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1.
Preprint in English | bioRxiv | ID: ppbiorxiv-430269

ABSTRACT

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. SummaryFeyaerts et al. demonstrate that an integrated analysis of plasma and single-cell proteomics differentiates COVID-19 severity and reveals severity-specific biological signatures associated with the dysregulation of the JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks and the mobilization of the renin-angiotensin and hemostasis systems.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-20170365

ABSTRACT

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) admissions1-9 A better understanding of symptoms, comorbidities and medication used for preexisting conditions in patients with COVID-19 could help healthcare workers identify patients at increased risk of developing more severe disease10,11. 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 phenotypes13-18, we found that patients age, sex and ethnic group were minimally predictive when compared to patients 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.

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