Frontiers in Microbiology (May 2023)

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

  • Vita Petrone,
  • Marialaura Fanelli,
  • Martina Giudice,
  • Nicola Toschi,
  • Nicola Toschi,
  • Allegra Conti,
  • Christian Maracchioni,
  • Marco Iannetta,
  • Claudia Resta,
  • Chiara Cipriani,
  • Martino Tony Miele,
  • Francesca Amati,
  • Massimo Andreoni,
  • Loredana Sarmati,
  • Paola Rogliani,
  • Giuseppe Novelli,
  • Giuseppe Novelli,
  • Giuseppe Novelli,
  • Enrico Garaci,
  • Guido Rasi,
  • Paola Sinibaldi-Vallebona,
  • Paola Sinibaldi-Vallebona,
  • Antonella Minutolo,
  • Claudia Matteucci,
  • Emanuela Balestrieri,
  • Sandro Grelli,
  • Sandro Grelli

DOI
https://doi.org/10.3389/fmicb.2023.1155624
Journal volume & issue
Vol. 14

Abstract

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

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