A resource for the network representation of cell perturbations caused by SARS-CoV-2 infection

Livia Perfetto, Elisa Micarelli, Marta Iannuccelli, Prisca Lo Surdo, Giulio Giuliani, Sara Latini, Giusj Monia Pugliese, Giorgia Massacci, Simone Vumbaca, Federica Riccio, Claudia Fuoco, Serena Paoluzi, Luisa Castagnoli, Gianni Cesareni, Luana Licata, Francesca Sacco

Research output: Contribution to journalArticlepeer-review

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has caused more than 2.3 million casualties worldwide and the lack of effective treatments is a major health concern. The development of targeted drugs is held back due to a limited understanding of the molecular mechanisms underlying the perturbation of cell physiology observed after viral infection. Recently, several approaches, aimed at identifying cellular proteins that may contribute to COVID-19 pathology, have been reported. Albeit valuable, this information offers limited mechanistic insight as these efforts have produced long lists of cellular proteins, the majority of which are not annotated to any cellular pathway. We have embarked in a project aimed at bridging this mechanistic gap by developing a new bioinformatic approach to estimate the functional distance between a subset of proteins and a list of pathways. A comprehensive literature search allowed us to annotate, in the SIGNOR 2.0 resource, causal information underlying the main molecular mechanisms through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses affect the host-cell physiology. Next, we developed a new strategy that enabled us to link SARS-CoV-2 interacting proteins to cellular phenotypes via paths of causal relationships. Remarkably, the extensive information about inhibitors of signaling proteins annotated in SIGNOR 2.0 makes it possible to formulate new potential therapeutic strategies. The proposed approach, which is generally applicable, generated a literature-based causal network that can be used as a framework to formulate informed mechanistic hypotheses on COVID-19 etiology and pathology.

Original languageEnglish
Article number450
JournalGenes
Volume12
Issue number3
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Causal network
  • Enrichment analysis
  • High-throughput experiments
  • Signaling pathways
  • The coronavirus disease 2019 (COVID-19)

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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