are AHR2‐dependent.

Genetic Predictors of Cardiovascular Mortality During Intensive Glycemic Control in Type 2 Diabetes: Findings From the ACCORD Clinical Trial

OBJECTIVE To identify genetic determinants of increased cardiovascular mortality among subjects with type 2 diabetes who underwent intensive glycemic therapy in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. RESEARCH DESIGN AND METHODS A total of 6.8 million common variants were analyzed for genome-wide association with cardiovascular mortality among 2,667 self-reported white subjects in the ACCORD intensive treatment arm. Significant loci were examined in the entire ACCORD white genetic dataset (n = 5,360) for their modulation of cardiovascular responses to glycemic treatment assignment and in a Joslin Clinic cohort (n = 422) for their interaction with long-term glycemic control on cardiovascular mortality. RESULTS Two loci, at 10q26 and 5q13, attained genome-wide significance as determinants of cardiovascular mortality in the ACCORD intensive arm (P = 9.8 × 10−9 and P = 2 × 10−8, respectively). A genetic risk score (GRS) defined by the two variants was a significant modulator of cardiovascular mortality response to treatment assignment in the entire ACCORD white genetic dataset. Participants with GRS = 0 experienced a fourfold reduction in cardiovascular mortality in response to intensive treatment (hazard ratio [HR] 0.24 [95% CI 0.07–0.86]), those with GRS = 1 experienced no difference (HR 0.92 [95% CI 0.54–1.56]), and those with GRS ≥2 experienced a threefold increase (HR 3.08 [95% CI 1.82–5.21]). The modulatory effect of the GRS on the association between glycemic control and cardiovascular mortality was confirmed in the Joslin cohort (P = 0.029). CONCLUSIONS Two genetic variants predict the cardiovascular effects of intensive glycemic control in ACCORD. Further studies are warranted to determine whether these findings can be translated into new strategies to prevent cardiovascular complications of diabetes.

A genome-wide study of lipid response to fenofibrate in Caucasians: a combined analysis of the GOLDN and ACCORD studies.

Background Fibrates are commonly prescribed for hypertriglyceridemia, but they also lower LDL cholesterol and increase HDL cholesterol. Large interindividual variations in lipid response suggest that some patients may benefit more than others and genetic studies could help identify such patients. Methods We carried out the first genome-wide association study of lipid response to fenofibrate using data from two well-characterized clinical trials: the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Study. Genome-wide association study data from both studies were imputed to the 1000 Genomes CEU reference panel (phase 1). Lipid response was modeled as the log ratio of the post-treatment lipid level to the pretreatment level. Linear mixed models (GOLDN, N=813 from 173 families) and linear regression models (ACCORD, N=781) adjusted for pretreatment lipid level, demographic variables, clinical covariates, and ancestry were used to evaluate the association of genetic markers with lipid response. Among Caucasians, the results were combined using inverse-variance weighted fixed-effects meta-analyses. The main findings from the meta-analyses were examined in other ethnic groups from the HyperTG study (N=267 Hispanics) and ACCORD (N=83 Hispanics, 138 African Americans). Results A known lipid locus harboring the pre-B-cell leukemia homeobox 4 (PBX4) gene on chromosome 19 is important for LDL cholesterol response to fenofibrate (smallest P=1.5×10−8). The main results replicated with nominal statistical significance in Hispanics from ACCORD (P<0.05). Conclusion Future research should evaluate the usefulness of this locus to refine clinical strategies for lipid-lowering treatments.

Set membership experimental design for biological systems

BackgroundExperimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources.ResultsIn this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained.ConclusionsThe practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models.

Genetic Variants in HSD17B3, SMAD3, and IPO11 Impact Circulating Lipids in Response to Fenofibrate in Individuals With Type 2 Diabetes

Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin‐treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed‐up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P < 5 × 10‐6). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q < 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q < 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor‐beta (TGF‐β) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D.

Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure.

Robust computational approaches are needed to characterize systems-level responses to chemical perturbations in environmental and clinical toxicology applications. Appropriate characterization of response presents a methodological challenge when dealing with diverse phenotypic endpoints measured using in vivo systems. In this article, we propose an information-theoretic method named Aggregate Entropy (AggE) and apply it to scoring multiplexed, phenotypic endpoints measured in developing zebrafish (Danio rerio) across a broad concentration-response profile for a diverse set of 1060 chemicals. AggE accurately identified chemicals with significant morphological effects, including single-endpoint effects and multi-endpoint responses that would have been missed by univariate methods, while avoiding putative false-positives that confound traditional methods due to irregular correlation structure. By testing AggE in a variety of high-dimensional real and simulated datasets, we have characterized its performance and suggested implementation parameters that can guide its application across a wide range of experimental scenarios.

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.

Grammatical evolution support vector machines for predicting human genetic disease association

Identifying genes that predict common, complex human diseases is a major goal of human genetics. This is made difficult by the effect of epistatic interactions and the need to analyze datasets with high-dimensional feature spaces. Many classification methods have been applied to this problem, one of the more recent being Support Vector Machines (SVM). Selection of which features to include in the SVM model and what parameters or kernels to use can often be a difficult task. This work uses Grammatical Evolution (GE) as a way to choose features and parameters. Initial results look promising and encourage further development and testing of this new approach.

Genetic Variants in CPA6 and PRPF31 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 < 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 <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.