Shared host genetic landscape of respiratory viral infection

Abstract

Respiratory viruses represent a major global health burden. Although these viruses have different life cycles, they may depend on common host genetic factors, which could be targeted by broad-spectrum host-directed therapies. We used genome-wide CRISPR screens and advanced data analytics to map a network of host genes that support infection by nine human respiratory viruses [influenza A virus, parainfluenza virus, human rhinovirus, respiratory syncytial virus, human coronavirus (HCoV)-229E, HCoV-NL63, HCoV-OC43, Middle East respiratory syndrome-related coronavirus, and severe acute respiratory syndrome-related coronavirus 2]. We explored shared pathways using knowledge graphs to inform on pharmacological targets. We selected and validated STT3A/B proteins of the N-oligosaccharyltransferase complex as host targets of broad-spectrum antiviral small molecules. Our work highlights the commonalities of viral host genetic dependencies and the feasibility of using this information to develop broad-spectrum antiviral agents.

Keywords: CRISPR; functional genomics; genome-wide; host-directed therapy; respiratory virus.

Fig. 1.

Profile of individual CRISPR ko screen. Significance scores are presented as –log10(FDR) on the y-axis for genes distributed along the x-axis (ordered by gene rank). Top 10 genes by FDR in each screen are labeled (ties method, first). Points colored as hit enrichment (red) for proviral genes or depletion (blue) for antiviral genes at FDR < 0.05 threshold, no hit (gray), or known receptor hit (black). Note that the previously identified viral receptor is in the top 10 hits for each screen when known (ACE2, ANPEP, DPP4, and ICAM1). Panel legends correspond to Pathogen/Cell line/screening endpoint/time after infection.

Fig. 2.

Functional interaction network of protein hubs across respiratory viral CRISPR screens. This analysis used n = 1,363 genes that scored as positive hits for one or more pathogens. (A) We leverage the Reactome Functional Interactions gene–gene graph as our source of truth for the underlying biological network. In addition, we build a vector of numbers termed “CRISPR profiles” consisting of the minus log10 aggregated P-values and the graph embeddings. We study the network localization (hubs, pathways) and the similarities in common response to pathogen perturbations by means of the similarities of the CRISPR profiles. For clarity, and with few exceptions, we only label genes associated with four or more pathogens. (B–E) Knowledge graphs of molecular systems that are densely sampled in panel to provide additional information (protein–protein interaction between viral and host proteins, BioGRID data, genetic associations, and paralogue information). (B) V-ATPase, a multi-subunit enzyme that mediates acidification of eukaryotic intracellular organelles. (C) Oligomeric Golgi (COG) complex, a master regulator of membrane trafficking at the Golgi. (D) Hypusination, a two-step enzyme-mediated post-translational modification of the eukaryotic translation factor eIF5A. (E) N-linked glycosylation, membrane-associated enzyme complex OST. Additional densely sampled areas of the network are discussed in SI Appendix, Table S2.

Fig. 3.

Validation of STT3A and STT3B as targets of antiviral strategies. To confirm results from the network integration, we evaluated the effect on viral replication of genetic perturbation of (A) the extended OST complex, and (B) the catalytic subunits STT3A and STT3B. Virus infection levels or survival in STT3A or STT3B heterogenous knockout pools was compared to cells transduced with intergenic sgRNA controls (nontarget), sgRNAs targeting genes known to block infection and additional negative controls where indicated. Perturbation of STT3A and/or STT3B increased survival and/or reduced infection against HRV, IAV, PIV, and HCoV-229E. Data are mean ± SEM pooled from three to four independent experiments normalized to the average effect on the nontarget controls. *P < 0.05 compared to Nontarget #1 and Nontarget #2 determined by ANOVA with Tukey’s multiple comparison test. (C) NGI-1 inhibits the replication of multiple respiratory viruses. Dose–response curves of NGI-1 activity and effect on cell viability for glycosylation reporter in H1 Hela cells, PIV infection model in A549 cells, HCoV-299E infection model in MRC-5 cells, SARS-CoV2 infection model in A549 cells with ACE2/TMPRSS2 overexpression, IAV infection model in MDCK cells, HRV infection model in H1HeLa cells, or RSV infection model in HEp-2 cells. Mean values from at least two separate experiments were shown ± SD. Effective concentration (EC50) and cytotoxic concentration (CC50) values were calculated from nonlinear fit of dose–response curves (in the graphs). ND = not determined.