IPEC-J2 cells used in this study were kept in our laboratory. The IPEC-J2 cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS, GIBCO, Australia), and 1% Pen-Strep [penicillin (100 U/ml), streptomycin (0.1 mg/ml); Solarbio, Beijing, China]. Cells were cultured at 37°C in a humidified incubator set at 5% CO₂.

The IPEC-J2 cells were seeded in 100 mm cell culture dishes and cultured overnight in an incubator at 37°C with 5% CO₂. PDCoV at a 1 multiplicity of infection (MOI) was used for adsorption in monolayer of IPEC-J2 cells for 1 h at 37°C with 5% CO₂, then the inoculum was removed and washed three times with phosphate-buffered saline (PBS), DMEM supplemented with 2% FBS and 1% pancreatin was subsequently added, and incubation was continued at 37°C with 5% CO₂ for 24 h.

The cells were collected and washed with ice-cold PBS three times. For metabolomic analysis, 500 μl ice-cold methanol-water (4:1, V/V, MACKLIN, Shanghai, China) was added to scrape off the cells, then transferred the collected cells to a 1.5 ml centrifuge tube, and added another 500 μl ice-cold methanol-water (4:1, V/V) and stored in −80°C; For transcriptomic analysis, cells were collected, and total RNA was extracted by RNAiso Plus (TaKaRa, Dalian, China) according to the manufacturer's instructions and the extracted RNA was stored in −80°C.

Transcriptome sequencing and differential expression analysis were provided by Novogene (Beijing, China). Genes with adjust P ≤ 0.05 and |log₂FC| ≥ 1 were considered as differentially expressed genes (DEGs). Function classification and enrichment analysis of DEGs were performed using GOseq software based on GO database (http://geneontology.org/page/go-enrichment-analysis). Based on the annotations of DEGs, transcripts were classified into three categories, including biological process (BP), cellular component (CC) and molecular function (MF). Pathway enrichment was analyzed based on the KEGG database (https://www.kegg.jp/kegg/kegg1.html) and carried out using the KOBAS 3.0 web server (with a P ≤ 0.05) at each comparison level for different genotypes and treatments.

Extraction of metabolites and LC-MS/MS analysis were provided by Novogene (Beijing, China). The identified differential metabolites were combined by the VIP value of the PLS-DA model (≥1), the P-value of student's t-test (≤0.05) and |log₂FC| (≥0.5). The eligible metabolites were considered as significantly changed. The KEGG database was used to annotate and display the differential metabolites. Other analyses included partial least-squares discriminant analysis (PLS-DA) and pathway enrichment.

Relative expression levels of selected genes were examined by RT-qPCR using AceQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). The relative gene expression levels of the 8 genes were normalized to GAPDH expression used the 2^−ΔΔCT method. The primers used in RT-qPCR were listed in Table 1. The RT-qPCR reactions were performed on the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific, Massachusetts, USA).

The cells were washed three times with PBS and lysed with RIPA Cell Lysis Buffer, and then equal amounts of protein were subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE). The samples were transferred to PVDF membranes, and the membranes were next blocked with 5% skim milk powder dissolved in TBST (TBS containing 2% Tween 20) for 2 h at room temperature. The membranes were then incubated with specific primary antibodies for 12 h, washed three times with TBST, and incubated with secondary antibodies for 2 h. The bands were imaged using a Tanon-5200 Chemiluminescent Imaging System (Tanon Science & Technology Co., Ltd.).

Statistical analysis was performed by Statistical Program for Social Sciences (SPSS 16.0, SPSS Inc., Chicago, IL, USA). Data were presented as the mean ± standard deviation (SD), and differences between control and experimental groups employed Student's t-test and one-way analysis of variance (ANOVA). For all analysis, P-values of < 0.05 were considered statistically significant (^*P < 0.05; ^**P < 0.01).

To determine the transcriptome changes in the peak of PDCoV replication, RNA sequencing was pursued in PDCoV infected IPEC-J2 cells at 24 h. Principal component analysis (PCA) showed that the samples of PDCoV-infected group and mock group were well separated (Figure 1A), indicating that the gene expression pattern of PDCoV-infected cells was differ from mock-infected cells.

A total of 1,401 DEGs were detected, of which 565 DEGs were upregulated and 836 DEGs were downregulated. All DEGs identified were mapped to the volcano map (Figure 1B). Gene expression profiles with significant difference was presented in the heat map (Figure 1C). To further validate the accuracy of transcriptome data, we randomly selected ANXA2, TFRC and TIRF1 from upregulated DEGs, PSAP, ACSL5C3, VNN2, and ENPP from downregulated DEGs for RT-qPCR analysis. Consistent with the RNA sequencing data, the mRNA levels of ANXA2, TFRC, and TIRF1 were significantly increased and the mRNA levels of PSAP, ACSL5C3, VNN2, and ENPP were significantly decreased, indicating the reliability of the RNA sequencing results (Figure 1D).

All DEGs were annotated to the Gene Ontology (GO) database (http://geneontology.org), and enriched to 528 signaling pathways. The top 10 pathways with the highest enrichment in each category were selected for display (Figure 1E). GO analysis in BP was mainly related with defense response, cell adhesion, and biological adhesion; CC was mainly enriched in external encapsulating structure, extracellular matrix and collagen-containing extracellular matrix; MF was mainly enriched in calcium ion binding, enzyme regulator activity and ligand–gated ion channel activity. Furthermore, 1,401 DEGs were annotated to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.kegg.jp/kegg/pathway.html), and assigned to 68 different KEGG pathways. The KEGG bubble plots show the top 20 enriched signaling pathways (Figure 1F), relating to neuroactive ligand–receptor interaction, cytokine-cytokine receptor interaction, MAPK signaling pathway, chemokine signaling pathway and ras signaling pathway.

To further investigate the different metabolites involved in the cellular responses to PDCoV infection, metabolomic changes were carried out on PDCoV infected IPEC-J2 cells by LC/MS. The PDCoV-infected and mock groups were easily distinguished, and the PLS-DA model was reliable with a large gap and good intra-group clustering within groups, indicating that PDCoV infection caused the metabolite changes (Figure 2A). All identified metabolites were plotted as volcano maps (Figure 2B), and 254 differential metabolites were identified in the PDCoV-infected group compared to the mock group, in which red dots represented upregulated 112 metabolites and blue dots represented downregulated 142 metabolites. The top 10 upregulated metabolites upon PDCoV infection were Methionine, Pidotimod, PG(22:5/22:5), L-Valine, DL-α-Aminocaprylicacid, 5-Aminopentanoate, Cuminaldehyde, N-Acetyl-aspartic acid, L-(+)-Citrulline, N-Acetylaspartic acid. Meanwhile, the top 10 downregulated metabolites were LPG 20:1, N1-methyl-5-methoxy-2-({2-[(methylamino)carbonyl]phenyl}thio) benzamide, Prostaglandin H2, Penicillin G, DL-Carnitine, 6-methyl-4-(morpholinomethyl)-2H-chromen-2-one, N1-pyrazin-2-yl-4chlorobenzamide, L-Glutathione (reduced), D-Mannose 6-phosphate, DL-Arginine. Additionally, Hierarchical clustering was further displayed to show the expression patterns of the differential metabolites between and within the PDCoV and mock group (Figure 2C).

Subsequently, differential expressed metabolites were enriched based on the KEGG database (http://www.kegg.jp/kegg/pathway.html), the bubble map lists the top 20 most enriched KEGG pathways (Figure 2D), which specifically contained Biosynthesis of cofactors, Nucleotide metabolism, Protein digestion and absorption, Biosynthesis of amino acids and Central carbon metabolism in cancer. Notably, the expression of various amino acids markedly changed. A significant upregulation of L-glutamine and a significant downregulation of gamma-Glutamylcysteine indicated that PDCoV infection resulted in glutamine accumulation. Furthermore, we also found that PDCoV infection altered the nucleotide metabolism of host cells, with significant changes in the expression of various nucleotides essential for cell proliferation and viral replication. The nucleotides may be consumed by PDCoV for its own replication. In addition, the expression of 5′-Adenylic acid was significantly downregulated, which also suggested the consumption of nucleotides of host cells upon PDCoV infection. Overall, these results suggested that PDCoV utilize numerous host components like amino acids and nucleotides to complete their life cycle.

To reveal the molecules and signaling pathways associated with the response to PDCoV infection, we combined analysis of the transcriptome and metabolome between the mock and PDCoV groups. DEGs and differentially accumulated metabolites (DAMs) caused by PDCoV infection were mapped onto the KEGG pathway map. DEGs and DAMs were enriched to PI3K-Akt signaling pathway (P < 0.01) and ferroptosis signaling pathway (P < 0.01; Figure 3). Specifically, 32 key genes and one metabolite (5′-Adenylic acid) were mapped to the PI3K/Akt signaling pathway. In addition, seven differentially expressed key genes (ACSL5, TFRC, SLC39A8, LPCAT3, SLC7A11, SAT2, and CP) and two DAMs (Mevalonic acid, gamma-Glutamylcystein) were participated in ferroptosis signaling pathway (Figure 4).

Ferroptosis is an important cellular non-apoptotic form of regulated cell death that plays a role in several viral infections (23, 24). To further explore the specific role of ferroptosis pathway plays in PDCoV infection, we used erastin, a classical activator of ferroptosis (22), to pretreated with IPEC-J2 cells, and then infected with PDCoV at an MOI of 1. RT-qPCR (Figures 5A, B) and western blotting (Figure 5C) results indicated that erastin treatment hampered the transcription of viral genes and the expression of viral protein.