Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection
has emerged as a pandemic. Paucity of information concerning the virus
and therapeutic interventions have made SARS-CoV-2 infection a genuine
threat to global public health. Therefore, there is a growing need for
understanding the molecular mechanism of SARS-CoV-2 infection at
cellular level. To address this, we undertook a systems biology
approach by analyzing publicly available RNA-seq datasets of SARS-CoV-2
infection of different cells and compared with other lung pathogenic
infections. Our study identified several key genes and pathways
uniquely associated with SARS-CoV-2 infection. Genes such as
interleukin (IL)-6, CXCL8, CCL20, CXCL1 and CXCL3 were upregulated,
which in particular regulate the cytokine storm and IL-17 signaling
pathway. Of note, SARS-CoV-2 infection strongly activated IL-17
signaling pathway compared with other respiratory viruses.
Additionally, this transcriptomic signature was also analyzed to
predict potential drug repurposing and small molecule inhibitors. In
conclusion, our comprehensive data analysis identifies key molecular
pathways to reveal underlying pathological etiology and potential
therapeutic targets in SARS-CoV-2 infection.
Subject terms: Computational biology and bioinformatics, Immunology,
Infectious diseases, Inflammation, Innate immunity
Introduction
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel
strain of the coronavirus family and the causative viral strain of the
coronavirus disease 2019 (COVID-19) pandemic. Most of the cases are
acute with symptoms like fever, shortness of breath and fatigue.
However, growing evidence suggests that higher mortality is associated
with further longer-term health complications. Clinical manifestations
of SARS-CoV-2 have been reported in which patients develop flu or
pneumonia-like respiratory syndrome along with organ damage such as
liver and heart^[30]1–[31]4. A recent work has shown that ACE2 (host
cell receptor for SARS-CoV-2 entry) expression levels do not differ
based on age, sex or ethnicity^[32]5. This part explains the
wide-spread transmission of the virus and raises the possibility of
immune response as critical factor for mortality risk. Indeed, high
level of pro-inflammatory cytokines were evident both in Middle East
Respiratory Syndrome (MERS) coronavirus and Severe Acute Respiratory
Syndrome coronavirus (SARS-CoV) infection. This results in the
infiltration of immune cells, thereby promoting lung
injury^[33]6,[34]7. Severely ill COVID-19 patients also demonstrated
higher levels of pro-inflammatory cytokines in bronchoalveolar lavage
fluid (BALF) and peripheral blood mononuclear cells (PBMC)^[35]8. In
addition, most of the COVID-19 patients exhibited an increase in
inflammatory monocytes and neutrophils^[36]9,[37]10.
Despite enormous effort, SARS-CoV-2 infection susceptibility index
remains elusive. We have observed myriad of events including
respiratory organ failure, multiple organ injury associated with
cytokine-storm-mediated inflammation. Notably, about 60% of SARS-CoV
infected patients had liver damage^[38]11 and this phenomenon was also
evident in MERS-CoV infection to a lesser extent^[39]12. Interestingly,
COVID-19 patients have also showed similar trend of liver damage
associated with abnormal liver function^[40]13. However, whether this
liver damage is due to viral infection itself or associated with the
drugs used for treatment, still needs to be elucidated. Reportedly,
cardiac injury has been observed in COVID-19 patients^[41]14,[42]15 and
a meta-analysis study also supports the notion of multiple organ damage
due to SARS-CoV-2 infection.
Understanding the hyperinflammation and type of immune response is of
utmost importance to design an effective therapy. The innate immune
system mounts the first line of defense against viral infection at an
early stage. Innate sensing of viral material initiates antiviral
response by producing type I interferon (IFN) through interferon
regulatory factors (IRFs) and elicits a pro-inflammatory cytokine
response via NF-κB-dependent pathways^[43]16. Reportedly, monocytes
from elderly subjects showed diminished IFNα/β level while producing
pro-inflammatory cytokines upon stimulation^[44]17. This suggests that
aging specifically impairs the IFN activation but not pro-inflammatory
cytokine production. Indeed, COVID-19 and SARS-CoV patients exhibited
hypercytokinemia^[45]18,[46]19 with an aberrant IFN response. Hence, a
more comprehensive and concerted study involving different in vitro or
in vivo models are required to understand the interplay between virus
and host innate immune response.
Here, we sought to characterize spatial distribution of host
transcriptome in different cells upon SARS-CoV-2 infection and whether
the infection generates unique and distinctive transcriptomic
signature. In this regard, we have analyzed and integrated publicly
available gene expression datasets. First, we have analyzed RNA-seq
data from SARS-CoV-2-infected normal human bronchial epithelial (NHBE)
cells, A549 cells (human lung carcinoma), primary human airway
epithelial (pHAE) cultures, cardiomyocytes and liver organoids. To
further understand, we have compared these data with other respiratory
viruses such as SARS-CoV, MERS, Influenza A virus (IAV), and
Respiratory Syncytial Virus (RSV). SARS-CoV-2 infection elicited a
clearly distinctive host response in multiple cells compared with those
observed in other viral infections. A computational analysis was
carried out to predict potential drug candidates based on their ability
to reverse SARS-CoV-2-mediated host transcriptional signature.
Methods
Dataset and sample information
The NCBI Gene Expression Omnibus (GEO) database
([47]http://www.ncbi.nlm.nih.gov/geo/) has been queried using keywords
“SARS-CoV-2” and “COVID-19”. The datasets were selected with stringent
selection of data generated on “Homo-sapiens”. Finally, following
datasets were included for this study: [48]GSE147507^[49]20,
[50]GSE150392^[51]21, [52]GSE153970^[53]22 and [54]GSE151803^[55]23.
Briefly, three independent biological replicates of primary human
lung/airway epithelial cells or A549 cells that were mock treated or
infected with SARS-CoV-2 at a Multiplicity Of Infection (MOI) of 2
([56]GSE147507) or 0.25 ([57]GSE153970), for 24 h and 48 h
respectively. A low MOI of 0.1 was used for 72 h in [58]GSE150392 using
iPSC derived cardiomyocytes. Liver organoid infections were performed
at a MOI of 0.1 for 24 h in GSE 151,803. SARS-CoV-2 (USA-WA1/2020) was
used in all these studies. In addition to SARS-CoV-2, [59]GSE47960 and
[60]GSE100504 have been used to analyze host response to SARS-CoV and
MERS viruses respectively. Primary human lung/airway epithelial cells
were infected at an MOI of 2 and 5 for SARS-CoV and MERS, respectively
in these studies. [61]GSE147507 dataset was used for RSV- and
IAV-infected host transcriptome. A complete list containing cell types,
MOI, infection period has been prepared as supplementary table 1 (see
supplementary information file).
Data processing and analysis
Raw sequencing reads were quality controlled and aligned to the human
genome (hg19) using the RNA-Seq Alignment App on Basespace (Illumina,
CA, USA), followed by differential expression analysis using
DESeq2^[62]24. Differentially expressed genes (DEGs) were characterized
for each sample (p adjusted-value < 0.05 and log2FC > 1). Volcano plots
were constructed using custom scripts in R. A large volume of DEGs were
identified with low count and highly dispersed genes, hence shrinkage
estimator ‘apeglm’^[63]25 was used in order to remove noise and
visualize the DEGs with significant differences in mock treated vs
SARS-CoV-2 treated group. Particularly this statistical approach
preserves true, large differences in log fold change (LFC) across
conditions and is superior to common methods in ranking of genes by LFC
in presence of low counts. In brief, apeglm utilizes heavy-tailed
Cauchy distribution instead of normal distribution on the effect sizes,
with fixed shape and scale adapted to the distribution of observed
maximum likelihood estimates (MLE) for all genes. Then a LaPlace
approximation was used to provide shrinkage estimates and corresponding
standard deviation. This eliminated further needs for filtering rules
or pseudo counts and maximizes the power of the current data to
estimate the effect size for each gene.
Functional enrichment analysis
A web-based tool, Metascape^[64]26 was used for functional enrichment
of DEGs. Metascape queries publicly available databases, e.g. Gene
Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG)^[65]27 and
assigns DEGs to their respective enriched pathways by calculating the
pairwise similarity between any two terms. Hypergeometric test and
Benjamini–Hochberg p-value correction algorithm have been used to
identify statistically significant enriched ontology terms. The
Bioconductor cluster Profiler package v3.14.3^[66]28 and STRING
v11^[67]29 have also been used to develop gene to pathway and
protein–protein interaction (PPI) networks of upregulated DEGs and
Cytoscape application v3.7.1^[68]30 was used for plotting genes based
on the count and p-value.
Disease similarities and drug predictive analysis
DisGeNET database^[69]31 was employed to identify disease similarities
with enriched DEGs. A minimum count of 3 genes, with a p-value < 0.01
and an enrichment factor > 1.5 (the enrichment factor is the ratio
between the observed counts and the counts expected by chance) were
collected and grouped into clusters based on their membership
similarities. Accumulative hypergeometric distribution was employed for
calculating p-value. The L1000FDW web tool^[70]32 was utilized for
potential drug candidate search for the treatment of SARS-CoV-2
infection. L1000FWD computes similarity between a given set of genes
with the Library of Integrated Network-based Cellular Signatures
(LINCS)-L1000 data and predicts compounds or drugs that reverse the
input transcriptomic signature.
Results
DEGs of SARS-CoV-2 infection
In order to determine and compare host cell response against SARS-CoV-2
infection, we first analyzed various publicly available RNA-sequencing
datasets. We identified numerous DEGs in lung, liver and heart cells
(Fig. [71]1A). Due to large number of DEGs, we employed more stringent
parameter and used ‘apeglm’ algorithm in Deseq2 program to identify
highly differential genes. This significantly narrowed down the gene
list and interestingly most of the DEGs belong to an upregulated
category (Fig. [72]1A). We could not find any significant association
among those few downregulated genes. For subsequent analysis, mostly
upregulated genes have been used. Notably, chemokine genes (CXCL1,
CXCL3, CXCL5, CXCL8, CCL20) and ISGs (IFITM1, IFI44L, IFI27) dominated
this list (See supplementary file 1 for the gene lists).
Figure 1.
[73]Figure 1
[74]Open in a new tab
DEGs of SARS-CoV-2 infection. (A) Volcano plots indicating DEGs of
A549, NHBE, pHAE, Liver organoids and Cardiomyocytes upon SARS-CoV-2
infection for 24 h. DEGs (p-adjusted < 0.05) with a log2FC of more than
2 are indicated in red. Non-significant DEGs with a log2FC of more than
2 are indicated in green. (B) Circos plot showing overlapping among the
genes significantly upregulated following SARS-CoV-2 infection using
purple lines. (C) Hierarchical clustering of the top most enriched
diseases by the upregulated genes from different datasets. Dendogram is
colored by the p values where grey cells indicate the lack of
significant enrichment.
An MA plot showing A549 cells had very few DEGs compared with other
cells used in the study (See Figure S1). However, supplementation with
putative SARS-CoV-2 receptor ACE2 in A549 cells resulted in higher
number of modulated transcriptome (see Figure S2A). A circos plot
showing association of DEGs among various datasets. Particularly, NHBE
cells share many DEGs with pHAE cells (Fig. [75]1B). Then, the DEGs
were screened for their approximate association in disease-gene
networks using DisGeNET database. Strikingly, enrichment of our
upregulated genes could link most of the clinical features such as
pneumonia, influenza, myocardial ischemia, hemorrhagic shock, etc.
observed in COVID-19 patients. (Fig. [76]1C).
Functional annotation and pathway enrichment of DEGs
Next, we performed gene set functional enrichment analysis using KEGG
pathway. Circos plot showing many distinct genes in one dataset are
ontologically connected to the ontological features in another dataset
(Fig. [77]2A). Key pathways shared by genes from most of the datasets
were cytokine-cytokine receptor signaling, IL-17 signaling pathway,
NOD-like receptor-mediated signaling, and Measles (Fig. [78]2B).
Interestingly, very few genes were upregulated in A549 cells and were
not enriched into IL-17 signaling pathway. We then analyzed
transcriptome of A549 cells supplemented with putative SARS-CoV-2
receptor, ACE2. ACE2 receptor supplementation induced expression of
transcripts that were shared with other cells (See Figure S2A) and
enriched in IL-17-mediated signaling pathways (See Figure S2B).
Figure 2.
[79]Figure 2
[80]Open in a new tab
SARS-CoV-2 infection induces inflammatory signaling. (A) Circos plot
showing the overlapping among genes significantly upregulated upon
SARS-CoV-2 infection of different cells. Purple lines represent shared
genes by various cells. Blue lines represent the different genes that
fall in the same ontology term. (B) Hierarchical clustering of the top
most enriched KEGG pathways from different datasets. Dendogram is
colored by the p values where grey cells indicate the lack of
significant enrichment. (C) Circos plot showing collated shared genes
from different datasets, genes are assigned to their designated KEGG
pathways.
DEGs were then ranked based on their frequency of distribution in
datasets and number of associated pathways. We selected top 50
upregulated genes that were present in at least two different cells.
Pathway enrichment analysis based on gene count assigned in KEGG
pathway for upregulated DEGs revealed IL-17 signaling, NOD-like
receptor-mediated signaling, and the TNF signaling (Fig. [81]2C)
amongst the most regulated pathways by these genes. Among other
statistically significant enriched terms, pathways related to NF-
[MATH: κ :MATH]
B and rheumatoid arthritis were also evident (Fig. [82]2C). In
addition, analyzed data from BALF collected from COVID-19 patients in
China (Xiong et al. 2020) followed a similar trend of enrichment
identified from our analysis (See Figure S3). However, we did not
observe strong similarities with our host transcriptome data when PBMC
transcripts were used for analysis from the same study group in China.
SARS-CoV-2 transcriptomes are enriched into IL-17 pathway
To further our understanding of the SARS-CoV-2-driven IL-17-enriched
transcriptome, we have analyzed microarray data from 24 to 96 h and
24–48 h for SARS-CoV and MERS infection, respectively. SARS-CoV and
MERS upregulated genes mainly enriched into MAPK, NF-
[MATH: κ :MATH]
B and TNF-α signaling pathways (Fig. [83]3A,B). Next, we compared genes
that were upregulated in SARS-CoV, SARS-CoV-2- and MERS-infected cells
(Fig. [84]3C). Enrichment of 40 shared genes showed ‘Measles’,
‘NOD-like receptor signaling’, ‘TLR signaling’, ‘RLR signaling’, and
‘TNF signaling’ (Fig. [85]3D). IL-17 signaling was also among the
enriched pathway, however it was not as strongly activated as we
observed in all the datasets of SARS-CoV-2 infection.
Figure 3.
[86]Figure 3
[87]Open in a new tab
SARS-CoV, SARS-CoV-2 and MERS-infected transcriptome activates innate
viral sensing pathways. (A) Hierarchical clustering of the top most
enriched terms in KEGG pathway by genes significantly upregulated at
different time points (24 – 96 h) upon SARS-CoV infection. (B)
Hierarchical clustering of the top most enriched terms in KEGG pathway
by genes significantly upregulated at different time points (24 – 48 h)
upon MERS infection. Dendogram is colored by the p values, and grey
cells indicate the lack of significant enrichment. (C) Shared DEGs in
SARS-CoV, SARS-CoV-2 and MERS infection. Venn diagram depicting genes
shared and/or unique between each comparison. (D) Barplot showing most
enriched KEGG pathways upon collation of 40 shared DEGs from SARS-CoV,
SARS-CoV-2 and MERS infection. Y-axis represents KEGG pathway and
X-axis represents p-value (-log). Higher –log P indicates smaller and
more significant p-value.
Next, we performed a gene set enrichment of all uniquely upregulated
DEGs for each of the infections. We have observed during time course
infection of SARS-CoV and MERS, grouping all DEGs from different time
points enriched into TLR and RLR signaling pathways, along with the NF-
[MATH: κ :MATH]
B and MAPK pathways. Of note, SARS-CoV-2-infected transcripts did not
trigger RLR signaling while induced strong cytokine responses mediated
by NF-
[MATH: κ :MATH]
B signaling (Fig. [88]4A). On the other hand, SARS-CoV-2-infected
unique transcriptomes predominantly enriched into ‘IL-17 signaling
pathway’ while both SARS-CoV and MERS-infected unique transcripts did
not. This data indicates that SARS-CoV-2 infection induces a plethora
of chemokines that mediates a strong inflammatory response driven by
IL-17 signaling (Fig. [89]4B).
Figure 4.
[90]Figure 4
[91]Open in a new tab
SARS-CoV-2 unique genes trigger IL-17 signaling pathway. (A) Dotplot
visualization of enriched KEGG terms in SARS-CoV, SARS-CoV-2 and MERS
infection showing all upregulated genes. Gene enrichment analyses were
performed using Network analyst against the KEGG pathway. (B) Dotplot
visualization of enriched KEGG terms in SARS-CoV, SARS-CoV-2 and MERS
infection of uniquely upregulated genes in each infection. Gene
enrichment analyses were performed using Network analyst against the
KEGG pathway. The color of the dots represents the false discovery rate
(FDR) value for each enriched KEGG term, and size represents the number
of hit genes found in the datasets. (C) Barplot showing gene ontology
for enriched biological process from genes upregulated in IL-17 pathway
upon SARS-CoV-2 infection only. Biological processes were ranked based
on combined score from the P value calculated by Fisher exact test and
multiplying that with Z-score.
We then sought to relate these unique IL-17 specific transcripts to the
biological processes. Notably, ‘leukocyte aggregation’, ‘positive
regulation of granulocytes/neutrophils’, and ‘fever generation’ were
the top biological processes modulated by these transcripts
(Fig. [92]4C). This demonstrated striking similarities between
phenotypes observed in COVID-19 patients and biological processes
controlled by the IL-17 specific transcripts.
Comparative analysis with other respiratory virus infections
We extended our analysis further by including transcriptional data from
respiratory syncytial virus (RSV) and Influenza A Virus (IAV).
Hierarchical clustering of transcriptional responses positioned
SARS-CoV-2 along with other coronaviruses while RSV and IAV belong to
the same cluster (Fig. [93]5A). Further analysis showed that SARS-CoV-2
transcriptional regulation was mainly controlled by NF-
[MATH: κ :MATH]
B (RELA) while IRF mediated signaling was not activated. On the
contrary, both SARS-CoV and MERS mediated transcriptional responses
triggered IRF mediated signaling cascade (Fig. [94]5B).
Figure 5.
[95]Figure 5
[96]Open in a new tab
SARS-CoV-2 distinctively activates NF-
[MATH: κ :MATH]
B- but not IRF-mediated signaling. (A) Hierarchical clustering of the
top most enriched terms in KEGG pathway by genes significantly
upregulated upon SARS-CoV, SARS-CoV-2, MERS, RSV and IAV infection. (B)
Hierarchical clustering of the top most enriched transcription factors
for significantly upregulated genes using TRRUST database. Dendogram is
colored by the p values, and grey cells indicate the lack of
significant enrichment. (C) Enrichment network visualization for
results from all datasets, where nodes are represented by pie charts
with color codes depicting their designated datasets.
Next, we developed protein–protein interaction (PPI) networks with the
upregulated DEGs in respiratory infections using cytoscape. KEGG
categories “IL-17 signaling”, “Influenza A signaling” and “TNF
signaling” formed most densely connected subnetworks, which are mainly
composed of ribosomal proteins and chemokines, respectively. Most of
the genes in IL-17 signaling were representative of SARS-CoV-2-infected
transcriptome as denoted by color (Fig. [97]5C). On the other hand,
Influenza A signaling pathway was shared by most of the respiratory
viruses and possibly produce similar phenotypes observed in those
respiratory illness mediated by viral infection.
Potential therapeutic intervention based on transcriptome signature
Reverse signature perturbation analysis was performed using the
upregulated DEGs identified upon SARS-CoV-2 infection (Fig. [98]6).
Potential drugs have been clustered according to their ability to
reverse the upregulated DEGs using the web-based tool L1000CDS2. Among
the identified drugs; Saracatinib showed in vitro inhibition of MERS
replication^[99]33. Besides, Dasatinib and Imatinib from our list have
also been reported to inhibit SARS-CoV and MERS infection in Vero E6
cells^[100]34. This suggests that the repurposing of drugs based on
host transcriptome may be an effective strategy until a safe and
effective vaccine is developed.
Figure 6.
[101]Figure 6
[102]Open in a new tab
SARS-CoV-2 transcriptome-based drug prediction. L1000CDS2 visualization
of drug-induced signature. Significantly upregulated top 50 genes found
in at least two datasets were used as input dataset. Blue square
represents drug that may inhibit gene(s).
Discussion
Viruses utilize host cell machinery and modulate host cell
transcriptome either for their replication or for evading host immune
responses. Hence, transcriptional profiling of host cell would help us
realize changes in the genetic landscape during viral infection. In
this regard, we have analyzed differentially expressed genes in
SARS-CoV-2-mediated infection using various in vitro cellular systems.
Datasets used in our study are mostly obtained using various cells from
lungs along with cardiomyocytes and liver organoids. Despite being a
respiratory virus, SARS-CoV-2 has been reported to impair liver
function^[103]35 and has active transcription site in cardiomyocytes of
deceased patients^[104]36. Notably, ACE2 expression is high in
cardiomyocytes^[105]37 and in general the expression is relatively
higher in liver and heart when compare with lungs^[106]38. Thus, it is
important to carry out a comparative transcriptional response in cells
from different organs.
We observed a consistent enrichment of cytokine response in all the
datasets used in this meta-analysis. Notably, monocyte or neutrophil
recruiting chemokines such as CCL20, CXCL1, CXCL3 and CXCL8 have been
upregulated. This data is in sync with the elevated level of
circulating neutrophils observed in COVID-19 patients along with a
decrease in lymphocytes^[107]39–[108]41.
Pattern recognition receptors such as Toll-like receptors (TLRs) and
RIG-I like receptors (RLRs) sense viral single or double-stranded RNA
to produce inflammatory cytokines and type I IFN^[109]42. We observed
that IRF7 and IRF9 were upregulated in SARS-CoV-2 infection. This in
turn may stimulate production of interferon stimulated genes (ISGs) to
produce an antiviral state. Indeed, several ISGs such as IFI1-3, IFI27,
IFITM3, OAS1 and OAS3 have been upregulated. This possibly indicates
initial host cellular response in order to inhibit viral replication.
However, severe viral load may overwhelm type I IFN response and
dictate outcome of infection. Of note, SARS-CoV and MERS-CoV infection
exhibited similar outcome depending on the timing of type I IFN
production. A delayed type I IFN production results in higher viral
replication, cellular damage and production of cytokine storm, whereas
an earlier production of type I IFN could protect from lethal
effect^[110]43,[111]44.
COVID-19 patients with severe respiratory symptoms had higher levels of
Tumor necrosis factor-α (TNF-α) and IL-6^[112]45. It is worth
mentioning that IL-6 was not only expressed in SARS-CoV-2-infected
cells but was also involved with most of the enriched pathways in our
analysis. A recent study^[113]45,[114]46 showed effective inhibition of
cytokine storm by blocking IL-6 with monoclonal antibody Tocilizumab,
supporting the notion that IL-6- mediated inflammation contributes to
the disease severity. Both IL-6 and IL-17 have been reported with
protecting virally infected cells from apoptosis and thereby promoting
viral persistence^[115]47.
All the SARS-CoV-2 datasets used in this study showed particularly
strong inflammatory response triggered with IL-17 activation. Previous
studies observed elevated IL-17 expression was conjoined with impaired
IRF7 upregulation and IFNα responses in herpes simplex virus 2
infection of aged mice. Indeed, IL-17 inhibition could prevent death of
aged mice during viral infection^[116]48,[117]49. Our data analysis
indicated the importance of IL-17 in SARS-CoV-2 infection. Meanwhile
IL-17 has also been reported to be increased in critically ill patients
compared with non-severe patients^[118]3. However, whether this
upregulation is the direct effect of SARS-CoV-2 infection or not
remains unclear. A very recent study published in April 2021 showed
that SARS-CoV-2 open reading frame 8 (ORF8) binds to IL17 receptor and
activates IL17 signaling pathway. Blocking IL17RA with an antibody
reduced IL17 mediated inflammation in lung and liver in SARS-CoV2 ORF8
pseudovirus infected mice^[119]50. Indeed, a 382-nucleotide deletion
variant of SARS-CoV-2 with abolished ORF8 expression was reported with
milder symptoms in hospitalized patients in Taiwan and
Singapore^[120]51,[121]52. This report clearly supports our finding on
SARS-CoV-2 distinctively initiates IL17 mediated inflammatory response
and may aggravate disease severity. Of note, pro-inflammatory cytokine
storms have already been reported to be damaging in other organs of
COVID-19 patients^[122]53. Hence, blocking IL-17 could be a viable
strategy to reduce multiple organ damage and disease severity.
It is still obscure how viruses induce a proinflammatory response
whilst keeping immune homeostasis at bay. SARS-CoV-2 or H1N1 2009
infections showed higher IL-1RA and IL-6 levels along with very low
viral load in the lung in severely ill people^[123]54. This raises an
interesting possibility as to whether viruses blindside the innate
immune system by maintaining a low amplification or self-replication
state while causing local inflammation or tissue damage. Our
comparative transcriptional analysis of SARS-CoV-2 and other viruses
supports this notion. As evident from our observation, NF-κB is the
most significant transcription factor that modulates host
transcriptional responses upon SARS-CoV-2 infection. Despite producing
ISGs, an absence of IRF3 or IRF7 regulated transcriptional processes
was notable in SARS-CoV-2 infection. On the other hand, SARS-CoV and
MERS transcriptomics were at least partly regulated by IRF1, IRF3 and
IRF7. Of note, a recent work showed that SARS-CoV-2 proteases could
cleave IRF3 directly and resulted in blunted IFN production^[124]55.
This accumulating evidence suggests that SARS-CoV-2 launches a
pro-inflammatory response while specifically blocking antiviral
responses.
Despite showing a variable clinical spectrum, severe COVID-19 patients
commonly exhibited shortness of breath, and production of
pro-inflammatory cytokines such as IL-6, IL-8, IL-1β,
IL-1RA^[125]3,[126]56. Classic clinical symptoms predominantly indicate
lung associated phenotypes, however recent evidence indicated that
mortality rate is higher in cardiac patients^[127]57,[128]58. Our gene
set enrichment also showed that SARS-CoV-2 infected cardiomyocytes have
higher expression of genes that may induce myocardial ischemia and
cerebral arterial infarction.
A consistent observation in each of our SARS-CoV-2 datasets was the
presence of chemokines and inflammatory cytokines. Hence, we used a
reverse transcriptome signature approach for predicting drugs that are
approved by FDA or in clinical trials. This analysis predicted that
Dasatinib, CGP-60474, Canertinib, Alvocidib, Saracatinib etc. can
perturb and reverse host transcriptional signature of SARS-CoV-2
infection. Reportedly, CGP-60474 could inhibit IL-6 and TNFα to
alleviate LPS-induced sepsis in mice^[129]59.
We have observed distinct transcriptional responses mediated by
SARS-CoV-2 in cell culture-based studies. However, MERS and IAV
datasets used in our study have different MOI than that of SARS-CoV and
SARS-CoV-2. MERS have higher MOI but similar infection period of
24–48 h than two other coronaviruses used in the analysis. On the other
hand, IAV infection was done at slightly higher MOI than SARS-CoV or
SARS-CoV-2. Interestingly, MERS infection showed late viral peak
compare with SARS-CoV and SARS-CoV-2^[130]60–[131]62, whilst IAV showed
faster inflammatory and antiviral responses than SARS-CoV-2^[132]63.
This clearly explains the reasoning of using slightly higher MOI for
MERS and shorter infection period with IAV.
Despite comprehensive analysis of the publicly available datasets,
there may be some limitations as well. As the percentage of lethality
is higher in aged population, future studies using aged or senescent
cells would clarify the discrepancies between young and aged in terms
of infection and mortality rate. In addition, all the studies used a
single cell type in the absence of immune cells, which may not reflect
true intercellular communications take place in vivo. As activation of
immune system varies in age groups, further research should be done
using a multicellular system. As we have seen that these cellular
models of infection secret numerous chemokines and particularly able to
trigger immune cell activation and chemotaxis, future studies should be
directed towards validating our data in moderate and severe COVID-19
patients.
Taken together, our comprehensive data analysis showed that SARS-CoV-2
initiated a distinct IL-17-driven inflammatory response irrespective of
the cells used in various studies (Fig. [133]7). Overall, our data
analysis showed unique and distinctive SARS-CoV-2-induced
transcriptional responses compared with other respiratory viruses.
Figure 7.
[134]Figure 7
[135]Open in a new tab
Summary of transcriptional differences among SARS-CoV-2 and other
respiratory viruses. SARS-CoV-2 infection initiates a predominant IL-17
enriched chemokine transcriptional response whilst producing a low to
moderate antiviral response by impairing interferon regulatory factors
driven transcriptional process. This results in disproportionate immune
response and recruitment of innate immune cells that ultimately leads
to complications such as ARDS, sepsis or fibrosis in COVID-19 patients,
On the other hand, other respiratory viruses trigger both inflammatory
and antiviral transcriptional response in host cells, and thereby
maintain a steady immune homeostasis.
Supplementary Information
[136]Supplementary Information 1.^ (920.7KB, docx)
[137]Supplementary Information 2.^ (30.7KB, xlsx)
Acknowledgements