I-REFF diagrams: enhancing transparency in systematic review through interactive reference flow diagrams.

Systematic review methods are recognized for their rigor and transparency and are widely adapted to frameworks that cover review types such as systematic reviews, scoping reviews, and systematic evidence maps. Reporting guidelines help promote better systematic review practices and detailed documentation of the review process for different types of health research (e.g., PRISMA-Preferred Reporting Items for Systematic Reviews and Meta-Analyses; CONSORT-Consolidated Standards of Reporting Trials; and STROBE-Strengthening the Reporting of Observational Studies in Epidemiology). Transparency in the systematic review process and reporting of results is one of the key advantages of the methods and particularly important for hazard and risk assessments due to the high level of scrutiny these reviews face from scientific, political, and public communities. Data visualizations are important to clearly convey information from a review by helping readers perceive, understand, and assess the displayed information easily and quickly. The study flow diagram is a required element of a systematic review and maps out the number of included and excluded records identified, and the reasons for exclusion. Static literature flow diagrams help viewers readily understand the general review methodology and summarize the number of records included or excluded at each stage of the review. However, such diagrams can be time-consuming to develop and maintain during a systematic review or scoping review, and they provide limited summary-level information. We explored how the use of online systematic review tools such as DistillerSR coupled with visualization software such as Tableau can efficiently generate an Interactive REFerence Flow (I-REFF) diagram that is linked to the literature screening data, thus requiring minimal preparation, and resulting in a simplified process for updating the diagram. Furthermore, I-REFF diagrams enhance transparency and traceability by not only summarizing the records in the review but also allowing viewers to follow specific records throughout the review process. We present an example I-REFF diagram and discuss recommendations for key interactive elements to include in these diagrams and how this workflow can improve efficiency and result in an accessible and transparent interactive literature flow diagram without advanced programming.

Systematic evidence mapping informs a class-based approach to assessing personal care products and pubertal timing.

BACKGROUND: Personal care products (PCPs) contain many different compounds and are a source of exposure to endocrine disrupting chemicals (EDCs), including phthalates and phenols. Early-life exposure to EDCs commonly found in PCPs has been linked to earlier onset of puberty. OBJECTIVE: To characterize the human and animal evidence on the association between puberty-related outcomes and exposure to PCPs and their chemical constituents and, if there is sufficient evidence, identify groups of chemicals and outcomes to support a systematic review for a class-based hazard or risk assessment. METHODS: We followed the OHAT systematic review framework to characterize the human and animal evidence on the association between puberty-related health outcomes and exposure to PCPs and their chemical constituents. RESULTS: Ninety-eight human and 299 animal studies that evaluated a total of 96 different chemicals were identified and mapped by key concepts including chemical class, data stream, and puberty-related health outcome. Among these studies, phthalates and phenols were the most well-studied chemical classes. Most of the phthalate and phenol studies examined secondary sex characteristics and changes in estradiol and testosterone levels. Studies evaluating PCP use and other chemical classes (e.g., parabens) had less data. CONCLUSIONS: This systematic evidence map identified and mapped the published research evaluating the association between exposure to PCPs and their chemical constituents and puberty-related health outcomes. The resulting interactive visualization allows researchers to make evidence-based decisions on the available research by enabling them to search, sort, and filter the literature base of puberty-related studies by key concepts. This map can be used by researchers and regulators to prioritize and target future research and funding to reduce uncertainties and address data gaps. It also provides information to inform a class-based hazard or risk assessment on the association between phthalate and phenol exposures and puberty-related health outcomes.

Becoming aWARE: The Development of a Web-Based Tool for Autism Research and the Environment.

A sharp rise in autism spectrum disorder (ASD) prevalence estimates, beginning in the 1990s, suggested factors additional to genetics were at play. This stimulated increased research investment in nongenetic factors, including the study of environmental chemical exposures, diet, nutrition, lifestyle, social factors, and maternal medical conditions. Consequently, both peer- and non-peer-reviewed bodies of evidence investigating environmental contributors to ASD etiology have grown significantly. The heterogeneity in the design and conduct of this research results in an inconclusive and unwieldy 'virtual stack' of publications. We propose to develop a Web-based tool for Autism Research and the Environment (aWARE) to comprehensively aggregate and assess these highly variable and often conflicting data. The interactive aWARE tool will use an approach for the development of systematic evidence maps (SEMs) to identify and display all available relevant published evidence, enabling users to explore multiple research questions within the scope of the SEM. Throughout tool development, listening sessions and workshops will be used to seek perspectives from the broader autism community. New evidence will be indexed in the tool annually, which will serve as a living resource to investigate the association between environmental factors and ASD.

Assessment of key characteristics, methodology, and effect size measures used in meta-analysis of human-health-related animal studies.

Since the early 1990s the number of systematic reviews (SR) of animal studies has steadily increased. There is, however, little guidance on when and how to conduct a meta-analysis of human-health-related animal studies. To gain insight about the methods that are currently used we created an overview of the key characteristics of published meta-analyses of animal studies, with a focus on the choice of effect size measures. An additional goal was to learn about the rationale behind the meta-analysis methods used by the review authors. We show that important details of the meta-analyses are not fully described, only a fraction of all human-health-related meta-analyses provided rationales for their decision to use specific effect size measures. In addition, our data may suggest that authors make post-hoc decisions to switch to another effect size measure during the course of their meta-analysis, and possibly search for significant effects. Based on analyses in this paper we recommend that review teams: 1) publish a review protocol before starting the conduct of a SR, prespecifying all methodological details (providing special attention to the planned meta-analysis including the effect size measure and the rational behind choosing a specific effect size, prespecifying subgroups and restricting the number of subgroup analyses), 2) always use the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist to report your SR of animal studies, and 3) use the random effects model (REM) in human-health-related meta-analysis of animal studies, unless the assumptions for using the fixed effect model (FEM) are all met.

Identifying environmental factors that influence immune response to SARS-CoV-2: Systematic evidence map protocol.

BACKGROUND: Widespread environmental contamination can directly interact with human immune system functions. Environmental effects on the immune system may influence human susceptibility to respiratory infections as well as the severity of infectious diseases, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, the efficacy of vaccines to respiratory diseases may be impacted by environmental exposures through immune perturbations. Given the quick pace of research about COVID-19 and associated risk factors, it is critical to identify and curate the streams of evidence quickly and effectively. OBJECTIVE: We developed this systematic evidence map protocol to identify and organize existing human and animal literature on high-priority environmental chemical classes (Per- and polyfluoroalkyl substances, pesticides, phthalates, quaternary ammonium compounds, and air pollutants) and their potential to influence three key outcomes: (1) susceptibility to respiratory infection, including SARS-CoV-2 (2) severity of the resultant disease progression, and (3) impact on vaccine efficacy. The result of this project will be an online, interactive database which will show what evidence is currently available between involuntary exposures to select environmental chemicals and immune health effects, data gaps that require further research, and data rich areas that may support further analysis. SEARCH AND STUDY ELIGIBILITY: We will search PubMed for epidemiological or toxicological literature on select toxicants from each of the chemical classes and each of the three outcomes listed above. STUDY APPRAISAL AND SYNTHESIS OF METHODS: For each study, two independent reviewers will conduct title and abstract screening as well as full text review for data extraction of study characteristics. Study quality will not be evaluated in this evidence mapping. The main findings from the systematic evidence map will be visualized using a publicly available and interactive database hosted on Tableau Public.

Evaluation of a semi-automated data extraction tool for public health literature-based reviews: Dextr.

INTRODUCTION: There has been limited development and uptake of machine-learning methods to automate data extraction for literature-based assessments. Although advanced extraction approaches have been applied to some clinical research reviews, existing methods are not well suited for addressing toxicology or environmental health questions due to unique data needs to support reviews in these fields. OBJECTIVES: To develop and evaluate a flexible, web-based tool for semi-automated data extraction that: 1) makes data extraction predictions with user verification, 2) integrates token-level annotations, and 3) connects extracted entities to support hierarchical data extraction. METHODS: Dextr was developed with Agile software methodology using a two-team approach. The development team outlined proposed features and coded the software. The advisory team guided developers and evaluated Dextr's performance on precision, recall, and extraction time by comparing a manual extraction workflow to a semi-automated extraction workflow using a dataset of 51 environmental health animal studies. RESULTS: The semi-automated workflow did not appear to affect precision rate (96.0% vs. 95.4% manual, p = 0.38), resulted in a small reduction in recall rate (91.8% vs. 97.0% manual, p < 0.01), and substantially reduced the median extraction time (436 s vs. 933 s per study manual, p < 0.01) compared to a manual workflow. DISCUSSION: Dextr provides similar performance to manual extraction in terms of recall and precision and greatly reduces data extraction time. Unlike other tools, Dextr provides the ability to extract complex concepts (e.g., multiple experiments with various exposures and doses within a single study), properly connect the extracted elements within a study, and effectively limit the work required by researchers to generate machine-readable, annotated exports. The Dextr tool addresses data-extraction challenges associated with environmental health sciences literature with a simple user interface, incorporates the key capabilities of user verification and entity connecting, provides a platform for further automation developments, and has the potential to improve data extraction for literature reviews in this and other fields.

Developing a database of systematic reviews of animal studies.

Systematic reviews (SRs) are common practice in clinical and public health research, but less common in non-human animal research. Systematic reviews of animal studies can be valuable to inform clinical research, to evaluate the need for further animal experiments on a given topic, and to assess the hazard of an environmental exposure in the evaluation of toxicological studies. In the last 10 years, there has been an increase in the number of SRs of animal research, as well as several publications with detailed guidance on how to perform high-quality systematic reviews of experimental animal studies. In order to evaluate current analytical approaches used in SRs of animal studies, easily identify all systematic reviews on a specific topic, and subsequently the original animal studies and their results and promote awareness and understanding of these emerging approaches, we compiled a database of SRs of animal studies. The database was developed using a rigorous, systematic approach and covers a broad range of research fields: preclinical research, toxicology, environmental health, and veterinary medicine. The database currently includes 3113 SRs of animal studies (search date June 2019). In addition to bibliographical information, data on whether or not a risk of bias assessment and meta-analysis were conducted were extracted. For future users, the search features of the database provide users with a platform to identify and select SRs with a particular characteristic for export to Microsoft Word or Microsoft Excel. From there, users may perform additional data extraction to meet their research needs. The database is freely available at www.Mendeley.com (link). The database provides methodologists a comprehensive source that can be used to explore and advance the current methodology applied to SRs of animal studies, and can help researchers to easily identify all systematic reviews on a specific topic, and subsequently the original animal studies and their results and avoid duplication and unnecessary animal research.

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

BACKGROUND: In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor. METHODS: Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods. RESULTS: On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process. CONCLUSION: SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.

Systematic evidence maps as a novel tool to support evidence-based decision-making in chemicals policy and risk management.

BACKGROUND: While systematic review (SR) methods are gaining traction as a method for providing a reliable summary of existing evidence for health risks posed by exposure to chemical substances, it is becoming clear that their value is restricted to a specific range of risk management scenarios - in particular, those which can be addressed with tightly focused questions and can accommodate the time and resource requirements of a systematic evidence synthesis. METHODS: The concept of a systematic evidence map (SEM) is defined and contrasted to the function and limitations of systematic review (SR) in the context of risk management decision-making. The potential for SEMs to facilitate evidence-based decision-making are explored using a hypothetical example in risk management priority-setting. The potential role of SEMs in reference to broader risk management workflows is characterised. RESULTS: SEMs are databases of systematically gathered research which characterise broad features of the evidence base. Although not intended to substitute for the evidence synthesis element of systematic reviews, SEMs provide a comprehensive, queryable summary of a large body of policy relevant research. They provide an evidence-based approach to characterising the extent of available evidence and support forward looking predictions or trendspotting in the chemical risk sciences. In particular, SEMs facilitate the identification of related bodies of decision critical chemical risk information which could be further analysed using SR methods, and highlight gaps in the evidence which could be addressed with additional primary studies to reduce uncertainties in decision-making. CONCLUSIONS: SEMs have strong and growing potential as a high value tool in resource efficient use of existing research in chemical risk management. They can be used as a critical precursor to efficient deployment of high quality SR methods for characterising chemical health risks. Furthermore, SEMs have potential, at a large scale, to support the sort of evidence summarisation and surveillance methods which would greatly increase the resource efficiency, transparency and effectiveness of regulatory initiatives such as EU REACH and US TSCA.

A scoping review of the health and toxicological activity of bisphenol A (BPA) structural analogues and functional alternatives.

Recent studies report widespread usage or exposure to a variety of chemicals with structural or functional similarity to bisphenol A (BPA), referred to as BPA analogues or derivatives. These have been detected in foodstuffs, house dust, environmental samples, human urine or blood, and consumer products. Compared to BPA, relatively little is known about potential toxicity of these compounds. This scoping review aimed to summarize the human, animal, and mechanistic toxicity data for 24 BPA analogues of emerging interest to research and regulatory communities. PubMed was searched from March 1, 2015 to January 5, 2019 and combined with the results obtained from literature searches conducted through March 23, 2015, in The National Toxicology Program's Research Report 4 (NTP RR-04), "Biological Activity of Bisphenol A (BPA) Structural Analogues and Functional Alternatives". Study details are presented in interactive displays using Tableau Public. In total, 5748 records were screened for inclusion. One hundred sixty seven studies were included from NTP RR-04 and 175 studies were included from the updated literature search through January 2019. In total, there are 22, 117, and 221 human epidemiological, experimental animal, or in vitro studies included. The most frequently studied BPA analogues are bisphenol S (BPS), bisphenol F (4,4-BPF), and bisphenol AF (BPAF). Notable changes in the literature since 2015 include the growing body of human epidemiological studies and in vivo studies conducted in zebrafish. Numerous new endpoints were also evaluated across all three evidence streams including diabetes, obesity, and oxidative stress. However, few studies have addressed endpoints such as neurodevelopmental outcomes or impacts on the developing mammary or prostate glands, which are known to be susceptible to disruption by BPA. Further, there remains a critical need for better exposure information in order to prioritize experimental studies. Moving forward, researchers should also ensure that full dose responses are performed for all main effects in order to support hazard and risk characterization efforts. The evidence gathered here suggests that hazard and risk characterizations should expand beyond BPA in order to consider BPA structural and functional analogues.

Human and animal evidence of potential transgenerational inheritance of health effects: An evidence map and state-of-the-science evaluation.

BACKGROUND: An increasing number of reports suggest early life exposures result in adverse effects in offspring who were never directly exposed; this phenomenon is termed "transgenerational inheritance." Given concern for public health implications for potential effects of exposures transmitted to subsequent generations, it is critical to determine how widespread and robust this phenomenon is and to identify the range of exposures and possible outcomes. OBJECTIVES: This scoping report examines the evidence for transgenerational inheritance associated with exposure to a wide range of stressors in humans and animals to identify areas of consistency, uncertainty, data gaps, and to evaluate general risk of bias issues for the transgenerational study design. METHODS: A protocol was developed to collect and categorize the literature into a systematic evidence map for transgenerational inheritance by health effects, exposures, and evidence streams following the Office of Health Assessment and Translation (OHAT) approach for conducting literature-based health assessments. RESULTS: A PubMed search yielded 63,758 unique records from which 257 relevant studies were identified and categorized into a systematic evidence map by evidence streams (46 human and 211 animal), broad health effect categories, and exposures. Data extracted from the individual studies are available in the Health Assessment Workspace Collaborative (HAWC) program. There are relatively few bodies of evidence where multiple studies evaluated the same exposure and the same or similar outcomes. Studies evaluated for risk of bias generally had multiple issues in design or conduct. CONCLUSIONS: The evidence mapping illustrated that risk of bias, few studies, and heterogeneity in exposures and endpoints examined present serious limitations to available bodies of evidence for assessing transgenerational effects. Targeted research is suggested to addressed inconsistencies and risk of bias issues identified, and thereby establish more robust bodies of evidence to critically assess transgenerational effects - particularly by adding data on exposure-outcome pairs where there is some evidence (i.e., reproductive, metabolic, and neurological effects).

SWIFT-Review: a text-mining workbench for systematic review.

BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus. METHODS: Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics. RESULTS: Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation. CONCLUSIONS: Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

Prioritizing Environmental Chemicals for Obesity and Diabetes Outcomes Research: A Screening Approach Using ToxCast™ High-Throughput Data.

BACKGROUND: Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is challenging. This review is intended to help researchers generate hypotheses about chemicals that may contribute to diabetes and to obesity-related health outcomes by summarizing relevant findings from the U.S. Environmental Protection Agency (EPA) ToxCast™ high-throughput screening (HTS) program. OBJECTIVES: Our aim was to develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high-throughput screening data. METHODS: We identified ToxCast™ assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and ß cell function, adipocyte differentiation, and feeding behavior) and presented chemical screening data against those assay targets to identify chemicals of potential interest. DISCUSSION: The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature. Testing hypotheses based on ToxCast™ data will also help assess the predictive utility of this HTS platform. CONCLUSIONS: More research is required to put these screening-level analyses into context, but the information presented in this review should facilitate the development of new hypotheses. CITATION: Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. 2016. Prioritizing environmental chemicals for obesity and diabetes outcomes research: a screening approach using ToxCast™ high-throughput data. Environ Health Perspect 124:1141-1154; http://dx.doi.org/10.1289/ehp.1510456.

Evaluation of the association between maternal smoking, childhood obesity, and metabolic disorders: a national toxicology program workshop review.

BACKGROUND: An emerging literature suggests that environmental chemicals may play a role in the development of childhood obesity and metabolic disorders, especially when exposure occurs early in life. OBJECTIVE: Here we assess the association between these health outcomes and exposure to maternal smoking during pregnancy as part of a broader effort to develop a research agenda to better understand the role of environmental chemicals as potential risk factors for obesity and metabolic disorders. METHODS: PubMed was searched up to 8 March 2012 for epidemiological and experimental animal studies related to maternal smoking or nicotine exposure during pregnancy and childhood obesity or metabolic disorders at any age. A total of 101 studies-83 in humans and 18 in animals-were identified as the primary literature. DISCUSSION: Current epidemiological data support a positive association between maternal smoking and increased risk of obesity or overweight in offspring. The data strongly suggest a causal relation, although the possibility that the association is attributable to unmeasured residual confounding cannot be completely ruled out. This conclusion is supported by findings from laboratory animals exposed to nicotine during development. The existing literature on human exposures does not support an association between maternal smoking during pregnancy and type 1 diabetes in offspring. Too few human studies have assessed outcomes related to type 2 diabetes or metabolic syndrome to reach conclusions based on patterns of findings. There may be a number of mechanistic pathways important for the development of aberrant metabolic outcomes following perinatal exposure to cigarette smoke, which remain largely unexplored. CONCLUSIONS: From a toxicological perspective, the linkages between maternal smoking during pregnancy and childhood overweight/obesity provide proof-of-concept of how early-life exposure to an environmental toxicant can be a risk factor for childhood obesity.

Estrogen receptor-α mediates diethylstilbestrol-induced feminization of the seminal vesicle in male mice.

BACKGROUND: Studies have shown that perinatal exposure to the synthetic estrogen diethylstilbestrol (DES) leads to feminization of the seminal vesicle (SV) in male mice, as illustrated by tissue hyperplasia, ectopic expression of the major estrogen-inducible uterine secretory protein lactoferrin (LF), and reduced expression of SV secretory protein IV (SVS IV). OBJECTIVES: The present study was designed to evaluate the role of the estrogen receptor (ER) in this action by using ER-knockout (ERKO) mice. METHODS: Wild-type (WT), ERα-null (αERKO), and ERß-null (ßERKO) male mice were treated with either vehicle or DES (2 µg/day) on neonatal days 1-5. These mice were divided into two groups: In the first group, intact mice were sacrificed at 10 weeks of age; in the second group, mice were castrated at 10 weeks of age, allowed to recover for 10 days, treated with dihydrotestosterone (DHT) or placebo, and sacrificed 2 weeks later. Body weights and SV weights were recorded, and mRNA expression levels of Ltf (lactoferrin), Svs4, and androgen receptor (Ar) were assessed. RESULTS: In DES-treated intact mice, SV weights were reduced in WT and ßERKO mice but not in αERKO mice. DES-treated WT and ßERKO males, but not αERKO males, exhibited ectopic expression of LF in the SV. DES treatment resulted in decreased SVS IV protein and mRNA expression in WT males, but no effect was seen in αERKO mice. In addition, DES-treated ßERKO mice exhibited reduced Svs4 mRNA expression but maintained control levels of SVS IV protein. In DES-treated castrated mice, DHT implants restored SV weights to normal levels in αERKO mice but not in WT mice, suggesting full androgen responsiveness in αERKO mice. CONCLUSIONS: These data suggest that DES-induced SV toxicity and feminization are primarily mediated by ERα; however, some aspects of androgen response may require the action of ERß.

Spontaneous airway hyperresponsiveness in estrogen receptor-alpha-deficient mice.

RATIONALE: Airway hyperresponsiveness is a critical feature of asthma. Substantial epidemiologic evidence supports a role for female sex hormones in modulating lung function and airway hyperresponsiveness in humans. OBJECTIVES: To examine the role of estrogen receptors in modulating lung function and airway responsiveness using estrogen receptor-deficient mice. METHODS: Lung function was assessed by a combination of whole-body barometric plethysmography, invasive measurement of airway resistance, and isometric force measurements in isolated bronchial rings. M2 muscarinic receptor expression was assessed by Western blotting, and function was assessed by electrical field stimulation of tracheas in the presence/absence of gallamine. Allergic airway disease was examined after ovalbumin sensitization and exposure. MEASUREMENTS AND MAIN RESULTS: Estrogen receptor-alpha knockout mice exhibit a variety of lung function abnormalities and have enhanced airway responsiveness to inhaled methacholine and serotonin under basal conditions. This is associated with reduced M2 muscarinic receptor expression and function in the lungs. Absence of estrogen receptor-alpha also leads to increased airway responsiveness without increased inflammation after allergen sensitization and challenge. CONCLUSIONS: These data suggest that estrogen receptor-alpha is a critical regulator of airway hyperresponsiveness in mice.

Estrogen receptor beta mediates gender differences in ischemia/reperfusion injury.

Under hypercontractile conditions associated with increased intracellular calcium, male hearts show enhanced ischemia/reperfusion injury compared to female hearts. Our aim in this study was to identify the specific estrogen receptor involved in this gender difference. Following brief treatment with isoproterenol, isolated mouse hearts were subjected to ischemia and reperfusion. Postischemic contractile function and infarct size were measured in wild-type (WT) male and female hearts, and female hearts lacking functional alpha estrogen receptor (alpha ERKO), or the beta estrogen receptor (beta ERKO). WT male hearts exhibited significantly less functional recovery and more necrosis than WT females. alpha ERKO female hearts exhibited ischemia/reperfusion injury similar to that observed in WT females, whereas beta ERKO females exhibited significantly less functional recovery than WT females and were similar to WT males. These data suggest that estrogen, through the beta-estrogen receptor, plays a role in the protection observed in the female heart. Furthermore, we identified genes that were differentially expressed in beta ERKO female hearts compared to alpha ERKO and WT female hearts, and found altered expression of a number of metabolism genes, which may be important in ischemic injury. We further showed that WT female hearts have increased ratio of carbohydrate to fatty acid metabolism relative to WT males.

Estrogen receptor knockout mice as a model for endocrine research.

The biological effects of estrogen in mammalian target tissues are important for multiple organ systems including the male and female reproductive tract and the neuroendocrine, skeletal, and cardiovascular systems. Numerous physiological effects of estradiol are modulated by the estrogen receptor (ER), a Class I member of the nuclear receptor superfamily. However, more recent studies have also implicated nongenomic effects of estrogen, which may involve a membrane-binding site. The two forms of the ER are the classical estrogen receptor-alpha (ERalpha) and the more recently discovered estrogen receptor-beta (ERbeta). Gene-targeting techniques were used to generate mice lacking either functional ERalpha (alphaERKO), ERbeta (betaERKO), or both ERs (alphabetaERKO) to provide a model for evaluating estrogen receptor action. These knockout models provide a unique tool to study the effects of estrogen in the context of the whole animal and to discern the role of each ER in various tissues. The reproductive phenotypes as well as some of the nonreproductive phenotypes of the different ERKO models are summarized.

Regulation of mouse renal CYP2J5 expression by sex hormones.

Mouse CYP2J5 is abundant in kidney and active in the metabolism of arachidonic acid to epoxyeicosatrienoic acids. Western blots of microsomes prepared from mouse kidneys demonstrate that after puberty, CYP2J5 protein is present at higher levels in male mice than in female mice. Northern analysis reveals that CYP2J5 transcripts are more abundant in adult male versus female kidneys, indicating that gender differences in renal CYP2J5 expression are regulated at a pretranslational level. Castration of male mice results in decreased renal CYP2J5 expression, and treatment of castrated male mice or female mice with 5alpha-dihydrotestosterone increases expression to levels that approximate those in intact male mice. In contrast, treatment of ovariectomized female mice or castrated male mice with 17beta-estradiol causes a further reduction in CYP2J5 expression. Growth hormone-deficient (lit/lit) mice respond similarly to castration and 5alpha-dihydrotestosterone treatment, indicating that the androgen effects are not mediated by alterations in the growth hormone secretory pattern. Mice that lack a functional androgen receptor (Tfm hemizygous) have reduced levels of renal CYP2J5 and do not respond to 5alpha-dihydrotestosterone treatment. Similarly, wild-type male mice treated with flutamide, an androgen antagonist, exhibit reduced renal CYP2J5 levels. Female estrogen receptor-alpha knockout (alphaERKO) mice, which are known to have elevated circulating testosterone levels, have significantly increased renal CYP2J5 expression compared with wild-type female mice, and these differences are abrogated by ovariectomy or treatment with flutamide. Based on these data, we conclude that the renal expression of CYP2J5 is up-regulated by androgen and down-regulated by estrogen.