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Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19

Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV.

Results

In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients.

Conclusions

Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.

Background

The transcriptome of living organisms including RNA transcripts is studied by transcriptomics assays [1]. The coding genes of organisms are present in the DNA, which are transcribed to mRNA by transcription. Two major techniques have widespread applications in the transcriptomics analysis of cell mRNA pool: the first one is microarray which is used for detection of a labeled mRNA or surrogate marker by an immobilized probe [2] and the second one is RNA-Seq which is a high-throughput sequencing technique, capable to differentiate various types of RNA including miRNA, tRNA, and rRNA [3]. The outputs of RNA-Seq analysis can be validated by quantitative reverse transcription PCR (RT-qPCR) as a complementary method in transcriptomics [4]. Recently, the transcriptome of infected human tissue samples was detected by analyzing the site of infection [5] to elucidate the response of patients to external pathogens and understand the molecular regulatory mechanisms of infections [6]. Two models, including cell lines for in vitro and animal models for in vivo assays are commonly considered for transcriptomics analysis of human responses to infectious agents [7]. In vitro and in vivo cell lines/animals interactions with pathogens and their host-specific responses to the pathogens can be evaluated by transcriptomics techniques. The interactions of the infected cell lines with immune system cellular components can be through recognition with specific T cells and natural killer (NK) cells as the crucial factors of the adaptive and innate immunity and reflected in the transcriptional responses [8,9,10]. In addition, the patterns of gene expression as a biomarker can be evaluated by transcriptomics techniques to discriminate the different stages of the infections and may influence physiological pathways.

Previously, transcriptome analyses of host-pathogen interactions were conducted on the viral infections including herpesvirus [11], cytomegalovirus [12, 13], influenza virus [14], human respiratory syncytial virus [15], measles virus [16], rubella virus [17], dengue virus [18], rabies virus [17], ebolavirus [19, 20], and human immunodeficiency virus (HIV) [21]. Moreover, studding the transcriptome of Calu-3 cell line (human lung cancer cell line) after infection with severe acute respiratory syndrome coronavirus (SARS-CoV) revealed that recognition receptors and the interleukin 17 (IL-17) pathway were activated [22]. In addition, another analysis was performed on the transcriptome of Calu-3 cell line following Middle East respiratory syndrome coronavirus (MERS-CoV) infection to identify suitable inhibitory and antiviral compounds [23].

Since December 2019, SARS-CoV-2 was emerged as a novel member of coronaviruses in Wuhan, China [24] and soon after spread around the world [25, 26]. In a study in 2005 that was performed on the prophylactic and therapeutic supremacies of chloroquine, as an anti-malaria agent, on primate cells infected with SARS-CoV, it was defined that chloroquine increases the endosomal pH and intervene with glycosylation of angiotensin-converting enzyme 2 (ACE2) as the receptor of SARS-CoV on the alveolar cell surface [27]. Although chloroquine and hydroxychloroquine sulfate, as the anti-malaria agents, were prescribed for treatment of coronavirus disease 2019 (COVID-19) at the beginning of pandemic [28], the ocular toxicity and cardiotoxicity of chloroquine and hydroxychloroquine sulfate should be considered [29, 30].

In a study performed by Wang C et al. in 2020, it was defined that the spike (S) protein of Wuhan strain of SARS-CoV-2 contains 1273 amino acid residues and the S protein of Urbani strain of SARS-CoV includes 1255 amino acid residues that showed about 77.5% similarity between the amino acid residues of S protein of both viruses. The study on the receptor binding domain (RBD) of S protein from both SARS-CoV and SARS-CoV-2 by enzyme linked immunosorbent assay (ELISA) and human 47D11 antibody revealed similar affinities of the human 47D11 antibody to ACE2 receptor of host cells through S1B domain of both SARS-CoV and SARS-CoV-2. In this study, 25 human and non-human cell lines from various organs and tissues were inoculated with SARS-CoV and SARS-CoV-2 and finally similar tropism of both viruses to ACE2 receptor was confirmed; meanwhile, they declared that the observed differences could be due to different infection capacity in the target organs such lungs, kidneys, and brain, resulting different cytopathic-effects in the tested cell lines [31,32,33].

Since the recent emergence of SARS-CoV-2 and considering its high transmission rate [34] which have affected all aspects of human life including health and economy [35, 36], it would be an urgent matter to combat against it and find an effective vaccine and/or potential therapeutics to treat COVID-19. Within this context, we try to expand the transcriptomics data of the interaction of lung cells with SARS-CoV to infection of bronchoalveolar cells with SARS-CoV-2. Due to the high percentage of similarity between SARS-CoV and SARS-CoV-2, this approach could be a potential solution in the pharmaceutical studies to explore an influential compound to treat COVID-19 efficiently. In this study and through the in silico and system biology approaches, we checked the transcriptomics data of infection of lung bronchial cells with SARS-CoV and believe that the results can be potentially expanded to infection of host lung bronchial epithelial cells with SARS-CoV-2 in COVID-19 patients.

Methods

mRNA Microarray Assay

The microarray assay on the mRNAs from 2B4 cell line (human bronchial epithelial cell) after infection with SARS-CoV was performed using Affymetrix GeneChip Human Genome U133 Plus 2.0 [HG-U133_Plus_2] by Yoshikawa et al. in 2010 [37]. The transcriptomics data of the infected 2B4 cell line was recorded for three different time durations including 12 h, 24 h, and 48 h post infection with SARS-CoV and the analysis was adjusted on p-value< 0.05 and Log2-fold change 1.0. This transcription profile was annotated in the Expression Atlas (https://www.ebi.ac.uk/gxa/home) hosted by EMBL-EBI database [38].

Gene Ontology (GO) Analysis

The GO analysis was conducted to identify the functionality of altered 2B4 cell line genes after infection with SARS-CoV. Through a computational approach, the function of altered genes was predicted by both human- and machine-readable data and the obtained results were filtered by Fisher’s exact test [39], False Discovery Rate (FDR)<1.0. This statistical significance removed the proportion of false positives among all upregulated and downregulated genes from 2B4 cell line genes after infection with SARS-CoV.

Reactome Pathways Analysis

The Reactome pathways analysis was conducted to explore the biochemical and physiological pathways in the infected 2B4 cell line with SARS-CoV. The same as GO analysis, Fisher’s exact test [39] and FDR<1.0 was employed to statistically identify the activated Reactome pathways in the 2B4 cell line genes after infection with SARS-CoV.

Results

mRNA Microarray Assay

The microarray mRNA analysis of the annotated data in the Expression Atlas database [38] revealed that no significant gene regulation occurs in the host cells within 24 h. All genes related to the interaction of 2B4 cell line and SARS-CoV were expressed after 48 h, in which most of the affected genes were downregulated, while just REL (c-Rel proto-oncogene) gene was upregulated (Fig. 1).

Fig. 1
figure1

Expression variations of cellular genes after SARS-CoV infection. The 2B4 cell line was infected with SARS-CoV and following 48 h incubation the gene expression was analyzed by microarray method. Most of the affected genes showed slight downregulation. Colors indication: dark red for low level upregulation, dark green for low level downregulation, and light green for high level downregulation. The analysis was adjusted on p-value< 0.05 and Log2-fold change 1.0

The microarray analysis on the expression of SARS-CoV genes in all three different durations (12 h, 24 h, and 48 h) of interaction with 2B4 cell line demonstrated almost similar expression level of SARS-CoV genes between 7 h to 9 h after infection of 2B4 cell line (Fig. 2).

Fig. 2
figure2

Boxplots show the array intensity distributions of SARS-CoV genes. The outlier computation method between the distribution of the pooled data and each array’s distribution derived from Kolmogorov-Smirnov statistic Ka. Asterisks indicate inaccurate results in the experiments. Purple bars show the expression level of SARS-CoV genes after 12 h, orange bars show the expression level of SARS-CoV genes after 24 h, and yellow bars show the expression level of SARS-CoV genes after 48 h. All three experiments demonstrate that SARS-CoV genes are expressed between 7 h and 9 h post infection of alveolar cells

Gene Ontology (GO) Analysis

In this analysis, the top ten GO as a network were visualized that their gene products were attributed to some vital cellular functions of the infected 2B4 cell line with SARS-CoV. Three out of ten GO including GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) displayed the highest association in the GO network after infection of 2B4 cell line with SARS-CoV (Fig. 3) (Table 1).

Fig. 3
figure3

Enrichment of top ten gene ontology (GO) after infection of 2B4 cell line with SARS-CoV. The GO analysis shows the top ten cellular genes are regulated after SARS-CoV infection

Table 1 The top ten GO analysis and Reactome pathways after infection of 2B4 cell line with SARS-CoV

Reactome Pathways Analysis

In this analysis, the top ten Reactome pathways as a network were visualized that showed a significant attribution in the biochemical and physiological pathways after infection of 2B4 cell line with SARS-CoV. One out of ten Reactome pathways including R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected 2B4 cell line with SARS-CoV (Fig. 4) (Table 1).

Fig. 4
figure4

Enrichment of top ten Reactome pathways after infection of 2B4 cell line with SARS-CoV. The Reactome pathways show the top ten cellular pathways and networks are enriched after SARS-CoV infection

Discussion

The study of literature demonstrated that the blood transcriptome of the infected host with SARS-CoV could be mined by reverse vaccinology and system vaccinology technologies and consequently a potential vaccine candidate could be designed through the production of recombinant proteins against COVID-19 [40]. The exploration of pathogenesis mechanisms of SARS-CoV infection can provide more detailed information to design drugs against COVID-19 through the regulation of immune response [41]. The transcriptomic gene expression profiles of Yellow Fever vaccine 17D (YF-17D) were analyzed by a GO system named Vaccine Investigation Ontology (VIO) to identify the association of various variables in the vaccinated population. Hence, not only GO analysis has a potential application in the microarray assay after infection and/or vaccination to detect the profiles of gene expression, but also the Reactome analysis pathway tools are employed to explore the enriched pathways after infection and/or vaccination [42]. Therefore, the p-value based on FDR < 0.05 as the significance cut-offs was applied in the GO analysis and Reactome pathway enrichment pathways in the evaluation of transcriptomic gene expression profiles by microarray assay in SARS-CoV infection, which has a potential to be expanded to CIVID-19.

In a study performed by Mizutani et al. in 2005, the in vitro and in vivo signaling pathways in SARS-CoV infection were illustrated [43]. In response to stressors, p38 mitogen-activated protein kinase (MAPK) is expressed so two isoforms of MAPK including p38α and p38β are expressed in all tissues, while the expression of other two isoforms including p38γ and p38δ is tissue-specific so the inhibition of p38α and/or p38β MAPKs in SARS-CoV infection would be occurred by SB203580 inhibitor. In addition, the interaction between MAPK kinase 6 (MKK6) and protein kinase R (PKR) activates p38 MAPK [44]. The activation of PKR is done by MKK6 through double-stranded RNA, while the activation of MKK6 is done by PKR through poly(rI;rC). Apoptosis signal-regulatory kinase (ASK1), transforming growth factor (TGF)-β-activating kinase (TAK1), and MAPKKK4 were categorized as the upstream targets of p38MAPK [45], while MAP kinase-interacting kinase 1 (MNK1), mitogen and stress-activated protein kinase 1 (MSK1), and MAPK-activated protein kinase 2 and 3 (MAPKAPK 2 and 3) were known as the downstream targets of p38MAPK [46]. On the other hand, the 7a protein from SARS-CoV induces phosphorylation and apoptosis of p38 MAPK in 293 T cells [47], while the induction of p38 MAPK pathway induces actin reorganization in COS-1 cells leading to devoid of growth factors [48].

The microarray analysis on the infected 2B4 cell line with SARS-CoV was performed by Yoshikawa et al in 2010 [37]. The results of this study revealed that REL gene was upregulated. REL is a member of nuclear factor-κB (NF-κB) family of transcription factors. It was defined that the pathogenesis of SARS-CoV is associated with stimulated induction of proinflammatory cytokines by activation of at least five pathways including NF-κB, NF-AT, IRF-3, IRF-7, ATF-2/jun, and jun/fos (AP-1) [49]. Similarly in COVID-19, it can be expected that the activation of REL gene can lead to cytokine storm in the infected lung with SARS-CoV-2. Cytokine storm is an uncontrolled systemic inflammatory response that may lead to multi-organ failure and death in COVID-19.

Downregulation of peroxiredoxin 5 (PRDX5) gene levels by SARS-CoV infection in the 2B4 cell line reduces the intracellular hydrogen peroxide (H2O2) levels to minimize oxidative stress in the infected cells [50]. The expression of matrix metalloproteinase 1 (MMP1) gene was downregulated in SARS-CoV infection, while MMP1 gene is overexpressed in influenza H7N9, which may lead to deposition of collagen in lungs and consequently gas exchange problem due to fibrosis [51, 52]. Therefore, it seems that the occurrence mechanism of acute respiratory distress syndrome (ARDS) in COVID-19 and influenza infection is initiated by two different mechanisms. Neurofilament triplet L protein (NEFL) gene can be downregulated in the SARS-CoV infection, while NEFL gene is upregulated in Zika virus infected cells [53]. Another downregulated gene in the SARS-CoV infection is nitric oxide synthase traffic inducer (NOSTRIN) so this downregulation can induce the secretion of some proinflammatory chemokines and cytokines including CCL2, CCL5, and IL-6 [54]. This is in controversy with the high secretion of IL-6 in COVID-19 which is an initiator mechanism for C-reactive protein (CRP) production and cytokine storm [55]. Another study showed that phenylalanine hydroxylase (PAH) gene was downregulated in SARS-CoV infection leading to activation of immune system through upregulation of T helper 1 (Th1) responses [56]. Notably, the cytokine storm is usually amplified by activation and secretion of cytokines from Th1 lymphocytes [57]. Lower expression of BCL2 Interacting Protein 3 like (BNIP3L) gene was experienced in SARS-CoV infection, while downregulation of this gene was associated with a reduction of NK cell memory and their response to cytomegalovirus infection [58]. Dual specificity protein phosphatase 4 (DUSP4) gene was downregulated in SARS-CoV infection with the association with the skew of Th1 toward Th2 immune response leading to susceptibility to viral infections and inflammation, however, may control the occurrence of cytokine storm by IL-6, IL-12 and tumor necrosis factor α (TNFα) as well as increasing the amount of prostaglandin PGE2 [59]. Another study showed that the coding gene for hydroxysteroid 17-beta dehydrogenase 2 (HSD17B2) was downregulated in SARS-CoV infection, which is a consequence of stimulation of IL-1, IL-6, and TNFα as well as induction of inflammation. On the other hand, the coding gene for Musashi-2 (MSI2) RNA-binding protein was downregulated in SARS-CoV infection. The loss of function of MSI2 gene affects hematopoietic cells and consequently the development of leukocytes leading to suppression of the innate immune responses to infections [60, 61]. In SARS-CoV infection, the downregulation of N-myc downstream-regulated 1 (NDRG1) gene was led to induction of the proinflammatory signals and activation of a cytokine storm with C-C Motif Chemokine Ligand such as CCL2, CCL5, CCL23, CCL26, C-X-C Motif Chemokine Ligands such CXCL10, CXCL11, CXCL16, Vav Guanine Nucleotide Exchange Factor 3 (VAV3), signal transducer and activator of transcription 1 (STAT1), and SHC-transforming protein 3 (SHC3) [62]. In addition to aforementioned genes, the coding gene for transmembrane emp24 domain-containing protein 4 (TMED4) was downregulated in SARS-CoV infection. TMED4 can interact with interleukin-1 receptor-like 1 so the production of proinflammatory cytokines including IL-6 and IL-8 was induced [63]. Another downregulated gene in the SARS-CoV infection was transmembrane protein 184A (TMEM184A), which was necessary for heparin responses in endothelial cells and vascular smooth muscle cells with interruption effect on the anti-inflammatory responses of endothelial cells to heparin [64]. The reduction in the production level of taurine and induction of inflammatory responses was experienced by downregulation of nebulin (NEB) gene in SARS-CoV infection [65]. In addition, mitogen-activated protein kinase (MAPK) signaling pathway was activated after downregulation of HCG11 gene leading to increased production of inflammatory cytokines [66]. Another downregulated gene was cysteine synthase (CYS1). Previous studies have shown that the presence of some amino acids such as cysteine are essential for the optimization of immune system and immunomodulatory properties including regulation of proinflammatory cytokines and T-cell proliferation, so the presence of cysteine-rich proteins in the diet can improve the function of immune system during the infections. CYS1 downregulation can be led to the activation of NF-κB, stimulation of macrophages, and cytokine storm by over production of IL-6 and TNF-α [67, 68]. Moreover, an association was reported between the downregulation of glutamine and serine-rich protein 1 (QSER1) and bromodomain containing 1 (BRD1) genes, which consequently induce the infiltration of B-cells and activate humoral immune responses [69]. The results obtained from mRNA microarray assay [37] revealed that the most downregulated genes in the infected bronchial epithelial cells with SARS-CoV induce signaling pathways and interleukin-producing cells toward an overactivation of immune system leading to cytokine storm, which the mentioned outcomes can be expanded to COVID-19 as well.

GO analysis defined top three GOs including GO:0016192, GO:0006886, and GO:0006888, which are intermediate transportation forms for exocytosis of assembled SARS-CoV proteins by smooth-wall vesicles to plasma membrane [70], intracellular transportation of 3a protein from SARS-CoV with the significant role of YXXΦ motif [71], and cycling S protein through the endoplasmic reticulum (ER)-Golgi system [72]; respectively. Due to similar proteins of SARS-CoV and SARS-CoV-2, the results of GO analysis from SARS-CoV can be expanded to COVID-19 as well.

The Reactome pathway analysis revealed that PTEN homolog plays a crucial role in the SARS-CoV infection through activation of dendritic cells, production of hyperactive B-cells and uncontrolled T-cells, and secretion of proinflammatory cytokines including interferons (IFNs), TNF-α, IL-10, IL-4, and granulocyte monocyte-colony stimulating factor (GM-CSF) [73]. Therefore, similar to SARS-CoV infection, PTEN Reactome pathway can regulate several Reactome pathways and immune responses in COVID-19.

Conclusions

Based on the high percentage of similarity of SARS-CoV and SARS-CoV-2, the results of many former studies on SARS-CoV can be expanded to SARS-CoV-2 to accelerate the pharmaceutical and vaccine explorations against COVID-19. Study on the single cell transcriptomics of infected bronchial epithelial cells with SARS-CoV demonstrates several immunity regulation factors and pathways that can be expanded to SARS-CoV-2 immunological pathogenesis and capable to lead a cytokine storm in the COVID-19 patients. As the overall conclusion, the transcriptomics data from SARS-CoV infection has the potential to be a golden pattern in the future pharmaceutical and vaccine studies against COVID-19.

Availability of Data and Materials

All data analyzed in this study were prepared from Expression Atlas database and were included in this article.

Abbreviations

ACE2:

Angiotensin-converting enzyme 2

ARDS:

Acute respiratory distress syndrome

ASK1:

Apoptosis signal-regulatory kinase

BNIP3L:

BCL2 Interacting Protein 3 like

BRD1:

Bromodomain containing 1

COVID-19:

Coronavirus disease 2019

CRP:

C-reactive protein

CYS1:

Cysteine synthase

ELISA:

Enzyme linked immunosorbent assay

ER:

Endoplasmic reticulum

FDR:

FalseDiscoveryRate

GM-CSF:

Granulocyte monocyte-colony stimulating factor

GO:

Gene ontology

HSD17B2:

Hydroxysteroid 17-beta dehydrogenase 2

IFN:

Interferon

IL:

Interleukin

MAPK:

Mitogen-activated protein kinase

MAPKAPK:

MAPK-activated protein kinase

MERS-CoV:

Middle East respiratory syndrome coronavirus

MKK6:

MAPK kinase 6

MMP1:

Matrix metalloproteinase 1

MNK1:

MAP kinase-interacting kinase 1

MSI2:

Musashi-2

MSK1:

Mitogen and stress-activated protein kinase 1

NDRG1:

N-myc downstream-regulated 1

NEB:

Nebulin

NEFL:

Neurofilament triplet L protein

NF-κB:

Nuclear factor-κB

NK cell:

Natural killer cell

NOSTRIN:

Nitric oxide synthase traffic inducer

PAH:

Phenylalanine hydroxylase

PKR:

Protein kinase R

PRDX5:

Peroxiredoxin 5

PTEN:

Phosphatase and tensin

QSER1:

Glutamine and serine-rich protein 1

RBD:

Receptor binding domain

RT-qPCR:

Quantitative reverse transcription PCR

S:

Spike

SARS-CoV-2:

Severe Acute Respiratory Syndrome Coronavirus 2

SHC3:

SHC-transforming protein 3

STAT1:

Signal transducer and activator of transcription 1

TAK1:

Transforming growth factor (TGF)-β-activating kinase

Th:

T helper

TMED4:

Transmembrane emp24 domain-containing protein 4

TNFα:

Necrosis factor α

VAV3:

Vav Guanine Nucleotide Exchange Factor 3

References

  1. 1.

    Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13(5):e1005457.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Govindarajan R, Duraiyan J, Kaliyappan K, Palanisamy M. Microarray and its applications. J Pharm Bioallied Sci. 2012;4(Suppl 2):S310–2.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Nadal-Ribelles M, Islam S, Wei W, Latorre P, Nguyen M, de Nadal E, et al. Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nat Microbiol. 2019;4(4):683–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18(9):1509–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Crosetto N, Bienko M, van Oudenaarden A. Spatially resolved transcriptomics and beyond. Nat Rev Genet. 2015;16(1):57–66.

    CAS  PubMed  Google Scholar 

  6. 6.

    Gliddon HD, Herberg JA, Levin M, Kaforou M. Genome-wide host RNA signatures of infectious diseases: discovery and clinical translation. Immunology. 2018;153(2):171–8.

    CAS  PubMed  Google Scholar 

  7. 7.

    Waddell SJ, Butcher PD, Stoker NG. RNA profiling in host-pathogen interactions. Curr Opin Microbiol. 2007;10(3):297–302.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Pathan N, Hemingway CA, Alizadeh AA, Stephens AC, Boldrick JC, Oragui EE, et al. Role of interleukin 6 in myocardial dysfunction of meningococcal septic shock. Lancet. 2004;363(9404):203–9.

    CAS  PubMed  Google Scholar 

  9. 9.

    Tran P, Ahmad R, Xu J, Ahmad A, Menezes J. Host's innate immune response to fungal and bacterial agents in vitro: up-regulation of interleukin-15 gene expression resulting in enhanced natural killer cell activity. Immunology. 2003;109(2):263–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, Belcher CE, et al. Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci U S A. 2002;99(2):972–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Tombacz D, Balazs Z, Csabai Z, Moldovan N, Szucs A, Sharon D, et al. Characterization of the dynamic Transcriptome of a Herpesvirus with long-read single molecule real-time sequencing. Sci Rep. 2017;7:43751.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhang Q, Lai MM, Lou YY, Guo BH, Wang HY, Zheng XQ. Transcriptome altered by latent human cytomegalovirus infection on THP-1 cells using RNA-seq. Gene. 2016;594(1):144–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Schwartz M, Stern-Ginossar N. The Transcriptome of Latent Human Cytomegalovirus. J Virol. 2019;93(11):e00047–19.

  14. 14.

    Cao Y, Zhang K, Liu L, Li W, Zhu B, Zhang S, et al. Global transcriptome analysis of H5N1 influenza virus-infected human cells. Hereditas. 2019;156:10.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Dapat C, Oshitani H. Novel insights into human respiratory syncytial virus-host factor interactions through integrated proteomics and transcriptomics analysis. Expert Rev Anti-Infect Ther. 2016;14(3):285–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Haralambieva IH, Zimmermann MT, Ovsyannikova IG, Grill DE, Oberg AL, Kennedy RB, et al. Whole Transcriptome profiling identifies CD93 and other plasma cell survival factor genes associated with measles-specific antibody response after vaccination. PLoS One. 2016;11(8):e0160970.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Haralambieva IH, Oberg AL, Ovsyannikova IG, Kennedy RB, Grill DE, Middha S, et al. Genome-wide characterization of transcriptional patterns in high and low antibody responders to rubella vaccination. PLoS One. 2013;8(5):e62149.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Sessions OM, Tan Y, Goh KC, Liu Y, Tan P, Rozen S, et al. Host cell transcriptome profile during wild-type and attenuated dengue virus infection. PLoS Negl Trop Dis. 2013;7(3):e2107.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Speranza E, Connor JH. Host Transcriptional Response to Ebola Virus Infection. Vaccines (Basel). 2017;5(3):30.

    Google Scholar 

  20. 20.

    Cross RW, Speranza E, Borisevich V, Widen SG, Wood TG, Shim RS, et al. Comparative Transcriptomics in Ebola Makona-Infected Ferrets, Nonhuman Primates, and Humans. J Infect Dis. 2018;218(suppl_5):S486–S95.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Golumbeanu M, Desfarges S, Hernandez C, Quadroni M, Rato S, Mohammadi P, et al. Proteo-Transcriptomic dynamics of cellular response to HIV-1 infection. Sci Rep. 2019;9(1):213.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Josset L, Menachery VD, Gralinski LE, Agnihothram S, Sova P, Carter VS, et al. Cell host response to infection with novel human coronavirus EMC predicts potential antivirals and important differences with SARS coronavirus. mBio. 2013;4(3):e00165–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Liang R, Wang L, Zhang N, Deng X, Su M, Su Y, et al. Development of Small-Molecule MERS-CoV Inhibitors. Viruses. 2018;10(12):721.

    CAS  PubMed Central  Google Scholar 

  24. 24.

    Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Euro Surveill. 2020;25(4):2000058.

    PubMed Central  Google Scholar 

  25. 25.

    Zolfaghari Emameh R, Nosrati H, Taheri RA. Combination of biodata mining and computational Modelling in identification and characterization of ORF1ab Polyprotein of SARS-CoV-2 isolated from Oronasopharynx of an Iranian patient. Biol Proced Online. 2020;22:8.

  26. 26.

    Zolfaghari Emameh, R., Falak, R. & Bahreini, E. Application of System Biology to Explore the Association of Neprilysin, Angiotensin-Converting Enzyme 2 (ACE2), and Carbonic Anhydrase (CA) in Pathogenesis of SARS-CoV-2. Biol Proced Online 22, 11 (2020). https://doi.org/10.1186/s12575-020-00124-6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Vincent MJ, Bergeron E, Benjannet S, Erickson BR, Rollin PE, Ksiazek TG, et al. Chloroquine is a potent inhibitor of SARS coronavirus infection and spread. Virol J. 2005;2:69.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Principi N, Esposito S. Chloroquine or hydroxychloroquine for prophylaxis of COVID-19. Lancet Infect Dis. 2020;S1473–3099(20)30296–6.

  29. 29.

    Ruamviboonsuk P, Lai TYY, Chang A, Lai CC, Mieler WF, Lam DSC, et al. Chloroquine and Hydroxychloroquine retinal toxicity consideration in the treatment of COVID-19. Asia Pac J Ophthalmol (Phila). 2020;9(2):85–7.

    Google Scholar 

  30. 30.

    Kamp TJ, Hamdan MH, January CT. Chloroquine or Hydroxychloroquine for COVID-19: Is Cardiotoxicity a Concern? J Am Heart Assoc. 2020;9:e016887.

    PubMed  Google Scholar 

  31. 31.

    Wang C, Li W, Drabek D, Okba NMA, van Haperen R, Osterhaus A, et al. A human monoclonal antibody blocking SARS-CoV-2 infection. Nat Commun. 2020;11(1):2251.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Hattermann K, Muller MA, Nitsche A, Wendt S, Donoso Mantke O, Niedrig M. Susceptibility of different eukaryotic cell lines to SARS-coronavirus. Arch Virol. 2005;150(5):1023–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ou X, Liu Y, Lei X, Li P, Mi D, Ren L, et al. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun. 2020;11(1):1620.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Liu Y, Gayle AA, Wilder-Smith A, Rocklov J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med. 2020;27(2):taaa021.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Bowie C, Hill T. Exit strategy to control covid-19 and relaunch the economy. BMJ. 2020;369:m1851.

    PubMed  Google Scholar 

  36. 36.

    McKee M, Stuckler D. If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future. Nat Med. 2020;26(5):640–2.

    CAS  PubMed  Google Scholar 

  37. 37.

    Yoshikawa T, Hill TE, Yoshikawa N, Popov VL, Galindo CL, Garner HR, et al. Dynamic innate immune responses of human bronchial epithelial cells to severe acute respiratory syndrome-associated coronavirus infection. PLoS One. 2010;5(1):e8729.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Papatheodorou I, Moreno P, Manning J, Fuentes AM, George N, Fexova S, et al. Expression atlas update: from tissues to single cells. Nucleic Acids Res. 2020;48(D1):D77–83.

    PubMed  Google Scholar 

  39. 39.

    Jafari M, Ansari-Pour N. Why, when and how to adjust your P values? Cell J. 2019;20(4):604–7.

    PubMed  Google Scholar 

  40. 40.

    Maruyama SR, Carvalho B, Gonzalez-Porta M, Rung J, Brazma A, Gustavo Gardinassi L, et al. Blood transcriptome profile induced by an efficacious vaccine formulated with salivary antigens from cattle ticks. NPJ Vaccines. 2019;4:53.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Barton AJ, Hill J, Pollard AJ, Blohmke CJ. Transcriptomics in human challenge models. Front Immunol. 2017;8:1839.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ong E, Sun P, Berke K, Zheng J, Wu G, He Y. VIO: ontology classification and study of vaccine responses given various experimental and analytical conditions. BMC Bioinformatics. 2019;20(Suppl 21):704.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Mizutani T, Fukushi S, Saijo M, Kurane I, Morikawa S. JNK and PI3k/Akt signaling pathways are required for establishing persistent SARS-CoV infection in Vero E6 cells. Biochim Biophys Acta. 2005;1741(1–2):4–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Lee CH, Chen RF, Liu JW, Yeh WT, Chang JC, Liu PM, et al. Altered p38 mitogen-activated protein kinase expression in different leukocytes with increment of immunosuppressive mediators in patients with severe acute respiratory syndrome. J Immunol. 2004;172(12):7841–7.

    CAS  PubMed  Google Scholar 

  45. 45.

    Spaziani A, Alisi A, Sanna D, Balsano C. Role of p38 MAPK and RNA-dependent protein kinase (PKR) in hepatitis C virus core-dependent nuclear delocalization of cyclin B1. J Biol Chem. 2006;281(16):10983–9.

    CAS  PubMed  Google Scholar 

  46. 46.

    Freshney NW, Rawlinson L, Guesdon F, Jones E, Cowley S, Hsuan J, et al. Interleukin-1 activates a novel protein kinase cascade that results in the phosphorylation of Hsp27. Cell. 1994;78(6):1039–49.

    CAS  PubMed  Google Scholar 

  47. 47.

    Kopecky-Bromberg SA, Martinez-Sobrido L, Palese P. 7a protein of severe acute respiratory syndrome coronavirus inhibits cellular protein synthesis and activates p38 mitogen-activated protein kinase. J Virol. 2006;80(2):785–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Surjit M, Liu B, Jameel S, Chow VT, Lal SK. The SARS coronavirus nucleocapsid protein induces actin reorganization and apoptosis in COS-1 cells in the absence of growth factors. Biochem J. 2004;383(Pt 1):13–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    DeDiego ML, Nieto-Torres JL, Jimenez-Guardeno JM, Regla-Nava JA, Castano-Rodriguez C, Fernandez-Delgado R, et al. Coronavirus virulence genes with main focus on SARS-CoV envelope gene. Virus Res. 2014;194:124–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Shahid M, Idrees M, Butt AM, Raza SM, Amin I, Rasul A, et al. Blood-based gene expression profile of oxidative stress and antioxidant genes for identifying surrogate markers of liver tissue injury in chronic hepatitis C patients. Arch Virol. 2020;165(4):809–22.

    CAS  PubMed  Google Scholar 

  51. 51.

    Morales MM, Pires-Neto RC, Inforsato N, Lancas T, da Silva LF, Saldiva PH, et al. Small airway remodeling in acute respiratory distress syndrome: a study in autopsy lung tissue. Crit Care. 2011;15(1):R4.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Chen J, Cui G, Lu C, Ding Y, Gao H, Zhu Y, et al. Severe infection with avian influenza a virus is associated with delayed immune recovery in survivors. Medicine (Baltimore). 2016;95(5):e2606.

    CAS  Google Scholar 

  53. 53.

    Jiang X, Dong X, Li SH, Zhou YP, Rayner S, Xia HM, et al. Proteomic analysis of Zika virus infected primary human fetal neural progenitors suggests a role for Doublecortin in the pathological consequences of infection in the cortex. Front Microbiol. 2018;9:1067.

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Chakraborty S, Ain R. Nitric-oxide synthase trafficking inducer is a pleiotropic regulator of endothelial cell function and signaling. J Biol Chem. 2017;292(16):6600–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Liu F, Li L, Xu M, Wu J, Luo D, Zhu Y, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Murr C, Grammer TB, Meinitzer A, Kleber ME, Marz W, Fuchs D. Immune activation and inflammation in patients with cardiovascular disease are associated with higher phenylalanine to tyrosine ratios: the ludwigshafen risk and cardiovascular health study. J Amino Acids. 2014;2014:783730.

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Silva R, Morgado JM, Freitas A, Couceiro A, Orfao A, Regateiro F, et al. Influence of pro- and anti-inflammatory cytokines in Th1 polarization after allogeneic stimulation. Int J Biomed Sci. 2005;1(1):46–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    O'Sullivan TE, Johnson LR, Kang HH, Sun JC. BNIP3- and BNIP3L-mediated Mitophagy promotes the generation of natural killer cell memory. Immunity. 2015;43(2):331–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Lang R, Raffi FAM. Dual-Specificity Phosphatases in Immunity and Infection: An Update. Int J Mol Sci. 2019;20(11):2710.

    CAS  PubMed Central  Google Scholar 

  60. 60.

    Belew MS, Bhatia S, Keyvani Chahi A, Rentas S, Draper JS, Hope KJ. PLAG1 and USF2 co-regulate expression of Musashi-2 in human hematopoietic stem and progenitor cells. Stem Cell Reports. 2018;10(4):1384–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Sawai CM, Babovic S, Upadhaya S, Knapp D, Lavin Y, Lau CM, et al. Hematopoietic stem cells are the major source of multilineage hematopoiesis in adult animals. Immunity. 2016;45(3):597–609.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Cheng J, Xie HY, Xu X, Wu J, Wei X, Su R, et al. NDRG1 as a biomarker for metastasis, recurrence and of poor prognosis in hepatocellular carcinoma. Cancer Lett. 2011;310(1):35–45.

    CAS  PubMed  Google Scholar 

  63. 63.

    Aber R, Chan W, Mugisha S, Jerome-Majewska LA. Transmembrane emp24 domain proteins in development and disease. Genet Res (Camb). 2019;101:e14.

    Google Scholar 

  64. 64.

    Farwell SL, Kanyi D, Hamel M, Slee JB, Miller EA, Cipolle MD, et al. Heparin decreases in tumor necrosis factor alpha (TNFalpha)-induced endothelial stress responses require Transmembrane protein 184A and induction of dual specificity phosphatase 1. J Biol Chem. 2016;291(10):5342–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Sztal TE, McKaige EA, Williams C, Oorschot V, Ramm G, Bryson-Richardson RJ. Testing of therapies in a novel nebulin nemaline myopathy model demonstrate a lack of efficacy. Acta Neuropathol Commun. 2018;6(1):40.

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Xu Y, Zheng Y, Liu H, Li T. Modulation of IGF2BP1 by long non-coding RNA HCG11 suppresses apoptosis of hepatocellular carcinoma cells via MAPK signaling transduction. Int J Oncol. 2017;51(3):791–800.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Grimble RF. The effects of sulfur amino acid intake on immune function in humans. J Nutr. 2006;136(6 Suppl):1660S–5S.

    CAS  PubMed  Google Scholar 

  68. 68.

    Ruth MR, Field CJ. The immune modifying effects of amino acids on gut-associated lymphoid tissue. J Anim Sci Biotechnol. 2013;4(1):27.

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Fryland T, Christensen JH, Pallesen J, Mattheisen M, Palmfeldt J, Bak M, et al. Identification of the BRD1 interaction network and its impact on mental disorder risk. Genome Med. 2016;8(1):53.

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Lim YX, Ng YL, Tam JP, Liu DX. Human Coronaviruses: A Review of Virus-Host Interactions. Diseases. 2016;4(3):26.

    PubMed Central  Google Scholar 

  71. 71.

    Minakshi R, Padhan K. The YXXPhi motif within the severe acute respiratory syndrome coronavirus (SARS-CoV) 3a protein is crucial for its intracellular transport. Virol J. 2014;11:75.

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    McBride CE, Li J, Machamer CE. The cytoplasmic tail of the severe acute respiratory syndrome coronavirus spike protein contains a novel endoplasmic reticulum retrieval signal that binds COPI and promotes interaction with membrane protein. J Virol. 2007;81(5):2418–28.

    CAS  PubMed  Google Scholar 

  73. 73.

    Brandmaier A, Hou SQ, Demaria S, Formenti SC, Shen WH. PTEN at the interface of immune tolerance and tumor suppression. Front Biol (Beijing). 2017;12(3):163–74.

    Google Scholar 

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Acknowledgements

We thank the Research Deputy and COVID-19 National Committee of the National Institute of Genetic Engineering and Biotechnology (NIGEB) of the Islamic Republic of Iran for preparing the condition to perform this study. No funding organizations had any role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.

Funding

To perform this study, RZE received a research grant support from the National Institute of Genetic Engineering and Biotechnology (NIGEB) of the Islamic Republic of Iran.

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All authors participated in the design of the study. RZE designed and carried out the search to find microarray mRNA results from SARS-CoV infection. RZE and HN performed biodata mining related to downregulation and upregulation of genes. RZE and RF evaluated the immunological regulatory effects of downregulated and upregulated genes. MK worked on the mRNA microarray assay and visualization of the related results. ME contributed in drafting of the introduction and English editing of the manuscript, artwork preparation of the figures, and preparing the manuscript for submission to the journal. RZE drafted the first version of the manuscript. All authors participated in writing further versions and read and approved the final manuscript.

Corresponding author

Correspondence to Reza Zolfaghari Emameh.

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The data of the microarray mRNA analysis in this study was obtained from Expression Atlas database.

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The authors declare that they have no conflict of interests.

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Zolfaghari Emameh, R., Nosrati, H., Eftekhari, M. et al. Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19. Biol Proced Online 22, 16 (2020). https://doi.org/10.1186/s12575-020-00127-3

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Keywords

  • Transcriptomics
  • Bronchial epithelial cells
  • Immunological regulations
  • Signaling pathways
  • Cytokine storm
  • Gene ontology
  • Reactome pathways
  • Severe acute respiratory syndrome
  • SARS-CoV-2
  • COVID-19