Proteomic and Phosphoproteomic Analysis of Chicken Embryo

Proteomic and Phosphoproteomic Analysis of Chicken Embryo Fibroblasts Infected with Cell Culture-Attenuated and Vaccine Strains of Marek's Disease Vir...
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Proteomic and Phosphoproteomic Analysis of Chicken Embryo Fibroblasts Infected with Cell Culture-Attenuated and Vaccine Strains of Marek’s Disease Virus Ko-yi Chien,† Kevin Blackburn,† Hsiao-Ching Liu,‡ and Michael B. Goshe*,† †

Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh North Carolina 27695, United States ‡ Department of Animal Science, North Carolina State University, Raleigh North Carolina 27695, United States S Supporting Information *

ABSTRACT: Vaccination is an effective strategy to reduce the loss of chickens in the poultry industry caused by Marek’s Disease (MD), an avian lymphoproliferative disease. The vaccines currently used are from attenuated serotype 1 Marek’s disease virus (MDV) or naturally nononcogenic MDV strains. To prepare for future immunity breaks, functional genomic and proteomic studies have been used to better understand the underlying mechanisms of MDV pathogenicity and the effects induced by the vaccine viruses. In this study, a combined approach of quantitative GeLC−MSE and qualitative ERLIC/IMAC/LC−MS/MS analysis were used to identify abundance changes of proteins and the variations of phosphorylation status resulting from the perturbations due to infection with an attenuated oncogenic virus strain (Md11/75C) and several nononcogenic virus strains (CVI988, FC126 and 301B) in vitro. Using this combined approach, several signal transduction pathways mapped by the identified proteins were found to be altered at both the level of protein abundance and phosphorylation. On the basis of this study, a kinase-dependent pathway to regulate phosphorylation of 4E-BP1 to modulate assembly of the protein translation initiation complex was revealed. The differences of 4E-BP1 phosphorylation patterns as well as the measured abundance changes among several other proteins that regulate host transcriptional and translational activities across the virus strains used in this study provide new insight for future functional and biochemical characterization of specific proteins involved in MDV pathogenesis. KEYWORDS: proteomics, phosphoproteomics, liquid chromatography, mass spectrometry, Marek’s Disease Virus, phosphorylation

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lesions. The first successful attenuated strain used as a vaccine was strain HPRS-164 followed by the worldwide application of CVI988 against highly virulent MDV strains (vv and vv+).5,6 Md11/75C, used in a previous study for the mild pathogenic MDV strain for chicken cell infection,7 was also attenuated by over 75 cell passages from the original Md11 strain of MDV and showed to be protective for very virulent virus, such as the Md5 strain.8,9 Interestingly, even though Md11/75C has a highly protective efficacy toward high virulent MDV strains, it was shown to fail in protecting against MD by some strains while serotype 3 or other nononcogenic MDV virus vaccines were effective.9 Another drawback of using an attenuated virus vaccine is the possibility for the serotype 1 strain to regain its oncogenicity postinoculation. For example, an oncogenic revertant has been observed in several lines of chickens inoculated with the Md11/75C strain.10,11 Serotype 2 and 3 viruses are naturally nononcogenic strains of MDV and also provide different levels of protection against MD

INTRODUCTION Marek’s Disease (MD) is a contagious disease caused by Marek’s Disease Virus (MDV), a double-stranded DNA herpesvirus with oncogenic pathogenicity in susceptible chicken populations. Vaccination against MD has been applied for more than 30 years to protect infected birds from developing tumors. Although vaccination reduces mortality of infected birds, it does not block viral replication and dissemination. Hence, administration of MD vaccines has to be thorough and as early as possible, usually on 18-day-old embryos or one-day-old chicks. The MDV strains are classified into 3 serotypes: serotype 1 (MDV1), serotype 2 (MDV2) and serotype 3, which includes the apathogenic strains of turkey origin (herpesvirus of turkeys, HVT). Among the three serotypes, only serotype 1 contains oncogenic MDVs, and they are further categorized according to virulence into mild (m), virulent (v), very virulent (vv) and very virulent plus (vv+) strains.1−3 Commercial vaccines used for controlling MD are all live virus vaccines. Many serotype 1 viruses have been attenuated by serial passages in cell culture to reduce their oncogenicity but retain their antigenicity to boost host immunity against MD-induced © 2012 American Chemical Society

Received: May 25, 2012 Published: October 29, 2012 5663

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lesions. Initially, chickens were found to be protected from MD lesions by prior exposure to avirulent viruses, which were later identified as serotype 2 MDV strains12,13 such as 301B, which has been licensed for use as a bivalent or polyvalent vaccine with other serotypes10,14,15 Some strains of serotype 3, or HVT, including FC126 have been licensed for use as vaccines in the United States and many other countries but now are more widely employed in combination with CVI988 and/or serotype 2 strains for more protective power against highly virulent MDV infections.9,15 Both serotype 2 and 3 strains are genetically close to serotype 1 MDV, except for a segment of the inverted repeat regions of the serotype 1 virus that contains genes involved in its oncogenicity16,17 that have no homologous counterparts in both serotype 2 and 3 viruses. Other than attenuated or nononcogenic viruses, recombinant vaccines are also attractive alternatives in the field of vaccine development.18−21 The development of recombinant vaccines against MDV infection is driven by the evolution of virus virulence, and the resulting immunization is no longer effective. The recombinant technologies are 2-fold: (1) expressing MDV antigens on herpesvirus genome vectors that are not oncogenic in vitro and in vivo such as HVT,21−24 gene-deleted serotype 1 MDV25,26 or fowlpox virus19,20,27 and (2) inserting genes from other avian viruses into MDV vectors to acquire a protective effect from both MDV and the chosen virus.28−32 Other derivative strategies include using a Meq-deleted MDV clone to construct a bacterial artificial chromosome,33 an RNAi technique delivered by viral vectors,24 and recombinant viral antigens such as glycoprotein B21,22,34,35 as immune-eliciting agents. Unfortunately, despite all of the efforts, there is still no licensed recombinant vaccine on the market. The lack of such a vaccine is partly due to our limited knowledge about how the interactions between viral and host gene products contribute to pathogenicity such that no proper proteins can be selected as effective MD preventing targets. However, this seems to be a logical direction for future vaccine development since the virus vaccines utilized so far cannot induce sterilizing immunity, and the coexistence of virulent MDV and vaccine viruses appear to increase the mutation rate of the viral genome to evolve into more virulent strains.2,36,37 The mechanism of how a vaccine acts in the prevention of MDV-induced tumorgenesis is still not fully understood. It is speculated that the introduction of a vaccine virus in the host can activate both antibody and cell-mediated immune responses before the immune system is weakened by virulent MDV. That is, higher profiles of cytokine expression, such as interleukins or interferon, are produced after vaccination followed by MDV challenge.38,39 Hence, the efficient and long-lasting presentation of exogenous antigen may play a role in eliciting T or B cells to undergo clonal expansion and facilitate antigen recognition. To obtain some insight regarding this mechanism, a study to investigate the differences that vaccine viruses and Md11/75C produce in the proteome and phosphoproteome of infected CEF cells was performed. By using proteomic tools, our short-term objective was to capture an overall picture of protein network alterations from which more information would be obtained regarding the differences between virulent and vaccine strains of MDV in the host cells in order to provide new avenues of study for developing more effective MDV vaccines. Overall this work represents the first application of using comprehensive proteomic tools to compare the protein abundance and phosphorylation profiles upon infection with multiple MDV strains in chicken cells.

Article

MATERIALS AND METHODS

Cells, Viruses, and Materials

Specific-pathogen free (SPF) eggs were obtained from SPAFAS (Charles River Laboratories, CT). Virus stock of MDV strains (serotype 1: Md11/75C, passage number: 79 (p.79), CVI988, p.43; serotype 2: 301B, p.15; serotype 3: FC126, p.11) were obtained from the Avian Disease and Oncology Laboratory (East Lansing, MI). Sequencing grade-modified trypsin was purchased from Promega (www.promega.com). β-Casein (at least 95% βcasein and containing αS1 and αS2-casein) and urea were from Sigma (www.sigmaaldrich.com). Acetonitrile (HPLC grade) and formic acid (ACS reagent grade) were from Sigma-Aldrich (www.sigmaaldrich.com). Acetone (optima grade) was purchased from Fisher Scientific (www.fishersci.com). Ammonium bicarbonate and guanidinium chloride (GdmCl) were from Fluka (www.sigmaaldrich.com). Water was distilled and purified using a High-Q 103S water purification system (www.high-q. com). All other reagents and chemicals were purchased from Sigma-Aldrich unless otherwise stated. Cell Culture and Virus Infection

Chicken embryo fibroblast (CEF) cells were prepared from 11day-old embryos as previously described.40 Secondary CEF cells were maintained in RPMI medium supplemented with 1% fetal calf serum, 2 mM glutamine and 100 units penicillin g/mL at 37 °C with 5% CO2 and seeded to 80−90% confluency in 150-mm Petri dishes for virus infection. Infection was performed by inoculating 106 plaque forming units (pfu) of Md11/75C, CVI988, FC126 and 301B strains of MDV virus, respectively, or medium only as the mock infection. For infected cells, cell morphology was observed and cells were harvested when cytopathic effects became clearly visible in about 80% of the cells. Protein Extraction and Digestion

Infected CEF cells were harvested and lysed using 1 mL of lysis buffer (5 mM magnesium chloride, 150 mM potassium chloride, 10 mM HEPES, pH 7.5, 1 mM EGTA, 0.2% NP-40, and 25 mM PMSF), containing phosphatase inhibitor with 1 mM sodium vanadate (Na3VO4), 20 mM sodium fluoride and protease inhibitor cocktail containing 2 mM AEBSF, 300 nM aprotinin, 130 μM bestatin, 1 mM EDTA, 14 μM E-64 and 1 μM leupeptin (Sigma-Aldrich, P2714). Collected cell lysate was sonicated on ice and centrifuged at 7500 rpm (rotor FA21, www.piramoon. com) to remove cell debris. The supernatant was transferred into a new 50 mL-tube and a total of 4−5 volumes of cold acetone were added. The samples were mixed thoroughly and incubated at −20 °C overnight to promote protein precipitation. Precipitated proteins were collected by centrifuging at 8500 rpm before the solution phase was discarded. Acetone precipitates were air-dried and 1 mL of 6 M GdmCl in 50 mM ammonium bicarbonate, pH 8.2, was added to resolubilize the proteins. Total protein concentration was determined by the BCA assay (www.piercenet.com) using serially diluted BSA as a standard. Based on the protein concentration measured by the BCA assay and visual inspection of the protein obtained via Coomassie stained SDS-PAGE gels, additional cultures of samples were assessed and prepared to achieve a sample containing 10 mg of protein for cells infected with each virus, thus in essence the biological replicates were pooled. Overall, the amount of cells used for this data set was large enough to represent a general condition of each treatment and thus represent pooled biological replicates of which an aliquot was used for LC−MSE analysis (described below), a pooling method 5664

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μm i.d. × 1 cm length) and desalted using a flow rate of 10 μL/ min and then separated by in-line gradient elution onto a 75 μm i.d. × 25 cm column packed with Waters BEH C18 stationary phase (1.7 μm particles) at a flow rate of 300 nL/min. The linear gradient was from 2 to 40% mobile phase B over 60 min, where mobile phase A contained 0.1% formic acid in water and mobile phase B contained 0.1% formic acid in acetonitrile. Synthetic human [Glu1]-fibrinopeptide B solution (300 fmol/μL) delivered by the NanoLockSpray source at a flow rate of 0.6 μL/min was used as the external calibrant and monitored every 30 s during each LC−MSE and LC−MS/MS analysis. In-gel digested peptides from each gel fraction were spiked with 120 fmol of the yeast alcohol dehydrogenase (ADH) internal standard (120 fmol per injection in order to monitor instrument performance but not for determining absolute protein quantification) and then subjected to LC−MSE analysis where the Q-Tof was operated in data-independent acquisition (DIA) mode with MS data (m/z 50−1990) acquired using alternating 2 s scans of low and elevated collision energy. Data were collected at a constant collision energy of 4 V during the low-energy MS scans, while a step from 15 to 30 V of collision energy was applied for CID during the high-energy MSE scans. For each gel fraction, at least 3 injections were performed as technical replicates. For LC−MS/MS analysis of the ERLIC/IMAC-enriched fractions, the Q-Tof was operated in a data-dependent acquisition (DDA) mode with a full MS scan (m/z 400−1900) followed by MS/MS scans (m/z 100−1990) for CID of the top 8 most intense precursor ions detected from the previous MS scan where an ion exclusion time for precursor selection of 200 s was applied. All the data was acquired using V-mode at a resolving power of at least 10 000 full width at half height (fwhh) at m/z 786.

that has been shown to provide meaningful quantitative proteomic data between samples.41 After pooling, an equal amount of protein from each sample was thermally denatured in a boiling water bath for 8 min followed by reduction with 8 mM tris(2-carboxyethyl)phosphine (www.piercenet.com) at 37 °C for 30 min and alkylation with 10 mM iodoacetamide for 1 h in the dark. Samples were diluted with 50 mM ammonium bicarbonate, pH 8.2, so that the final concentration of GdmCl was lower than 1 M followed by trypsin addition using a 1:80 trypsin-to-protein ratio. Trypsin digestion was performed at 37 °C overnight. The resulting tryptic peptides were desalted by solid-phase extraction (SPE) using an Alltech Prevail C18 cartridge and the solvent was evaporated via vacuum centrifugation before the peptides were stored at −20 °C until further fractionation was performed. SDS-PAGE Separation

For samples that were separated using 1D-SDS-PAGE, two aliquots containing 30 μg of protein were loaded on NuPAGE Novex Bis-Tris 4−12% mini gels (www.invitrogen.com) and electrophoresed at 90 V for the initial 5 min and 150 V until the dye front had migrated to the bottom of the gel. Gels were stained for 3 h using the Colloidal Blue Stain Kit (Invitrogen) and destained in water overnight. Proteins were separated using 2 lanes per sample and 7 gel-fractions were excised from each lane where the corresponding segments for each sample were pooled into one microfuge tube for in-gel digestion. For in-gel digestion, the gel pieces were destained in destaining solution containing 100 mM ammonium bicarbonate, pH 8.2, /acetonitrile (1/1, v/v) by shaking the tubes for 30 min at 35 °C. The same destaining step was repeated until most of the dye was removed from the gel slices. Protein reduction and alkylation was performed using the same concentration and incubation conditions as described for in-solution digestion. Trypsin digestion was performed by adding 20 μL of a 10 ng/μL trypsin solution into each gel fraction. For those fractions with larger gel pieces, 50 mM ammonium bicarbonate, pH 8.2, was supplemented as needed to immerse the gel pieces. Digestion was performed overnight at 37 °C. Peptides were extracted from the gel pieces using an extraction solution containing acetonitrile/2% formic acid (1/1, v/v) by rigorous shaking or vortexing. Extracted peptides were dried using vacuum centrifugation and stored at −20 °C until LC−MSE could be performed.

Data Processing and Peptide/Protein Identification

LC−MSE raw data files were processed using ProteinLynx Global Server version 2.4 (PLGS 2.4) with lock mass calibration to generate product ion spectra (i.e., MSE spectra) for subsequent database searching using the ion accounting algorithm. Static modifications with carboxamidomethylation for Cys residues and variable modifications for oxidation on Met residues and phosphorylation on Ser, Thr and Tyr residues were specified. A protein database containing 23,590 nonredundant protein entries was constructed containing Gallus gallus, Gallid herpesvirus 2, 3 and Meleagrid herpesvirus 1 protein entries along with bovine casein proteins, human keratins and trypsin protein sequences. An in-house program was written to filter out redundant entries, which were defined as two or more proteins with different GI numbers but containing identical protein sequences or a partial fragment which can be completely matched into a larger protein, and collapsed into a single entry in the final database. The same database was also used for DDA database searches. For DDA data analysis, raw data were processed on PLGS 2.4 to generate pkl files, which were then uploaded to an in-house server for Mascot database searching. The Mascot search parameters were set at a significance threshold of a p-value 50% of the raw data within a treatment group. To further sort the combined 1002 proteins from Elucidator processing, a self-organizing map function was utilized to analyze

Pathway Analysis

All proteomic data were subjected to pathway analysis using cross-referenced, multiple databases containing chicken, human and mouse knowledge-based information. The bioinformatic 5666

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(i.e., tandem IMAC) to enhance overall phosphopeptide detection. In the same FC126-infected sample, the comparison between the first and second IMAC enrichment using the same exclusion list resulted in 140 and 172 phosphopeptides, respectively, with only 46 found in common (Figure 3B). Similar results were also obtained from the other samples (Figure S3, Supporting Information). By using these strategies, a total of 361 unique host phosphopeptides, about 25% of the total number of host peptides identified, were detected in FC126-infected CEF cells (Table 2 and Figure 4A). The reason for this relatively low percentage compared to other samples was due to the fact that the nonphosphorylated peptides were too abundant to be effectively removed by IMAC and thus were detected by LC− MS/MS analysis. Phosphoproteomic Profile Changes between Infected CEF Cells

All five infected samples were processed for phosphopeptide enrichment and DDA LC−MS/MS analysis, but the CVI988infected sample was the only sample containing very few phosphopeptides despite the fact that the spiked casein standard phosphopeptides were detected at levels similar to the other samples. For this reason, conclusions regarding phosphorylation cannot be reliably discussed for the CVI988 sample. The overall enrichment percentage for phosphopeptides across the remaining four samples was about 40% for phosphopeptides and 45% for phosphoproteins as shown in Figure 4A. Among the four comparable samples, Md11/75C-infected cells resulted in the highest percentage of phosphopeptides being identified after enrichment (∼60%) while FC126-infected samples were found to contain the lowest percentage of phosphopeptides (∼30%). As to the number of unique peptides, the mock-infected sample contained the largest number of host phosphopeptides (451), but Md11/75C-infected cells had the most combined phosphopeptides (471) which included 26 viral proteins (Table 2). Interestingly, even though the percentage of phosphopeptides was the lowest in the FC126-infected sample, there were a total of 46 unique viral phosphopeptides identified, which was the most among all samples. Thus, upon different treatments, the enrichment of phosphopeptides varied and reflected the striking dynamic changes in the phosphoproteins between the infected CEF cells. Despite the lower proportion of phosphopeptides enriched in some of the samples, all of the infected samples possessed unique phosphopeptides with some overlaps between samples. In this analysis, each sample was analyzed twice with the second replicate injected using an exclusion list comprised of m/z values and their respective retention times for the nonphosphorylated peptides identified by the first injection to address switching errors in DDA analysis. The Venn diagram shown in Figure 4B illustrate the unique phosphopeptides only found in virusinfected CEF cells but not in mock-infected samples. In contrast, Figure 4C presents the Venn diagram for unique phosphopeptides only found in mock-infected cells when compared with each of the three virus-infected cells. Taken together, there were approximately 200 unique phosphopeptides detected in mockand Md11/75C-infected cells and about 150 unique phosphopeptides present in FC126- and 301B-infected cells. Only 159 out of 1127 phosphopeptides were ubiquitously identified in all four samples.

Figure 1. GeLC−MSE analysis for global proteomics. Gel image of protein cell lysates from different infections. Each lane is labeled according to the MDV strain used for infection (Lanes 1−5) while the protein molecular weight standard ladder is shown in lane 6. The lanes are segmented as indicated by the white lines with designated fraction numbers shown at the left.

expression trends of the identified proteins. A total of 9 clusters were classified based on the measure of the Pearson correlation as shown in Figure 2. Many distinctive features can be compared across the various viruses. For example, among the 9 clusters, clusters B and C contain proteins of lower abundance in the Md11/75C-infected CEF cells while cluster H contains the proteins that are more abundant upon Md11/75C infection. On the other hand, clusters B, C, F and I contain proteins that are present more abundantly in samples infected with mock or protective strains. Optimization for Phosphopeptide Detection

Enrichment for phosphopeptides was performed using a method combining ERLIC fractionation, IMAC enrichment, and LC− MS/MS data analysis.7 Due to the variation in sample complexity and peptide compositions, slightly modified strategies for DDA were applied in order to gain a maximal number of phosphopeptides that could be identified for each sample. For instance, the composition of peptides in FC126-infected cells possessed an exceptionally higher abundance of nonphosphorylated peptides; therefore, a peptide exclusion list for each ERLIC/IMAC fraction was applied to avoid these abundant peptides from being repeatedly selected for CID in order to facilitate precursor ion selection of relatively low abundant peptides. For the FC126-infected sample, the two injections with or without ion exclusion resulted in 140 and 159 phosphopeptides identified, respectively, among which only 65 of them were common in both analyses (Figure 3A). Due to the complexity from each fraction, an additional IMAC enrichment was conducted using the flow-through from the original IMAC step 5667

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Figure 2. Protein expression trends derived from combined data sets for infected CEF cells. The x-axis of each panel represents the infection condition: from left to right are cells infected with mock, Md11/75C, CVI988, FC126 and 301B. The y-axis represents the log intensity of a normalized protein abundance based on the mean of the total TIC. Each line of designated colors associated with each panel represents a different protein.

Comparisons of Proteome and Phosphoproteome Coverage

their relatively higher abundance compared to other phosphopeptides in the sample after ERLIC/IMAC enrichment. However, this may not always be the case for the phosphopeptides present in dynamic stoichiometries in response to various stimuli. For example, a G. gallus protein with the GI number of 253970482, corresponding to the sarcolemma associated protein, was identified by both GeLC−MSE and ERLIC/ IMAC/LC−MS/MS methods. This protein was found to be phosphorylated in only the mock- and 301B-infected CEF cells but not in the other samples, whereas for the abundance measurement, the normalized z-score in mock-, Md11/75C-, FC126-, and 301B-infected CEF cells after intensity scaling and statistical analyses using the Rosetta Elucidator was 1.524, 0.485, −0.738 and −0.421, respectively. In this case, it is clear that the phosphorylation state of a protein does not reflect its relative protein abundance and vice versa. Thus, the chance of a phosphopeptide being identified correlates with the absolute abundance of the specifically enriched phosphopeptide which can be independent of the overall abundance of its corresponding protein. To better analyze the proportion of individual phosphorylation events, an LC−MS/MS analysis of the IMAC flow-through of all pooled ERLIC fractions may represent a reasonable way to compare the relative peak intensities or identification frequency between the nonphosphorylated and

Although both of the GeLC−MSE and ERLIC/IMAC/LC−MS/ MS methods provided nearly 2000 protein identifications, the overlap between each method was limited. In fact, only 450 out of the 1790 unique proteins (∼25%) were shared by both methods, with only 100 phosphoproteins identified in the GeLC−MSE data set (Figure 5). This protein coverage difference reflects both the sample preparation and data acquisition disparities for each method. For GeLC−MSE, 60 μg of protein lysate was fractionated by SDS-PAGE, in gel digested, and then analyzed by a DIA approach, whereas with ERLIC/IMAC/LC−MS/MS phosphopeptide fractionation and enrichment were performed with 5 mg of digested protein lysate, then analyzed by a DDA approach. Consequently, GeLC−MSE analysis was able to provide quantitative information on proteins not identified by phosphopeptides using ERLIC/IMAC/LC−MS/MS analysis. In contrast, ERLIC/IMAC/LC−MS/MS provided enhanced detection of phophopeptides for less abundant proteins that were not readily detected at a global proteomic level using GeLC−MSE despite its utilization of a DIA acquisition strategy. Therefore, for any phosphopeptides identified, without support of data regarding protein abundance by GeLC−MSE, it was assumed that they were uniquely identified in each sample due to 5668

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Figure 3. Enhancing phosphopeptide detection using various LC−MS/ MS approaches exemplified by the FC126-infected CEF cells. (A) Comparison of a conventional top 8 DDA analysis and a top 8 DDA analysis utilizing a peptide exclusion list to prevent selected nonphosphorylated peptides from being repeatedly fragmented. The exclusion list is based on the search results of the first LC−MS/MS acquisition using the conventional method. Ion exclusion was performed by using an m/z window of 0.7 Da and a retention time window of 15 min. (B) Application of tandem IMAC. An additional IMAC enrichment (IMAC2) from the flow-through of the first IMAC enrichment (IMAC1) was performed and results in enhanced identification of phosphopeptides.

Figure 4. Differential phosphorylation between infected CEF cells revealed by ERLIC/IMAC/LC−MS/MS analysis. (A) Percentage of phosphopeptides (black bar) and phosphoproteins (white bar) detected after enrichment by LC−MS/MS in CEF cells infected with different virus strains. (B) Venn diagram of the unique phosphopeptides that are only detected in virus-infected cells when compared with mock-infected cells. (C) Venn diagram of the phosphopeptides that are only detected in the mock-infected CEF cells when compared with individual virusinfected samples.

phosphorylated counterparts. However, the complexity of the IMAC flow-through may still affect the identification of nonphosphopeptides due to DDA precursor selection and will greatly impact the accuracy of the quantification. Functional Proteomics

According to the proteome clusters shown in Figure 2, clusters B and C, which represent the proteins at lower abundance levels in attenuated oncogenic strain Md11/75C-infected CEF cells, contain 194 unique proteins out of 323 chicken proteins that can be converted into SwissProt accession numbers, and clusters A and H, which represent the high protein abundance levels of Md11/75C and may be involved with the mild pathogenicity of Md11/75C, contain 154 out of 184 unique Swiss-Prot accession numbers. Initially GO term annotation was performed for these two clustered subgroups to elucidate the overall characteristics of proteins that may be associated with Md11/75C viral pathogenicity. In total, 45% of the proteins were predicted to be involved with a variety of cellular processes in both groups (Figure S4A, Supporting Information). Moreover, proteins associated with “interactions with other cells or organisms”, “localization”, and “developmental process” displayed higher levels in clusters B and C, which represented the lower-expressed

Figure 5. Differential identification of proteins from proteome and phosphoproteome analyses of the MDV-infected CEF cells. The overall proteins identified by GeLC−MSE and ERLIC/IMAC/LC−MS/MS analysis were examined for their disparities. There were 450 unique proteins found in both methods in which only 100 were determined to be phosphorylated.

Table 2. Number of Unique Phosphorylated and Nonphosphorylated Peptides Identified in Mock- and Virus-infected Samples using ERLIC/IMAC/LC−MS/MS Analysis

Mock Md11/75C FC126 301B Total

unique host phosphopeptides

unique viral phosphopeptides

unique host nonphosphopeptides

unique viral nonphosphopeptides

total

% phosphopeptides

451 445 361 344 1127

0 26 46 24 97

812 313 1070 745 1787

0 5 28 2 35

1263 789 1505 1115 3046

36 60 27 33 40

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Table 3. Molecular Pathways Mapped for Data Derived from Both GeLC−MSE and ERLIC/IMAC/LC−MS/MS Analysis Methods using the Reactome Pathway Database (p < 0.05) qualitatively matched data

P-value

Metabolism of proteins Protein folding Chaperonin-mediated protein folding Cooperation of Prefoldin and TriC/CCT in actin and tubulin folding Metabolism of carbohydrates

pathway (Gallus gallus, Reactome)

30/89 16/28 16/23 16/22 19/68

3.6 × 10−11 1.7 × 10−11 1.9 × 10−13 6.3 × 10−14 5.1 × 10−7

Muscle contraction Smooth muscle contraction Axon guidance

20/59 14/21 31/194

4.8 × 10−4 1.2 × 10−5 6.8 × 10−5

Gene expression Translation tRNA aminoacylation Cytosolic tRNA aminoacylation F-actin capping protein binds to elogating F-actin

32/268 7/14 8/34 8/23 4/4

8.8 × 10−3 3.9 × 10−4 3.9 × 10−3 2.2 × 10−4 1.8 × 10−3

8/49 8/34 5/25 4/10 6/31

3.5 × 10−2 3.9 × 10−3 4.1 × 10−2 5.3 × 10−3 3.1 × 10−2

13/78

1.7 × 10−2

Innate immune response mediated by toll like receptors TLR3 cascade Membrane trafficking Golgi to ER retrograde transport Pyruvate metabolism and citric acid (TCA) cycle Apoptosis

provided valuable insight. For our final analysis, the Reactome database was used for primary pathway mapping of quantitative and qualitative proteomic data, and the remaining gaps were filled in with the KEGG and InnateDB database analyses. For qualitative data, protein IDs, which were converted to UniProt accession numbers, including those identified from GeLC−MSE fractions and those found to be phosphorylated in ERLIC/IMAC/LC−MS/MS analysis, were imported to the Reactome Web site for pathway analysis. A total of 891 protein entries were uploaded and 434 pathway events were matched from 238 uploaded protein entries. For this data set, which only contained information regarding the presence or absence of a protein, 16 pathways were assigned with a probability higher than 98% (Table 3). Due to the unexpected low number of matches in the Reactome chicken database, the same set of data converted to human and mouse gene identifiers was uploaded to InnateDB for human and mouse database searches and returned results with significantly more matches although many of them are redundant owing to the multiple databases used. A list of possible pathways derived from the InnateDB algorithm is provided in Table S3, Supporting Information, and demonstrates that the lack of a chicken knowledge-based database drastically reduces the overall matches and impedes a more comprehensive interpretation of the proteomic data via pathway analysis. Lastly, these qualitative data were also uploaded to the KEGG genome Web site for pathway mapping against the chicken database directly using the NCBI GI numbers as identifiers. Despite these limitations, some interesting insights were revealed. An example of a highly matched pathway, RNA transportrelated network, is presented in Figure 6A and demonstrates the proteome coverage of our data and the measurable changes in protein levels upon different MDV infections. After overlaying the quantification data on the top of the qualitative data, it was revealed that several proteins, which undergo changes in abundance, are located in the translation initiation complex

proteins responding to Md11/75C infection, while in clusters A and H, more proteins are related to “regulation”. The cellular component GO term comparison between the two subgroups showed increased abundance in the proteins located in the “plasma membrane”, “other intracellular compartments”, “cytoskeleton”, “ER and ribosome” for the B and C clusters (Figure S4B). A drastic increase in nuclear protein abundance for the A and H clusters are also worth noting. In terms of molecular functions, proteins involved in “binding” played important roles in both groups but seemed more dominant in the Md11/75C lower abundance group (Figure S4C). One has to keep in mind that the proteins of decreased abundance in the B and C clusters do not necessarily mean these proteins are irrelevant to the pathogenicity; on the contrary, these proteins may influence viral activities in negatively regulated loops. Due to the limited resources for chicken protein network analysis, the proteomic data were interpreted using crossreferenced, multiple databases containing chicken, human and mouse knowledge-based information. Among all the bioinformatic tools available online, Reactome (www.reactome.org) is one that provides a chicken protein interaction and pathway database although a portion of the data entries were referenced from human orthologs. This program uses protein entries with or without expression values for pathway mapping and visualization. The KEGG genome Web site (www.genome.jp/kegg/) allows chicken specific pathway mapping using experimental-derived protein or DNA data, but no statistical analysis is provided for the output search results. InnateDB (www.innatedb.ca) is another user-friendly online tool for quantitative as well as qualitative data analysis incorporating seven different pathway databases for data mapping. Since InnateDB only contains human and mouse databases, a series of conversions from the chicken to the human and mouse orthologs limited the analysis to about 30% of the entire combined proteomic data set, however the comprehensive coverage of pathway information obtained from InnateDB still 5670

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Figure 6. Example of a highly mapped pathway from the combined qualitative and quantitative data derived from the proteome and phosphoproteome analysis of virus-infected CEF cells. (A) Highly abundant proteins found in the master pathway of RNA transport from the GeLC−MSE data set. Infected CEF samples are framed with the designated colors: mock (gold), Md11/75C (dark blue), CVI988 (light blue), FC126 (green) and 301B (pink). Phosphorylation is highlighted by an encircled P with corresponding filled-in colors as previously indicated. The proteins and/or phosphorylation events identified to have a higher abundance in more than two samples are in red. (B) Group of four proteins expressing the same overall abundance pattern peaking for the Md11/75C strain of MDV. The x-axis indicates the infection condition and the y-axis represents the z-score intensity measured by GeLC−MSE analysis. The proteins profiled are: the eIF2 gamma subunit (open diamond), eIF4H (closed triangle), eIF5A2 (closed circle) and the PABPC4 (closed square).

(Figure 6A) and the spliceosome (Figure S5, Supporting Information). A number of phosphorylation changes were also detected in various translation initiation factors, nuclear pore complex, RNA transport, and signal transduction including

MAPK and mTOR pathways. In addition, an interesting pattern for a group of proteins that plays various roles in translation initiation and was previously reported to be involved in either virus pathogenesis or cell transformation was also observed as 5671

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infected cells when a phosphotyrosine (Y35) was identified with a phosphorylation at T47 on the same peptide, indicating that a double-phosphorylation event had occurred. Although T37 in human had been identified to be phosphorylated in the initial phosphorylation step, there was no detectable phosphorylation in the homologous residue T38 in chicken 4E-BP1. In Md11/ 75C- and 301B-infected cells, phosphorylation on T38 was still absent, but an additional phosphoserine at S74 was observed in both samples. Despite the extensive phosphopeptide fractionation and enrichment, it is interesting that phosphorylation at T38 in chicken 4E-BP1 was not detected since T37 in human has been consistently reported to be highly phosphorylated. The fact that the T47 was consistently identified as phosphorylated in the three infected CEF samples multiple times and considering that it should be colocalized with T38 on the same tryptic peptide, it is unlikely that the phosphopeptide was not enriched during sample preparation if the phosphorylation event was indeed happening at a substantial level at T38. The best explanation is that the phosphorylation on T38 does not occur or does so at an undetectable level. This is similar to the situation for phosphorylation of residue S66 in chicken 4E-BP1 which has been shown to be essential as S65 in human for dissociation of 4E-BP1 from eIF4E.57,58 In contrast, phosphorylation at residue T71 for chicken 4E-BP1 was identified multiple times across the four samples both in the mock- and virus-infected cells. Considering the analytical difficulties of detecting a doubly phosphorylated peptide, we were still able to detect the same peptide with another phosphorylation site at S74 in two of the four samples. The product ions clearly indicate both phosphorylated residues of the peptide (Figure S6, Supporting Information) and strongly suggest that there are indeed unique differences regulating the phosphorylation of 4E-BP1 in chicken cells after MDV infection. Since the phosphorylation sites identified in the chicken samples are rather novel, a phosphorylation site prediction analysis was conducted on the NetPhos 2.0 Server using the 4EBP1 protein sequence.60 The output of the prediction results show that most of our LC−MS/MS identified phosphorylation sites were calculated to be successfully assigned (score > 0.5) except for S74 which elicited a score just below the cutoff threshold (Figure S7, Supporting Information). However, after manually examining the four spectra assigned to be the phosphopeptide with S74 phosphorylation, two of them contained distinctive product ions pinpointing S74 as the phosphorylation site while the other two spectra provided less informative but still distinguishable product ions to indicate that S74 was phosphorylated. Compared to the neighboring T71, phosphorylation at S74 displayed a lower frequency of identification, which may indicate a lower abundance of this phosphopeptide, although the fragmentation preference for T71 with a C-terminal proline residue may also contribute to this bias. Interestingly, the measured abundance of eIF4E, the binding partner of 4E-BP1, shows a correlation with the degree of 4E-BP1 phosphorylation status during viral infection across all MDV strains analyzed. As shown in Figure 7B, the samples with a higher degree of phosphorylated 4E-BP1, especially those containing phosphorylation at S74, were also detected with a higher abundance of eIF4E. Due to the fact that the measured abundance of eIF4E in our system was comprised of at least three different formsthe free eIF4E, the cap-bound eIF4E and the 4E-BP-bound eIF4Ethe abundance change of eIF4E may be explained as (1) more free eIF4E was released and thus was

shown in Figure 6B. The four proteins extracted from the master data set all exhibit high abundance in Md11/75C-infected cells compared to the other samples. Among these, eIF5A2 was reported to be highly relevant to cellular oncogenesis42−44 while the cytoplasmic poly(A)-binding proteins (PABPC) were thought to be involved in some virus-mediated mRNA processing to alter the RNA turnover rate in the host.45−47 Regarding the fact that our data mapping required a series of protein ID conversions and that some of the proteins may be identified as different isomers that were not included by the program, it is reasonable to assume that there would be more proteins to be mapped to these pathways and complexes if our knowledge for chicken protein interactions was more extensively characterized.

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DISCUSSION

Differential Phosphorylation of 4E-BP1 upon Virus Infection

The infection of virus often leads to the hijacking of the host translational machinery to benefit viral replication and propagation. Some viruses manage to increase host protein synthesis activities to enhance viral protein production via a variety of strategies.48,49 A well-known mechanism involves competitive binding of the translation initiation factor complex eIF4G, a scaffolding component of eIF4F, and the regulatory 4Ebinding protein (4E-BP) to host translational initiation factor eIF4E, which is an mRNA cap-binding protein. 4E-BP acts as a translational repressor and sequesters eIF4E when the former is hypophosphorylated to prevent the assembly of eIF4F complex. Several studies have reported that the phosphorylation of 4EBP,50−54 as well as other translational initiation factors,55 dramatically affects viral replication and pathogenicity in herpesvirus-infected cells. Research conducted by multiple groups indicates variations on the phosphorylation sequences identified for inducing eIF4E dissociation. Sonenburg’s group has proposed that the regulation of 4E-BP phosphorylation is through a two-step process: initial phosphorylation events on the N-terminus of 4E-BP in a FRAP/mTOR-dependent regulation that may be modulated by external stimuli followed by subsequent phosphorylation events in the C-terminus of the protein for inducing dissociation from eIF4E.56,57 It was speculated that the first set of phosphorylation events may direct the second set of phosphorylation events by recruiting cognate kinases. Mass spectrometry data along with two-dimensional isoelectric focusing/SDS-PAGE and immunoblotting with phosphospecific antibodies pinpointed the phosphorylation sites at T37 and T46 of human 4E-BP1 for the prerequisite phosphorylations and the following phosphorylations at S65 and T70 upon serum stimulation, all of which contain the consensus Ser/Thr-Pro motif.56,57 These experimental data do not unanimously agree with other groups on the order of some of the phosphorylation events, possibly due to the variation of experimental conditions and stimuli. For example, Lawrence’s group demonstrated that the phosphorylation on S65 depends on the phosphorylation of T37/T46, T70 and S112 upon amino acid and insulin stimulation,58 and a similar sequence was observed by Ayuso et al. in ischemia-reperfusion stressed brain tissue as well.59 In our phosphoproteome analysis, however, we consistently identified T47 and T71 of chicken 4E-BP1, homologous residues of human T46 and T70, respectively, which also are contained in the consensus Ser/Thr-Pro sequence (Figure 7A and Figure S6, Supporting Information). An exception occurred for the FC1265672

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Figure 7. Proteomic changes in 4E-BP1 and eIF4E caused by MDV infection. (A) Phosphorylation sites of 4E-binding protein 1 (4E-BP1) identified in the infected CEF cells. The sequence coverage for this protein in the FC126-infected sample is shown in yellow along with the phosphorylated T46 and T71 residues in green. The schematic illustration of the phosphorylation sites for each sample is shown in the first column of the table with phosphorylated residues presented in vertical green bars and other residues presented in vertical yellow bars. (B) Abundance changes for eIF4E (GI:118090396) across all the samples measured by GeLC−MSE analysis. The x-axis indicates the infection condition and the y-axis represents the zscore intensities. From left to right: mock, Md11/75C, CVI988, FC126, and 301B- infected cells. (C) The proposed pathway regulating the assembly of the translation initiation complex by MDV proteins that presumably activate mTOR-mediated phosphorylation of 4E-BP1. The overall abundance measured for eIF4E reflects at least three forms: the free form, the cap-bound form and the 4E-BP1-bound form ([4E]measured = [4E]free + [4E]cap‑bound + [4E]BP‑bound).

edu/), thus suggesting that 4E-BP phosphorylation may play a regulatory role for eIF4E . Although the upstream cascade regulating human 4E-BP1 phosphorylation has been extensively studied, very little has been concluded regarding its role in virus infections. A few reports suspected that the increase of phosphorylation was due to the blockage of the activity of host antiviral protein tuberous sclerosis complex (TSC) by HSV-1 serine/threonine protein kinase Us3, 48,54 since it was prevalently recognized that the

prevented from being removed from the pool of the translational machinery in an unknown mechanism as indicated in Figure 7C, or (2) the infection indeed elicited eIF4E gene expression and the abundance of eIF4E was not directly linked to the phosphorylation status of 4E-BP1. However, previous microarray studies of HVT infected CEF cells did not show significant changes of the eIF4E mRNA expression in the course of their experimental design (within 72 h postinfection)61 or in the plaques caused by virus infection (http://www.chickest.udel. 5673

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MSE analysis indicated that proteins involved with “interaction with cells and organisms” and “developmental process”such as PDZ and LIM domain protein 7, different isoforms of actins, integrin and Thy-1 antigendisplayed reduced abundance in the cell-culture attenuated, oncogenic strain Md11/75C infection. In contrast, proteins involved in “regulation” and “located in the nucleus”such as annexin A, ubiquitinationrelated proteins, serine/threonine protein phosphatase, and heat shock protein 70displayed higher abundance in the Md11/ 75C-infected cells. Our data suggest two possibilities to explain the differences caused by Md11/75C compared to the other vaccine strains. The first possibility can be attributed to reducing the interaction/communication between the infected and the uninfected cells, which implies that poorer antigen-presenting signals may be elicited upon infection with the more virulent strain and thus the innate immune response may be delayed or diminished. For the second possibility, alterations in signal transduction and transcription cascades could be induced which enables the virus to manipulate host gene expression. Apart from the fact that the secreted cytokines, which compose the majority of the changes in gene expression upon infection, are not easily detected by cellular proteomics, our proteomics data still did not correlate very well with the transcriptomic data. This could possibly be due to the infection time course and the lack of the consideration for protein degradation processes in microarray studies. Considering that certain proteins under various dynamic changes during a very short period of time, and the lack of proper synchronization of cell status regarding the progression of virus infection, which is difficult to accomplish because MDV is a cell-associated virus, only the dramatic changes that occurred at the moment that the cells were harvested can be traced. Although our studies seem limited, an interesting snapshot of the moment is observed in the initiation of the protein translation. Other than the phosphorylation changes regarding 4E-BP1 mentioned earlier, a group of four different proteins displayed a similar abundance pattern across all five infected CEF samples (Figure 6B). These proteins have been determined to regulate translation initiation and include the eIF2 gamma subunit, eIF4H, eIF5A2, and PABPC4. The eIF2 gamma subunit promotes the ternary complex with GTP and Met-tRNA to recruit the binding of the ribosome 40S subunit71 and is related to the start codon selection for protein synthesis.72,73 Through a functional genomics approach, eIF2 gamma was once reported as a cofactor of hepatitis C virus internal ribosome entry sitemediated translation.74 The factor eIF4H enhances the eIF4A helicase activity and was reported to form a tripartite complex with virion host shutoff protein (Vhs) and eIF4A to regulate the stability of host mRNAs.75 It is also worth noting that eIF5A2 was found to be overexpressed in highly aggressive carcinoma cells42,76 and was regarded as an adverse prognostic marker in some cancer patients.43,77 PABPC4 is a cytoplasmic poly(A)binding protein, also called an inducible PABP, and was thought to regulate telomerase activities, RNA stabilities and cell growth in some virus-infected cells.45,46 Together, these observations imply that these proteins, their binding partners, or other proteins that regulate their expression and degradation, may represent therapeutic targets for a future herpesvirus vaccine or drug development. Hence, the data set derived from combining both proteomic and phosphoproteomic strategies of MDVinfected CEF cells is a starting point for future biological investigations to further elucidate the key factors for establishing the defensive response against viral infection.

phosphorylation of 4E-BP1 is through an mTOR-sensitive pathway that can be regulated by Akt/protein kinase B and its downstream TSC62,63 as shown in Figure S8, Supporting Information. Chuluunbaatar et al. noticed the depletion of the Us3 kinase gene resulted in a remarkable increase of hypophosphorylated 4E-BP1 which in turn, diminished viral replication, but the depletion of TSC2 can rescue Us3-deficient viral replication.54 Similar reports have not been found anywhere regarding MDV infection, thus whether the same kinase cascade can take place in chicken cells remains to be determined. In the current study, only one phosphopeptide of the Us3 protein from FC126-infected cells was identified by our ERLIC/IMAC/LC− MS/MS approach, but similar proteins were not identified and quantified in the GeLC−MSE analysis. Therefore, no speculation can be made regarding the possible role of Us3 to produce the differential phosphorylation status of 4E-BP1 in different MDVinfected cells. While considering the phosphoproteomic changes upon biological perturbations, it is ideal to compare data with protein abundance changes to the data enriched from the samples with identified phosphorylation sites in order to achieve a more comprehensive analysis. However, due to the wide dynamic range of protein abundances and the low stoichiometries of phosphorylation events, this goal is nearly impossible to achieve even when extensive fractionation is employed beyond those described in this study. Figure S8, Supporting Information, shows the mTOR signaling pathway that was mentioned earlier involving the phosphorylation of 4E-BP1. Several proteins that occupy different areas of the pathway have been identified by the quantitative GeLC−MSE or the qualitative phosphopeptideenriched measurements, however, no proteins were found in both data sets. This indicates that the two approaches combined provide better characterization for this pathway, but the lack of complementarity prevents a direct link between the change in protein abundance and its phosphorylation. Therefore, a more specific coimmunoprecipitation study may be a better way to simultaneously quantify and enrich for specific interactive partners of interest for more detailed characterization including the identification of phosphorylation sites. Thus, the proteome analysis conducted in this study serves as an initial entry point for identifying candidates for future biochemical investigations. Host Proteins Undergo Abundance Changes upon Infection with Various Strains of MDV

CEF cells are known to be a good host for virus propagation and replication in vitro. One role of fibroblasts in the early stage of virus infection at least partly involves antigen presentation64−66 and the production of cytokines (e.g., interferons)67−69 as the immediate alarm or barrier against pathogen invasion, whereas viruses utilize their own unique proteins to turn the host into a propagation-friendly environment. From the transcriptome analysis conducted by several laboratories, infection of CEF cells with either the oncogenic (vvMDV)70 or the HVT61 strain of MDV revealed unique differential expression of host genes. According to Morgan et al., the infection of CEF with vvMDV results in the increased expression of genes related to inflammation, cell growth, antigen presentation and interferonmediated responses;70 on the other hand, infection with HVT results in higher expression of signal transduction and transcription, cytoskeleton, apoptosis, and immune response while interferon-mediated genes also exhibited altered behaviors especially interferon-γ and its inducible genes.61 GO annotation of our comprehensive quantitative data derived from GeLC− 5674

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avirulent Marek’s disease virus (strain CVI 988) and its use in laboratory vaccination trials. Avian Dis. 1972, 16, 108−25. (6) Rispens, B. H.; van Vloten, H.; Mastenbroek, N.; Maas, J. L.; Schat, K. A. Control of Marek’s disease in the Netherlands. II. Field trials on vaccination with an avirulent strain (CVI 988) of Marek’s disease virus. Avian Dis. 1972, 16, 126−38. (7) Chien, K.; Liu, H.; Goshe, M. B. Development and application of a phosphoproteomic method using electrostatic repulsion-hydrophilic interaction chromatography (ERLIC), IMAC, and LC−MS/MS analysis to study Marek’s Disease Virus infection. J. Proteome Res. 2011, 10, 4041−53. (8) Witter, R. L. Attenuated revertant serotype 1 Marek’s disease viruses: safety and protective efficacy. Avian Dis. 1991, 35, 877−91. (9) Witter, R. L. Protection by attenuated and polyvalent vaccines against highly virulent strains of Marek’s disease virus. Avian Pathol. 1982, 11, 49−62. (10) Witter, R. L.; Silva, R. F.; Lee, L. F. New serotype 2 and attenuated serotype 1 Marek’s disease vaccine viruses: selected biological and molecular characteristics. Avian Dis. 1987, 31, 829−40. (11) Witter, R. L.; Lee, L. F. Polyvalent Marek’s disease vaccines: safety, efficacy and protective synergism in chickens with maternal antibodies. Avian Pathol. 1984, 13, 75−92. (12) Biggs, P. . M.; Milne, B. S. Biological properties of a number of Marek’s disease virus isolates. In Oncogegesis and Herpesviruses; International Agency for Research on Cancer: Lyon, France, 1972; pp 88−94. (13) Jurajda, V.; Halouzka, R. Isolation and study of biological properties of non-oncogenic Marek’s disease herpesviruses in chickens. 1. Characterization in vitro. Vet. Med. 1992, 37, 531−4. (14) Witter, R. L. New serotype 2 and attenuated serotype 1 Marek’s Disease vaccine viruses: comparative efficacy. Avian Dis. 1987, 31, 752. (15) Witter, R. L. Control strategies for Marek’s Disease: a perspective for the future pathotypes and evolution of MD vaccines: current technology. Poult. Sci. 1998, 77, 1197−1203. (16) Majerc, V.; Ney, E.; Kingham, B. F.; Kopa, J.; Schmidt, C. J. The genome of herpesvirus of turkeys: comparative analysis with Marek ’ s disease viruses. J. Gen. Virol. 2001, 82, 1123−1135. (17) Osterrieder, N.; Kamil, J. P.; Schumacher, D.; Tischer, B. K.; Trapp, S. Marek’s disease virus: from miasma to model. Nat. Rev. Microbiol. 2006, 4, 283−94. (18) Sondermeijer, P. J.; Claessens, J. A.; Jenniskens, P. E.; Mockett, A. P.; Thijssen, R. A.; Willemse, M. J.; Morgan, R. W. Avian herpesvirus as a live viral vector for the expression of heterologous antigens. Vaccine 1993, 11, 349−58. (19) McMillen, J. K.; Cochran, M. D.; Junker, D. E.; Reddy, D. N.; Valencia, D. M. The safe and effective use of fowlpox virus as a vector for poultry vaccines. Dev. Biol. Stand. 1994, 82, 137−45. (20) Nazerian, K.; Witter, R. L.; Lee, L. F.; Yanagida, N. Protection and synergism by recombinant fowl pox vaccines expressing genes from Marek’s disease virus. Avian Dis. 1996, 40, 368−76. (21) Ross, L. J.; Binns, M. M.; Tyers, P.; Pastorek, J.; Zelnik, V.; Scott, S. Construction and properties of a turkey herpesvirus recombinant expressing the Marek’s disease virus homologue of glycoprotein B of herpes simplex virus. J. Gen. Virol. 1993, 74 (Pt 3), 371−7. (22) Ross, N.; O’Sullivan, G.; Coudert, F. Influence of chicken genotype on protection against Marek’s disease by a herpesvirus of turkeys recombinant expressing the glycoprotein B (gB) of Marek’s disease virus. Vaccine 1996, 14, 187−9. (23) Tarpey, I.; Davis, P. J.; Sondermeijer, P.; van Geffen, C.; Verstegen, I.; Schijns, V. E. J. C.; Kolodsick, J.; Sundick, R. Expression of chicken interleukin-2 by turkey herpesvirus increases the immune response against Marek’s disease virus but fails to increase protection against virulent challenge. Avian Pathol. 2007, 36, 69−74. (24) Lambeth, L. S.; Zhao, Y.; Smith, L. P.; Kgosana, L.; Nair, V. Targeting Marek’s disease virus by RNA interference delivered from a herpesvirus vaccine. Vaccine 2009, 27, 298−306. (25) Lee, L. F.; Lupiani, B.; Silva, R. F.; Kung, H.-J.; Reddy, S. M. Recombinant Marek’s disease virus (MDV) lacking the Meq oncogene

CONCLUSION In this study, approaches to conduct a comprehensive proteomic analysis implementing quantitative GeLC−MSE for global proteome identification and qualitative phosphoproteome profiling by ERLIC/IMAC/LC−MS/MS were used to study changes in CEF cells infected with virulent or vaccine strains of MDV. Within the 1002 unique proteins and 539 phosphoproteins identified by both approaches, which include 1127 phosphopeptides, several pathways were well characterized such as RNA transport and protein synthesis which represent the most significant changes detectable upon virus infection. For further investigations, stable isotope-coded approaches may be required to process samples under the same conditions in order to render simultaneous protein and phosphopeptide quantification for between sample comparisons. The comprehensive protein analysis used in this study provides several interesting candidates including eIF4E and 4E-BP1for further analysis, such as quantifying their phosphorylation dynamics and their interaction networks using targeted affinity purification.

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ASSOCIATED CONTENT

S Supporting Information *

List of all peptide identifications for the quantitative proteomics and qualitative phosphoproteomics and additional analysis results mentioned in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Department of Molecular and Structural Biochemistry, North Carolina State University, 128 Polk Hall, Campus Box 7622, Raleigh, NC 27695-7622. Phone: 919-513-7740. Fax: 919-5152047. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the United States Department of Agriculture (NRI 2005-35604-15420 and NRI 2006-35204-17351) and a Major Research Instrumentation grant from the National Science Foundation (DBI-0619250). We thank the research agencies of North Carolina State University and the North Carolina Agricultural Research Service for continued support of our infectious disease and bioanalytical mass spectrometry research. We also thank Dr. Mark Burke and Dr. Sarah Schwartz at DHMRI for providing access to Rosetta Elucidator and assistance in data uploading.

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