Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19 (preprint)

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

Age-dependent and Independent Symptoms and Comorbidities Predictive of COVID-19 Hospitalization (preprint)

The coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome (SARS)-CoV-2, continues to burden medical institutions around the world by increasing total hospitalization and Intensive Care Unit (ICU) admissions. A better understanding of symptoms, comorbidities and medication used for pre-existing conditions in patients with COVID-19 could help healthcare workers identify patients at increased risk of developing more severe disease. Here, we have used self-reported data (symptoms, medications and comorbidities) from more than 3 million users from the COVID-19 Symptom Tracker app12 to identify previously reported and novel features predictive of patients being admitted in a hospital setting. Despite previously reported association between age and more severe disease phenotypes, we found that patient's age, sex and ethnic group were minimally predictive when compared to patient's symptoms and comorbidities. The most important variables selected by our predictive algorithm were fever, the use of immunosuppressant medication, mobility aid, shortness of breath and fatigue. It is anticipated that early administration of preventative measures in COVID-19 positive patients (COVID+) who exhibit a high risk of hospitalization signature may prevent severe disease progression.