Cell host response to infection with novel human coronavirus EMC predicts potential antivirals and important differences with SARS coronavirus

Abstract

A novel human coronavirus (HCoV-EMC) was recently identified in the Middle East as the causative agent of a severe acute respiratory syndrome (SARS) resembling the illness caused by SARS coronavirus (SARS-CoV). Although derived from the CoV family, the two viruses are genetically distinct and do not use the same receptor. Here, we investigated whether HCoV-EMC and SARS-CoV induce similar or distinct host responses after infection of a human lung epithelial cell line. HCoV-EMC was able to replicate as efficiently as SARS-CoV in Calu-3 cells and similarly induced minimal transcriptomic changes before 12 h postinfection. Later in infection, HCoV-EMC induced a massive dysregulation of the host transcriptome, to a much greater extent than SARS-CoV. Both viruses induced a similar activation of pattern recognition receptors and the interleukin 17 (IL-17) pathway, but HCoV-EMC specifically down-regulated the expression of several genes within the antigen presentation pathway, including both type I and II major histocompatibility complex (MHC) genes. This could have an important impact on the ability of the host to mount an adaptive host response. A unique set of 207 genes was dysregulated early and permanently throughout infection with HCoV-EMC, and was used in a computational screen to predict potential antiviral compounds, including kinase inhibitors and glucocorticoids. Overall, HCoV-EMC and SARS-CoV elicit distinct host gene expression responses, which might impact in vivo pathogenesis and could orient therapeutic strategies against that emergent virus.

Importance: Identification of a novel coronavirus causing fatal respiratory infection in humans raises concerns about a possible widespread outbreak of severe respiratory infection similar to the one caused by SARS-CoV. Using a human lung epithelial cell line and global transcriptomic profiling, we identified differences in the host response between HCoV-EMC and SARS-CoV. This enables rapid assessment of viral properties and the ability to anticipate possible differences in human clinical responses to HCoV-EMC and SARS-CoV. We used this information to predict potential effective drugs against HCoV-EMC, a method that could be more generally used to identify candidate therapeutics in future disease outbreaks. These data will help to generate hypotheses and make rapid advancements in characterizing this new virus.

FIG 1

HCoV-EMC replicates at a level similar to that of SARS-CoV in human epithelial cells. Triplicate wells of Calu-3 2B4 cells were infected with HCoV-EMC (MOI, 5). Medium from each well was collected and analyzed by plaque assay for viral growth kinetics in VeroE6 cells. The corresponding cells were harvested for transcriptomic analysis. SARS-CoV titers after infection of Calu-3 2B4 cells at an MOI of 5 were determined using the same method (19). The error bars represent the standard deviations among triplicate cell samples.

FIG 2

HCoV-EMC induces more and different transcriptional changes than SARS-CoV at similar times postinfection. (A) Number of up-regulated (red) and down-regulated (green) differentially expressed (DE) genes after infection with HCoV-EMC and SARS-CoV compared to time-matched mock-infected controls. The criterion used for differential expression analysis is a q value of <0.01 as determined by Limma’s empirical Bayes moderated t test and a |log₂ FC| of >1. (B) Percentage of DE genes under the condition shown on the x axis that intersect with DE genes under the condition shown on the y axis. To identify overlap among genes changing in the same direction, up- and down-regulated signatures are intersected separately, and the average of the two percentages is shown by a white-to-blue gradient (0% to 100%).

FIG 3

HCoV-EMC massively dysregulates the host transcriptome at late times postinfection and triggers both similar and unique pathways compared to those induced by SARS-CoV. (A) Heatmap depicting the expression values of 12,392 genes DE after infection with HCoV-EMC at late times postinfection (union of DE genes at 18 and 24 hpi). Genes were clustered into four main sets: set I includes 1,599 genes that are significantly up-regulated after infection with both HCoV-EMC and SARS-CoV; set II includes 1,875 genes that are significantly down-regulated after infection with both HCoV-EMC and SARS-CoV; set III includes 3,922 genes that are significantly up-regulated after infection with HCoV-EMC but not DE with SARS-CoV; and set IV includes 4,996 genes that are significantly down-regulated after infection with HCoV-EMC but not DE with SARS-CoV. (B) For each of the four clusters, the top 5 enriched canonical pathways are reported. Enrichment score (ES) was defined as –log₁₀(P value) of enrichment. Red lines depict the limit of significance (P < 0.01).

FIG 4

The antigen presentation pathway is specifically down-regulated after infection with HCoV-EMC. (A) Temporal gene expression changes of the 22 genes belonging to the antigen presentation pathway specifically down-regulated after HCoV-EMC infection. (B) Temporal changes for the same set of genes after SARS-CoV infection. Note that the time scale and log ₂ FC ranges are not the same in panels A and B. (C) Quantitative RT-PCR measurement of the antigen presentation pathway genes expression following HCoV-EMC infection.

FIG 5

Expression values and major upstream regulators for the 207 genes dysregulated early and constantly after infection with HCoV-EMC. (A) Heatmap depicting the expression values of 207 genes whose expression changed early after infection with HCoV-EMC (at 0, 3, 7, and/or 12 hpi) and remained up- or down-regulated later in infection (18 and 24 hpi). The color key on the left indicates the direction of changes across infection, with red depicting genes significantly up-regulated at at least one time postinfection and green showing genes significantly down-regulated. Genes were clustered based on their expression values across samples using Pearson correlation and complete linkage function. (B) Top 5 activated upstream regulators and top 5 inhibited upstream regulators of the early signature. The prediction of activation state is based on the global direction of changes of the 207 genes throughout infection with HCoV-EMC. Red lines depict the limit of significance (|z score| > 2). (C) Analysis of HCoV-EMC and SARS-CoV replication following SB203580 pre- or posttreatment (5 µM). IFN-α posttreatment was used as a reference. *, P < 0.01(Student’s t test).