AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks.

The Adverse Outcome Pathway (AOP) framework describes the progression of a toxicity pathway from molecular perturbation to population-level outcome in a series of measurable, mechanistic responses. The controlled, computer-readable vocabulary that defines an AOP has the ability to, automatically and on a large scale, integrate AOP knowledge with publically available sources of biological high-throughput data and its derived associations. To support the discovery and development of putative (existing) and potential AOPs, we introduce the AOP-DB, an exploratory database resource that aggregates association relationships between genes and their related chemicals, diseases, pathways, species orthology information, ontologies, and gene interactions. These associations are mined from publically available annotation databases and are integrated with the AOP information centralized in the AOP-Wiki, allowing for the automatic characterization of both putative and potential AOPs in the context of multiple areas of biological information, referred to here as "biological entities". The AOP-DB acts as a hypothesis-generation tool for the expansion of putative AOPs, as well as the characterization of potential AOPs, through the creation of association networks across these biological entities. Finally, the AOP-DB provides a useful interface between the AOP framework and existing chemical screening and prioritization efforts by the US Environmental Protection Agency.

The PhenX Toolkit: Recommended Measurement Protocols for Social Determinants of Health Research.

Health disparities are driven by unequal conditions in the environments in which people are born, live, learn, work, play, worship, and age, commonly termed the Social Determinants of Health (SDoH). The availability of recommended measurement protocols for SDoH will enable investigators to consistently collect data for SDoH constructs. The PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a web-based catalog of recommended measurement protocols for use in research studies with human participants. Using standard protocols from the PhenX Toolkit makes it easier to compare and combine studies, potentially increasing the impact of individual studies, and aids in comparability across literature. In 2018, the National Institute on Minority Health and Health Disparities provided support for an initial expert Working Group to identify and recommend established SDoH protocols for inclusion in the PhenX Toolkit. In 2022, a second expert Working Group was convened to build on the work of the first SDoH Working Group and address gaps in the SDoH Toolkit Collections. The SDoH Collections consist of a Core Collection and Individual and Structural Specialty Collections. This article describes a Basic Protocol for using the PhenX Toolkit to select and implement SDoH measurement protocols for use in research studies. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol: Using the PhenX Toolkit to select and implement SDoH protocols.

Linking complex disease and exposure data-insights from an environmental and occupational health study.

The disparate measurement protocols used to collect study data are an intrinsic barrier to combining information from environmental health studies. Using standardized measurement protocols and data standards for environmental exposures addresses this gap by improving data collection quality and consistency. To assess the prevalence of environmental exposures in National Institutes of Health (NIH) public data repositories and resources and to assess the commonality of the data elements, we analyzed clinical measures and exposure assays by comparing the Caribbean Consortium for Research in Environmental and Occupational Health study with selected NIH environmental health resources and studies. Our assessment revealed that (1) environmental assessments are widely collected in these resources, (2) biological assessments are less prevalent, and (3) NIH resources can help identify common data for meta-analysis. We highlight resources to help link environmental exposure data across studies to support data sharing. Including NIH data standards in environmental health research facilitates comparing and combining study data, and the use of NIH resources and adoption of standard measures will allow integration of multiple studies and increase the scientific impact of individual studies.

Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection.

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit ( https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies.

The PhenX Toolkit: Measurement Protocols for Assessment of Social Determinants of Health.

INTRODUCTION: Social determinants are structures and conditions in the biological, physical, built, and social environments that affect health, social and physical functioning, health risk, quality of life, and health outcomes. The adoption of recommended, standard measurement protocols for social determinants of health will advance the science of minority health and health disparities research and provide standard social determinants of health protocols for inclusion in all studies with human participants. METHODS: A PhenX (consensus measures for Phenotypes and eXposures) Working Group of social determinants of health experts was convened from October 2018 to May 2020 and followed a well-established consensus process to identify and recommend social determinants of health measurement protocols. The PhenX Toolkit contains data collection protocols suitable for inclusion in a wide range of research studies. The recommended social determinants of health protocols were shared with the broader scientific community to invite review and feedback before being added to the Toolkit. RESULTS: Nineteen social determinants of health protocols were released in the PhenX Toolkit (https://www.phenxtoolkit.org) in May 2020 to provide measures at the individual and structural levels for built and natural environments, structural racism, economic resources, employment status, occupational health and safety, education, environmental exposures, food environment, health and health care, and sociocultural community context. CONCLUSIONS: Promoting the adoption of well-established social determinants of health protocols can enable consistent data collection and facilitate comparing and combining studies, with the potential to increase their scientific impact.

CureSCi Metadata Catalog-Making sickle cell studies findable.

OBJECTIVES: To adopt the FAIR principles (Findable, Accessible, Interoperable, Reusable) to enhance data sharing, the Cure Sickle Cell Initiative (CureSCi) MetaData Catalog (MDC) was developed to make Sickle Cell Disease (SCD) study datasets more Findable by curating study metadata and making them available through an open-access web portal. METHODS: Study metadata, including study protocol, data collection forms, and data dictionaries, describe information about study patient-level data. We curated key metadata of 16 SCD studies in a three-tiered conceptual framework of category, subcategory, and data element using ontologies and controlled vocabularies to organize the study variables. We developed the CureSCi MDC by indexing study metadata to enable effective browse and search capabilities at three levels: study, Patient-Reported Outcome (PRO) Measures, and data element levels. RESULTS: The CureSCi MDC offers several browse and search tools to discover studies by study level, PRO Measures, and data elements. The "Browse Studies," "Browse Studies by PRO Measures," and "Browse Studies by Data Elements" tools allow users to identify studies through pre-defined conceptual categories. "Search by Keyword" and "Search Data Element by Concept Category" can be used separately or in combination to provide more granularity to refine the search results. This resource helps investigators find information about specific data elements across studies using public browsing/search tools, before going through data request procedures to access controlled datasets. The MDC makes SCD studies more Findable through browsing/searching study information, PRO Measures, and data elements, aiding in the reuse of existing SCD data.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Ontology-based semantic mapping of chemical toxicities.

This study was undertaken to evaluate the use of ontology-based semantic mapping (OS-Mapping) in chemical toxicity assessment. Nineteen chemical-species phenotypic profiles (CSPPs) were constructed by ontologically annotating the toxicity responses reported in more than seven hundred published studies of ten chemicals on six vertebrate species. The CSPPs were semantically compared to more than 29,000 publicly available phenotypic profiles of genes, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, and diseases based on a cross-species phenotype ontology. OS-Mapping was shown to differentiate chemical toxicities among themselves as well as within and across species. It also revealed cases of chemical by species interactions. In addition to confirming similar MOAs (mechanisms of action) for a few chemicals, OS-Mapping also generated novel insights into the MOAs underlying some seemingly different, yet phenotypically similar, classes of chemicals. The nature of a unified cross-species phenotype ontology and its representation of diverse knowledge domains allowed the construction of a complete phenotypic continuum for the 17α-ethynylestradiol_fathead minnow across the biological levels of organization, which complemented a similar one derived from the Comparative Toxicogenomics Database but based primarily on 17α-ethynylestradiol-induced molecular phenotypes. Overall, OS-Mapping has been demonstrated to offer a powerful approach to help bridge the gap between the molecular and non-molecular phenotypes of chemicals characterized by using high throughput or traditional omics methods and their apical endpoints of greater regulatory relevance, which are typically phenotypes found at the higher levels of biological organization. OS-Mapping also enables comparative toxicity assessment among chemicals, both within and across species. Furthermore, the semantic analysis of phenotypes can reveal additional novel MOAs for some well-known chemicals and discover candidate MOAs for chemicals that are less molecularly characterized. A full phenotypic continuum based on OS-Mapping will also be conducive to the future development of adverse outcome pathways. As phenomics continues to advance and the ontological annotation of literature becomes more automated, the power of OS-Mapping will be further enhanced.

An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia.

Research consortia play a key role in our understanding of how environmental exposures influence health and wellbeing, especially in the case of catastrophic events such as the Deepwater Horizon oil spill. A common challenge that prevents the optimal use of these data is the difficulty of harmonizing data regarding the environmental exposures and health effects across the studies within and among consortia. A review of the measures used by members of the Deepwater Horizon Research Consortia highlights the challenges associated with balancing timely implementation of a study to support disaster relief with optimizing the long-term value of the data. The inclusion of common, standard measures at the study design phase and a priori discussions regarding harmonization of study-specific measures among consortia members are key to overcoming this challenge. As more resources become available to support the use of standard measures, researchers now have the tools needed to rapidly coordinate their studies without compromising research focus or timely completion of the original study goals.

Creating a Structured AOP Knowledgebase via Ontology-Based Annotations.

INTRODUCTION: The Adverse Outcome Pathway framework is increasingly used to integrate data generated based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. MATERIALS AND METHODS: Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016 using terms from existing biological ontologies. RESULTS: AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from fourteen ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. DISCUSSION: This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new approaches described here for annotating key events will also promote the visualization and analysis of genomics data in an AOP context.