Expression profile of HERVs and inflammatory mediators detected in nasal mucosa as a predictive biomarker of COVID-19 severity

• Published in: Frontiers in microbiology Vol. 14; p. 1155624
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 22.05.2023
• Subjects: biomarker; COVID-19; HERV; human endogenous retroviruses; inflammation; Microbiology; respiratory outcome; COVID-19; HERV; respiratory outcome; biomarker; human endogenous retroviruses; inflammation
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2023.1155624

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