viruses

Article Comparative Proteomic Profiling: Cellular Metabolisms Are Mainly Affected in Senecavirus A-Inoculated Cells at an Early Stage of Infection

Fuxiao Liu 1,† , Bo Ni 2,† and Rong Wei 2,*

1 College of Veterinary Medicine, Qingdao Agricultural University, Qingdao 266109, China; [email protected] 2 Surveillance Laboratory of Livestock Diseases, China Animal Health and Epidemiology Center, Qingdao 266032, China; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work.

Abstract: Senecavirus A (SVA), also known as Seneca Valley virus, belongs to the genus Senecavirus in the family Picornaviridae. SVA can cause vesicular disease and epidemic transient neonatal losses in pigs. This virus efficiently propagates in some non-pig-derived cells, like the baby hamster kidney (BHK) cell line and its derivate (BSR-T7/5). Conventionally, a few proteins or only one protein is selected for exploiting a given mechanism concerning cellular regulation after SVA infection in vitro. Proteomics plays a vital role in the analysis of protein profiling, protein-protein interactions, and protein-directed metabolisms, among others. Tandem mass tag-labeled liquid chromatography- tandem mass spectrometry combined with the parallel reaction monitoring technique is increasingly used for proteomic research. In this study, this combined method was used to uncover separately



 proteomic profiles of SVA- and non-infected BSR-T7/5 cells. Furthermore, both proteomic profiles were compared with each other. The proteomic profiling showed that a total of 361 differentially Citation: Liu, F.; Ni, B.; Wei, R. expressed proteins were identified, out of which, 305 and 56 were upregulated and downregulated Comparative Proteomic Profiling: in SVA-infected cells at 12 h post-inoculation, respectively. GO (Gene Ontology) and KEGG (Kyoto Cellular Metabolisms Are Mainly Encyclopedia of Genes and Genomes) enrichment analyses showed that cellular metabolisms were Affected in Senecavirus A-Inoculated Cells at an Early Stage of Infection. affected mainly in SVA-inoculated cells at an early stage of infection. Therefore, an integrated Viruses 2021, 13, 1036. https:// metabolic atlas remains to be explored via metabolomic methods. doi.org/10.3390/v13061036 Keywords: Senecavirus A; proteomics; differentially expressed protein; enrichment analysis; Academic Editor: Petri Susi metabolism; pathway

Received: 13 April 2021 Accepted: 18 May 2021 Published: 31 May 2021 1. Introduction Senecavirus A (SVA), previously known as Seneca Valley virus, is an emerging pathogen Publisher’s Note: MDPI stays neutral in China [1,2]. SVA infection is characterized by porcine vesicular lesions on coronary with regard to jurisdictional claims in bands as well as snout and oral cavities, clinically indistinguishable from those caused published maps and institutional affil- by foot-and-mouth disease virus, swine vesicular disease virus and vesicular stomati- iations. tis virus [3,4]. SVA is classified into the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense, single-stranded and nonsegmented RNA, approximately 7300 nucleotides in length, with a 30 poly (A) tail but without a 50 capped structure. The genome contains 50 and 30 untranslated regions and a single long open reading frame of Copyright: © 2021 by the authors. polyprotein precursor [5]. Morphologically, mature virion is a non-enveloped icosahedral Licensee MDPI, Basel, Switzerland. particle with a diameter of approximately 27 nm. Purified virion reveals a sphere-like This article is an open access article shape as evidenced by transmission electron microscopy [6]. Some pig-derived cells, such distributed under the terms and as testis (CRL-1746) and kidney (PK-15) cell lines, are susceptible to SVA infection. SVA conditions of the Creative Commons can also propagate efficiently in some non-pig-derived cells, like the baby hamster kidney Attribution (CC BY) license (https:// (BHK) cell line and its derivate (BSR-T7/5). Additionally, SVA is an oncolytic virus, with creativecommons.org/licenses/by/ 4.0/). selective tropism for some tumors with neuroendocrine characteristics [7–9].

Viruses 2021, 13, 1036. https://doi.org/10.3390/v13061036 https://www.mdpi.com/journal/viruses Viruses 2021, 13, 1036 2 of 14

SVA infection triggers a variety of metabolic and biochemical changes in its host cells through virus-specific or -nonspecific mechanisms [10–13]. For example, SVA has been demonstrated to have the ability to induce autophagy or (and) apoptosis of host cells. The SVA-induced autophagy was evidenced by detecting autophagosome formation, GFP-LC3 puncta and accumulation of LC3-II proteins in cultured PK-15 and BHK-21 cells [10]. The SVA-induced apoptosis resulted from the SVA 3C protease, by which the NF-κB-p65 was cleaved at the late stage of infection [13]. Additionally, the innate immune system plays an important role in SVA infection. Wen et al. (2019) demonstrated that SVA replication could induce the degradation of RIG-I in HEK-293T, SW620 and SK6 cells. Overexpression of RIG-I significantly inhibited SVA propagation. The viral 2C and 3C proteins notably reduced Sev- or RIG-I-induced interferon-β production [12]. According to conventional methods, a few host and (or) virus proteins are generally selected for exploiting a given mechanism of cellular regulation against viral infection. Integrated omics can assist in deciphering complex networks in vitro or in vivo and espe- cially in unveiling the interaction among related molecules to affect disease outcome [14]. Proteomic research plays a crucial role in analyzing protein-protein interactions, profiles and kinetics of protein expression, as well as complex regulatory mechanisms. Owing to its high-efficiency, -sensitivity and -throughput characteristics, the tandem mass tag (TMT)- labeled method has been broadly used for quantitative analysis of proteomics [15–17]. Parallel reaction monitoring (PRM) is a novel technique, is better than conventional meth- ods to verify proteins, and provides reliable results of quantitative proteomics [18–20]. Additionally, with the development of bioinformatics, the efficiency of large-scale data processing has been greatly improved in the field of proteomic studies. The strategy, TMT-based quantitative analysis with PRM verification, has been gradually adopted for proteomic profiling [21–23]. We recently rescued a wild-type SVA (CH-LX-01-2016) from its cDNA clone using reverse genetics. The rescued virus could rapidly replicate in BSR-T7/5 cells and induced typical cytopathic effect (CPE) as early as 24 hpi, or even earlier [24]. In the present study, in order to uncover a proteomic profile of SVA-infected cells for further comparison to that of non-infected cells, the rescued SVA at passage 5 (P5) was used to inoculate BSR-T7/5 cells, subsequently subjected to a series of treatments for the proteomic analysis using TMT- labeled nano-liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by PRM verification. Comparative proteomic analysis showed that cellular metabolisms were affected mainly at the early stage of SVA infection.

2. Materials and Methods 2.1. Cell Line and Virus The BSR-T7/5 cells [25], derived from the BHK cell line, were cultured at 37 ◦C with 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM), supplemented with 4% fetal bovine serum and containing penicillin (100 U/mL), streptomycin (100 µg/mL), amphotericin B (0.25 µg/mL) and G418 (500 µg/mL). The wild-type SVA was rescued previously from a cDNA clone [24], genetically derived from an isolate, CH-LX-01-2016 (Genbank access No.: KX751945) [26].

2.2. Sample Preparation BSR-T7/5 cells were seeded into several T175 flasks for culture at 37 ◦C. Cell monolay- ers at 90% confluency were separately inoculated with the P5 SVA (MOI = 2.5), and the others served as non-infected controls. There were three SVA-infected samples (S1, S2 and S3) and three non-infected controls (C1, C2 and C3). At 12 h post-inoculation (hpi), SVA- and non-infected supernatants were removed from flasks. Cell monolayers were gently washed with D-PBS three times and then independently harvested into sterile 50 mL cen- trifuge tubes using cell scrapers. After centrifugation, cell pellets were suspended in 300 µL of SDT-lysis buffer (4% SDS, 100 mM DTT, 150 mM Tris-HCl pH 8.0), followed by boiling for 5 min, ultrasonic treatment for 2 min, and boiling for another 5 min. Undissolved Viruses 2021, 13, 1036 3 of 14

cellular debris was removed by centrifugation at 15,000× g for 20 min. The supernatants were collected and quantified with the BCA Protein Assay Kit (Bio-Rad, Richmond, VA, USA), followed by SDS-PAGE analysis.

2.3. Protein Digestion Digestion of protein (300 µg for each sample) was performed according to the pro- cedure of filter-aided sample preparation (FASP), as described previously [27]. Briefly, 300 µg of protein was added to DTT at a final concentration of 100 mM, boiled for 5 min, and then cooled to room temperature. The DTT and other low-molecular-weight compo- nents were removed using 200 µL of UA buffer (8 M urea, 150 mM Tris-HCl pH 8.0) by repeated ultrafiltration (Microcon units, 10 kD), facilitated by centrifugation at 12,000× g for 15 min. A total of 100 µL of IAA (50 mM iodoacetamide in UA buffer) was added to the concentrate, shaken at 600 rpm for 1 min, and incubated for 30 min in darkness, followed by centrifugation at 12,000× g for 10 min. A total of 100 µL of UA buffer was added to the concentrate, followed by centrifugation three times at 12,000× g for 10 min; then, 100 µL of TEAB buffer was added to the concentrate, followed by centrifugation three times at 14,000× g for 10 min. Forty µL of trypsin buffer (6 µg trypsin in 40 µL TEAB buffer) was added to the concentrate, subsequently shaken at 600 rpm for 1 min, and incubated at 37 ◦C for 16 h. The digests were collected by centrifugation at 12,000× g for 10 min using another Microcon device. The collected peptides were resolved in 0.1% TFA solution, and desalted on C18 Cartridges.

2.4. TMT Labeling of Peptides Peptides were labeled with TMT reagents (Thermo Fisher, Waltham, MA, USA), as described previously [28]. Briefly, the collected peptides were lyophilized, and then reconstituted further in 100 µL of 200 mM TEAB solution. For labeling, each TMT reagent was dissolved in 41 µL of anhydrous acetonitrile using a vortex agitator for 5 min, followed by centrifugation. Then, 41 µL of the TMT reagent was mixed with 40 µL of the peptide solution and incubated at room temperature for 1 h. The reaction was terminated by addition of 8 µL of 5% hydroxylamine, followed by incubation for 15 min. Finally, the labeled peptide samples were lyophilized and stored at −80 ◦C.

2.5. High-pH Reversed-Phase Fractionation TMT-labeled peptide mixture was fractionated as described by Gu et al. (2020), with slight modifications [29]. A Waters XBridge BEH130 column (C18, 3.5 µm, 2.1 × 150 mm) was used on an Agilent 1290 HPLC operating at 0.3 mL/min. The buffer A of mobile phase consisted of 10 mM ammonium formate with water, and the buffer B consisted of 10 mM ammonium formate with 90% (v/v) acetonitrile. Both buffers were adjusted to pH 10 with ammonium hydroxide. A total of fifteen fractions were collected using a 2 min interval with 30 min gradient and were further combined into ten fractions by a concatenation strategy. The fractions were lyophilized and resuspended in 0.1% FA for nano-LC-MS/MS analysis.

2.6. Nano-LC-MS/MS Analysis The nano-LC-MS/MS analysis was performed on a Q-Exactive HF-X mass spectrom- eter that was coupled to Easy nLC 1200 system (Thermo Fisher, Waltham, MA, USA). Solution A was 0.1% formic acid aqueous solution; solution B was a mixed solution of 0.1% formic acid, 80% acetonitrile and water. The column was first equilibrated with 100% solution A. The sample was injected into a Trap column (100 µm × 20 mm, 5 µm C18, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) and subjected to gradient separa- tion through a chromatography column (75 µm × 150 mm, 3 µm C18, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) at a flow rate of 300 nL/min. The linear gradient was set as follows: 0–3 min, linear gradient from 2 to 7% buffer B; 3–48 min, linear gradient from 7 to 35% buffer B; 48–53 min, linear gradient from 35 to 90% buffer B; 53–60 min, buffer B maintained at 90%. Viruses 2021, 13, 1036 4 of 14

The peptides were separated and subjected to a data-dependent acquisition (DDA) analysis for 60 min using the Q-Exactive HF-X mass spectrometer. The parameters were set as follows: detection mode: positive mode; parent ion scanning range: 300–1800 m/z; first-order MS resolution: 60,000 at m/z 200; AGC target: 3e6; first-level maximum IT: 50 ms. Peptide secondary MS was obtained as follows: for each full scan, MS data was acquired using a top-20 method for dynamically choosing the most intense parent ion from second-order MS (MS2) scan. The parameters were set as follows: MS2 resolution: 15,000 at m/z 200; AGC target: 1e5; level-2 maximum IT: 50 ms; MS2 activation type: HCD; isolation window: 1.6 m/z; normalized collision energy: 32 eV.

2.7. Integrated Analysis of Proteomic Data Proteome DiscovererTM Software (version 2.4, San Jose, CA, USA) [30] was used to search MS/MS spectra against the database UniProt Cricetulus griseus 56565-20201207.fasta using the SEQUEST HT search engine (downloaded on 7 December 2020 and including 56565 protein sequences). The search parameters used were as follows: Type: Reporter ion MS2; Isobaric labels: TMT 6plex; Enzyme: Trypsin; Reporter mass tolerance: 0.005 Da; Max Missed Cleavages: 2; Peptide Tolerance: 10 ppm; MS/MS Tolerance: 0.02 Da; Fixed modi- fications: Carbamidomethyl (C); Variable modifications: Oxidation (M), Acetyl (Protein N-term), Deamidation (N, Q), TMT (K) and TMT (N-term); Database: UniProt Cricetulus griseus 56565-20201207.fasta; Database pattern: Target-Reverse; Percolator (FDR): ≤0.01; Protein quantification: Razor and unique peptides were used for protein quantification.

2.8. Quantitative Analysis by LC-PRM/MS To verify the expression levels of different proteins obtained by the TMT-labeled proteomic analysis, a total of twenty proteins were selected for further quantitative analysis by LC-PRM/MS, as described by Li et al. (2019), with slight modifications [23]. Briefly, 2 µg of peptide from each sample was taken for LC-PRM/MS analysis. After sample loading, chromatographic separation was performed using the EASY-nLC nano-HPLC system (Thermo Fisher, Waltham, MA, USA), with two buffers. Solution A was 0.1% formic acid aqueous solution, and solution B was a mixture of 0.1% formic acid, 95% acetonitrile and water. The column was first equilibrated with 95% solution A. The sample was injected into a Trap column (100 µm × 20 mm, 5 µm C18, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) and then subjected to gradient separation through a chromatography column (75 µm × 150 mm, 3 µm C18, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) at a flow rate of 300 nL/min. The liquid phase separation gradient was as follows: 0–5 min, linear gradient of B liquid from 2 to 5%; 5–45 min, linear gradient of B liquid from 5 to 23%; 45–50 min, linear gradient of B liquid from 23 to 40%; 50–52 min, linear gradient of B liquid from 40 to 100%; 52–60 min, B liquid maintained at 100%. The peptides were separated and then subjected to targeted PRM/MS using the Q- Exactive HF-X mass spectrometer (Thermo Fisher, Waltham, MA, USA) for 60 min. The parameters were set as follows: detection mode: positive mode; parent ion scanning range: 300–1200 m/z; first-order MS resolution: 60,000 at m/z 200; AGC target: 3e6; first-level maximum IT: 50 ms. Peptide secondary MS was obtained as follows: for each full scan, target peptides of precursor m/z were sequentially selected based on the inclusion list for MS2 scan. The parameters were set as follows: MS2 resolution: 17,500 at m/z 200; AGC target: 1e6; level-2 maximum IT: 100 ms; MS2 activation type: HCD; isolation window: 1.6 Th; normalized collision energy: 27 eV. The raw data were analyzed using the Skyline 4.1 [31] to obtain the signal intensities of individual peptide sequences.

3. Results 3.1. Proteomic Profiles of SVA-and Non-Infected Cells SVA induced no typical CPE on BSR-T7/5 cells at 12 hpi (Figure1A). Cells were independently collected from the six groups (S1, S2, S3, C1, C2 and C3) and then subjected to a series of treatments for quantitative analysis, showing that protein concentrations of Viruses 2021, 13, 1036 5 of 14

group S1, S2, S3, C1, C2 and C3 were 22.72, 23.92, 17.56, 15.33, 12.15 and 13.55 µg/µL, respectively, as measured by the BCA Protein Assay Kit. There was no observable difference among these six groups through the SDS-PAGE analysis (Figure1B). To explore differences of protein expressions between SVA- and non-infected cells, quantitative proteomic analysis was carried out to characterize the protein samples from the six groups using the TMT- labeled nano-LC-MS/MS.

Figure 1. Sample preparation for SDS-PAGE analysis. SVA- and non-infected BSR-T7/5 cells at 0 and 12 hpi (A). Cell samples are collected from six groups for further lysis, followed by SDS-PAGE analysis (B).

A total of 5301 proteins were identified and quantified across the six groups (Sup- plementary file S1). Protein-related data were processed and comprehensively evaluated for obtaining proteomic profiles. Figure2A–D represented actual distributions of molec- ular weight, protein length, score and isoelectric point for all identified proteins, respec- tively. The titin (UniProt accession No.: G3HAC6) and histone H1 (UniProt accession No.: Q9QUY8) were determined to have the maximum (4004.6 kD) and minimum (1.5 kD) molecular weights, respectively. Figure2E–J revealed corresponding relations between the molecular weight and the number of peptide-spectrum matches (PSMs), the number of unique peptides, the number of peptides, score, sequence coverage as well as isoelectric point for each identified protein, respectively.

3.2. Analysis of Differentially Expressed Proteins (DEPs) The volcano plot was generated by the GraphPad Prism software (Version 8.0) to display the p value versus the fold change (FC) for all 5301 identified proteins (Figure3A ). Using a 1.2-fold increase or decrease in protein expression as a benchmark for a significant change, a total of 361 DEPs (p < 0.05) were identified, out of which, 305 and 56 were upregulated (Supplementary file S2) and downregulated (Supplementary file S3) in SVA- infected cells at 12 hpi, respectively. The heatmap showed hierarchical clustering of 361 DEPs (FC > 1.2 or < 0.833 and p value < 0.05) (Figure3B). Figure3C–F represented actual distributions of molecular weight, protein length, FC and p value for all upregulated proteins, respectively. Figure3G,H revealed corresponding relations between the molecular weight and the FC as well as p value for each upregulated protein, respectively. Figure3I–L represented actual distributions of molecular weight, protein length, FC and p value for all downregulated proteins, respectively. Figure3M,N revealed corresponding relations Viruses 2021, 13, 1036 6 of 14

between the molecular weight and the FC as well as p value for each downregulated protein, respectively.

(A) (B) (C) (D) 13 12 4 3 103 10 10 11 10 9 102 103 102 8 Score 7 1 102 101 6

10 Isoelectric point Protein length (aa) 5 Molecular weight (kD) 4 100 101 100 3 Identified proteins Identified proteins Identified proteins Identified proteins (up to 5,301) (up to 5,301) (up to 5,301) (up to 5,301) (E) (F) (G)

103 103 103

102 102 102

101 101 101 Molecular weight (kD) Molecular weight (kD) Molecular weight (kD)

0 10 100 100 0 1 2 3 10 10 10 10 0 4 8 12 16 20 21 41 61 81 10 0 10 1 10 2 Number of PSMs Number of unique peptides Number of peptides

(H) ( I ) (J)

103 103 103

102 102 102

101 101 101 Molecular weight (kD) Molecular weight (kD) Molecular weight (kD)

100 100 100 10 0 10 1 10 2 10 3 0 10 20 30 40 50 60 70 80 90 100 3 4 5 6 7 8 9 10 11 12 13 Score Sequence coverage (%) Isoelectric point

Figure 2. Comprehensive proteomic profiling of six groups. Violin plot-exhibited distributions of molecular weight (A), protein length (B), score (C) and isoelectric point (D) for all 5301 identified proteins. Corresponding relations between the molecular weight and the number of PSMs (E), the number of unique peptides (F), the number of peptides (G), score (H), sequence coverage (I) and isoelectric point (J) for each identified protein.

3.3. GO (Gene Ontogy) Enrichment Analysis DEPs were subjected to analysis in the GO database. According to the GO enrichment analysis, 248, 147 and 258 DEPs were assigned to the categories “biological process (BP)” (Supplementary file S4), “cell component (CC)” (Supplementary file S5) and “molecular function (MF)” (Supplementary file S6), respectively. Diverse biological changes were identified in multiple GO terms at 12 hpi: the numbers of statistically significant (p < 0.05) BP, CC and MF enrichments were 262, 55 and 146, respectively (Supplementary files S4–S6). The top 10 statistically significant GO terms were separately selected from these three categories and are shown in Figure4A. Major significant enrichments of the BP, CC and MF are shown in Figure4B–D, respectively. The most significant enrichments of BP, CC and MF concerned the small molecule metabolic process (GO: 0044281, p value: 1.38E-14, including 49 DEPs), the intracellular (GO: 0005622, p value: 7.04E-12, including 109 DEPs) Viruses 2021, 13, 1036 7 of 14

and the catalytic activity (GO: 0003824, p value: 2.73E-11, including 163 DEPs), respectively (Supplementary S4–S6).

(A) (C) (D) (G) Up Down 6 Stable

3 5 102 10 102

4 value p Protein length (aa) 10 Molecular weight (kD)

3 Molecular weight (kD) 2 1 10 1 -log 10 10

2 Upregulated DEPs Upregulated DEPs 1.1 1.3 1.5 1.7 1.8 2.8 3.8 4.8 (up to 305) (up to 305) FC of upregulated DEP

1 (E) (F) (H) 4.35 0.05

0 3.35 -2 -1 0 1 2 0.04 log2 FC 2.35

S vs C Heatmap 1.35 0.03 102 (B) 1.35 Groups Groups 1.5 CgPICR_009041 value CgPICR_020769 CgPICR_018803 C H671_3g9535 CgPICR_010007 I79_006261 1 S CgPICR_017208 1.30 H671_1g2241 p I79_011912 0.02 I79_011659 I79_007455 KDELR1 0.5 CgPICR_017686 CgPICR_011069 I79_016323 CgPICR_006008

I79_010126 Fold change CgPICR_000180 0 CgPICR_012971 CgPICR_008241 I79_017496 I79_004015 1.25 I79_015951 I79_022499 −0.5 I79_009890 I79_010231 I79_025189 I79_009336 CgPICR_003863 H671_3g11312 −1 CgPICR_007196 0.01 I79_006608 CgPICR_003086 I79_007823

I79_022777 Molecular weight (kD) H671_1g3844 −1.5 CgPICR_004929 1.20 COX3 CgPICR_014255 CgPICR_008495 CgPICR_006886 CgPICR_009870 CgPICR_009865 I79_008715 1 I79_003159 CgPICR_004303 I79_008693 10 CgPICR_007095 CgPICR_019938 CgPICR_009015 I79_009246 I79_016396 I79_007183 1.15 0.00 H671_3g9676

CgPICR_020446 Non-infected H671_4g11543 CgPICR_018496 I79_006541 CgPICR_000211 I79_009070 0.00 0.01 0.02 0.03 0.04 0.05 CgPICR_015023 CgPICR_001928 Upregulated DEPs I79_011730 Upregulated DEPs I79_014883 I79_024824 H671_xg20713 I79_014532 H671_1g1566 I79_000029 (up to 305) p value of upregulated DEP CgPICR_019770 (up to 305) CgPICR_001093 I79_010662 I79_001796 I79_007683 I79_001896 CgPICR_013364 CgPICR_012876 CgPICR_018558 CgPICR_007466 I79_010647 I79_017919 H671_1g3558 H671_3g8749 I79_018641 I79_012486 CgPICR_007359 CgPICR_008442 I79_007785 CgPICR_009020 I79_008740 CgPICR_017354 I79_011670 I79_014970 I79_004074 (I) (J) (M) CgPICR_007243 I79_023186 H671_5g15146 I79_016329 I79_003584 H671_4g11512 I79_011650 CgPICR_007229 CgPICR_006426

CgPICR_006854 SVA-infected CgPICR_010203 I79_019072 CgPICR_016567 I79_009944 H671_5g15145 I79_015735 MRI1 CgPICR_000547 CgPICR_011773 I79_004955 CgPICR_002359 I79_001773 CgPICR_019795 I79_018968 CgPICR_015262 I79_018639 I79_013070 CgPICR_012493 CgPICR_006449 CgPICR_006055 I79_000697 I79_000989 CgPICR_011919 CgPICR_012176 Uba1 I79_001065 CgPICR_004399 CgPICR_016973 3 H671_1g3232 I79_012527 CgPICR_009144 2 2 AARS 10 CgPICR_013023 CgPICR_013988 10 10 APRT H671_4g12611 CgPICR_010116 CgPICR_001474 CgPICR_003853 PAICS I79_001893 CgPICR_001216 CgPICR_011574 H671_4g13041 CgPICR_021286 I79_013257 I79_002807 CgPICR_006595 I79_004748 CgPICR_017699 CgPICR_014818 I79_015854 H671_4g12995 CgPICR_021131 CgPICR_016064 I79_022042 I79_010525 MTAP IMPDH2 HMGCS1 I79_017867 CgPICR_019983 I79_008123 I79_017373 I79_012784 CgPICR_004277 I79_018801 I79_014176 CgPICR_005126 H671_6g15449 CgPICR_014060 I79_017241 Protein length (aa) I79_018916 CgPICR_009031 I79_010927 CgPICR_002589 H671_1g1786 2 CgPICR_018801 H671_1g3843 I79_008535 10 Molecular weight (kD) 1 CgPICR_011308

CgPICR_003582 Molecular weight (kD) 1 I79_024883 10 I79_022301 I79_004230 10 H671_5g14786 CgPICR_001195 I79_009065 H671_1g1760 ilk CgPICR_012366 I79_023849 H671_5g15004 CgPICR_001679 CgPICR_008581 CgPICR_002495 I79_021963 CgPICR_015363 I79_009045 I79_005522 CgPICR_020542 CgPICR_003573 CgPICR_002732 CgPICR_015963 CgPICR_003679 CgPICR_000805 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 H671_21468 I79_020676 GAPDH Downregulated DEPs Downregulated DEPs CgPICR_007415 H671_7g18194 CgPICR_018598 CgPICR_003787 I79_000269 PRDX1 CgPICR_011489 (up to 56) FC of downregulated DEP CgPICR_011864 (up to 56) I79_003519 H671_4g13394 I79_007657 I79_013867 I79_004322 CgPICR_002087 CgPICR_007714 I79_016489 H671_xg20712 H671_21728 I79_015066 CgPICR_011481 CgPICR_018709 I79_016693 CgPICR_011025 I79_017418 H671_2g6806 I79_020492 I79_002802 I79_019851 CgPICR_009035 H671_1g1503 I79_019852 0.85 0.05 3 H671_2g6290 I79_004606 CgPICR_006504 (K) (L) (N) 10 I79_020028 I79_008559 CgPICR_007766 I79_012071 CgPICR_019677 I79_006652 I79_017921 CgPICR_002554 H671_5g13622 CgPICR_017858 I79_012023 H671_1g3085 OSGEP 0.80 I79_006260 I79_007370 CgPICR_007703 CgPICR_009159 CgPICR_004128 CgPICR_010023 0.04 H671_8g19566 CgPICR_009792 H671_3g9663 H671_5g14938 I79_016325 I79_003583 CgPICR_002649 I79_009193 I79_009926 0.75 H671_5g14835 I79_024382 I79_014826 I79_012342 CgPICR_003865 H671_6g15713 I79_001117 H671_6g15480 I79_002236 CgPICR_005295 I79_014274 CgPICR_012506 I79_011599 0.03 2 I79_012127 I79_012806 I79_013985 0.70 10 I79_006899 I79_013744 I79_020092 I79_023295 ADSS CgPICR_016044 I79_021010 I79_004349 I79_017474 CgPICR_006999 I79_024172 I79_001138 value I79_009067 CgPICR_008404 CgPICR_018338 H671_5g14818 0.65 CgPICR_016985

I79_009507 p I79_022246 0.02 CgPICR_017890 TUBA1C PGK1 CgPICR_016021 CgPICR_006479 H671_3g8659 DOHH I79_015153

I79_024353 Fold change I79_021805 CgPICR_016951 I79_018157 ADK 0.60 I79_002464 I79_018514 CgPICR_019760 H671_1g1573 CgPICR_001878 I79_015306 CgPICR_018409 I79_021181 I79_000085 0.01 CgPICR_002600 1 CgPICR_003527 I79_012224

I79_000914 Molecular weight10 (kD) I79_016573 CgPICR_013959 CgPICR_005124 0.55 CgPICR_013108 I79_022935 GMPR H671_7g18528 CgPICR_001447 I79_017854 H671_3g8530 H671_1g1770 H671_5g14907 I79_011325 I79_002021 CgPICR_009323 I79_001830 I79_020709 I79_018965 0.50 0.00 H671_3g11188 I79_022931 H671_3g8864 I79_022096 CgPICR_021901 CgPICR_007427 I79_000357 C3 C1 C2 S1 S2 S3 Downregulated DEPs Downregulated DEPs 0.00 0.01 0.02 0.03 0.04 0.05 C3 C1 C2 S1 S2 S3 (up to 56) (up to 56) p value of downregulated DEP

Figure 3. Profiles of upregulated and downregulated DEPs. Volcano plot of all 5301 proteins identified in the six groups (A). Up: upregulated DEPs (FC > 1.2 and p value < 0.05); Down: downregulated DEPs (FC < 0.833 and p value < 0.05); Stable: proteins neither significantly upregulated nor significantly downregulated (0.833 ≤ FC ≤ 1.2 or p value ≥ 0.05). Heatmap with hierarchical clustering of 361 DEPs (FC > 1.2 or < 0.833 and p value < 0.05) (B). The intensity of color reflects the degree of change. Red: highly expressed DEPs; blue: lowly expressed DEPs. Violin-plot-exhibited distributions of molecular weight (C), protein length (D), FC (E) and p value (F) for all 305 upregulated DEPs. Corresponding relations between the molecular weight and FC (G) and p value (H) for each upregulated DEP. Violin-plot-exhibited distributions of molecular weight (I), protein length (J), FC (K) and p value (L) for all 56 downregulated DEPs. Corresponding relations between the molecular weight and FC (M) as well as p value (N) for each downregulated DEP. Viruses 2021, 13, 1036 8 of 14

Biological Process Cell Component Molecular Function (A) (B) small molecule metabolic process 49 (1.38e-14) organic acid metabolic process 33 (8.72e-13) 60% glycosyl compound metabolic process 21 (5.31e-12)

Number of proteins organophosphate metabolic process 32 (1.36e-10) 50% 150 cofactor metabolic process 20 (2.04e-10) generation of precursor metabolites and energy 15 (1.82e-08) 40% carbohydrate derivative metabolic process 28 (2.20e-08) 100

30% single-organism catabolic process 19 (7.76e-08) single-organism carbohydrate metabolic process 19 (4.72e-07)

20% carbohydrate metabolic process 23 (1.68e-06) 50 Percent of proteins organic substance catabolic process 24 (2.08e-06)

10% cellular amino acid metabolic process 15 (3.01e-06) phosphorus metabolic process 55 (8.92e-06)

0% 0 organonitrogen compound metabolic process 90 (4.19e-05) s s cell single-organism biosynthetic process 21 (6.01e-05) cell part cytoplasm intracellular cytosolic part ligaseanion activity bindingligase activity oxidation-reduction process 32 (7.68e-05) protein complex catalytic activity intracellular part catalytic complex translocon complex hydro-lyase activity heterocycle metabolic process 65 (6.39e-04) magnesium ion binding and related compounds oxoacid metabolic process carbon-oxygen lyase activity nucleoside metabolic process aminoacyl-tRNA cellular aromatic compound metabolic process 65 (7.01e-04) organic acid metabolic process y, forming carbon-oxygen bonds small moleculecarboxylic metabolic acid processmetabolicribonucleoside process metabolic process phosphopyruvate hydratase activity purine nucleoside metabolic process phosphopyruvate hydratase complex organic cyclic compound metabolic process 65 (1.18e-03) purine ribonucleosidenucleoside diphosphatemetabolic process phosphorylation ligase activit purine-containing compound metabolic proce cellular nitrogen compound metabolic process 78 (2.21e-03) , forming aminoacyl-tRNA 0% 7% 14% 21% 28% 35% Percent of proteins ligase activity Percent of genes

cytoplasm 54 (7.25e-08) ligase activity, forming carbon-oxygen bonds 8 (1.45e-05) (C) catalytic complex 14 (2.58e-05) (D) anion binding 66 (1.79e-05) carbon-oxygen lyase activity 8 (2.21e-05) nucleosome 5 (7.59e-03) enzyme activator activity 11 (3.33e-05) EKC/KEOPS complex 1 (1.48e-02) ribonucleotide binding 57 (8.91e-05) GMP reductase complex 1 (1.48e-02) nucleotide binding 66 (1.77e-04) gamma-tubulin complex 1 (1.48e-02) nucleoside phosphate binding 66 (1.77e-04) DNA replication factor C complex 1 (1.48e-02) nucleoside-triphosphatase regulator activity 9 (3.15e-04) exocyst 2 (1.52e-02) oxidoreductase activity, acting on CH-OH group of donors 8 (6.32e-04) nucleoside binding 49 (3.31e-03) heterotrimeric G-protein complex 2 (1.76e-02) coenzyme binding 8 (7.03e-03) cytoplasmic side of membrane 2 (1.76e-02) oxidoreductase activity, acting on a sulfur group of donors 3 (1.81e-02) extrinsic component of plasma membrane 2 (2.01e-02) cytoskeletal protein binding 13 (2.05e-02) cell cortex 2 (2.55e-02) calmodulin binding 3 (2.66e-02) primosome complex 1 (2.93e-02) unfolded protein binding 4 (2.93e-02) intracellular organelle part 27 (3.29e-02) transferase activity, transferring phosphorus-containing groups 31 (3.21e-02) chaperone binding 2 (3.59e-02) nucleotide-activated protein kinase complex 1 (4.37e-02) cis-trans isomerase activity 4 (4.09e-02) adherens junction 2 (4.84e-02) palmitoyl hydrolase activity 1 (4.29e-02)

0% 4% 8% 12% 16% 20% 0% 5% 10% 15% 20% 25% PercentPercent of of proteins genes PercentPercent of of proteins genes

Figure 4. GO enrichment analysis of statistically significant DEPs in SVA-infected cells at 12 hpi. Top 10 statistically significant GO terms of three categories (biological process, cell component and molecular function) (A). Major GO terms that are significantly enriched in biological processes (B), cell component (C), and molecular function (D). Each bar is followed by the number of DEPs with the corresponding p value.

3.4. KEGG (Kyoto Encyclopedia of Genes and Genomes) Enrichment Analysis DEPs were subjected to analysis in the KEGG database. The results (Supplemen- tary file S7) showed that the KEGG pathways were classified into four categories: A (metabolism), B (genetic information processing), C (environmental information process- ing) and D (cellular processes) (Figure5A). The metabolism was predominant in all four categories and contained the most KEGG pathways, up to 17 (not including the “metabolic pathways”). In contrast, there was only one KEGG pathway, separately identified in cat- egory B and D. The “metabolic pathways” (Pathway ID: cge01100; p value: 1.4 × 10−6) was identified to contain the highest number of DEPs, up to 30. Other important KEGG enrichments, classified into the four KEGG categories, are shown in Figure5A. The top 10 KEGG signaling pathways contained seven metabolism-related ones (including the “metabolic pathways”) (Figure5B), implying that the metabolic change was predominant in SVA-infected cells.

3.5. Protein-Protein Interaction (PPI) Analysis As shown in Supplementary file S7, a total of 25 KEGG pathways showed their p values < 0.05. To clarify the relations between DEPs and KEGG paths in cells, 24 KEGG pathways (not including the “metabolic pathways”) were further analyzed by searching the STRING database (Version 11.0) [32]. The resulting network map of PPI is illustrated in Figure5C. Out of 24 KEGG pathways, 11 were metabolism-related ones, accounting for nearly 46%. A total of 43 DEPs, including 36 upregulated and 7 downregulated ones, were identified to be related to these 24 KEGG pathways. The DEP with the highest FC was ATP-dependent 6-phosphofructokinase (gene ID: I79_000029), involved in five KEGG pathways. The purine metabolism was linked to the most DEPs, up to eight upregulated ones, and simultaneously possessed the lowest p value. The pyrimidine metabolism had the second lowest p value, and was linked to five upregulated DEPs. Out of seven downregulated DEPs, only one (gene ID: I79_016396) was related to the metabolism (fatty acid metabolism), and the other six mainly concerned non-metabolic pathways, such as the oxidative phosphorylation and Parkinson’s disease. Viruses 2021, 13, 1036 9 of 14

Metabolic pathways 30 (1.40e-06) (A) Carbon metabolism 6 (4.44e-04) A0 (B) Fatty acid metabolism 3 (1.49e-02) Fructose and mannose metabolism 4 (1.52e-04) Galactose metabolism 3 (1.95e-03) Pentose phosphate pathway 3 (2.33e-03) AA Butanoate metabolism 2 (2.01e-02) Pentose and glucuronate interconversions 2 (2.59e-02) Pyruvate metabolism 2 (4.09e-02) 6 A Sulfur metabolism 2 (2.63e-03) AB Oxidative phosphorylation 4 (2.58e-02) Synthesis and degradation of ketone bodies 2 (3.81e-03) AC Purine metabolism 8 (1.14e-05) AD Pyrimidine metabolism 5 (8.59e-05) 4 AH

One carbon pool by folate 2 (8.57e-03) p value Terpenoid backbone biosynthesis 2 (1.50e-02) AI 10 Caffeine metabolism 1 (3.86e-02) AJ AK Drug metabolism - other enzymes 3 (4.59e-02) -log(Pvalue) p<0.01

Aminoacyl-tRNA biosynthesis 3 (1.49e-02) BB B -log 2 AMPK signaling pathway 5 (2.93e-03) CB C FoxO signaling pathway 4 (2.63e-02) p<0.05 Ras signaling pathway 5 (4.89e-02) Gap junction 3 (4.81e-02) DC D 0 0% 3% 6% 9% 12% 15% Percent of proteins

A : Metabolism A0 : Global and overview maps AA : Carbohydrate metabolism Purine metabolism Sulfur metabolism AB : Energy metabolism Metabolic pathways Carbon metabolism Galactose metabolism AC : Lipid metabolism Pyrimidine metabolism AMPK signaling pathway AD : Nucleotide metabolism Pentose phosphate pathway AH : Metabolism of cofactors and vitamins AI : Metabolism of terpenoids and polyketides Fructose and mannose metabolism AJ : Biosynthesis of other secondary metabolites AK : Xenobiotics biodegradation and metabolism B : Genetic Information Processing BB : Translation Synthesis and degradation of ketone bodies C : Environmental Information Processing CB : Signal transduction D : Cellular Processes DC : Cellular community - eukaryotes

(C) Pentose and glucuronate interconversions

I79_015000 Fructose and mannose metabolism I79_021963 I79_009926 Galactose metabolism I79_015306 Pentose phosphate pathway I79_000029 I79_023216 Gap junction I79_018355 H671_4g13394 I79_018133 FoxO signaling pathway H671_5g14786 I79_023445 I79_008223 AMPK signaling pathway Uba1 I79_009246 Carbon metabolism I79_002427 I79_004712 One carbon pool by folate I79_016396 I79_014297 Non-alcoholic fatty liver disease I79_017373 I79_011670 Caffeine metabolism Fatty acid metabolism H671_7g18194 Parkinson disease I79_017235 IMPDH2 I79_017921 I79_016325 nat Pyruvate metabolism Ras signaling pathway COX3 H671_3g9535 I79_006261 I79_009507 Drug metabolism - other enzymes GMPR I79_009782 I79_022246 Terpenoid backbone biosynthesis I79_019587 Purine metabolism Oxidative phosphorylation H671_7g18193 H671_4g13027 Synthesis and degradation of ketone bodies I79_013471 I79_024353 AARS Aminoacyl-tRNA biosynthesis Pyrimidine metabolism I79_000085 Butanoate metabolism I79_012110 Sulfur metabolism I79_010525 I79_012527

down up low high

DEPs KEGG pathway (–log10 p value )

Figure 5. KEGG pathways with significant enrichments of DEPs in SVA-infected cells at 12 hpi. Classification of KEGG pathway enrichments (A). The KEGG pathways are classified into four categories: metabolism, genetic information processing, environmental information processing and cellular processes. Each bar is followed by the number of DEPs with the corresponding p value. Top 10 statistically significant KEGG pathways (B). Two p values, 0.05 and 0.01, are independently marked with dashed lines. Network map of interactions among DEPs (C). Twenty-four KEGG pathways and forty-three DEPs are marked with squares and circles, respectively. Upregulated and downregulated DEPs are marked with red and green circles, respectively.

3.6. PRM of Twenty Upregulated DEPs PRM mass spectrometry was used to verify the target peptides found in the TMT- labeled nano-LC-MS/MS analysis of the SVA-infected cells. To confirm the proteomic data, 20 upregulated DEPs (Supplementary file S8) were selected for LC-PRM/MS analysis on all six groups. PRM-related data, including the original AUC, normalized AUC, peptide quantification, protein quantification and average basepeak, are listed in Supplementary S9. Forty optimal peptides were selected as candidates, for which Skyline analysis results are shown in Supplementary files S10 and S11. The normalized peak area of peptide segment was used to analyze quantitatively the target peptide segments from different samples. Mass intensities of 40 candidate peptides were compared between the S and C groups (Figure6A). Figure6B,C represented actual distributions of FC and p values for these 40 candidate peptides, respectively. Figure6D showed the relation between both Viruses 2021, 13, 1036 10 of 14

parameters of each candidate peptide. The PRM quantitative results (Supplementary file S9, Sheet-protein quantification) showed similar trends to those of TMT-based proteomic anal- ysis on 19 candidate proteins, except the zinc finger CCCH domain-containing protein 14 (gene ID: I79_007785), whose FC was less than 1.0 (Figure6E), implying that the TMT-based proteomic data were reliable. Out of the 19 DEPs that were confirmed to be upregulated, the heat shock protein 75 kDa (mitochondrial) (gene ID: I79_016693) revealed its FC value to be less than 1.2 (Figure6E) but still with the upregulated trend. (A)

Tubulin alpha chain Annexin (Fragment) Adenosylhomocysteinase

AVFVDLEPTVIDEIR SIQFVDWCPTGFK SEISGDLAR SLEDALSSDTSGHFR SEIDLFNIR GISEETTTGVHNLYK VAVVAGYGDVGK IILLAEGR

1.0e+08

6e+08 3e+08 3e+08 6e+08 6e+08 6e+08

2e+09 7.5e+07

4e+08 2e+08 2e+08 4e+08 4e+08 4e+08 C C C C C C C C 5.0e+07 S S S S S S S S mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity

1e+09

1e+08 2e+08 2e+08 1e+08 2e+08 2e+08 2.5e+07

0.0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 C S C S C S C S C S C S C S C S

Chromosome segregation 1-like protein Ubiquitin-activating enzyme E1 Heat shock protein 75 kDa, mitochondrial Actin-depolymerizing factor

QLSDAISIIGR SPSAVQQDNLDEDLIR LAGTQPLEVLEAVQR AFLEALQNQAETSSK DISELQHEEFYR QTQVSVLPEGGETPLFK TGAQELLR YIETDPANR 4e+08 2.5e+08 1.0e+08 8e+07

2.0e+08 1e+08 3e+08 1.2e+08 7.5e+07 1e+08 2e+08 6e+07

1.5e+08

C C C C C C C C 2e+08 8.0e+07 S S S S 5.0e+07 S 4e+07 S S S mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity 1.0e+08 5e+07 5e+07 1e+08

4.0e+07 2.5e+07 2e+07 1e+08 5.0e+07

0.0e+00 0e+00 0e+00 0e+00 0.0e+00 0e+00 0e+00 0.0e+00 C S C S C S C S C S C S C S C S

Developmentally-regulated Elongation factor 1-gamma Zinc finger CCCH domain-containing protein 14 Glutamine amidotransferase GTP-binding protein 1

ILGLLDTHLK QVLEPSFR QAFPNTNR AISEAQESVTK TTNYSAVPQK EEIVLLTHGDSVDK DFPETNNILK IGFVGFPSVGK 6e+08

5e+07 3e+07 5e+08 3e+08 5e+06 2.0e+07 3e+08 1.5e+07

4e+07 4e+08 4e+06 1.5e+07 2e+07 2e+08 2e+08 3e+07 C 3e+08 C C 1.0e+07 C 3e+06 C C C C S S S S S S S S mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity 1.0e+07 2e+07 2e+08 2e+06 1e+08 1e+07 1e+08 5.0e+06 5.0e+06 1e+08 1e+06 1e+07

0e+00 0e+00 0e+00 0.0e+00 0e+00 0e+00 0.0e+00 0e+00 C S C S C S C S C S C S C S C S

Nucleoside diphosphate kinase Ubiquitinyl hydrolase 1 Isoleucyl-tRNA synthetase Kinesin-like protein

VMLGETNPADSK NIIHGSDSVESAEK YYGLQILENVIK DFEEYPEHR LYLINSPVVR YIIEELNVR VIVGGVDLLAK YTSLQEEAQGK

12500000 1.5e+07 8e+07 8e+07

2.0e+08 2.0e+07 5e+07 3e+07

10000000 6e+07 6e+07 4e+07 1.5e+08 1.5e+07 1.0e+07

2e+07 7500000

C C C 3e+07 C C C C C 4e+07 S S S S 4e+07 S S S S

mass intensity mass intensity 1.0e+08 mass intensity 1.0e+07 mass intensity mass intensity mass intensity mass intensity mass intensity 5000000 2e+07 5.0e+06 1e+07 2e+07 2e+07 5.0e+07 5.0e+06 2500000 1e+07

0e+00 0.0e+00 0.0e+00 0e+00 0e+00 0e+00 0 0.0e+00 C S C S C S C S C S C S C S C S

Serine/threonine-protein Calcium-activated neutral proteinase 1 Post-proline cleaving enzyme Integrin-linked protein kinase phosphatase 2A activator Peroxiredoxin-6-like protein

SDGTETSTNLHQK QSNPLLIHVDTK LNENHSGELWK WIDETPPVDQPSR LPFPIIDDK LSILYPATTGR NFDEILR DLYQIILK 6e+06 4e+06 2.0e+08 1.0e+08

6e+06

1e+07 3e+06 2e+08 1e+08 1.5e+08 7.5e+07 4e+06

C 4e+06 C C C C C C C S S S S S S S S 2e+06 1.0e+08 5.0e+07 mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity mass intensity

5e+06 5e+07 1e+08 2e+06 2e+06 1e+06 5.0e+07 2.5e+07

0e+00 0e+00 0e+00 0e+00 0e+00 0.0e+00 0.0e+00 0e+00 C S C S C S C S C S C S C S C S

(B) (C) (D) (E) 3.2 0.65 0.65 2.4 0.45 0.45 2.2 2.7 0.25 2.0 0.25 0.05 2.2 0.04 1.8 0.05 1.6

value 0.05 0.03 1.7 p 0.04 1.4 Fold change Fold change 0.02 0.03 1.2 1.2 0.02 0.01 1.0

0.01 value of candidate peptide 0.7 0.00 p 0.00 0.8 Candidate peptides (up to 40) Candidate peptides (up to 40) 0.8 1.3 1.8 2.3 2.8 Selected DEPs (up to 20) Fold change of candidate peptide Figure 6. PRM analysis of 20 upregulated DEPs in SVA-infected cells at 12 hpi. Comparison of mass intensities corresponding to 40 candidate peptides between the S and C groups (A). Each DEP is involved in one to three candidate peptides, framed by a rectangle. Violin-plot-exhibited distributions of FC (B) and p value (C) for all 40 candidate peptides. Corresponding relation between FC and p value for each candidate peptide (D). Violin-plot-exhibited distribution of FC for all 20 DEPs (E). Viruses 2021, 13, 1036 11 of 14

4. Discussion SVA is an emerging virus that has recently affected numerous pig farms in China [33–36]. The SVA CH-LX-01-2016 was a China isolate [26], for which a reverse genetics system was developed in our earlier study. Using this system, we rescued one wild-type [24] and two reporter-tagged [37,38] SVAs. The wild-type SVA, initially recovered from its cDNA clone, contained no point mutation at P0. Even though the P0 SVA was serially passaged in vitro for 10 times, there were only four single-nucleotide polymorphisms identified in viral genome [24]. Therefore, the P5 SVA stock was speculated to contain no more than four low-frequency point mutations. In other words, it was a relatively “pure” stock without its own quasispecies. Due to no impact of viral quasispecies at P5 on cells, it was appropriate for the analysis of intracellular proteome after virus infection. Prior to SVA inoculation, the BSR-T7/5 cells were additionally subjected to PCR detection to confirm no mycoplasma contamination in cell culture (data not shown). SVA-induced robust CPEs, like cell rounding, lysis and detachment, can appear as early as 24 hpi, or even earlier [24]. In order to reduce the interference of CPEs with protein profiling, cell samples were collected from both S and C groups as early as 12 hpi. Proteomic analysis plays a crucial role in revealing cellular metabolisms, viral replica- tion, antiviral responses, and virus–host interactions at the protein level [21,39,40]. Using the technique of TMT-labeled nano-LC-MS/MS in this study, a total of 5301 proteins were identified and quantified across the six groups (Supplementary file S1). Most of these pro- teins showed their molecular weights ranging from 20 to 150 kD (Figure2A). Comparative proteomic analysis indicated 361 DEPs, including 305 upregulated and 56 downregulated ones in cells at 12 hpi. The BP, CC and MF were quantified by the GO annotation and enrichment analysis, suggesting that DEPs were significantly enriched (p < 0.05) in BP and MF. KEGG pathway annotation and enrichment analysis revealed that a total of 25 statistically significant pathways were identified (Supplementary file S7), and DEPs were mainly enriched in the metabolisms, up to 11, including purine, pyrimidine, fructose and mannose, carbon, galactose, sulfur, fatty acid, butanoate, caffeine, pyruvate and drug (other enzymes) metabolisms. Based on the results of GO and KEGG enrichment analyses, it can be concluded that cellular metabolisms are mainly affected in SVA-infected cells at 12 hpi. The TMT as a chemical label facilitates sample multiplexing in MS-based quantification and identification of biological macromolecules [41]. The TMT-labeled nano-LC-MS/MS facilitated our comparative proteomic analysis between SVA- and non-infected cells. More recently, Li et al. (2020) used another method, iTRAQ-labeled LS-MS/MS, to analyze comparatively the proteomes between SVA (isolate, SVV-CH-SD)- and non-infected tes- ticular cells at 24 hpi [11]. Since they use a different labeling strategy, SVA strain, cell line and hpi from those in our current study, proteomic data are incomparable between these two studies. Besides the proteomic analysis, Wang et al. (2020) recently reported a transcriptomic profile of SVA-infected pig kidney cells. Type I interferons were demon- strated to be upregulated at the highest level, implying that they played a critical role in immune responses against SVA infection at an early stage in a host [42]. However, we did not observe a similar result concerning the significant upregulation of type I interferons, possibly attributed to the non-pig-derived cell line used for SVA propagation in our study. The PRM assay has emerged as an alternative method of targeted quantification. It can be performed both in the high resolution and in the high mass accuracy mode on a mass spectrometer [43]. In this study, the PRM technique was used for analysis of 20 upregulated DEPs with high FC values to verify the proteomic profile. Nineteen candidate DEPs were demonstrated to have similar trends to those of the TMT-labeled nano-LC-MS/MS analysis, suggesting that the comparative proteomic data were reliable and could be used for further research. A total of 305 upregulated and 56 downregulated DEPs were identified in the TMT-labeled nano-LC-MS/MS analysis. Due to lack of interesting proteins in the list of downregulated DEPs, we did not perform the PRM analysis on downregulated DEPs. Out of all DEPs, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was found to be upregulated (Supplementary file S2, FC value: 1.30; p value: 0.0015). In general, the Viruses 2021, 13, 1036 12 of 14

GAPDH is known to be stably expressed at high levels in most tissues and cells and is therefore widely used as an internal control for Western blotting. Interestingly, our study demonstrated that the GAPDH was an unreliable internal control for the comparative analysis on protein quantification of BSR-T7/5 cells infected with SVA. To sum up, in the present study, the TMT-labeled nano-LC-MS/MS was used to uncover the proteomic atlases of SVA- and non-infected cells at the early stage of infection. The LC-PRM/MS subsequently confirmed that the proteomic data were reliable. The comparative proteomic analysis revealed a total of 305 upregulated and 56 downregulated DEPs. Out of these, a large number of DEPs were involved in cellular metabolisms, suggesting that SVA, at its early stage of infection, mainly interferes with various metabolic pathways in BSR-T7/5 cells. An integrated metabolic atlas remains to be explored via metabolomic methods.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/v13061036/s1, Supplementary file S1: Proteomic profile, Supplementary file S2: Upregulated DEPs, Supplementary file S3: Downregulated DEPs, Supplementary file S4: Biological process, Sup- plementary file S5: Cell component, Supplementary file S6: molecular function, Supplementary S7: KEGG pathways, Supplementary file S8: Upregulated DEPs, Supplementary file S9: PRM-related data, Supplementary file S10: Secondary spectra of candidate peptides, Supplementary file S11: Skyline analyses of candidate peptides. Author Contributions: Experimental design, R.W.; analysis of data, F.L. and B.N.; writing—original draft preparation, F.L.; review, B.N. and R.W.; funding acquisition, B.N. and R.W. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Innovation Fund, funded by China Animal Health and Epidemiology Center. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets supporting the conclusions of this article are included within the article and its additional files. The mass spectrometry proteomics data have been de- posited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024947. Acknowledgments: We gratefully thank the Shanghai Bioprofile Biotechnology Co., Ltd. (Shanghai, China) for providing technical assistance in LC-MS/MS and LC-PRM/MS. We also thank Xianggan Cui for his help in data analysis and Qianqian Wang for her help in cell culture. Conflicts of Interest: The authors declare no conflict of interest.

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