Multi-Omics Analyses Reveal Metabolic Alterations Regulated by Orf Virus in Primary Ovine Fetal Turbinate Cells

Abstract

Orf virus (ORFV) is a member of the Parapoxvirus genus of the Poxviridae family causing contagious diseases in sheep, goats, and wild ungulates, with zoonotic potential in humans. Although many viruses, including poxviruses, are known to utilize the host cellular machinery to reproduce viral particles, the metabolic changes induced by ORFV remain unclear. In the present study, non-targeted metabolomics and proteomics were employed to investigate the impact of ORFV infection on the host cellular metabolism network. A total of 301 metabolites and 802 proteins were significantly altered during the early stages of ORFV infection, and most of them were involved in cellular lipid metabolism, amino acid metabolism, nucleotide metabolism, and glucose metabolism. We further determined the effect of the host’s metabolic system on ORFV replication using the TCID₅₀ assay. Virus titers were significantly decreased in the absence of glucose or when treated with the de novo fatty acid synthesis inhibitor, indicating that glucose metabolism and de novo fatty acid synthesis pathway were required for ORFV replication. However, glutamine did not affect viral titers. Our findings provide insights into ORFV–host interactions, which are critical for developing new preventive or therapeutic strategies against ORFV by targeting altered metabolic pathways.

1. Introduction

Orf, also known as contagious ecthyma (CE) or contagious pustular dermatitis, is a common viral disease predominantly affecting sheep, goats, and, to a lesser extent, humans and wild ruminants, and is widely distributed in sheep flocks worldwide [1,2,3]. Infected animals usually develop characteristic papulopustular eruptions and ulcerative and proliferative lesions on the skin and mucous membranes of the lips, tongue, eyelids, and udders. Generally, the lesions are self-limiting without treatment, often resolving spontaneously within 3 to 6 weeks; however, in severe cases, systemic symptoms are accompanied by a secondary bacterial infection or mixed infections [4,5,6]. In recent years, the disease has emerged as a significant zoonotic threat with escalating annual mortality rates, causing severe economic losses [7,8]. Despite numerous research efforts, effective antiviral treatment for ORFV infection remains limited, hindered by gaps in understanding the complex interactions between the virus and the host cell.

Viruses usually rely on host cell metabolism to provide energy and biosynthetic precursors needed for their own replication and survival [9]. To date, multiple studies have indicated that metabolic alterations may facilitate virus replication in host cells. For example, lipid metabolic alterations induced by hepatitis B virus (HBV) infection were observed in host cells and in HBV transgenic mice [10]. Several viruses, including Vaccinia Virus (VACV) and Kaposi’s Sarcoma Herpesvirus (KSHV), require glutamine metabolism for efficient replication, thereby depriving infected cells of exogenous glutamine, which leads to a significant decrease in infectious virus production [11,12]. Newcastle disease virus (NDV) has been proven to remodel the plasma phospholipid metabolism network in chickens [13]. To bolster self-replication in host cells, Influenza A Virus (IAV) hijacks organelles to synthesize a large number of metabolites required for viral replication, as well as energy for virion packaging [14]. These studies have provided abundant evidence of a close link between host metabolism and viral proliferation and pathogenesis. However, the metabolic changes caused by ORFV infection remain unknown.

Cellular metabolism is a complex network of biochemical processes that organisms utilize to convert nutrients into energy for their survival, which plays an important role in viral life cycle in a highly dynamic manner [15]. In the present study, we investigated the impact of ORFV infection on host cell metabolism using a non-targeted metabolomics approach based on mass spectrometry (LC-MS). Multiomics profiling revealed the metabolic changes in host cells caused by ORFV, involving cellular lipid metabolism, amino acid metabolism, nucleotide metabolism, and glucose metabolism. We believe that the results of this study will provide an important reference for future research on ORFV pathogenesis and the development of new preventive or therapeutic strategies targeting the altered metabolic pathway.

2. Materials and Methods

2.1. Cells and Virus

Primary ovine fetal turbinate (OFTu) cells were cultured in Dulbecco’s modified Eagle medium (DMEM) (GIBCO, Invitrogen, Grand Island, NY, USA), supplemented with 8% fetal bovine serum (FBS; Hyclone, sourced from Tauranga, New Zealand), 2 mM L-glutamine, 100 U of penicillin per ml, and 100 μg of streptomycin in an incubator at 37 °C with 5% CO₂. The ORFV strain, ORFV-CL24 (GenBank accession number PV126639), was isolated and maintained in our laboratory.

2.2. Sample Collection

When the OFTu cells reached high confluence (>90%), they were mock-infected or infected with the ORFV-CL24 strain at a multiplicity of infection (MOI) of 0.1. The virus inoculums was removed by washing with phosphate-buffered saline (PBS) and incubating in DMEM for the indicated times in 5% CO₂ at 37 °C. The cytopathic effect (CPE) in primary OFTu cell cultures induced by ORFV-CL24 strain was examined daily under a light microscope. The infected cells were gently washed twice with precooled PBS, then quenched by the addition of 1 mL of 60% aqueous methanol solution, and collected at 0 h, 24 h, and 48 h using cell scrapers. The samples were centrifuged at 1000 rpm for 1 min at 4 °C to remove the supernatant, and the pellet was quickly frozen using liquid nitrogen. Finally, the samples were freeze-dried and stored at −80 °C until metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS). To identify ORFV infection, cells and cell culture supernatants were frozen and thawed three times to release the virus, then centrifuged at 1500 rpm for 15 min. The supernatants were collected for virus titration, and the cells were lysed for Western blotting.

2.3. Virus Titration

OFTu cells were mock-infected or infected with ORFV-CL24 and harvested at 24 and 48 h post-infection (hpi), respectively. Virus titer was quantified by 50% tissue culture infectious dose (TCID₅₀). Briefly, the samples were inoculated onto confluent monolayers of OFTu cells in 96-well plates (100 μL/well) and titrated in triplicate using 10-fold serial dilutions (10⁻¹ to 10⁻⁹). Infectivity titers (TCID₅₀/mL) were then calculated using the Reed–Muench method. To detect infectious particle production in ORFV-infected cells treated with pharmacological inhibitors, including TOFA, C75, and Etomoxir, cells were collected at 24 hpi, and virus titers in each group were determined by the TCID₅₀ assay. Each treatment was performed in triplicate.

2.4. Western Blotting

Target proteins were analyzed by 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes. The membranes were then blocked with 5% skim milk in Tris-buffered saline with Tween 20 (TBST) and incubated overnight at 4 °C with primary antibodies, followed by a further incubation with horseradish peroxidase (HRP)-conjugated secondary antibodies. The protein bands were visualized using ECL Western Blotting Detection Reagents (Beyotime, Shanghai, China) and captured with a UVP gel documentation system (UVP, LLC., Phoenix, AZ, USA). GAPDH served as a loading control.

2.5. Metabolite Extraction

After the samples were slowly thawed at 4 °C, 1 mL precooled methanol was added to the Eppendorf tubes, and the mixture was vortexed for 60 s. Then, the samples were treated with ultrasound for 15 min in ice water, followed by centrifugation at 12,000 rpm for 10 min at 4 °C. The supernatant (450 µL) was transferred to a new Eppendorf tube, and the extracts were dried in a vacuum concentrator. The vacuum-dried extracts were dissolved in 150 µL of a 2-chlorobenzalanine (4 ppm) 80% methanol solution, and the supernatant was filtered through a 0.22 µm membrane to obtain the prepared samples for LC–MS. Finally, the supernatant (60 µL) was transferred into a fresh 2 mL LC/MS glass vial, and approximately 20 µL from each sample was combined to form a quality control (QC) sample for LC–MS detection.

2.6. Untargeted Metabolomics Analysis

Chromatographic separation of each prepared sample was accomplished in a Thermo Ultimate 3000 system equipped with an ACQUITY UPLC^® HSS T3 column (2.1 × 150 mm × 1.8 μm; Waters Co., Ltd., Milford, MA, USA). The column was maintained at 40 °C. The autosampler temperature was set to 8 °C. Gradient elution of analytes was carried out with 0.1% formic acid in ddH₂O and 0.1% formic acid in acetonitrile, or 5 mM ammonium formate in ddH₂O and acetonitrile at a flow rate of 0.25 mL/min. A total amount of 2 μL of each sample was injected after equilibration.

Subsequently, mass spectrometric detection of metabolites was performed using a Thermo Q Exactive Focusmass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with an ESI ionization source. Simultaneous MS1 and MS/MS (full MS mode with data-dependent acquisition, DDA) was applied. The parameters were as follows: sheath gases pressure, 30 arbitrary (arb) units; aux gas pressure, 10 arb; spray voltage, 3.50 kV for ESI (+), and −2.50 kV for ESI (−); capillary temperature, 325 °C; MS1 scan range, mass/charge (m/z) 81–1000; MS1 resolving power, 70,000 FWHM; number of data-dependent scans per cycle, 10; MS/MS resolving power, 17,500 FWHM; normalized collision energy, 30 eV; dynamic exclusion time, automatic. The raw data were converted to the mzXML format (XCMS input file format) using Proteowizard (v3.0.8789). The peak identification, peak filtration, and peak alignment were accomplished using the XCMS package in R (v.3.3.2). The preprocessing results generated a data matrix containing retention time, peak intensity (relative ratio of the peak area), mass-to-charge ratio (m/z), and identification information. The differential metabolites were obtained and subjected to subsequent correlation analysis and KEGG enrichment analysis.

2.7. Protein Extraction and LC-MS/MS Analysis

The cells with ORFV-CL24 or mock-infected cells were lysed by sonication with four volumes of lysis buffer (8 M urea, 1% TritonX-100, 10 mM dithiothreitol, and 1% protease inhibitor). The cell lysates were centrifuged at 3000× g for 10 min, and the supernatants were further centrifuged at 12,000× g for 10 min at 4 °C. Then, the supernatant was transferred to a new centrifuge tube, and the protein concentration was determined using a BCA kit (Thermo Fisher Scientific, Waltham, MA, USA). The protein samples were subjected to SDS–PAGE and in-gel digestion, followed by LC–MS/MS analysis.

For LC–MS/MS analysis, the protein samples were digested using trypsin (Promega, Madison, WI, USA). Briefly, the trypsin was added at a 1:50 trypsin-to-protein mass ratio for 12 h and a 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion. Subsequently, the tryptic peptides were dissolved in 0.1% formic acid and separated using a NanoElute ultrahigh-performance liquid chromatography (UHPLC) system according to the manufacturer’s instructions. The nanoHPLC was online coupled to a timsTOF Pro mass spectrometer (Bruker, Karlsruhe, Germany) with a CaptiveSpray ion source (Bruker, Karlsruhe, Germany). The electrospray voltage applied was set at 1.75 kV for the proteome analysis. Both the original peptide ion and its secondary fragments were detected and analyzed at the time-of-flight (TOF) detector, with an MS/MS scan ranging from 300 to 1500 m/z. The timsTOF Pro instrument was operated in parallel accumulation serial fragmentation (PASEF) mode with 20 PASEF scans per acquisition cycle. The raw data files from the mass spectrometer were examined using Proteome DiscoverTM Software 2.2. The peptide identifications were performed throughout the search procedure on the Uniprot website (https://www.uniprot.org/, accessed on 23 October 2024) with a false discovery rate (FDR) of less than 1%. Subsequently, the sample correlation, functional annotation, and other bioinformatics analyses were performed using GO and KEGG enrichment.

2.8. Cell Viability Assay

The CCK-8 assay was used to evaluate the cytotoxicityof TOFA (an allosteric inhibitor of acetyl-CoA carboxylase-α), C75 (a synthetic fatty acid synthase (FASN) inhibitor) and Etomoxir ((R)-(+)-Etomoxir) (a carnitine palmitoyl transferase (CPT-1a) inhibitor). Briefly, OFTu cells grown in 96-well culture plates were incubated with various concentrations of the indicated chemicals in triplicate for 24 h at 37 °C. Afterward, the chemical-containing medium was thoroughly removed, and medium containing 10% CCK-8 reagent was added. Following incubation for 2 h, the absorbance was measured at 450 nm using a microplate reader (TECAN, AG, Mannedorf, Switzerland).

2.9. Statistical Analysis

Single-dimensional statistical analysis was performed using SPSS Statistics for Windows (Version 26.0). Student’s t-test and multiple analyses of variance were used to compare the raw data. The level of significance was set at a p value of <0.05, and the level of high-significance was set at a p value of <0.01.

3. Results

3.1. Replication Level of ORFV-CL24 in OFTu Cells

To analyze metabolites and proteins in OFTu cells infected with ORFV-CL24, we first confirmed ORFV replication. The cytopathic effect (CPE) was evident at 24 and 48 hpi, characterized by rounded cells, cytolysis, and detachment of dead cells (Figure 1A). The infectious viral loads in the cell supernatants were determined by the TCID₅₀ analysis. As shown in Figure 1B, the viral titer was significantly elevated at 48 hpi, indicating a high level of infection. Additionally, the expression level of ORFV 011 protein was detected by Western blotting. ORFV 011 protein also increased with increasing infection time (Figure 1C), which was consistent with the elevation of viral titer within 48 h. These results indicated the successful replication of ORFV-CL24 in OFTu cells. Consequently, 24 h and 48 h were selected for non-target metabolomics and proteomics analysis.

3.2. Multivariate Analysis of Cellular Metabolites and Proteins Under ORFV-CL24 Infection

To comprehensively acquire reliable data on the mock-infected and infected groups, we employed principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to analyze metabolite composition. The results of PCA (Figure 2A) and OPLS-DA (Figure 2B) revealed significant differences in metabolite concentrations between the mock-infected (0 hpi) and infected groups (24 hpi and 48 hpi), suggesting that ORFV-CL24 infection altered the metabolic profiles. Additionally, we conducted a proteomics analysis of ORFV-CL24-infected (12 hpi, 24 hpi, and 48 hpi) or mock-infected (0 hpi) OFTu cells. Subsequently, we measured relative performance, including distributional variability and protein intensity, by plotting violin plots (Figure 2C), Pearson correlation coefficient (PCC) plots (Figure 2D), and PCA plots (Figure 2E). Taken together, the resulting data indicated that the infection model was reliable and stable, which could be exploited for further investigations.

3.3. Significant Alterations in Metabolic and Proteomic Responses to ORFV-CL24 Infection

To better understand the effects of ORFV infection on host metabolism, we conducted a non-targeted metabolomic analysis on OFTu cells infected with the ORFV-CL24 strain for 24 h and 48 h. The differential metabolites across the three groups are presented as a heat map, a volcano map, and difference scatter plots. Trends in differential metabolites were evaluated using a hierarchical clustering heatmap (Figure 3A), indicating significant differences between the mock-infected (0 hpi) and infected groups (24 hpi and 48 hpi). According to the importance of variable weights (VIP value ≥ 1), the differentially enriched metabolites were filtered with a p-adjusted value < 0.05, |log₂ FC| ≥ 1.0 in each pairwise comparison, which were visualized via a volcano plot (Figure 3B) and multiple group difference scatter plots (Figure 3C). Each point in the volcanic map represents a metabolite. A total of 301 metabolites (216 upregulated, 85 downregulated) were significantly altered after ORFV infection. The number of upregulated metabolites increased gradually over time. These results indicated that ORFV infection induced significant changes in metabolite levels.

Using LC-MS/MS proteomics, differentially expressed proteins were identified to determine altered protein expression after ORFV infection. The statistical criteria (p value < 0.05 and FC > 1.5 or FC < 1/1.5) were applied to detect differentially expressed proteins. A significant difference was observed in the heat map depicting hierarchical clustering of the protein data (Figure 3D). The radar map of differential proteins demonstrated the expression levels in samples from mock-infected (0 hpi) and infected groups (12 hpi, 24 hpi, and 48 hpi) (Figure 3E). A total of 802 differential proteins were identified, 328 of which were increased in ORFV-CL24-infected cells, while 474 proteins were decreased compared with the mock-infected group (Figure 3F). In summary, ORFV infection significantly altered the cellular metabolism and cellular protein composition.

3.4. KEGG Functional Classification of Differential Metabolites and Proteins

The screened differential metabolites and proteins were mapped to the corresponding KEGG database for metabolic and protein pathway analysis. As illustrated in Figure 4A, KEGG pathway enrichment analysis revealed significant enrichment mainly in lipid metabolism, nucleotide metabolism, amino acid metabolism, and carbon metabolism. Thus, the occurrence of differentially exclusive metabolites could be explained by changes in metabolic pathways in ORFV-infected OFTu cells. Compared with the mock-infected group (0 hpi), 7, 11, 15, 7, and 13 differential metabolites were, respectively, enriched in lipid metabolism, nucleotide metabolism, amino acid metabolism, carbon metabolism, and cofactor and vitamin metabolism pathways at 24 hpi. In addition, 10, 8, 20, 8, and 11 differential metabolites were mainly enriched in lipid metabolism, nucleotide metabolism, amino acid metabolism, carbon metabolism, and cofactor and vitamin metabolism pathways in comparison with the infected group (48 hpi). Furthermore, KEGG pathway enrichment analysis was performed for the identified differentially expressed proteins. As shown in Figure 4B–D, the differentially expressed proteins were mainly enriched in metabolic pathways, including lipid metabolism, amino acid metabolism, carbon metabolism, and nucleotide metabolism, which were highly consistent with the alterations in cellular metabolism.

3.5. KEGG Pathway Enrichment Analysis of the Significantly Altered Metabolites and Proteins in ORFV-Infected Cells

To explore in depth the pathways associated with metabolic and proteomic changes, enrichment analysis was performed on metabolites and proteins that were significantly up- or downregulated in ORFV-infected cells compared with mock cells. The results of the enrichment analysis of differential metabolites at 24 hpi and 48 hpi, with the top 20 pathways, are shown in Figure 5A,B as significant histograms. Furthermore, a p-value < 0.05 was set as the cut-off criterion, indicating significant enrichment of KEGG pathways. At 24 hpi, the significantly enriched pathways included 2-oxocarboxylic acid metabolism, ABC transporters, phenylalanine metabolism, biosynthesis of amino acids, necroptosis, endocytosis, Rap1 signaling pathway, sulfur relay system, and autophagy–animal pathway (Figure 5A). At the same time, these pathways, including 2-oxocarboxylic acid metabolism, amino digestion biosynthesis, phenylalanine absorption, aminoacyl-tRNA biosynthesis, central carbon metabolism in cancer, protein signaling and metabolism, vitamin B6 metabolism, mTOR signaling pathway, as well as valine, leucine, and isoleucine biosynthesis, were significantly enriched at 48 hpi. Additionally, the differential protein enrichment analysis revealed that three major pathways, including steroid hormone biosynthesis, the PPAR signaling pathway, and the viral protein interaction with cytokine and cytokine receptor pathway, were significantly enriched at 12 hpi (Figure 5C). At 24 hpi, KEGG analyses revealed that the differentially expressed proteins were enriched in metabolic and signaling pathways, including steroid biosynthesis, steroid hormone biosynthesis, PPAR signaling pathway, and JAK-STAT signaling pathway (Figure 5D). Moreover, we found that several pathways, including steroid biosynthesis, pyrimidine metabolism, fatty acid biosynthesis, fatty acid signaling pathway, and PPAR pathway, were enriched at 48 hpi (Figure 5E).

3.6. Analysis of the Significantly Altered Amino Acid Metabolism in ORFV−Infected Cells

Based on the non-target metabolomics results, the differential metabolites involved in amino acid metabolic pathways were selected for further analysis. The characteristics of differential metabolites were displayed by cluster heatmap plots (Figure 6A) and KEGG significant bubble maps (Figure 6B). The abundance of differential metabolites in amino acid metabolic pathways at different time points of infection was illustrated using a cluster heat map. As shown in Figure 6A, the amounts of amino acids, including tyrosine, valine metabolite intermediates, leucine, leucine metabolite intermediates, phenylalanine, ketoleucine, arginine, methionine, histidine, acetyl−glutamate, as well as acetyl−glutamine, were significantly upregulated. The levels of oxidized glutathione and asparagine metabolites were significantly downregulated in the ORFV−infected cells compared to those in the mock−infected cells. Meanwhile, KEGG enrichment analyses indicated that ORFV infection resulted in a large extent of changes in the metabolism pathways of amino acids, especially those referring to the biosynthesis of amino acids (Figure 6B). In addition, proteomic analysis revealed that ORFV−CL24 infection significantly upregulated glutathione S−transferase (GST), an important antioxidant enzyme. Thus, the increase in GST content and the decrease in oxidized glutathione content might be a protective response of host cells to viral infection. In summary, ORFV induced a significant alteration of amino acid metabolism.

3.7. Analysis of the Significantly Altered Lipid Metabolism in ORFV-Infected Cells

To further identify lipid metabolism associated with ORFV infection, differential metabolites involved in lipid metabolic pathways were selected and analyzed using cluster heatmaps and KEGG significant bubble maps (Figure 7). In the present study, the abundance of differential metabolites in lipid metabolic pathways at different time points of infection was illustrated using a cluster heat map. As shown in Figure 7A, membrane lipid components sphingomyelin and phosphatidylcholine were decreased after ORFV-CL24 infection, whereas the levels of sphingosine and lysophosphatidylcholine increased significantly at 24 hpi. Fatty acid synthesis was increased at 24 hpi and then decreased at 48 hpi. These results indicated that viruses might utilize cellular lipids and host lipid metabolism pathways to aid in replication and propagation. Additionally, KEGG enrichment analyses indicated that ORFV infection resulted in a large extent of changes in host lipid metabolism pathways, including phospholipid metabolism and sphingolipid metabolism (Figure 7B). Furthermore, the proteomic analysis revealed that ORFV-CL24 infection significantly upregulated stearoyl-CoA desaturase A (SCD) and fatty acid desaturase 2 (FADS2), key enzymes of the fatty acid synthesis pathway, indicating that ORFV infection promoted unsaturated fatty acid synthesis; 7-dehydrocholesterol reductase (DHCR7) was also significantly upregulated at 48 hpi, indicating that ORFV infection may promote cholesterol synthesis; sphingosine kinase 1 (SPHK1) was significantly decreased after ORFV-CL24 infection, SPHK1 catalyzed sphingosine phosphorylation to sphingosine 1-phosphate (S1P), while metabolome result analysis showed that sphingosine content, a metabolite of sphingomyelin, increased at 24 hpi; glyceryl monoester lipase (MGLL) was significantly decreased after ORFV-24 infection, suggesting that ORFV infection may inhibit triglyceride breakdown. Combined with omics results, ORFV infection promotes fatty acid and cholesterol synthesis and alters sphingomyelin metabolism, indicating that ORFV-CL24 infection is tightly connected to host lipid metabolism.

3.8. Analysis of the Significantly Altered Carbohydrate Metabolism in ORFV-Infected Cells

To further determine the changes in carbohydrate metabolism in ORFV-infected cells, the differential metabolites classified into carbohydrate metabolism (e.g., amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, and galactose metabolism) were subjected to analysis using cluster heatmap plots (Figure 8A) and KEGG significant bubble maps (Figure 8B). The abundance of differential metabolites involved in carbohydrate metabolism pathways at different time points of infection was illustrated using a cluster heat map. As shown in Figure 8A, the levels of carbohydrate, including 6-phosphogluconic acid, digalacturonic acid, N-acetyl-d-glucosamine, and myo-inositol, were significantly elevated at 24 hpi. At 48 hpi, D-glucosamine 6-phosphate, an intermediate of monosaccharide and amylolysis D-Maltose content, and cis-aconitate, an intermediate of the tricarboxylic acid cycle (TCA), were significantly increased. To fully understand their potential role in ORFV infection, the differential metabolites involved in carbohydrate metabolism were further characterized using KEGG enrichment analysis. As displayed in Figure 8B, the amino sugar and nucleotide sugar metabolism, galactose metabolism, as well as starch and sucrose metabolism were commonly significantly enriched. Additionally, proteomic analysis revealed that ORFV-CL24 infection significantly upregulated the expression of Dihydrolipoamide S-Succinyltransferase (DLST) at 48 hpi, a key enzyme in oxidative decarboxylation of the TCA; pyruvate carboxylase (PC) is a key step in the TCA that catalyzes the conversion of pyruvate to oxaloacetate, and the expression of PC is also significantly upregulated at 24 h and 48 h after ORFV-CL24 infection; dihydrolipoamide S-acetyltransferase (DLAT) is one of the components of pyruvate dehydrogenase complex (PDC) and is a bridge between glycolysis and the TCA, and DLAT expression is significantly upregulated at 48 h after ORFV-CL24 infection. Combined with omics results, ORFV-CL24 infection promotes the TCA and alters glucose metabolism.

3.9. Analysis of the Significantly Altered Nucleotide Metabolism in ORFV-Infected Cells

To comprehensively evaluate the effect of ORFV infection on cellular nucleotide metabolism, the differential metabolites involved in nucleotide metabolic pathways were selected for further analysis using cluster heatmap plots (Figure 9A) and KEGG significant bubble maps (Figure 9B). As shown in Figure 9A, the amounts of deoxyguanosine, hypoxanthine, xanthosine, uracil, and uridine significantly increased at 24 hpi, while significant decreases were observed at 48 hpi. Additionally, the levels of metabolites, including adenosine 5′-diphosphoribose, uridine 5′-diphosphate, guanosine 5′-triphosphate, and thymidine 5′-diphosphate, increased significantly with increasing infection time. Meanwhile, KEGG enrichment analyses indicated that the differential metabolites involved in nucleotide metabolism were mainly enriched in purine and pyrimidine metabolism (Figure 9B). Furthermore, proteomics analysis revealed that ORFV-CL24 infection significantly upregulated the expression of deoxycytidine kinase (DCK) at 48 hpi, which catalyzes the phosphorylation of deoxycytidine and generates deoxycytidine triphosphate (dCTP) involved in DNA synthesis and repair processes; ribonucleotide regulatory subunit M2 (RRM2) is a key component of ribonucleotide reductase (RNR), which is responsible for converting ribonucleotides into deoxyribonucleotides, which are precursors of DNA synthesis, and ORFV-CL24 infection significantly upregulated the expression of RRM2 at 48 h; ribonucleotide kinase 1 (TK1) is one of the key enzymes of DNA synthesis, and ORFV-CL24 infection significantly upregulated the expression of TK1 at 48 h. Combined with omics results, ORFV-CL24 infection of OFTu cells alters nucleotide metabolism and promotes DNA synthesis.

3.10. The Effect of the Pharmacological Inhibitors on the Viability of OFTu Cells

The viability of OFTu cells treated with TOFA, C75 or Etomoxir at different concentrations was evaluated by the CCK-8 assay. As shown in Figure 10A, the cytotoxicity effect of TOFA, C75 and Etomoxir on OFTu cells in the concentration range of 0–50 µmol/L, 0–20 µmol/L and 0–200 µmol/L was not significant. According to these results, the concentration of 30 µmol/L TOFA, 20 µmol/L C75 and 200 µmol/L Etomoxir was used for subsequent experiments.

3.11. ORFV Replication Requires Glucose and Does Not Require Glutamine

The metabolomic profiles of ORFV-infected cells suggested that there was an increase in the levels of TCA intermediates, the intermediates of the pentose phosphate cycle, and some amino acids, including glutamine, histidine, acetyl-glutamate, and acetyl-glutamine. Therefore, we hypothesized that ORFV-induced changes in lipid, amino acid, and glucose metabolic activities were necessary for the virus’s life cycle. To test whether glucose and glutamine are involved in ORFV replication, OFTu cells infected with ORFV (MOI = 0.1) were grown in DMEM supplemented without any supplement or supplemented with glucose and/or glutamine, respectively. At 24 hpi, virus titers in each group were determined by the TCID₅₀ assay. Each treatment was performed in triplicate. As shown in Figure 10B, compared with the mock group, the viral titers were significantly decreased in ORFV-infected cells supplemented without glucose, and no obvious change was observed in infected cells supplemented without glutamine. These results indicated that glucose was required for ORFV replication, whereas glutamine had no obvious effect on ORFV replication capacities.

3.12. ORFV Replication Exploits De Novo Fatty Acid Synthesis Without β-Oxidation of Fatty Acids

To further ascertain the role of lipid metabolism in ORFV life cycle, we used the pharmacological inhibitors TOFA (an allosteric inhibitor of acetyl-CoA carboxylase-α), C75 (a synthetic fatty acid synthase (FASN) inhibitor) and Etomoxir ((R)-(+)-Etomoxir) (a carnitine palmitoyl transferase (CPT-1a) inhibitor) to block fatty acid synthesis or inhibit fatty acid oxidation. Subsequently, we investigated whether inhibition of fatty acid synthesis or fatty acid oxidation impacts infectious particle production. The production of infectious particles was determined using the TCID₅₀ assay. As illustrated in Figure 10C, inhibition of de novo fatty acid synthesis using TOFA or C75 significantly reduced virus titers. However, the virus titers did not change significantly even after specific blockade of fatty acid β-oxidation (Figure 10D).

4. Discussion

As a typical member of the Parapoxvirus genus of the Poxviridae family, ORFV is the causative agent of Orf, which mainly affects sheep, goats, and other ruminant populations, resulting in significant economic losses and posing a threat to the flocks and global public health [16,17,18]. In contrast to many other DNA viruses, poxviruses replicate entirely within the cytoplasm of infected cells rather than in the nucleus [19,20]. Thus, it has been widely accepted that poxviruses depend on their effective manipulation of host factors and cellular metabolic systems to support the energetic and biosynthetic requirements for successful replication. Recent studies have shown that host proteins are critically required for ORFV infection and replication [21,22,23]. However, the metabolic profile of ORFV-infected cells has yet to be investigated. In the present study, we explored alterations in metabolic levels following ORFV infection using a combination of untargeted metabolomics and proteomics. The results demonstrated that ORFV can modify the metabolic characteristics of infected cells, providing new insights into ORFV–host interactions. Furthermore, the data may lead to an improved understanding of the infection mechanism.

Over the past several years, the idea has emerged that viruses depend on the metabolism of infected cells to proliferate. Therefore, it is not surprising that viral infection triggers alterations in the metabolic status of host cells to facilitate virus production. Although an increasing number of studies have focused on the role of metabolism during viral infections, no reports have examined alterations in metabolic levels induced by ORFV. To explore this knowledge gap, we utilized liquid chromatography coupled to mass spectrometry (LC-MS/MS) to analyze the metabolic changes induced by ORFV-CL24 infection of host cells. Metabolites, as end-products of cellular regulatory processes, play critical roles in controlling cellular structure and signaling [24,25]. To obtain a reliable metabolic model, the appropriately sensitive cells for metabolomics analysis were determined by evaluating the replication level of ORFV-CL24 in primary OFTu cells. In previous reports, comparison of the ORFV sensitivity of cell cultures (sheep or bovine), including MDBK cells, primary neonatal bovine testicular cells, bovine Sertoli cells, sheep testis (ST), sheep kidney (SK), revealed that the primary cell cultures of sheep origin, including sheep testis (ST), sheep kidney (SK), were the most suitable for cultivation of Orf viruses [26,27,28]. Our results showed that the cytopathic effect (CPE), including cell rounding, cytolysis, and detachment of dead cells, was observed at 24 and 48 hpi, which was similar to the changes observed in other cell cultures of sheep origin. In the present study, we analyzed non-target metabolomics and proteomics results to preliminarily determine that ORFV-CL24 infection significantly promoted fatty acid synthesis, upregulated several enzymes and intermediates related to the TCA, increased enzymes and intermediates in the DNA synthesis pathway, and altered cellular amino acid anabolism. Therefore, we speculated that such changes in the levels of these metabolites, likely resulting from ORFV targeting and interference with metabolic pathways, including lipid, amino acid, carbohydrate, and nucleotide metabolism, might benefit viral replication.

To clarify, ORFV-CL24 was closely correlated with host metabolism, so we further performed a more in-depth analysis of metabolic pathways potentially associated with ORFV replication. Although glucose and glutamine have been demonstrated to act as the main energy sources during multiple viral infections, including adenovirus, human cytomegalovirus (HCMV), Zika virus (ZIKV), and influenza virus, their requirements for efficient ORFV-CL24 replication have not yet been identified [29,30,31,32,33]. Herein, we initially explored whether glucose or glutamine was required for ORFV replication using glucose- and glutamine-deprived assays. The viral titers were significantly decreased in ORFV-infected cells supplemented without glucose, and no obvious change was observed in infected cells supplemented without glutamine, indicating ORFV replication relies largely on glucose and is not reliant on glutamine. In addition, we further assess the role of lipid metabolism in the ORFV life cycle by investigating the impact of inhibiting fatty acid synthesis or fatty acid oxidation on infectious particle production. Inhibition of de novo fatty acid synthesis using TOFA or C75 significantly reduced virus titers; however, virus titers did not change even with specific blockade of fatty acid β-oxidation. The results indicated that ORFV replication exploited de novo fatty acid synthesis rather than β-oxidation.

In summary, this study confirmed that ORFV-CL24 infection altered multiple metabolic pathways using multiomics profiling. Additionally, we confirmed that fatty acid synthesis and glucose might be participated in ORFV-CL24 replication using interference experiments. Overall, we provided a comprehensive and in-depth understanding of the host metabolism induced by ORFV-CL24 and a valuable reference for future research into ORFV–host interactions and the development of new preventive or therapeutic strategies against ORFV by targeting the altered metabolic pathways.

5. Conclusions

In this study, we performed multi-omics analyses (metabolomics and proteomics) to determine the metabolic and proteomic changes in OFTu cells induced by ORFV-CL24. Multiomics profiling showed clear changes in several metabolites and proteins involved in cellular lipid, amino acid, nucleotide, and glucose metabolism. Furthermore, the metabolic and proteomic changes in key pathways were analyzed and discussed in relation to metabolic processes. Additionally, we performed a more in-depth analysis of metabolic pathways potentially associated with ORFV replication. Our results revealed that ORFV-CL24 was largely reliant on glucose to meet increased energy demands and that de novo fatty acid synthesis was involved in ORFV-CL24 replication. The results of this study will provide an important reference for future research on ORFV pathogenesis and the development of new preventive or therapeutic strategies targeting the altered metabolic pathway.