Journal of Translational Medicine (Dec 2021)

Multi-omics approach to COVID-19: a domain-based literature review

  • Chiara Montaldo,
  • Francesco Messina,
  • Isabella Abbate,
  • Manuela Antonioli,
  • Veronica Bordoni,
  • Alessandra Aiello,
  • Fabiola Ciccosanti,
  • Francesca Colavita,
  • Chiara Farroni,
  • Saeid Najafi Fard,
  • Emanuela Giombini,
  • Delia Goletti,
  • Giulia Matusali,
  • Gabriella Rozera,
  • Martina Rueca,
  • Alessandra Sacchi,
  • Mauro Piacentini,
  • Chiara Agrati,
  • Gian Maria Fimia,
  • Maria Rosaria Capobianchi,
  • Francesco Nicola Lauria,
  • Giuseppe Ippolito

DOI
https://doi.org/10.1186/s12967-021-03168-8
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 18

Abstract

Read online

Abstract Background Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. Methods The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. Results The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. Conclusions Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity.

Keywords