Quantitative Proteomics Using Stable Isotope Labeling with Amino Acids in Cell Culture Reveals Changes in the Cytoplasmic, Nuclear, and Nucleolar Proteomes in Vero Cells Infected with the Coronavirus Infectious Bronchitis Virus

Abstract

Virus-host interactions involve complex interplay between viral and host factors, rendering them an ideal target for proteomic analysis. Here we detail a high throughput quantitative proteomics analysis of Vero cells infected with the coronavirus infectious bronchitis virus (IBV), a positive strand RNA virus that replicates in the cytoplasm. Stable isotope labeling with amino acids in cell culture (SILAC) was used in conjunction with LC-MS/MS to identify and quantify 1830 cellular and two viral proteins from IBV-infected cells. Fractionation of cells into cytoplasmic, nuclear, and nucleolar extracts was used to reduce sample complexity and provide information on the trafficking of proteins between the different compartments. Each fraction showed a proportion of proteins exhibiting ≥2-fold changes in abundance. Ingenuity Pathway Analysis revealed that proteins that changed in response to infection could be grouped into different functional categories. These included proteins regulated by NF-κB- and AP-1-dependent pathways and proteins involved in the cytoskeleton and molecular motors. A luciferase-based reporter gene assay was used to validate the up-regulation of AP-1- and NF-κB-dependent transcription in IBV-infected cells and confirmed using immunofluorescence. Immunofluorescence was used to validate changes in the subcellular localization of vimentin and myosin VI in IBV-infected cells. The proteomics analysis also confirmed the presence of the viral nucleocapsid protein as localizing in the cytoplasm, nucleus, and nucleolus and the viral membrane protein in the cytoplasmic fraction. This research is the first application of SILAC to study total host cell proteome changes in response to positive sense RNA virus infection and illustrates the versatility of this technique as applied to infectious disease research.

Virus infection and the host cell response involve a complex interplay of cellular and viral networks. Many viruses attempt to subvert host cell processes to increase the efficiency of virus infection, and likewise the cell utilizes a number of responses to generate an antiviral state. Previous studies have generally targeted defined cellular and viral pathways, and quantitative high throughput proteomics has not yet been applied widely to the study of virus infection in host cells. Coronaviruses are no exception to this, and infection has been demonstrated to result in the subversion of specific host cell processes, for example the cell cycle (1–4), apoptosis (5–7), and suppression of interferon (8, 9).

Coronaviruses generally cause respiratory and gastrointestinal infections in humans and animals. Examples include severe acute respiratory syndrome coronavirus (SARS-CoV),¹ which infects humans and animals (10), and infectious bronchitis virus (IBV), a disease of poultry that causes significant morbidity and mortality (11). Coronaviruses are characterized by having the largest positive sense single-stranded RNA genome in the virome and a replication process in the cytoplasm that involves discontinuous transcription during negative strand synthesis (12). Thus, upon infection, the genome acts as a messenger RNA for the synthesis of the virus RNA replicase polyprotein, which then transcribes a nested set of virus subgenomic mRNAs and replicates genomic RNA. With some exceptions, the order of a gene along the coronavirus genome determines its mRNA abundance with the 3′-most genes (encoding the nucleocapsid (N) and membrane (M) proteins) having the most abundant mRNAs and the 5′-most genes (encoding the viral replicase) having the least abundant mRNAs. Viral protein abundance generally correlates with the abundance of the corresponding mRNA. Coronavirus replication is associated with membranes (13) and centered on double membrane vesicles that form in the cytoplasm of infected cells through the induction of autophagy (14, 15), a cellular stress response. Virus assembly occurs in the ER-Golgi intermediate compartment (16). As well as localizing to sites of replication, coronavirus proteins have been shown to target discrete cellular structures, such as the nucleolus (17) and mitochondria (18). In the case of the nucleolus, the observation of localization of a viral protein (N protein) to this structure was initially thought unusual given the site of viral RNA replication and that transcription of the virus is cytoplasmic. The N protein is a viral RNA-binding protein (19–21) and chaperone (22). However, many viruses target proteins to the nucleolus in many cases independent of the site of genome replication, and examples can be found from DNA, RNA, and retroviruses (23–25).

Proteomics has been applied to the interaction of coronaviruses with the host cell. For example, in SARS-CoV-infected cells, two-dimensional electrophoresis followed by ESI-MS/MS and isotope-coded affinity tag technology coupled to two-dimensional LC-MS/MS identified and quantified 355 proteins with 186 proteins showing a 1.5-fold or greater alteration between infected and mock-infected cells (26). In this study, high throughput quantitative proteomics using stable isotope labeling with amino acids in cell culture (SILAC) was used to investigate whether the Vero cell proteome changed in coronavirus-infected cells using IBV. Vero cells are commonly used in the propagation and study of many different animal and human pathogens, including SARS-CoV (27), respiratory syncytial virus (28), influenza virus (29), parainfluenza virus type 5 (30), and eukaryotic parasites (31). Vero cells are interferon-deficient and do not secret type 1 interferon when infected by viruses (they still have the interferon α/β receptor). However, this allows productive growth of pathogens and infection and reduces the bystander effect in uninfected cells. Given their common use in investigating host-pathogen interactions, antiviral screening (32), and vaccine production (33), a map of the Vero cell proteome is advantageous. To reduce sample complexity and to study the interaction of IBV with different regions of the Vero cell, the cytoplasmic, nuclear, and nucleolar proteomes were analyzed. Many cellular proteins were shown to increase and decrease in abundance in response to IBV infection. Bioinformatic analyses of protein networks and pathways predicted that signaling cascades could be induced in IBV-infected cells, and this was validated experimentally. To our knowledge, this research is the first application of SILAC to study total host cell proteome changes in response to positive sense RNA virus infection and illustrates the versatility of this technique as applied to infectious disease research.

EXPERIMENTAL PROCEDURES

Virus and Cells

Vero cells were grown at 37 °C with 5% CO₂ in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum and penicillin/streptomycin. IBV (Beaudette-United States strain) was propagated in Vero cells, and the virus was harvested at 24 h postinfection. Virus titer was calculated by plaque assay titration in Vero cells. For SILAC experiments, cells were grown in media containing either unlabeled arginine and lysine amino acids (R0K0) or containing ¹³C-labeled arginine and lysine amino acids (R6K6) and supplemented with 10% dialyzed fetal calf serum and penicillin/streptomycin for 10 rounds of cell division. Cells were then grown in 500-cm² dishes until 60% confluent and infected with virus/mock-infected at a multiplicity of infection (m.o.i.) of 1. All cells were confirmed to be Mycoplasma-free.

Cellular Fractionation into Cytoplasmic, Nuclear, and Nucleolar Fractions

Cellular fractionation was carried out as in Andersen et al. (34) with the number of sonication repeats increased to 10 repeats of 10 s on, 10 s off.

Gel Electrophoresis and In-gel Digestion

Each sample was reduced in SDS-PAGE loading buffer containing 10 mm DTT and alkylated in 50 mm iodoacetamide prior to being boiled, then separated by one-dimensional SDS-PAGE (4–12% Bis-Tris Novex minigel, Invitrogen), and visualized by colloidal Coomassie staining (Novex, Invitrogen). The entire protein gel lane was excised and cut into 10 gel slices each. Every gel slice was subjected to in-gel digestion with trypsin (35). The resulting tryptic peptides were extracted by 1% formic acid, acetonitrile; lyophilized in a SpeedVac (Helena Biosciences); and resuspended in 1% formic acid.

LC-MS/MS

Trypsin-digested peptides were separated using an Ultimate U3000 (Dionex Corp.) nanoflow LC system consisting of a solvent degasser, micro- and nanoflow pumps, flow control module, UV detector, and a thermostated autosampler. 10 ml of sample (a total of 2 mg) was loaded with a constant flow of 20 ml/min onto a PepMap C₁₈ trap column (0.3-mm inner diameter × 5 mm; Dionex Corp.). After trap enrichment, peptides were eluted off onto a PepMap C₁₈ nanocolumn (75 mm × 15 cm; Dionex Corp.) with a linear gradient of 5–35% solvent B (90% acetonitrile with 0.1% formic acid) over 65 min with a constant flow of 300 nl/min. The HPLC system was coupled to an LTQ Orbitrap XL (Thermo Fisher Scientific Inc.) via a nano-ES ion source (Proxeon Biosystems). The spray voltage was set to 1.2 kV, and the temperature of the heated capillary was set to 200 °C. Full-scan MS survey spectra (m/z 335–1800) in profile mode were acquired in the Orbitrap with a resolution of 60,000 after accumulation of 500,000 ions. The five most intense peptide ions from the preview scan in the Orbitrap were fragmented by collision-induced dissociation (normalized collision energy, 35%; activation Q, 0.250; and activation time, 30 ms) in the LTQ after the accumulation of 10,000 ions. Maximal filling times were 1000 ms for the full scans and 150 ms for the MS/MS scans. Precursor ion charge state screening was enabled, and all unassigned charge states as well as singly charged species were rejected. The dynamic exclusion list was restricted to a maximum of 500 entries with a maximum retention period of 90 s and a relative mass window of 10 ppm. The lock mass option was enabled for survey scans to improve mass accuracy (36). The data were acquired using Xcalibur software.

Quantification and Bioinformatics Analysis

Quantification was performed with MaxQuant version 1.0.7.4 (37) and was based on two-dimensional centroid of the isotope clusters within each SILAC pair. To minimize the effect of outliers, protein ratios were calculated as the median of all SILAC pair ratios that belonged to peptides contained in the protein. The percentage of variability of the quantitation was defined as the standard deviation of the natural logarithm of all ratios used for obtaining the protein ratio multiplied by a constant factor of 100.

The generation of the peak list, SILAC- and extracted ion current-based quantitation, calculated posterior error probability and false discovery rate based on search engine results, peptide to protein group assembly, and data filtration and presentation were carried out using MaxQuant. The derived peak list was searched with the Mascot search engine (version 2.1.04; Matrix Science, London, UK) against a concatenated database combining 80,412 proteins from the International Protein Index (IPI) human protein database version 3.6 (forward database) and the reversed sequences of all proteins (reverse database). Alternatively, database searches were done using Mascot (Matrix Science) as the database search engine, and the results were saved as a peptide summary before quantification using MSQuant (SourceForge). Parameters allowed included up to three missed cleavages and three labeled amino acids (arginine and lysine). Initial mass deviation of precursor ions and fragment ions were up to 7 ppm and 0.5 Da, respectively. The minimum required peptide length was set to 6 amino acids. To pass statistical evaluation, posterior error probability (PEP) for peptide identification (MS/MS spectra) should be below or equal to 0.1. The required false positive rate (FPR) was set to 5% at the peptide level. False positive rates or PEPs for peptides were calculated by recording the Mascot score and peptide sequence length-dependent histograms of forward and reverse hits separately and then using Bayes' theorem to derive the probability of a false identification for a given top scoring peptide. At the protein level, the false discovery rate was calculated as the product of the PEP of the peptides of a protein where only peptides with distinct sequences were taken into account. If a group of identified peptide sequences belongs to multiple proteins and these proteins cannot be distinguished with no unique peptide reported, these proteins are reported as a protein group in MaxQuant. Proteins were quantified if at least one MaxQuant-quantifiable SILAC pair was present. Identification was set to a false discovery rate of 1% with a minimum of two quantifiable peptides. The set value for FPR/PEP at the peptide level ensures that the worst identified peptide has a probability of 0.05 of being false, and proteins are sorted by the product of the false positive rates of their peptides where only peptides with distinct sequences are recognized. During the search, proteins are successively included starting with the best identified proteins until a false discovery rate of 1% is reached, an estimation based on the fraction of reverse protein hits. Enzyme specificity was set to trypsin allowing for cleavage N-terminal to proline and between aspartic acid and proline. Carbamidomethylation of cysteine was searched as a fixed modification, whereas N-acetyl protein and oxidation of methionine were searched as variable modifications.

Protein Pathway Analysis

Data were analyzed through the use of Ingenuity Pathways Analysis (Ingenuity® Systems). Networks were generated using data sets containing gene identifiers and corresponding expression values and uploaded into the application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. A cutoff of 2.0 was set to identify genes whose expression was significantly differentially regulated. These genes, called focus genes, were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Networks of these focus genes were then algorithmically generated based on their connectivity. Graphical representations of the molecular relationships between genes/gene products were generated. Genes or gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature or from canonical information stored in the Ingenuity Pathways Knowledge Base. Human, mouse, and rat orthologs of a gene are stored as separate objects in the Ingenuity Pathways Knowledge Base but are represented as a single node in the network. The intensity of the node color indicates the degree of up- (red) or down (green)-regulation. Nodes are displayed using various shapes that represent the functional class of the gene product.

Confocal Microscopy

Coverslips containing fixed samples were mounted onto slides using Vectorshield containing DAPI (Vector Laboratories) to allow visualization of cell nuclei. Confocal images were captured on an LSM510 META microscope (Carl Zeiss Ltd.) equipped with 40× and 63×, numerical aperture 1.4, oil immersion lenses. Pinholes were set to allow optical sections of 1 μm to be acquired. All fluorescence was measured in the linear range as the detector is a photomultiplier, and the range indicator was utilized to ensure that no saturated pixels were obtained on image capture. Images were averaged eight times. Live cell work was conducted as described (38).

Western Blot

Cellular fractions were obtained as detailed above. Total protein was assayed by BCA assay (Pierce). 5 mg of protein was resolved by 12% SDS-PAGE and transferred to PVDF membranes (Millipore) using a Bio-Rad semidry transfer apparatus. ECL detection was used to detect proteins.

Antibodies

IBV proteins were detected with chicken anti-IBV polyclonal serum (1:20,000; Charles River). Anti-RelA, -p50, -nucleolin, -fibrillarin, -myosin VI, and -tubulin antibodies were obtained from Abcam, and anti-lamin B was from Calbiochem. The anti-vimentin antibody was a kind donation from Tom Wileman (University of East Anglia). Horseradish peroxidase-conjugated secondary antibodies for Western blotting were obtained from Sigma, and Alexa Fluor-conjugated secondary antibodies (A488 and A568) were obtained from Molecular Probes (Invitrogen).

Assay for Induction of AP-1

80% confluent cell monolayers in 12-well dishes were co-transfected with 100 ng of AP-1 reporter plasmid and 10 ng of Renilla reporter plasmid per well using Lipofectamine 2000 (Invitrogen) according to the manufacturer's protocol in serum-free media. After 24-h incubation, the transfection mixture was replaced with either plain PBS (mock), IBV in PBS (m.o.i. of 5), or Sendai virus in PBS (100 hemagglutinin units/well). Plates were then rocked at 37 °C for 1 h before PBS was replaced with 400 μl of full media and incubated at 37 °C for 24 h. Cells were lysed and harvested according to the Promega Dual-Luciferase kit for the luciferase assay. Control plates of transfection mixture only and media only were included to rule out background reactivity (data not shown).

Assay for Detection of NF-κB

80% confluent cell monolayers in 12-well dishes were co-transfected with 100 ng of NF-κB or positive regulatory domain II (PRDII) reporter plasmid and 10 ng of Renilla reporter plasmid per well using Lipofectamine 2000 (Invitrogen) in serum-free media according to the manufacturer's instructions. After 24-h incubation, the transfection mixture was replaced with either plain PBS (mock), IBV in PBS (m.o.i. of 5), or Sendai virus in PBS (100 hemagglutinin units/well). Plates were then rocked at 37 °C for 1 h before PBS was replaced with 400 μl of full media and incubated at 37 °C for 24 h with 5% CO₂. Cells were lysed and harvested according to the Promega Dual-Luciferase kit for the luciferase assay. Control plates of transfection mixture only and media only were included to rule out background reactivity (data not shown).

RESULTS

Quantitative high throughput proteomics analysis using SILAC (Fig. 1A) was used to investigate the potential changes in the Vero cell proteome in response to IBV infection (Fig. 1B). To reduce sample complexity, maximize protein coverage, and reflect where different proteins can traffic, the cell was fractionated into samples enriched in cytoplasmic, nuclear, and nucleolar proteins (Fig. 1C). These samples were validated using antibodies to known cellular and viral proteins that were enriched in each fraction (Fig. 1D). β-Tubulin, a cytoplasmic marker protein, was present in the cytoplasmic fractions, absent in the nuclear fractions, but enriched in the nucleolar fraction from IBV-infected cells. Lamin B and fibrillarin, markers for the nucleus and nucleolus, respectively, were detected in their respective fractions. The IBV N protein was detected in all fractions prepared from infected cells. The viral M protein was present in the cytoplasmic fraction only. No proteins corresponding to the known molecular weights of viral proteins were detected in fractions prepared from mock-infected cells. In total, 1830 cellular proteins were both identified and quantified by LC-MS/MS. These were divided between the cytoplasmic (818 proteins; supplemental Table 1), nuclear (506 proteins; supplemental Table 2), and nucleolar fractions (506 proteins; supplemental Table 3). Where only single peptides hits were obtained for proteins, spectra have been included (supplemental Fig. 1). Also, for the nucleolar proteome, proteins identified by single peptides were included only if they were present in the human nucleolar proteome database (52). Two viral proteins were detected in this analysis, the N and the M proteins. The N protein was identified in the nuclear, cytoplasmic, and nucleolar proteomes, and the M protein was identified in the cytoplasm only (Fig. 2) and validated the Western blot data presented in Fig. 1D.

Fig. 1.

A, diagrammatic representation of the high throughput quantitative experimental approach taken in this study to investigate changes in the cellular proteome in response to infection with IBV. B, immunofluorescence analysis using confocal microscopy of IBV-infected cells, examples of which are marked with arrows. C, cytoplasmic (C), nuclear (Nu), and nucleolar (No) proteomes purified from mock- (−) and IBV-infected (+) cells. Molecular mass is shown to the left. D, Western blot analysis of the cytoplasmic (C), nuclear (Nu), and nucleolar (No) extracts using antibodies to specific markers for each fraction, tubulin-1 for the cytoplasm, lamin B-1 for the nucleus, and fibrillarin for the nucleolus.

Fig. 2.

Amino acid sequence of IBV N and M protein sequences showing peptide coverage (bold and italic) on proteins identified from nuclear, cytoplasmic, and nucleolar extracts. Mascot score and percent peptide coverage are provided.

For quantitative analysis, previous investigations using SILAC have applied ratio cutoffs ranging from near 1.0- to 2-fold (39, 40). An analysis investigating changes in cellular proteins in SARS-CoV-infected cells by two-dimensional gel electrophoresis used a ratio of 1.5-fold as a basis for comparison (26). In this study, a 2.0-fold cutoff was used to identify proteins whose abundance may have changed in IBV-infected cells. However, as can be seen from Fig. 1B, not all cells were infected with IBV. A known feature of IBV infection is that virus spreads through initiating new foci of infection and cell-to-cell fusion (41). Thus, infection is not synchronous, and not all cells will be infected. Hence, changes in the cellular proteome in response to IBV infection in cell culture may be under-representative of what is occurring in an individual cell. Comparison between the mock- and IBV-infected cells indicated for all three proteomes that certain proteins changed in abundance. In the cytoplasmic proteome, one protein (0.1%) showed ≥2.0-fold reduction in the cytoplasm of IBV-infected cells, and 66 proteins (8.1%) showed a ≥2.0-fold increase in the cytoplasm of IBV-infected cells (Table I). In the nuclear proteome, 35 proteins (6.9%) showed ≥2.0-fold reduction in the nucleus of IBV-infected cells, and five proteins (1.0%) showed ≥2.0-fold increase in the nucleus of IBV-infected cells (Table II). In the nucleolar proteome, 12 proteins (2.4%) showed ≥2.0-fold reduction in the nucleolus of IBV-infected cells, and 22 proteins (4.5%) showed ≥2.0-fold increase in the nucleolus of IBV-infected cells (Table III).

Table I. Cytoplasmic proteins identified by LC-MS/MS demonstrating ≥2-fold change in abundance in response to IBV infection.

Notes on protein function were obtained from annotations in the UniProt database. ssDNA, single-stranded DNA; dsDNA, double-stranded DNA.

Protein ID	Name	Ratio	Peptides	Sequence coverage	PEP	Function
				%
Proteins present in increased abundance in IBV-infected cells
IPI00299554	KIF14	121.5	13	0.7	0.030844	Essential for cytokinesis
IPI00910009	cDNA FLJ53554	119.4	15	6.6	0.017233	Unknown
IPI00152881	Shroom3	21.8	7	0.5	0.021639	Cell morphology
IPI00289501	VGF	4.4	5	10.4	3.51 × 10−11	Regulation of cell-cell interactions
IPI00902514	Histone H2A	4.3	3	11.2	0.013631	DNA binding, nucleosome component
IPI00876941	Caveolin 1 variant	4.1	3	13.4	6.75 × 10−11	A scaffolding protein within caveolar membranes
IPI00418471	Vimentin	3.6	137	63.7	9.49 × 10−297	Cytoskeleton
IPI00004669	pp-GaNTase 2	3.4	2	4.4	0.001276	O-Linked glycosylation
IPI00220739	mPR	3.2	3	14.9	2.37 × 10−17	Progesterone receptor
IPI00218466	SEC61A1	3.0	2	6	0.001145	Insertion of membrane or secretory proteins into the ER
IPI00102069	eIF 3	3.0	3	10.2	6.86 × 10−9	Protein synthesis
IPI00910513	cDNA FLJ78119	3.0	3	4.4	0.004264	Highly similar to Homo sapiens G1 to S phase transition 1
IPI00855785	Fibronectin	2.9	8	3.7	2.07 × 10−34	Cell adhesion, cell morphology
IPI00166293	Histone H2B type 3-B	2.9	10	35.7	7.54 × 10−36	DNA binding, nucleosome component
IPI00296190	C10orf58	2.8	2	10	0.003213	Unknown
IPI00010951	Epiplakin	2.8	2	3.7	1.51 × 10−21	Cytoskeleton
IPI00328319	RBBP4	2.7	2	9.4	5.23 × 10−20	Chromatin remodeling
IPI00019353	Acylglycerol kinase	2.7	2	11.1	0.008944	Lipid kinase
IPI00232533	eIF 1A	2.6	3	11.8	0.001923	Protein biosynthesis
IPI00017726	Type II HADH	2.6	2	12.3	0.000319	Mitochondrial tRNA maturation
IPI00398002	Plectin-1	2.6	216	30.5	0	Structural/regulatory component of cytoskeleton
IPI00030847	TM9SF3	2.5	3	6.3	7.02 × 10−29	Unknown
IPI00025831	CYP3A5	2.5	4	8.6	7.24 × 10−28	Oxidation of a variety of compounds
IPI00013122	p50Cdc37	2.5	2	5.6	8.13 × 10−8	Promotes kinase interaction with the HSP90 complex
IPI00847322	Superoxide dismutase	2.5	8	42.8	4.65 × 10−60	Removal of oxygen radicals
IPI00554788	Keratin	2.4	16	21.4	9.04 × 10−84	Filament reorganization
IPI00642584	KIAA0090	2.4	2	2.9	0.001148	Unknown
IPI00294840	AIM1	2.3	3	2.7	2.50 × 10−8	Interacts with cytoskeleton
IPI00024976	hTom22	2.3	3	31	3.03E-06	Import of cytoplasmic mitochondrial proteins
IPI00759493	cDNA FLJ76284	2.3	2	9	0.004796	Unknown
IPI00237884	AKAP 250	2.3	3	0.8	2.69 × 10−7	Anchoring protein for protein kinases A and C
IPI00021695	PMCA1	2.3	3	3.7	9.83 × 10−17	Calcium export
IPI00465432	Nodal modulator 2	2.2	4	4.3	0.000351	Nodal signaling
IPI00026970	hSPT16	2.2	5	4.8	6.53 × 10−5	Nucleosome reorganization
IPI00027175	Sorcin	2.2	2	11.1	0.000415	Calcium ion binding
IPI00220487	ATP5H	2.2	11	44.1	6.23 × 10−15	ATP synthesis
IPI00873223	cDNA FLJ56155	2.2	2	4.6	6.81 × 10−12	Nucleotidyltransferase activity
IPI00020557	LRP	2.1	2	0.6	6.32 × 10−6	Involved in endocytosis, phagocytosis, cell growth
IPI00003269	κ-Actin	2.1	6	14.1	1.00 × 10−19	Cytoskeleton, cell motility
IPI00005537	L12mt	2.1	4	18.2	3.92 × 10−9	Mitochondrial ribosome component
IPI00156689	VAT1	2.1	6	14.8	1.04 × 10−22	Zinc ion binding, oxidoreductase activity
IPI00017283	IleRS	2.1	5	4.5	0.000219	Isoleucine-tRNA ligase
IPI00018783	ITPase	2.1	2	19.1	0.000154	Nucleotide metabolism
IPI00334190	SLP-2	2.1	6	33.7	4.23 × 10−73	Associated with the cytoskeleton
IPI00008982	P5CS	2.1	4	3	9.05 × 10−19	Proline biosynthesis
IPI00219097	HMG-2	2.1	2	7.2	0.00367	Binds ssDNA and unwinds dsDNA
IPI00215719	RPL18	2.1	2	12.8	3.42 × 10−7	Large ribosome subunit
IPI00386271	SLC25A12	2.1	5	11.1	2.05 × 10−18	Calcium-dependent mitochondrial aspartate and glutamate carrier
IPI00014424	eEF1A-2	2.1	8	50.5	5.11 × 10−90	Protein biosynthesis
IPI00010845	CI-23kD	2.1	2	4.3	0.004255	Core subunit of the mitochondrial membrane respiratory chain
IPI00554481	4F2hc	2.1	4	4.8	2.29 × 10−13	Amino acid transport, required for cell growth
IPI00015476	ASCT1	2.0	3	4.9	0.001305	Sodium-dependent amino acid transport
IPI00296337	DNPK1	2.0	18	5.8	1.01 × 10−53	Sensor for DNA damage and functions in DNA repair
IPI00003925	PDHE1-B	2.0	6	17.3	3.61 × 10−14	Conversion of pyruvate to acetyl-CoA and CO2
IPI00022793	TP-β	2.0	4	8.6	1.50 × 10−12	Fatty acid oxidation
IPI00419258	HMG-1	2.0	8	21.9	1.19 × 10−7	Binds ssDNA and unwinds dsDNA
IPI00029264	Cytochrome c1	2.0	2	8.9	4.24 × 10−6	Mitochondrial respiratory chain
IPI00299571	PDIA6	2.0	13	20.7	1.72 × 10−65	Catalyzes the rearrangement of disulfide bonds
IPI00011253	40 S ribosomal protein S3	2.0	15	50.6	1.30 × 10−18	Ribosomal protein
IPI00029737	LACS 4	2.0	15	23.5	9.19 × 10−33	Synthesis of cellular lipids and degradation via β-oxidation
IPI00021338	PDC-E2	2.0	5	6.2	0.000723	Catalyzes the conversion of pyruvate to acetyl-CoA and CO2
IPI00328415	B5R	2.0	5	14.6	2.29 × 10−15	Desaturation and elongation of fatty acids, cholesterol biosynthesis
IPI00014230	gC1qBP	2.0	3	12.4	2.44 × 10−20	Immune response, complement activation
IPI00021048	Myoferlin	2.0	12	5.4	2.37 × 10−42	Plasma membrane repair
IPI00871843	TGM2	2.0	3	4.9	2.12 × 10−8	Catalyzes protein cross-linking, polyamine-protein conjugation
IPI00513768	ATAD3C	2.0	13	3.9	0.003615	Nucleoside triphosphatase activity
Proteins present in decreased abundance in IBV-infected cells
IPI00873829	ACAA2	5.2	2	7.8	9.51 × 10−9	Acyltransferase

Table II. Nuclear proteins identified by LC-MS/MS demonstrating ≥2-fold change in abundance in response to IBV infection.

Notes on protein function were obtained from annotations in the UniProt database. G6P, glucose-6-phosphate; LDH, lactate dehydrogenase; pol, polymerase; PG, phosphoglycerate.

Protein ID	Name	Ratio	Peptides	Sequence coverage	FPR	Function
				%
Proteins present in increased abundance in IBV-infected cells
IPI00816452	Myosin VI	90.4	7	1.2	0.010476	Motor protein, p53-responsive, enhances RNA pol II transcription
IPI00646779	Tubulin β-6 chain	10.8	5	8.7	1.21 × 10−41	Major constituent of microtubules
IPI00171438	TXNDC5	3.9	2	6	0.001762	Thioredoxin activity
IPI00658097	HMGN1	2.8	5	18.1	2.60 × 10−14	Nucleosome binding
IPI00293845	RIF1	2.0	2	0.4	0.003549	Cell cycle, peak expression in late G2/S phase
Proteins present in decreased abundance in IBV-infected cells
IPI00414676	HSP90-β	2.0	6	11.5	4.66 × 10−72	Chaperone, stress response
IPI00219757	GSTP1-1	2.0	1	18.1	0.004648	Antiapoptotic, glutathione conjugation
IPI00295386	CBR1	2.0	3	6.1	2.29 × 10−23	Catalyzes the reduction of a range of carbonyl compounds
IPI00184848	ABCB11	2.0	1	1.3	2.58 × 10−61	ATP-dependent export of bile salts and sodium
IPI00479186	PKM2	2.0	9	17.9	7.24 × 10−97	ATP generation
IPI00887678	LOC654188	2.0	6	13	2.23 × 10−10	Peptidyl-prolyl isomerase activity
IPI00376344	Myosin Ib	2.0	5	6.4	6.81 × 10−76	Motor protein, cell migration and vesicular transport
IPI00375531	NDK A	2.0	3	7.9	1.73 × 10−6	Nucleoside triphosphate synthesis, cell cycle
IPI00843975	Ezrin	2.0	6	15	9.31 × 10−71	A determinant of cell shape
IPI00333770	Zizimin-3	2.0	2	0.6	1.04 × 10−5	Potential guanine nucleotide exchange factor
IPI00554481	4F2hc	2.0	7	8.9	1.16 × 10−93	Animo acid transport, required for cell growth
IPI00747849	ATP1B1	2.1	2	4.6	0.001511	Ion transport
IPI00105407	AKR1B10	2.1	2	12	4.27 × 10−24	Acts as all-trans-retinaldehyde reductase
IPI00413641	Aldose reductase	2.1	2	5.7	0.000461	NADPH-dependent reduction of carbonyl-containing compounds
IPI00219675	p21-Rac1	2.1	3	13.3	0.005477	Small GTPase; roles in cell polarization, cell migration
IPI00910781	G6P isomerase	2.2	5	4.6	4.60 × 10−32	Carbohydrate degradation
IPI00219365	Moesin	2.2	3	12.8	5.74 × 10−59	Plasma membrane structure
IPI00910979	Pyruvate kinase	2.3	2	22.1	1.44 × 10−164	Pyruvate kinase
IPI00219217	LDH B-chain	2.3	3	9.9	3.67 × 10−16	l-Lactate dehydrogenase activity
IPI00297477	U2 snRNP A	2.3	2	9.4	0.003467	mRNA splicing via the spliceosome
IPI00465248	α-Enolase	2.4	9	27.9	8.17 × 10−102	Glycolysis, growth control, hypoxia tolerance
IPI00216298	Thioredoxin	2.4	2	10.5	0.004965	Participates in redox reactions, cell motion, and proliferation
IPI00218914	Retinal dehydrogenase 1	2.5	12	20	1.44 × 10−12	Cofactor and retinol metabolism
IPI00010896	CLIC1	2.5	3	15.8	1.28 × 10−10	Chloride ion channel activity, pH-dependent
IPI00003269	κ-Actin	2.5	1	19.1	5.94 × 10−47	Cell motility, cytoskeleton
IPI00453476	PG mutase	2.5	6	12.6	1.56 × 10−12	Glycolysis, phosphoglycerate mutase
IPI00169383	Phosphoglycerate kinase 1	2.6	4	5.8	0.00015	Glycolysis, primer recognition protein
IPI00032826	Hsc70-interacting protein	2.6	6	3.5	0.005148	Chaperone
IPI00795918	NCAM-1	2.7	6	5.2	0.002325	Cell adhesion
IPI00643920	Transketolase	2.8	4	5.5	4.12 × 10−15	Connects the pentose phosphate pathway to glycolysis
IPI00299024	BSAP1	2.9	2	35.2	2.38 × 10−43	Cell growth/projection
IPI00219301	MARCKS	2.9	1	4.5	0.000205	Cytoskeleton, actin binding
IPI00219219	Galectin-1	3.1	1	34.8	8.32 × 10−63	Apoptosis, cell differentiation, cell proliferation
IPI00216691	Profilin-1	3.2	2	34.3	1.09 × 10−8	Modulates actin polymerization
IPI00217466	Histone H1c	4.5	1	19.5	8.35 × 10−100	Nucleosome formation and structure

Table III. Nucleolar proteins identified by LC-MS/MS demonstrating ≥2-fold change in abundance in response to IBV infection.

Notes on protein function were obtained from annotations in the UniProt database. snRNP, small nuclear ribonucleoprotein; COX, cytochrome c oxidase; NPC, nuclear pore complex; PDI, protein-disulfide isomerase.

Protein ID	Name	Ratio	Peptides	Sequence coverage	FPR	Function
				%
Proteins present in increased abundance in IBV-infected cells
IPI00019590	tPA	7.8	2	4.8	1.15 × 10−13	Tissue remodeling, degradation, cell migration
IPI00328840	Aly	6.5	1	4.2	7.76 × 10−7	mRNA splicing and export
IPI00012066	PCBP2	3.0	1	5.5	2.76 × 10−10	RNA binding
IPI00741608	eIF 5A-1	2.7	2	8.4	1.94 × 10−14	Protein biosynthesis
IPI00853009	CELF-1	2.6	2	5.5	2.67 × 10−6	Alternative splicing, mRNA translation and stability
IPI00023860	NAP1L1	2.5	4	13.8	1.02 × 10−9	Modulation of chromatin formation and regulation of cell proliferation
IPI00298994	Talin-1	2.5	3	1.9	0.000312	Cytoskeleton anchoring at plasma membrane
IPI00028122	PSIP1	2.5	2	4.9	2.44 × 10−6	Cell differentiation and stress responses
IPI00290204	U1 snRNP 70 kDa	2.4	1	3.7	0.000982	RNA splicing
IPI00744851	DSP	2.3	1	7.1	0.001515	Cytoskeleton
IPI00007818	CPSF3	2.3	2	4.2	5.31 × 10−6	mRNA processing
IPI00031627	RPB1	2.3	2	1.2	8.39 × 10−8	Transcription
IPI00011654	Tubulin β chain	2.2	5	17.6	2.37 × 10−22	Major constituent of microtubules
IPI00477040	NUP188	2.2	2	1.8	6.37 × 10−6	May function as part of the nuclear pore complex
IPI00030275	HSP75	2.1	3	4.3	1.23 × 10−11	Chaperone
IPI00003377	SFRS7	2.1	4	20.6	1.26 × 10−23	Pre-mRNA splicing, can modulate alternative splicing in vitro
IPI00011698	SAP18	2.1	2	22.2	4.86 × 10−14	Transcriptional repression.
IPI00646899	RPL10	2.1	1	6.1	4.72 × 10−8	Ribosome constituent
IPI00221222	PC4	2.0	1	10.2	7.45 × 10−8	Transcriptional regulation
IPI00555744	RPL14	2.0	1	5.5	9.09 × 10−44	RNA binding, ribosomal protein
IPI00163505	RBM39	2.0	1	2.8	7.78 × 10−17	Transcriptional coactivator, mRNA processing/splicing
IPI00397904	NUP93	2.0	2	3.3	8.64 × 10−11	Part of the nuclear pore complex, required for correct NPC assembly
IPI00217357	CARP-1	2.0	3	3.7	5.20 × 10−5	Cell cycle regulation, cell proliferation, and potential apoptosis signaling
Proteins present in decreased abundance in IBV-infected cells
IPI00294744	PARN	2.0	5	9.7	9.65 × 10−17	Nonsense-mediated mRNA decay, 3′ poly(A) tail degradation
IPI00220362	HSP10	2.0	3	33.3	2.07 × 10−10	Stress response
IPI00010796	PDI	2.1	7	16.3	1.14 × 10−47	Chaperone, disulfide bond creation, rearrangement, and dissolution
IPI00054042	GTFII-I	2.2	3	2.9	0.001192	Transcription, coordinates complex formation at the c-FOS promoter
IPI00024662	CBX5	2.2	1	6.8	1.19 × 10−5	Chromatin binding, transcriptional repression
IPI00178440	EF-1β	2.3	1	6.7	2.73 × 10−11	Translation elongation activity
IPI00328987	Bystin	2.3	1	3.4	0.001063	Cell adhesion, ribosome biogenesis
IPI00182540	Catenin δ1	2.3	1	1.5	4.90 × 10−5	Cell adhesion, transcription, transcriptional regulation
IPI00024911	ERp29 precursor	2.4	2	9.6	2.90 × 10−18	Chaperone
IPI00006579	COX IV-1	2.8	2	14.8	0.001749	Cytochrome c oxidase activity
IPI00383815	GFAP	3.1	4	8.2	1.26 × 10−19	Class III intermediate filament
IPI00062120	S100A16	3.4	2	15.5	1.05 × 10−13	Calcium-binding nucleolar protein

Bioinformatic Analyses of Vero Cell Proteome

Data sets of the total number of proteins identified from the cytoplasmic, nuclear, and nucleolar fractions were uploaded to Ingenuity Pathway Analysis, which then assigned identified proteins into different molecular and cellular functional classes (see supplemental Table 4 for definitions) based on underlying biological evidence from the literature database. The relative proportion of proteins in each separate functional class is shown as a series of pie charts (Fig. 3), and the data indicated that the proteome differed between the cytoplasm, nucleus, and nucleolus. For example, the relative proportion of proteins involved in RNA post-transcriptional modification and gene expression was greater in the nuclear and nucleolar proteomes than in the cytoplasmic proteome, whereas in the cytoplasmic proteome, the relative proportion of proteins involved in protein degradation was greater than in the nuclear or nucleolar proteome.

Fig. 3.

Analysis of relative proportion of different protein groups and functional classes (indicated in legend) in nuclear, cytoplasmic, and nucleolar proteomes of Vero cells. For orientation, the first grouping, protein synthesis, is to the right of the 12 o'clock position. Protein synthesis, cellular growth and proliferation, and gene expression are indicated as PS, CG&P, and GE, respectively.

Network Pathway Analysis Predicted Novel Interactions with Cellular Signaling Cascades

Ingenuity Pathway Analysis was used to determine whether proteins that changed in abundance could be mapped to specific functional networks. For the cytoplasmic and nuclear proteomes, network analysis suggested that proteins involved in cellular assembly, organization, morphology, signaling, and growth changed in abundance in IBV-infected cells (Figs. 4 and 5 for the cytoplasmic and nuclear proteomes, respectively). Many of these proteins were linked to the NF-κB and AP-1 signaling cascades as were proteins associated with the nucleolar proteome (Fig. 6). Although the response of NF-κB to various SARS-CoV proteins was evaluated in several overexpression systems expressing different virus proteins, there were differing results (42–44). The response of AP-1 in coronavirus-infected cells or NF-κB in IBV-infected cells has not been determined previously. AP-1 is a transcription factor composed of dimeric basic region-leucine zipper proteins that belong to the Jun, Fos, Maf, and activating transcription factor subfamilies (45). Similar to NF-κB, AP-1 regulates a wide range of cellular processes, including differentiation, proliferation, survival, and death (45). Specific proteins up-regulated in IBV-infected cells and known to be associated with the AP-1 network included HSPB1 (heat shock protein 27-kDa protein 1, HSP27), whose transcription is dependent on NF-κB (46) and in turn can increase activation of Erk1/2 (47) and p38 mitogen-activated protein kinase (MAPK) cascades (48). NF-κB is a protein complex, which among other proteins is composed of RelA and p50. These proteins can traffic to the nucleus to activate NF-κB-responsive genes.

Fig. 4.

Ingenuity Pathway Analysis of proteins predominately associated with assembly and organization in nuclear proteome. Proteins shaded in green indicate 2-fold or more depleted in IBV-infected cells, and proteins shaded in red indicate 2-fold or more increased in the nuclear proteome. Proteins in gray were identified in the analysis but did not meet the 2-fold specified cutoff value between mock- and IBV-infected cells. Proteins in white are those identified from the Ingenuity Pathways Knowledge Base. The shapes are indicative of the molecular class (i.e. protein family) (see supplemental Fig. 2 for the legend). Lines connecting the molecules indicate molecular relationships. There are two line styles; dashed lines indicate indirect interactions, and solid lines indicate direct interactions. The style of the arrows indicates specific molecular relationships and the directionality of the interaction (A acts on B).

Fig. 5.

Ingenuity Pathway Analysis of proteins predominately associated with cellular assembly and organization in cytoplasmic proteome. Proteins shaded in green indicate 2-fold or more depleted in IBV-infected cells, and proteins shaded in red indicate 2-fold or more increased in the nuclear proteome. Proteins in gray were identified in the analysis but did not meet the 2-fold specified cutoff value between mock- and IBV-infected cells. Proteins in white are those identified from the Ingenuity Pathways Knowledge Base. The shapes are indicative of the molecular class (i.e. protein family) (see supplemental Fig. 2 for the legend). Lines connecting the molecules indicate molecular relationships. There are two line styles; dashed lines indicate indirect interactions, and solid lines indicate direct interactions. The style of the arrows indicates specific molecular relationships and the directionality of the interaction (A acts on B).

Fig. 6.

Ingenuity Pathway Analysis of proteins predominately associated with cell growth and interaction in nucleolar proteome. Proteins shaded in green indicate 2-fold or more depleted in IBV-infected cells, and proteins shaded in red indicate 2-fold or more increased in the nuclear proteome. Proteins in gray were identified in the analysis but did not meet the 2-fold specified cutoff value between mock- and IBV-infected cells. Proteins in white are those identified from the Ingenuity Pathways Knowledge Base. The shapes are indicative or the molecular class (i.e. protein family) (see supplemental Fig. 2 for the legend). Lines connecting the molecules indicate molecular relationships. There are two line styles; dashed lines indicate indirect interactions, and solid lines indicate direct interactions. The style of the arrows indicates specific molecular relationships and the directionality of the interaction (A acts on B).

Validation of Signaling Pathway Activation in IBV-infected Cells

To validate the network pathway analysis and determine whether AP-1 and/or NF-κB was up-regulated in IBV-infected cells, a reporter gene system was used in which expression of the reporter protein luciferase was under the control of promoter(s) recognized by either the AP-1 or NF-κB transcription complex. As a positive control, Sendai virus, which has been shown previously to stimulate signaling cascades, was used to infect cells (Fig. 7A). The data indicated that luciferase activity was ∼2.5-fold higher in IBV-infected cells compared with mock-infected cells and therefore suggested that AP-1-driven transcription increased as a result of virus infection (Fig. 7A). Two independent systems were used to measure intracellular NF-κB p50/RelA heterodimer activity. The NF-κB promoter, which is synthetic, has three NF-κB (concanavalin A)-dependent elements and the PRDII promoter, and which occurs in native systems, is activated by NF-κB to produce interferon β. Both reporter systems give rise to expression of a luciferase gene, allowing the relative amount of luciferase present to be analyzed, reflecting promoter activity and hence NF-κB activation. Cells were either mock-infected or infected with IBV and transfected with the appropriate reporter gene constructs. The data indicated that for both reporter gene systems the level of luciferase in IBV-infected cells was greater than in mock-infected cells. There was an ∼6- and 4-fold increase in luciferase activity in IBV-infected cells with reporter gene using the PRDII- or concanavalin A promoter-based assay, respectively (Fig. 7A).

Fig. 7.

A, histogram of the luciferase reporter gene assays to assess the relative level of AP-1- and NF-κB-driven transcription in mock-, Sendai virus-, and IBV-infected cells. Data are an average of 12 independent experiments. B, indirect immunofluorescence confocal microscopy analysis of the localization of either p50 or RelA (both green) in either mock- or IBV-infected (red) cells. Nuclei are stained with DAPI, and a merged image is also presented. ConA, concanavalin A.

As an independent validation, we predicted that if the canonical pathway was initiated upon infection with IBV NF-κB p50/RelA will be activated and there will be a redistribution of the p50/RelA heterodimer from cytoplasm to nucleus within the cell. To distinguish this, cells were infected with IBV, and indirect immunofluorescence confocal microscopy was used to determine the localization of p50 and RelA in mock- and IBV-infected cells (Fig. 7B). The data indicated that in mock-infected cells p50 and RelA were distributed throughout the cytoplasm and nucleus. In contrast, in IBV-infected cells, the localization of p50 and RelA was predominantly nuclear.

Cellular Scaffolding and Trafficking Proteins Were Redistributed in IBV-infected Cells

The quantitative proteomics analysis revealed that several different cellular proteins involved in cell morphology and the cytoskeleton changed in abundance in IBV-infected cells. For example, MYO6, a protein involved in endocytic vesicle trafficking (49) and cargo transport (50), was enriched in the nuclear proteome (90-fold increase; Table I) in IBV-infected cells. Vimentin is a major component of type III intermediate filaments and was increased in abundance in the cytoplasmic proteome of IBV-infected cells (Table II). The subcellular localization of these two proteins was investigated in IBV-infected cells and compared with mock-infected cells using indirect immunofluorescence confocal microscopy. The data indicated that in mock-infected cells the subcellular localization of MYO6 was predominately cytoplasmic (Fig. 8A), whereas in IBV-infected cells, MYO6 also exhibited perinuclear and nuclear punctate staining (Fig. 8A), which verified the increased nuclear abundance of MYO6 determined by the SILAC analysis (Table I). In IBV-infected cells, the abundance and distribution of vimentin were different from those in mock-infected cells. In mock-infected cells, vimentin was distributed in the cytoplasm with enrichment in the perinuclear region (Fig. 8B), whereas in IBV-infected cells that form syncytia, this enrichment was lost (Fig. 8B), again validating the change in the amount of vimentin determined by the SILAC analysis (Table II). β-Tubulin was identified as being 2.2-fold increased in the nucleolar fraction from IBV-infected cells by SILAC analysis (Table III). This again was validated by the observed increase of β-tubulin in nucleolar fractions from IBV-infected cells by Western blot (Fig. 1D).

Fig. 8.

Indirect immunofluorescence analysis using confocal microscopy of MYO6 (A) or vimentin (B) in mock- or IBV-infected cells. MYO6 and vimentin are shown in green, nuclei are stained with DAPI (blue), and IBV proteins are shown in red. A merged image is also presented. For A, two examples of IBV-infected cells are shown: top, the MYO6 cytoplasmic signal is in the same linear range as the MYO6 signal in the mock-infected cells; bottom, the MYO6 signal in the nucleus is in the linear range. Note that the IBV-infected cells identified in B are an example of virus-induced syncytia.

Changes in Nucleolar Proteome in Response to Virus Infection Are Not Conserved

The interaction of virus infection with the nucleolus has been predicted to alter the nucleolar proteome (23, 25, 51). This hypothesis was investigated in a recent quantitative proteomics analysis using SILAC coupled to LC-MS/MS that revealed only specific nucleolar proteins changed in abundance in response to adenovirus (a double-stranded DNA virus that replicates in the nucleus) infection rather than total global changes (52). A similar observation was found in this study in that the abundance of only specific nucleolar proteins changed in response to IBV infection. Comparison of the amounts of proteins that were common to both data sets indicated that not all of the same proteins were altered in virus-infected cells (Fig. 9).

Fig. 9.

Comparison of relative abundance of proteins identified in nucleolus in cells infected with either IBV (black) or adenovirus (gray). Selected gene names are used as identifiers.

DISCUSSION

Virus infection has multiple effects on host cell processes, such as signaling pathways and morphology. High throughput quantitative proteomics using SILAC is an ideal methodology to map such changes from the perspective of a total cell as it is suited to unbiased comparison analysis (39), e.g. virus-infected versus uninfected cells. Previously, to our knowledge, SILAC analysis has only been applied to the interaction of three viruses with the host cell, hepatitis C virus (53), pseudorabies virus (PrV) (40), and adenovirus. Hepatitis C virus is a positive strand RNA virus, and SILAC was used to investigate its interaction with host cell lipid rafts. In this study, 1036 proteins were identified by SILAC with LC-MS/MS compared with 100 proteins identified by two-dimensional gel electrophoresis (53). PrV I-like adenovirus is a double-stranded DNA virus that replicates in the nucleus. For PrV I, SILAC was used in conjunction with two-dimensional gel electrophoresis and phosphoprotein enrichment to identify 2600 proteins representing 565 genes. Changes were identified in proteins associated with intracellular transport and trafficking as well as HSP27 (40). For adenovirus infection, SILAC was applied to study changes in the nucleolar proteome in response to infection. More frequently, two-dimensional gel electrophoresis has been used to investigate the interaction of a number of different RNA viruses that replicate in the cytoplasm with subcellular compartments and/or the host cell. For example, 24 different proteins were identified as changing in the nuclear proteome of respiratory syncytial virus-infected cells (54). These included components of the cytoskeleton, RNA helicases, and heat shock proteins (54). Analysis of infectious bursal disease virus-infected cells identified 51 altered cellular proteins, including cytoskeletal proteins, ribosomal proteins, translation factors, and heat shock proteins (55). Analysis of dengue virus-infected cells identified changes in 17 differentially expressed proteins (56).

In this study of Vero cells infected with IBV, a total of 1830 cellular and two viral proteins was identified and quantified. Vero cell lines are commonly used in studying virus-host cell interactions as it is permissive to infection by many different viruses. The viral N protein was present in all three samples and validated previous overexpression and infection experiments where N protein, which contains appropriate trafficking signals (57, 58), was shown to be predominately localized to the cytoplasm but also to the nucleus and nucleolus (59–61). M protein was identified in the cytoplasmic proteome from IBV-infected cells but not in the nuclear or nucleolar proteome. This protein has been shown previously to localize to the Golgi region in IBV-infected cells (62, 63) and therefore also provides an internal control for the purity of the different extracts. No other viral proteins were detected. However, compared with the N and the M proteins, these proteins are less abundant and might therefore have been below the threshold level of detection.

SILAC coupled to LC-MS/MS differentiated changes in the host cell in response to infection with IBV. These were investigated using techniques independent of subcellular fractionation, including functional assays on replicate samples and indirect immunofluorescence confocal microscopy of selected proteins. Network pathway analysis indicated that in the proteomes examined cellular proteins could be linked to signaling cascades such as NF-κB, MAPK, and ERK. The interaction of IBV with these pathways has not been described previously and in general in coronaviruses has only been examined in the context of overexpression of specific viral proteins rather than in virus-infected cells. For example, overexpression of different SARS-CoV proteins has been reported to have differing effects, either activation or inhibition on NF-κB activity (42, 43, 64). AP-1 has been shown to be induced in cells overexpressing the SARS-CoV spike glycoprotein (65) and N protein (66). From the pathway analysis, we predicted that AP-1 and NF-κB signaling would be activated in IBV-infected cells. This was examined using a reporter gene-based system in which the presence of AP-1 and NF-κB activates the transcription (and subsequent expression) of luciferase. The data indicated that AP-1 was activated 2.5-fold in IBV-infected cells, similar to that reported in the overexpression studies of SARS-CoV proteins (65, 66). AP-1 proteins have important effects on cellular proliferation (67), and activation of proteins within this pathway could account for observed ablation of D-type cyclins in IBV-infected cells (1, 68). Likewise, this is the first description of the activation of NF-κB in IBV-infected cells and together with the independent observations that p50 and RelA traffic to the nucleus in IBV-infected cells confirms that coronaviruses can activate this pathway. These observations were a direct and independent validation of the bioinformatics analysis of the quantitative proteomics data.

One of the major findings was that the abundance and distribution of proteins involved in maintenance of cellular structure were altered in IBV-infected cells. This confirms previous observations that have shown rearrangement of Golgi apparatus (69) and microtubules (70) or highlighted the role of actin in assembly and budding (71) in coronavirus-infected cells. Two proteins specifically validated using immunofluorescence were MYO6 and vimentin, neither of which has been identified previously as being altered in coronavirus-infected cells. MYO6 showed enhanced punctate nuclear staining and staining of the perinuclear region in IBV-infected cells reminiscent of the distribution of MYO6 in cells treated with α-amanitin, an inhibitor of RNA polymerase II (72). This pattern of redistribution has been described for MYO6 as a p53-induced stress response (73). In IBV-infected cells, vimentin accumulated in the cytoplasm in virus-induced syncytia. Vimentin has been shown previously to be disrupted in the RNA viruses, infectious bursal disease virus (55), bluetongue virus (74), and dengue virus-infected cells (75), and in the DNA viruses, African swine virus (76) and vaccinia virus-infected cells (77). In many of these cases, vimentin has been suggested to have a role in virus assembly and egress.

Analysis of the nucleolar proteome identified and quantified 506 proteins, a proportion of which showed either increased or decreased abundance in virus-infected cells. This validates the hypotheses that the nucleolar proteome can be altered in RNA virus-infected cells (23). The data indicated that in IBV-infected cells there was no significant difference in the abundance of the proteins that are responsible for the formation of the nucleolus (78), such as nucleolin, nucleophosmin, and fibrillarin, potentially indicating that proteome changes as a result of virus infection are to specific proteins rather than generic to the nucleolus. This reflected the findings of a quantitative proteomics analysis of the nucleolus in cells infected with adenovirus (52). Other viruses, such as the DNA virus herpes simplex virus type 1, which replicates in the nucleus and some of whose proteins localize to the nucleolus (79, 80), can induce the redistribution of nucleolin and fibrillarin in virus-infected cells (81, 82). Similarly, adenovirus infection resulted in the redistribution of nucleolin and nucleophosmin (83). However, how these alterations may affect nucleolar function is not known. Interestingly, upstream binding factor can be recruited to adenovirus DNA replication centers without an apparent ablation in rRNA synthesis or alteration in the localization of RNA polymerase I (84). In IBV-infected cells, proteins associated with RNA post-transcriptional modifications, such as splicing, cleavage of mRNA, and processing of mRNA, showed differential abundance in the nucleolus. Changes in the abundance of such proteins may account for the regulation of specific host cell mRNAs observed in coronavirus-infected cells (85).

Similar to that observed in a quantitative analysis of PrV-infected cells (40), the majority of the cellular proteome remained relatively unchanged in IBV-infected cells, suggesting that specific changes occur in response to virus infection. High throughput quantitative proteomics using SILAC is an ideal methodology to study the cellular proteome during virus infection. The use of multiple isotope labeling lends itself to the simultaneous analysis of different strains of the same virus that show different pathogenic phenotypes to determine whether changes in the cellular proteome can be correlated with infectivity.