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COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection.
Francesco Messina; Emanuela Giombini; Chiara Agrati; Francesco Vairo; Tommaso Ascoli Bartoli; Samir Al Moghazi; Mauro Piancentini; Markus Maeurer; Alimuddin Zumla; Maria R. Capobianchi; Francesco Nicola Lauria; Giuseppe Ippolito.
Affiliation
  • Francesco Messina; National Instritute for Infectious Diseases "L. Spallanzani" - IRCCS
  • Emanuela Giombini; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Chiara Agrati; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Francesco Vairo; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Tommaso Ascoli Bartoli; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Samir Al Moghazi; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Mauro Piancentini; Department of Biology, University of Rome "Tor Vergata," Rome, Italy.
  • Markus Maeurer; Champalimaud Centre for the Unknown, Lisbon, Portugal; I. Medizinische Klinik Johannes Gutenberg-Universitat, University of Mainz, 55131 Mainz, Germany.
  • Alimuddin Zumla; Department of Infection, Division of Infection and Immunity, University College London, and National Institutes of Health and Research Biomedical Research Centr
  • Maria R. Capobianchi; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Francesco Nicola Lauria; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
  • Giuseppe Ippolito; National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
Preprint in English | bioRxiv | ID: ppbiorxiv-082487
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
BackgroundEpidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information. MethodsWe investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis. The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells. ResultsAlthough the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines. ConclusionsIn this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.
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Full text: Available Collection: Preprints Database: bioRxiv Type of study: Experimental_studies / Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: bioRxiv Type of study: Experimental_studies / Prognostic study Language: English Year: 2020 Document type: Preprint
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