T-CLEARE: A Pilot Community-Driven Tissue-Clearing Protocol Repository.

Selecting and implementing a tissue-clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue-clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that many articles do not provide sufficient detail to replicate or reproduce experimental results. To help address this issue, we have developed a novel, freely available repository of tissue-clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue-clearing protocols did not perform well (negative results). The goal of T-CLEARE is to provide a forum for the community to share evaluations and modifications of tissue-clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

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.

Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

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.

Standard metadata for 3D microscopy.

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.

Common Data Elements for National Institute of Mental Health-Funded Translational Early Psychosis Research.

The National Institutes of Health has established the PhenX Toolkit as a web-based resource containing consensus measures freely available to the research community. The National Institute of Mental Health (NIMH) has introduced the Mental Health Research Core Collection as part of the PhenX Toolkit and recently convened the PhenX Early Psychosis Working Group to generate the PhenX Early Psychosis Specialty Collection. The Working Group consisted of two complementary panels for clinical and translational research. We review the process, deliberations, and products of the translational research panel. The Early Psychosis Specialty Collection rationale for measure selection as well as additional information and protocols for obtaining each measure are available on the PhenX website (https://www.phenxtoolkit.org). The NIMH strongly encourages investigators to use instruments from the PhenX Mental Health Research Collections in NIMH-funded studies and discourages use of alternative measures to collect similar data without justification. We also discuss some of the potential advances that can be achieved by collecting common data elements across large-scale longitudinal studies of early psychosis.

The Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium: design, methods, and study population.

BACKGROUND: Single birth cohort studies have been the basis for many discoveries about early life risk factors for childhood asthma but are limited in scope by sample size and characteristics of the local environment and population. The Children's Respiratory and Environmental Workgroup (CREW) was established to integrate multiple established asthma birth cohorts and to investigate asthma phenotypes and associated causal pathways (endotypes), focusing on how they are influenced by interactions between genetics, lifestyle, and environmental exposures during the prenatal period and early childhood. METHODS AND RESULTS: CREW is funded by the NIH Environmental influences on Child Health Outcomes (ECHO) program, and consists of 12 individual cohorts and three additional scientific centers. The CREW study population is diverse in terms of race, ethnicity, geographical distribution, and year of recruitment. We hypothesize that there are phenotypes in childhood asthma that differ based on clinical characteristics and underlying molecular mechanisms. Furthermore, we propose that asthma endotypes and their defining biomarkers can be identified based on personal and early life environmental risk factors. CREW has three phases: 1) to pool and harmonize existing data from each cohort, 2) to collect new data using standardized procedures, and 3) to enroll new families during the prenatal period to supplement and enrich extant data and enable unified systems approaches for identifying asthma phenotypes and endotypes. CONCLUSIONS: The overall goal of CREW program is to develop a better understanding of how early life environmental exposures and host factors interact to promote the development of specific asthma endotypes.

Recommendations and Challenges of the Clinical Services Panel of the PhenX Early Psychosis Working Group.

Coordinated specialty care (CSC) is a promising multielement treatment for the care of individuals experiencing the onset of schizophrenia. The community mental health block grant program has increased federal support for CSC programs. In order to maximize the number of sites capable of science-to-service or service-to-science translation, the National Institute of Mental Health funded a supplement to the PhenX toolkit consisting of measures for early psychosis. The early psychosis working group included translational research and clinical services panels. The clinical services panel was charged with identifying low-burden and psychometrically sound measures for use in routine clinical settings. The 19 new clinical measures complement existing measures already in the toolkit. Measures cover a range of domains, including symptoms, social and occupational functioning, well-being, medication adherence and side effects, physical activity, and shared decision making and person-centered care. Several challenges are also discussed. The review process underscored the challenges facing nonacademic sites in collecting even low-burden assessments.

Standard measures for sickle cell disease research: the PhenX Toolkit sickle cell disease collections.

Standard measures and common data elements for sickle cell disease (SCD) will improve the data quality and comparability necessary for cross-study analyses and the development of guidelines that support effective treatments and interventions. In 2014, the National Institutes of Health, National Heart, Lung, and Blood Institute (NHLBI) funded an Administrative Supplement to the PhenX Toolkit (consensus measures for Phenotypes and eXposures; https://www.phenxtoolkit.org/) to identify common measures to promote data comparability across SCD research. An 11-member Sickle Cell Disease Research and Scientific Panel provided guidance to the project, establishing a core collection of SCD-related measures and defining the scope of 2 specialty collections: (1) cardiovascular, pulmonary, and renal complications, and (2) neurology, quality-of-life, and health services. For each specialty collection, a working group of SCD experts selected high-priority measures using a consensus process that included scientific community input. The SCD measures were released into the Toolkit in August 2015. The 25 measures included in the core collection are recommended for use by all NHLBI-funded investigators performing human-subject SCD research. The 10 neurology, quality-of-life, and health services measures and 14 cardiovascular, pulmonary, and renal measures are recommended for use within these specialized research areas. For SCD and other researchers, PhenX measures will promote collaborations with clinicians and patients, facilitate cross-study analysis, accelerate translational research, and lead to greater understanding of SCD phenotypes and epigenetics. For clinicians, using PhenX measures will help elucidate the etiology, progression, and treatment of SCD, leading to improved patient care and quality of life.

Proceedings of a Sickle Cell Disease Ontology workshop - Towards the first comprehensive ontology for Sickle Cell Disease.

Sickle cell disease (SCD) is a debilitating single gene disorder caused by a single point mutation that results in physical deformation (i.e. sickling) of erythrocytes at reduced oxygen tensions. Up to 75% of SCD in newborns world-wide occurs in sub-Saharan Africa, where neonatal and childhood mortality from sickle cell related complications is high. While SCD research across the globe is tackling the disease on multiple fronts, advances have yet to significantly impact on the health and quality of life of SCD patients, due to lack of coordination of these disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a disease-specific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge. The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes. The objective of the workshop was to bring together experts in SCD from around the world to contribute their expertise to the development of various aspects of the SCD ontology.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using biofilter, and gene-environment interactions using the Phenx Toolkit*.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value < 1 × 10(-4), as well as an overall model with p-value < 0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value < 1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

Data compatibility in the addiction sciences: an examination of measure commonality.

The need for comprehensive analysis to compare and combine data across multiple studies in order to validate and extend results is widely recognized. This paper aims to assess the extent of data compatibility in the substance abuse and addiction (SAA) sciences through an examination of measure commonality, defined as the use of similar measures, across grants funded by the National Institute on Drug Abuse (NIDA) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Data were extracted from applications of funded, active grants involving human-subjects research in four scientific areas (epidemiology, prevention, services, and treatment) and six frequently assessed scientific domains. A total of 548 distinct measures were cited across 141 randomly sampled applications. Commonality, as assessed by density (range of 0-1) of shared measurement, was examined. Results showed that commonality was low and varied by domain/area. Commonality was most prominent for (1) diagnostic interviews (structured and semi-structured) for substance use disorders and psychopathology (density of 0.88), followed by (2) scales to assess dimensions of substance use problems and disorders (0.70), (3) scales to assess dimensions of affect and psychopathology (0.69), (4) measures of substance use quantity and frequency (0.62), (5) measures of personality traits (0.40), and (6) assessments of cognitive/neurologic ability (0.22). The areas of prevention (density of 0.41) and treatment (0.42) had greater commonality than epidemiology (0.36) and services (0.32). To address the lack of measure commonality, NIDA and its scientific partners recommend and provide common measures for SAA researchers within the PhenX Toolkit.

PhenX RISING: real world implementation and sharing of PhenX measures.

BACKGROUND: The purpose of this manuscript is to describe the PhenX RISING network and the site experiences in the implementation of PhenX measures into ongoing population-based genomic studies. METHODS: Eighty PhenX measures were implemented across the seven PhenX RISING groups, thirty-three of which were used at more than two sites, allowing for cross-site collaboration. Each site used between four and 37 individual measures and five of the sites are validating the PhenX measures through comparison with other study measures. Self-administered and computer-based administration modes are being evaluated at several sites which required changes to the original PhenX Toolkit protocols. A network-wide data use agreement was developed to facilitate data sharing and collaboration. RESULTS: PhenX Toolkit measures have been collected for more than 17,000 participants across the PhenX RISING network. The process of implementation provided information that was used to improve the PhenX Toolkit. The Toolkit was revised to allow researchers to select self- or interviewer administration when creating the data collection worksheets and ranges of specimens necessary to run biological assays has been added to the Toolkit. CONCLUSIONS: The PhenX RISING network has demonstrated that the PhenX Toolkit measures can be implemented successfully in ongoing genomic studies. The next step will be to conduct gene/environment studies.

Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots.

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, 2580 cataract cases and 1367 controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) Biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 529,431 single nucleotide polymorphisms (SNPs) with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using the Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use; these environmental factors have been previously associated with the formation of cataracts. We found a total of 288 models that exhibit an interaction term with a p-value ≤ 1×10(-4) associated with cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

The PhenX Toolkit pregnancy and birth collections.

PURPOSE: Pregnancy and childbirth are normal conditions, but complications and adverse outcomes are common. Both genetic and environmental factors influence the course of pregnancy. Genetic epidemiologic research into pregnancy outcomes could be strengthened by the use of common measures, which would allow data from different studies to be combined or compared. Here, we introduce perinatal researchers to the PhenX Toolkit and the Collections related to pregnancy and childbirth. METHODS: The Pregnancy and Birth Collections were drawn from measures in the PhenX Tooklit. The lead author selected a list of measures for each Collection, which was reviewed by the remaining authors and revised on the basis of their comments. We chose the measures we thought were most relevant for perinatal research and had been linked most strongly to perinatal outcomes. RESULTS: The Pregnancy and Birth Health Conditions Collection includes 24 measures related to pregnancy and fertility history, maternal complications, and infant complications. The Pregnancy and Birth Outcome Risk Factors Collection includes 43 measures of chemical, medical, psychosocial, and personal factors associated with pregnancy outcomes. CONCLUSIONS: The biological complexity of pregnancy and its sensitivity to environmental and genomic influences suggest that multidisciplinary approaches are needed to generate new insights or practical interventions. To fully exploit new research methods and resources, we encourage the biomedical research community to adopt standard measures to facilitate pooled or meta-analyses.

Using PhenX measures to identify opportunities for cross-study analysis.

The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.

The PhenX Toolkit: get the most from your measures.

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

Hydrogen bonding and packing density are factors most strongly connected to limiting sites of high flexibility in the 16S rRNA in the 30S ribosome.

BACKGROUND: Conformational flexibility in structured RNA frequently is critical to function. The 30S ribosomal subunit exists in different conformations in different functional states due to changes in the central part of the 16S rRNA. We are interested in evaluating the factors that might be responsible for restricting flexibility to specific parts of the 16S rRNA using biochemical data obtained from the 30S subunit in solution. This problem was approached taking advantage of the observation that there must be a high degree of conformational flexibility at sites where UV photocrosslinking occurs and a lack of flexibility inhibits photoreactivity at many other sites that are otherwise suitable for reaction. RESULTS: We used 30S x-ray structures to quantify the properties of the nucleotide pairs at UV- and UVA-s4U-induced photocrosslinking sites in 16S rRNA and compared these to the properties of many hundreds of additional sites that have suitable geometry but do not undergo photocrosslinking. Five factors that might affect RNA flexibility were investigated - RNA interactions with ribosomal proteins, interactions with Mg2+ ions, the presence of long-range A minor motif interactions, hydrogen bonding and the count of neighboring heavy atoms around the center of each nucleobase to estimate the neighbor packing density. The two factors that are very different in the unreactive inflexible pairs compared to the reactive ones are the average number of hydrogen bonds and the average value for the number of neighboring atoms. In both cases, these factors are greater for the unreactive nucleotide pairs at a statistically very significant level. CONCLUSION: The greater extent of hydrogen bonding and neighbor atom density in the unreactive nucleotide pairs is consistent with reduced flexibility at a majority of the unreactive sites. The reactive photocrosslinking sites are clustered in the 30S subunit and this indicates nonuniform patterns of hydrogen bonding and packing density in the 16S rRNA tertiary structure. Because this analysis addresses inter-nucleotide distances and geometry between nucleotides distant in the primary sequence, the results indicate regional and global flexibility of the rRNA.