Biology subjects

Capobianchi, M. R.

Publications and source records attributed to Capobianchi, M. R..

5 recordsLinked to original sources

bioRxiv2020

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↗

bioRxiv2020

Extremely potent human monoclonal antibodies from convalescent Covid-19 patients

Human monoclonal antibodies are safe, preventive and therapeutic tools, that can be rapidly developed to help restore the massive health and economic disruption caused by the Covid-19 pandemic. By single cell sorting 4277 SARS-CoV-2 spike protein specific memory B cells from 14 Covid-19 survivors, 453 neutralizing antibodies were identified and 220 of them were expressed as IgG. Up to 65,9% of monoclonals neutralized the wild type virus at a concentration of >500 ng/mL, 23,6% neutralized the virus in the range of 100 - 500 ng/mL and 9,1% had a neutralization potency in the range of 10 - 100 ng/mL. Only 1,4% neutralized the authentic virus with a potency of 1-10 ng/mL. We found that the most potent neutralizing antibodies are extremely rare and recognize the RBD, followed in potency by antibodies that recognize the S1 domain, the S-protein trimeric structure and the S2 subunit. The three most potent monoclonal antibodies identified were able to neutralize the wild type and D614G mutant viruses with less than 10 ng/mL and are good candidates for the development of prophylactic and therapeutic tools against SARS-CoV-2. One Sentence SummaryExtremely potent neutralizing human monoclonal antibodies isolated from Covid-19 convalescent patients for prophylactic and therapeutic interventions.

immunology↗

bioRxiv2020

Synthetic Antibodies neutralize SARS-CoV-2 infection of mammalian cells

Coronaviruses (CoV) are a large family of enveloped, RNA viruses that circulate in mammals and birds. Three highly pathogenic strains have caused zoonotic infections in humans that result in severe respiratory syndromes including the Middle East Respiratory Syndrome CoV (MERS), Severe Acute Respiratory Syndrome CoV (SARS), and the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Here, we describe a panel of synthetic monoclonal antibodies, built on a human IgG framework, that bind to the spike protein of SARS-CoV-2 (the causative agent of COVID-19), compete for ACE2 binding, and potently inhibit SARS-CoV-2. All antibodies that exhibited neutralization potencies at sub-nanomolar concentrations against SARS-CoV-2/USA/WA1 in Vero E6 cells, also bound to the receptor binding domain (RBD), suggesting competition for the host receptor ACE2. These antibodies represent strong immunotherapeutic candidates for treatment of COVID-19.

biochemistry↗

bioRxiv2020

Identification of neutralizing human monoclonal antibodies from Italian Covid-19 convalescent patients

In the absence of approved drugs or vaccines, there is a pressing need to develop tools for therapy and prevention of Covid-19. Human monoclonal antibodies have very good probability of being safe and effective tools for therapy and prevention of SARS-CoV-2 infection and disease. Here we describe the screening of PBMCs from seven people who survived Covid-19 infection to isolate human monoclonal antibodies against SARS-CoV-2. Over 1,100 memory B cells were single-cell sorted using the stabilized prefusion form of the spike protein and incubated for two weeks to allow natural production of antibodies. Supernatants from each cell were tested by ELISA for spike protein binding, and positive antibodies were further tested for neutralization of spike binding to receptor(s) on Vero E6 cells and for virus neutralization in vitro. From the 1,167 memory B specific for SARS-CoV-2, we recovered 318 B lymphocytes expressing human monoclonals recognizing the spike protein and 74 of these were able to inhibit the binding of the spike protein to the receptor. Finally, 17 mAbs were able to neutralize the virus when assessed for neutralization in vitro. Lead candidates to progress into the drug development pipeline will be selected from the panel of neutralizing antibodies identified with the procedure described in this study. One Sentence SummaryNeutralizing human monoclonal antibodies isolated from Covid-19 convalescent patients for therapeutic and prophylactic interventions.

immunology↗

bioRxiv2020

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↗