A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program (preprint)

Background As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. Methods and Findings In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. Conclusions The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

Changes in Treatment and Severity of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program (preprint)

Objectives The purpose of this study was to examine how the treatment and severity of multisystem inflammatory syndrome in children (MIS-C) has changed over more than two years of the COVID-19 pandemic in the United States. Methods Electronic health record data were retrieved from the PEDSnet network as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative. The study included data for children ages 0 to 20 years hospitalized for MIS-C from March 1, 2020 through July 20, 2022. Descriptive statistics for MIS-C treatments and laboratory results were computed for three time periods of interest: March 1, 2020 to May 31, 2021 (pre-Delta); June 1 to December 31, 2021 (primarily Delta); January 1 to July 20, 2022 (primarily Omicron). Standardized differences measured the effect size of the difference between Omicron and pre-Omicron cohorts. Results The study included 946 children with a diagnosis of MIS-C. The largest differences in the Omicron period compared to prior years were decreases in the percentage of children with abnormal troponin (effect size = 0.40), abnormal lymphocytes (effect size = 0.33), and intensive care unit (ICU) visits (effect size = 0.34). There were small decreases in the Omicron period for the majority of treatments and abnormal laboratory measurements examined, including infliximab, anticoagulants, furosemide, aspirin, IVIG without steroids, echocardiograms, mechanical ventilation, platelets, ferritin, and sodium. Conclusions This study provides the first evidence that the severity of MIS-C declined in the first half of the year 2022 relative to prior years of the COVID-19 pandemic in the United States.

Real-world Use of Nirmatrelvir-Ritonavir in COVID-19 Outpatients During the Emergence of Omicron Variants BA.2/BA2.12.1 (preprint)

Background: Ritonavir-boosted Nirmatrelvir (NMV-r), a protease inhibitor with in vitro activity against SARS-CoV-2, has been shown to reduce risk of progression to severe COVID-19 among high-risk individuals during the Delta-variant phase. We sought to determine the effectiveness of NMV-r against Omicron lineage variants BA.2/BA2.12.1, and assess for evidence of a clinical rebound effect. Methods: We conducted a retrospective observational cohort study of non-hospitalized adult patients with SARS-CoV-2 infection from March 26th, 2022 to June 23rd, 2022, using records from a statewide health system linked to vaccine and mortality data. Propensity score matching was performed on NMV-r treated outpatients with outpatients not treated with antiviral therapy. The primary outcome was 28-day all-cause hospitalization; secondary outcomes were COVID-19-related hospitalization, 28-day all-cause mortality, and 28-day ED visits. Logistic regression was used to determine NMV-r treatment effectiveness; subgroup analyses were performed to assess for heterogeneity in treatment effect. Results: Of 14,953 SARS-CoV-2 infected outpatients, 3,614 NMV-r treated patients were matched to 4,835 untreated outpatients. NMV-r was associated with significantly lower odds of 28-day all-cause hospitalization as compared to no antiviral treatment [31 (0.9%) vs. 64 (1.3%), adjusted odds ratio (aOR): 0.48 (95% CI 0.31-0.75)]. NMV-r was also associated with lower odds of COVID-19 related hospitalization [aOR (95% CI): 0.42 (0.25-0.68)] and 28-day all-cause mortality [aOR (95% CI): 0.05 (0.00-0.38)]. Using ED visits within 28 days as a surrogate for rebound symptoms, we observed no clinically evident rebound effect with NMV-r treatment [140 (3.9%) vs 205 (4.2%), aOR: 0.81 (95% CI 0.65-1.02), p = 0.075]. Conclusion: Real-world evidence during an Omicron BA.2/BA2.12.1 predominant period demonstrated an association of NMV-r treatment with reduced 28-day hospitalization and all-cause mortality, and without an increase in rebound symptoms as assessed by ED visits within 28 days after treatment.

Multisystem Inflammatory Syndrome in Children Managed in the Outpatient Setting: An EHR-based cohort study from the RECOVER program (preprint)

Using electronic health record data combined with primary chart review, we identified 7 children across 8 pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) who were managed as outpatients. These findings should prompt a discussion about modifying the case definition to allow for such a possibility.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program (preprint)

BackgroundMore than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). ObjectiveTo identify risk factors associated with PASC/long-COVID. DesignRetrospective case-control study. Setting31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Patients8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system. MeasurementsRisk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. ResultsAmong 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. ConclusionsThis national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course. KEY POINTSO_ST_ABSQuestionC_ST_ABSWhat risk factors are associated with post-acute sequelae of SARS-CoV-2 (PASC) in the National COVID Cohort Collaborative (N3C) EHR Cohort? FindingsThis national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, specific comorbidities, and the number of physicians per capita. MeaningClinicians can use these risk factors to identify patients at high risk for PASC while they are still in the acute phase of their infection and also to support targeted enrollment in clinical trials for preventing or treating PASC.

Coding Long COVID: Characterizing a new disease through an ICD-10 lens (preprint)

Naming a newly discovered disease is always challenging; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux. The deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Our results include a characterization of common diagnostics, treatment-oriented procedures, and medications associated with U09.9-coded patients, which give us insight into current practice patterns around Long COVID. We also established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into three major categories: cardiopulmonary, neurological, and metabolic. We aim to apply the patterns gleaned from this analysis to flag probable Long COVID cases occurring prior to the existence of U09.9, thus establishing a mechanism to ensure patients with earlier cases of Long-COVID are no less ascertainable for current and future research and treatment opportunities.

Real-World Evidence of the Neutralizing Monoclonal Antibody Sotrovimab for Preventing Hospitalization and Mortality in COVID-19 Outpatients (preprint)

Background: It is not known whether sotrovimab, a neutralizing monoclonal antibody (mAb) treatment authorized for early symptomatic COVID-19 patients, is effective against the SARS-CoV-2 Delta variant to prevent progression to severe disease and mortality. Methods: Observational cohort study of non-hospitalized adult patients with SARS-CoV-2 infection from October 1st 2021 - December 11th 2021, using electronic health records from a statewide health system plus state-level vaccine and mortality data. We used propensity matching to select 3 patients not receiving mAbs for each patient who received outpatient sotrovimab treatment. The primary outcome was 28-day hospitalization; secondary outcomes included mortality and severity of hospitalization. Results: Of 10,036 patients with SARS-CoV-2 infection, 522 receiving sotrovimab were matched to 1,563 not receiving mAbs. Compared to mAb-untreated patients, sotrovimab treatment was associated with a 63% decrease in the odds of all-cause hospitalization (raw rate 2.1% versus 5.7%; adjusted OR 0.37, 95% CI 0.19-0.66) and an 89% decrease in the odds of all-cause 28-day mortality (raw rate 0% versus 1.0%; adjusted OR 0.11, 95% CI 0.0-0.79), and may reduce respiratory disease severity among those hospitalized. Conclusion: Real-world evidence demonstrated sotrovimab effectiveness in reducing hospitalization and all-cause 28-day mortality among COVID-19 outpatients during the Delta variant phase.

Acute upper airway disease in children with the omicron (B.1.1.529) variant of SARS-CoV-2: a report from the National COVID Cohort Collaborative (N3C) (preprint)

Background: Reports of SARS-CoV-2 causing laryngotracheobronchitis (commonly known as croup) have been limited to small case series. Early reports suggest the Omicron (B.1.1.529) strain of SARS-CoV-2 (the dominant circulating US strain since the week of 12/25/2021) replicates more efficiently in the conducting airways. This may increase the risk of a croup phenotype in children as they have smaller airway calibers. Methods: Description of the incidence, change over time, and characteristics of children with SARS-CoV-2 and upper airway infection (UAI) diagnoses within the National COVID Cohort Collaborative (N3C) before and during the rise of the Omicron variant. We compare the demographics, comorbidities, and clinical outcomes of hospitalized SARS-CoV-2 positive children with and without UAI. Results: SARS-CoV-2 positive UAI cases increased to the highest number per month (N = 170) in December 2021 as the Omicron variant became dominant. Of 15,806 hospitalized children with SARS-CoV-2, 1.5% (234/15,806) had an UAI diagnosis. Those with UAI were more likely to be male, younger, white, have asthma and develop severe disease as compared to those without UAI. Conclusions: Pediatric acute UAI cases have increased during the Omicron variant surge with many developing severe disease. Improved understanding of this emerging clinical phenotype could aid in therapeutic decision-making and healthcare resource planning.

Children with SARS-CoV-2 in the National COVID Cohort Collaborative (N3C) (preprint)

Importance: SARS-CoV-2 Objective: To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C) Design: Prospective cohort study of encounters with end dates before May 27th, 2021. Setting: 45 N3C institutions Participants: Children < 19-years-old at initial SARS-CoV-2 testing Main Outcomes and Measures: Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs MIS-C contrasts for children infected with SARS-CoV-2. Results: 728,047 children in the N3C were tested for SARS-CoV-2; of these, 91,865 (12.6%) were positive. Among the 5,213 (6%) hospitalized children, 685 (13%) met criteria for severe disease: mechanical ventilation (7%), vasopressor/inotropic support (7%), ECMO (0.6%), or death/discharge to hospice (1.1%). Male gender, African American race, older age, and several pediatric complex chronic condition (PCCC) subcategories were associated with higher clinical severity (p [≤] 0.05). Vital signs (all p [≤] 0.002) and many laboratory tests from the first day of hospitalization were predictive of peak disease severity. Children with severe (vs moderate) disease were more likely to receive antimicrobials (71% vs 32%, p < 0.001) and immunomodulatory medications (53% vs 16%, p < 0.001). Compared to those with acute COVID-19, children with MIS-C were more likely to be male, Black/African American, 1-to-12-years-old, and less likely to have asthma, diabetes, or a PCCC (p < 0.04). MIS-C cases demonstrated a more inflammatory laboratory profile and more severe clinical phenotype with higher rates of invasive ventilation (12% vs 6%) and need for vasoactive-inotropic support (31% vs 6%) compared to acute COVID-19 cases, respectively (p <0.03). Conclusions: In the largest U.S. SARS-CoV-2-positive pediatric cohort to date, we observed differences in demographics, pre-existing comorbidities, and initial vital sign and laboratory test values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.

Characterizing Long COVID: Deep Phenotype of a Complex Condition (preprint)

Importance: Since late 2019, the novel coronavirus SARS-CoV-2 has given rise to a global pandemic and introduced many health challenges with economic, social, and political consequences. In addition to a complex acute presentation that can affect multiple organ systems, there is mounting evidence of various persistent long-term sequelae. The worldwide scientific community is characterizing a diverse range of seemingly common long-term outcomes associated with SARS-CoV-2 infection, but the underlying assumptions in these studies vary widely making comparisons difficult. Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 infection (PASC or long COVID), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations of long COVID. Observations: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts of individuals three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to Human Phenotype Ontology (HPO) terms. Conclusions and Relevance: Patients and clinicians often use different terms to describe the same symptom or condition. Addressing the heterogeneous and inconsistent language used to describe the clinical manifestations of long COVID combined with the lack of standardized terminologies for long COVID will provide a necessary foundation for comparison and meta-analysis of different studies. Translating long COVID manifestations into computable HPO terms will improve the analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared or pooled more effectively. Furthermore, mapping lay terminology to HPO for long COVID manifestations will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, which may improve the stratification and thereby diagnosis and treatment of long COVID.

A Multi-center, Prospective, Observational-cohort controlled study of Clinical Outcomes following COVID-19 Convalescent plasma therapy in hospitalized COVID-19 patients (preprint)

BackgroundThe SARS-CoV2 pandemic has caused high inpatient mortality and morbidity throughout the world. COVID19 convalescent plasma has been utilized as a potential therapy for patients hospitalized with COVID19 pneumonia. This study evaluated the outcomes of hospitalized COVID19 patients treated with COVID19 convalescent plasma in a prospective, observational multicenter trial. MethodsFrom April 2020 through August 2020, hospitalized COVID19 patients at 16 participating hospitals in Colorado were enrolled and treated with COVID19 convalescent plasma (CCP) and compared to hospitalized patients with COVID19 who were not treated with convalescent plasma. Plasma antibody levels were determined following the trial given that antibody tests were not approved at the initiation of the trial. CCP-treated and untreated COVID19 hospitalized patients were matched using propensity scores followed by analysis for length of hospitalization and inpatient mortality. Results542 total hospitalized COVID19 patients were enrolled at 16 hospitals across the region. A total of 468 hospitalized COVID19 patients were entered into propensity score matching with 188 patients matched for analysis in the CCP-treatment and control arms. Fine-Gray models revealed increased length of hospital stay in CCP-treated patients and no change in inpatient mortality compared to controls. In subgroup analysis of CCP-treated patients within 7 days of admission, there was no difference in length of hospitalization and inpatient mortality. ConclusionsThese data show that treatment of hospitalized COVID19 patients with CCP did not significantly improve patient hospitalization length of stay or inpatient mortality.

Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information (preprint)

Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. As the worldwide scientific community forges ahead with efforts to characterize a wide range of outcomes associated with SARS-CoV-2 infection, the proliferation of available data has made it clear that formal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction (preprint)

BackgroundThe majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and FindingsIn a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. ConclusionsThis is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Seroconversion stages COVID19 into distinct pathophysiological states (preprint)

COVID19 is a heterogeneous medical condition involving a suite of underlying pathophysiological processes including hyperinflammation, endothelial damage, thrombotic microangiopathy, and end-organ damage. Limited knowledge about the molecular mechanisms driving these processes and lack of staging biomarkers hamper the ability to stratify patients for targeted therapeutics. We report here the results of a cross-sectional multi-omics analysis of hospitalized COVID19 patients revealing that seroconversion status associates with distinct underlying pathophysiological states. Seronegative COVID19 patients harbor hyperactive T cells and NK cells, high levels of IFN alpha, gamma and lambda ligands, markers of systemic complement activation, neutropenia, lymphopenia and thrombocytopenia. In seropositive patients, all of these processes are attenuated, observing instead increases in B cell subsets, emergency hematopoiesis, increased markers of platelet activation, and hypoalbuminemia. We propose that seroconversion status could potentially be used as a biosignature to stratify patients for therapeutic intervention and to inform analysis of clinical trial results in heterogenous patient populations.

Scientific publications and COVID-19 "research pivots" during the pandemic: An initial bibliometric analysis (preprint)

An examination is presented of scientific research publication trends during the global coronavirus (COVID-19) pandemic in 2020. After reviewing the timing of the emergence of the pandemic in 2020 and the growth of governmental responses, available secondary and sources are used to highlight impacts of COVID-19 on scientific research. A bibliometric analysis is then undertaken to analyze developments in COVID-19 related scientific publications through to October of 2020 by broad trends, fields, countries, and organizations. Two publication data sources are used: PubMed and the Web of Science. While there has been a massive absolute increase in PubMed and Web of Science papers directly focused on COVID-19 topics, especially in medical, biological science, and public health fields, this is still a relatively small proportion of publication outputs across all fields of science. Using Web of Science publication data, the paper examines the extent to which researchers across all fields of science have pivoted their research outputs to focus on topics related to COVID-19. A COVID-19 research pivot is defined as the extent to which the proportion of output in a particular research field has shifted to a focus on COVID-19 topics in 2020 (to date) compared with 2019. Significant variations are found by specific fields (identified by Web of Science Subject Categories). In a top quintile of fields, not only in medical specialties, biomedical sciences, and public health but also in subjects in social sciences and arts and humanities, there are relatively high to medium research pivots. In lower quintiles, including other subjects in science, social science, and arts and humanities, low to zero COVID-19 research pivoting is identified. Version NoteThis working paper is Version 1, completed on December 6, 2020. As further data becomes available, it may be updated.

Identification of potential coagulation pathway abnormalities in SARS-Cov-2 infection; insights from bioinformatics analysis (preprint)

Abnormal coagulation parameters have been explored in a significant number of severe COVID-19 patients, linked to poor prognosis and increased risk of organ failure. Here, to uncover the potential abnormalities in coagulation pathways, we analyzed the RNA-seq data (GEO147507) obtained from the treatment of three pulmonary epithelial cell lines with SARS-CoV-2. The significant differentially expressed genes (DEGs) were subjected to Enrichr database for KEGG pathway enrichment analysis and gene ontology (GO) functional annotation. The STRING database was used to generate PPI networks for identified DEGs. We found three upregulated procoagulant genes (SERPINE1, SERPINA5, and SERPINB2) belong to the serine protease inhibitor (serpin) superfamily that inhibit tissue plasminogen activator (t-PA) and urokinase plasminogen activator (u-PA) in the fibrinolysis process. In conclusion, we suggest the fibrinolysis process, especially the blockage of t-PA and u-PA inhibitors, a potential target for more study in treating coagulopathy in severe COVID-19 cases.

Spike Protein of SARS-CoV-2 Activates Macrophages and Contributes to Induction of Acute Lung Inflammations in Mice (preprint)

BackgroundCoronavirus disease 2019 (COVID-19) patients exhibit multiple organ malfunctions with a primary manifestation of acute and diffuse lung injuries. The Spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial to mediate viral entry into host cells; however, whether it can be cellularly pathogenic and contribute to pulmonary hyper-inflammations in COVID-19 is not well known. Methods and FindingsIn this study, we developed a Spike protein-pseudotyped (Spp) lentivirus with the proper tropism of SARS-CoV-2 Spike protein on the surface and tracked down the fate of Spp in wild type C57BL/6J mice receiving intravenous injection of the virus. A lentivirus with vesicular stomatitis virus glycoprotein (VSV-G) was used as the control. Two hours post-infection (hpi), Spp showed more than 27-75 times more viral burden in the lungs than other organs; it also exhibited about 3-5 times more viral burden than VSV-G lentivirus in the lungs, liver, kidney and spleen. Acute pneumonia was evident in animals 24 hpi. Spp lentivirus was mainly found in LDLR+ macrophages and pneumocytes in the lungs, but not in MARC1+ macrophages. IL6, IL10, CD80 and PPAR-{gamma} were quickly upregulated in response to infection of Spp lentivirus in the lungs in vivo as well as in macrophage-like RAW264.7 cells in vitro. We further confirmed that forced expression of the Spike protein in RAW264.7 cells could significantly increase the mRNA levels of the same panel of inflammatory factors. ConclusionsOur results demonstrate that the Spike protein of SARS-CoV-2 alone can induce cellular pathology, e.g. activating macrophages and contributing to induction of acute inflammatory responses.