The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints.

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

The physiological and biochemical basis of potency thresholds modeled using human estrogen receptor alpha: implications for identifying endocrine disruptors.

The endocrine system functions by interactions between ligands and receptors. Ligands exhibit potency for binding to and interacting with receptors. Potency is the product of affinity and efficacy. Potency and physiological concentration determine the ability of a ligand to produce physiological effects. The kinetic behavior of ligand-receptor interactions conforms to the laws of mass action. The laws of mass action define the relationship between the affinity of a ligand and the fraction of cognate receptors that it occupies at any physiological concentration. We previously identified the minimum ligand potency required to produce clinically observable estrogenic agonist effects via the human estrogen receptor-alpha (ERα). By examining data on botanical estrogens and dietary supplements, we demonstrated that ERα ligands with potency lower than one one-thousandth that of the primary endogenous hormone 17ß-estradiol (E2) do not produce clinically observable estrogenic effects. This allowed us to propose a Human-Relevant Potency Threshold (HRPT) for ERα ligands of 1 × 10-4 relative to E2. Here, we test the hypothesis that the HRPT for ERα arises from the receptor occupancy by the normal metabolic milieu of endogenous ERα ligands. The metabolic milieu comprises precursors to hormones, metabolites of hormones, and other normal products of metabolism. We have calculated fractional receptor occupancies for ERα ligands with potencies below and above the previously established HRPT when normal circulating levels of some endogenous ERα ligands and E2 were also present. Fractional receptor occupancy calculations showed that individual ERα ligands with potencies more than tenfold higher than the HRPT can compete for occupancy at ERα against individual components of the endogenous metabolic milieu and against mixtures of those components at concentrations found naturally in human blood. Ligands with potencies less than tenfold higher than the HRPT were unable to compete successfully for ERα. These results show that the HRPT for ERα agonism (10-4 relative to E2) proposed previously is quite conservative and should be considered strong evidence against the potential for disruption of the estrogenic pathway. For chemicals with potency 10-3 of E2, the potential for estrogenic endocrine disruption must be considered equivocal and subject to the presence of corroborative evidence. Most importantly, this work demonstrates that the endogenous metabolic milieu is responsible for the observed ERα agonist HRPT, that this HRPT applies also to ERα antagonists, and it provides a compelling mechanistic explanation for the HRPT that is grounded in basic principles of molecular kinetics using well characterized properties and concentrations of endogenous components of normal metabolism.

Kinetically-derived maximal dose (KMD) indicates lack of human carcinogenicity of ethylbenzene.

The kinetically-derived maximal dose (KMD) is defined as the maximal external dose at which kinetics are unchanged relative to lower doses, e.g., doses at which kinetic processes are not saturated. Toxicity produced at doses above the KMD can be qualitatively different from toxicity produced at lower doses. Here, we test the hypothesis that neoplastic lesions reported in the National Toxicology Program's (NTP) rodent cancer bioassay with ethylbenzene are a high-dose phenomenon secondary to saturation of elimination kinetics. To test this, we applied Bayesian modeling on kinetic data for ethylbenzene from rats and humans to estimate the Vmax and Km for the Michaelis-Menten equation that governs the elimination kinetics. Analysis of the Michaelis-Menten elimination curve generated from those Vmax and Km values indicated KMD ranges for venous ethylbenzene of 8-17 mg/L in rats and 10-18 mg/L in humans. Those venous concentrations are produced by inhalation concentrations of around 200 ppm ethylbenzene, which is well above typical human exposures. These KMD estimates support the hypothesis that neoplastic lesions seen in the NTP rodent bioassay occur secondary to saturation of ethylbenzene elimination pathways and are not relevant for human risk assessment. Thus, ethylbenzene does not pose a credible cancer risk to humans under foreseeable exposure conditions. Cancer risk assessments focused on protecting human health should avoid endpoint data from rodents exposed to ethylbenzene above the KMD range and future toxicological testing should focus on doses below the KMD range.

Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models.

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Range of the perfluorooctanoate (PFOA) safe dose for human health: An international collaboration.

Many government agencies and expert groups have estimated a dose-rate of perfluorooctanoate (PFOA) that would protect human health. Most of these evaluations are based on the same studies (whether of humans, laboratory animals, or both), and all note various uncertainties in our existing knowledge. Nonetheless, the values of these various, estimated, safe-doses vary widely, with some being more than 100,000 fold different. This sort of discrepancy invites scrutiny and explanation. Otherwise what is the lay public to make of this disparity? The Steering Committee of the Alliance for Risk Assessment (2022) called for scientists interested in attempting to understand and narrow these disparities. An advisory committee of nine scientists from four countries was selected from nominations received, and a subsequent invitation to scientists internationally led to the formation of three technical teams (for a total of 24 scientists from 8 countries). The teams reviewed relevant information and independently developed ranges for estimated PFOA safe doses. All three teams determined that the available epidemiologic information could not form a reliable basis for a PFOA safe dose-assessment in the absence of mechanistic data that are relevant for humans at serum concentrations seen in the general population. Based instead on dose-response data from five studies of PFOA-exposed laboratory animals, we estimated that PFOA dose-rates 10-70 ng/kg-day are protective of human health.

A novel approach to calculating the kinetically derived maximum dose.

The kinetically derived maximal dose (KMD) provides a toxicologically relevant upper range for the determination of chemical safety. Here, we describe a new way of calculating the KMD that is based on sound Bayesian, theoretical, biochemical, and toxicokinetic principles, that avoids the problems of relying upon the area under the curve (AUC) approach that has often been used. Our new, mathematically rigorous approach is based on converting toxicokinetic data to the overall, or system-wide, Michaelis-Menten curve (which is the slope function for the toxicokinetic data) using Bayesian methods and using the "kneedle" algorithm to find the "knee" or "elbow"-the point at which there is diminishing returns in the velocity of the Michaelis-Menten curve (or acceleration of the toxicokinetic curve). Our work fundamentally reshapes the KMD methodology, placing it within the well-established Michaelis-Menten theoretical framework by defining the KMD as the point where the kinetic rate approximates the Michaelis-Menten asymptote at higher concentrations. By putting the KMD within the Michaelis-Menten framework, we leverage existing biochemical and pharmacological concepts such as "saturation" to establish the region where the KMD is likely to exist. The advantage of defining KMD as a region, rather than as an inflection point along the curve, is that a region reflects uncertainty and clarifies that there is no single point where the curve is expected to "break;" rather, there is a region where the curve begins to taper off as it approaches the asymptote (Vmax in the Michaelis-Menten equation).

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways.

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Different as night and day: Behavioural and life history responses to varied photoperiods in Daphnia magna.

Nearly all animal species have utilized photoperiod to cue seasonal behaviours and life history traits. We investigated photoperiod responses in keystone species, Daphnia magna, to identify molecular processes underlying ecologically important behaviours and traits using functional transcriptomic analyses. Daphnia magna were photoperiod-entrained immediately posthatch to a standard control photoperiod of 16 light/ 8 dark hours (16L:8D) relative to shorter (4L:20D, 8L:16D, 12L:12L) and longer (20L:4D) day length photoperiods. Short-day photoperiods induced significantly increased light-avoidance behaviours relative to controls. Correspondingly, significant differential transcript expression for genes involved in glutamate signalling was observed, a critical signalling pathway in arthropod light-avoidance behaviour. Additionally, period circadian protein and proteins coding F-box/LRR-repeat domains were differentially expressed which are recognized to establish circadian rhythms in arthropods. Indicators of metabolic rate increased in short-day photoperiods which corresponded with broadscale changes in transcriptional expression across system-level energy metabolism pathways. The most striking observations included significantly decreased neonate production at the shortest day length photoperiod (4L:20D) and significantly increased male production across short-day and equinox photoperiods (4L:20D, 8L:16D and 12L:12D). Transcriptional expression consistent with putative mechanisms of male production was observed including photoperiod-dependent expression of transformer-2 sex-determining protein and small nuclear ribonucleoprotein particles (snRNPs) which control splice variant expression for genes like transformer. Finally, increased transcriptional expression of glutamate has also been shown to induce male production in Daphnia pulex via photoperiod-sensitive mechanisms. Overall, photoperiod entrainment affected molecular pathways that underpin critical behavioural and life history traits in D. magna providing fundamental insights into biological responses to this primary environmental cue.

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials.

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

A decision analysis framework for estimating the potential hazards for drinking water resources of chemicals used in hydraulic fracturing fluids.

Despite growing concerns over the potential for hydraulic fracturing to impact drinking water resources, there are limited data available to identify chemicals used in hydraulic fracturing fluids that may pose public health concerns. In an effort to explore these potential hazards, a multi-criteria decision analysis (MCDA) framework was employed to analyze and rank selected subsets of these chemicals by integrating data on toxicity, frequency of use, and physicochemical properties that describe transport in water. Data used in this analysis were obtained from publicly available databases compiled by the United States Environmental Protection Agency (EPA) as part of a larger study on the potential impacts of hydraulic fracturing on drinking water. Starting with nationwide hydraulic fracturing chemical usage data from EPA's analysis of the FracFocus Chemical Disclosure Registry 1.0, MCDAs were performed on chemicals that had either noncancer toxicity values (n=37) or cancer-specific toxicity values (n=10). The noncancer MCDA was then repeated for subsets of chemicals reported in three representative states (Texas, n=31; Pennsylvania, n=18; and North Dakota, n=20). Within each MCDA, chemicals received scores based on relative toxicity, relative frequency of use, and physicochemical properties (mobility in water, volatility, persistence). Results show a relative ranking of these chemicals based on hazard potential, and provide preliminary insight into chemicals that may be more likely than others to impact drinking water resources. Comparison of nationwide versus state-specific analyses indicates regional differences in the chemicals that may be of more concern to drinking water resources, although many chemicals were commonly used and received similar overall hazard rankings. Several chemicals highlighted by these MCDAs have been reported in groundwater near areas of hydraulic fracturing activity. This approach is intended as a preliminary analysis, and represents one possible method for integrating data to explore potential public health impacts.

Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards.

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high-throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline-based meta-regression can be used to integrate data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an in vitro point of departure of 0.751 µM and risk-specific in vitro concentrations of 0.29 µM and 0.28 µM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta-regression, may allow risk assessors to identify points of departure and risk-specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.

Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.

Overview of Chronic Oral Toxicity Values for Chemicals Present in Hydraulic Fracturing Fluids, Flowback, and Produced Waters.

Concerns have been raised about potential public health effects that may arise if hydraulic fracturing-related chemicals were to impact drinking water resources. This study presents an overview of the chronic oral toxicity values-specifically, chronic oral reference values (RfVs) for noncancer effects, and oral slope factors (OSFs) for cancer-that are available for a list of 1173 chemicals that the United States (U.S.) Environmental Protection Agency (EPA) identified as being associated with hydraulic fracturing, including 1076 chemicals used in hydraulic fracturing fluids and 134 chemicals detected in flowback or produced waters from hydraulically fractured wells. The EPA compiled RfVs and OSFs using six governmental and intergovernmental data sources. Ninety (8%) of the 1076 chemicals reported in hydraulic fracturing fluids and 83 (62%) of the 134 chemicals reported in flowback/produced water had a chronic oral RfV or OSF available from one or more of the six sources. Furthermore, of the 36 chemicals reported in hydraulic fracturing fluids in at least 10% of wells nationwide (identified from EPA's analysis of the FracFocus Chemical Disclosure Registry 1.0), 8 chemicals (22%) had an available chronic oral RfV. The lack of chronic oral RfVs and OSFs for the majority of these chemicals highlights the significant knowledge gap that exists to assess the potential human health hazards associated with hydraulic fracturing.

A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions.

BACKGROUND: The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. OBJECTIVE: Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. METHODS: New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. DISCUSSION: NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. CONCLUSIONS: While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

Loss of Hif-2α Rescues the Hif-1α Deletion Phenotype of Neonatal Respiratory Distress In Mice.

Hypoxia is a state of decreased oxygen reaching the tissues of the body. During prenatal development, the fetus experiences localized occurrences of hypoxia that are essential for proper organogenesis and survival. The response to decreased oxygen availability is primarily regulated by hypoxia-inducible factors (HIFs), a family of transcription factors that modulate the expression of key genes involved in glycolysis, angiogenesis, and erythropoiesis. HIF-1α and HIF-2α, two key isoforms, are important in embryonic development, and likely are involved in lung morphogenesis. We have recently shown that the inducible loss of Hif-1α in lung epithelium starting at E4.5 leads to death within an hour of parturition, with symptoms similar to neonatal respiratory distress syndrome (RDS). In addition to Hif-1α, Hif-2α is also expressed in the developing lung, although the overlapping roles of Hif-1α and Hif-2α in this context are not fully understood. To further investigate the independent role of Hif-2α in lung epithelium and its ability to alter Hif-1α-mediated lung maturation, we generated two additional lung-specific inducible Hif-α knockout models (Hif-2α and Hif-1α+Hif-2α). The intrauterine loss of Hif-2α in the lungs does not lead to decreased viability or observable phenotypic changes in the lung. More interestingly, survivability observed after the loss of both Hif-1α and Hif-2α suggests that the loss of Hif-2α is capable of rescuing the neonatal RDS phenotype seen in Hif-1α-deficient pups. Microarray analyses of lung tissue from these three genotypes identified several factors, such as Scd1, Retlnγ, and Il-1r2, which are differentially regulated by the two HIF-α isoforms. Moreover, network analysis suggests that modulation of hormone-mediated, NF-κB, C/EBPα, and c-MYC signaling are central to HIF-mediated changes in lung development.

Can data science inform environmental justice and community risk screening for type 2 diabetes?

BACKGROUND: Having the ability to scan the entire country for potential "hotspots" with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies. OBJECTIVES: Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots. METHODS: Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California. RESULTS: We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor. CONCLUSIONS: This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.