The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

AXL/MERTK inhibitor ONO-7475 potently synergizes with venetoclax and overcomes venetoclax resistance to kill FLT3-ITD acute myeloid leukemia.

FMS-like Tyrosine Kinase 3 (FLT3) mutation is associated with poor survival in acute myeloid leukemia (AML). The specific Anexelekto/MER Tyrosine Kinase (AXL) inhibitor, ONO-7475, kills FLT3-mutant AML cells with targets including Extracellular- signal Regulated Kinase (ERK) and Myeloid Cell Leukemia 1 (MCL1). ERK and MCL1 are known resistance factors for Venetoclax (ABT-199), a popular drug for AML therapy, prompting the investigation of the efficacy of ONO-7475 in combination with ABT-199 in vitro and in vivo. ONO-7475 synergizes with ABT-199 to potently kill FLT3-mutant acute myeloid leukemia cell lines and primary cells. ONO-7475 is effective against ABT-199-resistant cells including cells that overexpress MCL1. Proteomic analyses revealed that ABT-199-resistant cells expressed elevated levels of pro-growth and anti-apoptotic proteins compared to parental cells, and that ONO-7475 reduced the expression of these proteins in both the parental and ABT-199-resistant cells. ONO-7475 treatment significantly extended survival as a single in vivo agent using acute myeloid leukemia cell lines and PDX models. Compared to ONO-7474 monotherapy, the combination of ONO-7475/ABT-199 was even more potent in reducing leukemic burden and prolonging the survival of mice in both model systems. These results suggest that the ONO-7475/ABT-199 combination may be effective for AML therapy.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

Fire Does Not Strongly Affect Genetic Diversity or Structure of a Common Treefrog in the Endangered Florida Scrub.

Fire regimes influence natural populations of organisms in diverse ways, via direct effects on population dynamics as well as indirect effects on habitat and ecosystem processes. Although many amphibian species have evolved to persist in fire-dependent ecosystems, the effects of fire on the genetic diversity of amphibian populations remain relatively unexplored. We examined how different aspects of fire history relate to population genetic diversity and structure of an abundant anuran, Hyla femoralis, in a large, intact area of Florida scrub containing hundreds of seasonally inundated ponds. Specifically, we assessed the overall population genetic structure and examined whether variation in time since fire, fire intensity, or historical fire frequency at breeding sites explained spatial variation in genetic diversity. Based on our sampling of 17 breeding aggregations within the 2,100-ha study area, neither recent nor frequent fire reduce genetic diversity or restrict connectivity among ponds for H. femoralis. Overall, mean effective population sizes were large (average range = 68-572). We detected a positive trend between effective population size (Ne) and average intensity of the most-recent fire, with this factor explaining 42% of the variation in Ne. Our results contrast with previous studies that consistently demonstrate strong relationships between fire history and population genetic structure of scrub-associated lizard species, suggesting that H. femoralis is resilient to a wide range of fire regimes. More generally, our study contributes to understanding the roles of life-history characteristics and environmental unpredictability in shaping organisms' responses to fire.

Treatment of Low-Flow Vascular Malformations of the Extremities Using MR-Guided High Intensity Focused Ultrasound: Preliminary Experience.

Five patients with painful vascular malformations of the extremities that were refractory to standard treatment and were confirmed as low-flow malformations on dynamic contrast-enhanced magnetic resonance (MR) imaging were treated with MR imaging-guided high intensity focused ultrasound. Daily maximum numeric rating scale scores for pain improved from 8.4 ± 1.5 to 1.6 ± 2.2 (P = .004) at a median follow-up of 9 months (range, 4-36 mo). The size of the vascular malformations decreased on follow-up MR imaging (median enhancing volume, 8.2 mL [0.7-10.1 mL] before treatment; 0 mL [0-2.3 mL] after treatment; P = .018) at a median follow-up of 5 months (range, 3-36 mo). No complications occurred.

YAP/TAZ Are Mechanoregulators of TGF-ß-Smad Signaling and Renal Fibrogenesis.

Like many organs, the kidney stiffens after injury, a process that is increasingly recognized as an important driver of fibrogenesis. Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are related mechanosensory proteins that bind to Smad transcription factors, the canonical mediators of profibrotic TGF-ß responses. Here, we investigated the role of YAP/TAZ in the matrix stiffness dependence of fibroblast responses to TGF-ß In contrast to growth on a stiff surface, fibroblast growth on a soft matrix led to YAP/TAZ sequestration in the cytosol and impaired TGF-ß-induced Smad2/3 nuclear accumulation and transcriptional activity. YAP knockdown or treatment with verteporfin, a drug that was recently identified as a potent YAP inhibitor, elicited similar changes. Furthermore, verteporfin reduced YAP/TAZ levels and decreased the total cellular levels of Smad2/3 after TGF-ß stimulation. Verteporfin treatment of mice subjected to unilateral ureteral obstruction similarly reduced YAP/TAZ levels and nuclear Smad accumulation in the kidney, and attenuated renal fibrosis. Our data suggest that organ stiffening cooperates with TGF-ß to induce fibrosis in a YAP/TAZ- and Smad2/3-dependent manner. Interference with this YAP/TAZ and TGF-ß/Smad crosstalk likely underlies the antifibrotic activity of verteporfin. Finally, through repurposing of a clinically used drug, we illustrate the therapeutic potential of a novel mechanointerference strategy that blocks TGF-ß signaling and renal fibrogenesis.

Recombinant N-Terminal Slit2 Inhibits TGF-ß-Induced Fibroblast Activation and Renal Fibrosis.

Fibrosis and inflammation are closely intertwined injury pathways present in nearly all forms of CKD for which few safe and effective therapies exist. Slit glycoproteins signaling through Roundabout (Robo) receptors have been described to have anti-inflammatory effects through regulation of leukocyte cytoskeletal organization. Notably, cytoskeletal reorganization is also required for fibroblast responses to TGF-ß Here, we examined whether Slit2 also controls TGF-ß-induced renal fibrosis. In cultured renal fibroblasts, which we found to express Slit2 and Robo-1, the bioactive N-terminal fragment of Slit2 inhibited TGF-ß-induced collagen synthesis, actin cytoskeletal reorganization, and Smad2/3 transcriptional activity, but the inactive C-terminal fragment of Slit2 did not. In mouse models of postischemic renal fibrosis and obstructive uropathy, treatment with N-terminal Slit2 before or after injury inhibited the development of renal fibrosis and preserved renal function, whereas the C-terminal Slit2 had no effect. Our data suggest that administration of recombinant Slit2 may be a new treatment strategy to arrest chronic injury progression after ischemic and obstructive renal insults by not only attenuating inflammation but also, directly inhibiting renal fibrosis.

Phylogeography of the arid-adapted Malagasy bullfrog, Laliostoma labrosum, influenced by past connectivity and habitat stability.

The rainforest biome of eastern Madagascar is renowned for its extraordinary biodiversity and restricted distribution ranges of many species, whereas the arid western region of the island is relatively species poor. We provide insight into the biogeography of western Madagascar by analyzing a multilocus phylogeographic dataset assembled for an amphibian, the widespread Malagasy bullfrog, Laliostoma labrosum. We find no cryptic species in L. labrosum (maximum 1.1% pairwise genetic distance between individuals in the 16S rRNA gene) attributable to considerable gene flow at the regional level as shown by genetic admixture in both mtDNA and three nuclear loci, especially in central Madagascar. Low breeding site fidelity, viewed as an adaptation to the unreliability of standing pools of freshwater in dry and seasonal environments, and a ubiquitous distribution within its range may underlie overall low genetic differentiation. Moreover, reductions in population size associated with periods of high aridity in western Madagascar may have purged DNA variation in this species. The mtDNA gene tree revealed seven major phylogroups within this species, five of which show mostly non-overlapping distributions. The nested positions of the northern and central mtDNA phylogroups imply a southwestern origin for all extant mtDNA lineages in L. labrosum. The current phylogeography of this species and paleo-distributions of major mtDNA lineages suggest five potential refugia in northern, western and southwestern Madagascar, likely the result of Pleistocene range fragmentation during drier and cooler climates. Lineage sorting in mtDNA and nuclear loci highlighted a main phylogeographic break between populations north and south of the Sambirano region, suggesting a role of the coastal Sambirano rainforest as a barrier to gene flow. Paleo-species distribution models and dispersal networks suggest that the persistence of some refugial populations was mainly determined by high population connectivity through space and time.

Slit2 prevents neutrophil recruitment and renal ischemia-reperfusion injury.

Neutrophils recruited to the postischemic kidney contribute to the pathogenesis of ischemia-reperfusion injury (IRI), which is the most common cause of renal failure among hospitalized patients. The Slit family of secreted proteins inhibits chemotaxis of leukocytes by preventing activation of Rho-family GTPases, suggesting that members of this family might modulate the recruitment of neutrophils and the resulting IRI. Here, in static and microfluidic shear assays, Slit2 inhibited multiple steps required for the infiltration of neutrophils into tissue. Specifically, Slit2 blocked the capture and firm adhesion of human neutrophils to inflamed vascular endothelial barriers as well as their subsequent transmigration. To examine whether these observations were relevant to renal IRI, we administered Slit2 to mice before bilateral clamping of the renal pedicles. Assessed at 18 hours after reperfusion, Slit2 significantly inhibited renal tubular necrosis, neutrophil and macrophage infiltration, and rise in plasma creatinine. In vitro, Slit2 did not impair the protective functions of neutrophils, including phagocytosis and superoxide production, and did not inhibit neutrophils from killing the extracellular pathogen Staphylococcus aureus. In vivo, administration of Slit2 did not attenuate neutrophil recruitment or bacterial clearance in mice with ascending Escherichia coli urinary tract infections and did not increase the bacterial load in the livers of mice infected with the intracellular pathogen Listeria monocytogenes. Collectively, these results suggest that Slit2 may hold promise as a strategy to combat renal IRI without compromising the protective innate immune response.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

Concatenation and concordance in the reconstruction of mouse lemur phylogeny: an empirical demonstration of the effect of allele sampling in phylogenetics.

The systematics and speciation literature is rich with discussion relating to the potential for gene tree/species tree discordance. Numerous mechanisms have been proposed to generate discordance, including differential selection, long-branch attraction, gene duplication, genetic introgression, and/or incomplete lineage sorting. For speciose clades in which divergence has occurred recently and rapidly, recovering the true species tree can be particularly problematic due to incomplete lineage sorting. Unfortunately, the availability of multilocus or "phylogenomic" data sets does not simply solve the problem, particularly when the data are analyzed with standard concatenation techniques. In our study, we conduct a phylogenetic study for a nearly complete species sample of the dwarf and mouse lemur clade, Cheirogaleidae. Mouse lemurs (genus, Microcebus) have been intensively studied over the past decade for reasons relating to their high level of cryptic species diversity, and although there has been emerging consensus regarding the evolutionary diversity contained within the genus, there is no agreement as to the inter-specific relationships within the group. We attempt to resolve cheirogaleid phylogeny, focusing especially on the mouse lemurs, by employing a large multilocus data set. We compare the results of Bayesian concordance methods with those of standard gene concatenation, finding that though concatenation yields the strongest results as measured by statistical support, these results are found to be highly misleading. By employing an approach where individual alleles are treated as operational taxonomic units, we show that phylogenetic results are substantially influenced by the selection of alleles in the concatenation process.

Review of the systematic status of Sceloporus arenicolus Degenhardt and Jones, 1972 with an estimate of divergence time.

The sagebrush lizards (Sceloporus graciosus group) consist of four taxa (S. graciosus graciosus, S. graciosus gracilis, S. graciosus vandenburgianus, and S. arenicolus) distributed in western North America. Of these, S. arenicolus is morphologically, behaviorally, and ecologically distinct as well as geographically disjunct from the other taxa, occurring only in the Mescalero-Monahans Sandhills of southeastern New Mexico and adjacent Texas. Sceloporus arenicolus is a taxon of concern because of its small range and habitat alteration due to land use practices. Understanding evolutionary relationships among members of the S. graciosus group, and especially S. arenicolus, has important implications for conservation. We examine the phylogenetic relationship of S. arenicolus relative to the three recognized subspecies of S. graciosus at mitochondrial and nuclear loci for populations sampled throughout the ranges of these taxa. Additionally, we estimate the divergence time and clade age of S. arenicolus. We find that the S. graciosus group is in need of major taxonomic revision, and also confirm that S. arenicolus is a genetically distinct and divergent lineage. These results bear important consequences for conservation and management.

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.

BACKGROUND: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery. METHODS AND FINDINGS: ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS). CONCLUSIONS: We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.