AUTHOR=Petrone Vita , Fanelli Marialaura , Giudice Martina , Toschi Nicola , Conti Allegra , Maracchioni Christian , Iannetta Marco , Resta Claudia , Cipriani Chiara , Miele Martino Tony , Amati Francesca , Andreoni Massimo , Sarmati Loredana , Rogliani Paola , Novelli Giuseppe , Garaci Enrico , Rasi Guido , Sinibaldi-Vallebona Paola , Minutolo Antonella , Matteucci Claudia , Balestrieri Emanuela , Grelli Sandro TITLE=Expression profile of HERVs and inflammatory mediators detected in nasal mucosa as a predictive biomarker of COVID-19 severity JOURNAL=Frontiers in Microbiology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1155624 DOI=10.3389/fmicb.2023.1155624 ISSN=1664-302X ABSTRACT=Introduction

Our research group and others demonstrated the implication of the human endogenous retroviruses (HERVs) in SARS-CoV-2 infection and their association with disease progression, suggesting HERVs as contributing factors in COVID-19 immunopathology. To identify early predictive biomarkers of the COVID-19 severity, we analyzed the expression of HERVs and inflammatory mediators in SARS-CoV-2-positive and -negative nasopharyngeal/oropharyngeal swabs with respect to biochemical parameters and clinical outcome.

Methods

Residuals of swab samples (20 SARS-CoV-2-negative and 43 SARS-CoV-2-positive) were collected during the first wave of the pandemic and expression levels of HERVs and inflammatory mediators were analyzed by qRT-Real time PCR.

Results

The results obtained show that infection with SARS-CoV-2 resulted in a general increase in the expression of HERVs and mediators of the immune response. In particular, SARS-CoV-2 infection is associated with increased expression of HERV-K and HERV-W, IL-1β, IL-6, IL-17, TNF-α, MCP-1, INF-γ, TLR-3, and TLR-7, while lower levels of IL-10, IFN-α, IFN-β, and TLR-4 were found in individuals who underwent hospitalization. Moreover, higher expression of HERV-W, IL-1β, IL-6, IFN-α, and IFN-β reflected the respiratory outcome of patients during hospitalization. Interestingly, a machine learning model was able to classify hospitalized vs not hospitalized patients with good accuracy based on the expression levels of HERV-K, HERV-W, IL-6, TNF-a, TLR-3, TLR-7, and the N gene of SARS-CoV-2. These latest biomarkers also correlated with parameters of coagulation and inflammation.

Discussion

Overall, the present results suggest HERVs as contributing elements in COVID-19 and early genomic biomarkers to predict COVID-19 severity and disease outcome.