In ternationa l Scholars Journa ls African Journal of Pig Farming ISSN 2375-0731 Vol. 7 (9), pp. 001-010, September, 2019. Available online at www.internationalscholarsjournals.org © International Scholars Journals Author(s) retain the copyright of this article. Full Length Research Paper Analysis of pig serum proteins based on shotgun liquid chromatography-tandem mass spectrometry Keshan Zhang, Yongjie Liu, Youjun Shang, Haixue Zheng, Jianhong Guo, Hong Tian, Ye Jin, Jijun He and Xiangtao Liu* State Key Laboratory of Veterinary Etiological Biology, National Foot and Mouth Disease Reference Laboratory, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Science, Xujiaping No.1, Lanzhou, Gansu, 730046, PR China. Accepted 12 June, 2019 Recent advances in proteomics technologies have opened up significant opportunities for future applications. We used shotgun liquid chromatography, coupled with tandem mass spectrometry (LC-MS/MS) to determine the proteome profile of healthy pig serum. Samples of venous blood were collected and subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis separation and in-gel trypsin digestion. The peptides were then processed using shotgun LC-MS/MS. Serum proteins were subjected to protein identification and bioinformatics analysis. A total of 392 proteins were identified, and 179 were annotated according to their molecular functions and biological processes, excluding 142 hypothetical proteins and 71 immune globulins. To the best of our knowledge, this represents the first porcine serum proteomics analysis based on shotgun LC-MS/MS. This method and the resulting proteomics information may prove valuable for ensuring good animal welfare practice and for monitoring swine health and disease status. Key words: Analysis, pig serum, shotgun coupled with tandem mass spectrometry (LC-MS/MS). INTRODUCTION Serum is a major body fluid. Serum composition thus reflects the overall health status of the individual animal and is often used to monitor health and disease in farm animals (Bendixen et al., 2011; Eckersall et al., 1996). Studies of protein distribution characteristics in serum may provide significant information to help unravel the mechanisms of disease and for the identification of biomarkers associated with new drug targets and early diagnosis (Issaq et al., 2007; Wan et al., 2006). Human serum protein maps have already been established (Millioni et al., 2012). Detailed serum protein two- *Corresponding author. E-mail: hnxiangtao@hotmail.com. Abbreviations: LC-MS/MS, Liquid chromatography-tandem mass spectrometry; 2-D PAGE, two-dimensional polyacrylamide gel electrophoresis; MWs, molecular weights; pI, isoelectric point; PRRSV, porcine reproductive and respiratory syndrome virus; PCV-2, porcine circovirus-2; PRV, pseudorabies virus; HCV, hog cholera virus; FMDV, foot-and- mouth disease virus. dimensional gel electrophoresis (2-DE) identification maps have been described for healthy pigs, and 27 high- to-medium-abundance plasma proteins, including some examples of infection/inflammation-regulated proteins in healthy Landrace × Large white pigs (Miller et al., 2009). Proteome analysis is most commonly accomplished using a combination of 2-DE to separate and visualize proteins and mass spectrometry (MS) for protein identifi- cation (Gygi et al., 2000a). However, the disadvantages of this technique include extensive sample handling, a limited dynamic range, and difficulties in resolving low- abundance proteins with extreme isoelectric points (pIs) and molecular weights (MWs), as well as hydrophobic proteins such as membrane proteins (Corthals et al., 2000; Gygi et al., 2000b; Oh-Ishi et al., 2000). Liquid chromatography, coupled with tandem mass spectro- metry (LC−MS/MS), represents a powerful technique for the proteomic analysis of complex samples, where peptide masses may still overlap, even with a high- resolution mass spectrometer (Adams and Zubarev, 2005; Wysocki et al., 2005). LC-MS/MS has been increasingly used for the accurate detection of changes in app:ds:early app:ds:diagnosis Figure 1. Separation of serum proteins by one- dimensional SDS-PAGE. Samples of 150 g of proteins were separated on 12% bis-Tris gels and stained to allow protein identification. Four sections were excised and subsequently used for digestion. protein profiles and to infer biological function (Aebersold and Mann, 2003; Crockett et al., 2005). The shotgun LC- MS/MS proteomics method has been used to identify thousands of proteins in human body fluids, including blood, seminal plasma and tear fluid. In the present study, gel-LC-MS/MS and bioinformatics analysis methods were used to develop a pig serum protein profile. These results are expected to provide valuable information to assist in the practice of good animal welfare, and for monitoring swine health and disease status. MATERIALS AND METHODS Animals Four Landrace femal pigs, aged about six months, were bought from a local farm and bred in separated rooms. All the pigs were free of the following pathogenic agents: porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus-2 (PCV-2), pseudorabies virus (PRV), hog cholera virus (HCV) and foot-and- mouth disease virus (FMDV), which were detected by polymerase chain reaction or reverse transcription-polymerase chain reaction (datas not shown). The animal experiments were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals, issued by the Council for the International Organizations of Medical Sciences. Sample preparation and sodium dodecyl sulfate- polyacrylamide gel electrophoresis separation Samples of intravenous (iv) blood were collected, they were incubated at 37°C for 2 h, then 4°C for 6 h; at last serum were separated at 5000 rpm, for 5 min. The serum protein concentration was determined by quantitative kit (GE) according to the instructions and stored at -80°C until use (Hsieh et al., 2006). Four serum samples (150 g) were pooled and denatured at 100°C for 5 min in an equal volume of 2× protein loading buffer [0.1 M Tris buffer, pH 6.8, 4% sodium dodecyl sulfate (SDS), 0.2% mercapto- ethanol, 40% glycerol, and 0.002% bromophenol blue], and subjected to SDS-polyacrylamide gel electrophoresis. Samples were separated using 12% homogeneous SDS polyacrylamide slab gels and Tris-glycine-SDS buffer (10 mM Tris, 50 mM glycine, 0.1% SDS, pH 8.0) using a Bio-Rad mini-protean tera system (Bio-Rad). Electrophoresis was carried out at a constant current of 15 mA/gel followed by 30 mA for about 1.5 h until the bromophenol blue reached the bottom of the gel. The gels were then stained with Coomassie brilliant blue G250 (Sigma, USA). Images were acquired using a GS-800 densitometer (Bio-Rad, Hercules, CA). In-gel digestion The in-gel trypsin digestion of proteins was conducted according to Wilm et al. (1996). The protein lane of the stained gel was cut into four slices (A, B, C, and D), depending on protein molecular weight (MW) (Figure 1). Each slice was diced into 1 × 1 mm pieces and subjected to in-gel tryptic digestion. The gel pieces were rinsed three times using Milli-Q water and destained with 0.2 ml of 100 mM NH4HCO3 in 50% acetonitrile for 45 min at 37°C, until complete depigmentation. The gel pieces were then dried in a vacuum centrifuge. 10 µl of 10 mM dithiothreitol in 100 mM NH4HCO3, sufficient to cover the gel pieces, was added to the proteins at 56°C for 1 h. After cooling to room temperature, the dithiothreitol solution was replaced with the same volume of 55 mM iodoacetamide in 100 mM NH4HCO3. After 45 min incubation at room temperature in the dark, the gel pieces were washed with 100 L of 100 mM NH4HCO3 for 10 min, dehydrated in 100 L of acetonitrile, swollen by rehydration in 100 L of 100 mM NH4HCO3, and shrunk again by adding the same volume of acetonitrile. The proteins were subsequently digested with 20 ng/L porcine trypsin (modified proteomics grade, Sigma) overnight at 37°C. Peptides were extracted by one change of 20 mM NH4HCO3 and three changes of 5% formic acid in 50% acetonitrile (20 min for each change) at room temperature (Li et al., 2009; Zhang et al., 2007). Shotgun LC-MS/MS analysis The extracted peptides from each gel piece were analyzed using an Ettan MDLC system (GE Healthcare, USA). In this system, samples were desalted on RP trap columns (Zorbax 300 SB C18, Agilent Technologies, USA), and then separated on an RP column (150 m internal diameter, 100 mm long, Column Technology Inc., Fremont, CA). Mobile phase A (0.1% formic acid in HPLC-grade water) and mobile phase B (0.1% formic acid in acetonitrile) were selected. 20 g of tryptic peptide mixture was loaded onto the columns and separation was carried out at a flow rate of 2 L/min using a linear gradient of 4 to 50% B for 120 min. A Finnigan LTQ linear ion trap MS (Thermo Electron, USA), equipped with an electrospray Table 1. Numbers of peptides and proteins identified in porcine serum. Parameter Number of protein Percentage (%) Total peptides 5390 100 Total proteins 848 15.7 Protein groups 392 46.2 immune globulin 71 17.1 annotated proteins 179 45.7 Hypothetical proteins 142 36.2 interface, was connected to the LC setup to detect the eluted peptides. Data-dependent MS/MS spectra were obtained simul- taneously. Each scan cycle consisted of one full MS scan in profile mode, followed by five MS/MS scans in centroid mode with the following Dynamic exclusion settings: repeat count 2, repeat duration 30 s and exclusion duration 90 s. Each sample was analyzed in triplicate. Protein identification and bioinformatics analysis Peptides and proteins were identified using Biowork 3.2 software (Thermo Finnigan, San Jose, CA), which uses the MS and MS/MS spectra of peptide ions to search against the Suina protein database. MASCOT protein scores (based on combined MS and MS/MS spectra) > 72 were considered statistically significant (p ≤ 0.05). We accepted individual MS/MS spectra with a statistically significant (confidence interval ≥ 95%) ion score (based on MS/MS spectra). The protein identification and annotation criteria were based on Delta CN (≥ 0.1) and Xcorr (one charge ≥ 1.9, two charges ≥ 2.2 and three charges ≥ 3.75). Protein classification was performed using Gene Ontology Annotation (GOA; http://www.ebi.ac.uk/goa/), according to their molecular functions and biological processes. The subcellular locations of different proteins were predicated with PSORT (http://psort.hgc.jp/). RESULTS Serum protein SDS-PAGE separation Serum proteins were separated by one dimensional SDS- PAGE and the gel was cut into four pieces, according to MW, for shotgun LC-MS/MS analysis (Figure 1). Identification of proteins A total of 5390 peptides were detected by shotgun LC- MS/MS. A total of 848 proteins were identified, some of which belonged to the same peptides groups. Thus, 392 proteins were finally identified (Table 1). Characterization of protein profile The molecular mass and pI value distributions of the 392 identified serum proteins were analyzed. Their molecular masses ranged from 3.2 to 360 kDa, with most being between 10 and 100 kDa (Figure 2A). The pIs of the proteins ranged from 4 to 11.6 (Figure 2B), with most being between 5 and 7. The predicted 2-DE distribution (Figure 3) showed that the pIs of about 95% of the identified proteins were between 4 and 10, representing proteins that are usually difficult to separate by 2-DE. About 40% of the identified proteins fell outside the typical limits of protein resolution obtained by 2-DE. Furthermore, about 14 proteins had higher pIs (> 10), which are also usually difficult to separate by 2-DE, but these proteins were also identified successfully by shotgun LC-MS/MS. Bioinformatics analysis A total of 189 peptides were annotated according to the GOA database and were classified on the basis of molecular function or biological process. They could be divided into about five functional molecular groups (Figure 4A): the classical protein group (101, 32.6% of 189 annotated peptides) and the cellular protein group (88, 31.7%) were the most common. The classical serum protein group can be further sub-classified into five subgroups, based on their specific functions (Figure 4B); most proteins were proteases or other enzymes (46, 47.9%), common circulating blood proteins (19, 19.0%), or coagulation and complement factors (18, 18.8%) which are important categories of classical serum proteins. The cellular protein group is also sub-classified into five subgroups according to their function or biological process (Figure 4C): signaling channels, hormone regulation, the cytoskeleton, the nucleus, and cellular metabolic secretions. DISCUSSION Serum contains many high-abundance proteins that perform various housekeeping functions, as well as numerous secreted or shed low-abundance proteins that are critical for signal transduction and regulatory events. During necrosis, apoptosis, and hemolysis, cell contents may be released into the serum. In a certain time period, the presence, absence or concentration of a specific protein from serum may be related with the http://www.ebi.ac.uk/goa/ http://psort.hgc.jp/ app:ds:on app:ds:on app:ds:on app:ds:on app:ds:basis app:ds:basis Figure 2. Distributions of molecular weights and pI values for proteins identified by LC-MS/MS, (A) Distribution of molecular weights. (B) Distribution of pI values. Figure 3. Theoretical 2-DE distribution of proteins from porcine serum. The theoretical pIs and MWs of the proteins were calculated using compute pI/Mw tools according to protein amino acid sequence or ID. Figure 4. Categorization of 189 serum proteins by molecular functions or biological processes. (A) All identified proteins. (B) Classical serum proteins. (C) cellular proteins. pathophysiological performance of body, and the presence of these components in blood reinforces the significance of a proteomic approach to identifying biomarkers for disease status. Previous proteomic characterizations of pig serum have used two dimensional PAGE (Miller et al., 2009). Result shows (Figure 3) 40% proteins that are usually difficult to separate by 2-DE. This study used the shotgun LCMS/MS proteomics technique combined with informatics analysis to determine the proteome profile of pig serum. This technique represents an efficient strategy for swine serum proteomics research, and overcame the disadvantage of 2DE that cannot separate polarity protein. To reduce the individual differences, serum from four pigs were merged together and used for gel electrophoresis separation. In order to identify proteins according to molecular weight, brand A and B mixed, and brand C and D mixed, respectively (Figure 1). In this study, given profile of serum protein from porcine, we identified a total of 392 proteins (Table 1), of which 189 (Table 2) were annotated and classified based on their molecular function or biological process (Figure 4). As we expected, besides immune globulin, albumin, apolipoprotein, hemoglobin and actin, which are both ubiquitous in the red blood cells were successfully identified. These represented the main serum proteins, and are involved in the combination and transportation of small molecules (Alaupovic, 1996; Bondarenko et al., 2002). Few coagulation or complement factors associated with whole process of blood coagulation were identified successfully as well. Sodium channel protein, transmembrane channel-like protein related to signaling pathways and receptors were identified, with important functions in signal transduction (Naren et al., 1997). The identified proteins also included 46 kinds of proteases or enzymes related to many important biological processes, such as biosynthesis, metabolic regulation, nucleotide replication, damage repair, transcription and posttranslational modification. Fibronectins involved in cell adhesion, cell motility, opsonization, wound healing, and Table 2. List of 189 annotated serum proteins. Protein name PepCount Unique Cover MW kD pI Accession PepCount percent number Common circulating blood protein 15 Albumin 787 52 74.1 69.4 5.92 gi|833798 Alpha-2-macroglobulin 91 37 32.2 167 5.58 gi|311256211 Ceruloplasmin 71 26 33.7 121.8 5.72 gi|311269519 Apolipoprotein B 31 22 12.3 300 6.19 gi|951375 Apolipoprotein A-I 63 15 52.1 30.3 5.48 gi|461519 Apolipoprotein A-II 8 3 26 11.1 7.73 gi|297747304 Apolipoprotein C-III 4 2 28.1 107 4.76 gi|416627 Hemoglobin subunit beta 33 9 72.1 16.2 7.1 gi|3041678 Porcine hemoglobin 30 9 67.8 16 6.76 gi|5542425 Haptoglobin 22 9 30.8 38.5 6.51 gi|41019122 Hemopexin precursor 37 12 45.3 51.3 6.59 gi|47522736 Apolipoprotein E 12 7 24.6 36.6 5.62 gi|461527 Apolipoprotein D 3 2 13.7 21.5 4.76 gi|311269822 Angiotensinogen-like 3 2 6.3 37.8 8.74 gi|311271188 Spectrin alpha chain 2 2 1.7 284.9 5.2 gi|311246557 Coagulation and complement factor 19 Complement C3 272 55 46.8 186.8 6.09 gi|47522844 Coagulation factor X protein 1 1 2.3 53.1 5.28 gi|113205818 Coagulation factor IX 1 1 2.7 45.5 5.19 gi|60392241 Complement component C3 70 12 60.5 33.4 5.69 gi|295656640 Complement factor B 36 11 22.6 85.9 7.45 gi|162138242 Complement component 4 65 20 19.5 192.5 6.8 gi|158537756 Complement C2 1 1 3 83.3 7.95 gi|156120138 Complement component C5 11 6 5.9 188.6 6.49 gi|37677940 Complement component C6 5 3 5.1 105.3 6.92 gi|148226535 Complement component C7 precursor 1 1 1.5 93.1 6.7 gi|47523630 Complement component C8A 6 4 13.1 66 5.61 gi|147905213 Complement component C8B 7 3 7.4 69.2 8.14 gi|148235410 Component C8G 2 2 10.9 22.3 5.59 gi|148223227 Complement component C9 5 1 3.3 62.3 5.92 gi|148233690 Complement factor I 11 4 11.8 67.1 8.06 gi|311262683 Complement C1 5 2 15.8 26.5 9.43 gi|51491906 Blood coagulation factor XIV 9 4 13.9 51.8 6.23 gi|571399 Coagulation factor XII 1 1 3.6 68 6.98 gi|35039077 Galectin-8 1 1 6.3 36.3 7.86 gi|218664463 Protease inhibitors 7 Serpin A3-1 89 17 36.9 60.9 8.46 gi|311261515 Inter-alpha-trypsin inhibitor 40 15 22.8 102.1 6.42 gi|48374067 Inhibitor of carbonic anhydrase 34 13 26.9 77.6 5.88 gi|47523160 Alpha-1protease inhibitor 16 6 26.8 47.2 5.54 gi|1703026 Clusterin precursor 12 6 20.4 51.7 5.62 gi|47522770 Plasma protease C1 inhibitor 7 2 6.5 54.6 6.77 gi|178056710 Plasminogen activator inhibitor 2 1 4.7 44.8 8.6 gi|311259199 Table 2. Count’d. Blood transport and binding proteins 9 Serotransferrin 258 46 74.7 77 6.93 gi|136192 Vitamin D-binding protein 12 6 24.3 24.5 5.02 gi|5186337 Hemoglobin subunit alpha 10 6 57.4 150.3 8.76 gi|122465 Transthyretin 7 5 48 16.1 6.29 gi|1717817 C4b-binding protein alpha chain-like 4 3 8.3 67.4 6.14 gi|311265150 Transgelin-2-like 3 2 12.6 54.7 6.04 gi|311254018 Polyadenylate-binding protein 4-like 2 1 3.8 70.5 9.33 gi|311258948 Polypyrimidine tract-binding protein 1 1 1 5 59.9 9.24 gi|47523538 Telethonin binding protein 1 1 9.6 18.9 5.38 gi|224809550 Channel and receptor derived proteins 15 Voltage-dependent anion-selective Channel protein 1 3 1 6.7 30.7 8.62 gi|7505046 Signal sequence receptor, alpha 2 1 5.2 32 4.36 gi|297632426 Calreticulin 2 1 7 48.3 4.32 gi|290750002 Signal recognition particle 68 kda protein 1 1 2.4 70.4 8.65 gi|311266756 Lycine receptor subunit alpha-1 1 1 1.4 50.2 8.93 gi|311274089 Sodium channel protein 1 1 0.8 206.8 4.92 gi|311266955 Transmembrane channel-like protein 1 1 1.8 92.5 5.91 gi|311245910 Phosphoinositide 3-kinase adapter protein 1 1 1 1.6 100.7 5.64 gi|194041783 Insulin receptor substrate 4 1 1 2.3 53.1 8.83 gi|258590765 Transient receptor potential cation channel 1 1 0.9 236.2 8.54 gi|311245919 Sodium channel and clathrin linker 1 1 1 6.4 11 4.98 gi|311262562 Mitochondrial import receptor subunit TOM34 1 1 3.1 50.9 9.42 gi|311274903 Calmodulin-like 1 1 14.8 16.8 4.09 gi|311252670 Syntaxin-3-like 1 1 3.9 48.9 8.44 gi|311247613 Protease or other enzymes 44 Alpha-1-antichymotrypsin 2 32 9 40.2 46.7 6.28 gi|47523270 Plasminogen 18 9 15.8 90.6 7 gi|146345485 Fumarate hydratase 20 8 11.8 13.9 6.3 gi|47523636 Antithrombin-III 28 7 24.8 52.4 5.84 gi|194018664 Prothrombin precursor 19 7 18.8 70.1 5.62 gi|172072659 Membrane primary amine oxidase-like 12 6 15.7 78.3 6.61 gi|311267153 Glyceraldehyde 3-phosphate dehydrogenase 18 4 29.4 35.8 8.57 gi|2407184 Serum paraoxonase/arylesterase 1 8 4 18.8 39.9 5.29 gi|167621416 Kininogen-1 isoform 2 5 4 11 43.8 6.64 gi|311269761 Plasma kallikrein 8 3 7.9 72.3 7.78 gi|47522962 L-lactate dehydrogenase B chain 5 3 17.7 36.6 5.57 gi|1107387 Beta-enolase 19 2 9.22 47.1 8.05 gi|113205948 Pyruvate kinase isozymes 7 2 5.4 64.9 7.98 gi|311260850 ATP synthase subunit alpha, mitochondrial 6 2 7.8 59.7 9.21 gi|297591975 ATP-dependent RNA helicase A 1 1 4.5 44.6 5.53 gi|311264941 Alpha-1-antichymotrypsin 1 5 2 13.8 24.7 5.22 gi|9968809 Transketolase 4 2 9.3 67.8 7.21 gi|162952052 Carbonic anhydrase 1 3 2 12.7 29 6.67 gi|194037099 Pig muscle 3-phosphoglycerate kinase 3 2 8.5 43.4 8.78 gi|13399644 ADP/ATP translocase 1-like isoform 1 3 1 6.7 24.7 10.89 gi|311254417 Phosphoglycerate mutase 1-like isoform 2 3 1 10.5 28.9 6.51 gi|194041795 Ribose-phosphate pyrophosphokinase 3 1 4.7 34.8 8 gi311276762| Carboxypeptidase B2 2 1 4.3 48.6 6.83 gi|194040626 Transmembrane protease serine 4-like 2 1 2.5 64.5 8.22 gi|311264000 Table 2. Count’d. Bifunctional aminoacyl-trna synthetase 2 1 1 161.1 1.35 gi|311265228 Glucosamine--fructose-6-phosphate 2 1 2.7 59.5 7.01 gi|311249541 Aminotransferase 2 1 2.7 59.5 7.01 gi|311249541 Polypeptide N-acetylgalactosaminyltransferase 2 1 4.8 64.2 8.63 gi|194042623 Threonyl-trna synthetase 1 1 4 37.7 6.28 gi|311273548 Nicotinamide N-methyltransferase 1 1 9 29.5 5.63 gi|118573081 Phosphoinositide 3-kinase adapter protein 1 1 1.5 107.1 5.78 gi|28860138 Mismatch repair endonuclease PMS2 1 1 1.9 94 6.31 gi|311250873 Alanyl-trna synthetase, cytoplasmic-like 1 1 23.5 8.6 6.12 gi|311257020 Adenylosuccinate synthetase 1 1 2 50.1 8.72 gi|189031714 Inorganic pyrophosphatase 1 1 6.6 27.5 5.44 gi|311271315 Ubiquitin carboxyl-terminal hydrolase 22-A 1 1 2.2 94.4 7.34 gi|311276293 Tyrosine-protein kinase 1 1 1 122.6 6.68 gi|311249266 Ubiquitin-conjugating enzyme E2 L3 1 1 16.2 17.9 8.68 gi|297591969 Serum paraoxonase/arylesterase 1 7 4 14.3 45.6 6.36 gi|118403912 Cholinephosphotransferase 1 1 2.2 42.3 9.05 gi|311262709 Serine/threonine-protein kinase 25-like 1 1 3.5 48.7 6.18 gi|311273415 Serine/threonine-protein kinase Nek5 1 1 0.1 79 8.67 gi|311266294 Rho gtpase-activating protein 23-like 1 1 1 131.8 9.06 gi|311268532 Carbonyl reductase [NADPH] 3-like 1 1 6.1 30.7 5.57 gi|311270205 Cytokines or homones 1 Interleukin enhancer-binding factor 2 3 2 13.1 43.1 5.19 gi|311254260 Other extracellular or secreted 55 Spreading factor 6 4 10.7 52.6 5.6 gi|1351418 Heparin cofactor 2 6 4 11.2 55.8 6.5 gi|194043402 Matrin-3-like isoform 1 2 2 5.1 94.7 5.87 gi|311250254 Elongation factor 1-alpha 2 6 1 6.3 50.2 9.33 gi|311263706 60S ribosomal protein L15 2 1 7.8 17.7 11.6 gi|6174950 40S ribosomal protein S5-like isoform 1 2 1 9.8 22.9 9.73 gi|311259613 Vitamin K-dependent protein S 2 1 5.9 27.6 5 gi|311270126 Leucine-rich alpha-2-glycoprotein 2 1 3.8 29.7 7.02 gi|311248408 Macrosialin-like isoform 1 1 1 4.3 42.8 9.29 gi|311261974 Fibronectin isoform 3 27 18 13.9 239.7 5.72 gi|311273025 Histidine-rich glycoprotein 56 12 25.3 61.5 7.2 gi|311269757 Gelsolin 30 11 25.7 84.8 5.93 gi|121118 Alpha-1B-glycoprotein 42 10 30.8 54.4 5.99 gi|311259609 Alpha-2-HS-glycoprotein 59 8 36.3 38.8 5.5 gi|311269753 60 kda heat shock protein 21 8 25.1 60.9 5.7 gi|194044029 Actin 50 6 31.2 41.7 5.29 gi|311250866 Heat shock cognate 71kda protein 26 5 25.9 50.4 5.41 gi|311264120 Heat shock 70 kda protein 1B 18 5 17.5 70.1 5.6 gi|56748897 Heat shock cognate protein HSP 90-beta 13 5 12.3 83.2 4.96 gi|31160516 Heat shock protein HSP 90-alpha 8 3 7.4 84.7 4.93 gi|47522774 T-complex protein 1 subunit alpha-like 13 4 15.4 60.3 5.71 gi|194033404 Vimentin-like 13 4 11.5 70.3 6.01 gi|257096532 Complex of Bdellastasin With Porcine Trypsin 27 3 22.4 23.4 8 gi|257472074 Clathrin heavy chain 7 3 3.8 191.6 5.48 gi|224492556 Fetuin-B-like 4 3 15.4 41.2 7.4 gi|31126975 Zinc-alpha-2-glycoprotein-like 3 3 17.1 34.4 5.88 gi|311250971 Eukaryotic translation initiation factor 3 1 1 16.8 12.6 4.84 gi|311253491 Lumican-like 7 2 11.4 38.8 5.82 gi|194037683 Table 2. Count’d. Ubiquitin-like modifier-activating enzyme 1 5 2 3.4 114.6 5.54 gi|311276235 Elongation factor 1-gamma 5 2 11.4 50 6.15 gi|311247489 Fascin 3 2 8.5 54.7 6.07 gi|226372953 40S ribosomal protein S15 3 2 28.3 17 10.39 gi|51338618 Zinc finger protein 7 1 1 1.9 76.3 9.13 gi|311253237 Zinc finger protein AEBP2-like 1 1.4 1.4 54.2 5.13 gi|311250679 Zinc finger protein 425-like 1 1 2.6 70.6 9.74 gi|311264767 Polyubiquitin-C-like 1 1 1.2 80.8 9.36 gi|31127000 Adiponectin 1 1 10.3 15.5 8.94 gi|33694199 Troponin T 1 1 6.11 31.2 5.92 gi|66773803 Transcription activator BRG1 1 1 1.4 165 8.82 gi|311248656 Mitogen-activated protein kinase 9 1 1 4 48.4 5.5 gi|311249537 Cytotoxic T-lymphocyte protein 4 1 1 4.5 24.4 5.42 gi|12644505 Centromere protein F-like 1 1 0.5 351.5 5.07 gi|311265008 Transcription factor AP-2 gamma 1 1 4.9 49.1 7.69 gi|178056536 Cell division control protein 42 homolog 1 1 6.8 21.3 5.76 gi122063302 94 kda glucose-regulated protein 1 1 2.1 92.5 4.75 gi|17865698 Ubiquilin-4-like 1 1 3.3 63.9 5.14 gi|311254132 Annexin A5-like 1 1 3.9 42.9 5.16 gi|311262609 Apoptosis regulator protein 1-like 1 1 2.2 71.8 5.43 gi|311271288 C-reactive protein 1 1 11.3 24.9 5.75 gi|628999899 Myosin-9 1 1 1 227 5.51 gi|311255169 ADP-ribosylation factor 1-like 1 1 15 20.7 6.31 gi|311249487 Golgi membrane protein 1-like 1 1 5.9 27.2 4.51 gi|311265509 40S ribosomal protein S28 1 1 17.5 9.1 11.03 gi|45268967 78 kda glucose-regulated protein 4 4 9.5 73.8 5.68 gi|194033595 Leucine-rich PPR motif-containing protein 1 1 2.1 87 7.55 gi311252711 Sytokeleton or nuclear related 24 Heterogeneous nuclear ribonucleoprotein A1 10 4 20.9 34.2 9.27 gi|116175259 Eukaryotic initiation factor 4A-I 11 3 14.5 46.1 5.32 gi|154147660 Tubulin beta chain isoform 1 6 3 14 49.7 4.78 gi|194040122 Histone H2A type 2-C-like 7 2 37.2 14 10.9 gi|311254405 Plastin-2 isoform 1 3 2 6.4 70.2 5.25 gi|194040624 Collagen alpha-3(VI) 1 1 0.8 32.1 7.33 gi|194043712 Microtubule-associated protein 4-like 3 1 1.87 116.9 5.03 gi|311268808 Heterogeneous nuclear ribonucleoprotein A/B 3 1 6.3 32 8.31 gi|162951821 Histone H3.1-like 3 1 23.5 15.4 11.13 gi|311259879 RNA-binding protein FUS-like 3 1 6.2 52.5 9.4 gi|311251250 Plastin-3 isoform 1 2 1 2.4 63.9 5.73 gi|311276826 Heterogeneous nuclear ribonucleoprotein F 2 1 4.1 45.7 5.32 gi|311271228 Vinculin 2 1 1.7 123.9 5.62 gi|50403675 Heterogeneous nuclear ribonucleoprotein Q 1 1 2.9 69.6 8.68 gi|194035295 Actin related protein 1 1 8.3 19.7 8.53 gi|19556223 Actin 50 6 31.2 41.7 5.29 gi|311250866 Nucleolysin TIAR isoform 2 1 1 4.6 43.4 8.1 gi|311271911 Heterogeneous nuclear ribonucleoprotein D0 1 1 9.5 22.9 9.47 gi|311262905 Small nuclear ribonucleoprotein E 1 1 27.2 10.8 9.46 gi|147903209 Histone H3.2 1 1 23.5 15.4 11.27 gi|311254411 Histone h1t-like 1 1 5.2 22.2 11.58 gi194039830 Nucleophosmin-like isoform 1 1 1 4.8 32.6 4.61 gi|311273930 Nuclear envelope pore membrane protein POM 1 1 7.3 31.2 11.43 gi|311265618 Sister chromatid cohesion protein PDS5 1 1 3.6 37.1 7.66 gi|311262027 maintenance of cell shape were identified (Hakkinen et al., 2010). Some organellar proteins were found (including 40S ribosomal protein S15 and 40S ribosomal protein S28), as well as a few eukaryotic translation initiation factors. Heat-shock proteins (HSPs) are specific proteins that can protect cells and play an important role in growth, development, differentiation and other physiological activities (Arrigo and Simon, 2010; Burel et al., 1992). In a word, this overview map of pig serum protein provided a large number of reference information. According to function information of proteins, we can make a particular study of partially serum protein in some aspect of disease. Additionally, as can be seen from the sub-cellular localization of identified proteins, these proteins distributed mainly in nucleus, cytoplasm, extracellular matrix, mitochondrion cytoskeleton, and perform their functions in these areas. Therefore, sub- cellular localization of protein from serum has potential values in research on diseases. Due to the limited number of pig proteins available in the public databases, protein annotation for some of the proteins was impossible and a number of peptide mass fingerprinting was unmatched effectively. The identifi- cation of total proteins in pig serum will be achievable as soon as the complete and accurately annotated genome and protein sequence databases for pig become available. ACKNOWLEDGEMENTS This work was supported by the earmarked fund for China Agriculture Research System (CARS-39). The authors wish to thank the journal editors and anonymous reviewers for their editing and revision of the manuscript. REFERENCES Adams CM, Zubarev RA (2005). Distinguishing and quantifying peptides and proteins containing D-amino acids by tandem mass spectrometry. Anal. Chem. 77(14):4571-80. Aebersold R, Mann M (2003). Mass spectrometry-based proteomics. Nature 422(6928):198-207. Alaupovic P (1996). Significance of apolipoproteins for structure, function, and classification of plasma lipoproteins. Methods Enzymol. 263:32-60. Arrigo AP, Simon S (2010). Expression and functions of heat shock proteins in the normal and pathological mammalian eye. Curr. Mol. Med. 10(9):776-93. Bendixen E, Danielsen M, Hollung K, Gianazza E, Miller I (2011). Farm animal proteomics--a review. J. Proteomics 74(3):282-93. Bondarenko PV, Chelius D, Shaler TA (2002). Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Anal. Chem. 74(18):4741-4749. Burel C, Mezger V, Pinto M, Rallu M, Trigon S, Morange M (1992). Mammalian heat shock protein families. Expression and functions. Experientia 48(7):629-634. Corthals GL, Wasinger VC, Hochstrasser DF, Sanchez JC (2000). The dynamic range of protein expression: a challenge for proteomic research. Electrophoresis 21(6):1104-1115. Crockett DK, Lin Z, Vaughn CP, Lim MS, Elenitoba-Johnson KS (2005). Identification of proteins from formalin-fixed paraffin-embedded cells by LC-MS/MS. Lab. Invest. 85(11):1405-1415. Eckersall PD, Saini PK, McComb C (1996). The acute phase response of acid soluble glycoprotein, alpha(1)-acid glycoprotein, ceruloplasmin, haptoglobin and C-reactive protein, in the pig. Vet. Immunol. Immunopathol. 51(3-4):377-85. Gygi SP, Corthals GL, Zhang Y, Rochon Y, Aebersold R (2000a). Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. USA 97(17):9390-9395. Gygi SP, Corthals GL, Zhang Y, Rochon Y, Aebersold R (2000b). Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. USA 97:17-9390. Hakkinen KM, Harunaga JS, Doyle AD, Yamada KM (2010). Direct Comparisons of the Morphology, Migration, Cell Adhesions, and Actin Cytoskeleton of Fibroblasts in Four Different Three-Dimensional Extracellular Matrices. Tissue Engineering Part A p.1708. Hsieh SY, Chen RK, Pan YH, Lee HL (2006). Systematical evaluation of the effects of sample collection procedures on low©\molecular©\weight serum/plasma proteome profiling. Proteomics 6(10):3189-3198. Issaq HJ, Xiao Z, Veenstra TD (2007). Serum and plasma proteomics. Chem. Rev. 107(8):3601-3620. Li J, Chen X, Fan W, Moghaddam SHH, Chen M, Zhou Z, Yang H, Chen J, Zhong B (2009). Proteomic and bioinformatic analysis on endocrine organs of domesticated silkworm, Bombyx mori L. for a comprehensive understanding of their roles and relations. J. Proteome Res. 8(6):2620-2632. Miller I, Wait R, Sipos W, Gemeiner M (2009). A proteomic reference map for pig serum proteins as a prerequisite for diagnostic applications. Res. Vet. Sci. 86(2):362-367. Millioni R, Tolin S, Fadini GP, Falda M, van Breukelen B, Tessari P, Arrigoni G (2012). High confidence and sensitivity four-dimensional fractionation for human plasma proteome analysis. Amino Acids. Naren AP, Nelson DJ, Xie W, Jovov B, Pevsner J, Bennett MK, Benos DJ, Quick MW, Kirk KL (1997). Regulation of CFTR chloride channels by syntaxin and Munc 18 isoforms. Nature 390(6657):302-304. Oh-Ishi M, Satoh M, Maeda T (2000). Preparative two-dimensional gel electrophoresis with agarose gels in the first dimension for high molecular mass proteins. Electrophoresis 21(9):1653-1669. Wan J, Sun W, Li X, Ying W, Dai J, Kuai X, Wei H, Gao X, Zhu Y, Jiang Y (2006). Inflammation inhibitors were remarkably up©\regulated in plasma of severe acute respiratory syndrome patients at progressive phase. Proteomics 6(9):2886-2894. Wysocki VH, Resing KA, Zhang Q, Cheng G (2005). Mass spectrometry of peptides and proteins. Methods 35(3):211-222. Zhang L, Lun Y, Yan D, Yu L, Ma W, Du B, Zhu X (2007). Proteomic analysis of macrophages: a new way to identify novel cell-surface antigens. J. Immunol. Methods 321(1-2):80-85. app:ds:in app:ds:in app:ds:in app:ds:in app:ds:word