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Transcriptome analysis of PK-15 cells expressing CSFV NS4A

BMC Veterinary Research volume 18, Article number: 434 (2022) Cite this article

Abstract

Background

Classical swine fever (CSF) is a severe disease of pigs that results in huge economic losses worldwide and is caused by classical swine fever virus (CSFV). CSFV nonstructural protein 4 A (NS4A) plays a crucial role in infectious CSFV particle formation. However, the function of NS4A during CSFV infection is not well understood. 

Results

In this study, we used RNA-seq to investigate the functional role of CSFV NS4A in PK-15 cells. A total of 3893 differentially expressed genes (DEGs) were identified in PK-15 cells expressing NS4A compared to cells expressing the empty vector (NC). Twelve DEGs were selected and further verified by RT‒qPCR. GO and KEGG enrichment analyses revealed that these DEGs were associated with multiple biological functions, including cell adhesion, apoptosis, host defence response, the inflammatory response, the immune response, and autophagy. Interestingly, some genes associated with host immune defence and inflammatory response were downregulated, and some genes associated with host apoptosis and autophagy were upregulated.

Conclusion

CSFV NS4A inhibits the innate immune response, and suppresses the expression of important genes associated with defence response to viruses and inflammatory response, and regulates cell adhesion, apoptosis and autophagy.

Peer Review reports

Background

Classical swine fever virus (CSFV) is a causative pathogen of classical swine fever (CSF), one of the most economically disastrous diseases in the swine industry [1, 2]. The major symptoms of CSF include hyperthermia and systemic bleeding. Host cell defence mechanisms are essential for resistance to viral infection, such as the innate immune response and apoptosis. However, the innate immune response is inhibited by CSFV due to weak induction of antiviral cytokines, including type I interferon (IFN), and proinflammatory cytokines, such as IFN-β and interleukin (IL)-6 [3,4,5]. In addition, CSFV does not induce cell death [6, 7]. CSFV infection triggers a complete autophagic response to promote virus replication [8].

CSFV belongs to the Flaviviridae family and the Pestivirus genus [9]. It is a single-stranded positive-sense RNA virus. The CSFV genome is approximately 12.3 kb in length and encodes a polyprotein precursor that is further proteolytically processed into four structural proteins (C, Erns, E1 and E2) and eight nonstructural proteins (Npro, p7, NS2, NS3, NS4A, NS4B, NS5A and NS5B) [10, 11]. CSFV NS4A is an 8 kDa protein that is located in the nucleus and cytoplasm [12]. NS4A is a cofactor of the NS3 protease that assists the cleavage of downstream nonstructural proteins and is involved in the formation of infectious virions. CSFV NS4A induces IL-8 production by enhancing the mitochondrial antiviral signalling protein (MAVS) pathway and promoting CSFV replication [12]. However, the function of NS4A during CSFV infection needs to be further elucidated.

RNA-seq is a powerful approach to quantify and identify differentially expressed genes on a genome-wide scale and has been employed to study various viral infections and viral proteins [13, 14]. The aim of the current study was to use the RNA-seq method to investigate the functional role of CSFV NS4A in PK-15 cells. The results may provide novel information that will increase our understanding of the roles of NS4A in the pathogenesis of CSFV.

Results

Construction of PK-15 cells expressing NS4A

To analyse the function of CSFV NS4A, we first constructed PK-15 cells expressing NS4A by lentiviral infection. PK-15 cells were infected with lentiviral products overexpressing NS4A and screened with puromycin. Subsequently, the cells were examined via fluorescence detection of the GFP reporter and western blotting with a Flag antibody. As shown in Fig. 1a, green fluorescence in PK-15 cells expressing the empty vector (NC) or PK-15 cells expressing NS4A (NS4A) was visible under an inverted fluorescence microscope, and the percentage of positively infected cells was ~ 100%. However, no green fluorescence was visible in noninfected PK-15 cells. Additionally, NS4A expression was confirmed by western blot analysis using an anti-Flag mAb in NS4A cells (Fig. 1b), indicating that PK-15 cells expressing CSFV NS4A were successfully constructed.

Fig. 1
figure 1

Confirmation of PK-15 cells expressing NS4A. a Confirmation of PK-15 cells expressing NS4A by fluorescence detection of the GFP reporter. Mock represents PK-15 cells; NC represents PK-15 cells expressing the empty vector; NS4A represents PK-15 cells expressing NS4A. b Western blotting for NS4A expression in PK-15 cells expressing NS4A. β-Actin was used as an internal control. The blots were cropped from the original blot by using Adobe Photoshop CS6, and the full-length original blots and multiple exposure images are included in additional files 3, 4 and 5

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RNA sequencing and read assembly

In this study, sequencing libraries were prepared in triplicate, and RNA-seq was used to compare the gene expression levels of PK-15 cells expressing NS4A with those of PK-15 cells expressing the empty vector. RNA-seq generated 287,527,934 raw reads, with an average of 47,921,322 reads per sample. After ribosomal RNA and low-quality reads were discarded, a total of 272,843,899 clean reads were generated, with an average of 45,473,983 reads per sample. On average, 94.90% of the sequences was mapped onto the Sus scrofa reference genome (GCF_000003025.6_Sscrofa11.1). Among the mapped reads, 91.12% were mapped to a single position (Table 1).

Table 1 Statistics of the RNA-seq datasets
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Differentially expressed genes (DEGs) in PK-15 cells expressing CSFV NS4A

To compare the DEGs in the NC PK-15 cells with those in the PK-15 cells expressing NS4A, we identified DEGs with volcano plots (Fig. 2a). In all, 3893 genes were identified as DEGs in PK-15 cells expressing NS4A compared to NC PK-15 cells; 1987 DEGs were upregulated, and 1906 were downregulated (Fig. 2b). Additional file 1 shows the DEGs that were upregulated and downregulated. The most upregulated gene in PK-15 cells expressing NS4A was found to be mucin 12 gene (MUC12), which increased 68.95 fold change. The most downregulated gene in PK-15 cells expressing NS4A was found to be IL-10, which reduced 188.61 fold change. In addition, important immune response genes were downregulated, including TLR2, TRIF and STAT1 genes associated with TLR pathway, NOD1, NLRP3 and ERK genes associated with NLR pathway and RIG-I, MDA5, LGP2 and IFN-ε genes associated with RLR pathway. IL-1β, CXCL10, IL-10, and TNF-α genes, important inflammatory factors, were significantly downregulated. ITGB1/2/6/8, NECTIN2/3/4 and CLDN1/6/7/12/16, cell adhesion regulator genes, were significantly downregulated. However, other important cell adhesion genes were upregulated in PK-15 cells expressing NS4A, such as VEGF-A and VEGF-B. ATG3 genes associated with autophagy were upregulated. Furthermore, we screened 49 DEGs related to the defence response to viruses (Fig. 3a) and 38 DEGs related to apoptosis (Fig. 3b), and the DEGs were further analysed according to hierarchical clustering. Numerous genes associated with defence response to viruses were mainly some interferon stimulating genes (ISGs), and these genes were significantly downregulated, such as IFITM1/3, MX1, IFIT1, OAS1, BST-2 and ISG15. The significantly upregulated important genes related to apoptosis were FADD, Bid, Bax, Caspase 8, p53, ATF4, and DDIT3, whereas the Fos gene was significantly downregulated in PK-15 cells expressing NS4A compared to NC PK-15 cells. The analysis of RNA-seq data revealed that CSFV NS4A was involved in cell adhesion, cell apoptosis, host defence response, the inflammatory response, the immune response, and autophagy.

Fig. 2
figure 2

Statistical analysis of the DEGs. a Volcano plot displaying the DEGs in PK-15 cells expressing NS4A. In the figure, the two vertical grey lines represent the threshold of 2 times differential expression, and the horizontal grey line is the threshold of a P value = 0.05. Red dots indicate groups of upregulated genes, green dots indicate groups of downregulated genes, and grey dots indicate non-significantly differentially expressed genes. b The number of upregulated genes and downregulated genes

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Fig. 3
figure 3

Expression profiles (heatmaps) of viral defence response genes and apoptosis genes in PK-15 cells expressing NS4A. The samples NC_1, NC_2, NC_3 and NS4A_1, NS4A_2, NS4A_3 are similar, but three independent experiments were performed. Each column is a sample, and each row represents the expression level of the same gene in different samples. The colour in the heatmap represents the gene expression change. Red indicates high gene expression, green indicates low gene expression, and white indicates unaltered gene expression. The DEGs are labelled on the right of the heatmap. a Heatmap of viral defence response genes. b Heatmap of apoptosis genes in response to viral infection

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Gene Ontology (GO) analyses and KEGG pathway analyses of DEGs

To explore the biological functions of the DEGs, GO enrichment analysis was conducted. The DEGs were classified into three main categories: biological process (BP), cellular component (CC), and molecular function (MF). Additional file 2 shows that these DEGs were enriched with 4397 GO terms, including 637 GO terms in the CC category, 867 GO terms in the MF category, and 2893 terms in the BP category. According to P < 0.05 as the standard, these DEGs were significantly enriched with 320 GO terms, and the top 30 GO terms are listed in Fig. 4a. The most significantly enriched term was nucleolus (GO: 0005634), followed by RNA binding (GO: 0003723), rRNA processing (GO: 0006364), and others. Many DEGs were enriched with the apoptotic process (GO: 0006915), defence response to virus (GO: 0051607) and negative regulation of viral genome replication (GO: 0045071) terms, such as Bid, DDIT3, TNF, ISG15, MX1, OAS1, BST2, IFIT1, and IFITM1.

Fig. 4
figure 4

Functional enrichment analysis of DEGs expressed in PK-15 cells expressing NS4A. a Top 30 GO terms of the DEGs. The GO terms were classified into 3 categories, including BP, CC, and MF. The top 10 GO terms with the smallest P values or most significant enrichment in each GO category were selected. b Top 20 KEGG pathways of the DEGs. The top 20 pathways with the smallest P values or most significant enrichment were selected. The KEGG pathway database is obtained in KEGG software from the Kanehisa laboratory

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KEGG pathway analysis is used for predicting the biological processes and phenotypic traits of genes. To analyse the function of CSFV NS4A, the DEGs were mapped to reference canonical signalling pathways via KEGG database analysis. According to a P value < 0.05 as the standard, the DEGs were significantly enriched in 30 pathways, and the top 20 enriched KEGG pathways are shown in Fig. 4b. The most significant pathway was cytokine‒cytokine receptor interaction (ko04060), in which 65 genes were differentially expressed. In addition, the apoptosis pathway (ko04210) and NOD-like receptor signalling pathway (ko04621) were found among these pathways, which are related to apoptosis and the immune system.

Validation of RNA-seq data by real-time quantitative PCR (RT‒qPCR)

To validate the gene expression results obtained from RNA-seq, we tested the expression of important apoptosis-related genes (ATF4, Bax, p53), inflammatory response genes (IL-10, TNF-α, CXCL10), innate immune response genes (NOD1, NLRP3, TRIM21, TRIM25, TRIM40) and autophagy genes (ATG3) using RT‒qPCR. As shown in Fig. 5, the mRNA expression of ATF4, Bax, p53 and ATG3 were significantly upregulated, and the mRNA expression of IL-10, TNF-α, CXCL10, NOD1, NLRP3, TRIM25 and TRIM40 were significantly downregulated in NS4A-expressing cells. Comparison of the RNA-seq results and RT‒qPCR results of the selected genes showed that the two detection methods were largely consistent.

Fig. 5
figure 5

Validation of the RNA-seq data by RT‒qPCR. To confirm the results from RNA-seq, RT‒qPCR was performed using total RNA from NS4A- or NC-expressing PK-15 cells. β-Actin was used as a reference. The y-axis represents the normalized fold changes in the expression of these selected genes. The error bars represent the mean ± SD (n = 3). *, P < 0.05; **, P < 0.01; ***, P < 0.001

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Discussion

As a highly contagious and fatal viral disease, CSF has caused great economic losses in the swine industry worldwide [1, 2]. Many researchers have worked to reveal the pathogenesis of CSFV and have already made certain achievements. However, the functions of CSFV proteins need to be further studied. CSFV NS4A is a small nonstructural protein, and little is known about its function. In this study, RNA-seq was used to explore the effect of CSFV NS4A in PK-15 cells to analyse the function of NS4A during CSFV infection.

RNA-seq revealed 3893 DEGs in PK-15 cells expressing CSFV NS4A. These DEGs were related to cell adhesion, apoptosis, host defence response, the inflammatory response, the immune response, autophagy and other terms. Mucin 12 gene (MUC12) is a member of the mucin family and play an essential role in forming protective mucous barriers on epithelial surfaces. We observed that MUC12 were significantly upregulated in PK-15 cells expressing NS4A. CSFV infection causes diffuse haemorrhaging in the skin, kidneys, and other organs of pigs [15]. We found that VEGF-A and VEGF-B were upregulated in PK-15 cells expressing NS4A. VEGF is a multifunctional cytokine and leads to a powerfully increased vascular permeability [16], which may be closely related to haemorrhagic symptoms. In addition, cell adhesion molecules, such as integrin subunit beta 1/2/6/8 (ITGB1/2/6/8), nectin cell adhesion molecule 2/3/4 (NECTIN2/3/4) and the tight junction (TJ) protein claudin-1/6/7/12/16 (CLDN1/6/7/12/16), were significantly downregulated in NS4A-expressing cells; these molecules contribute to the maintenance and regulation of vascular permeability [17,18,19].

The virus replication process includes adsorption, internalization, uncoating, biosynthesis, assembly and release. A previous study has demonstrated that NS4A promotes CSFV replication [12]. However, the molecular mechanism has remained unclear. In this study, we observed that some important genes associated with defence response to viruses (IFITM1/3, MX1, IFIT1, OAS1, BST-2 and ISG15) were significantly downregulated in NS4A-expressing cells. Interferon-induced transmembrane protein 1/3 (IFITM1/3) inhibits the entry of viruses into the host cell cytoplasm and prevents subsequent viral fusion and release of viral contents into the cytosol. IFITM1/3 overexpression significantly inhibits CSFV replication [20]. Interferon-induced GTP-binding protein Mx1 (MX1) targets viruses through binding and inactivation of their ribonucleocapsids and inhibits CSFV replication [21]. Interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) inhibits virus replication and translation initiation. Upregulation of the IFIT1 gene is closely linked to the replication inhibition of many viruses, including hepatitis E virus and hepatitis C virus (HCV) [22, 23]. 2’-5’-Oligoadenylate synthetase 1 (OAS1) activates latent RNase L, which results in viral RNA degradation and inhibition of virus replication and mediates the antiviral effect against HCV, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and dengue virus (DENV) [24,25,26]. Bone marrow stromal cell antigen 2 (BST-2) efficiently blocks the release of diverse mammalian enveloped viruses and represses the replication of HCV, Newcastle disease virus (NDV), vesicular stomatitis virus (VSV), and herpes simplex virus (HSV) [27]. Through covalent binding with the host and viral target proteins, interferon-stimulating gene 15 (ISG15) inhibits the release of viral particles and hinders virus replication. Previous studies have shown that the activity of ISG15 inhibits CSFV replication via inhibition of autophagy by ISGylating BECN1 [28]. The results indicate that CSFV NS4A might facilitate CSFV replication by reducing the expression of these antiviral genes.

In the early stages of virus infection, the innate immune system can be triggered to limit the spread of viruses. First, multiple pattern recognition receptors (PRRs) are activated by sensing of pathogen-associated molecular patterns (PAMPs), such as viral proteins and nucleic acids, leading to induction of cytokines and type I interferons. The intracellular PRRs mainly include Toll-like receptors (TLRs), RIG-I-like receptors (RLRs) and NOD-like receptors (NLRs) [29,30,31,32]. Previous studies have demonstrated that CSFV infection upregulates the expression of TLR2, TLR4 and TLR7 and downregulates the expression of TLR3 and TIR-domain-containing adapter-inducing interferon-β (TRIF) [5]. In addition, CSFV is recognized by RIG-I and MDA5 receptors to activate MAVS expression [3, 33]. CSFV infection can sufficiently activate assembly of the NLRP3 inflammasome [34]. In our study, downregulation of NLRs, such as NOD1, NLRP3, ERK and IL-1β, was observed. The TLR pathway was also inhibited, as evidenced by the downregulation of TLR2, TRIF, tumour necrosis factor-alpha (TNF-α) and STAT1. We further found that NS4A expression reduced the expression of RIG-I, MDA5, LGP2, IFN-ε and CXCL10, suggesting that the RLR pathway was also inhibited. TRIM proteins have emerged as important positive and negative regulators of PRR signalling pathways. Many TRIM proteins play central roles in the host defence against virus infection[35]. We observed that the expression of TRIM25 and TRIM40 were downregulated in NS4A-expressing cells. These results indicate that NS4A acts as a negative regulator of host innate defence and inhibits the innate immune response during CSFV infection, which might promote persistent CSFV infection.

Apoptosis plays an important role in preventing the virus from replicating and producing progeny viruses [36]. In vitro, CSFV infection does not induce apoptosis or pathological cell changes. Previous studies have shown that CSFV Npro, Erns and NS2 proteins can inhibit apoptosis [7, 37, 38]. However, the effect of CSFV NS4A on apoptosis is unknown. Here, we observed that the expression of many apoptosis-associated genes was changed. The proto-oncogenes Fos and TNF-α, which have been associated with apoptotic cell death [39, 40], were downregulated in NS4A-expressing cells. In addition, FADD, Bid, Bax, Caspase 8, p53, ATF4, and CHOP, which are important apoptosis-associated genes [41,42,43], were upregulated in NS4A-expressing cells. Our results provide a reference for studying the relationship between NS4A and apoptosis.

Cells adapt to poor survival conditions through the initiation and execution of autophagy [44]. CSFV infection triggers a complete autophagic response to promote virus replication [8]. Autophagy-related 3 (ATG3), which is a ubiquitin-like-conjugating enzyme involved in autophagy [45], was upregulated in NS4A-expressing cells, implying that NS4A might be involved in the induction of autophagy during CSFV infection. Proinflammatory cytokines are involved in a wide variety of processes, stimulating the activation and migration of immune cells to infected sites and promoting virus clearance. Our results showed that the expression of IL-1β, CXCL10, IL-10, and TNF-α was reduced in NS4A-expressing cells. IL-1β and CXCL10, important inflammatory factors, are induced during CSFV infection [46, 47]. Additionally, TNF-α, a prototypical proinflammatory cytokine with pleiotropic activities, is secreted in CSFV-infected pigs and limits the replication of CSFV in cell culture [48]. These results indicate that CSFV NS4A might inhibit these important proinflammatory responses to promote virus replication, which is consistent with our previous results [12]. However, further studies are necessary to elucidate the mechanism of cytokine regulation in NS4A-expressing cells.

This finding indicates that CSFV NS4A might contribute to virus replication by inhibiting host cell protection and inducing host cell autophagy. Taken together, the findings of this study provide useful information for further understanding the function of CSFV NS4A during CSFV infection.

Conclusion

In summary, our findings revealed 3893 DEGs in PK-15 cells expressing CSFV NS4A compared to NC cells via RNA-seq. These DEGs were involved in inflammatory responses, cell adhesion, apoptosis, host defence response, the immune response, autophagy and other processes. Interestingly, NS4A protein expression in PK-15 cells resulted in downregulation of genes involved in host immune defence and inflammatory response and upregulation of genes involved in host apoptosis and autophagy. Based on above results, this finding indicates that CSFV NS4A inhibits the innate immune response, and suppresses the expression of important genes associated with defence response to viruses and inflammatory response, and regulates cell adhesion, apoptosis and autophagy.

Methods

Cell culture and plasmid construction

HEK293T (ATCC, CRL-11,268) and PK-15 cells (ATCC, CCL-33) were preserved in our laboratory and cultured in high-glucose DMEM (GIBCO, UK) supplemented with 10% heat-inactivated foetal bovine serum (FBS) (HyClone, USA) at 37 °C and 5% CO2. The pCDH-CMV-NS4A-Flag vector encoding the CSFV NS4A protein with a Flag fusion protein at its C-terminus was constructed by cloning the NS4A gene into the overexpression lentivector pCDH-CMV-MCS-EF1-GreenPuro (CD513B-1) (SBI, Mountain View, CA, USA) using EcoR I and BamH I restriction enzymes. The primers are listed in Supplementary Table S1, and the plasmid was confirmed by restriction digestion and sequencing.

Construction of PK-15 cells expressing NS4A

HEK293T cells were cotransfected with the pCDH-CMV-NS4A-Flag plasmids and three other plasmids (pGag/Pol, pRev, pVSVG) using TurboFect (Thermo Fisher Scientific, USA). After 16 h, the medium was replaced with Advanced DMEM. The culture supernatants containing lentivirus were collected after 48 h. PK-15 cells were infected with lentiviral products expressing NS4A and then selected with 6 µg/mL puromycin (Thermo, USA) for 10 days. To confirm that PK-15 cells expressed NS4A, green fluorescence in PK-15 cells was confirmed to be visible under an inverted fluorescence microscope (Nikon, Japan). Then, NS4A gene expression was analysed by western blotting in PK-15 cells expressing NS4A. In brief, PK-15 cells expressing NS4A were harvested and lysed. Protein samples were separated by 12% SDS‒PAGE. According to the size of target proteins and the indication of protein marker, the unused parts of the separating gel and stacking gel were cut off. The proteins were transferred onto PVDF membranes (Millipore, USA). The membranes were incubated with an anti-β-actin mouse mAb (Tianjin Sungene Biotech, KM9001, China) or anti-Flag mouse mAb (CWBIO, China) at 4 °C overnight and then incubated with HRP-conjugated goat anti-mouse IgG (1:5000) (Bioss, China) for 2 h at room temperature. Then, the signal was detected using an automatic chemiluminescence image analysis system (Tanon, 5200, China).

RNA isolation and library preparation

Total RNA was extracted using TRIzol reagent according to the manufacturer’s protocol. RNA purity and quantification were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then, the libraries were constructed using a TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Transcriptome sequencing and analysis were conducted by OE Biotech Co., Ltd. (Shanghai, China).

RNA sequencing and differentially expressed gene analysis

The libraries were sequenced on an Illumina HiSeq X Ten platform, 150 bp paired-end reads were generated, and raw reads for each sample were generated. The raw data (raw reads) in FASTQ format were first processed using Trimmomatic [49], and the low-quality reads were removed to obtain clean reads. Then, the clean reads were mapped to the Sus scrofa genome (GCF_000003025.6_Sscrofa11.1) using HISAT2 [50]. The FPKM value [51] of each gene was calculated using Cufflinks [52], and the read counts of each gene were obtained by HTSeqcount [53]. Differential expression analysis was performed using the DESeq R package. A P value < 0.05 and fold change > 2 or fold change < 0.5 were set as the thresholds for significant differential expression.

GO functional enrichment and pathway analysis

Hierarchical cluster analysis of the DEGs was performed to demonstrate the expression patterns of the genes in different groups and samples. GO enrichment and KEGG [54] pathway enrichment analyses of the DEGs were performed using R based on the hypergeometric distribution. The KEGG pathway database from the Kanehisa laboratory was used for KEGG pathway enrichment analyses [55,56,57].

RT‒qPCR to validate gene expression

To validate the RNA sequencing results, 12 genes were selected and verified by RT‒qPCR with the specific primers in Supplementary Table S1. Total cellular RNA was extracted with TRIzol (Invitrogen, USA). Subsequently, the total RNA was firstly treated with DNase to remove genomic DNA, and cDNA was synthesized using a PrimeScript RT reagent kit (Vazyme, China). The relative mRNA expression was calculated with an ABI7500 system (Biosystem, CA, USA), with β-actin as the reference, using UltraSYBR Mixture (CWBIO, China) according to the manufacturer’s protocol. The data were analysed by using the 2−∆∆Ct method. All experiments were performed in triplicate, and the results represent the mean ± standard deviation (SD).

Statistical analysis

The RT‒qPCR experiments were performed at least three times, and the results represent the mean ± standard deviation (SD) of three replicates. The data were analysed by one-way ANOVA and Bonferroni post hoc test using SPSS software (version 18.0). A P value < 0.05 was considered to indicate significance.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its additional files, and the dataset analysed during the current study is available in the GEO repository at the NCBI under accession number GSE212691.

References

  1. Dreier S, Zimmermann B, Moennig V, Greiser-Wilke I. A sequence database allowing automated genotyping of classical swine fever virus isolates. J Virol Methods. 2007;140:95–9.

    Article  CAS  Google Scholar 

  2. Luo Y, Li S, Sun Y, Qiu HJ. Classical swine fever in China: a minireview. Vet Microbiol. 2014;172:1–6. https://doi.org/10.1016/j.vetmic.2014.04.004.

    Article  Google Scholar 

  3. Dong W, Lv H, Li C, Liu Y, Wang C, Lin J, et al. MAVS induces a host cell defense to inhibit CSFV infection. Arch Virol. 2018;163:1805–21.

    Article  CAS  Google Scholar 

  4. Fiebach AR, Guzylack-Piriou L, Python S, Summerfield A, Ruggli N. Classical swine fever virus N(pro) limits type I interferon induction in plasmacytoid dendritic cells by interacting with interferon regulatory factor 7. J Virol. 2011;85:8002–11.

    Article  CAS  Google Scholar 

  5. Cao Z, Guo K, Zheng M, Ning P, Li H, Kang K, et al. A comparison of the impact of Shimen and C strains of classical swine fever virus on toll-like receptor expression. J Gen Virol. 2015;96:1732–45. https://doi.org/10.1099/vir.0.000129.

    Article  CAS  Google Scholar 

  6. Bensaude E, Turner JL, Wakeley PR, Sweetman DA, Pardieu C, Drew TW, et al. Classical swine fever virus induces proinflammatory cytokines and tissue factor expression and inhibits apoptosis and interferon synthesis during the establishment of long-term infection of porcine vascular endothelial cells. J Gen Virol. 2004;85:1029–37.

    Article  CAS  Google Scholar 

  7. Johns HL, Bensaude E, La Rocca SA, Seago J, Charleston B, Steinbach F, et al. Classical swine fever virus infection protects aortic endothelial cells from pIpC-mediated apoptosis. J Gen Virol. 2010;91:1038–46.

    Article  CAS  Google Scholar 

  8. Pei J, Zhao M, Ye Z, Gou H, Wang J, Yi L, et al. Autophagy enhances the replication of classical swine fever virus in vitro. Autophagy. 2014;10:93–110. https://doi.org/10.4161/auto.26843.

    Article  CAS  Google Scholar 

  9. Ji W, Guo Z, Ding NZ, He CQ. Studying classical swine fever virus: making the best of a bad virus. Virus Res. 2015;197:35–47.

    Article  CAS  Google Scholar 

  10. Lamp B, Riedel C, Roman-Sosa G, Heimann M, Jacobi S, Becher P, et al. Biosynthesis of classical swine fever virus nonstructural proteins. J Virol. 2011;85:3607–20.

    Article  CAS  Google Scholar 

  11. Thiel HJ, Stark R, Weiland E, Rumenapf T, Meyers G. Hog cholera virus: molecular composition of virions from a pestivirus. J Virol. 1991;65:4705–12.

    Article  CAS  Google Scholar 

  12. Dong W, Lv H, Guo K, Wang T, Ouyang Y, Jin M, et al. Classical swine fever virus infection and its NS4A protein expression induce IL-8 production through MAVS signaling pathway in swine umbilical vein endothelial cells. Front Microbiol. 2017;8:2687.

    Article  Google Scholar 

  13. He Y, Wang A, Chen S, Wu Z, Zhang J, Wang M, et al. Differential immune-related gene expression in the spleens of duck Tembusu virus-infected goslings. Vet Microbiol. 2017;212:39–47. https://doi.org/10.1016/j.vetmic.2017.08.002.

    Article  CAS  Google Scholar 

  14. Park IB, Choi YC, Lee KT, Chun T. Transcriptome analysis of pig macrophages expressing porcine reproductive and respiratory syndrome virus non-structural protein 1. Vet Immunol Immunopathol. 2021;231:110147. https://doi.org/10.1016/j.vetimm.2020.110147.

    Article  CAS  Google Scholar 

  15. Moennig V, Floegel-Niesmann G, Greiser-Wilke I. Clinical signs and epidemiology of classical swine fever: a review of new knowledge. Vet J. 2003;165:11–20. https://doi.org/10.1016/s1090-0233(02)00112-0.

    Article  CAS  Google Scholar 

  16. Weis SM, Cheresh DA. Pathophysiological consequences of VEGF-induced vascular permeability. Nature. 2005;437:497–504. https://doi.org/10.1038/nature03987.

    Article  CAS  Google Scholar 

  17. Tang QH, Zhang YM, Xu YZ, He L, Dai C, Sun P. Up-regulation of integrin beta3 expression in porcine vascular endothelial cells cultured in vitro by classical swine fever virus. Vet Immunol Immunopathol. 2010;133:237–42. https://doi.org/10.1016/j.vetimm.2009.07.005.

    Article  CAS  Google Scholar 

  18. Bekes I, Lob S, Holzheu I, Janni W, Baumann L, Wockel A, et al. Nectin-2 in ovarian cancer: how is it expressed and what might be its functional role? Cancer Sci. 2019;110:1872–82. https://doi.org/10.1111/cas.13992.

    Article  CAS  Google Scholar 

  19. Sun W, Wu W, Jiang N, Ge X, Zhang Y, Han J, et al. Highly pathogenic PRRSV-infected alveolar macrophages impair the function of pulmonary microvascular endothelial cells. Viruses. 2022;14:452.

    Article  Google Scholar 

  20. Li C, Zheng H, Wang Y, Dong W, Liu Y, Zhang L, et al. Antiviral role of IFITM proteins in classical swine fever virus infection. Viruses. 2019;11:126.

    Article  CAS  Google Scholar 

  21. Zhou J, Chen J, Zhang XM, Gao ZC, Liu CC, Zhang YN, et al. Porcine Mx1 protein inhibits classical swine fever virus replication by targeting nonstructural protein NS5B. J Virol. 2018;92:e02147-17.

    Article  Google Scholar 

  22. Pingale KD, Kanade GD, Karpe YA. Corrigendum. Hepatitis E virus polymerase binds to IFIT1 to protect the viral RNA from IFIT1-mediated translation inhibition. J Gen Virol. 2022;103:001711. https://doi.org/10.1099/jgv.0.001711.

    Article  Google Scholar 

  23. Ishida Y, Kakuni M, Bang BR, Sugahara G, Lau DT, Tateno-Mukaidani C, et al. Hepatic IFN-Induced protein with tetratricopeptide repeats regulation of HCV infection. J Interferon Cytokine Res. 2019;39:133–46. https://doi.org/10.1089/jir.2018.0103.

    Article  CAS  Google Scholar 

  24. Lin RJ, Yu HP, Chang BL, Tang WC, Liao CL, Lin YL. Distinct antiviral roles for human 2’,5’-oligoadenylate synthetase family members against dengue virus infection. J Immunol. 2009;183:8035–43. https://doi.org/10.4049/jimmunol.0902728.

    Article  CAS  Google Scholar 

  25. Wickenhagen A, Sugrue E, Lytras S, Kuchi S, Noerenberg M, Turnbull ML, et al. A prenylated dsRNA sensor protects against severe COVID-19. Science. 2021;374:eabj3624. https://doi.org/10.1126/science.abj3624.

    Article  CAS  Google Scholar 

  26. Zhao Y, Kang H, Ji Y, Chen X. Evaluate the relationship between polymorphisms of OAS1 gene and susceptibility to chronic hepatitis C with high resolution melting analysis. Clin Exp Med. 2013;13:171–6. https://doi.org/10.1007/s10238-012-0193-6.

    Article  CAS  Google Scholar 

  27. Chen S, Huang X, Xie Q, Liu Q, Zhu H. The role of long noncoding RNA BST2-2 in the innate immune response to viral infection. J Virol. 2022;96:e0020722.

    Article  Google Scholar 

  28. Li C, Wang Y, Zheng H, Dong W, Lv H, Lin J, et al. Antiviral activity of ISG15 against classical swine fever virus replication in porcine alveolar macrophages via inhibition of autophagy by ISGylating BECN1. Vet Res. 2020;51:22.

    Article  CAS  Google Scholar 

  29. Brennan K, Bowie AG. Activation of host pattern recognition receptors by viruses. Curr Opin Microbiol. 2010;13:503–7.

    Article  CAS  Google Scholar 

  30. Kanneganti TD. Central roles of NLRs and inflammasomes in viral infection. Nat Rev Immunol. 2010;10:688–98.

    Article  CAS  Google Scholar 

  31. Mikula I Jr, Pastorekova S, Mikula I. Sr. Toll-like receptors in immune response to the viral infections. Acta Virol. 2010;54:231–45.

    Article  CAS  Google Scholar 

  32. Loo YM, Gale M. Jr. Immune signaling by RIG-I-like receptors. Immunity. 2011;34:680–92.

    Article  CAS  Google Scholar 

  33. Dong XY, Liu WJ, Zhao MQ, Wang JY, Pei JJ, Luo YW, et al. Classical swine fever virus triggers RIG-I and MDA5-dependent signaling pathway to IRF-3 and NF-kappaB activation to promote secretion of interferon and inflammatory cytokines in porcine alveolar macrophages. Virol J. 2013;10:286.

    Article  Google Scholar 

  34. Fan S, Yuan J, Deng S, Chen Y, Xie B, Wu K, et al. Activation of Interleukin-1beta release by the classical swine fever virus is dependent on the NLRP3 Inflammasome, which affects Virus Growth in Monocytes. Front Cell Infect Microbiol. 2018;8:225. https://doi.org/10.3389/fcimb.2018.00225.

    Article  CAS  Google Scholar 

  35. van Gent M, Sparrer KMJ, Gack MU. TRIM proteins and their roles in antiviral host defenses. Annu Rev Virol. 2018;5:385–405. doi:https://doi.org/10.1146/annurev-virology-092917-043323.

    Article  CAS  Google Scholar 

  36. Griffin DE, Hardwick JM. Regulators of apoptosis on the road to persistent alphavirus infection. Annu Rev Microbiol. 1997;51:565–92.

    Article  CAS  Google Scholar 

  37. Ruggli N, Bird BH, Liu L, Bauhofer O, Tratschin JD, Hofmann MA. N(pro) of classical swine fever virus is an antagonist of double-stranded RNA-mediated apoptosis and IFN-alpha/beta induction. Virology. 2005;340:265–76.

    Article  CAS  Google Scholar 

  38. Tang Q, Guo K, Kang K, Zhang Y, He L, Wang J. Classical swine fever virus NS2 protein promotes interleukin-8 expression and inhibits MG132-induced apoptosis. Virus Genes. 2011;42:355–62. https://doi.org/10.1007/s11262-011-0582-z.

    Article  CAS  Google Scholar 

  39. Asim M, Chaturvedi R, Hoge S, Lewis ND, Singh K, Barry DP, et al. Helicobacter pylori induces ERK-dependent formation of a phospho-c-Fos c-Jun activator protein-1 complex that causes apoptosis in macrophages. J Biol Chem. 2010;285:20343–57. doi:https://doi.org/10.1074/jbc.M110.116988.

    Article  CAS  Google Scholar 

  40. Kim H, Ray R. Evasion of TNF-alpha-mediated apoptosis by hepatitis C virus. Methods Mol Biol. 2014;1155:125–32. https://doi.org/10.1007/978-1-4939-0669-7_11.

    Article  Google Scholar 

  41. Obeng E. Apoptosis (programmed cell death) and its signals - a review. Braz J Biol. 2021;81:1133–43. https://doi.org/10.1590/1519-6984.228437.

    Article  CAS  Google Scholar 

  42. Ouyang L, Shi Z, Zhao S, Wang FT, Zhou TT, Liu B, et al. Programmed cell death pathways in cancer: a review of apoptosis, autophagy and programmed necrosis. Cell Prolif. 2012;45:487–98. https://doi.org/10.1111/j.1365-2184.2012.00845.x.

    Article  CAS  Google Scholar 

  43. Zhou X, Jiang W, Liu Z, Liu S, Liang X. Virus infection and death receptor-mediated apoptosis. Viruses. 2017;9:316.

    Article  Google Scholar 

  44. Chang C, Su H, Zhang D, Wang Y, Shen Q, Liu B, et al. AMPK-Dependent phosphorylation of GAPDH triggers Sirt1 activation and is necessary for Autophagy upon glucose starvation. Mol Cell. 2015;60:930–40. https://doi.org/10.1016/j.molcel.2015.10.037.

    Article  CAS  Google Scholar 

  45. Radoshevich L, Debnath J. ATG12-ATG3 and mitochondria. Autophagy. 2011;7:109–11. https://doi.org/10.4161/auto.7.1.13998.

    Article  Google Scholar 

  46. Micaroni M, Stanley AC, Khromykh T, Venturato J, Wong CX, Lim JP, et al. Rab6a/a’ are important golgi regulators of pro-inflammatory TNF secretion in macrophages. PLoS One. 2013;8:e57034. https://doi.org/10.1371/journal.pone.0057034.

    Article  CAS  Google Scholar 

  47. Auray G, Talker SC, Keller I, Python S, Gerber M, Liniger M, et al. High-resolution profiling of Innate Immune responses by Porcine dendritic cell subsets in vitro and in vivo. Front Immunol. 2020;11:1429. https://doi.org/10.3389/fimmu.2020.01429.

    Article  CAS  Google Scholar 

  48. Liniger M, Gerber M, Renzullo S, Garcia-Nicolas O, Ruggli N. TNF-mediated inhibition of classical swine fever virus replication is IRF1-, NF-kappaB- and JAK/STAT signaling-dependent. Viruses. 2021;13:2017.

    Article  CAS  Google Scholar 

  49. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. https://doi.org/10.1093/bioinformatics/btu170.

    Article  CAS  Google Scholar 

  50. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60. https://doi.org/10.1038/nmeth.3317.

    Article  CAS  Google Scholar 

  51. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:R22. https://doi.org/10.1186/gb-2011-12-3-r22.

    Article  CAS  Google Scholar 

  52. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5. https://doi.org/10.1038/nbt.1621.

    Article  CAS  Google Scholar 

  53. Anders S, Pyl PT, Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9. https://doi.org/10.1093/bioinformatics/btu638.

    Article  CAS  Google Scholar 

  54. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:D480-4. https://doi.org/10.1093/nar/gkm882.

    Article  CAS  Google Scholar 

  55. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi:https://doi.org/10.1093/nar/28.1.27.

    Article  CAS  Google Scholar 

  56. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28:1947–51. doi:https://doi.org/10.1002/pro.3715.

    Article  CAS  Google Scholar 

  57. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2022. https://doi.org/10.1093/nar/gkac963.

    Article  Google Scholar 

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Acknowledgements

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Funding

This study was supported by the National Natural Science Foundation of China (31902274; 31902246), the Key Technologies Research and Development Program of Henan Province (222102110312), the Key Scientific Research Project of University in Henan Province (20A230005; 21B230004), the Doctoral Science Foundation of Henan University of Animal Husbandry and Economy (2019HNUAHEDF020; 2019HNUAHEDF019), the Key Disciplines of Veterinary Medicine of Henan University of Animal Husbandry and Economy (XJXK202202) and the Innovation Team of Henan University of Animal Husbandry and Economy (2018KYTD16).

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Authors and Affiliations

  1. Key Laboratory of Veterinary Biological Products, College of Veterinary Medicine, Henan University of Animal Husbandry and Economy, 450046, Zhengzhou, China

    Huifang Lv, Zhifeng Peng, Bingxin Jia, Huiyuan Jing, Sufang Cao, Zhikun Xu & Wang Dong

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  1. Huifang Lv
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  2. Zhifeng Peng
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  7. Wang Dong
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Contributions

HFL analysed and interpreted the transcriptome data of PK-15 cells expressing NS4A. HYJ and ZFP constructed the plasmids and PK-15 cells expressing NS4A. SFC carried out RNA isolation, library preparation and sequencing. ZKX and BXJ validated gene expression by RT‒qPCR. WD and HFL were major contributors to the writing of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wang Dong.

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Supplementary Information

Additional file 1:

 Supplementary Table S2. Up-regulated and down-regulated genes in CSFV NS4A expressing PK-15 cells compared to those in NC expressing PK-15 cells.

Additional file 2:

 Supplementary Table S3. The GO terms of DEGs.

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Additional file 3.

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Additional file 4.

12917_2022_3533_MOESM5_ESM.jpg

Additional file 5.

Additional file 6:

 Supplementary Table S1. Primers used in this study.

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Lv, H., Peng, Z., Jia, B. et al. Transcriptome analysis of PK-15 cells expressing CSFV NS4A. BMC Vet Res 18, 434 (2022). https://doi.org/10.1186/s12917-022-03533-9

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Keywords

  • RNA-seq
  • Classical swine fever virus
  • NS4A
  • Virus replication
  • Immune defence

BMC Veterinary Research

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