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. 2022 Mar 15;14(3):611.
doi: 10.3390/v14030611.

A BioID-Derived Proximity Interactome for SARS-CoV-2 Proteins

Danielle G May  1 , Laura Martin-Sancho  2 , Valesca Anschau  2 , Sophie Liu  3 , Rachel J Chrisopulos  1 , Kelsey L Scott  1 , Charles T Halfmann  1 , Ramon Díaz Peña  2 , Dexter Pratt  3 , Alexandre R Campos  2 , Kyle J Roux  1   4
Affiliations

A BioID-Derived Proximity Interactome for SARS-CoV-2 Proteins

Danielle G May et al. Viruses. .

Abstract

The novel coronavirus SARS-CoV-2 is responsible for the ongoing COVID-19 pandemic and has caused a major health and economic burden worldwide. Understanding how SARS-CoV-2 viral proteins behave in host cells can reveal underlying mechanisms of pathogenesis and assist in development of antiviral therapies. Here, the cellular impact of expressing SARS-CoV-2 viral proteins was studied by global proteomic analysis, and proximity biotinylation (BioID) was used to map the SARS-CoV-2 virus-host interactome in human lung cancer-derived cells. Functional enrichment analyses revealed previously reported and unreported cellular pathways that are associated with SARS-CoV-2 proteins. We have established a website to host the proteomic data to allow for public access and continued analysis of host-viral protein associations and whole-cell proteomes of cells expressing the viral-BioID fusion proteins. Furthermore, we identified 66 high-confidence interactions by comparing this study with previous reports, providing a strong foundation for future follow-up studies. Finally, we cross-referenced candidate interactors with the CLUE drug library to identify potential therapeutics for drug-repurposing efforts. Collectively, these studies provide a valuable resource to uncover novel SARS-CoV-2 biology and inform development of antivirals.

Keywords: BioID; COVID-19; SARS-CoV-2; TurboID; interactome; proximity labeling.

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Conflict of interest statement

Sanford Research has licensed BioID reagents to BioFront Technologies. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
BioID2-viral fusion protein expression in A549 cells. (A) Viral proteins were fused to either the N- or C-terminus of the BioID2 promiscuous biotin ligase. The schematic shows the orientation of NSPs (yellow), structural proteins (green), and ORF proteins (purple) fused to BioID2 to scale. (B) A549 human lung cells stably expressing BioID2-fusion proteins were assessed for fusion-protein expression and localization (red) and promiscuous biotinylation (green) following the addition of exogenous biotin. Scale bar 10 µm.
Figure 2
Figure 2
Examples of COVID-19 Proteomics website functionality. (A) Volcano plot analysis of changes in global protein abundance. (B) Half volcano plots showing enriched PPI candidates following BioID method. (C) Functional enrichment analysis of PPI candidates. (D) Protein–drug tree for viral proteins, PPI candidates, and known drug interactors.
Figure 3
Figure 3
Analysis of proteins influenced by expression of viral structural E, M, and N proteins. (A) Volcano plot visualization of global changes in host-protein expression in cells expressing viral proteins Envelope (E), Membrane (M), or Nucleocapsid (N). Protein identifications are available on the COVID-19 Proteomics Resource website. (B) Metascape analysis of significantly upregulated proteins including enriched terms, cluster visualization, and interactions. (C) Metascape analysis of significantly downregulated proteins including enriched terms, cluster visualization, and interactions.
Figure 4
Figure 4
Network analysis of SARS-CoV-2 interactors. (A) The network containing the 876 identified SARS-CoV-2 interactors was subjected to supervised community detection, and the resulting hierarchy is shown. Each node represents a cluster of interconnected proteins and each edge (marked by an arrow) represents containment of one community (target) by another (source). Indicated are enriched biological processes as determined by gProfiler. (B–E) Asterisks (*) denote selected zoom-in insets from the hierarchy. Nodes represent human proteins, and edges are interactions from STRING.
Figure 5
Figure 5
Enriched pathway analysis of PPIs for selected SARS-CoV-2 viral baits. (A–D). High-confidence associations between indicated SARS-CoV-2 proteins (hexagons) and human proteins (circles/nodes). Node color is proportional to the p value (the darkest, the lowest the p value). Human–human interactions as determined by STRING are represented by dashed edges. Human–viral interactions are indicated with solid edges, and their thickness are proportional to the log2FC (the thickest, the highest log2FC).
Figure 6
Figure 6
Integrated bioinformatics analysis of datasets from cells expressing individual RDRP-complex viral proteins. (A) The network containing the 123 proteins identified as NSP7/8/12 unique interactors, globally upregulated, or globally downregulated was subjected to supervised community detection and the resulting hierarchy is shown. Each node represents a cluster of interconnected proteins and each edge (marked by an arrow) represents containment of one community (target) by another (source). Indicated are enriched biological processes as determined by gProfiler. (B) Gene ontology enrichment of biological processes performed by unique host-proteins identified in proximity labeling studies for NSP7/8/12.
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