BACKGROUND:Comprehensive environmental risk characterization, encompassing physical, chemical, social, ecological, and lifestyle stressors, necessitates innovative approaches to handle the escalating complexity. This is especially true when considering individual and population-level diversity, where the myriad combinations of real-world exposures magnify the combinatoric challenges. The GeoTox framework offers a tractable solution by integrating geospatial exposure data from source-to-outcome in a series of modular, interconnected steps. RESULTS:Here, we introduce the GeoTox open-source R software package for characterizing the risk of perturbing molecular targets involved in adverse human health outcomes based on exposure to spatially-referenced stressor mixtures. We demonstrate its usage in building computational workflows that incorporate individual and population-level diversity. Our results demonstrate the applicability of GeoTox for individual and population-level risk assessment, highlighting its capacity to capture the complex interplay of environmental stressors on human health. CONCLUSIONS:The GeoTox package represents a significant advancement in environmental risk characterization, providing modular software to facilitate the application and further development of the GeoTox framework for quantifying the relationship between environmental exposures and health outcomes. By integrating geospatial methods with cutting-edge exposure and toxicological frameworks, GeoTox offers a robust tool for assessing individual and population-level risks from environmental stressors. GeoTox is freely available at https://niehs.github.io/GeoTox/ .

Per-and polyfluoroalkyl substances (PFAS) are a class of widely used chemicals with limited human health effects data relative to the diversity of structures manufactured. To help fill this data gap, an extensive in vivo developmental toxicity screen was performed on 139 PFAS provided by the US EPA. Dechorionated embryonic zebrafish were exposed to 10 nominal water concentrations of PFAS (0.015-100 mu M) from 6 to 120 h postfertilization (hpf). The embryos were assayed for embryonic photomotor response (EPR), larval photomotor response (LPR), and 13 morphological endpoints. A total of 49 PFAS (35%) were bioactive in one or more assays (11 altered EPR, 25 altered LPR, and 31 altered morphology). Perfluorooctanesulfonamide (FOSA) was the only structure that was bioactive in all 3 assays, while Perfluorodecanoic acid (PFDA) was the most potent teratogen. Low PFAS volatility was associated with developmental toxicity (p < 0.01), but no association was detected between bioactivity and five other physicochemical parameters. The bioactive PFAS were enriched for 6 supergroup chemotypes. The results illustrate the power of a multi-dimensional in vivo platform to assess the developmental (neuro)toxicity of diverse PFAS and in the acceleration of PFAS safety research.

Toxicological evaluation of chemicals using early-life stage zebrafish (Danio rerio) involves the observation and recording of altered phenotypes. Substantial variability has been observed among researchers in phenotypes reported from similar studies, as well as a lack of consistent data annotation, indicating a need for both terminological and data harmonization. When examined from a data science perspective, many of these apparent differences can be parsed into the same or similar endpoints whose measurements differ only in time, methodology, or nomenclature. Ontological knowledge structures can be leveraged to integrate diverse data sets across terminologies, scales, and modalities. Building on this premise, the National Toxicology Program's Systematic Evaluation of the Application of Zebrafish in Toxicology undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency. To accomplish this, zebrafish researchers were asked to assess images of zebrafish larvae for morphological malformations in two surveys. In the first survey, researchers were asked to annotate observed malformations using their own terminology. In the second survey, researchers were asked to annotate the images from a list of terms and definitions from the Zebrafish Phenotype Ontology. Analysis of the results suggested that the use of ontology terms increased consistency and decreased ambiguity, but a larger study is needed to confirm. We conclude that utilizing a common data standard will not only reduce the heterogeneity of reported terms but increases agreement and repeatability between different laboratories. Thus, we advocate for the development of a zebrafish phenotype atlas to help laboratories create interoperable, computable data.

Background Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps. Objective We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public. Methods The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox ( ToxPiToolbox.tbx ) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability. Results Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States. Significance The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org .

Flame retardant chemicals (FRCs) commonly added to many consumer products present a human exposure burden associated with adverse health effects. Under pressure from consumers, FRC manufacturers have adopted some purportedly safer replacements for first-generation brominated diphenyl ethers (BDEs). In contrast, second and third-generation organophosphates and other alternative chemistries have limited bioactivity data available to estimate their hazard potential. In order to evaluate the toxicity of existing and potential replacement FRCs, we need efficient screening methods. We built a 61-FRC library in which we systemically assessed developmental toxicity and potential neurotoxicity effects in the embryonic zebrafish model. Data were compared to publicly available data generated in a battery of cell-based in vitro assays from ToxCast, Tox21, and other alternative models. Of the 61 FRCs, 19 of 45 that were tested in the ToxCast assays were bioactive in our zebrafish model. The zebrafish assays detected bioactivity for 10 of the 12 previously classified developmental neurotoxic FRCs. Developmental zebrafish were sufficiently sensitive at detecting FRC structure-bioactivity impacts that we were able to build a classification model using 13 physicochemical properties and 3 embryonic zebrafish assays that achieved a balanced accuracy of 91.7%. This work illustrates the power of a multi-dimensional in vivo platform to expand our ability to predict the hazard potential of new compounds based on structural relatedness, ultimately leading to reliable toxicity predictions based on chemical structure.

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

While the COVID-19 pandemic presents a global challenge, the U.S. response places substantial responsibility for both decision-making and communication on local health authorities. To better support counties and municipalities, we integrated baseline data on relevant community vulnerabilities with dynamic data on local infection rates and interventions into a Pandemic Vulnerability Index (PVI). The PVI presents a visual synthesis of county-level vulnerability indicators that can be compared in a regional, state, or nationwide context. We describe use of the PVI, supporting epidemiological modeling and machine-learning forecasts, and deployment of an interactive, web Dashboard. The Dashboard facilitates decision-making and communication among government officials, scientists, community leaders, and the public to enable more effective and coordinated action to combat the pandemic.

The Houston-Galveston-Brazoria (HGB) region faces numerous environmental and public health challenges from both natural disasters and industrial activity, but the historically disadvantaged communities most often impacted by such risks have limited ability to access and utilize big data for advocacy efforts. We developed HGBEnviroScreen to identify and prioritize regions of heightened vulnerability, in part to assist communities in understanding risk factors and developing environmental justice action plans. While similar in objectives to existing environmental justice tools, HGBEnviroScreen is unique in its ability to integrate and visualize national and local data to address regional concerns. For the 1090 census tracts in the HGB region, we accrued data into five domains: (i) social vulnerability, (ii) baseline health, (iii) environmental exposures and risks, (iv) environmental sources, and (v) flooding. We then integrated and visualized these data using the Toxicological Prioritization Index (ToxPi). We found that the highest vulnerability census tracts have multifactorial risk factors, with common drivers being flooding, social vulnerability, and proximity to environmental sources. Thus, HGBEnviroScreen is not only helping identify communities of greatest overall vulnerability but is also providing insights into which domains would most benefit from improved planning, policy, and action in order to reduce future vulnerability.

Addressing the complex relationship between public health and environmental exposure requires multiple types and sources of data. An important source of chemical data derives from high-throughput screening (HTS) efforts, such as the Tox21/ToxCast program, which aim to identify chemical hazard using primarily in vitro assays to probe toxicity. While most of these assays target specific genes, assessing the disease-relevance of these assays remains challenging. Integration with additional data sets may help to resolve these questions by providing broader context for individual assay results. The Comparative Toxicogenomics Database (CTD), a publicly available database that builds networks of chemical, gene, and disease information from manually curated literature sources, offers a promising solution for contextual integration with HTS data. Here, we tested the value of integrating data across Tox21/ToxCast and CTD by linking elements common to both databases (i.e., assays, genes, and chemicals). Using polymarcine and Parkinson’s disease as a case study, we found that their union significantly increased chemical-gene associations and disease-pathway coverage. Integration also enabled new disease associations to be made with HTS assays, expanding coverage of chemical-gene data associated with diseases. We demonstrate how integration enables development of predictive adverse outcome pathways using 4-nonylphenol, branched as an example. Thus, we demonstrate enhancements to each data source through database integration, including scenarios where HTS data can efficiently probe chemical space that may be understudied in the literature, as well as how CTD can add biological context to those results.

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.

Background: Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates. Results: We detail the implementation of a new graphical user interface for ToxPi (Toxicological Prioritization Index) that provides interactive visualization, analysis, reporting, and portability. The interface is deployed as a stand-alone, platform-independent Java application, with a modular design to accommodate inclusion of future analytics. The new ToxPi interface introduces several features, from flexible data import formats (including legacy formats that permit backward compatibility) to similarity-based clustering to options for high-resolution graphical output. Conclusions: We present the new ToxPi interface for dynamic exploration, visualization, and sharing of integrated data models. The ToxPi interface is freely-available as a single compressed download that includes the main Java executable, all libraries, example data files, and a complete user manual from http://toxpi.org.

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.

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.

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 μM 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.

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.

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.

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.

Children exposed to maternal smoking during pregnancy exhibit increased risk for many adverse health effects. Maternal smoking influences methylation in newborns at specific CpG sites (CpGs). Here, we extend evaluation of individual CpGs to gene-level and pathway-level analyses among 1062 participants in the Norwegian Mother and Child Cohort Study (MoBa) using the Illumina 450 K platform to measure methylation in newborn DNA and maternal smoking in pregnancy, assessed using the biomarker, plasma cotinine. We used novel implementations of bioinformatics tools to collapse epigenome-wide methylation data into gene- and pathway-level effects to test whether exposure to maternal smoking in utero differentially methylated CpGs in genes enriched in biologic pathways. Unlike most pathway analysis applications, our approach allows replication in an independent cohort.