{"doi":"10.1101/2020.05.07.082487","title":"COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection","abstract":"Abstract Background Epidemiological, 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. Methods We 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. Results Although 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. Conclusions In 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":119545,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":247270,"name":"Emanuela Giombini","orcid":"0000-0003-2237-9755","position":1,"is_corresponding":false},{"id":228459,"name":"Chiara Agrati","orcid":"0000-0002-5252-0927","position":2,"is_corresponding":false},{"id":85538,"name":"Francesco Vairo","orcid":"0000-0002-8375-7468","position":3,"is_corresponding":false},{"id":554702,"name":"Tommaso Ascoli Bartoli","orcid":"0000-0001-7654-8695","position":4,"is_corresponding":false},{"id":555343,"name":"Samir Al Moghazi","orcid":null,"position":5,"is_corresponding":false},{"id":240148,"name":"Mauro Piacentini","orcid":"0000-0003-2919-1296","position":6,"is_corresponding":false},{"id":228967,"name":"Franco Locatelli","orcid":"0000-0002-7976-3654","position":7,"is_corresponding":false},{"id":314583,"name":"Gary Kobinger","orcid":"0000-0003-0612-2298","position":8,"is_corresponding":false},{"id":228081,"name":"Markus Maeurer","orcid":"0000-0002-8436-8077","position":9,"is_corresponding":false},{"id":85550,"name":"Alimuddin Zumla","orcid":"0000-0002-5111-5735","position":10,"is_corresponding":false},{"id":85541,"name":"Maria Rosaria Capobianchi","orcid":"0000-0003-3465-0071","position":11,"is_corresponding":false},{"id":449033,"name":"F. Lauria","orcid":"0000-0001-8621-8841","position":12,"is_corresponding":false},{"id":85534,"name":"Giuseppe Ippolito","orcid":"0000-0002-1076-2979","position":13,"is_corresponding":false},{"id":555344,"name":"COVID 19 INMI Network Medicine for IDs Study Group","orcid":null,"position":14,"is_corresponding":false},{"id":492165,"name":"Francesco Messina","orcid":"0000-0001-8076-7217","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T23:14:08.313144Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}