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.

BACKGROUND: Coronavirus disease-2019 (COVID-19) is a growing pandemic with an increasing death toll that has been linked to various comorbidities as well as racial disparity. However, the specific characteristics of these at-risk populations are still not known and approaches to lower mortality are lacking. METHODS: We conducted a retrospective electronic health record data analysis of 25,326 subjects tested for COVID-19 between 2/25/20 and 6/22/20 at the University of Alabama at Birmingham Hospital, a tertiary health care center in the racially diverse Southern U.S. The primary outcome was mortality in COVID-19-positive subjects and the association with subject characteristics and comorbidities was analyzed using simple and multiple linear logistic regression. RESULTS: The odds ratio of contracting COVID-19 was disproportionately high in Blacks/African- Americans (OR 2.6; 95%CI 2.19-3.10; p<0.0001) and in subjects with obesity (OR 1.93; 95%CI 1.64-2.28; p<0.0001), hypertension (OR 2.46; 95%CI 2.07-2.93; p<0.0001), and diabetes (OR 2.11; 95%CI 1.78-2.48; p<0.0001). Diabetes was also associated with a dramatic increase in mortality (OR 3.62; 95%CI 2.11-6.2; p<0.0001) and emerged as an independent risk factor in this diverse population even after correcting for age, race, sex, obesity and hypertension. Interestingly, we found that metformin treatment was independently associated with a significant reduction in mortality in subjects with diabetes and COVID-19 (OR 0.33; 95%CI 0.13-0.84; p=0.0210). CONCLUSION: Thus, these results suggest that while diabetes is an independent risk factor for COVID-19- related mortality, this risk is dramatically reduced in subjects taking metformin, raising the possibility that metformin may provide a protective approach in this high risk population.

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.

We reported previously a model of polyglutamine repeat disorders with insertion of 146 CAG repeats into the murine hypoxanthine phosphoribosyl transferase locus (Hprt^(CAG)146; Ordway et al. [1997] Cell 91:753–763), which does not normally contain polyglutamine repeats. These mice develop an adult‐onset neurologic phenotype of incoordination, involuntary limb clasping, seizures, and premature death. Histologic analysis demonstrates widespread ubiquinated neuronal intranuclear inclusions (NIIs). We now report characterization of the age of onset of behavioral abnormalities, correlated with the time course of occurrence of NIIs in several brain regions, and the occurrence of NIIs in non‐neuronal tissues. Onset of behavioral abnormalities occurred at approximately 22 weeks of age. There was variable time course of expression of NIIs in several brain regions. Assessment of several non‐neuronal tissues revealed nuclear inclusions in hepatocytes and choroid plexus epithelium. γ‐Aminobutyric acid (GABA)/benzodiazepine receptors, dopamine D1‐like and D2‐like receptors, and type 2 vesicular monoamine transporter (VMAT2) binding sites were assayed before and after the onset of behavioral abnormalities. GABA/benzodiazepine receptors were unchanged either before or after the onset of behavioral abnormalities in any region analyzed, whereas striatal D1‐like and D2‐like receptors were diminished after but not before the onset of symptoms. Dorsal striatal VMAT2 binding sites were decreased before the onset of behavioral changes. Mitochondrial electron transport chain components were assayed with histochemical methods before and after the onset of behavioral changes. There was no change in behaviorally presymptomatic or symptomatic animals. Hprt^(CAG)146 mice did not exhibit increased susceptibility to the mitochondrial toxin 1‐Methyl‐4‐phenyl‐1,2,3,6‐tetrahydropyridine. Hprt^(CAG)146 mice are a useful model for studying polyglutamine repeat disorders. J. Comp. Neurol. 465:205–219, 2003.

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.