Analysis of differentially expressed genes related to cell death in porcine kidney-15 cells at 24 and 48 hours post porcine parvovirus infection

Tingting Lu Department of Veterinary Medicine, Henan University of Animal Husbandry and Economy, Zhengzhou, China

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Xinghui Song Department of Veterinary Medicine, Henan University of Animal Husbandry and Economy, Zhengzhou, China

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Li Zhao Department of Veterinary Medicine, Henan University of Animal Husbandry and Economy, Zhengzhou, China

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Xia Ma Department of Veterinary Medicine, Henan University of Animal Husbandry and Economy, Zhengzhou, China

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Abstract

OBJECTIVE

This study aims to identify and characterize differentially expressed genes (DEGs) associated with porcine parvovirus (PPV)-induced cell death in porcine kidney-15 (PK-15) cells. By analyzing the biological processes enriched by these DEGs and exploring their interaction networks, we aim to gain a deeper understanding of the molecular mechanisms underlying PPV-mediated cell death.

METHODS

After infecting cultured PK-15 cells with PPV for 24 and 48 hours, cell viability and cysteine-requiring aspartate protease-3 (caspase-3) activity were assessed using an enzyme marker. Apoptosis was observed using fluorescence microscopy. The genome-wide gene expression levels were analyzed through RNA sequencing. The functional enrichment of DEGs was analyzed using the Kyoto Encyclopedia of Genes and Genomes database, and the protein-protein interaction network was generated using the Search Tool for the Retrieval of Interacting Genes/Proteins database.

RESULTS

Porcine parvovirus inhibits cell viability, boosts caspase-3 activity, and enhances cell death at 24 and 48 hours postinfection (HPI). Porcine parvovirus–infected cells showed 547 DEGs at 24 HPI and 1,765 at 48 HPI. Different forms of cell death were enriched in 149 genes that were upregulated at both 24 and 48 HPI. More DEGs associated with cell death were involved at 48 than at 24 HPI. These DEGs are involved in multiple signaling pathways and interact within a complex protein network.

CONCLUSIONS

Porcine parvovirus infection of PK-15 cells induces multiple cell death–related DEGs and signaling pathways.

CLINICAL RELEVANCE

Our study presents a promising approach to investigating the mechanism of PPV infection, with a particular focus on the induction of cell death.

Abstract

OBJECTIVE

This study aims to identify and characterize differentially expressed genes (DEGs) associated with porcine parvovirus (PPV)-induced cell death in porcine kidney-15 (PK-15) cells. By analyzing the biological processes enriched by these DEGs and exploring their interaction networks, we aim to gain a deeper understanding of the molecular mechanisms underlying PPV-mediated cell death.

METHODS

After infecting cultured PK-15 cells with PPV for 24 and 48 hours, cell viability and cysteine-requiring aspartate protease-3 (caspase-3) activity were assessed using an enzyme marker. Apoptosis was observed using fluorescence microscopy. The genome-wide gene expression levels were analyzed through RNA sequencing. The functional enrichment of DEGs was analyzed using the Kyoto Encyclopedia of Genes and Genomes database, and the protein-protein interaction network was generated using the Search Tool for the Retrieval of Interacting Genes/Proteins database.

RESULTS

Porcine parvovirus inhibits cell viability, boosts caspase-3 activity, and enhances cell death at 24 and 48 hours postinfection (HPI). Porcine parvovirus–infected cells showed 547 DEGs at 24 HPI and 1,765 at 48 HPI. Different forms of cell death were enriched in 149 genes that were upregulated at both 24 and 48 HPI. More DEGs associated with cell death were involved at 48 than at 24 HPI. These DEGs are involved in multiple signaling pathways and interact within a complex protein network.

CONCLUSIONS

Porcine parvovirus infection of PK-15 cells induces multiple cell death–related DEGs and signaling pathways.

CLINICAL RELEVANCE

Our study presents a promising approach to investigating the mechanism of PPV infection, with a particular focus on the induction of cell death.

Porcine parvovirus (PPV) is considered a major cause of reproductive disease in pigs, characterized by stillbirth, mummification, embryonic death, and infertility.1,2 Porcine parvovirus demonstrates the capacity to traverse the placental barrier in sows, even if they have been vaccinated, potentially resulting in fetal infection and mortality.3 Porcine parvovirus has been a persistent global issue for the pig industry and continues to be one of the most prevalent and significant sources of infection leading to infertility. The PPV genome is single-stranded DNA that is approximately 5 kb in length. It encodes 3 nonstructural (NS) proteins, NS1, NS2, and NS3, involved in viral replication as well as 3 structural proteins, viral protein1 (VP1), VP2, and VP3.4

Porcine parvovirus has been proven to replicate in the cells of the heart, spleen, kidney, endometrium, lung, and small intestine through PCR experiments.5,6 Furthermore, PPV infection has been reported to trigger apoptosis in a porcine kidney-15 (PK-15) cell line through the activation of the p53 signaling pathway.7,8 According to those reports, the proliferation and viability of PK-15 cells are influenced by PPV, varying with the infection's concentration and duration. Besides, PPV infection also influences the expression of inflammatory cytokines, such as IL-6, IL-12, and tumor necrosis factor-α (TNF-α). It has been demonstrated that the expression of IL-6 is induced by activating the nuclear factor κ B (NF-κB) and Toll-like receptor (TLR)-9 signaling pathways in PK-15 cells.9,10 However, the molecular mechanisms at the genome-wide level by which PPV impacts host cells following infection remain unexplained.

The investigation of the variations in transcript levels in host cells with PPV infection, screening for genes with differential expression, and clustering of these gene functions are essential for understanding the effects of PPV on host cells.

Methods

Cells and virus

Porcine kidney-15 cells (ATCC; CCL-33), which can be used for the proliferation and characterization of a wide range of viruses, were purchased from Pricella Life Science & Technology Co Ltd. Porcine parvovirus type 1 (PPV1) was generously provided by the Veterinary Research Institute of Binzhou in China. Dulbecco modified Eagle medium (Gibco BRL) supplemented with 10% fetal bovine serum (Every Green) was used to culture PK-15 cells at 37 °C with 5% CO2.

Cell viability assessment

The effects of PPV on PK-15 cell viability were assessed using the Cell Counting Kit-8 (CCK-8; Beyotime). Cells were inoculated into a 96-well plate with approximately 10,000 cells per well. After 4 hours of incubation, the culture medium was replaced. Subsequently, 3 blank wells without cells were set up, 3 wells with uninfected cells (control) were retained, and 1 multiplicity of infection (MOI) of PPV was added to the other 3 wells with cells. The incubation continued for 24 and 48 hours. Then, the culture medium was aspirated, and 100 µL of fresh serum-free culture medium and 10 µL of CCK-8 solution were added. The cells were then incubated for 1 hour in the cell culture incubator. Absorbance values were measured at 450 nm using an enzyme marker, and cell viability was calculated using the formula: cell viability (%) = [A (PPV) − A (blank)]/[A (control) − A (blank)] X 100.

Cysteine-requiring aspartate protease-3 activity assay

Cysteine-requiring aspartate protease-3 (caspase-3) is a key enzyme in the apoptotic process. Cysteine-requiring aspartate protease-3 catalyzes the production of yellow p-nitroaniline (pNA) from the substrate acetyl-Asp-Glu-Val-Asp pNA, enabling the detection of caspase-3 activity by measuring the absorbance at 405 nm using the Caspase 3 Activity Assay Kit (Beyotime).

Cells were inoculated into a 6-well plate and infected with PPV (1 MOI) when the cells reached the exponential phase. After 24 and 48 hours postinfection (HPI) of continuing incubation, the adherent cells were digested with trypsin and harvested by centrifugation at 600 X g for 5 minutes at 4 °C. The lysate was added and incubated in an ice bath for 15 minutes, then centrifuged at 4 °C for 20 minutes at 20,000 X g. The supernatant of each sample was transferred to a precooled centrifuge tube, and the protein concentration was determined using the Bradford Protein Assay Kit (Beyotime). The caspase-3 activity was measured immediately according to the protocol of the Caspase 3 Activity Assay Kit. The absorbance resulting from the caspase-3–catalyzed production of pNA in the sample is determined by subtracting the A405 of the blank control from the A405 of the sample. By comparing the sample's production of pNA with the standard curve, the amount can be calculated. A standard curve was plotted by measuring the absorbance values of pNA provided in the assay kit, which were diluted at concentrations of 0, 10, 20, 50, 100, and 200 μM, using a 405-nm wavelength. One unit of enzyme activity is defined as the amount of enzyme that can cleave 1 nmol of acetyl-Asp-Glu-Val-Asp pNA to produce 1 nmol of pNA in 1 hour at 37 °C when the substrate is saturated.

Morphology observation and apoptosis analysis

The changes in cell morphology were observed using an inverted microscope, and the Annexin V-FITC Apoptosis Detection Kit from Beyotime Biotech Inc was used to detect cell death. Porcine kidney-15 cells were inoculated into a 12-well plate and infected with PPV (1 MOI) at 24 and 48 HPI. In preparation for the assay, the 12-well plate was rotated horizontally at 1,000 rpm for 5 minutes. Aspirating the supernatant, adding 1 mL of PBS to each well, and centrifuging the plate again for 5 minutes were performed, and the supernatant was ultimately discarded. Then, 195 μL of Annexin V-FITC buffer and 5 μL of Annexin V-FITC were added and mixed gently. Afterward, 5 μL of propidium iodide was added and gently mixed. The sample was incubated for 10 minutes in the dark and then observed under a fluorescence microscope within 1 hour.

Ribonucleic acid extraction

Porcine kidney-15 cells were seeded in 6-well plates and infected with PPV (1 MOI) when they grew to 80% coverage. Then, cells were collected from the control and PPV-infected groups at 24 and 48 HPI, respectively. There were 3 replicates of each experiment. The culture solution was poured off from the 6-well plate, and the total RNA was extracted using TRNzol Universal Reagent from TIANGEN Biotech (BEIJING) Co Ltd according to the protocol. On a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), RNA quantity and quality were examined. The Agilent 2100 system (Agilent Technologies) and LabChip GX kits (PerkinElmer) were used to assess the stability of RNA.

Library construction and RNA sequencing

The VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina was used to create cDNA libraries, and VAHTSTM DNA Clean Beads (Vazyme) were utilized to purify the product. To ensure the quality of the product, an effective concentration higher than 2 nM was used as a standard. Ribonucleic acid sequencing (RNA-seq) was performed in paired-end 150-bp mode using the Illumina NovaSeq 6000 platform (Illumina). Adapter sequences and low-quality reads (reads with more than 10% N and in which the number of bases with a quality value ≤ 10 constitutes over 50% of the entire read) were removed from the raw sequencing data before saving it in FASTQ format. Afterward, the screened clean data were aligned to the reference genome Sscrofa11.1 (http://asia.ensembl.org/index.html) using HISAT2 software.11 The reads were mapped, and the transcripts were quantified after being assembled using the StringTie method.12

Correlation assessment and principal component analysis of biological replicates

Three biological replicates of each sample were examined using the Pearson correlation coefficient.13 Principal component (PC) analysis (PCA) is a method of applying variance decomposition to multidimensional data using the R package gmodels. We performed PCA based on the fragments per kb of exon model per million mapped fragments (FPKM) values of each gene in the samples.

Differentially expressed genes analysis

Fragments per kb of exon model per million mapped fragments measurements were used to compute the expression levels of each transcript. DESeq214 was used to identify differentially expressed genes (DEGs) with the screening criteria of fold change ≥ 1.5 and false discovery rate < 0.05. TBtools was used to generate a heatmap of the DEGs.15

Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction analysis

The DEGs’ functional enrichment was analyzed in comparison to the genomic background. The Kyoto Encyclopedia of Genes and Genomes pathway–related database was used to enhance the understanding of gene functions. To assess the statistical enrichment of DEGs among Kyoto Encyclopedia of Genes and Genomes pathways, KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/) was utilized.16

By merging the outcomes of DEGs with pairs of interactions identified in the Search Tool for the Retrieval of Interacting Genes/Proteins database, a network of DEG interactions was created.17

Reverse transcription–quantitative PCR analysis

Six DEGs were selected to verify the accuracy and consistency of the RNA-seq findings using reverse transcription–quantitative PCR (RT-qPCR). The primers for these genes, which were designed using the TBtools program (http://github.com/CJ-Chen/TBtools/releases), are listed in Supplementary Table S1; GAPDH was used as an internal control. HiScript II Reverse Transcriptase from Vazyme was used to convert the extracted RNA into cDNA, and Thermo Fisher Scientific's ABI 7500 Fast Real-time PCR system was used to perform quantitative PCR using qPCR SYBR Green Master Mix (Vazyme). The PCR settings were 95 °C for 30 seconds, followed by 40 cycles of 95 °C for 3 seconds, and 60 °C for 30 seconds. The machine was then set to run its standard melt curve acquisition protocol, which consisted of the following steps: 95 °C for 15 seconds, 60 °C for 60 seconds, and 95 °C for 15 seconds. Three biological replicates were set up for each trial. The relative expression of mRNAs was calculated using the 2-ΔΔCt method.

Statistical analysis

The statistical analysis of cell viability and RT-qPCR was conducted using SPSS, version 20.0 (IBM Corp). Data were obtained as mean ± SD from 3 separate experiments for each group. The Student t test was used to compare 2 groups in triplicate. Significant differences between data were indicated by an asterisk.

Results

Effect of PPV infection on PK-15 cells

To understand the effect of PPV infection at 24 and 48 HPI on cell proliferation, we assessed cell viability using the CCK-8 assay. The results showed that the cell viability was significantly inhibited after 24 and 48 HPI in PPV-infected cells compared to uninfected cells (Figure 1). To further understand the effect of PPV on apoptosis, we compared the relative caspase-3 activity in PPV-infected cells versus control cells. There was a significant increase in enzyme activity in PPV-infected cells compared to uninfected cells at both 24 and 48 HPI (Figure 1). Furthermore, caspase-3 activity significantly increased, rising from 1.11 to 3.44 (U/mg protein) as the cell culture time extended from 24 hours to 48 hours. In the infected cells, this activity doubled from 2.54 to 4.96 (U/mg protein) over the same period (Supplementary Table S2).

Figure 1
Figure 1

The effect of porcine parvovirus (PPV) infection on porcine kidney-15 (PK-15) cells was examined. Porcine kidney-15 cells were infected with 1 multiplicity of infection PPV; cell viability (A) and caspase-3 enzyme activity (B) were examined. Morphological changes and apoptosis were observed in infected and uninfected PK-15 cells under fluorescence microscopy (200X; C). Data are presented as mean ± SD. **P < .01; ***P < .001. HPI = Hours postinfection. PI = Propidium iodide.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

By observing the cellular morphology through microscopy, we noted that the cells exhibited cytopathic effects at 24 HPI. At 48 HPI, the cell boundaries became indistinct, and a significant number of cells underwent lysis and died. By using fluorescent staining to observe apoptosis and necrosis, we discovered that PPV resulted in cell death that becomes apparent over time. We also found that uninfected cells experienced a significant increase in mortality within 48 hours, which was accelerated by PPV infection (Figure 1).

Correlation assessment and PCA analysis

In order to understand the changes brought to the cells by PPV infection at the transcriptional level, the transcriptome of 12 samples was analyzed, and a total of 83.04 Gb of clean data was obtained. The clean data of each sample reached 6.43 Gb. Forty-three to 55 million reads were obtained from each sample. The proportion of mapped reads to total reads was higher than 95.8%, and the quality score 30 (Q30) base percentage of each sample was not less than 95.15% (Supplementary Table S3). To assess the consistency of samples, the correlation coefficient among 3 replicates was determined. As the correlation coefficient approaches 1, the reproducibility among the 3 parallel tests will be higher. The findings indicated that the 3 biological replicates for each of the 4 groups in our study were highly consistent. R2 values ranged from 0.995 to 0.999 for control cells at 24 HPI (Con24h-1, Con24h-2, and Con24h-3); from 0.994 to 0.998 for the 3 replicates of PPV-infected cells at 24 HPI (PPV24h); 0.998 for control cells at 48 HPI (Con48h); and from 0.996 to 0.998 for PPV-infected cells at 48 HPI (PPV48h-1, PPV48h-2, and PPV48h-3; Figure 2).

Figure 2
Figure 2

Analysis of genes among different sample groups. Heatmap of correlation in gene expression among 4 groups (A) and principal component (PC) analysis of 4 groups (B). Con24h = Control cells at 24 HPI. Con48h = Control cells at 48 HPI. PPV24h = PPV-infected cells at 24 HPI. PPV48h = PPV-infected cells at 48 HPI.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

The results of the PCA indicated that the PCs converged among the 3 biological replicates of the individual samples. The plot is divided into 2 PCs, PC1 and PC2. PC1 is labeled with a percentage (84%), indicating that there are significant changes in the PCs of the cells cultured at 24 hours and 48 hours. Specifically, PC2 accounts for 13.8% of the variance and effectively distinguishes the samples between the PPV-infected cells and the control cells. This indicates that the effect of infection and the passage of time are both contributing to the variance in the data. Among the 4 groups of samples, the PCs of Con24h and PPV24h were closer together compared to the other groups (Figure 2).

Differentially expressed genes induced by PPV infection

To better understand the dynamic alterations that occurred in PK-15 cells, the DEGs 24 and 48 hours after PPV infection were examined. After 24 hours of infection, 547 DEGs were identified, with 217 genes upregulated and 330 genes downregulated. After 48 hours of infection, a total of 1,081 upregulated DEGs and 684 downregulated DEGs were identified (Figure 3; Supplementary Table S4). Among these DEGs, 149 were upregulated whereas 90 genes were downregulated at both 24 and 48 HPI (Figure 3). The 149 PPV-induced genes were enriched in “cellular processes” related to various types of cell death, including “necroptosis,” “phagosome,” and “ferroptosis” as well as signaling pathways categorized under “environmental signaling pathways” that are also linked to cell death. This suggested that multiple types of cell death occurred during PPV infection, whereas genes downregulated by PPV were primarily associated with other physiological functions of the cell, such as “focal adhesion” and “gap junction” (Figure 3).

Figure 3
Figure 3

Differentially expressed genes were analyzed by volcano plot (A). Green dots in the figure represent downregulated differentially expressed genes (DEGs), red dots represent upregulated DEGs, and black dots represent nondifferentially expressed genes. The Venn diagram (B) shows the number of upregulated and downregulated genes at 24 and 48 HPI. The top 3 or 4 Kyoto Encyclopedia of Genes and Genomes functional enrichment functions in terms of “cellular processes” and “environmental information processing” were identified for genes that were coupregulated and codownregulated at 24 and 48 HPI in the Venn diagram (C). ECM = Extracellular matrix. FC = Fold change. FDR = False discovery rate. MAPK = Mitogen-activated protein kinase. NF-κB = Nuclear factor κ B. PI3K = Phosphoinositide 3-kinase. TNF = Tumor necrosis factor.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

Kyoto Encyclopedia of Genes and Genomes analysis of cell death–related pathways

Porcine kidney-15 cells undergo necrosis and apoptosis after PPV infection. We counted the number of DEGs involved in relevant signaling pathways resulting from PPV infection at 24 and 48 HPI to comprehend the changes in the signaling pathways related to cell death. We classify the signaling pathways associated with cell death into 3 categories: cellular processes, environmental information, and organismal systems. In the classification of cellular processes, we observed that PPV infection induced more DEGs at 48 than at 24 HPI. For example, there were only 12 DEGs involved in the phagosome signaling pathway at 24 HPI, with 8 upregulated and 4 downregulated. In contrast, there were 27 DEGs at 48 HPI, of which 25 were upregulated. Besides, 7 and 6 DEGs were involved in the necroptosis and apoptosis signaling pathways at 24 HPI, whereas 17 and 19 DEGs were involved at 48 HPI. Similarly, the number of DEGs at 48 HPI was much higher than at 24 HPI in the categories “environmental signaling processes” and “organismal systems,” indicating that more DEGs induced by PPV infection at 48 HPI are involved in these signaling pathways (Figure 4; Supplementary Tables S5 and S6).

Figure 4
Figure 4

Enrichment of Kyoto Encyclopedia of Genes and Genomes pathways related to cell death at 24 (A) and 48 (B) HPI. HIF = Hypoxia-inducible factor. JAK = Janus kinase. mTOR = Mammalian target of rapamycin. NOD = Nucleotide-binding oligomerization domain. STAT = Signal transducer and activator of transcription.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

Analysis of the protein-protein interaction network of DEGs

We identified the DEGs in the signaling pathways responsible for cell death and mapped the network of interactions among these proteins. The complexity of these protein interactions was demonstrated by the data, with the majority of the proteins being involved in multiple signaling pathways. Among these, nuclear factor kappa B inhibitor alpha (NFKBIA) is involved in apoptosis, IL-17, NF-κB, TNF, TLR, and nucleotide-binding oligomerization domain–like receptor signaling pathways. It interacted with numerous proteins expressed by DEGs, including protein kinase C γ, cyclin E2, C-X-C motif chemokine ligand 8, TLR2, cyclin-dependent kinase 1, myeloid differentiation factor 88, intercellular adhesion molecule 1, TLR3, signal transducer and activator of transcription (STAT)-3, nitric oxide synthase 2, IL-6, TLR1, and CCAAT enhancer binding protein β. Hypoxia-inducible factor (HIF)-1A participates in the HIF-1, autophagy, and mitophagy signaling pathways. It interacts with adenosine receptor A2a, STAT3, receptor tyrosine-protein kinase erbB-2, nitric oxide synthase 2, Bcl2-interacting protein 3 (BNIP3) and endothelin-1. Bcl2-interacting protein 3 is involved in autophagy and mitophagy signaling pathways, suggesting that PPV infection may regulate cell death through BNIP3-induced autophagy. Cyclic AMP-dependent transcription factor (activating transcription factor 4) is at the intersection of mitophagy, apoptosis, mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K)-Akt, and TNF signaling pathways. Besides, it interacts with Egl-9 family HIF-3 (EGLN3), protein kinase C γ type isoform X1 (protein kinase C γ), MAP kinase-interacting serine/threonine-protein kinase 1, and other proteins. IL-6 is involved in HIF-1, IL-17, Janus kinase–STAT, PI3K-Akt, TNF, TLR, and nucleotide-binding oligomerization domain–like signaling pathways. Tumor necrosis factor receptor superfamily member 6 precursor (FAS) is involved in necroptosis, apoptosis, p53, MAPK, and TNF signaling pathways. IL-1A participates in the MAPK and necroptosis signaling pathways. Ras-related protein rab-7b (RAB7B) is involved in the phagosome, mitophagy, and autophagy signaling pathways (Figure 5).

Figure 5
Figure 5

Protein-protein interaction of DEGs involved in the signaling pathways associated with cell death. The different font colors in the figure represent the distinct signaling pathways in which the protein is involved. Some proteins are involved in multiple signaling pathways and are distinguished by different colored horizontal lines underneath in addition to the letters.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

Validation of gene expression by RT-qPCR

From the important genes mentioned in the aforementioned protein-protein interaction network, we selected a total of 6 genes for RNA-seq validation by RT-qPCR. This selection includes BNIP3 and HIF1A, both of which exhibit high expression levels with FPKM values exceeding 10. Tumor necrosis factor receptor superfamily member 6 is classified as moderately expressed with FPKM values ranging from 1 to 10. RAB7B is categorized under low expression, with FPKM values that are below 1. Additionally, we included EGLN3 and IL1A genes that demonstrate a dynamic range of expression, transitioning from low to high levels. Our results evaluated the consistency of gene expression change trends measured by RT-qPCR and RNA-seq. The RT-qPCR outcomes were generally consistent with those obtained by RNA-seq. The expression levels of BNIP3 and EGLN3 were highest at 48 HPI, whereas IL1A, HIF1A, and RAB7B expression levels increased after PPV infection and peaked at 48 HPI. Among these, RAB7B, which had a low expression level, displayed relatively high variability among the 3 replicates. Interestingly, while the expression level of FAS generally decreased over time, PPV infection resulted in its upregulation (Figure 6).

Figure 6
Figure 6

Comparison of expression levels of 6 DEGs determined by RNA sequencing (RNA-seq) and reverse transcription–quantitative PCR (RT-qPCR). Data are presented as mean ± SD. *P < .05; **P < .01; ***P < .001; ****P < .0001.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.06.0164

Discussion

Parvoviruses present a diverse group of viruses affecting both animals and humans, with significant differences in the diseases they cause. Canine parvovirus (CPV) and PPV are particularly impactful in animals, causing severe gastroenteritis and reproductive failure, respectively.1,18 In humans, parvovirus B19 (B19) is prevalent, leading to erythema infectiosum and, in certain cases, anemia.19 Several studies have reported that viruses belonging to the parvovirus family induce apoptosis in various host cells. From these reports, CPV causes cell death by activating caspase-3, -8, -9, and -12.19 This indicated a complex interplay between different cell death mechanisms, such as the extrinsic and intrinsic apoptosis pathways and the endoplasmic reticulum stress pathway. Unlike apoptosis induced by PPV infection, apoptosis caused by CPV infection is p53 independent.18 Cell cycle arrest and apoptosis occur in human B19-infected cells. During the infection process, the B19 diminishes the expression of the mRNA for baculoviral inhibitor of apoptosis repeat-containing protein 3, which is known for its role in inhibiting apoptosis. Our results also indicate that the expression of baculoviral inhibitor of apoptosis repeat-containing protein 3 in cells infected with PPV for 48 hours significantly increased compared to the control group cells.1921 Human bocavirus infection of HeLa cells induces apoptosis via a mitochondria-mediated pathway.19,22 Similarly, infection with the minute virus of mice fibrotropic strain promotes the expression of caspases-3 and -9, BAX, Apaf-l, and cytochrome C in transformed fibroblast cells, thereby activating the mitochondria-mediated apoptotic pathway.23 Previous study in our laboratory has shown that PPV induces apoptosis in PK-15 cells, with increased expression of cytochrome c, Apaf-1, and caspase-9, demonstrating similar mechanisms of cell death induction.8

In this research, we aimed to achieve a more comprehensive understanding of gene expression alterations following PPV infection of cells at a genome-wide scale. We identified a significant number of DEGs at 24 and 48 HPI, many of which are involved in various signaling pathways associated with cell death, either directly or indirectly. The identified forms of cell death induced by the virus include apoptosis,24,25 necroptosis,26 autophagy,27 ferroptosis,28 pyroptosis,29 and necrosis.30 Crosstalk between these types of cell death and many diverse signaling pathways controls it.31 For example, the PI3K-AKT signaling pathway regulates various cellular processes, such as autophagy and apoptosis.32 MAPK signaling pathway was reported to regulate cell apoptosis.33 Rap1 and NF-κB signaling pathways were reported to regulate the genes involved in cell apoptosis.34 Calcium signaling also plays a role in cell death through autophagy.35 Our research revealed that following PPV infection, alterations in gene expression occurred in the majority of these pathways.

Among the DEGs involved in these signaling pathways, we identified some interesting genes, including BNIP3 and HIF1A, whose expression considerably increased 48 hours after PPV infection. The BNIP3 signaling pathway is a group of mitochondrial autophagy pathways widely distributed in humans and mammals. It consists of members of the Bcl-2 subfamily that contain only the BH3 structural domain. This domain inhibits the function of the antiapoptotic protein Bcl-2 and activates mitochondrial autophagy. This pathway is regulated by HIF.3638 HIF1 is an oxygen-sensitive transcriptional activator that induces changes in transcription in response to hypoxia. These modified genes are associated with angiogenesis, iron metabolism, glucose metabolism, and cell proliferation/survival.39,40 EGLN3 is a proline hydroxylase whose primary function is to regulate HIF factors and is mainly responsible for degrading the HIF-α subunit.41 It has been reported that influenza A virus infection increased the expression of HIF-1α at both mRNA and protein levels and induced cell ferroptosis by activating the HIF-1α/iNOS/VEGF signaling pathway.42 In SARS-CoV-2 (the virus that causes coronavirus disease 2019) infection, the viral ORF3a protein can induce the expression of HIF-1α by causing mitochondrial damage and the production of mitochondrial reactive oxygen species. This upregulation of HIF-1α subsequently promotes the infection and replication of SARS-CoV-2, induces the overexpression of cellular inflammatory factors, and accelerates cellular damage.43 Our results suggest that PPV infection promotes mitochondrial autophagy by inducing BNIP3 and HIF1A expression, thereby facilitating cell death. The results also indicate that EGLN3 expression is significantly higher at 48 HPI, suggesting its potential involvement in the HIF-1 signaling pathway at this time by regulating HIF1A. Comparing these findings, it is evident that the impact of viral infections on the expression of HIF-1α is complex and involves various intracellular signaling pathways and the physiological state of the host cell. Further research is necessary to determine the precise effects of specific viral infections on the expression and function of HIF-1α.

Building on our findings related to cell death mechanisms, we delved into the role of specific receptors and genes in mediating these processes. Tumor necrosis factor receptor superfamily member 6 is one of the TNF receptors that serves as a model for research on the apoptotic signaling pathway. Tumor necrosis factor receptor superfamily member 6 autoaggregates as a homotrimer at the plasma membrane without the assistance of its native ligand Fas ligand, and this process is essential for inducing cell death.44,45 Our results showed that the expression level of FAS was significantly lower in cells cultured for 48 compared to 24 hours, whereas their expression levels increased significantly at both 24 and 48 hours after PPV infection. This suggests that PPV infection regulates multiple signaling pathways, such as necroptosis, apoptosis, p53, MAPK, and TNF signaling pathways, through the induction of FAS expression, promoting cell death. IL1A is 1 of the necroptosis-related genes46 and functions in inflammatory diseases.47,48 Our RNA-seq and RT-qPCR data demonstrated that PPV infection increased IL1A expression by over tenfold at both 24 and 48 HPI. This indicates that PPV infection promotes cellular necroptosis.

In addition to the changes observed in PPV-infected cells, we also noted that uninfected cells underwent physiological changes during the 24 to 48 HPI in culture. We found that DEGs in this process were also involved in cell death–related signaling pathways. Infection with PPV accelerated this process. It is difficult to attribute cell death induced by PPV infection to a specific gene or protein because multiple DEGs resulting from PPV infection are involved in various signaling pathways, forming a complex network of protein interactions. Further research into the role of PPV in the cell death process, beyond the normal apoptosis that occurs in cells over time, is necessary.

Although our research has identified numerous DEGs associated with cell death, along with their intricate interconnections, a deeper investigation into their specific roles in the context of viral infection and their overall influence on cellular health is warranted. This approach will help elucidate the complex dynamics at play and potentially uncover novel therapeutic targets. While this study sheds light on the molecular mechanisms underlying PPV-mediated cell death in vitro, the in vitro environment does not fully replicate the complexities of a living organism. Therefore, further research employing in vivo models is warranted to validate these findings and gain a more holistic perspective on PPV pathogenesis.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org.

Acknowledgments

The raw data associated with this study has been deposited at the National Center for Biotechnology Information (NCBI) (accession: PRJNA1066973).

Disclosures

The authors have nothing to disclose. No AI-assisted technologies were used in the generation of this manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 32102659 and 32072906; the PhD research startup funding in Henan University of Animal Husbandry and Economy, grant number 2022HNUAHEDF028; the Key Discipline of Veterinary Medicine of Henan University of Animal Husbandry and Economy, grant number XJXK202202; and the Henan Provincial Science and Technology Research Project (242102110002).

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