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Biology subjects

Lauria, F. N.

Publications and source records attributed to Lauria, F. N..

2 recordsLinked to original sources

Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome.

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level. Host conditions and comorbidities were identified as risk factors for severe and fatal disease courses, but the mechanisms of interaction between host and SARS-CoV-2 determining the grade of COVID- 19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred on published PPI, using an explorative algorithm (Random Walk with Restart) triggered by all the 28 proteins of SARS-CoV-2, or each single viral protein one-by-one. The functional analysis for all proteins, linked to many aspects of COVID-19 pathogenesis, allows to identify the subcellular districts, where SARS-CoV-2 proteins seem to be distributed, while in each interactome built around one single viral protein, a different response was described, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, an explorative network-based approach was applied to Bradykinin Storm, highlighting a possible direct action of ORF3a and NS7b to enhancing this condition. This network-based model for SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.

microbiology

COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection.

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.

bioinformatics