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Cell Host Response to Infection with Novel Human Coronavirus EMC
Predicts Potential Antivirals and Important Differences with SARS
Coronavirus
Laurence Josset,
a
Vineet D. Menachery,
b,c
Lisa E. Gralinski,
b,c
Sudhakar Agnihothram,
b,c
Pavel Sova,
a
Victoria S. Carter,
a
Boyd L. Yount,
b,c
Rachel L. Graham,
b,c
Ralph S. Baric,
b,c
Michael G. Katze
a
Department of Microbiology, School of Medicine, University of Washington, Seattle, Washington, USA
a
; Department of Epidemiology
b
and Department of Microbiology
and Immunology,
c
University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
L.J. and V.D.M. contributed equally to this work.
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 postin-
fection. 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 infec-
tion with HCoV-EMC, and was used in a computational screen to predict potential antiviral compounds, including kinase inhib-
itors and glucocorticoids. Overall, HCoV-EMC and SARS-CoV elicit distinct host gene expression responses, which might im-
pact 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 hy-
potheses and make rapid advancements in characterizing this new virus.
Received 5 March 2013 Accepted 12 April 2013 Published 30 April 2013
Citation Josset L, Menachery VD, Gralinski LE, Agnihothram S, Sova P, Carter VS, Yount BL, Graham RL, Baric RS, Katze MG. 2013. Cell host response to infection with novel
human coronavirus EMC predicts potential antivirals and important differences with SARS coronavirus. mBio 4(3):e00165-13. doi:10.1128/mBio.00165-13.
Invited Editor Michael Buchmeier, University of California, Irvine Editor Michael Buchmeier, University of California, Irvine
Copyright © 2013 Josset et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported
license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
Address correspondence to Michael G. Katze, honey@u.washington.edu.
In September 2012, a novel human coronavirus, HCoV-EMC,
was reported to health authorities from two cases of acute respi-
ratory syndrome with renal failure (1–4). The most recent update
by the WHO identified a total of 17 confirmed cases of human
infection, including 11 deaths, suggesting a mortality rate of ~65%
(5). All of these individuals had a history of recent travel to the
Middle East. Identification of clusters of coronavirus cases indi-
cates that HCoV-EMC can be transmitted from human to human
(6) and raises concern about a possible outbreak of this virus,
similar to the one caused by a related virus, the severe acute respi-
ratory syndrome-related coronavirus (SARS-CoV), in 2002–2003.
SARS-CoV, originating in China, spread throughout Asia and to
other continents and affected more than 8,000 people (7, 8). The
overall mortality during the outbreak was estimated at 9.6% (7).
While the mortality rate of HCoV-EMC cannot be assessed with
certainly, it could be more pathogenic than SARS-CoV.
HCoV-EMC belongs to the genus betacoronavirus, as does
SARS-CoV. However, HCoV-EMC is more closely related to the
bat coronaviruses HKU4 and HKU5 (lineage 2C) than it is to
SARS-CoV (lineage 2B) (2, 9). Less than 50% amino acid sequence
identity is conserved in the replicase domains between SARS-CoV
and HCoV-EMC. Another important difference between the two
viruses is that they do not use the same host cell receptor for
infection (10). Indeed, it was clearly shown that human
angiotensin-converting receptor 2 (hACE2), used by SARS-CoV,
is not the HCoV-EMC receptor (10). Dipeptidyl peptidase 4 was
recently identified as the HCoV-EMC receptor (11). This receptor
is conserved among different species such as bats and humans,
RESEARCH ARTICLE
May/June 2013 Volume 4 Issue 3 e00165-13 ®mbio.asm.org 1
partially explaining the large host range of HCoV-EMC. This was
somewhat surprising, as coronaviruses generally show strict host
specificity.
While recent identification of the crystal structure of HCoV-
EMC protease suggests that a wide-spectrum CoV protease inhib-
itor could block the catalytic site (12), there is currently no proven
antiviral treatment for HCoV-EMC. Viruses rely on host factors to
replicate and often hijack cellular processes initiated in response
to infection to ensure efficient replication (13). Targeting cellular
responses has been shown to inhibit viral replication (13, 14).
Furthermore, immunomodulatory drugs that reduce the exces-
sive host inflammatory response to respiratory viruses, as seen
with influenza virus infections, have therapeutic benefit (reviewed
in reference 15). Several genome-based drug repurposing strate-
gies successfully identified known drugs that could be reused to
treat lung cancer, inflammatory bowel disease (16), and influenza
virus infection (14). Such an approach has the advantage of accel-
erating treatment availability, which could be crucial in case of an
outbreak of an emerging pathogen.
Overall, differences in viral sequences, host cell receptor, and
host range indicate that HCoV-EMC and SARS-CoV may have
distinct strategies for interacting with their hosts. This fact could
impact treatment strategies. To begin to assess this question, we
compared the host response of human cells to HCoV-EMC and
SARS-CoV infection using global transcriptomic profiling. Our
goal was to gain a rapid and comprehensive assessment of the host
response to HCoV-EMC infection that could guide research on
this emerging virus. Importantly, we used this information to
computationally predict antiviral treatment and identified a
broad down-regulation of the antigen presentation pathway that
may be important in vivo for the development of an adaptive im-
mune response.
RESULTS
SARS-CoV and HCoV-EMC have similar replication kinetics
but different cytopathic effects in a human epithelial cell line.
To characterize HCoV-EMC, we sought a cell line that mimics the
human airway. Calu-3 cells, derived from epithelial cells lining the
human conducting airway, can be differentiated into polarized
ciliated cells and permit robust replication of several respiratory
viruses, including SARS-CoV, influenza A virus, and respiratory
syncytial virus (RSV) (17–20). Therefore, Calu-3 cells were in-
fected with HCoV-EMC using a multiplicity of infection (MOI) of
5, and results were compared with those for Calu-3 cells infected
with SARS-CoV Urbani at the same MOI. The viruses replicated
to similar levels, with both peaking at ⬎10
7
PFU at 24 h postinfec-
tion (hpi) (Fig. 1). While SARS-CoV has been previously shown to
maintain steady replication and cell viability to 72 hpi (19),
HCoV-EMC induced substantial cytopathic effect at 18 to 24 hpi,
with significant cell rounding and detaching. Together, these data
suggest that although HCoV-EMC and SARS-CoV exhibit similar
replication kinetics, they elicit different host responses in lung
epithelial cells.
HCoV-EMC induces earlier and different transcriptional
changes than SARS-CoV. To assess the global host transcriptional
response following infection with HCoV-EMC, samples of in-
fected Calu-3 cells were collected throughout a 24-h time course
postinfection. Genes that were differentially expressed (DE) com-
pared to time-matched mock-infected controls were determined
using the statistical cutoff of a qvalue of ⬍0.01 and an absolute
log
2
(FC) of ⬎1. These genes were compared to DE genes from
Calu-3 cells infected with SARS-CoV across a 72-h time course
using the same statistical cutoff (Fig. 2).
As previously observed (19), SARS-CoV is able to replicate
actively with a surprisingly small number of significant transcrip-
tional changes before 24 hpi (from 5 DE genes at 0 hpi to 47 genes
at 12 hpi) (Fig. 2A). Similarly, HCoV-EMC induced fewer tran-
scriptional changes before 12 hpi than after. However, HCoV-
EMC replication at early times postinfection induced more
changes than SARS-CoV, with the number of DE genes ranging
from 28 at 0 hpi to 206 genes at 12 hpi (Fig. 2A). At later times
postinfection, a massive host response was observed during
HCoV-EMC infection, with 6,532 DE genes at 18 hpi and 11,664
genes at 24 hpi, while SARS-CoV induced changes of only 792
genes at 24 hpi with maximum changes at 48 and 54 hpi of 6,496
and 6,498 genes, respectively.
To evaluate the similarity of transcriptional dysregulation be-
tween HCoV-EMC and SARS-CoV, we determined the percent-
age of overlap among DE genes changing in the same direction at
each time point (Fig. 2B). The intersection between up- or down-
regulated DE genes for each condition was calculated separately
and then averaged to determine the percentage of intersecting
genes (Fig. 2B). The overlap between signatures at late times
postinfection for a single virus was very high; for example, 89% of
the genes DE by HCoV-EMC at 18 hpi were also DE at 24 hpi, and
on average 77% of the DE genes at 24 hpi with SARS-CoV were
also DE at later times postinfection. However, the overlap between
SARS-CoV and HCoV-EMC at the same time point was low; for
example, only 3% of the DE genes in response to HCoV-EMC at
24 hpi were also DE by SARS-CoV at this time point. Of note, the
intersection of DE genes between the two infections was higher
when later times postinfection for SARS-CoV were compared
with earlier time points for HCoV-EMC. On average 22% of the
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.
Josset et al.
2®mbio.asm.org May/June 2013 Volume 4 Issue 3 e00165-13
HCoV-EMC signatures from 12 to 24 hpi overlapped the signa-
tures of SARS-CoV from 36 to 72 hpi. Together, these results
indicate that HCoV-EMC induced both a more robust and a
largely different host response compared to that induced by
SARS-CoV at similar times postinfection. However, changes in-
duced from 12 to 24 hpi after HCoV-EMC were more similar to
changes induced late after SARS-CoV infection (24 to 72 hpi).
HCoV-EMC massively dysregulates the host transcriptome
at late times postinfection. At late times postinfection, HCoV-
EMC induced drastic changes in the host transcriptome with
12,392 DE genes at 18 hpi and/or 24 hpi. To characterize this late
signature, we compared the log
2
fold change (log
2
FC) expression
values of these 12,392 genes after infection with SARS-CoV or
HCoV-EMC (Fig. 3 and www.systemsvirology.org). There were
3,474 genes (28%) expressed similarly to those in SARS-CoV-
infected samples at the same or later times postinfection and 8,918
genes (72%) expressed differently than during SARS-CoV infec-
tion. These genes were further clustered according to their expres-
sion pattern in HCoV-EMC infected cells (clusters I and III con-
tain genes up-regulated after HCoV-EMC infection at 18 to
24 hpi; clusters II and IV contain down-regulated genes). Enrich-
ment in canonical pathways for each of this cluster was performed
and is shown Fig. 3B.
Genes that were up-regulated by both HCoV-EMC and SARS-
CoV (cluster I), though later by SARS-CoV than by HCoV-EMC,
were primarily related to viral recognition and the activation of
innate immune pathways: the IL-17-related pathway and activa-
tion of interferon regulatory factor (IRF) by cytosolic pattern rec-
ognition receptors. Genes down-regulated by both viruses (cluster
II) were related to metabolism.
Genes specifically up-regulated by HCoV-EMC were statisti-
cally enriched in only 2 pathways: cAMP-mediated signaling and
protein ubiquitination. Because ubiquitination is involved in the
innate immune antiviral response (21), this could represent an
interesting mechanism to explore further. Finally, genes specifi-
cally down-regulated after HCoV-EMC infection were largely re-
lated to the antigen presentation pathway. In addition, the other
identified canonical pathways are strongly related to lymphocyte
signaling processes. Together, these data suggest that HCoV-EMC
may quickly move to interfere with elements of the adaptive im-
mune response, in contrast to SARS-CoV.
The antigen presentation pathway is broadly down-
regulated after HCoV-EMC infection. As down-regulation of the
antigen presentation pathway may have important implications
for the development of the adaptive response, we more closely
examined changes in this pathway after infection with HCoV-
EMC (Fig. 4). Twenty-two genes related to major histocompati-
bility complex (MHC) class I, MHC class II, or antigen presenta-
tion were found to be down-regulated after HCoV-EMC infection
(Fig. 4A), while in contrast, the vast majority of these genes were
up-regulated by SARS-CoV infection after 36 hpi (Fig. 4B). Inter-
estingly, transcriptional regulators of MHC class I (NLRC5) and
MHC class II (CIITA) were transiently up-regulated early after
HCoV-EMC infection (at 12 and 0 hpi, respectively). Other genes
were down-regulated starting at 12 hpi. Importantly, both MHC
class I genes (HLA-A,-B,-C,-E, and -G) and class II genes (HLA-
DMB,-DPA1,-DPB1,-DQA1,-DRA,-DRB1,-DRB3,-DRB4, and
-DRB5) had decreased expression values after infection. Several
genes from the MHC I pathway, including proteasome genes
(PSMB8 and PSMB9), genes from the peptide-loading complex
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 qvalue of ⬍0.01 as determined by Limma’s empirical Bayes moderated ttest and a |log
2
FC| of ⬎1. (B)
Percentage of DE genes under the condition shown on the xaxis that intersect with DE genes under the condition shown on the yaxis. 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 bya
white-to-blue gradient (0% to 100%).
Host Response to HCoV-EMC
May/June 2013 Volume 4 Issue 3 e00165-13 ®mbio.asm.org 3
(PDIA3 and TAPBP), and B2M, were down-regulated, as well as
the gene for the invariant chain (CD74), which belongs to the
MHC II pathway. Down-regulation of genes within MHC I and II
pathways was confirmed by quantitative reverse transcription-
PCR (RT-PCR), with all 9 genes tested having significantly de-
creased expression in HCoV-EMC samples (Fig. 4C). Together,
the data show that HCoV-EMC and SARS-CoV induce opposite
regulation of the entire antigen presentation pathway.
Early and sustained transcriptional changes may be reverted
by kinase inhibitors and glucocorticoids. With the goal of iden-
tifying possible drugs that will modulate the host response
throughout infection and from early times postinfection, we fo-
cused on characterizing the early transcriptional changes induced
by HCoV-EMC that remained stable throughout the infection.
Among the 348 genes DE early after infection (at 0, 3, 7, and/or
12 hpi), 207 (59%) remained dysregulated, with the same pattern
at later times postinfection. We chose to focus on this 207-gene
signature to avoid transient events that were specific to one time
postinfection and to exclude genes with inconsistent expression
patterns across the course of infection. Expression values for these
genes are displayed in Fig. 5A and are available at www.systems
virology.org. This early signature was enriched in genes related to
the inflammatory response with IL-17, tumor necrosis factor 2
(TNFR2)-related pathways, and predicted activation of che-
motaxis of leukocytes (see Table S1 in the supplemental material).
None of the 207 genes were DE early after SARS-CoV infection;
−40 4
Log2FC
ES
Activation of IRF by Cytosolic Pattern Recognition Receptors
Hematopoiesis from Pluripotent Stem Cells
Diff. Reg. of Cytokine Prod. in Mac. and Th by IL−17A and IL−17F
Role of IL−17F in Allergic Inflammatory Airway Diseases
Role of IL−17A in Arthritis
Ethanol Degradation II
Superpathway of Cholesterol Biosynthesis
Noradrenaline and Adrenaline Degradation
Serine Biosynthesis
Xenobiotic Metabolism Signaling
Protein Ubiquitination Pathway
cAMP−mediated signaling
Type II Diabetes Mellitus Signaling
G−Protein Coupled Receptor Signaling
Hepatic Cholestasis
Antigen Presentation Pathway
Allograft Rejection Signaling
Cytotoxic T Lymphocyte−mediated Apoptosis of Target Cells
Wnt/β−catenin Signaling
Nur77 Signaling in T Lymphocytes
I
02468
II
02468
III
02468
IV
02468
B
HCoV-EMC SARS-CoV
I
II
III
IV
A
03712
18 24 0371224
30 36 48 54 60 72 hpi
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 –log
10
(Pvalue) of enrichment. Red lines depict the limit of significance (P⬍0.01).
Josset et al.
4®mbio.asm.org May/June 2013 Volume 4 Issue 3 e00165-13
however, expression of 51 genes (24.5%) was changed after 24 hpi.
This subset of 51 genes was enriched in cell viability molecules,
glucocorticoid receptor signaling, and IL-17-related pathways.
In an effort to identify potential drugs that could block the host
response to HCoV-EMC, we used the early 207-gene signature
and IPA (Ingenuity pathway analysis) upstream regulator analysis
(see Materials and Methods) to identify the upstream regulators
that may be responsible for gene expression changes observed
early after infection. Upstream regulators are defined as any mol-
ecule that can affect the expression of another molecule, including
genes
B2M
CD74
CIITA
HLA−A
HLA−B
HLA−C
HLA−DMB
HLA−DPA1
HLA−DPB1
HLA−DQA1
HLA−DRA
HLA−DRB1
HLA−DRB3
HLA−DRB4
HLA−DRB5
HLA−E
HLA−G
NLRC5
PDIA3
PSMB8
PSMB9
TAP BP
−3
−2
−1
0
1
2
Log2 FC
hpi
A
−2
−1
0
1
2
hpi
Log2 FC
B
01020
30 40 60 70500 5 10 15 20
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.
Host Response to HCoV-EMC
May/June 2013 Volume 4 Issue 3 e00165-13 ®mbio.asm.org 5
transcription factors, cytokines, micro-RNAs and drugs. The ac-
tivation state for each regulator was predicted based on global
direction of changes throughout infection for previously pub-
lished targets of this regulator. The predicted top five activated
and top five down-regulated regulators are shown in Fig. 5B. The
top five activated upstream regulators included genes and mole-
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 (|zscore| ⬎2). (C) Analysis of HCoV-EMC and SARS-CoV replication following SB203580 pre- or posttreatment (5
M).
IFN-
␣
posttreatment was used as a reference. *, P⬍0.01(Student’s ttest).
Josset et al.
6®mbio.asm.org May/June 2013 Volume 4 Issue 3 e00165-13
cules that are known to induce the activation of inflammatory
genes that were also found up-regulated in the early signature. The
top predicted inhibited regulators included four kinase inhibitors
(SB203580, LY294002, U0126, and SP600125) and one corticoste-
roid (triamcinolone acetonide). These molecules are known to
down-regulate the same genes that were up-regulated early and
across HCoV-EMC infection. In addition, several kinase inhibi-
tors (including SB203580, LY294002, and U0126) and a glucocor-
ticoid (dexamethasone) were also predicted to be negative regula-
tors of genes changed similarly after SARS-CoV and HCoV-EMC
infection at late times postinfection (see Table S2 in the supple-
mental material). Therefore, we hypothesize that treating cells
with these drugs might partially revert CoV signatures and inhibit
deleterious host responses and/or viral replication in the case of
both SARS-CoV and HCoV-EMC.
In silico and in vitro evidence for kinase inhibitors as poten-
tial anti-CoV. To test these hypotheses, we first analyzed whether
cells treated with identified kinase inhibitors have gene expression
profiles opposite to the one induced after viral infection. To this
end, we used Connectivity Map (cmap), which is a database of
more than 1,309 drug transcriptional signatures in several cell
lines (22). This data-driven tool allows the identification of mol-
ecules that induce similar or opposite transcriptional changes rel-
ative to the query signature, based on their connectivity scores.
The connectivity score is a value between ⫹1 and ⫺1, where a high
positive score indicates that the drug induces changes similar to
those induced by viral infection, while a high negative score indi-
cates that the drug reverses the expression of the HCoV-EMC
signature. Cmap includes transcriptional profiles of several kinase
inhibitors, including two compounds that were predicted to be
potential negative regulators of viral response in IPA upstream
regulator analysis (SB203580 and LY294002). SB203580 had a
connectivity score of ⫺0.733 in MCF7 cells (Data set S1 in the
supplemental material), and LY294002 had negative scores in 4 of
the 5 cell lines tested in cmap (⫺0.281 in HL60, ⫺0.146 in MCF7,
⫺0.252 in PC3, and ⫺0.811 in SKMEL5), indicating that these
two drugs reversed the expression profile of HCoV-EMC signa-
ture. Other related kinase inhibitors present in cmap are U0125 (a
derivative of U0126) and SB202190, which both had negative
scores in PC3 cells (⫺0.649 and ⫺0.406, respectively).
To further validate the potential effectiveness of identified ki-
nase inhibitors, we evaluated the ability of the top predicted neg-
ative upstream regulator, SB203580, to interfere with viral repli-
cation (Fig. 5C). Importantly, this kinase inhibitor was predicted
to regulate genes that were DE similarly by SARS-CoV and HCoV-
EMC at late times postinfection (see Table S2 in the supplemental
material) and could therefore inhibit both viruses’ replication.
Treating cells with 5
M SB203580 following SARS-CoV infection
resulted in a significant decrease of 20% in the log
10
viral titer at
24 hpi (P⬍0.01); these replication levels are similar to those in
interferon (IFN)-treated cells. SB203580 was less effective against
EMC-CoV, showing no efficacy with posttreatment; however,
pretreatment of infection resulted in significant decreases of 15%
and 7% of the log
10
viral titer, at 24 and 48 hpi, respectively (P⬍
0.01). Efficacy against SARS-CoV can be explained by the fact that
all genes that were DE after SARS-CoV infection were predicted to
be regulated by kinase inhibitors (cluster I and II genes [Fig. 3] and
additional genes specific to SARS-CoV [data not shown]), while
the 8,918 genes specific to HCoV-EMC (clusters III and IV
[Fig. 3]) did not include any kinase inhibitor in their upstream
negative regulators (see Table S2). In addition, host response dy-
namics to SARS-CoV and HCoV-EMC were different, with a de-
layed and more limited response to SARS-CoV allowing
SB203580 posttreatment to be effective. In contrast, EMC-CoV
rapidly induced host expression changes and thus required
SB203580 pretreatment for measurable effect. As genes specific to
HCoV-EMC were predicted to be negatively regulated by other
classes of molecules (like gemfibrozil, which targets PPAR
␣
;z⫽
2.96), it will be important to determine whether using such mol-
ecules in combination with kinase inhibitors like SB203580 could
provide a better inhibition of viral replication. Together, these
data demonstrate the efficacy of SB203580 against two unique
CoVs and validate in silico approaches to predict effective drug
treatment for emergent viruses.
DISCUSSION
HCoV-EMC was isolated from a patient who died from an acute
respiratory disease similar to that caused by SARS-CoV. However,
there are several indicators that the host responses to these two
viruses may be significantly different. Several cases of HCoV-EMC
infection have resulted in renal failure, which has rarely been ob-
served in SARS-CoV infection. In addition, SARS-CoV and
HCoV-EMC do not use the same cell receptor, and there are im-
portant differences in their genomic sequences. This study adds
strength to the assertion that “HCoV-EMC is not the same as
SARS-CoV” (23). Indeed, even though we identified specific char-
acteristics of the SARS-CoV response in the HCoV-EMC signa-
tures, HCoV-EMC induced robust and specific transcriptional re-
sponses that were distinct from those induced by SARS-CoV,
including the broad down-regulation of MHC molecules.
This study is the first global transcriptomic analysis of the cel-
lular response to HCoV-EMC infection. Kindler et al. performed
RNA-Seq on human airway epithelium (HAE) cells infected with
HCoV-EMC (24). However, their analysis was focused on viral
sequences and did not include a genome-wide analysis of the host
response. They did, however, use RT-qPCR (quantitative PCR) to
compare expression levels of a set of 15 genes, including IFN, RNA
sensor molecules, and IFN-stimulated genes (ISGs), following in-
fection with HCoV-EMC, SARS-CoV, or HCoV-229E (MOI 0.1).
In our study, we confirm that SARS-CoV and HCoV-EMC induce
a similar up-regulation of RNA sensor molecules, such as RIGI,
MDA5, and two of three genes of ISGF3 (IRF9 and STAT1) (genes
in cluster I [Fig. 3]). Of note, HCoV-EMC titers were up to 10
2
-
fold higher than those of SARS-CoV in HAE cells (24), whereas we
observed similar viral replication of the two CoVs in Calu-3 cells.
Lower replication of SARS-CoV in HAE cells might be explained
by the mixed cell population in these primary cultures, with likely
nonuniform expression of SARS-CoV receptor (ACE2). In con-
trast, Calu-3 2B4 cells used in our study are a clonal population of
Calu-3 cells sorted for ACE2 expression which support high rep-
lication of SARS-CoV. In addition, while Kindler et al. noted the
absence of induction of IFN-

at 3, 6, and 12 hpi (24), we found a
specific up-regulation of IFN-
␣
5and IFN-

1by HCoV-EMC at
18 and 24 hpi (genes in cluster III) and an up-regulation of IFN-
␣
21 by both SARS-CoV and HCoV-EMC at 24 hpi (cluster I)
(expression values for all DE genes are available at http://www
.systemsvirology.org). These data illustrate that HCoV-EMC and
SARS-CoV both trigger the activation of pattern recognition re-
ceptors but may subsequently induce different levels of IFN.
Moreover, there were stark differences in global downstream ISG
Host Response to HCoV-EMC
May/June 2013 Volume 4 Issue 3 e00165-13 ®mbio.asm.org 7
expression following infection with SARS-CoV or HCoV-EMC;
this analysis is discussed in detail elsewhere (V. D. Menachery et
al., submitted for publication).
Activation of similar innate viral-sensing pathways by HCoV-
EMC and SARS-CoV is not surprising given the conservation of
this mechanism to detect foreign RNA and familial relationships
of the viruses. We also found that both viruses induced proinflam-
matory cytokines related to IL-17 pathways. It has previously been
shown that IL-17A-related gene expression exacerbates severe re-
spiratory syncytial virus (RSV) or influenza virus infection (25,
26). IL-17A was predicted to be activated throughout infection
with HCoV-EMC and may induce immune-mediated pathology
that possibly contributes to a high mortality rate. IL-17A is known
to be produced by T-helper cells, but its expression in Calu3 cells
was increased up to 2-fold at 24 hpi after HCoV-EMC infection.
Interestingly, IL-17C and IL-17F, which can be produced by epi-
thelial cells under certain inflammatory conditions and which ac-
tivate pathways similar to IL-17A-mediated responses (27), were
increased earlier and to a greater extent following HCoV-EMC
infection (up to 3-fold at 18 hpi for IL-17C and 4-fold at 7 hpi for
IL-17F). Therefore, further study of the IL-17 response may pro-
vide interesting targets to limit lung injury (26).
A main difference between responses to HCoV-EMC and
SARS-CoV was the specific down-regulation of the antigen pre-
sentation pathway after HCoV-EMC infection. In contrast, these
genes were found to be up-regulated after SARS-CoV infection.
Several viruses have evolved mechanisms to inhibit both the MHC
class I (reviewed in references 28 and 29) and class II (reviewed in
reference 30) pathways. While expression of MHC class II is usu-
ally limited to professional antigen-presenting cells, human lung
epithelial cells constitutively express this complex (31). Our data
demonstrated down-regulation of the MHC class II transactivator
(CIITA) after HCoV-EMC infection, a finding that possibly ex-
plains decreases in MHC class II molecule expression; this is a
common viral strategy used to block that pathway (30). MHC
class II inhibition can prevent class II-mediated presentation of
endogenous viral antigens produced within infected cells and im-
pair the adaptive immune response. Similarly, MHC class I genes
were also down-regulated after HCoV-EMC infection; decreasing
expression of MHC class I can attenuate CD8 T-cell-mediated
recognition of infected cells and could allow immune evasion by
HCoV-EMC. Finally, PSMB8 and PSMB9, parts of the immuno-
proteasome, were also down-regulated by HCoV-EMC; these
components replace portions of the standard proteasome and en-
hance production of MHC class I binding peptides (32). In their
absence, proteins targeted for degradation may not generate pep-
tides that robustly bind MHC class I, thus limiting their presenta-
tion. Down-regulation of PSMB8 and PSMB9 could counteract
the host response to viral infection, including up-regulation of
ubiquitins and ubiquitin ligases observed during HCoV-EMC in-
fection (Fig. 3B) that may ineffectively target viral protein for deg-
radation. Together, the inhibition of MHC class I and II as well as
immunoproteasome construction may have an important impact
on the in vivo adaptive immune response against HCoV-EMC.
While there is no proven effective antiviral therapy against
SARS-CoV (33), several molecules have in vitro antiviral activity,
including ribavirin, lopinavir, and type I IFN, but their benefits for
patients are unclear (33). IFN-
␣
pretreatment of cells has been
shown to inhibit HCoV-EMC replication (24), but no direct an-
tiviral therapies have been reported. Targeting host factors impor-
tant for the virus, instead of the virus itself, has been investigated
for HIV (34) and influenza virus (13). For example, inhibiting
upstream regulators (such as NF-
B) that control the host re-
sponse to influenza virus infection has been shown to reduce virus
replication in vitro and in mice (35). Inhibition of immunophilins
that interact with the viral nonstructural protein 1 (Nsp1) resulted
in potent inhibition of SARS-CoV replication (36, 37). In this
study, we characterized upstream regulators predicted to be acti-
vated (e.g., NF-
B and IL-17, which could be targeted with spe-
cific inhibitors) and upstream regulators predicted to be inhib-
ited.
The top five inhibited regulators included one glucocorticoid
and four kinase inhibitors; these drugs may be able to directly
block part of the host response and impact viral replication/patho-
genesis. Among them, LY294002, a potent inhibitor of phospha-
tidylinositol 3 kinase (PI3K), has known antiviral activity, inhib-
iting the replication of influenza virus (38), vaccinia virus (39),
and HCMV (40). SB203580, an inhibitor of p38 MAPK, is also an
effective antiviral against the encephalomyocarditis virus (41),
RSV (42), and HIV (43). LY294002 and SB203580 were also iden-
tified in Connectivity Map, a database of drug-associated gene
expression profiles (22), as molecules reversing components of the
HCoV-EMC gene expression signature. Finally, SB203580
showed promising antiviral results against both HCoV-EMC and
SARS-CoV in our in vitro assay (Fig. 4C). Further extensive stud-
ies, including dose-response tests and tests of other kinases inhib-
itors, are ongoing. Nonetheless, these results validate our genome-
based drug prediction, which allows rapid identification of
effective antivirals. Despite central roles of PI3K and MAPK path-
ways in regulating multiple cellular processes, many kinase inhib-
itors targeting these pathways have been shown to be safe and well
tolerated in vivo (reviewed in references 44 and 45). It has been
hypothesized that mitogenic MAPK and survival PI3K/Akt path-
ways may be of major importance only during early development
of an organism and may be dispensable in adult tissues (13). Sev-
eral drugs targeting JNK, PI3K, and MEK have shown promising
therapeutic potential in humans against a variety of diseases, in-
cluding cancer and inflammatory disorder (44, 45). p38 MAPK
inhibitors have also been evaluated in humans, but the first gen-
eration of molecules, including SB203580, has a high in vivo tox-
icity (liver and/or central nervous system). However, develop-
ment of novel nontoxic inhibitors (e.g., ML3403) (46), more
selective molecules (e.g., AS1940477) (47), and administration via
inhalation (48) are promising strategies for use of this class of
inhibitor for treatment of pulmonary disease. Overall, these re-
sults indicate that kinase inhibitors could be used as broad anti-
CoV agents which might be combined with other host-targeting
molecules, like peroxisome proliferator-activated receptor
␣
(PPAR
␣
) agonists, to better inhibit HCoV-EMC replication.
In conclusion, using global gene expression profiling, we have
shown that HCoV-EMC induces a dramatic host transcriptional
response, most of which does not overlap the response induced by
SARS-CoV. This study highlights the advantages of high-
throughput “-omics” to globally and efficiently characterize
emerging pathogens. The robust host gene expression analysis of
HCoV-EMC infection provides a plethora of data to mine for
further hypotheses and understanding. Host response profiles can
also be used to quickly identify possible treatment strategies, and
we anticipate that host transcriptional profiling will become a gen-
Josset et al.
8®mbio.asm.org May/June 2013 Volume 4 Issue 3 e00165-13
eral strategy for the rapid characterization of future emerging vi-
ruses.
MATERIALS AND METHODS
Cells and virus. Calu-3 2B4 cells, a clonal population of Calu-3 cells
sorted for ACE2 expression, were grown in minimum essential media
(MEM; Gibco) supplemented with 20% fetal bovine serum (HyClone)
and 1% antibiotic antimycotic (Gibco). Viral titrations and propagation
HCoV-EMC were performed in VeroE6 cells using standard methods.
Human coronavirus EMC 2012 (HCoV-EMC) was received from Bart L.
Haagmans (Erasmus Medical Center, Rotterdam, Netherlands) via MTA.
Experiments with SARS-CoV were previously performed under the same
conditions using the same cells (19).
Calu-3 cell infections. All work was performed in a biosafety level 3
(BSL3) facility supported by redundant exhaust fans, and personnel pro-
tective equipment was worn, including Tyvek suits, hoods, and HEPA-
filtered powered air-purifying respirators as previously described (49).
Cells were washed with phosphate-buffered saline (PBS) and inoculated
with virus at an MOI of 5 or mock diluted in PBS for 40 min at 37°C.
Following inoculation, cells were washed 3 times, and fresh medium was
added to signify time zero. Triplicate samples of mock-infected and virus-
infected Calu-3 2B4 cells were harvested between 0 and 72 hpi.
RNA isolation and microarray processing. RNA isolation from
Calu-3 2B4 cells, subsequent hybridization to Agilent 4⫻44K human HG
arrays, and processing of raw data were performed as previously described
(18). We analyzed triplicates for each time postinfection except for 12 hpi,
for which one replicate did not pass RNA quality control and was there-
fore excluded. For direct comparison with SARS-CoV-infected cells, raw
data from HCoV-EMC experiments were quantile normalized together
with the SARS-CoV data set (GEO series accession number GSE33267).
All probes were required to meet Agilent Feature Extraction QC criteria
for all replicates at at least one infection time point (33,773 probes passed
QC filtering). For each sample, a log
2
fold change (log
2
FC) value was
calculated as the difference between log
2
normalized data for this sample
and the average of log
2
normalized data for time- and data set-matched
mock-infected samples.
Statistical analysis. Differential expression was determined by com-
paring SARS-CoV- or HCoV-EMC-infected replicates to time- and data
set-matched mock-infected controls, based on a linear model fit for each
probe using the R package Limma (50). Criteria for differential expression
were an absolute log
2
FC of 1 and a qvalue of ⬍0.01 calculated using a
moderated ttest with subsequent Benjamini-Hochberg correction. Dif-
ferentially expressed (DE) genes after HCoV-EMC infection at early times
postinfection were defined as all genes DE at 0, 3, 7, or 12 hpi, and genes
DE at late times postinfection were defined as all genes DE at 18 or 24 hpi.
To identify genes with similar patterns of variation at early and late times
postinfection, early and late signatures were intersected considering up-
regulated and down-regulated genes separately and then combined to
define the signature of 207 stable genes. To cluster the genes DE at late
times postinfection following HCoV-EMC infection, differential expres-
sion was also calculated directly between SARS-CoV- and HCoV-EMC-
infected samples using the same criteria. A comparison of similar times
postinfection was performed, except for HCoV-EMC-infected samples at
18 hpi, which were compared to SARS-CoV samples at 24 hpi. HCoV-
EMC samples at 24 hpi were compared with SARS samples at 24 hpi and
later times postinfection (30, 36, 48, 54, 60, and 72 hpi). Genes similarly
changed between HCoV-EMC and SARS-CoV (cluster I and II) were
defined as genes DE by both viruses compared to their time-matched
mocks and that were also not differentially changed between HCoV-EMC
and SARS-CoV in at least one of the previously listed comparisons. Genes
of cluster III and IV were genes DE at late times postinfection following
HCoV-EMC infection that were not similarly changed between HCoV-
EMC and SARS-CoV. To further cluster the genes based on their expres-
sion value after HCoV-EMC infection, log
2
FC values at 18 and 24 hpi
were averaged, and clusters I and III were genes with average log
2
FC
values of ⬎0, while cluster II and IV genes had average log
2
FC values of
⬍0.
Functional enrichment and upstream regulator analysis. Functional
analysis of statistically significant gene expression changes was performed
using Ingenuity Pathways Knowledge Base (IPA; Ingenuity Systems). For
all gene set enrichment analyses, a right-tailed Fisher’s exact test was used
to calculate a Pvalue determining the probability that each biological
function assigned to that data set was due to chance alone. All enrichment
scores were calculated in IPA using the probes that passed our QC filter as
the background data set.
Upstream regulator analysis, which was used to predict regulators and
infer their activation state, is based on prior knowledge of expected effects
between regulators and their known target genes according to the IPA
database. A zscore is calculated and determines whether gene expression
changes for known targets of each regulator are consistent with what is
expected from the literature (z⬎2, regulator predicted to be activated) or
are anti-correlated with the literature (z⬍2, regulator predicted to inhib-
ited).
Quantitative reverse transcription PCR (RT-PCR). RNA was reverse
transcribed using the QuantiTect reverse transcription kit (Qiagen). The
resulting cDNA samples were diluted 50⫻. Primer sets for SYBR green
quantitative RT-PCR were designed using Primer3 (51). Primer se-
quences are available in Table S3 in the supplemental material. Relative
gene expression in infected samples compared to that in mock-infected
cells was calculated using the 2
⫺⌬⌬CT
method (52), using the MYL6 gene
as a calibrator, as the expression of MYL6 did not significantly change in
HCoV-EMC-infected cells in the microarray data.
Connectivity map. To determine whether some drugs can reverse the
HCoV-EMC infection signature, we used the publicly available Connec-
tivity Map (cmap) database (build 02) (22). Cmap is a collection of
genome-wide transcriptional data from cultured human cells treated with
1,309 different compounds. We used the list of 207 DE genes that are
stable after infection with HCoV-EMC. Agilent probes were mapped to
Affymetrix U133A probe sets using the BioMart ID converter tool (53) in
order to query the cmap database. Results were filtered based on their
negative connectivity enrichment score.
Antiviral in vitro assay. For in vitro evaluation of the p38 MAPK
inhibitor SB203580 (Sigma), VeroE6 cells were incubated for 4 h with the
drug at 5
M prior infection with HCoV-EMC at an MOI of 0.01 (pre-
treatment condition), medium was replaced during the 40-min infection,
and medium containing SB203580 was added back following inoculation
(54). For the posttreatment condition, cells were treated with SB203580 at
5
M or type I IFN (100 U/ml; PBL Interferon Source) at 0 hpi following
the infection inoculation.
Publically available data. Normalized matrixes and expression
values can be found at https://www.systemsvirology.org/project/home/
Data%20%26%20Resources/Experimental%20Metadata/HCoV-EMC%20
infection%20in%20Calu-3%20cells/begin.view?. The other data set uti-
lized was Calu-3 cells infected with SARS-CoV at MOI 5 (GSE33267).
Microarray data accession numbers. Raw microarray data have been
deposited in NCBI’s Gene Expression Omnibus under accession number
GSE45042.
SUPPLEMENTAL MATERIAL
Supplemental material for this article may be found at http://mbio.asm.org
/lookup/suppl/doi:10.1128/mBio.00165-13/-/DCSupplemental.
Table S1, DOCX file, 0.1 MB.
Table S2, DOCX file, 0.1 MB.
Table S3, DOCX file, 0.1 MB.
Data set S1, XLS file, 0.5 MB.
ACKNOWLEDGMENTS
We thank Lynn Law and Marcus Korth for valuable feedback on the man-
uscript.
This project was funded in part by federal funds from the National
Institute of Allergy and Infectious Diseases, National Institutes of Health,
Host Response to HCoV-EMC
May/June 2013 Volume 4 Issue 3 e00165-13 ®mbio.asm.org 9
Department of Health and Human Services, under contract
HHSN272200800060C and grant U54AI081680. The findings and con-
clusions in this report are those of the authors and do not necessarily
reflect the views of the funding agency.
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