Article pubs.acs.org/jpr
Ion-Current-Based Temporal Proteomic Profiling of Influenza-AVirus-Infected Mouse Lungs Revealed Underlying Mechanisms of Altered Integrity of the Lung Microvascular Barrier Shichen Shen,‡,§ Jun Li,†,‡ Shannon Hilchey,# Xiaomeng Shen,‡,§ Chengjian Tu,†,‡ Xing Qiu,∥ Andrew Ng,§ Sina Ghaemmaghami,⊥ Hulin Wu,▽ Martin S. Zand,*,# and Jun Qu*,†,‡ †
Department of Pharmaceutical Sciences, SUNY at Buffalo, South Campus, Buffalo, New York 14214, United States New York State Center of Excellence in Bioinformatics & Life Sciences, 701 Ellicott Street, Buffalo, New York 14203, United States § Jacobs School of Medicine and Biomedical Sciences, SUNY at Buffalo, South Campus, Buffalo, New York 14214, United States ∥ Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Boulevard, Rochester, New York 14642, United States ⊥ Department of Biology, University of Rochester, 402 Hutchison Hall, Rochester, New York 14627, United States # Division of Nephrology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, New York 14642, United States ▽ Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, Texas 77030, United States ‡
S Supporting Information *
ABSTRACT: Investigation of influenza-A-virus (IAV)-infected lung proteomes will greatly promote our understanding on the virus-host crosstalk. Using a detergent-cocktail extraction and digestion procedure and a reproducible ioncurrent-based method, we performed the first comprehensive temporal analysis of mouse IAV infection. Mouse lung tissues at three time points post-inoculation were compared with controls (n = 4/group), and >1600 proteins were quantified without missing value in any animal. Significantly changed proteins were identified at 4 days (n = 144), 7 days (n = 695), and 10 days (n = 396) after infection, with low false altered protein rates (1.73−8.39%). Functional annotation revealed several key biological processes involved in the systemic host responses. Intriguingly, decreased levels of several cell junction proteins as well as increased levels of tissue metalloproteinase MMP9 were observed, reflecting the IAV-induced structural breakdown of lung epithelial barrier. Supporting evidence of MMP9 activation came from immunoassays examining the abundance and phosphorylation states of all MAPKs and several relevant molecules. Importantly, IAV-induced MMP gelatinase expression was suggested to be specific to MMP9, and p38 MAPK may contribute predominantly to MMP9 elevation. These findings help to resolve the long-lasting debate regarding the signaling pathways of IAV-induced MMP9 expression and shed light on the molecular mechanisms underlying pulmonary capillary-alveolar leak syndrome that can occur during influenza infection. KEYWORDS: influenza, bottom-up proteomics, ion-current-based quantification, host factors, microvascular barrier
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the M2 ion channel protein.2 An alternative anti-influenza strategy has thus been proposed, which targets host factors (i.e., proteins and signaling pathways) universally exploited or affected by the virus.3 Such a strategy is potentially beneficial for the inhibition of viral survival and propagation as well as the alleviation of respiratory complications (usually seen in severe cases of influenza) caused by IAV. Consequently, it is important to identify the host factors that are most relevant in terms of
INTRODUCTION
Pandemic outbreaks of influenza have often resulted in extensive morbidity and mortality throughout human history. The unprecedented 1918 “Spanish” influenza pandemic infected approximately 20 to 40% of the worldwide population and caused an estimated 20−50 million deaths.1 The influenza A virus (IAV) strain is a small, enveloped single-stranded RNA virus with a segmented genome and a propensity for frequent antigenic drift and reassortment. This genetic plasticity enables rapid mutation and evolution of IAV and the ability to escape current antiviral therapeutics targeting viral neuraminidase and © XXXX American Chemical Society
Received: October 3, 2015
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DOI: 10.1021/acs.jproteome.5b00927 J. Proteome Res. XXXX, XXX, XXX−XXX
Article
Journal of Proteome Research host−IAV interactions and host responses against IAV, and examination of the therapeutic relevance of these host factors is important for the development of novel anti-influenza strategies. Several pioneering studies have recently attempted to characterize such host factors relevant to influenza infection. Most of these relied on transcriptomic approaches such as genome-wide RNA interference screening, followed by validation at the protein level to identify proteins required for IAV entry and replication.3−6 For example, Konig et al. discovered 295 cellular cofactors necessary for the early replication of IAV in host cells, and additional validation confirmed 23 and 10 proteins as critical for the entry and postentry replication of IAV, respectively;3 however, transcriptional and translational data often do not correlate well,7,8 and investigation of IAV host factors on the mRNA levels alone may be suboptimal. Therefore, quantitative proteomic studies may provide more accurate information to discover host factors necessary for IAV pathogenicity and nonlinear relationships between transcript and protein levels indicating post-transcriptional regulation. For example, using two-dimensional difference gel electrophoresis (2D DIGE)9 and stable isotope labeling by amino acids in cell culture (SILAC),10 a number of proteins related to cellular innate and adaptive immune responses have been found to be dysregulated during IAV infection in in vitro experiments.11−14 Similar proteomic studies have identified virulence factors for IAV proteins NS1 and NS215 and identified SG15, MIF, PDCD5, and UCHL1 as targets for antisense RNA therapy of IAV infection.16 Proteome-wide investigation of tissue samples from animals post IAV infection would offer in vivo evidence of influenzainduced host responses. However, current proteomic methods have a number of technical challenges, rendering the comprehensive analysis of complex tissues in multiple replicates (e.g., ≥10) problematic. To achieve reliable proteomic profiling in tissues, the effects of technical and biological variability must be minimized to reduce false-positive discovery.17 Using a sizable number of biological replicates is therefore the most rational option17 but may be difficult for most current quantitative proteomic approaches. For gel-based approaches, the multiplexing capacity is usually quite limited.18 Labeling approaches, such as Super-SILAC,19 10-plex TMT,20 and isobaric tag for relative and absolute quantitation (iTRAQ),21 were developed to enable higher-plex quantitative analysis, but these methods also carry inherent deficiencies. For SuperSILAC, “heavy” internal standards may not be available for all model systems, and for iTRAQ and TMT, multiplexing capacities are still relatively limited.22 Another daunting challenge is how to obtain satisfactory protein coverage when analyzing many biological replicates,23 especially for those low abundance proteins of biological significance. This problem is more pronounced where high abundance tissue proteins and plasma proteins dominate lung proteome, as in this study. Although prefractionation techniques like multidimensional chromatography enable significantly better protein coverage,24,25 it is impractical to perform such procedures for a large number of biological replicates. Finally, a number of problems during the quantitative analysis of the proteomics data set still remain underrepresented, such as the false-positive in the discovery of significantly changed proteins,26−28 and the missing data (or missing value) issue when analyzing a large number of biological replicates.29
To address these challenges, we have developed and optimized a proteomic strategy allowing the robust and reliable profiling of lung tissues with many biological replicates and with extremely low levels of missing data. This approach was used to analyze the temporal proteomic changes in mouse lungs during IAV infection (4, 7, and 10 days versus uninfected 0 day control). The approach comprises of several key steps: (1) exhaustive protein extraction from mouse lung tissues utilizing a strong, detergent cocktail buffer coupled to a surfactant-aided precipitation/on-pellet digestion (SOD) procedure, yielding efficient and reproducible protein/peptide recovery;30,31 (2) highly efficient and reproducible nano liquid chromatography (LC) separation of divergent peptide species on a 75 cm long heated column, coupled to Orbitrap mass spectrometry (MS) enhanced by an ion-overfilling strategy for sensitive acquisition of precursor ion signals; and (3) a unique ion-current-based (ICB) MS1 quantification approach with superior quantitative accuracy and precision to conventional label-free methods.32 Proteins of interests were subjected to further validation via immunoassay.
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MATERIALS AND METHODS
Animal Experiments
All animal experiments were performed under institutional guidelines according to an IACUC-approved protocol and monitored by the University Committee on Animal Research at the University of Rochester Medical Center. Male C57BL/6 mice (The Jackson Laboratory, Bar Harbor, ME) 10−12 weeks of age (n = 16) were anesthetized with 2,2,2-tribromoethanol and were intranasally inoculated with 0.03 mL of 1 × 105 EID50 H3N2 A/Hong Kong/X31 IAV. Lungs from four mice were harvested on days 0, 4, 7, and 10 (indicated as 0d, 4d, 7d, and 10d) postinfluenza inoculation. On the day of lung harvest, individual mice were weighed, anesthetized with isoflurane gas (2-chloro-2-(difluoromethoxy)-1,1,1-trifluoro-ethane), and euthanized by CO2 inhalation. Lungs from each mouse were separated into left and right lobes, removed, and snap-frozen. Samples were stored at −80 °C until protein extraction. Sample Preparation and Protein Extraction
Snap-frozen mouse lungs were manually ground to fine powder under liquid nitrogen, and 10 mg of the resultant tissue powder was suspended in 400 μL of ice-cold lysis buffer (containing 50 mM Tris, 150 mM NaCl, 0.5% sodium deoxycholate and 2% SDS, 2% IGEPAL CA-630, pH 8.0) plus protease and phosphatase inhibitor cocktail tablets (Roche Applied Science, Indianapolis, IN). The mixture solution was homogenized with a Polytron homogenizer (Kinematica AG, Switzerland) by repeating the homogenization (at 15 000 rpm, 5−10s) and cooling (20s) loop for 5−10 times. Sample sonication was performed until the solution became pellucid, and the mixture was then centrifuged (20 000g, 4 °C, 1h). The supernatant was collected and the protein concentration for each sample was determined by bicinchoninic acid assay (BCA) (Pierce Biotechnology, Rockford, IL) before storage at −80 °C. Surfactant-Aided Precipitation/On-Pellet Digestion
100 μg of protein from each sample was reduced by the addition of 30 mM tris(2-caboxyl)phosphine (TCEP) for 10 min and further alkylated by the addition of 20 mM iodoacetamide (IAM) in darkness for 30 min, both conducted under 37 °C with rigorous vortexing at 200 rpm. The proteins were then precipitated by stepwise addition of 9 volumes of B
DOI: 10.1021/acs.jproteome.5b00927 J. Proteome Res. XXXX, XXX, XXX−XXX
Article
Journal of Proteome Research chilled acetone with vortexing and incubated at −20 °C overnight. After centrifugation (20 000g, 4 °C, 30 min) the supernatants were discarded and the pellet containing precipitated proteins was washed with 800 μL of chilled acetone/water mixture (85/15 v/v %) and dried. For the onpellet digestion part, a two-step enzyme addition strategy was employed, which included digestion-aided pellet dissolution, followed by complete cleavage. Trypsin at an enzyme/substrate ratio of 1:30 (w/w) was dissolved in 100 μL of Tris buffer (50 mM, pH 8.5) and added to the precipitated protein pellets and incubated at 37 °C for 6 h with constant vortexing (Eppendorf Thermomixer). The other portion of trypsin at an enzyme/ substrate ratio of 1:25 (w/w) was then added to the redissolved and partially cleaved proteins, and the mixture was incubated (37 °C) overnight. Digestion was terminated by the addition of 1% formic acid (FA). The supernatant for individual samples containing tryptic peptides derived from 6 μg of proteins was prepared for LC−MS analysis.
without compromising the mass accuracy and resolution. The dynamic exclusion was enabled with the following settings: repeat count = 1; repeat duration = 30 s; exclusion list size = 500; and exclusion duration = 40 s. The activation time was 30 ms with an isolation width of 3 Da for ITMS; the normalized activation energy was 35%, and the activation q was 0.25. Four samples at each time point (0d, 4d, 7d, and 10d) were randomly analyzed. Protein Identification and Ion-Current-Based Quantification
The individual raw files (.raw) generated by LC−MS analysis were matched to the Mus musculus UniProt-Swissprot protein database (released on June 2013) with 16 616 entries using the SEQUEST-embedded Proteome Discoverer (version 1.4.1.14, Thermo Scientific). Raw files were imported into the software, and DTA files were generated from MS2 spectra. The search parameters set were listed as follows: (1) precursor ion mass tolerance: 25 ppm; (2) fragment ion mass tolerance: 0.80 Da; (3) maximal missed cleavages permitted: 2; (4) fixed modification: carbamidomethylation of cysteine; and (5) dynamic modification: oxidation of methionine. Protein filtering and control of the false discovery rate (FDR) was accomplished by Scaffold (v4.3.2, Proteome Software, Inc.)33 using a target-decoy search strategy with a concatenated database containing both forward and reverse sequences.34 Filtering parameters were: (1) minimal peptide number: 2; (2) DeltaCN scores: 0.10; and (3) XCorr scores: 0.85, 1.2, 1.8, and 3.1 for singly, doubly, triply, and quadruply charged peptides. Chromatographic alignment and global intensity-based MS1 feature detection/extraction was performed using SIEVE (v2.1.377, Thermo Scientific).35 The principal procedures include: (1) chromatographic alignment among LC−MS/MS runs using the ChromAlign algorithm (quality control of LC− MS/MS runs was achieved by monitoring and benchmarking the alignment scores and base-peak-ion-current intensity); (2) determination of quantitative “frames” based on m/z and retention time in the aligned collective data set (only frames with high-quality area under the curve (AUC) data (e.g., a signal-to-noise ratio >10) were selected for quantification so that the reliability of quantification was guaranteed); (3) calculation of peptide ion intensities for each frame; and (4) identification of MS2 fragmentation scans associated with each frame. The output spectrum report (.xls) from Scaffold and the .sdb file from SIEVE were incorporated by in-house R scripts to generate a .csv file containing the assignment and quantitative information on each frame. Quantile normalization of the ion current intensities and aggregation from frame level to protein level were accomplished by another individual R package. The abundance ratio of individual proteins was calculated using the average ion current intensities of four replicates in each group. The evaluation of statistical significance between groups (specimens post IAV inoculation versus control) was carried out with a Student’s t test, with a p-value cutoff of 0.05. Significantly altered protein levels for each time point were determined by setting cutoff threshold based on the abundance ratio, p value calculated by Student’s t test, and adjusted p value calculated by multiple testing correction (sequential Fisher’s combined probability test).36 In the current study, a protein abundance ratio of >1.5 or
