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Publications

Journal Article Epublish

PubMed: 39595969 DOI: 10.3390/ijms252211899

Mass Spectrometry-Based Metabolomics Reveals a Salivary Signature for Low-Severity COVID-19.

Int J Mol Sci 25(22)

Authors: Iasmim Lopes de Lima, Alex Ap Rosini Silva, Carlos Brites, Natália Angelo da Silva Miyaguti, Felipe Raposo Passos Mansoldo, Sara Vaz Nunes, Pedro Henrique Godoy Sanches, Thais Regiani Cataldi, Caroline Pais de Carvalho, Adriano Reis da Silva, Jonas Ribeiro da Rosa, Mariana Magalhães Borges, Wellisson Vilarindo Oliveira, Thiago Cruz Canevari, Alane Beatriz Vermelho, Marcos Nogueira Eberlin, Andreia M Porcari

Affiliations

  • PPGEMN, School of Engineering, Mackenzie Presbyterian University, São Paulo 01302-907, SP, Brazil. MackGraphe-Mackenzie Institute for Research in Graphene and Nanotechnologies, Mackenzie Presbyterian Institute, São Paulo 01302-907, SP, Brazil.
  • MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University-USF, Bragança Paulista 12916-900, SP, Brazil.
  • LAPI-Laboratory of Research in Infectology, University Hospital Professor Edgard Santos (HUPES), Federal University of Bahia (UFBA), Salvador 40110-060, BA, Brazil.
  • BIOINOVAR-Biotechnology Laboratories, Biocatalysis, Bioproducts and Bioenergy, Institute of Microbiology Paulo de Góes, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-902, RJ, Brazil.
  • Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (USP/ESALQ), Piracicaba 13418-900, SP, Brazil.
  • PPGEMN, School of Engineering, Mackenzie Presbyterian University, São Paulo 01302-907, SP, Brazil.
  • MackGraphe-Mackenzie Institute for Research in Graphene and Nanotechnologies, Mackenzie Presbyterian Institute, São Paulo 01302-907, SP, Brazil.

Abstract

Omics approaches were extensively applied during the coronavirus disease 2019 (COVID-19) pandemic to understand the disease, identify biomarkers with diagnostic and prognostic value, and discover new molecular targets for medications. COVID-19 continues to challenge the healthcare system as the virus mutates, becoming more transmissible or adept at evading the immune system, causing resurgent epidemic waves over the last few years. In this study, we used saliva from volunteers who were negative and positive for COVID-19 when Omicron and its variants became dominant. We applied a direct solid-phase extraction approach followed by non-target metabolomics analysis to identify potential salivary signatures of hospital-recruited volunteers to establish a model for COVID-19 screening. Our model, which aimed to differentiate COVID-19-positive individuals from controls in a hospital setting, was based on 39 compounds and achieved high sensitivity (85%/100%), specificity (82%/84%), and accuracy (84%/92%) in training and validation sets, respectively. The salivary diagnostic signatures were mainly composed of amino acids and lipids and were related to a heightened innate immune antiviral response and an attenuated inflammatory profile. The higher abundance of thyrotropin-releasing hormone in the COVID-19 positive group highlighted the endocrine imbalance in low-severity disease, as first reported here, underscoring the need for further studies in this area.

MeSH

Machine Learning, Covid-19, Metabolomics, Viruses