Andrew Crouse

Background: We (and others) have previously identified five clinically distinct diabetes subtypes. Currently, few models to identify diabetes subtypes are readily accessible. Further, while COVID-19 has been associated with increased risk of new-onset diabetes, it remains unknown whether the pandemic is also associated with changes in diabetes subtype distribution. Methods: We used the electronic health records of patients diagnosed with diabetes from 2010 to 2019 at the Kirklin Clinic of the University of Alabama at Birmingham (UAB) to train models to assign diabetes subtypes previously identified by hierarchical clustering. We then applied the trained model to conduct a retrospective cluster analysis of electronic health records of patients diagnosed with diabetes from 2020 to 2024 at UAB. We further validated our findings using data from the 2015–2023 National Health and Nutrition Examination Surveys (NHANES). Results: The trained classification model had an average specificity of 98% and an average sensitivity of 93%. Using the model, we identified a significant difference in the distribution of type 2 diabetes subtypes in patients at UAB and in participants in NHANES. In particular, the proportion of patients with severe insulin-dependent diabetes or severe insulin-resistant diabetes subtypes increased from 42% to 61% and 31% to 40% at the UAB and in NHANES, respectively. Conclusions: The model presented here can facilitate the identification of diabetes subtypes. The proportions of patients with severe subtypes of diabetes have seemed to increase in the more recent years following the pandemic. Further studies are required to determine the potential causes of this phenomenon.

This report explores a therapeutic hypothesis for addressing the potential effects of a loss-of-function variant in the ZC4H2 gene. We describe a pediatric female (born 2020) with developmental delay associated with an early truncation variant in ZC4H2 . This genetic alteration is suspected to reduce levels of the functional ZC4H2 protein (encoded by the ZC4H2 gene). Variants in ZC4H2 are associated with Wieacker–Wolff and Miles–Carpenter syndrome, which are characterized by intellectual disability, developmental delay, congenital contractures, and seizures. The patient presents with multiple developmental manifestations, including developmental delay, intellectual disability, joint contractures and dislocations, coronal craniosynostosis, truncal hypotonia, laryngomalacia, rocker bottom feet, and intermittent esotropia. We hypothesize that targeting the downstream effects of ZC4H2 haploinsufficiency, such as synaptic dysfunction and impaired dendritic spine stability, may help mitigate the patient’s symptoms by restoring synaptic homeostasis. This report outlines the rationale for this therapeutic hypothesis, which is informed by genetic findings, prior literature, and recent mechanistic insights. Further studies are needed to validate underlying disease mechanisms and to evaluate the candidate therapy for safety and efficacy.

This report explores a therapeutic hypothesis for addressing the potential effects of a loss-of-function variant in the ZC4H2 gene. We describe a pediatric female (born 2020) with developmental delay associated with an early truncation variant in ZC4H2 . This genetic alteration is suspected to reduce levels of functional ZC4H2 protein (encoded by the ZC4H2 gene). Variants in ZC4H2 are associated with Wieacker-Wolff and Miles-Carpenter Syndrome, which are characterized by intellectual disability, developmental delay, congenital contractures, and seizures. The patient presents with multiple developmental manifestations, including developmental delay, intellectual disability, joint contractures and dislocations, coronal craniosynostosis, atrial septal defect, truncal hypotonia, laryngomalacia, rocker bottom feet, widely spaced nipples, and intermittent esotropia. We hypothesize that targeting the downstream effects of ZC4H2 haploinsufficiency, such as synaptic dysfunction and impaired dendritic spine stability, may help mitigate the patient’s symptoms by restoring synaptic homeostasis. This report outlines the rationale for this therapeutic hypothesis, which is informed by genetic findings, prior literature, and recent mechanistic insights. Further studies are needed to validate underlying disease mechanisms and to evaluate the candidate therapy for safety and efficacy.

The NIH NCATS Translator program now exposes > 200 million semantically harmonized edges, weaving together genomic, pharmacologic, and clinical evidence from over 20 Knowledge Providers—spanning resources such as ClinVar, DrugCentral, DisGeNET, SPOKE, SemMedDB, and real-world EHR aggregates. Leveraging Translator’s reasoning APIs and graph-machine-learning pipelines, our team has systematically surfaced candidate therapeutics for rare monogenic disorders and validated several in patient-specific studies. To democratize this workflow, we built a web-based interface featuring “Smart Queries” that identify drugs predicted to modulate a target gene or treat a disease. This talk will present (i) clinical successes achieved to date, (ii) a live walkthrough of the new UI, and (iii) practical guidance on integrating Translator into rare-disease research pipelines worldwide—accelerating the path from graph insight to patient impact.

The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

This report explores a therapeutic hypothesis for addressing the potential effects of a loss-of-function variant in the ZC4H2 gene. We describe a pediatric female (born 2020) with developmental delay associated with an early truncation variant in ZC4H2 . This genetic alteration is suspected to reduce levels of functional ZC4H2 protein (encoded by the ZC4H2 gene). Variants in ZC4H2 are associated with Wieacker-Wolff and Miles-Carpenter Syndrome, which are characterized by intellectual disability, developmental delay, congenital contractures, and seizures. The patient presents with multiple developmental manifestations, including developmental delay, intellectual disability, joint contractures and dislocations, coronal craniosynostosis, atrial septal defect, truncal hypotonia, laryngomalacia, rocker bottom feet, widely spaced nipples, and intermittent esotropia. We hypothesize that targeting the downstream effects of ZC4H2 haploinsufficiency, such as synaptic dysfunction and impaired dendritic spine stability, may help mitigate the patient’s symptoms by restoring synaptic homeostasis. This report outlines the rationale for this therapeutic hypothesis, which is informed by genetic findings, prior literature, and recent mechanistic insights. Further studies are needed to validate underlying disease mechanisms and to evaluate the candidate therapy for safety and efficacy.

MAPK8IP3- related neurodevelopmental disorders are a spectrum of rare conditions caused by de novo mutations in the MAPK8IP3 gene that encodes the JIP3 protein. These disorders are associated with a spectrum of neurodevelopmental symptoms that manifest in children and cause brain abnormalities, profound intellectual disabilities, movement disorders, and developmental delays. JIP3 is required for axonal transport of proteins and organelles between the soma and the synaptic terminal of neurons, a process critical for normal brain development and function. Homozygous loss-of-function mutations in JIP3 lead to impaired axonal transport and aggregation of cargo, which result in axonal swelling and stunted elongation. Despite these severe outcomes, disease mechanisms are poorly understood, and no current treatments are available. Here we conduct thorough morphological, behavioral, and motility phenotyping in the JIP3 knockout zebrafish and identify locomotor deficits and morphological abnormalities. To identify treatment options, we used insights from expert clinicians and the artificial intelligence tool, mediKanren, to identify drug candidates hypothesized to improve patient symptoms or compensate for the loss of JIP3 at the molecular level. We then prioritized drugs that are FDA-approved, safe for children, and readily available. These collective efforts identified amantadine and levodopa as candidate therapies and rescued motor phenotypes associated with JIP3 loss-of-function in zebrafish.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Several independent lines of evidence suggest that megakaryocytes are dysfunctional in severe COVID-19. Herein, we characterized peripheral circulating megakaryocytes in a large cohort of inpatients with COVID-19 and correlated the subpopulation frequencies with clinical outcomes. Using peripheral blood, we show that megakaryocytes are increased in the systemic circulation in COVID-19, and we identify and validate S100A8/A9 as a defining marker of megakaryocyte dysfunction. We further reveal a subpopulation of S100A8/A9+ megakaryocytes that contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protein and RNA. Using flow cytometry of peripheral blood and in vitro studies on SARS-CoV-2–infected primary human megakaryocytes, we demonstrate that megakaryocytes can transfer viral antigens to emerging platelets. Mechanistically, we show that SARS-CoV-2–containing megakaryocytes are nuclear factor κB (NF-κB)-activated, via p65 and p52; express the NF-κB–mediated cytokines interleukin-6 (IL-6) and IL-1β; and display high surface expression of Toll-like receptor 2 (TLR2) and TLR4, canonical drivers of NF-κB. In a cohort of 218 inpatients with COVID-19, we correlate frequencies of megakaryocyte subpopulations with clinical outcomes and show that SARS-CoV-2–containing megakaryocytes are a strong risk factor for mortality and multiorgan injury, including respiratory failure, mechanical ventilation, acute kidney injury, thrombotic events, and intensive care unit admission. Furthermore, we show that SARS-CoV-2+ megakaryocytes are present in lung and brain autopsy tissues from deceased donors who had COVID-19. To our knowledge, this study offers the first evidence implicating SARS-CoV-2+ peripheral megakaryocytes in severe disease and suggests that circulating megakaryocytes warrant investigation in inflammatory disorders beyond COVID-19.