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Prebiotic galactooligosaccharides (GOS) reduce anxiety-like behaviors in mice and humans. However, the biological pathways behind these behavioral changes are not well understood. To begin to study these pathways, we utilized C57BL/6 mice that were fed a standard diet with or without GOS supplementation for 3 weeks prior to testing on the open field. After behavioral testing, colonic contents and serum were collected for bacteriome (16S rRNA gene sequencing, colonic contents only) and metabolome (UPLC-MS, colonic contents and serum data) analyses. As expected, GOS significantly reduced anxiety-like behavior (i.e., increased time in the center) and decreased cytokine gene expression (Tnfa and Ccl2) in the prefrontal cortex. Notably, time in the center of the open field was significantly correlated with serum methyl-indole-3-acetic acid (methyl-IAA). This metabolite is a methylated form of indole-3-acetic acid (IAA) that is derived from bacterial metabolism of tryptophan. Sequencing analyses showed that GOS significantly increased Lachnospiraceae UCG006 and Akkermansia; these taxa are known to metabolize both GOS and tryptophan. To determine the extent to which methyl-IAA can affect anxiety-like behavior, mice were intraperitoneally injected with methyl-IAA. Mice given methyl-IAA had a reduction in anxiety-like behavior in the open field, along with lower Tnfa in the prefrontal cortex. Methyl-IAA was also found to reduce TNF-α (as well as CCL2) production by LPS-stimulated BV2 microglia. Together, these data support a novel pathway through which GOS reduces anxiety-like behaviors in mice and suggests that the bacterial metabolite methyl-IAA reduces microglial cytokine and chemokine production, which in turn reduces anxiety-like behavior.
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Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.
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Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.
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Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.
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Reconhecimento Automatizado de Padrão , Doenças Raras , Humanos , Doenças Raras/tratamento farmacológico , Doenças Raras/genética , Pessoal de Saúde , ConhecimentoRESUMO
Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.
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Reposicionamento de Medicamentos , National Center for Advancing Translational Sciences (U.S.) , Estados Unidos , Ciência Translacional BiomédicaRESUMO
Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, â¼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.
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Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.
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OBJECTIVE: Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases. MATERIALS AND METHODS: Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis. RESULTS: Thirty-seven disease clusters were created with a mean size of 87 diseases. We validate the clusters quantitatively via semantic similarity based on the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters are highly related. DISCUSSION: We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and drugs are enumerated for follow-up efforts. CONCLUSION: We lay out a method for clustering rare diseases using graph node embeddings. We develop an easy-to-maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems.
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Reconhecimento Automatizado de Padrão , Doenças Raras , Humanos , Doenças Raras/genética , Semântica , Fenótipo , Reposicionamento de MedicamentosRESUMO
BACKGROUND: Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data. METHODS: We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM. RESULTS: We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM. CONCLUSION: Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.
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Canabidiol , Glioblastoma , Humanos , Reposicionamento de Medicamentos , Glioblastoma/tratamento farmacológico , Doenças Raras , Desenvolvimento de MedicamentosRESUMO
BACKGROUND: As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles. METHODS: We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence. FINDINGS: We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, ßrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis. INTERPRETATIONS: These metabolites may be identified prior to infection to enable protective measures for these individuals. FUNDING: The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.
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COVID-19 , Doenças Transmissíveis , Influenza Humana , Humanos , Metaboloma , Estudos Prospectivos , Influenza Humana/epidemiologia , Metabolômica , Doenças Transmissíveis/etiologiaRESUMO
Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.
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Smokers (SM) have increased lung immune cell counts and inflammatory gene expression compared to electronic cigarette (EC) users and never-smokers (NS). The objective of this study is to further assess associations for SM and EC lung microbiomes with immune cell subtypes and inflammatory gene expression in samples obtained by bronchoscopy and bronchoalveolar lavage (n = 28). RNASeq with the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Macrophage subtypes revealed a two-fold increase in M0 (undifferentiated) macrophages for SM and EC users relative to NS, with a concordant decrease in M2 (anti-inflammatory) macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) between SM/NS, SM/EC users, and EC users/NS, respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages, respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were three bacteria genera-DEG correlations and three bacteria genera-macrophage subtype correlations. In this pilot study, SM and EC use were associated with an increase in undifferentiated M0 macrophages, but SM differed from EC users and NS for inflammatory gene expression. The data support the hypothesis that SM and EC have toxic lung effects influencing inflammatory responses, but this may not be via changes in the microbiome.
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BACKGROUND: Identifying individuals with a higher risk of developing severe coronavirus disease 2019 (COVID-19) outcomes will inform targeted and more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of preexisting autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. METHODS: A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease and hospitalization, were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. RESULTS: Of the 2 453 799 adults diagnosed with COVID-19, 191 520 (7.81%) had a preexisting AID diagnosis and 278 095 (11.33%) had a preexisting IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a preexisting AID (odds ratio [OR], 1.13; 95% confidence interval [CI]: 1.09-1.17; P < .001), IS exposure (OR, 1.27; 95% CI: 1.24-1.30; P < .001), or both (OR, 1.35; 95% CI: 1.29-1.40; P < .001) were more likely to have a life-threatening disease. These results were consistent when hospitalization was evaluated. A sensitivity analysis evaluating specific IS revealed that tumor necrosis factor inhibitors were protective against life-threatening disease (OR, 0.80; 95% CI: .66-.96; P = .017) and hospitalization (OR, 0.80; 95% CI: .73-.89; P < .001). CONCLUSIONS: Patients with preexisting AID, IS exposure, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.
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Autoimunidade , COVID-19 , Adulto , Humanos , COVID-19/epidemiologia , Estudos Retrospectivos , Hospitalização , Imunossupressores/uso terapêuticoRESUMO
Background Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data. Methods We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repositioning candidates for GBM. Results We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM. Conclusion Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing. This could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.
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COVID-19 highlighted the need for use of real-world data (RWD) in critical care as a near real-time resource for clinical, research, and policy efforts. Analysis of RWD is gaining momentum and can generate important evidence for policy makers and regulators. Extracting high quality RWD from electronic health records (EHRs) requires sophisticated infrastructure and dedicated resources. We sought to customize freely available public tools, supporting all phases of data harmonization, from data quality assessments to de-identification procedures, and generation of robust, data science ready RWD from EHRs. These data are made available to clinicians and researchers through CURE ID, a free platform which facilitates access to case reports of challenging clinical cases and repurposed treatments hosted by the National Center for Advancing Translational Sciences/National Institutes of Health in partnership with the Food and Drug Administration. This commentary describes the partnership, rationale, process, use case, impact in critical care, and future directions for this collaborative effort.
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Motivation: IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships. Results: The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude. Availability and implementation: IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Biologia Computacional , Metabolômica , Metabolômica/métodos , Espectrometria de Massas/métodos , Bases de Dados Factuais , Biologia Computacional/métodosRESUMO
The National Center for Advancing Translational Science (NCATS) seeks to improve upon the translational process to advance research and treatment across all diseases and conditions and bring these interventions to all who need them. Addressing the racial/ethnic health disparities and health inequities that persist in screening, diagnosis, treatment, and health outcomes (e.g., morbidity, mortality) is central to NCATS' mission to deliver more interventions to all people more quickly. Working toward this goal will require enhancing diversity, equity, inclusion, and accessibility (DEIA) in the translational workforce and in research conducted across the translational continuum, to support health equity. This paper discusses how aspects of DEIA are integral to the mission of translational science (TS). It describes recent NIH and NCATS efforts to advance DEIA in the TS workforce and in the research we support. Additionally, NCATS is developing approaches to apply a lens of DEIA in its activities and research - with relevance to the activities of the TS community - and will elucidate these approaches through related examples of NCATS-led, partnered, and supported activities, working toward the Center's goal of bringing more treatments to all people more quickly.
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Importance: Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. Objective: To examine, using data from the National COVID Cohort Collaborative (N3C), whether patients with pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure are at a higher risk of developing severe COVID-19 outcomes. Design setting and participants: A retrospective cohort of 2,453,799 individuals diagnosed with COVID-19 between January 1 st , 2020, and June 30 th , 2022, was created from the N3C data enclave, which comprises data of 15,231,849 patients from 75 USA data partners. Patients were stratified as those with/without a pre-existing diagnosis of AID and/or those with/without exposure to IS prior to COVID-19. Main outcomes and measures: Two outcomes of COVID-19 severity, derived from the World Health Organization severity score, were defined, namely life-threatening disease and hospitalization. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression models with and without adjustment for demographics (age, BMI, gender, race, ethnicity, smoking status), and comorbidities (cardiovascular disease, dementia, pulmonary disease, liver disease, type 2 diabetes mellitus, kidney disease, cancer, and HIV infection). Results: In total, 2,453,799 (16.11% of the N3C cohort) adults (age> 18 years) were diagnosed with COVID-19, of which 191,520 (7.81%) had a prior AID diagnosis, and 278,095 (11.33%) had a prior IS exposure. Logistic regression models adjusted for demographic factors and comorbidities demonstrated that individuals with a prior AID (OR = 1.13, 95% CI 1.09 - 1.17; p =2.43E-13), prior exposure to IS (OR= 1.27, 95% CI 1.24 - 1.30; p =3.66E-74), or both (OR= 1.35, 95% CI 1.29 - 1.40; p =7.50E-49) were more likely to have a life-threatening COVID-19 disease. These results were confirmed after adjusting for exposure to antivirals and vaccination in a cohort subset with COVID-19 diagnosis dates after December 2021 (AID OR = 1.18, 95% CI 1.02 - 1.36; p =2.46E-02; IS OR= 1.60, 95% CI 1.41 - 1.80; p =5.11E-14; AID+IS OR= 1.93, 95% CI 1.62 - 2.30; p =1.68E-13). These results were consistent when evaluating hospitalization as the outcome and also when stratifying by race and sex. Finally, a sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66-0.96; p =1.66E-2) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; p =1.06E-05). Conclusions and Relevance: Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.
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Objective: Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases. Materials and Methods: Data on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were used to convert nodes into vectors upon which k-means clustering was applied. We validated the disease clusters through semantic similarity and feature enrichment analysis. Results: A node embedding model was trained on the ontology enriched rare disease KG and k-means clustering was applied to the embedding vectors resulting in 37 disease clusters with a mean size of 87 diseases. We validate the disease clusters quantitatively by looking at semantic similarity of clustered diseases, using the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters were shown to be highly related. Discussion: We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and approved or investigational drugs are enumerated for follow-up efforts. Conclusion: Our study lays out a method for clustering rare diseases using the graph node embeddings. We develop an easy to maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems. Detailed subnetwork analysis and in-depth review of individual clusters may lead to translatable findings. Future work will focus on incorporation of additional data sources, with a particular focus on common disease data.