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[Preprint].2022 Jun 7:rs.3.rs-1354127.

doi: 10.21203/rs.3.rs-1354127/v2.

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

Yadi Zhou¹, Yuan Liu², Shagun Gupta^{2 3}, Mauricio I Paramo^{2 4}, Yuan Hou¹, Chengsheng Mao⁵, Yuan Luo⁵, Julius Judd⁴, Shayne Wierbowski^{2 3}, Marta Bertolotti², Mriganka Nerkar⁴, Lara Jehi⁶, Nir Drayman⁷, Vlad Nicolaescu⁸, Haley Gula⁸, Savaş Tay⁷, Glenn Randall⁸, John T Lis⁴, Cédric Feschotte⁴, Serpil C Erzurum⁶, Feixiong Cheng^{1 9 10}, Haiyuan Yu^{2 3}

Affiliations

¹ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US.
² Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US.
³ Department of Computational Biology, Cornell University, Ithaca, NY 14853, US.
⁴ Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US.
⁵ Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, US.
⁶ Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US.
⁷ Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, US.
⁸ Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, US.
⁹ Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, US.
¹⁰ Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, US.

PMID:35677070
PMCID: PMC9176654
DOI: 10.21203/rs.3.rs-1354127/v2

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

Yadi Zhou et al. Res Sq.2022.

[Preprint].2022 Jun 7:rs.3.rs-1354127.

doi: 10.21203/rs.3.rs-1354127/v2.

Authors

Affiliations

¹ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US.
² Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, US.
³ Department of Computational Biology, Cornell University, Ithaca, NY 14853, US.
⁴ Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, US.
⁵ Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, US.
⁶ Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, US.
⁷ Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, US.
⁸ Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, US.
⁹ Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, US.
¹⁰ Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, US.

PMID:35677070
PMCID: PMC9176654
DOI: 10.21203/rs.3.rs-1354127/v2

Update in

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.
Zhou Y, Liu Y, Gupta S, Paramo MI, Hou Y, Mao C, Luo Y, Judd J, Wierbowski S, Bertolotti M, Nerkar M, Jehi L, Drayman N, Nicolaescu V, Gula H, Tay S, Randall G, Wang P, Lis JT, Feschotte C, Erzurum SC, Cheng F, Yu H.Zhou Y, et al.Nat Biotechnol. 2023 Jan;41(1):128-139. doi: 10.1038/s41587-022-01474-0. Epub 2022 Oct 10.Nat Biotechnol. 2023.PMID:36217030Free PMC article.

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 (EC₅₀ ) 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.

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Conflict of interest statement

Competing interests

The authors declare that there are no competing interests.

Figures

**Extended Figure 1.. Characteristics of the interactome.**
(a) Overlap of the host factors among the four interactomes compared in this study. Heatmaps show the Jaccard indexes (green) and overlap coefficients (purple) of the host factors against other gene sets. Dots indicate FDR < 0.05 by Fisher’s exact test. In the box plots, boxes range from lower to upper quartiles, center lines indicate medians, whiskers show 1.5 × interquartile ranges, and crosses show mean values. (b) The overlap of the interactions in our interactome with the other three interactomes by considering the protein complexes and pathways. If two host factors interacting with the same viral protein are known to interact with each other in the literature, we consider the two viral-host interactions as overlapping. (c) Overlap of the host factors with the differentially expressed genes in SARS-CoV-2⁺ vs. SARS-CoV-2⁻ cells in seven cell types from COVID-19 patient samples. Epi - epithelial. (d) Overlap of the host factors with the differentially expressed genes from four bulk RNA-seq/proteomics datasets. (e,f) Biological characteristics of the SARS-CoV-2 host factors. The host factors have lower non-synonymous to synonymous substitutions (*dN/dS*) ratios (e) and lower evolutionary ratios (f) compared to random background (grey, mean ± standard deviation of 100 repeats using genes randomly selected by degree preserved node shuffling). Genes were sorted in ascending order in terms ofdN/dS ratio or evolutionary ratio.

**Extended Figure 2.. ZNF579 targets significantly overlap with the differentially expressed genes in SARS-CoV-2 infected patient samples.**
(a) Enriched KEGG pathways of genes associated with ZNF579 binding by ChIP-seq (ENCODE:ENCSR018MQH). Genes were considered to be bound by ZNF579 if a ChIP-seq peak overlapped with the promoter region (−1000 to transcription start site). (b) Overlap of ZNF579 targets and differentially expressed genes (DEGs) in bronchoalveolar lavage fluid (BALF) SARS-CoV-2⁺ vs. SARS-CoV-2⁻ samples. SeeMethods for the source of the single-cell dataset. Fisher’s exact tests show that the overlaps are significant (FDR < 0.05) for five cell types, including CD8, epithelial-ciliated, epithelial-secretory, macrophage, and monocyte. (c) The enriched pathways of the overlapping ZNF579 targets and DEGs in the five cell types. Pathways that are significantly enriched in at least two cell types are shown.

**Extended Figure 3.. Comparison of the drug screening results using different interactomes and their combinations.**
(a) 16 drugs identified by our interactome cannot be identified by any of the other three interactomes (and the interactome combined from them for 13 drugs) compared in this study. 6 of the top 23 drugs with desired anti-SARS-CoV-2 profiles are among these drugs. (b) Drugs identified by combining all four interactomes that could not be identified by any interactome individually. Three drugs (highlighted with a star) were found to have desired anti-SARS-CoV-2 profiles.

**Extended Figure 4.. Carvedilol indirectly targets the SARS-CoV-2 host factors through protein-protein interactions with its targets.**
(a) Individual target-level network proximities to the SARS-CoV-2 gene sets (all host factors, host factors for each viral protein, and gene sets by different functions from Reactome). Network proximities were computed using the “shortest” method (SeeMethods). (b) Potential mechanisms-of-action of carvedilol by exploring the protein-protein interactions of its targets and the SARS-CoV-2 host factors.

**Extended Figure 5.. Comparison of the drug screening results using different variations of the network proximity-based screening methods.**
(a) Network proximity-based drug screening using directed human protein-protein interactome vs. undirected human protein-protein interactome. (b) Network proximity-based drug screening using degree preserved edge shuffling vs. degree preserved node shuffling. PCC, Pearson correlation coefficient.

**Figure 1.. SARS-CoV-2-human protein interactome.**
(a) Pipelines using Y2H and AP/MS for detecting SARS-CoV-2-human protein-protein interactions. (b) Edges between viral proteins (diamonds) and human proteins (circles) represent protein-protein interactions. Edge colors indicate the methods used to detect the protein-protein interaction. Several biological processes that are significantly enriched in these human proteins (Fig. S2 and Table S2) are highlighted with yellow background. Human proteins that interact with only one SARS-CoV-2 protein are shown in the box connected to that specific protein. The interactome can be found in Table S1.

**Figure 2.. Characteristics of the interactome and validation of novel SARS-CoV-2-human interactions.**
(a) UpSet plot showing the overlap of SARS-CoV-2-human protein-protein interactions from four studies (Table S3). Each bar shows the interactions shared by only the marked studies at the bottom. Composition of each bar in terms of the source of the interactions are indicated by different colors.(b) Co-immunoprecipitation confirming ORF3a-ZNF579 interaction in HEK 293T cells following transfection with ORF3a-FLAG or empty vector.(c) Western blot showing levels of ZNF579 along with GAPDH as a loading control in 293T cells following transfection with ORF3a-FLAG or empty vector.(d) ChIP-seq for ZNF579 in MCF7 cells from the ENCODE consortium at theHSPA6 locus. Signal is log2 fold change over input. (e) Expression of HSPA6 after transfection with ORF3a-FLAG or empty vector. Two transfection replicates were probed with two primer pairs to HSPA6 at three different template dilutions in technical triplicate (18 total reactions for each condition). Expression is normalized to GAPDH and then to the empty vector average using the double-delta Ct method. (f-g) Co-immunoprecipitation confirming ORF7b-STT3A and ORF7b-Sec61 interactions in HEK293T cells following transfection with ORF7b-FLAG or empty vector and STT3A-MYC or Sec61-V5, respectively. (h) Co-immunoprecipitation confirming N-histone H1.4 interaction in HEK293T cells following transfection with N or empty vector and histone H1.4.

**Figure 3.. Discovery of interactome-based host-targeting therapies for COVID-19.**
(a) Workflow of drug repurposing for COVID-19 using our interactome. We ranked the drugs by their proximity to the SARS-CoV-2 host factors (Table S7), filtered the top drugs by their NCATS anti-SARS-CoV-2 profiles (Table S8), and finally analyzed their drug-outcome relationship using electronic health records (EHR) data (Table 1, Table S9–**S10**). (b) The top 23 drugs can target the SARS-CoV-2 host factors directly or through protein-protein interactions with their targets.

**Figure 4.. Population-based and experimental validation of interactome-predicted drugs.**
(**a-c**) Drug-outcome evaluation using the Northwestern Medicine Enterprise Data Warehouse (NMEDW) and Cleveland Clinic Foundation (CCF) COVID-19 databases. Odds ratio was used to evaluate the carvedilol effect to the positive laboratory test result of COVID-19. Patients were matched with propensity score using age, gender, race, and other comorbidities (Table 1) to reduce various confounding factors. (d) Experimental validation of the anti-SARS-CoV-2 activity of carvedilol showed an EC₅₀ value of 4.1 μM and low cell toxicity. EC₅₀, half maximal effective concentration; CC₅₀, half maximal cytotoxic concentration; SI, selectivity index (SI = CC₅₀/EC₅₀).

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This is a preprint.

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

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

Authors

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