Senescence Signatures Predict Hospitalization Risk and Severity in COVID-19 Patients

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with a particular risk for severe disease and mortality in the elderly population. The more aged you are the higher the risk for mortality and severity due to COVID-19. Why age is the single largest risk factor for severity in COVID-19 is not known. Together virus-induced cell senesence and aging are believed to play a central role in COVID-19 severity and pathogenesis. A deeper understanding of COVID-19 pathophysiology and the involvement of senescence/aging proteins is therefore required. This can help identify patients, at an earlier stage, who are more susceptible to acquiring a severe COVID-19 infection and those who are most likely to go on to develop post-COVID-19 syndrome. This early detection remains a major challenge however largely due to limited understanding of SARS-CoV-2 pathogenesis.

In this study, we investigate whether the levels of senescence-specific plasma proteins from COVID-19 patients can be utilized to predict severity and post-COVID-19 syndrome. We performed proteomic profiling of plasma from COVID-19 patients (n = 400) using the Olink Explore 384 Inflammation Panel. Data analysis identified differences in plasma concentrations of proteins, which are linked to senescence while considering patient hospitalization status, age, and their World Health Organization (WHO) clinical progression score.

The statistically significant changes were found in the senescence-associated plasma proteome of COVID-19 patients who were hospitalized, more aged, and those with severe WHO classification (TPPI, CXCL10, HGF, VEGFA, SIRPB1, IL-6, TNFRSF11B, and B4GALT1; p < 0.05) and which may be linked to post-COVID-19 syndrome. Epigenetic analysis of the methylome, using the GrimAge Clock, found that biological and chronological age did not correlate in hospitalized patients. We also identified that PTX3, CXCL10, KYNU, and SIRPB1 genes had increased promoter methylation in hospitalized patients.

Machine learning analysis showed that characteristic protein changes perform with a similar accuracy to that of a whole panel biomarker signature in terms of hospitalization, age, and WHO clinical progression score.

This study revealed senescence specific protein changes (sendotypes) in the plasma of COVID-19 patients, which can be used as determinants for predicting COVID-19 severity, viral signature persistence, and ultimately which may lead to post-COVID-19 syndrome. We propose that the identification of such sendotypes could be exploited for therapeutic intervention via senolytics in COVID-19.