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Volume 18 Supplement 1

Proceedings of the 27th International Conference on Genome Informatics: genomics

Transcriptome analysis reveals dynamic changes in coxsackievirus A16 infected HEK 293T cells

BMC Genomics volume 18, Article number: 933 (2017) Cite this article

Abstract

Background

Coxsackievirus A16 (CVA16) and enterovirus 71 (EV71) are two of the major causes of hand, foot and mouth disease (HFMD) world-wide. Although many studies have focused on infection and pathogenic mechanisms, the transcriptome profile of the host cell upon CVA16 infection is still largely unknown.

Results

In this study, we compared the mRNA and miRNA expression profiles of human embryonic kidney 293T cells infected and non-infected with CVA16. We highlighted that the transcription of SCARB2, a cellular receptor for both CVA16 and EV71, was up-regulated by nearly 10-fold in infected cells compared to non-infected cells. The up-regulation of SCARB2 transcription induced by CVA16 may increase the possibility of subsequent infection of CVA16/EV71, resulting in the co-infection with two viruses in a single cell. This explanation would partly account for the co-circulation and genetic recombination of a great number of EV71 and CVA16 viruses. Based on correlation analysis of miRNAs and genes, we speculated that the high expression of SCARB2 is modulated by down-regulation of miRNA has-miR-3605-5p. At the same time, we found that differentially expressed miRNA target genes were mainly reflected in the extracellular membrane (ECM)-receptor interaction and circadian rhythm pathways, which may be related to clinical symptoms of patients infected with CVA16, such as aphthous ulcers, cough, myocarditis, somnolence and potentially meningoencephalitis. The miRNAs hsa-miR-149-3p and hsa-miR-5001-5p may result in up-regulation of genes in these morbigenous pathways related to CVA16 and further cause clinical symptoms.

Conclusions

The present study elucidated the changes in 293T cells upon CVA16 infection at transcriptome level, containing highly up-regulated SCARB2 and genes in ECM-receptor interaction and circadian rhythm pathways, and key miRNAs in gene expression regulation. These results provided novel insight into the pathogenesis of HFMD induced by CVA16 infection.

Background

Hand, foot and mouth disease (HFMD) threatens infants and children globally [1] and is caused by two main pathogens, CVA16 and EV71, both of which are members of the Enterovirus genus in the Picornaviridae family. Infection with EV71 commonly causes HFMD cases with severe symptoms, such as aseptic meningoencephalitis, brainstem encephalitis, myelitis, myocarditis and pulmonary edema [2]. By contrast, infection with CVA16 generally induces mild and self-limiting clinical symptoms, such as vesicular maculopapular rash, ulcers and pharyngitis [3], and only occasionally results in severe and fatal cases with central nervous system inflammation [4–8]. CVA16 infections have contributed to the majority of HFMD cases for decades, and co-infection and genetic recombination between CVA16 and EV71 have occurred frequently, likely leading to large disease breakouts and evolution of both viruses [9–13]. Therefore, greater attention should be paid to investigations of CVA16 infection mechanisms.

CVA16/EV71 infections begin with the binding of cellular receptors of the host cell, which were demonstrated to be human scavenger receptor class B member 2 (SCARB2) expressed extensively in tissues and human P-selectin glycoprotein ligand-1 (PSGL-1) expressed primarily in lymphocytes [14, 15]. The receptors mediate entry and subsequent replication of viruses. Our group described that SCARB2-overexpressing cell lines significantly increase their susceptibility to CVA16/EV71 of various genotypes [16], similar susceptibility increasing effect was also proved in PSGL-1 overexpressing cells [15, 17]. The infection of these viruses causes the disruption of cellular pathways and events. Host cell molecular alterations include relocalization of far upstream element binding protein 2 (FBP2), heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) and hnRNP K, phosphorylation of MAPK/ERK and PI3K/Akt pathways to assist virus replication [18–21], and cleavage of eukaryotic translation initiation factor 4G (eIF4G) by virus protease to inhibit the synthesis of host proteins and induce apoptosis [22]. Cell death and apoptosis in tissues subsequently result in HFMD symptoms.

In recent years, many studies on HFMD have been conducted at the transcriptome level, most of which use assay technologies to identify markers (genes or miRNAs) of EV71-related clinical symptoms in different tissues or cells [23–27]. However, the molecular mechanisms underlying vesicular rash formation caused by CVA16 infection and the potential central nervous system inflammation caused by enterovirus are still largely unknown. In this study, RNA-seq and miRNA-seq technologies were used to profile the transcriptome alterations in CVA16-infected and non-infected (CVA16-non-infected) cells, with the aim of gaining greater insight into the underlying mechanisms of CVA16-host interactions which may be highly relevant to disease pathogenesis in vivo.

Results and discussion

mRNA and miRNA expression profiles

We analyzed mRNA and miRNA expression profiles based on mRNA-seq and miRNA-seq for CVA16-infected as well as CVA16-non-infected cells to determine how CVA16 infection affects the function of a cell and the related regulatory mechanisms. In total, 88.1 M and 72.4 M mRNA-seq reads were generated for CVA16-infected and CVA16-non-infected cells, respectively. After trimming low quality bases, uniquely mapped rates of reads of these groups were both about 67% (Table 1). The saturation evaluation (Additional file 1) showed that the sequencing data were sufficient for analysis of gene expression. For CVA16-infected and CVA16-non-infected cells, 15.1 M and 7.5 M short reads of miRNA sequences were generated, respectively. We mapped miRNA clean short reads to a human pre-miRNA database and obtained 20.39 and 23.97% mapped rates and detected 447 and 446 miRNAs for CVA16-infected and CVA16-non-infected cells, respectively (Table 2).

Table 1 Mapping results of RNA-seq data for CVA16-infected and CVA16-non-infected cell samples
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Table 2 Results of miRNA sequencing reads mapped to mirBase for CVA16-infected and CVA16-non-infected cell samples
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Among the identified 1954 differentially expressed genes, 1825 genes were up-regulated genes and 129 were down-regulated genes in CVA16-infected relative to CVA16-non-infected samples ((Differentially expressed genes are listed in Additional file 2, and the top 10 are shown in Fig. 1a). The 1954 differentially expressed genes were enriched in 822 third-level Gene Ontology (GO) terms (Additional file 3), mainly those involved in the regulation of cellular processes, cellular macromolecule metabolic processes and regulation of metabolic processes. The 51 differentially expressed miRNAs identified included 29 up-regulated miRNAs and 22 down-regulated miRNAs in CVA16-infected cells relative to CVA16-non-infected cells (Differentially expressed miRNAs are listed in Additional file 4, and the top 10 are shown in Fig. 1b). Therefore, 1323 differentially expressed target genes were predicted between CVA16-infected and CVA16-non-infected cells (Differentially expressed target genes are listed in Additional file 5, and the top 10 are shown in Fig. 1c).

Fig. 1
figure 1

Differential gene expression between CVA16-non-infected and CVA16-infected cell samples. a Top 10 differentially expressed genes. b Top 10 differentially expressed miRNAs. c Top 10 differentially expressed target genes. Blue represents CVA6-non-infected. Red represents CVA6- infected

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The expression regulation of SCARB2 in CVA16-infected cells

Virus infection is generally known to alter the expression of host genes and affect protein function. Thus, studying expression patterns of host genes may facilitate their use as diagnostic markers and therapeutic targets for diseases. CVA16 infection can cause clinical symptoms of HFMD, such as aphthous ulcers, cough and central nervous system dysfunctions. We found that the SCARB2 gene was the most differentially expressed, as its level in CVA16-infected cells was 10 times higher than that of CVA16-non-infected cells (Fig. 1a). By quantitative RT-PCR (qRT-PCR) analysis, the copy number of SCARB2 mRNA in CVA16-infected cells also was confirmed to be increased compared to that of the control (Fig. 2a). Furthermore, levels of the SCARB2 protein in CVA16-infected cells were shown to be increased in a dose-dependent pattern (Fig. 2b). SCARB2 is the cell membrane receptor for CVA16/EV71, and the up-regulation of its expression may increase the chance of co-infection with two viruses in a single cell. This finding would partly explain the large number of cases of EV71 and CVA16 co-circulation and genetic recombination. We also found that the miRNA hsa-miR-3605-5p, which targets the SCARB2 gene, was down-regulated in CVA16-infected cells. The miRNA hsa-miR-3605-5p plays an important role in the processes of metal ion transport, monovalent inorganic cation transport, intracellular transport and transmembrane transport. Similarly, the major function of SCARB2 in the cell is membrane transport [28]. Therefore, we inferred that the down-regulation of hsa-miR-3605-5p mediates the high expression of SCARB2.

Fig. 2
figure 2

SCARB2 mRNA and protein levels between CVA16-non-infected and CVA16-infected cell samples. a Fold change of SCARB2/β-actin mRNA copies detected by qRT-PCR. b SCARB2 protein levels detected by Western blot

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Function enrichment analysis of differentially expressed genes

These differentially expressed target genes between CVA16-infected and CVA16-non-infected cells were clustered into four functional pathways: ECM-receptor interaction, circadian rhythm, ABC transporters and lysine degradation (Table 3). We found that all of the differentially expressed target genes clustered in these four KEGG functional pathways were up-regulated (Fig. 3a-d). Some differentially expressed genes, regulated by miRNA, clustered in the ECM-receptor interaction pathway, which may be closely related to symptoms caused by CVA16 infection: aphthous ulcers, cough and myocarditis. Bakir-Gungor et al. [29] recently discovered that oral aphthous ulcers of Behcet’s disease may be correlated with the ECM-receptor interaction. Gunawardhana et al. [30] also found that inflammation of the airway is affected by the ECM-receptor interaction. The ECM in the heart and vascular wall consists of fibrous proteins and proteoglycans [31], which are important for maintenance of both the structure and function of the heart and vascular tissues [32]. Excessive deposition of ECM proteins has also been associated with many cardiac pathological entities such as myocardial fibrosis after myocardial infarction [33]. Therefore, we had reason to believe that the ECM-receptor interaction may be involved with symptoms of myocarditis, such as aphthous ulcers and cough, in some patients infected with CVA16. At the same time, we found that most of the differently expressed genes (9 out of 14) in the ECM-receptor interaction were modulated by the down-regulation of miRNAs hsa-miR-149-3p and hsa-miR-5001-5p (Fig. 4a). These genes regulated by hsa-miR-149-3p or hsa-miR-5001-5p, including ITGA3, AGRN, COL5A1, HSPG2, LAMA3, LAMA5, COL11A2, ITGA7 and ITGA2, are all related to diseases.

Table 3 Clustering functional pathways of differentially expressed miRNA target genes
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Fig. 3
figure 3

Differentially expressed genes between CVA16-non-infected and CVA16-infected cell samples clustered in four KEGG pathways. a ECM-receptor interaction pathway. b Circadian rhythm pathway. c ABC transporters pathway. d Lysine degradation pathway. Blue represents CVA6-non-infected. Red represents CVA6- infected

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Fig. 4
figure 4

Regulation relation networks between differentially expressed target genes clustered in KEGG pathways and differentially expressed miRNAs. Yellow represents up-regulated miRNAs. Green represents down-regulated miRNAs. Orange represents differentially expressed genes clustered in KEGG pathways. a ECM-receptor interaction pathway. b Circadian rhythm pathway

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Another observed enrichment pathway was the circadian rhythm pathway. Differentially expressed genes associated with the circadian rhythm pathway included NR1D1, CSNK1D, CLOCK, PER1 and PER2. These genes were all up-regulated and related to the sleep phase syndrome and circadian clock. In other words, somnolence caused by CVA16 infection was correlated with the circadian rhythm pathway. Radomski et al. found that patients suffering from meningoencephalitis demonstrated circadian disruptions in cortisol, prolactin and sleep-wake rhythms. Sleeping sickness at the stage of meningoencephalitis manifests itself as a significant disturbance in the circadian rhythm of sleep-wakefulness [34]. Buguet et al. also found that the stage of meningoencephalitis in patients suffering from human African trypanosomiasis (HAT, sleeping sickness) represented a dysregulation of the sleep-wake cycle and sleep structure, rather than hypersomnia, and this dysregulation was accompanied by a circadian dysrhythmia of hormonal secretions. This finding shown that the meningoencephalitis is related to the circadian rhythm [35]. Other reports have shown that meningoencephalitis is one of the typical symptoms after EV71 infection [36–38]. Meningoencephalitis has also frequently appeared as one of the symptoms of Coxsackie B virus infection [38, 39]. As CV infection has been reported to cause lethargic encephalitis and mild paralysis in children [40, 41], we deduced that CVA16 interferes with intracellular circadian rhythms leading to somnolence with meningoencephalitis. Here, we found that 80% (4 out of 5) of the differentially expressed target genes clustered in the circadian rhythm pathway also are regulated by down-regulation of the miRNAs hsa-miR-149-3p and hsa-miR-5001-5p (Fig. 4b). Therefore, we deduced that the down-regulation of these two miRNAs may result in up-regulation of genes in morbigenous pathways related with CVA16 and further cause clinical symptoms.

Conclusions

We examined the impact of infection with CVA16, a major causative pathogen of HFMD, using RNA-seq and miRNA-seq technologies to reveal the associated pathogenic mechanisms at the transcriptome level. We highlighted that the SCARB2 gene was up-regulated by nearly 10-fold in infected cells compared to non-infected 293T cells at the transcriptional and translational levels. SCABR2 serving as a cellular receptor plays an important role in co-infections of EV71 and CVA16, and has-miR-3605-5p regulates the high expression of SCARB2. We found that differentially expressed miRNA target genes were mainly concentrated in the ECM-receptor interaction and circadian rhythm pathways, which may be related to clinical symptoms of patients infected with CVA16, such as aphthous ulcers, cough, myocarditis and somnolence of meningoencephalitis. Down-regulation of miRNAs hsa-miR-149-3p and hsa-miR-5001-5p may result in up-regulation of genes in these morbigenous pathways related to CVA16 infection, which further leads to clinical phenotypes.

Methods

Cells and viruses

293T cells (derived from human embryonic kidney cells) were cultured as monolayers in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal calf serum (FCS). The CVA16 strain HN1129/CHN/2010 (B1b genotype, a gift kindly provided by the Henan Provincial Center for Disease Control and Prevention, China) was used for infection.

CVA16 infections

Ninety percent confluent monolayers of 293T cells were infected with CVA16 at 1 TCID50 per cell. Two hours later, cells were washed twice with PBS and cultured in fresh DMEM (10% FCS). Fifteen hours later, the cells were harvested for transcriptome and qRT-PCR analyses.

Next-generation sequencing of mRNA and miRNA and data processing

Sequence data were generated using Illuminna Hiseq 2000 following the manufacturer’s instructions for mRNA-seq and miRNA-seq. For mRNA-seq data, Fastx-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) was used to remove low quality reads and adapter sequences. Evaluation of mRNA-seq sequencing data showed that the sequencing quality was high (mean quality value was greater than 34). Reads (after removal of low quality reads and adapter sequences) were mapped to the human reference genome sequences (ENSEMBL62/GRCh37) with the Burrows-Wheeler Aligner (BWA) [42]. Only reads that were uniquely mapped to the reference genome sequences or were uniquely mapped to the junctions were chosen for subsequent analysis. Read densities for each gene were calculated by the number of uniquely mapped reads per kilobase per million mapped reads (RPKM). Differentially expressed genes were identified by DEGseq [43], which is an R-package available in Bioconductor (www.bioconductor.org/packages/2.7). A gene was considered to be significantly differentially expressed if the p-value and q-value were both less than 0.05.

For miRNA-seq data, if miRNA clean short reads (after removing low quality reads and adaptor sequences) were mapped to the whole human genome, both known miRNA and new miRNA could be obtained. However, the specificity of mapping short reads to the genome was very low due to the very short clean reads (<25 nt) and the complexity of the reference genome sequences. Therefore, in order to accurately observe known miRNA expression, we mapped clean short miRNA reads to human pre-miRNAs from the mirBase database (http://www.mirbase.org/) using SHRiMP2 software [44]. RPKM were computed to normalize the miRNA expression, and miRNAs were ignored when the number of mapped reads was less than 5. Differentially expressed genes were identified by DEGseq [43] using the same parameters as that for RNA-seq. To predict the gene target by miRNAs, two computational target prediction algorithms (RNAhybrid 2.1 [45] and miRanda 3.3 [46]) were used. The results predicted by both algorithms were combined, and the overlapping sequences were determined.

Functional profiling of differentially expressed genes

The Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/) was used to identify functional categories of differentially expressed genes. Following the instructions of the DAVID manual, differentially expressed genes in each sample were uploaded, and the function charts were generated. The functional groups with a P-value less than 0.05 and gene count greater than two were examined.

qRT-PCR

Total RNA was extracted from cells using TRIzol reagent (Invitrogen). Target genes were amplified from 100 ng total RNA using a One Step SYBR Prime Script RT-PCR Kit (TaKaRa) and CFX96 real-time PCR detection instrument (Bio-Rad). SCARB2 mRNA levels were analyzed with β-actin as the internal control. SCARB2 primer: sense, 5′-GTACTGAGGCATTTGACTCCT-3′; antisense, 5′-AGTTCCCTGTAGGTGTATGGC-3′. β-actin primer: sense, 5′-CCACCATGTACCCAGGCATT-3′; antisense, 5′-CCGGACTCATCGTACTCCTG-3′.

Western blot

293T cells were infected with CVA16 at 0, 10−4, 10−3, 10−2, 10−1 and 1 TCID50 per cell. After 2 h, the cells were washed twice with PBS and then cultured in fresh DMEM (10% FCS) and harvested after 15 h. The expression levels of SCARB2 in 293T cells were detected by Western blot with β-actin as the internal control. After treatment with lysis buffer, proteins were subjected to 10% SDS-PAGE, and transferred to nitrocellulose membranes. After blocking with 5% nonfat milk and washing in Tris-buffered saline–Tween solution, membranes were incubated with goat anti-SCARB2 primary antibody (1:2000, R&D Systems), followed by incubation with an alkaline phosphatase-conjugated rabbit anti-goat secondary antibody (1:5000, Sigma). The staining was carried out with NBT and BCIP solutions.

Abbreviations

CVA16:

Coxsackievirus A16

ECM:

Extracellular membrane

EV71:

Enterovirus 71

GO:

Gene Ontology

HFMD:

Hand, foot and mouth disease

RPKM:

Reads per kilobase per million mapped reads

SCARB2:

Scavenger receptor class B member 2

References

  1. Muehlenbachs A, Bhatnagar J, Zaki SR. Tissue tropism, pathology and pathogenesis of enterovirus infection. J Pathol. 2015;235(2):217–28.

    Article  PubMed  Google Scholar 

  2. Wang SM, Liu CC. Update of enterovirus 71 infection: epidemiology, pathogenesis and vaccine. Expert Rev Anti Infect Ther. 2014;12(4):447–56.

    Article  CAS  PubMed  Google Scholar 

  3. Mao Q, Wang Y, Yao X, Bian L, Wu X, Xu M, Liang Z. Coxsackievirus A16: epidemiology, diagnosis, and vaccine. Hum Vaccin Immunother. 2014;10(2):360–7.

    Article  PubMed  Google Scholar 

  4. Goto K, Sanefuji M, Kusuhara K, Nishimura Y, Shimizu H, Kira R, Torisu H, Hara T. Rhombencephalitis and coxsackievirus A16. Emerg Infect Dis. 2009;15(10):1689–91.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wang CY, Li Lu F, Wu MH, Lee CY, Huang LM. Fatal coxsackievirus A16 infection. Pediatr Infect Dis J. 2004;23(3):275–6.

    Article  PubMed  Google Scholar 

  6. Legay F, Leveque N, Gacouin A, Tattevin P, Bouet J, Thomas R, Chomelt JJ. Fatal coxsackievirus A-16 pneumonitis in adult. Emerg Infect Dis. 2007;13(7):1084–6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wright Jr HT, Landing BH, Lennette EH, Mc AR. Fatal infection in an infant associated with Coxsackie virus group A, type 16. N Engl J Med. 1963;268:1041–4.

    Article  PubMed  Google Scholar 

  8. Yang WS. Clinical characteristic analysis of 104 cases of children severe hand, foot and mouth disease. Chongqing Med. 2011;40:1722–7.

    Google Scholar 

  9. Zhao K, Han X, Wang G, Hu W, Zhang W, Yu XF. Circulating coxsackievirus A16 identified as recombinant type A human enterovirus. China Emerg Infect Dis. 2011;17(8):1537–40.

    PubMed  Google Scholar 

  10. Zhang Y, Zhu Z, Yang W, Ren J, Tan X, Wang Y, Mao N, Xu S, Zhu S, Cui A, et al. An emerging recombinant human enterovirus 71 responsible for the 2008 outbreak of hand foot and mouth disease in Fuyang city of China. Virol J. 2010;7:94.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Yip CC, Lau SK, Zhou B, Zhang MX, Tsoi HW, Chan KH, Chen XC, Woo PC, Yuen KY. Emergence of enterovirus 71 “double-recombinant” strains belonging to a novel genotype D originating from southern China: first evidence for combination of intratypic and intertypic recombination events in EV71. Arch Virol. 2010;155(9):1413–24.

    Article  CAS  PubMed  Google Scholar 

  12. Pan H, Zhu YF, Qi X, Zhang YJ, Li L, Deng F, Wu B, Wang SJ, Zhu FC, Wang H. Analysis on the epidemiological and genetic characteristics of enterovirus type 71 and Coxsackie A16 virus infection in Jiangsu, China. Zhonghua Liu Xing Bing Xue Za Zhi. 2009;30(4):339–43.

    CAS  PubMed  Google Scholar 

  13. Zhang HM, Li CR, Liu YJ, Liu WL, Fu D, Xu LM, Xie JJ, Tan Y, Wang H, Chen XC. To investigate pathogen of hand, foot and mouth disease in Shenzhen in 2008. Zhonghua Shi Yan He Lin Chuang Bing Du Xue Za Zhi. 2009;23(5):334–6.

    PubMed  Google Scholar 

  14. Yamayoshi S, Yamashita Y, Li J, Hanagata N, Minowa T, Takemura T, Koike S. Scavenger receptor B2 is a cellular receptor for enterovirus 71. Nat Med. 2009;15(7):798–801.

    Article  CAS  PubMed  Google Scholar 

  15. Nishimura Y, Shimojima M, Tano Y, Miyamura T, Wakita T, Shimizu H. Human P-selectin glycoprotein ligand-1 is a functional receptor for enterovirus 71. Nat Med. 2009;15(7):794–7.

    Article  CAS  PubMed  Google Scholar 

  16. Li X, Fan P, Jin J, Su W, An D, Xu L, Sun S, Zhang Y, Meng X, Gao F, et al. Establishment of cell lines with increased susceptibility to EV71/CA16 by stable overexpression of SCARB2. Virol J. 2013;10:250.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lin HY, Yang YT, Yu SL, Hsiao KN, Liu CC, Sia C, Chow YH. Caveolar endocytosis is required for human PSGL-1-mediated enterovirus 71 infection. J Virol. 2013;87(16):9064–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lin JY, Li ML, Huang PN, Chien KY, Horng JT, Shih SR. Heterogeneous nuclear ribonuclear protein K interacts with the enterovirus 71 5′ untranslated region and participates in virus replication. J Gen Virol. 2008;89(Pt 10):2540–9.

    Article  CAS  PubMed  Google Scholar 

  19. Lin JY, Li ML, Shih SR. Far upstream element binding protein 2 interacts with enterovirus 71 internal ribosomal entry site and negatively regulates viral translation. Nucleic Acids Res. 2009;37(1):47–59.

    Article  CAS  PubMed  Google Scholar 

  20. Lin JY, Shih SR, Pan M, Li C, Lue CF, Stollar V, Li ML. hnRNP A1 interacts with the 5′ untranslated regions of enterovirus 71 and Sindbis virus RNA and is required for viral replication. J Virol. 2009;83(12):6106–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wong WR, Chen YY, Yang SM, Chen YL, Horng JT. Phosphorylation of PI3K/Akt and MAPK/ERK in an early entry step of enterovirus 71. Life Sci. 2005;78(1):82–90.

    Article  CAS  PubMed  Google Scholar 

  22. Kuo RL, Kung SH, Hsu YY, Liu WT. Infection with enterovirus 71 or expression of its 2A protease induces apoptotic cell death. J Gen Virol. 2002;83:1367–76.

    Article  CAS  PubMed  Google Scholar 

  23. Bian L, Wang Y, Liu Q, Xia J, Long JE. Prediction of signaling pathways involved in enterovirus 71 infection by algorithm analysis based on miRNA profiles and their target genes. Arch Virol. 2015;160(1):173–82.

    Article  CAS  PubMed  Google Scholar 

  24. Xu LJ, Jiang T, Zhang FJ, Han JF, Liu J, Zhao H, Li XF, Liu RJ, Deng YQ, Wu XY, et al. Global transcriptomic analysis of human neuroblastoma cells in response to enterovirus type 71 infection. PLoS One. 2013;8(7):e65948.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Griffiths MJ, Ooi MH, Wong SC, Mohan A, Podin Y, Perera D, Chieng CH, Tio PH, Cardosa MJ, Solomon T. In enterovirus 71 encephalitis with cardio-respiratory compromise, elevated interleukin 1beta, interleukin 1 receptor antagonist, and granulocyte colony-stimulating factor levels are markers of poor prognosis. J Infect Dis. 2012;206(6):881–92.

    Article  CAS  PubMed  Google Scholar 

  26. Jia HL, He CH, Wang ZY, Xu YF, Yin GQ, Mao LJ, Liu CW, Deng L. MicroRNA expression profile in exosome discriminates extremely severe infections from mild infections for hand, foot and mouth disease. BMC Infect Dis. 2014;14:506.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang Y, Yang E, Pu J, Liu L, Che Y, Wang J, Liao Y, Wang L, Ding D, Zhao T, et al. The gene expression profile of peripheral blood mononuclear cells from EV71-infected rhesus infants and the significance in viral pathogenesis. PLoS One. 2014;9(1):e83766.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Eskelinen E-L, Tanaka Y, Saftig P. At the acidic edge: emerging functions for lysosomal membrane proteins. Trends Cell Biol. 2003;13(3):137–45.

    Article  CAS  PubMed  Google Scholar 

  29. Bakir-Gungor B, Remmers EF, Meguro A, Mizuki N, Kastner DL, Gul A, Sezerman OU. Identification of possible pathogenic pathways in Behcet’s disease using genome-wide association study data from two different populations. Eur J Hum Genet. 2015;23(5):678–87.

    Article  CAS  PubMed  Google Scholar 

  30. Gunawardhana LP, Gibson PG, Simpson JL, Benton MC, Lea RA, Baines KJ. Characteristic DNA methylation profiles in peripheral blood monocytes are associated with inflammatory phenotypes of asthma. Epigenetics. 2014;9(9):1302–16.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hong D, Zeng X, Xu W, Ma J, Tong Y, Chen Y. Altered profiles of gene expression in curcumin-treated rats with experimentally induced myocardial infarction. Pharmacol Res. 2010;61(2):142–8.

    Article  CAS  PubMed  Google Scholar 

  32. Ju H, Dixon IM. Extracellular matrix and cardiovascular diseases. Can J Cardiol. 1996;12(12):1259–67.

    CAS  PubMed  Google Scholar 

  33. Mackenna D. Role of mechanical factors in modulating cardiac fibroblast function and extracellular matrix synthesis. Cardiovasc Res. 2000;46(2):257–63.

    Article  CAS  PubMed  Google Scholar 

  34. Radomski MW. Disruptions in the secretion of cortisol, prolactin, and certain cytokines in human African trypanosomiasis patients. Bulletin De La Société De Pathologie Exotique. 1994;87:376–9.

    CAS  PubMed  Google Scholar 

  35. Buguet A. Is sleeping sickness a circadian disorder? The serotonergic hypothesis. Chronobiol Int. 1999;16(4):477–89.

    Article  CAS  PubMed  Google Scholar 

  36. Huang YF, Chiu PC, Chen CC, Chen YY, Hsieh KS, Liu YC, Lai PH, Chang HW. Cardiac troponin I: a reliable marker and early myocardial involvement with meningoencephalitis after fatal enterovirus71 infection. J Infect. 2003;46(4):238–43.

    Article  PubMed  Google Scholar 

  37. Cho HK, Lee H, Seo JW, Na YL, Kim HS, Hong YM, Lee SJ, Sun WL, Cheon DS, Ji YH. Enterovirus 71-associated hand, foot and mouth diseases with neurologic symptoms, a university hospital experience in Korea, 2009. Korean J Pediatr. 2010;53(5):639.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu JY, Wang SM, Chen IC, Yu CK, Liu CC. Hepatic damage caused by coxsackievirus B3 is dependent on age-related tissue tropisms associated with the coxsackievirus-adenovirus receptor. Pathog Dis. 2013;68(2):52–60.

    Article  PubMed  Google Scholar 

  39. Bissel SJ, Winkler CC, Deltondo J, Wang G, Williams K, Wiley CA. Coxsackievirus B4 myocarditis and meningoencephalitis in newborn twins. Neuropathology. 2014;34(5):429–37.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Suvisaari J. Childhood central nervous system viral infections and adult schizophrenia. Am J Psychiatry. 2003;160(6):1183–5.

    Article  PubMed  Google Scholar 

  41. Cree BC, Bernardini GL, Hays AP, Lowe G. A fatal case of coxsackievirus B4 meningoencephalitis. Jama Neurol. 2003;60(1):107–12.

  42. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26(1):136–8 (133).

    Article  PubMed  Google Scholar 

  44. David M. SHRiMP2: sensitive yet practical short read mapping. Bioinformatics. 2011;27(7):1011–2 (1012).

    Article  CAS  PubMed  Google Scholar 

  45. Kruger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34(Web Server issue):W451–4.

    Article  PubMed  PubMed Central  Google Scholar 

  46. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. Plos Biology. 2004;2(11):e363.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by The National Programs for High Technology Research and Development (863 Program; 2015AA020108), National Key Research Program of China grant (No. 2016YFB0201702), the Ministry of Science and Technology of the People’s Republic of China, grants from the National Science Foundation of China (No. 31271386 and No. 31471248), and grants from Jilin Provincial Science & Technology Department, China (No. 20130204013YY and No. 20140520001JH).

Declarations

This article has been published as part of BMC Genomics Volume 18 Supplement 1, 2016: Proceedings of the 27th International Conference on Genome Informatics: genomics. The full contents of the supplement are available online at http://bmcgenomics.biomedcentral.com/articles/supplements/volume-18-supplement-1.

Funding

Publication of this article was funded by grant (20140520001JH) from Jilin Provincial Science & Technology Department, China.

Availability of data and materials

The sequence data reported in this paper have been deposited in the genome sequence archive of Beijing Institute of Genomics, Chinese Academy of Sciences, gsa.big.ac.cn (accession no. PRJCA000127 (mRNA-seq) and PRJCA000128 (miRNA-seq).

Authors’ contributions

Conceived and designed the experiments: CJ and JX. Performed the experiments: JJ. Analyzed the data: RL, WS, RZ and XG. Contributed reagents/materials/analysis tools: RL, JJ, CJ, WS and JX. Wrote the paper: RL, WS. Provided comments on the manuscript: XG, FL, XS, WD, MC, JW and JX. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

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Not applicable.

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Author notes

    Authors and Affiliations

    1. National Engineering Laboratory for AIDS Vaccine, School of Life Sciences, Jilin University, Changchun, 130012, China

      Jun Jin, Chunlai Jiang, Wenwen Dai & Weiheng Su

    2. BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China

      Rujiao Li, Fang Liang, Meili Chen & Jingfa Xiao

    3. Key Laboratory for Molecular Enzymology and Engineering, the Ministry of Education, School of Life Sciences, Jilin University, Changchun, 130012, China

      Chunlai Jiang & Weiheng Su

    4. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China

      Ruosi Zhang, Xiaomeng Ge, Xin Sheng, Jiayan Wu & Jingfa Xiao

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    1. Jun Jin
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    Correspondence to Jingfa Xiao or Weiheng Su.

    Additional files

    Additional file 1:

    RNA-Seq saturation curves. The horizontal axis represents number of reads. The left vertical axis represents the number of genes, and the right vertical axis represents the correlation coefficient. Saturation test results showed that the sequencing data were sufficient for analysis of differences in gene expression. (PPTX 49 kb)

    Additional file 2:

    Differently expressed genes between CVA16-infected and CVA16-non-infected cell samples. (XLS 584 kb)

    Additional file 3:

    GO functional enrichment of differentially expressed genes between CVA16-infected and CVA16-non-infected cell samples. (PPTX 68 kb)

    Additional file 4:

    Differently expressed miRNA between CVA16-infected and CVA16-non-infected cell samples. (XLS 38 kb)

    Additional file 5:

    Differently expressed target genes between CVA16-infected and CVA16-non-infected cell samples. (XLS 403 kb)

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    Jin, J., Li, R., Jiang, C. et al. Transcriptome analysis reveals dynamic changes in coxsackievirus A16 infected HEK 293T cells. BMC Genomics 18 (Suppl 1), 933 (2017). https://doi.org/10.1186/s12864-016-3253-6

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    Keywords

    • RNA-seq
    • miRNA-seq
    • Hand, foot and mouth disease
    • Coxsackievirus A16
    • SCARB2
    • Gene expression and regulation

    BMC Genomics

    ISSN: 1471-2164

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