Influenza A Infection of Primary Human Airway Epithelial Cells Up

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Influenza A infection of primary human airway epithelial cells up-regulates proteins related to purine metabolism and ubiquitin-related signaling Andrea L Kroeker, Peyman Ezzati, Kevin Coombs, and Andrew J Halayko J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400464p • Publication Date (Web): 10 Jun 2013 Downloaded from http://pubs.acs.org on June 16, 2013

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Journal of Proteome Research Kroeker et al

Nuclear changes induced by influenza in HBAE

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Influenza A infection of primary human airway epithelial cells up-regulates proteins related to purine metabolism and ubiquitin-related signaling Andrea L. Kroeker1,2,3, Peyman Ezzati3, Kevin M. Coombs1,2,3,4, Andrew J. Halayko1,2

1

Department of Physiology, Faculty of Medicine, University of Manitoba, Winnipeg, Canada R3E 0J9

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Manitoba Institute of Child Health, Room 641 John Buhler Research Center, University of Manitoba, Winnipeg, Canada R3E 3P4

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Manitoba Center for Proteomics and Systems Biology, Room 799 John Buhler Research Centre, University of Manitoba, Winnipeg, Canada R3E 3P4

4

Department of Medical Microbiology, Faculty of Medicine, University of Manitoba, Winnipeg, Canada R3E 0J9

Andrea Kroeker (v)204.996-9453 [email protected] Peyman Ezzati [email protected] Corresponding Author: Dr. Kevin Coombs (v)204.789.3976 (f)204.480.1362 [email protected] Dr. Andrew Halayko (v)204.480.1327 [email protected]

Manuscript details:

Abstract: 166 words Main text: 4,853 words (excluding References and Tables) References: 66 Figures: 5 Tables: 2 Supplementary: 2 Tables

Running Title: Nuclear changes induced by influenza in HBAE

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Abbreviations HBAE hpi HSU IAV INF NS-1 PR8 SILAC

Human bronchial airway epithelial cells Hours post infection High-salt-8M-Urea lysis buffer Influenza A Virus Interferon Non-structural protein 1 Influenza strain A/PR/8/34 Stable Isotope Labeling of Amino Acids in Cell Culture


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Abstract Virus-host interactions are important determinants of virus replication and immune responses but they are not well defined. In this study we analyzed quantitative host protein alterations in nuclei-enriched fractions from multiple primary human bronchial airway epithelial (HBAE) cells infected by an H1N1 influenza A virus (A/PR/8/34). We first developed an effective detergent-free nuclear lysis method that was coupled with in-solution digestion and LCMS/MS. Using SILAC, we identified 837 HBAE nuclear proteins in three different donors and compared their responses to infection at 24 hours. Some proteins were altered in all three donors, of which 94 were up-regulated and 13 were down-regulated by at least 1.5-fold. Many of these up-regulated proteins clustered into purine biosynthesis, carbohydrate metabolism and protein modification. Down-regulated proteins were not associated with any specific pathways or processes. These findings further our understanding of cellular processes that are altered in response to influenza in primary epithelial cells and may be beneficial in the search for host proteins that may be targeted for antiviral therapy.



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Introduction Influenza A virus (IAV), is an enveloped, single-stranded RNA virus with a genome of 8 negative-sense segments that produce at least 15 distinct proteins depending on the strain, including NA, HA, NS1, NS2/NEP, M1, M2, NP, PA, PB1, PB1-F2, PB2 1, 2 as well as the more recently described N40 3, M42 4, PA-X 2 and NS3 5, 6 viral proteins. Throughout the virus lifecycle, these proteins all interact extensively with each other and localize to both the host cell’s cytoplasm and nucleus 7, 8. Some virus-host protein interactions elicit host proteome changes that are beneficial to virus replication, indeed some being essential for replication 9-12. Thus, these host proteins represent attractive drug targets for novel drug therapy. Several proteomic techniques including iTRAQ 13, 2DIGE-MS/MS 14, and SILAC 15 have been used to investigate the effect of infection by different influenza virus strains on host cells, principally continuous (transformed) epithelial cell lines 15-19 but also in primary human macrophages 13, primary human alveolar macrophages 20 and a macaque animal model 21-23. We recently demonstrated the response of primary human bronchial epithelial cells to influenza infection using SILAC analysis of cytoplasmic proteins 24. Studies of this nature have substantially contributed to understanding the innate immune response to virus infection and have identified numerous host factors that are attractive potential drug targets. Another well-researched aspect of influenza biology has focused on the assembly and function of the viral polymerase complex, including multiple proteomic studies in which interacting host proteins have been discovered 25-27. Importantly, in contrast to many RNA viruses, the influenza polymerase complex enters the nucleus with replication of the virus being reliant on nuclear localization. Thus, beyond understanding the impact of infection on host cytoplasmic protein responses, it is imperative to also delineate changes that occur in the host nuclear proteome to fully decipher viral replication mechanisms. Previous nuclear or nucleolar proteomic studies


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have already begun to identify nuclear-related processes that are altered in response to numerous viruses including respiratory syncytial virus 28-30, adenovirus 31, HIV-1 32, and corona infectious bronchitis virus 33. Two studies involving influenza have analyzed purified nuclei 13 from primary macrophages or 293T cell nucleoli 8. Taken together, these studies have identified altered expression of proteins involved in stress response 13, 28, 32, RNA/DNA/protein synthesis 13, 31-33, molecular transport 13, 29, and metabolic pathways 13, 32. However, most viral proteomic studies are performed on whole cell lysates. This is the first study to specifically analyze global nuclear changes induced by influenza in primary human epithelial cells.

Experimental Procedures

Cells and viruses Influenza virus strain A/PR/8/34 (H1N1) was grown in embryonated hens’ eggs from laboratory stocks, after which chorioallantoic fluid was harvested, aliquoted, and titered in MDCK cells by standard procedures 15. L929 mouse fibroblasts were routinely grown and passaged in Joklik’s modified MEM with 5% fetal bovine serum (FBS). Primary normal human bronchotracheal epithelial (HBAE) cells were obtained from 3 different “healthy donors” by Lonza Inc. and were certified as mycoplasma-, HIV-, HBV- and HCV-negative. These cells, their labeling for SILAC experiments, and infection with influenza have been previously described by us 24.

Optimization of Nuclear Protein Extraction



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For each lysis condition, 3 million L929 cells were washed 3 times in >50 volumes of ice-cold phosphate-buffered saline (PBS) and lysed for cytosolic proteins by adding NP-40 buffer (10mM Tris-HCl pH 7.4, 3mM CaCl2, 2mM MgCl2, 0.5% NP-40, 1.1µM pepstatin A) to the cells and incubating them for 30 minutes on ice. Nuclei were pelleted at 5000×g for 10 minutes and frozen at -80°C until further processing. Nuclear pellets were thawed on ice, washed 3 times in isotonic buffer (PBS + 12% w/v sucrose) and then resuspended in 100µl of one of various buffers: water, PBS, 0.5% NP40 in PBS, 0.5% NP40 + 1% DOC in PBS, 0.5% NP40 + 1% DOC + 0.1% SDS in PBS, high salt RIPA (50mM Tris, 500mM NaCl, 0.5% NP40, 1% DOC, pH 8.0), high salt detergent-free buffer (10mM HEPES, 620mM NaCl, 1mM DTT, 1mM MgCl2, pH 8.0) or 8M urea. Samples were subjected to three freeze-thaw cycles, sonicated at midrange setting with a probe (Vibra Cell, Sonics & Materials Inc.) for 10 × 3s, and centrifuged at 21,000×g for 5’ to pellet any insoluble material. The supernatant was saved as the soluble fraction and total protein content determined with a standard BCA protein assay. 50% of each of the soluble nuclear fractions and the insoluble nuclear pellets were mixed with 4× electrophoresis sample loading buffer and 1mM DTT and separated on 10% SDS-PAGE gels. Protein banding patterns were visualized with Coomassie Blue dye.

Isolation of Nuclear Proteins for SILAC analysis The above assays determined that a double extraction procedure that employed the high salt detergent-free buffer extraction, followed by 8M urea extraction, hereafter referred to as HSU, gave optimum results. Thus, HBAE cells were first labeled for SILAC, infected with influenza for 24h, harvested, equivalent amounts of infected and mock-infected samples mixed together, and fractionated into nucleus and cytosol as previously described 24. For this study, the nuclear pellets 6 ACS Paragon Plus Environment

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were thawed on ice, washed 3 times in isotonic buffer (PBS + 12% w/v sucrose) and then extracted by freeze/thaw/sonication as described above with 75µl high salt buffer. The supernatant was saved as soluble fraction 1 after which the pellet was resuspended in 25µl 8M urea, subjected to freezethaw/sonication/extraction and this supernatant, soluble fraction 2, was combined with soluble fraction 1. Total protein in the combined sample was then measured using a standard BCA protein assay.

2DLC-MS/MS and Peptide Identification We performed three biologic replicate infections and extractions with cells derived from each of the three healthy HBAE donors. One of the nine samples was lost during processing. One hundred micrograms of each of the eight remaining samples were separately diluted in 10mM ammonium bicarbonate, reduced, alkylated and digested with trypsin as described previously 24. Peptide fractionation was carried out using a 2D RP (reversed-phase) high pH – RP low pH peptide system as described previously 34. Thirty-two 1-min fractions were collected and concatenated using procedures described elsewhere 34, 35; the last 16 fractions were combined with the first 16 fractions in sequential order (i.e. #1 with #17; #2 with #18, etc.). Combined fractions were vacuumdried and re-dissolved for the second dimension RP separation (0.1% formic acid in water). The second dimension was run on a splitless nano-flow Tempo LC system (Eksigent, Dublin, CA) with 20 µL sample injection via a 300µm × 5mm PepMap100 pre-column (Dionex, Sunnyvale, CA) and a 100µm × 200mm analytical column packed with 5µm Luna C18(2) (Phenomenex, Torrance, CA). Both eluents A (water) and B (acetonitrile) contained 0.1 % formic acid as an ion-pairing modifier. A 0.33 % acetonitrile per minute linear gradient (0-30% B) was used for peptide elution, providing a total 1-hour run time per fraction in the second dimension. Mass spectrometry was performed as 7 ACS Paragon Plus Environment


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previously described 24. Briefly, a QStar Elite mass spectrometer (Applied Biosystems, Foster City, CA) was used in a data-dependent MS/MS acquisition mode. One-second survey MS spectra were collected (m/z 400–1500) followed by MS/MS measurements on the 3 most intense parent ions (80 counts/s threshold, +2 to +4 charge state, m/z 100–1500 mass range for MS/MS), using the manufacturer’s “smart exit” (spectral quality 5) settings. Previously targeted parent ions were excluded from repetitive MS/MS acquisition for 60 s (50 mDa mass tolerance). Spectra were identified using Analyst QS 2.0 (Applied Biosystems) software.

Peptide and Protein Identification and Quantification 16 “.wiff” files from Analyst were submitted simultaneously to Protein Pilot 4.0 (Applied Biosystems) for relative quantification and protein identification of each of the 8 replicate samples using the ParagonTM algorithm as the search engine. Peptides were identified by searching each MS/MS spectrum against a database of human protein sequences (NCBInr, released March 2011, downloaded from ftp://ftp.ncbi.nih.govrefseqH_sapiensmRNA_Prot, 37,391 entries) and quantified by calculating the area under both the light and heavy peaks of the identified peptide. The search parameters allowed for cysteine modification by iodoacetic acid and biological modifications programmed in the algorithm (i.e. phosphorylations, amidations, semitryptic fragments, etc.). The threshold for detecting proteins (unused protscore (confidence)) in the software was set to 2.0 to achieve 99% confidence with a false discovery rate of 1%, and identified proteins were grouped by the ProGroup algorithm (Applied Biosystems, Foster City, CA) to minimize redundancy. Protein ratios were calculated by the software using the ratios of contributing peptides. The bias correction option was used to correct for small pipetting errors.


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Bioinformatics and statistics In order to compare multiple biological replicates, protein ratios within each replicate were converted to a z-score which allowed protein ratios to be normalized to the mean and standard deviation of its individual experiment 15. Z-scores were calculated by converting protein ratios to log2 and then using the formula: Log2L:H[b] – Average of (log2 of each member, a….n) Z-score (σ) of [b] = ———————————————————————— Standard deviation of (log2 of each member, a….n) Thus, a protein with a z-score > 1.65σ or < -1.65σ indicates that protein’s differential expression lies outside the 90% confidence level and were considered significant. Gi numbers of all significantly regulated proteins were submitted to and analyzed by the DAVID bioinformatic suite at the NIAID, version 6.7 36, 37 where gene ontologies and pathways were examined with the “GO-FAT”, Panther, and Reactome databases. The gi numbers were also submitted to, and pathways constructed with, Ingenuity Pathway Analysis software (IPA).

Western blotting Mock and influenza-infected HBAE cells were scraped in cold PBS at 24hpi, pelleted at 1500×g for 5 minutes and fractionated into cytosolic and nuclear fractions. Briefly, cells were treated with 0.5% NP40 buffer (20mM Tris pH 7.5, 100 mM NaCl, 0.5% NP-40, 0.5 mM EDTA, 1/100 anti-protease cocktail, 1/100 phosphatase inhibitor cocktail) on ice for 30 minutes to extract cytosolic proteins. Nuclei were then pelleted at 5000×g for 10 minutes, washed 3 times in 12%sucrose in PBS, and lysed with HSU. 30µg of protein was loaded per lane into SDS-PAGE gels, separated and transferred to nitrocellulose membranes. Membranes were probed with primary 9 ACS Paragon Plus Environment


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antibodies for viral NP (F26-9 38, a kind gift from Dr. Mingyi Li), viral NS-1 (3F5 6), GAPDH (Cell Signaling), Mx1 (Origene), IFIT1 (Epitomics), STAT1 (Cell Signaling), FN1 (Millipore), beta-actin (Cell Signaling) Histone 3 (Abcam), cytochrome c (Cell Signaling) and rabbit or mouse HRPconjugated secondary antibodies (Cell Signaling). Bands were detected with ECL (Amersham) and an AlphaInnotech Imaging instrument.

RESULTS Sequential high salt and 8M urea lysis efficiently extracts nuclear proteins Because primary human lung cells were of limited quantity and are capable of undergoing only a few divisions, we initially screened several nuclear protein extraction techniques using L929 mouse fibroblast cells. We used a variety of extraction methods including PBS alone, 0.5%NP40, RIPA, high salt RIPA, a high salt detergent-free buffer and 8M urea. Using a BCA protein assay, we found that RIPA, high salt RIPA, detergent-free high salt and 8M-urea buffers all extracted the highest amount of total protein from three million L929 nuclei; on average: 70µg, 114µg, 110µg, and 97µg, respectively. However, when these extracts were separated by SDS-PAGE, different buffers were found to extract different protein species as indicated by different protein banding patterns in the gel (Figure 1, asterisks). We also tested whether sequential extraction processes would increase the efficiency of protein extraction. Sequential treatment with the high salt detergent-free buffer followed by 8M urea (HSU) resulted in negligible residual insoluble pellets, suggesting the combination treatment essentially solubilized all material in the nucleus, which was supported by recovery of the highest amounts of protein (127µg on average) compared to any of the individual treatments. A unique aspect of HSU is that it does not contain detergents and allows for simplified downstream sample processing for mass spectrometry. We therefore chose the HSU 10 ACS Paragon Plus Environment

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method for further HBAE SILAC experiments.

SILAC analysis of nuclei from influenza-infected HBAE cells We performed two or more independent SILAC experiments with primary human bronchial epithelial cell cultures from each of three donors. These consisted of three replicates for each of donors 1 and 2 and two replicates for donor 2. Briefly, cells were isotopically labeled, infected with influenza, mixed, lysed and nuclei were purified. Proteins were extracted from isolated nuclei using HSU, digested and subjected to 2DLC-MS/MS to identify and quantify protein pairs. Overall, a total of 837 unique proteins were detected and quantified and this list was enriched for nuclear annotated proteins (Figure 2B and 2C). Western blotting of cytoplasmic and nuclear fractions confirmed that the nuclear fraction was highly enriched for the nuclear marker Histone-3, contained little, if any, mitochondria as measured by cytochrome c, carried only slight contamination from the plasma membrane as measured by E-cadherin (Figure 3A), and contained little, if any, cytoplasmic contamination as measured by GAPDH (Figure 3B). Comparing the abundance of proteins in infected cultures to uninfected cultures revealed that 97% of all proteins were present in approximately equal amounts (near a 1:1 ratio). This indicates that IAV infection does not alter the abundance of most nuclear proteins and suggests that only a select subset of proteins are affected. To determine cutoff values for identifying proteins that are either significantly up- or down-regulated, ratios for infected:uninfected in each trial were first converted to z-scores to normalize each run to its own mean and standard deviation, thus allowing us to compare multiple runs. By converting an average z-score back into an average protein ratio, we determined that a z-score of 1.65 (90% confidence interval) corresponded to a 1.5-fold change and used this cut-off for further bioinformatic analyses. 11 ACS Paragon Plus Environment


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Proteins that were identified with only a single peptide and only detected in a single run were excluded from our dataset. Proteins identified with one peptide and found in multiple trials were included. We also excluded proteins from further analysis if they were not consistently up- or downregulated between replicate runs in each donor; here, “consistent” was defined as one of 2 runs or 2 of three runs having a significantly altered ratio (z-score > 1.65 or < -1.65) and the other runs not falling below the 60% threshold (z-score > 1.3 or < -1.3). Proteins were then categorized as having ratios that were similar or different among the three donors, and were finally filtered for those that were significantly regulated in all three donors or significantly regulated in only a single donor (based on z-score values of +/- 1.65). Outlier protein ratios consisted of “9999” and “0” and were excluded from the statistical analysis but reincorporated into datasets for further bioinformatic and biological analyses. In summary, we used stringent parameters to generate a dataset of proteins of interest based on: 99% confidence in protein identification; protein identification based on 2 or more peptide pairs; ratio >1.5 for altered regulation; and, the necessity for proteins to show consistent infected:uninfected ratios. In this manner we identified 94 and 13 nuclear proteins that were upand down-regulated, respectively, in multiple HBAE donors after A/PR/8/34-infection (listed in Tables 1 and 2). In addition, we found 19 and 14 nuclear proteins that were only up- or downregulated in a single donor in response to influenza infection. The identities, confidence limits and other statistical parameters for specific peptides used for quantitation of these proteins are listed in Tables 1 and 2. Six of the SILAC-identified non-regulated and regulated proteins also were examined by Western blotting (Figure 3). STAT1 was up-regulated both by Western blot analysis and by SILAC ratios, GAPDH and β-actin were not regulated according to both SILAC and Western blot analysis, and FN1 was down-regulated according to both analyses (Figure 3B). IFIT1


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and MX1 also were indicated as up-regulated by both analyses. Although there were differences in the absolute levels of regulation, which might be due to inherent differences in different methods’ levels of sensitivity and/or sampling differences (partially degraded proteins would have been detected by MS but would not have been measured by Western blot), the overall regulation trends were generally validated.

Consistently up-regulated proteins are associated with protein metabolism and modification, purine biosynthesis, cytoskeletal proteins and carbohydrate metabolism Gene ontology analyses using Panther, KEGG and Reactome classification terms were determined for proteins that were up-regulated in response to influenza infection at the 90% confidence intervals (z-score > 1.65, ratio >1.5). Many up-regulated proteins were associated with protein metabolism and modification, including protein folding (CCT7, CCT4, ERO1L, HSP90AA1, PPID, HSPA4, CCT2) and proteolysis (PSMD11, PSMC2, PSMD6, PSME1, PSMA2, LONP1, ISG15, UCHL1, NPEPPS, SCRN1) (Figure 4A). Other categories that were significantly represented (p-value < 0.01) included purine biosynthesis (ARPT, NME1-NME2, IMPDH2, PKM, PNP, ATIC, PRPS1, PAICS), and cytoskeleton proteins (ACTR1A, MX1, DSTN, EEF1G, DNML1, MAP1A, MAP1LC3B2, MYL12A) (Figure 4A). We also noted that numerous proteins that are typically associated with mitochondria and carbohydrate metabolism were detected and significantly up-regulated (p-value < 0.001) (LDH2, PCK2, PKM2, AKR1A1, AKR1B1, MDH1, MDH2, ALDH2, ADH5, IDH1) (Figure 4A). Additionally, canonical pathways and networks that were represented by influenza-altered proteins were constructed with IPA at the 90% confidence interval (Figure 5).



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Consistently down-regulated proteins are associated with cell adhesion Few down-regulated proteins were annotated as nuclear and were mainly associated with cell adhesion and extracellular matrix interactions (THBS1, LAMA3, CD44, FN1, TGFBI, ALDH18A1, PLOD2) (Figure 4B).

DISCUSSION Our lab has previously used SILAC to study cytosolic host pathways that are altered in response to influenza virus infection in both HBAE and the A549 cell line 15, 24. We extend this work to now analyze the nuclear response of primary human airway epithelial cells, thus providing a full assessment of the host response to virus infection. In addition, we also assayed cells from multiple donors to focus on responses that may be fundamental to influenza infection rather than those that may be specific to an individual donor, as was done in our earlier study 24. First, we demonstrated that nuclear proteins could be efficiently extracted by sequentially treating nuclei with high salt followed by 8M urea coupled with freeze-thaw cycles, DNase I digestion and sonication steps. We also determined that the nuclear fraction was enriched for nuclear-annotated proteins with little contamination from the cytosol, mitochondria or plasma membrane (Figure 3). To further exclude possible contaminants from our list, we determined whether regulated proteins were found exclusively in the nuclear fraction. As indicated in Table 1, 16 proteins were detected in both fractions; of these, 11 were up-regulated in the nucleus but unchanged in the cytoplasm, suggesting that they are not simply contaminants from the cytosol. The remaining 5 proteins (STAT1, IFIT1, ISG15, Mx1 and eIF4A1) were up-regulated in both fractions and could therefore be considered possible contaminants. However, we and others have shown with Western blots that STAT1, for example, is truly up-regulated in both cytoplasm and nucleus, indicating that interpretation of 14 ACS Paragon Plus Environment

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nuclear proteomic data is not simple. For instance, it is important to consider that many proteins shuttle between multiple cellular compartments, often in response to specific stimuli, and that these dynamics are not always well characterized. Other nuclear or nucleolar proteomic studies have also shown a surprising over-representation of proteins that have not typically been thought of as nuclear, e.g. proteins related to proteasomes and ubiquitination, cytoskeletal proteins 13, 39, and Mx1 translocating into the nucleus in response to HSV infection 40. Further subcellular proteomic studies will assist in characterizing multiple localizations of proteins and potentially elucidating novel functions associated with translocation processes.

Protein ubiquitination and proteolysis A large number of proteins involved in protein post-translational modification were found up-regulated in HBAE, of which the majority were related to the ubiquitin-proteasome system. The attachment of ubiquitin-like molecules to proteins plays an important role in cell homeostasis and affects the lifecycle of numerous viruses 41. With respect to influenza, for example, protein ubiquitination plays an essential role in both the antiviral response towards influenza infection 42-44 and replication 45. Many studies describe either the induction or prevention of protein proteolysis as playing a key role in viral replication. For example, a host antiviral protein, cyclophilin A, targets the influenza M1 protein for ubiquitin-mediated degradation and restricts influenza replication 44. The ubiquitin-proteasomal pathway has also been utilized by DNA tumour viruses 46 and adenoviruses 47 in causing cell cycle dysregulation in the host cell. In our current study we identified UCHL1 as up-regulated, which was recently implicated in induction of G0/G1 cell cycle arrest and apoptosis 48. This is consistent with the observation that influenza virus production is increased in cells that enter G0/G1 cell cycle arrest 15 ACS Paragon Plus Environment


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and that influenza may cause cell cycle dysregulation 50. CAND1, a cullin-RING ligase

involved in neddylation and cell cycle regulation 51 was also found up-regulated by influenza in HBAE. OTUB1 has multiple effects that depend on the protein being targeted. However, in the context of virus infection, it has been previously shown that OTUB1 can deubiquinate TRAF3 and TRAF6 leading to negative regulation of the interferon response 52. ISG15 was also noted to be up-regulated in the HBAE nuclei and was previously found to be up-regulated in the HBAE cytoplasm as well 24. ISG15 is commonly induced in response to many viruses and is conjugated to proteins in a similar fashion as ubiquitin. ISGylation facilitates antiviral responses to multiple viruses such as influenza 53, dengue virus 54, west nile virus 54 and retrovirus 55 but has conversely also been suggested to promote viral replication such as with hepatitis B 56 and C 57. Interestingly, proteasomal subunits have been described to have functions other than protein degradation, such as regulating gene expression 58. Previous studies have demonstrated that proteasomal subunits are required for efficient viral gene transcription of human cytomegalovirus 59 and genome replication of the rotavirus 60. Whether proteasomal subunits have non-degradative functions in the influenza lifecycle is not known. Our current study as well as others identified multiple up-regulated proteasomal subunits in response to influenza infection, for example: PSMA2, PSMC2, PSMD6, PSMD11, and PSME1 (this study), PSMA1, PSMA3, PSMA6, PSMA7, PSMB2 and PSMD7 13. In contrast, our earlier study with A549 cells detected many proteasomal subunits but none that were up-regulated and one (PSMA7) that was significantly down-regulated in response to influenza infection 15.

Purine biosynthesis


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Purine biosynthesis produces both adenine and guanine molecules, which are important precursors for DNA and RNA synthesis as well as for ATP and GTP production. Sindbis virus, for example, has been found to favour ATP synthesis in infected cells 61. Adenine is also used in the production of NAD+, which is synthesized either through the cytoplasmic de novo pathway or the nuclear salvage pathway 62. Nuclear functions of NAD+ include consumption by sirtuin proteins, which are thought to be involved in regulation of transcription through histone deacetylation 63. In addition, NAD+ is an essential cofactor of mitochondrial oxidoreductases of which many were found up-regulated in this study (LDHB, IDH1, ERO1L, CRYZ, PGD, VAT1, ADH5, MDH1, MDH2, AKR1B1, AKR1A1, ALDH1A1). Other up-regulated enzymes we identified that are involved in purine synthesis include PNP, ATIC, PRPS1, and PAICS. Furthermore, APRT was up-regulated; it is an enzyme linked to energy sensing and maintaining pools of nucleotides 64. We found IMPDH2, a rate-limiting enzyme in nucleotide biosynthesis, to be up-regulated in response to influenza. IMPDH2 has been targeted by multiple antiviral drugs such as mycophenolic acid against HCV 65 and reovirus 66. Other studies have also found considerable induction of oxidoreductases such as in PR8 infected alveolar macrophages 19, 20. In contrast, nucleotide metabolism was found down-regulated in primary human macrophages at 12- and 18hpi using H3N2 influenza strains 13. Surprisingly, we did not find any up- or down-regulated proteins related to gene expression or mRNA splicing as has typically been seen in other studies with influenza virus 13, 15. This may relate to the nature of the primary cells as opposed to continuously-cultured cells used in other studies.

CONCLUSION



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We have performed a comprehensive study of the nuclear proteins in primary human bronchial epithelial cells that are altered in abundance in response to H1N1 influenza infection. Using subcellular fractionation and optimized nuclear lysis conditions, we quantified abundance of 837 nuclear proteins before and after influenza A infection in primary epithelial cells from three different donors. Only a small fraction were changed after infection. Proteins that were found up-regulated in all donors were involved in protein metabolism and modification, purine biosynthesis and cytoskeletal proteins. Nuclear proteins that were reduced in abundance were not associated with a specific pathway or cellular process. On the other hand, some nuclear proteins were found up- or down-regulated in only a single donor and these were associated with protein folding and the cytoskeleton. Collectively these observations provide insight concerning nucleus-associated mechanisms exploited and relied upon by influenza virus for replication in primary human lung epithelial cells. These findings complement and greatly extend our previous data that deciphered changes in cytosolic proteins in host cells after influenza infection 24.


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ACKNOWLEDGEMENTS This work was supported by grant MOP-77759 from the Canadian Institute of Health Research (CIHR) awarded to A.J.H., and CIHR grants ROP-104906 and MOP-106713 to K.M.C.; from a Small Grant from the Manitoba Institute for Child Health to K.M.C., from a Manitoba Health Research Council Opportunities Grant awarded to A.J.H. and K.M.C., as well as by CIHR and MHRC graduate studentships awarded to A.L.K. The authors thank Dr. James House, Director, Animal Sciences for embryonated hens eggs in which some influenza virus stocks were grown, Dr. Mingyi Li for the gift of monoclonal F26-9 anti-NP antibody, Patty Sauder, Niaz Rahim and Saeedah Al-Mutairi for anti-influenza virus NS1 and NP monoclonal antibody purification, Matthew Stuart-Edwards for developing scripts for bioinformatics analysis and Dr. Oleg Krokhin for adapting and optimizing the 2DLC-MS/MS platform used for our experiments.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.



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Figure Legends

Figure 1 – Evaluation of nuclear protein extraction efficiencies using different lysis buffers. L929 nuclei were isolated, frozen, and resuspended in various buffers as indicated and centrifuged to pellet any insoluble material (pellet size indicated with +, ++ or +++; a – indicates non-visible pellet). The total amount of soluble protein (µg per 3 million cells) in the soluble supernatant was measured using a BCA assay and is indicated below each relevant lane. Protein banding patterns of ½ of each soluble (S) and insoluble (I) fraction were visualized using SDS-PAGE stained with Coomassie Blue. Asterisks indicate bands that appear differentially solubilized by the high salt or 8M urea treatments.

Figure 2 – Distributions of total, up-regulated and down-regulated proteins identified. (A) A total of 837 nuclear proteins were identified from three separate biological replicates in three different donors; 29% of all proteins were found in all donors, and 55% were found in two or more donors. (B) Cellular localizations of all proteins identified in cytoplasmic and nuclear fractions from HBAE SILAC experiments were mapped using gene ontology terms. Nuclear and cytoplasmic fractions were obtained from the same samples but processed separately (n = 8). (C) All proteins from cytoplasmic and nuclear fractions that had a nuclear gene ontology term were further categorized as containing only a nuclear localization term, as containing both nuclear and cytoplasmic terms but not others or as containing both nuclear and other terms. (D) The number of total up- and downregulated protein pairs identified in each trial at a confidence level of 90% (z-score > 1.65 or < 1.65). Specific peptides identified, measured and used to measure up-regulated and down-regulated proteins are listed in Supplementary Tables 1 and 2, respectively. 20 ACS Paragon Plus Environment

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Figure 3 – Western blotting validation. (A) Western blot confirmation of subcellular fractionation efficiency. (B) Western blotting was performed to confirm SILAC virus:mock ratios. SILAC ratios are an average of the three HBAE donors, Western blot ratios are from two donors that were not used in SILAC experiments. “Induced” was used to describe proteins that are clearly up-regulated but for each a ratio could not be determined because no value was obtained under non-infected conditions. “n.a.” was used to described protein ratios that were not found; for western blot experiments, the protein was assayed for but not detected; for mass spectrometry experiments these proteins were not identified. “n.d.” was used to designate viral proteins that were not identified due to the fact that their sequences were not present in the human database used for protein identification.

Figure 4 – Pathway and protein classification analysis of total proteins that were consistently up- or down-regulated using Panther databases. Lists of these consistently up- and down-regulated (A and B) protein IDs were uploaded into DAVID separately and analyzed for enrichment of categories belonging to biological processes, molecular functions and pathways at the 90% confidence interval. Functions specifically related to proteasome and ubiquitination or purine metabolism are indicated with a # or ^, respectively.

Figure 5 – Interactions between total proteins that were consistently up- or down-regulated. Protein IDs and ratios from Table 1 and 2 were combined and imported into the Ingenuity Pathways Analysis (IPA) tool from which interacting pathways were constructed. Up- and down-regulated



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proteins are denoted in red and green, respectively; grey proteins indicate that they were detected in our study but not regulated; white proteins interact with many proteins in the network but were not detected in this study. Any known direct connections between these proteins are indicated by solid lines; indirect interactions are not shown here.


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Table 1 – HBAE proteins increased > 1.5-fold Proteins detected in both cytoplasmic and nuclear fractions

#

Gene Symbol

Protein Name

GO: Nucleus

Virus/Mock Protein Ratio (Donor 1) Run Run Run AVG 1 2 3

Virus/Mock Protein Ratio (Donor 2) Run Run AVG 1 2


MDH2

mitochondrial malate dehydrogenase

Yes

2.3

2.7

2.4

2.5

3.6

2.5

3.0

--

--

--

--

PRKACA

cAMP-dependent protein kinase catalytic subunit alpha

Yes

6.2

--

--

6.2

--

--

--

--

--

--

--

CAND1

TIP120 protein

Yes

13.9

--

3.1

8.5

--

--

--

--

--

--

--

HSPA4

heat shock 70kDa protein 4

Yes

5.1

--

--

5.1

--

--

--

--

--

--

--

EDF1

endothelial differentiation-related factor 1

Yes

--

--

--

--

--

--

--

--

--

50.0

50.0

*

*STAT1

signal transducer and activator of transcription 1

Yes

--

--

--

--

--

--

--

4.3

--

--

4.3

#

PSME1

proteasome activator subunit 1

Yes

--

--

--

--

--

--

--

2.5

--

--

2.5

EEF1G

eukaryotic translation elongation factor 1 gamma

3.0

--

--

3.0

1.7

0.9

1.3

3.1

1.7

1.8

2.2

IDH1

isocitrate dehydrogenase [NADP] cytoplasmic

--

5.0

3.7

4.4

2.4

--

2.4

--

--

--

--

#

CUL4A

cullin 4A

6.8

--

--

6.8

--

--

--

--

--

--

--

DPYSL2

dihydropyrimidinase-like 2

3.0

17.2

9.0

9.7

--

--

--

--

--

--

--

--

--

--

--

--

--

--

3.0

--

--

3.0

PRKCSH

protein kinase C substrate 80K-H

*

*EIF4A1

eukaryotic translation initiation factor 4A

*

*MX1

myxovirus resistance protein 1

*ISG15 *IFIT1

# * *

--

--

--

--

1.9

--

1.9

--

--

--

--

--

--

--

--

--

--

--

2.9

--

6.7

4.8

ISG15 ubiquitin-like modifier

--

--

--

--

--

--

--

4.4

--

--

4.4

interferon-induced protein with tetratricopeptide repeats 1

--

--

--

--

--

--

--

5.4

--

--

5.4

Yes

*these proteins are also up-regulated in the cytoplasmic fraction as previously published for Donor 1 24 # related to proteolysis, ubiquitin or ubiquitin-like signaling ^ related to purine metabolism



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Proteins detected only in the nuclear fraction

^

Gene Symbol

Protein Name

GO: Nucleus


NAPRT1

nicotinate phosphoribosyltransferase

Yes

100&

100

PABPN1

polyadenylate-binding protein 2

Yes

100

100

CCT4

T-complex protein 1 subunit delta

Yes

PKM2

pyruvate kinase isozymes M1/M2

Yes

CCT2

T-complex protein 1 subunit beta

100

100

100

2.6

2.6

Yes

2.2

2.2

2.2

22.6

AKR1B1

aldose reductase

Yes

#

SCRN1

secernin-1

Yes

#

NPEPPS

puromycin-sensitive aminopeptidase

Yes

#

PSMD11

26S proteasome non-ATPase regulatory subunit 11

Yes

4.5

#

UCHL1

ubiquitin carboxyl-terminal hydrolase isozyme L1

Yes

5.2

SEPT2

septin-2

Yes

3.8

5.9

5.9

9.8

6.8 4.5

3.4

4.3

3.0

3.0

3.2

3.2

ADH5

alcohol dehydrogenase class-3

Yes

3.3

PSMA2

proteasome subunit alpha type-2

Yes

2.9

2.8

#

PSMD6

26S proteasome non-ATPase regulatory subunit 6

Yes

2.7

2.7

TRIM28

transcription intermediary factor 1-beta

Yes

2.5

2.5

PPP2R1A

serine/threonine-protein phosphatase 2A 65kDa regulatory subunit A

Yes

2.5

2.5

HMGB2

high-mobility group box 2

Yes

KARS

^


22.6

#

#


50.0

50.0

50.0

50.0

lysyl-tRNA synthetase

Yes

PSMC2

proteasome 26S ATPase subunit 2

Yes

S100P

S100 calcium binding protein P

Yes

10.1

EEF1A1

eukaryotic translation elongation factor 1 alpha 1

Yes

2.7

2.3

3.9

1.9

1.5

1.1

0.8

3.8

2.9

3.0

1.7

3.0

3.0

PGK1

phosphoglycerate kinase 1

2.3

2.3

2.0

2.2

LDHB

L-lactate dehydrogenase B

6.1

4.8

3.9

4.9

1.8

1.8

PNP

nucleoside phosphorylase

19.0

19.0

5.8

5.8

1.8

10.1

2.3


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PGM1

5.9

HSP90AA1

heat shock protein HSP 90-alpha

2.7

PAFAH1B2

platelet-activating factor acetylhydrolase IB subunit beta

PGD

6-phosphogluconate dehydrogenase, decarboxylating

3.1

3.8

3.0

3.0

3.5

3.2

3.2

obg-like ATPase 1 isoform 1

3.2

3.2

3.8

3.0

18.2

peptidyl-prolyl cis-trans isomerase D

7.93

7.93

ATIC

bifunctional purine biosynthesis protein PURH

7.0

7.0

2.6

16.7

16.7

7.4

retinal dehydrogenase 1

5.53

DPYSL3

dihydropyrimidinase-related protein 3

5.9

HPRT1

hypoxanthine phosphoribosyltransferase 1

GSTM3

glutathione S-transferase Mu 3

5.5

5.5

HIBADH

3-hydroxyisobutyrate dehydrogenase, mitochondrial

4.8

4.8

AKR1A1

alcohol dehydrogenase [NADP+]

MAP1A

microtubule-associated protein 1A

4.0

quinone oxidoreductase

3.6

9.22

5.9 14.6

14.6

3.9

3.9 4.0

3.1

3.3

BPNT1

3'(2'), 5'-bisphosphate nucleotidase 1

2.8

4.2

OTUB1

ubiquitin thioesterase OTUB1

3.1

3.1

microtubule-associated proteins 1A/1B light chain 3 beta 2

2.7

coiled-coil-helix-coiled-coil-helix domain-containing protein 6 2.6

3.5

3.1

2.5

2.5

3.4

3.0

VAT1

synaptic vesicle membrane protein VAT-1 homolog

PRPS1

phosphoribosyl pyrophosphate synthetase 1

5.1

5.1

GMPPB

GDP-mannose pyrophosphorylase B

4.9

4.9

LONP1

lon protease homolog, mitochondrial T-complex protein 1 subunit eta

2.6

16.7

PPID

CCT7

3.8

4.1

OLA1

CHCHD6

3.8

4.1

15.2

MAP1LC3B2

2.2

5.5

multifunctional protein ADE2 isoform 2

5.5

2.2

8.0

PAICS

lacritrin

3.9

4.8

3.8

2.2

4.7

4.8

3.5

ARP1 actin-related protein 1 homolog A, centractin alpha

3.2

10.2

10.2

mannose-1-phosphate guanyltransferase alpha

3.2

beta-2-microglobulin precursor

CRYZ

#

3.6

glycyl-tRNA synthetase

ALDH1A1

^

2.9

B2M

LACRT

#

5.9

GARS

ACTR1A

^

3 June 2013

phosphoglucomutase 1

GMPPA

^


2.3 2.6

2.3 2.6



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RAB5B, member RAS oncogene family

DSTN

Destrin

50.0

50.0

dynamin 1-like

50.0

50.0

DNM1L

2.3

2.3

RAB5B

16.2

AGR2

anterior gradient 2 homolog

16.2

PCK2

mitochondrial phosphoenolpyruvate carboxykinase 2

3.9

3.8 4.2

MYL12A

2.5

3.4 3.3

myosin, light chain 12A, regulatory, non-sarcomeric

2.3

PYGB

brain glycogen phosphorylase

4.2

4.2

MDH1

cytosolic malate dehydrogenase

4.1

4.1

SLC7A5

solute carrier family 7 (cationic amino acid transporter, y+ system), member 5

4.0

4.0

WARS

tryptophanyl-tRNA synthetase

3.2

3.2

HLA-B

major histocompatibility complex, class I, B

2.7

2.7

ERO1L

ERO1-like

2.7

2.7

transaldolase 1

2.6

2.6

electron transfer flavoprotein subunit alpha, mitochondrial

2.8

2.8

TALDO1 ETFA

*these proteins are also up-regulated in the cytoplasmic fraction as previously published for Donor 1 24 # related to proteolysis, ubiquitin or ubiquitin-like signaling ^ related to purine metabolism & Outlier protein ratios of “9999” were assigned a value of 100 for bioinformatics considerations


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Table 2 - HBAE proteins decreased to < 0.67-fold

Proteins detected in both cytoplasmic and nuclear fractions Gene Symbol

Protein Name

GO: Nucleus




fibronectin 1

0.4

0.5

0.5

0.5

--

--

--

0.2

--

--

0.2

*THSP1

thrombospondin 1

0.3

0.5

0.5

0.5

--

--

--

--

--

--

--

*TGFBI

transforming growth factor, beta-induced, 68kDa

0.3

--

0.5

0.4

--

--

--

--

--

--

--

*CD44

CD44 antigen

--

--

0.1

0.1

--

--

--

--

--

--

--

*FN1

*these proteins are also down-regulated in the cytoplasmic fraction

Proteins detected only in the nuclear fraction Gene Symbol

Protein Name

SRSF10

serine/arginine-rich splicing factor 10

PTHLH

parathyroid hormone-related protein

0.5

0.4

0.5

EIF5A2

eukaryotic translation initiation factor 5A-2

0.4

0.4

0.4

CD59

GO: Nucleus


0.7

CD59 glycoprotein

LAMA3

laminin alpha 3 subunit

0.3

0.6

PLOD2

procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2

0.3

0.6

PGAM5

serine/threonine-protein phosphatase PGAM5, mitochondrial

ALDH18A1 RPL10


0.6

0.7

0.7

0.5

0.5


0.2

0.5

0.4

0.5 0.4

0.4

0.4

delta-1-pyrroline-5-carboxylate synthetase

0.3

0.3

ribosomal protein L10

0.3

0.3



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References 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

Palese, P.; Shaw, M. L., Orthomyxoviridae: The viruses and their replication. In Fields Virology, 5 ed.; Knipe, D. M.; Howley, P. M., Eds. Lippincott Williams & Wilkins: Philadelphia, 2007; pp 16471689. Jagger, B. W.; Wise, H. M.; Kash, J. C.; Walters, K. A.; Wills, N. M.; Xiao, Y. L.; Dunfee, R. L.; Schwartzman, L. M.; Ozinsky, A.; Bell, G. L.; Dalton, R. M.; Lo, A.; Efstathiou, S.; Atkins, J. F.; Firth, A. E.; Taubenberger, J. K.; Digard, P., An Overlapping Protein-Coding Region in Influenza A Virus Segment 3 Modulates the Host Response. Science 2012, 337, (6091), 199-204. Wise, H. M.; Foeglein, A.; Sun, J.; Dalton, R. M.; Patel, S.; Howard, W.; Anderson, E. C.; Barclay, W. S.; Digard, P., A complicated message: Identification of a novel PB1-related protein translated from influenza A virus segment 2 mRNA. J Virol 2009, 83, (16), 8021-31. Wise, H. M.; Hutchinson, E. C.; Jagger, B. W.; Stuart, A. D.; Kang, Z. H.; Robb, N.; Schwartzman, L. M.; Kash, J. C.; Fodor, E.; Firth, A. E.; Gog, J. R.; Taubenberger, J. K.; Digard, P., Identification of a novel splice variant form of the influenza a virus m2 ion channel with an antigenically distinct ectodomain. PLoS Pathog 2012, 8, (11), e1002998. Selman, M.; Dankar, S. K.; Forbes, N. E.; Jia, J.-J.; Brown, E. G., Adaptive mutation in influenza A virus non-structural gene is linked to host switching and induces a novel protein by alternative splicing. Emerging Microbes and Infection 2012, 1, e42. Rahim, M. N.; Selman, M.; Sauder, P. J.; Forbes, N. E.; Stecho, W.; Xu, W.; Lebar, M.; Brown, E. G.; Coombs, K. M., Generation and characterization of a new panel of broadly-reactive monoclonal anti-NS1 antibodies for detection of Influenza A virus. Journal of General Virology 2013, 94, 592-604. Shapira, S. D.; Gat-Viks, I.; Shum, B. O. V.; Dricot, A.; de Grace, M. M.; Wu, L. G.; Gupta, P. B.; Hao, T.; Silver, S. J.; Root, D. E.; Hill, D. E.; Regev, A.; Hacohen, N., A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 2009, 139, (7), 1255-1267. Emmott, E.; Wise, H.; Loucaides, E. M.; Matthews, D. A.; Digard, P.; Hiscox, J. A., Quantitative proteomics using SILAC coupled to LC-MS/MS reveals changes in the nucleolar proteome in influenza A virus-infected cells. J Proteome Res 2010, 9, (10), 5335-45. Karlas, A.; Machuy, N.; Shin, Y.; Pleissner, K. P.; Artarini, A.; Heuer, D.; Becker, D.; Khalil, H.; Ogilvie, L. A.; Hess, S.; Maurer, A. P.; Muller, E.; Wolff, T.; Rudel, T.; Meyer, T. F., Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication. Nature 2010, DOI: 10.1038/nature08760. Brass, A. L.; Huang, I. C.; Benita, Y.; John, S. P.; Krishnan, M. N.; Feeley, E. M.; Ryan, B. J.; Weyer, J. L.; van der Weyden, L.; Fikrig, E.; Adams, D. J.; Xavier, R. J.; Farzan, M.; Elledge, S. J., The IFITM Proteins Mediate Cellular Resistance to Influenza A H1N1 Virus, West Nile Virus, and Dengue Virus. Cell 2009, 139, (7), 1243-1254. Hao, L. H.; Sakurai, A.; Watanabe, T.; Sorensen, E.; Nidom, C. A.; Newton, M. A.; Ahlquist, P.; Kawaoka, Y., Drosophila RNAi screen identifies host genes important for influenza virus replication. Nature 2008, 454, (7206), 890-U46. Konig, R.; Stertz, S.; Zhou, Y.; Inoue, A.; Hoffmann, H. H.; Bhattacharyya, S.; Alamares, J. G.; Tscherne, D. M.; Ortigoza, M. B.; Liang, Y.; Gao, Q.; Andrews, S. E.; Bandyopadhyay, S.; De Jesus, P.; Tu, B. P.; Pache, L.; Shih, C.; Orth, A.; Bonamy, G.; Miraglia, L.; Ideker, T.; Garcia-Sastre, A.; Young, J. A. T.; Palese, P.; Shaw, M. L.; Chanda, S. K., Human host factors required for influenza virus replication. Nature 2010, DOI: 10.1038/nature08699. Lietzen, N.; Ohman, T.; Rintahaka, J.; Julkunen, I.; Aittokallio, T.; Matikainen, S.; Nyman, T. A., Quantitative subcellular proteome and secretome profiling of influenza A virus-infected human primary macrophages. PLoS Pathog 2011, 7, (5), e1001340. Ohman, T.; Rintahaka, J.; Kalkkinen, N.; Matikainen, S.; Nyman, T. A., Actin and RIG-I/MAVS signaling components translocate to mitochondria upon influenza A virus infection of human primary macrophages. J Immunol 2009, 182, (9), 5682-92. Coombs, K. M.; Berard, A.; Xu, W.; Krokhin, O.; Meng, X.; Cortens, J. P.; Kobasa, D.; Wilkins, J.; Brown, E. G., Quantitative proteomic analyses of influenza virus-infected cultured human lung cells. J Virol 2010, 84, (20), 10888-906.


Page 29 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16.

17. 18. 19.

20.

21.

22.

23.

24.

25.

26. 27.

28.

29.

30.

31. 32.

33.


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Liu, N.; Song, W. J.; Wang, P.; Lee, K. C.; Chan, W.; Chen, H. L.; Cai, Z. W., Proteomics analysis of differential expression of cellular proteins in response to avian H9N2 virus infection in human cells. Proteomics 2008, 8, (9), 1851-1858. Zhang, L.; Zhang, X.; Ma, Q.; Ma, F.; Zhou, H., Transcriptomics and proteomics in the study of H1N1 2009. Genomics Proteomics Bioinformatics 2010, 8, (3), 139-44. Liu, H. C.; Hicks, J.; Yoo, D., Proteomic dissection of viral pathogenesis. Dev Biol (Basel) 2008, 132, 43-53. Li, C.; Bankhead, A., 3rd; Eisfeld, A. J.; Hatta, Y.; Jeng, S.; Chang, J. H.; Aicher, L. D.; Proll, S.; Ellis, A. L.; Law, G. L.; Waters, K. M.; Neumann, G.; Katze, M. G.; McWeeney, S.; Kawaoka, Y., Host regulatory network response to infection with highly pathogenic H5N1 avian influenza virus. J Virol 2011, 85, (21), 10955-67. Liu, L.; Zhou, J.; Wang, Y.; Mason, R. J.; Funk, C. J.; Du, Y., Proteome alterations in primary human alveolar macrophages in response to influenza a virus infection. J Proteome Res 2012, 11, (8), 4091-101. Zinman, G.; Brower-Sinning, R.; Emeche, C. H.; Ernst, J.; Huang, G. T.; Mahony, S.; Myers, A. J.; O'Dee, D. M.; Flynn, J. L.; Nau, G. J.; Ross, T. M.; Salter, R. D.; Benos, P. V.; Bar Joseph, Z.; Morel, P. A., Large scale comparison of innate responses to viral and bacterial pathogens in mouse and macaque. PLoS One 2011, 6, (7), e22401. Baas, T.; Baskin, C. R.; Diamond, D. L.; Garcia-Sastre, A.; Bielefeldt-Ohmann, H.; Tumpey, T. M.; Thomas, M. J.; Carter, V. S.; Teal, T. H.; Van Hoeven, N.; Proll, S.; Jacobs, J. M.; Caldwell, Z. R.; Gritsenko, M. A.; Hukkanen, R. R.; Camp, D. G., 2nd; Smith, R. D.; Katze, M. G., Integrated molecular signature of disease: analysis of influenza virus-infected macaques through functional genomics and proteomics. J Virol 2006, 80, (21), 10813-28. Brown, J. N.; Palermo, R. E.; Baskin, C. R.; Gritsenko, M.; Sabourin, P. J.; Long, J. P.; Sabourin, C. L.; Bielefeldt-Ohmann, H.; Garcia-Sastre, A.; Albrecht, R.; Tumpey, T. M.; Jacobs, J. M.; Smith, R. D.; Katze, M. G., Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections. Journal of Virology 2010, 84, (22), 12058-12068. Kroeker, A. L.; Ezzati, P.; Halayko, A. J.; Coombs, K. M., Response of primary human airway epithelial cells to Influenza infection – A quantitative proteomic study. Journal of Proteome Research 2012, 11, 4132-4136. Tafforeau, L.; Chantier, T.; Pradezynski, F.; Pellet, J.; Mangeot, P. E.; Vidalain, P. O.; Andre, P.; Rabourdin-Combe, C.; Lotteau, V., Generation and comprehensive analysis of an influenza virus polymerase cellular interaction network. J Virol 2011, 85, (24), 13010-8. Landeras-Bueno, S.; Jorba, N.; Perez-Cidoncha, M.; Ortin, J., The splicing factor proline-glutamine rich (SFPQ/PSF) is involved in influenza virus transcription. PLoS Pathog 2011, 7, (11), e1002397. Huarte, M.; Sanz-Ezquerro, J. J.; Roncal, F.; Ortin, J.; Nieto, A., PA subunit from influenza virus polymerase complex interacts with a cellular protein with homology to a family of transcriptional activators. J Virol 2001, 75, (18), 8597-604. Brasier, A. R.; Spratt, H.; Wu, Z.; Boldogh, I.; Zhang, Y. H.; Garofalo, R. P.; Casola, A.; Pashmi, J.; Haag, A.; Luxon, B.; Kurosky, A., Nuclear heat shock response and novel nuclear domain 10 reorganization in respiratory syncytial virus-infected A549 cells identified by high-resolution twodimensional gel electrophoresis. Journal of Virology 2004, 78, (21), 11461-11476. Munday, D. C.; Emmott, E.; Surtees, R.; Lardeau, C. H.; Wu, W.; Duprex, W. P.; Dove, B. K.; Barr, J. N.; Hiscox, J. A., Quantitative proteomic analysis of A549 cells infected with human respiratory syncytial virus. Mol Cell Proteomics 2010, 9, (11), 2438-59. Munday, D. C.; Hiscox, J. A.; Barr, J. N., Quantitative proteomic analysis of A549 cells infected with human respiratory syncytial virus subgroup B using SILAC coupled to LC-MS/MS. Proteomics 2010, 10, (23), 4320-34. Lam, Y. W.; Evans, V. C.; Heesom, K. J.; Lamond, A. I.; Matthews, D. A., Proteomics analysis of the nucleolus in adenovirus-infected cells. Mol Cell Proteomics 2010, 9, (1), 117-30. Jarboui, M. A.; Bidoia, C.; Woods, E.; Roe, B.; Wynne, K.; Elia, G.; Hall, W. W.; Gautier, V. W., Nucleolar Protein Trafficking in Response to HIV-1 Tat: Rewiring the Nucleolus. Plos One 2012, 7, (11). Emmott, E.; Rodgers, M. A.; Macdonald, A.; McCrory, S.; Ajuh, P.; Hiscox, J. A., Quantitative proteomics using stable isotope labeling with amino acids in cell culture reveals changes in the



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Kroeker et al

34.

35.

36. 37. 38.

39.

40.

41. 42.

43.

44. 45.

46.

47.

48.

49.

50.

51.



cytoplasmic, nuclear, and nucleolar proteomes in Vero cells infected with the coronavirus infectious bronchitis virus. Mol Cell Proteomics 2010, 9, (9), 1920-36. Spicer, V.; Yamchuk, A.; Cortens, J.; Sousa, S.; Ens, W.; Standing, K. G.; Wilkins, J. A.; Krokhin, O., Sequence-specific retention calculator. A family of peptide retention time prediction algorithms in reversed-phase HPLC: applicability to various chromatographic conditions and columns. Anal.Chem. 2007, 79, (22), 8762-8768. Dwivedi, R. C.; Spicer, V.; Harder, M.; Antonovici, M.; Ens, W.; Standing, K. G.; Wilkins, J. A.; Krokhin, O. V., Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal.Chem. 2008, 80, (18), 7036-7042. Dennis, G.; Sherman, B. T.; Hosack, D. A.; Yang, J.; Gao, W.; Lane, H. C.; Lempicki, R. A., DAVID: Database for annotation, visualization, and integrated discovery. Genome Biology 2003, 4, (9). Huang, D. W.; Sherman, B. T.; Lempicki, R. A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 2009, 4, (1), 44-57. Yang, M.; Berhane, Y.; Salo, T.; Li, M.; Hole, K.; Clavijo, A., Development and application of monoclonal antibodies against avian influenza virus nucleoprotein. J.Virol.Meth. 2008, 147, (2), 265274. Forbus, J.; Spratt, H.; Wiktorowicz, J.; Wu, Z.; Boldogh, I.; Denner, L.; Kurosky, A.; Brasier, R. C.; Luxon, B.; Brasier, A. R., Functional analysis of the nuclear proteome of human A549 alveolar epithelial cells by HPLC-high resolution 2-D gel electrophoresis. Proteomics 2006, 6, (9), 2656-2672. Numajiri Haruki, A.; Naito, T.; Nishie, T.; Saito, S.; Nagata, K., Interferon-inducible antiviral protein MxA enhances cell death triggered by endoplasmic reticulum stress. J. Interferon Cytokine Res. 2011, 31, 847-856. Gustin, J. K.; Moses, A. V.; Fruh, K.; Douglas, J. L., Viral takeover of the host ubiquitin system. Front Microbiol 2011, 2, 161. Belgnaoui, S. M.; Paz, S.; Samuel, S.; Goulet, M. L.; Sun, Q.; Kikkert, M.; Iwai, K.; Dikic, I.; Hiscott, J.; Lin, R., Linear Ubiquitination of NEMO Negatively Regulates the Interferon Antiviral Response through Disruption of the MAVS-TRAF3 Complex. Cell Host Microbe 2012, 12, (2), 211-22. Maelfait, J.; Roose, K.; Bogaert, P.; Sze, M.; Saelens, X.; Pasparakis, M.; Carpentier, I.; van Loo, G.; Beyaert, R., A20 (Tnfaip3) deficiency in myeloid cells protects against influenza A virus infection. PLoS Pathog 2012, 8, (3), e1002570. Liu, X.; Zhao, Z.; Xu, C.; Sun, L.; Chen, J.; Zhang, L.; Liu, W., Cyclophilin A restricts influenza A virus replication through degradation of the M1 protein. PLoS One 2012, 7, (2), e31063. Widjaja, I.; de Vries, E.; Tscherne, D. M.; Garcia-Sastre, A.; Rottier, P. J. M.; de Haan, C. A. M., Inhibition of the Ubiquitin-Proteasome System Affects Influenza A Virus Infection at a Postfusion Step. Journal of Virology 2010, 84, (18), 9625-9631. Scheffner, M.; Werness, B. A.; Huibregtse, J. M.; Levine, A. J.; Howley, P. M., The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Cell 1990, 63, (6), 1129-36. Querido, E.; Blanchette, P.; Yan, Q.; Kamura, T.; Morrison, M.; Boivin, D.; Kaelin, W. G.; Conaway, R. C.; Conaway, J. W.; Branton, P. E., Degradation of p53 by adenovirus E4orf6 and E1B55K proteins occurs via a novel mechanism involving a Cullin-containing complex. Genes Dev 2001, 15, (23), 3104-17. Xiang, T.; Li, L.; Yin, X.; Yuan, C.; Tan, C.; Su, X.; Xiong, L.; Putti, T. C.; Oberst, M.; Kelly, K.; Ren, G.; Tao, Q., The ubiquitin peptidase UCHL1 induces G0/G1 cell cycle arrest and apoptosis through stabilizing p53 and is frequently silenced in breast cancer. PLoS One 2012, 7, (1), e29783. He, Y.; Xu, K.; Keiner, B.; Zhou, J.; Czudai, V.; Li, T.; Chen, Z.; Liu, J.; Klenk, H. D.; Shu, Y. L.; Sun, B., Influenza A virus replication induces cell cycle arrest in G0/G1 phase. J Virol 2010, 84, (24), 12832-40. Dove, B. K.; Surtees, R.; Bean, T. J.; Munday, D.; Wise, H. M.; Digard, P.; Carroll, M. W.; Ajuh, P.; Barr, J. N.; Hiscox, J. A., A quantitative proteomic analysis of lung epithelial (A549) cells infected with 2009 pandemic influenza A virus using stable isotope labelling with amino acids in cell culture. Proteomics 2012, 12, (9), 1431-6. Gastaldello, S.; Callegari, S.; Coppotelli, G.; Hildebrand, S.; Song, M.; Masucci, M. G., Herpes virus deneddylases interrupt the cullin-RING ligase neddylation cycle by inhibiting the binding of CAND1. J Mol Cell Biol 2012, 4, (4), 242-51.


Page 31 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


52.

53.

54. 55. 56.

57.

58. 59.

60. 61.

62.

63. 64.

65.

66.


3 June 2013

Li, S.; Zheng, H.; Mao, A. P.; Zhong, B.; Li, Y.; Liu, Y.; Gao, Y.; Ran, Y.; Tien, P.; Shu, H. B., Regulation of virus-triggered signaling by OTUB1- and OTUB2-mediated deubiquitination of TRAF3 and TRAF6. J Biol Chem 2010, 285, (7), 4291-7. Tang, Y.; Zhong, G.; Zhu, L.; Liu, X.; Shan, Y.; Feng, H.; Bu, Z.; Chen, H.; Wang, C., Herc5 attenuates influenza A virus by catalyzing ISGylation of viral NS1 protein. J Immunol 2010, 184, (10), 5777-90. Dai, J.; Pan, W.; Wang, P., ISG15 facilitates cellular antiviral response to dengue and west nile virus infection in vitro. Virol J 2011, 8, 468. Pincetic, A.; Kuang, Z.; Seo, E. J.; Leis, J., The interferon-induced gene ISG15 blocks retrovirus release from cells late in the budding process. J Virol 2010, 84, (9), 4725-36. Zhu, Y.; Qin, B.; Xiao, C.; Lu, X.; Chen, L., Cell-type specific interferon stimulated gene staining in liver underlies response to interferon therapy in chronic HBV infected patients. Dig Dis Sci 2012, 57, (9), 2355-61. Chen, L.; Li, S.; McGilvray, I., The ISG15/USP18 ubiquitin-like pathway (ISGylation system) in hepatitis C virus infection and resistance to interferon therapy. Int J Biochem Cell Biol 2011, 43, (10), 1427-31. Kwak, J.; Workman, J. L.; Lee, D., The proteasome and its regulatory roles in gene expression. Biochim Biophys Acta 2011, 1809, (2), 88-96. Tran, K.; Mahr, J. A.; Spector, D. H., Proteasome subunits relocalize during human cytomegalovirus infection, and proteasome activity is necessary for efficient viral gene transcription. J Virol 2010, 84, (6), 3079-93. Lopez, T.; Silva-Ayala, D.; Lopez, S.; Arias, C. F., Replication of the rotavirus genome requires an active ubiquitin-proteasome system. J Virol 2011, 85, (22), 11964-71. Silva da Costa, L.; Pereira da Silva, A. P.; Da Poian, A. T.; El-Bacha, T., Mitochondrial bioenergetic alterations in mouse neuroblastoma cells infected with Sindbis virus: implications to viral replication and neuronal death. PLoS One 2012, 7, (4), e33871. Anderson, R. M.; Bitterman, K. J.; Wood, J. G.; Medvedik, O.; Cohen, H.; Lin, S. S.; Manchester, J. K.; Gordon, J. I.; Sinclair, D. A., Manipulation of a nuclear NAD+ salvage pathway delays aging without altering steady-state NAD+ levels. J Biol Chem 2002, 277, (21), 18881-90. Blander, G.; Guarente, L., The Sir2 family of protein deacetylases. Annu Rev Biochem 2004, 73, 41735. Onyenwoke, R. U.; Forsberg, L. J.; Liu, L.; Williams, T.; Alzate, O.; Brenman, J. E., AMPK directly inhibits NDPK through a phosphoserine switch to maintain cellular homeostasis. Mol Biol Cell 2012, 23, (2), 381-9. Pan, Q.; de Ruiter, P. E.; Metselaar, H. J.; Kwekkeboom, J.; de Jonge, J.; Tilanus, H. W.; Janssen, H. L.; van der Laan, L. J., Mycophenolic acid augments interferon-stimulated gene expression and inhibits hepatitis C Virus infection in vitro and in vivo. Hepatology 2012, 55, (6), 1673-83. Hermann, L. L.; Coombs, K. M., Inhibition of reovirus by mycophenolic acid is associated with the M1 genome segment. J Virol 2004, 78, (12), 6171-9.



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Figure 1 – Evaluation of nuclear protein extraction efficiencies using different lysis buffers. L929 nuclei were isolated, frozen, and resuspended in various buffers as indicated and centrifuged to pellet any insoluble material (pellet size indicated with +, ++ or +++; a – indicates non-visible pellet). The total amount of soluble protein (µg per 3 million cells) in the soluble supernatant was measured using a BCA assay and is indicated below each relevant lane. Protein banding patterns of ½ of each soluble (S) and insoluble (I) fraction were visualized using SDS-PAGE stained with Coomassie Blue. Asterisks indicate bands that appear differentially solubilized by the high salt or 8M urea treatments. 115x82mm (300 x 300 DPI)

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Figure 2 – Distributions of total, up-regulated and down-regulated proteins identified. (A) A total of 837 nuclear proteins were identified from three separate biological replicates in three different donors; 29% of all proteins were found in all donors, and 55% were found in two or more donors. (B) Cellular localizations of all proteins identified in cytoplasmic and nuclear fractions from HBAE SILAC experiments were mapped using gene ontology terms. Nuclear and cytoplasmic fractions were obtained from the same samples but processed separately (n = 8). (C) All proteins from cytoplasmic and nuclear fractions that had a nuclear gene ontology term were further categorized as containing only a nuclear localization term, as containing both nuclear and cytoplasmic terms but not others or as containing both nuclear and other terms. (D) The number of total up- and down-regulated protein pairs identified in each trial at a confidence level of 90% (zscore > 1.65 or < -1.65). Specific peptides identified, measured and used to measure up-regulated and down-regulated proteins are listed in Supplementary Tables 1 and 2, respectively. 184x184mm (300 x 300 DPI)



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Figure 3 – Western blotting validation. (A) Western blot confirmation of subcellular fractionation efficiency. (B) Western blotting was performed to confirm SILAC virus:mock ratios. SILAC ratios are an average of the three HBAE donors, Western blot ratios are from two donors that were not used in SILAC experiments. “Induced” was used to describe proteins that are clearly up-regulated but for each a ratio could not be determined because no value was obtained under non-infected conditions. “n.a.” was used to described protein ratios that were not found; for western blot experiments, the protein was assayed for but not detected; for mass spectrometry experiments these proteins were not identified. “n.d.” was used to designate viral proteins that were not identified due to the fact that their sequences were not present in the human database used for protein identification. 209x245mm (300 x 300 DPI)


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Figure 4 – Pathway and protein classification analysis of total proteins that were consistently up- or downregulated using Panther databases. Lists of these consistently up- and down-regulated (A and B) protein IDs were uploaded into DAVID separately and analyzed for enrichment of categories belonging to biological processes, molecular functions and pathways at the 90% confidence interval. Functions specifically related to proteasome and ubiquitination or purine metabolism are indicated with a # or ^, respectively. 228x469mm (300 x 300 DPI)



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Figure 5 – Interactions between total proteins that were consistently up- or down-regulated. Protein IDs and ratios from Table 1 and 2 were combined and imported into the Ingenuity Pathways Analysis (IPA) tool from which interacting pathways were constructed. Up- and down-regulated proteins are denoted in red and green, respectively; grey proteins indicate that they were detected in our study but not regulated; white proteins interact with many proteins in the network but were not detected in this study. Any known direct connections between these proteins are indicated by solid lines; indirect interactions are not shown here. 263x379mm (300 x 300 DPI)


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Influenza A Infection of Primary Human Airway Epithelial Cells Up

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