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.2023 Jan;41(1):128-139.
doi: 10.1038/s41587-022-01474-0. Epub 2022 Oct 10.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Affiliations

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Yadi Zhou et al. Nat Biotechnol.2023 Jan.

Abstract

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.

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Conflict of interest statement

Competing interests

The authors declare no competing interests.

Figures

Extended Data Fig. 1 |
Extended Data Fig. 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 (gray, mean ± standard deviation of 100 repeats using genes randomly selected by degree preserved node shuffling). Genes were sorted in ascending order in terms of dN/dS ratio or evolutionary ratio.
Extended Data Fig. 2 |
Extended Data Fig. 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. See Methods 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 Data Fig. 3 |
Extended Data Fig. 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 Data Fig. 4 |
Extended Data Fig. 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 (See Methods). (b) Potential mechanisms-of-action of carvedilol by exploring the protein-protein interactions of its targets and the SARS-CoV-2 host factors.
Extended Data Fig. 5 |
Extended Data Fig. 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.
Fig. 1 |
Fig. 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 (Supplementary Fig. 2 and Supplementary Table 2) 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 Supplementary Table 1.
Fig. 2 |
Fig. 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 (Supplementary Table 3). 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-IP confirming ORF3a–ZNF579 interaction in HEK293T cells following transfection with ORF3a–FLAG or empty vector. The experiment was repeated independently three times with similar results.c, Western blot showing levels of ZNF579 along with GAPDH as a loading control in HEK293T cells following transfection with ORF3a–FLAG or empty vector. Experiment was repeated independently three times with similar results.d, ChIP–seq for ZNF579 in MCF7 cells from the ENCODE consortium at theHSPA6 locus. Signal is log2FC over input.e, Expression ofHSPA6 after transfection with ORF3a–FLAG or empty vector (E.V.). Two transfection replicates were probed with two primer pairs toHSPA6 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 ΔΔCt method. Box plots display the median as the center line, the 1st and 3rd quartiles as hinges and 1.5× interquartile range as whiskers. Significance was assessed using the two-tailed Mann WhitneyU test.f,g Co-IP 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. Each experiment was repeated independently three times with similar results.h, Co-IP confirming N-histone H1.4 interaction in HEK293T cells following transfection with N or empty vector and histone H1.4. Experiment was repeated independently three times with similar results.
Fig. 3 |
Fig. 3 |. Discovery of interactome-based host-targeting therapies for COVID-19.
a, Work flow of drug repurposing for COVID-19 using our interactome. We ranked the drugs by their proximity to the SARS-CoV-2 host factors (Supplementary Table 7), filtered the top drugs by their NCATS anti-SARS-CoV-2 profiles (Supplementary Table 8) and finally analyzed their drug–outcome relationship using EHR data (Table 1 and Supplementary Table 9,10).b, The top 23 drugs can target the SARS-CoV-2 host factors directly or through protein–protein interactions with their targets.
Fig. 4 |
Fig. 4 |. Population-based and experimental validation of interactome-predicted drugs.
a–c, Drug–outcome evaluation using the NMEDW and CCF COVID-19 databases. Odds ratio was used to evaluate the carvedilol effect to the positive laboratory test result of COVID-19. Statistically significant atP < 0.05 and odds ratio < 1 indicate that the carvedilol user with lower odds of COVID-19 positive testing. The central diamond box denotes the odds ratio and the error bar denotes the 95% confidence interval. Patients were matched with propensity score using age, sex, race and other comorbidities (Table 1) to reduce various confounding factors. Statistics derived from cohort details shown in Table 1.d, Experimental validation of the anti-SARS-CoV-2 activity of carvedilol showed an EC50 value of 4.1 μM and low cell toxicity. CC50, half-maximal cytotoxic concentration; SI, selectivity index (SI = CC50/EC50).
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