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A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19.

ABSTRACT: Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC ₅₀ ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC9176654 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host’s immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [(half maximal effective concentration (EC 50 ) value of 4.1 µM]), suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

Project description:The SARS-CoV-2 virus causes severe acute respiratory syndrome (COVID-19) and has rapidly created a global pandemic. Patients that survive may face a slow recovery with long lasting side effects that can afflict different organs. SARS-CoV-2 primarily infects epithelial airway cells that express the host entry receptor Angiotensin Converting Enzyme 2 (ACE2) which binds to spike protein trimers on the surface of SARS-CoV-2 virions. However, SARS-CoV-2 can spread to other tissues even though they are negative for ACE2. To gain insight into the molecular constituents that might influence SARS-CoV-2 tropism, we determined which additional host factors engage with the viral spike protein in disease-relevant human bronchial epithelial cells (16HBEo - ). We found that spike recruited the extracellular proteins laminin and thrombospondin and was retained in the endoplasmatic reticulum (ER) by the proteins DJB11 and FBX2 which support re-folding or degradation of nascent proteins in the ER. Because emerging mutations of the spike protein potentially impact the virus tropism, we compared the interactome of D614 spike with that of the rapidly spreading G614 mutated spike. More D614 than G614 spike associated with the proteins UGGT1, calnexin, HSP7A and GRP78/BiP which ensure glycosylation and folding of proteins in the ER. In contrast to G614 spike, D614 spike was endoproteolytically cleaved, and the N-terminal S1 domain was degraded in the ER even though C-terminal 'S2 only' proteoforms remained present. D614 spike also bound more laminin than G614 spike, which suggested that extracellular laminins may function as co-factor for an alternative, 'S2 only' dependent virus entry. Because the host interactome determines whether an infection is productive, we developed a novel proteome-based cell type set enrichment analysis (pCtSEA). With pCtSEA we determined that the host interactome of the spike protein may extend the tropism of SARS-CoV-2 beyond mucous epithelia to several different cell types, including macrophages and epithelial cells in the nephron. An 'S2 only' dependent, alternative infection of additional cell types with SARS-CoV-2 may impact vaccination strategies and may provide a molecular explanation for a severe or prolonged progression of disease in select COVID-19 patients.

Project description:BackgroundUnderstanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics.MethodsWe leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments.ResultsThe all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions.ConclusionWe publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

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A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19.

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