Comparative Proteomics Analyses Reveal a Potential Biomarker for

Comparative Proteomics Analyses Reveal a Potential Biomarker for the Detection of Vancomycin-Intermediate Staphylococcus aureus Strains Jolyne Drummelsmith,*,† Eric Winstall,‡ Michel G. Bergeron,† Guy G. Poirier,‡ and Marc Ouellette† Centre de recherche en Infectiologie and Division de Microbiologie, Faculté de Médecine, Université Laval, CHUQ, Pavillon CHUL, 2705 boulevard Laurier, Quebec City, Quebec, Canada G1V 4G2, and Plate-forme Protéomique du Centre de génomique de Québec, Université Laval, CHUQ, Pavillon CHUL, 2705 boulevard Laurier, Quebec City, Quebec, Canada G1V 4G2 Received August 10, 2007

Vancomycin-intermediate Staphylococcus aureus (VISA) strains tend to develop during glycopeptide treatment of infections caused by methicillin-resistant S. aureus (MRSA). Rapid and effective detection methods for VISA strains are lacking, and mechanisms of resistance are unclear. Here, global comparative proteomic approaches have been used to identify potential biomarkers of intermediate vancomycin resistance. With the use of high-resolution two-dimensional gels and iTRAQ mass tagging, numerous proteins were found to be differentially expressed between clinical MRSA and VISA isolates of the same multilocus sequence type. One of these, the predicted lytic transglycosylase SAV2095 (SceDlike protein), was selected for further study based on both its high level of induction in Mu50 and its predicted role in modeling the cell wall, which is the target of vancomycin. Relative SAV2095 mRNA expression levels were compared between 25 MRSA and VISA/heterogeneous VISA clinical isolates by real-time RT-PCR. The SAV2095 mRNA was significantly induced in all VISA isolates relative to all MRSA strains (p < 0.001), and significant induction of SAV2095 was also seen for several potential heterogeneous VISA strains that appear vancomycin-sensitive by standard minimum inhibitory concentration-determining methods. Furthermore, strains selectedin vitro for increasing levels of resistance from four unrelated clinical MRSA isolates displayed concomitant increases in levels of SAV2095 expression. Together, these results suggest that SAV2095 expression level could serve as a molecular diagnostic marker for the rapid detection of VISA. Keywords: Staphylococcus aureus • vancomycin • antibiotic resistance • detection • biomarker

Introduction Staphylococcus aureus causes several severe diseases in humans, including bacterial endocarditis, pneumonia, and hospital- and community-acquired bacteremic infections. One of the few remaining treatments for cases involving methicillinresistant S. aureus (MRSA) is vancomycin (Vn). Treatment failures caused by S. aureus strains exhibiting intermediate levels of Vn resistance (Vn-intermediate S. aureus; VISA) occur globally.1–6 Recently, VISA was isolated from a patient not exposed to glycopeptides,7 and Vn-resistant staphylococci have been found in healthy carriers.8 The majority of instances of clinical S. aureus Vn resistance involve strains exhibiting intermediate levels of resistance * Corresponding author: Jolyne Drummelsmith, RC-709, CHUQ, pavillon CHUL, 2705, boulevard Laurier, Québec, QC, Canada G1V 4G2. Telephone: (418) 654-2705. Fax: (418) 654-2715. E-mail: [email protected]. † Centre de recherche en Infectiologie and Division de Microbiologie, Faculté de Médecine, Université Laval. ‡ Plate-forme Protéomique du Centre de génomique de Québec, Université Laval.

4690 Journal of Proteome Research 2007, 6, 4690–4702 Published on Web 11/13/2007

(minimum inhibitory concentration (MIC) of 4–16 mg/L). Such resistance generally develops during the course of Vn treatment and is not mediated by van gene acquisition as seen in enterococci9 and a few highly resistant clinical S. aureus isolates.10,11 In the majority of instances of clinical Vn resistance involving S. aureus, however, the van genes are absent, suggesting that resistance has emerged due to point mutations of specific genes or regulatory elements. The glycopeptide antibiotic Vn has an affinity for the peptide tail of the peptidoglycan (PG) precursor and binds these upon translocation of the lipidlinked monomer to the outer leaflet of the cell membrane.12 The bulky Vn molecule creates a physical block, preventing access to and cleavage of the D-alanyl-D-alanine by transpeptidases, and also possibly interfering with the transglycosylation reaction.13 This prevents the incorporation of new PG monomers into the cell wall and leads to cell death. Strong correlations between increased cell-wall thickness, decreased crosslinking of PG strands, and resistance levels exist in numerous strains, both clinical and derived in vitro.14 This appears to D-alanyl-D-alanine

10.1021/pr070521m CCC: $37.00

 2007 American Chemical Society

Vancomycin-Intermediate S. aureus decrease Vn penetration through the cell wall and prevent its interaction with nascent precursors.15 Attempts to measure the prevalence of intermediate Vn resistance in clinical isolates of S. aureus have produced widely varying estimates between different hospitals in the same country as well as different countries (reviewed in ref 16). Factors contributing to this variability include the difficulty of identifying resistant strains based on standard laboratory practices for MIC determination. One such example is the Japanese strain Mu3,17 which, although isolated from a clinical case where Vn therapy failed, tests as susceptible to Vn (MIC of 2 mg/L by conventional Etest). Efforts undertaken to resolve this inconsistency revealed the existence of small subpopulations of cells able to grow at higher drug concentrations.17 Several such strains have since been identified and are classified as heterogeneous VISA (hVISA). The gold standard VISA screening methodology is population analysis profiling,18 which is too cumbersome for routine diagnostic use. Other methods in use or being proposed are the Vn agar screen test and the standard Etest, which show poor sensitivity,19 and the macromethod Etest, which displays a higher sensitivity but somewhat poor specificity.19 In addition, these tests require cell culture and long incubations (48 h). If a reliable biomarker (e.g., gene, mutation, or level of gene product) could be found for VISA and hVISA strains, this would allow the development of a rapid and accurate method of detection. Intermediate Vn resistance is likely caused by numerous mutations, given the structural and regulatory complexity associated with the cell wall and the variety of phenotypic changes linked to resistance, and effective biomarkers could be among any of these. A number of transcriptomic analyses involving VISA strains have been carried out, using the sequenced VISA type strain Mu50 or strains of similar genetic background,20–23 but have not pursued the diagnostic potential of the differences noted. Few proteomics analyses have addressed the phenomenon of intermediate Vn resistance,24,25 and to our knowledge only one has looked for potential biomarkers.25 Here, we present a comparative proteomic analysis between the VISA type strain Mu50 and a Vn-sensitive MRSA strain CMRSA-2,27 both of multilocus sequence type (MLST) 5 and agr type II, the most common genetic background associated with VISA. Furthermore, we show that the constitutive expression of one of the genes identified through this study, SAV2095 (also known as SceD-like protein), is significantly increased in VISA/hVISA relative to MRSA and, as such, may be useful in the development of a rapid diagnostic test for the detection of these strains.

Experimental Section Strains Used. The clinical isolates tested in this study, as well as Vn resistance level by agar screen and Etest, MLST, and agr type (where determined), and country of origin are presented in Table 1. Isolates displaying increasing levels of Vn resistance were selected from four distinct MRSA backgrounds (CMRSA-1 to -4, Table 1) by successive passages in tryptic soy broth (TSB) containing increasing levels of Vn at 37 °C. MIC Determination. For some MRSA and all in vitro-derived VISA isolates, Vn MICs were determined using standard Etest methods. Briefly, colonies from a fresh overnight plate were resuspended in saline to a turbidity equivalent to 0.5 McFarland units. This suspension was swabbed onto Mueller-Hinton agar

research articles plates, allowed to dry, and overlaid with a Vn Etest strip (AB Biodisk). Plates were incubated at 37 °C for 48 h and interpreted. S. aureus strain ATCC29213 was used as a control (MIC ) 1.5 mg/L). For some MRSA strains, the macromethod Etest was also applied, using an inoculum equivalent to 2 McFarland units on Brain-Heart Infusion agar plates. Protein Sample Preparation. Overnight cultures of strains CMRSA-2 and Mu50 were diluted 1/100 in TSB containing 3 mg/L vancomycin for Mu50 (since intermediate Vn resistance in clinical isolates is unstable28) and harvested at similar culture densities (exponential phase, OD600nm ∼ 1.0). The equivalent of 20 OD600 units of cells were harvested, washed in PBS, and incubated in 220 µL of 20 mM Tris-HCl, pH 7.5, 50 µL of 1 mg/ mL lysostaphin, 3 µL of protease inhibitor cocktail, and 6 µL of DNase for 30 min at 37 °C (approximately 300 µL final volume). Subsequently, 700 µL of 2D lysis buffer (7 M urea, 2 M thiourea, 3% CHAPS, 20 mM DTT, 5 mM Tris-(2-carboxyethyl)phosphine (TCEP), 0.5% IPG buffer pH 4–7 (Amersham), and 0.25% IPG buffer pH 3–10 (Amersham)) was added, and samples were vortexed and incubated at RT for 2 h. Samples were centrifuged at maximum speed in a microcentrifuge for 2 min to remove insoluble material, and protein was quantitated using the 2D Quant Kit (Amersham). Two-Dimensional Gel Electrophoresis (2DE). In the first dimension, 150 µg of protein samples was run on 24 cm Immobiline DryStrips (Amersham) of pH ranges 4.5–5.5, 5.0–6.0, and 5.5–6.7 on an IPGphorII IEF system (Amersham) as recommended by the manufacturer. Strips were equilibrated in equilibration buffer (50 mM Tris-Cl, pH 8.8, 6 M urea, 30% glycerol, 2% SDS, and trace of bromophenol blue) containing 10 mg/mL DTT for 15 min and then in equilibration buffer containing 25 mg/mL iodoacetamide for 15 min, and sealed to 12% acrylamide gels made in-house using 0.5% agarose in standard Tris-glycine electrophoresis buffer. Second dimension SDS-PAGE were run in an EttanDALT apparatus (Amersham) at 40 mA/gel and 15 °C until the tracking dye was run off the gel. Proteins were visualized by Sypro Ruby fluorescence (Invitrogen). Gels were fixed overnight in 40% methanol and 7% acetic acid, stained for a minimum of 5 h, and then destained in 10% methanol and 7% acetic acid for 3 × 1 h. Gels were imaged with the ProXpress CCD camera-based scanner (Perkin-Elmer) at 100 µm resolution using 480 nm excitation and 620 nm emission filters. For each strain, 2D gels of 4 independent samples were analyzed using Progenesis PG240 v. 2006 (Nonlinear Dynamics). Spots were quantified within this software using the INCA processing algorithm and automatically matched using default settings. Matches were verified, and matching normalized spot volumes were compared by t test within the software to generate p-values. Mass Spectrometry for 2D Gel Protein Identification. Gel plugs containing the proteins of interest were excised using a ProXcision robot (Perkin-Elmer) and sent for LC-MS/MS analyses (Eastern Quebec Proteomics Centre, Centre Hospitalier de l’Université Laval, Quebec). Gel plugs were placed in 96-well plates and then washed with water. Tryptic digestions were performed on a MassPrep liquid handling robot (Micromass) according to the manufacturer’s specifications and using sequencing grade modified trypsin (Promega). After extraction from the gel into 50% acetonitrile/0.1% formic acid, peptides were lyophilized in a speed vacuum and resuspended in 10 µL of 0.1% formic acid solution. Peptide MS/MS spectra were obtained by capillary liquid chromatography (10 cm, 75 µm picofrit column) coupled with an LTQ (ThermoFinnigan, San Journal of Proteome Research • Vol. 6, No. 12, 2007 4691

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Drummelsmith et al.

Table 1. Strains Used in This Study standard Etesta name

origin

alias

CMRSA-1 CMRSA-2 CMRSA-3 CMRSA-4 CMRSA-5 Hungarian New York Brazilian Sa 501V F-182

Canada Canada Canada Canada Canada Hungary US-NY Portugal

98S-329 98S-1241 98S-1258 98S-1237

US-KN

HUSA304, BAA-39 NYBK2464, BAA-41 HSJ216, BAA-43 CCUG41787 ATCC43300

Mu50 Mu3 HIP5827 HIP5836 SA MER SA MER-S6 SA MER-S12 SA MER-S20 HIP06297 HIP06854 HIP07256 HIP07920 HIP07930 HIP08926 HIP09143 HIP09313 HIP09433 HIP09662 HIP09735 LIM 1 LIM 2 LIM 3 99.3795.V NRS49 HIP09740 HIP09737 BR 15 BR 5 LY-1999 0620–01 LY-1999 0620–02 LY-1999 0620–03 NRS68 HIP10540 HIP10267 C2000001227 IL NRS118 NRS126 P1V44 160013 HIP12864 HIP13057 HIP13036

Japan Japan US-MI US-NJ France France France France US-NY US-NJ US-RI US-RI US-NY US-CA US-OH US-TX US-MI US-WV US-NC France France France Scotland S. Korea US-CA US-CA Brazil Brazil Oman Oman Oman US-KN US-OH US-MD US-MN US-IL US-CA US-MA Belgium UK US-OK US-MI US-CN

NRS1 NRS2 NRS3, MI NRS4, NJ NRS11 NRS12 NRS13 NRS14 NRS17 NRS18 NRS19 NRS21 NRS22, USA600, 99758 NRS23 NRS24 NRS26 NRS27 NRS28 NRS29 NRS35 NRS36 NRS37 NRS39 N/A NRS51 NRS52 NRS54 NRS56 NRS63 NRS64 NRS65 N/A NRS73 NRS74 NRS76 NRS79, T78628 N/A N/A NRS272 NRS283 NRS402 NRS403 NRS404

C1V4 C1V8 C1V30 C2V4 C2V8 C2V24 C3V4 C3V8 C3V24 C4V8 C4V20

CMRSA-1 CMRSA-1 CMRSA-1 CMRSA-2 CMRSA-2 CMRSA-2 CMRSA-3 CMRSA-3 CMRSA-3 CMRSA-4 CMRSA-4

macro Etestb

(mg/L)

(mg/L)

MRSA Strains 1.5 1.5 2 1 -

8 3 3–4 2–4 -

VISA Strains 8 2 6 6 3 6 4 6 12 6 3 3 4 4 4 3 4 3 6 2 4 6 8 6 6 4 3 6 4 1.5 4 3 12 6 4 3 8 3 4 3 8 6 8

12 6 12 6 8 16 24 24 16 16 8 4 8 6 8 12 8 8 16 4 6 6 16 12 12 16 12 16 16 6 16 6 16 8 12 8 24 8 48 8 48 16 16

In Vitro Strains 5 10 16 6 9 30 3 8 20 5 8

agar screenc

MLST

agr

-

45d 5 239 36d

Ia II I III U1e U2e II I Ia III

34, 34, 34, 34, 34 53, 55, 57,

II II II II II II II II II II II I Ia II II I Ia I III I I I I II II II I I I I I II I II II II U1e II Ie III II II II

1, 58 17 3, 35, 59 3, 35, 59 7 7, 58 7 7 60, 58 www.narsa.net www.narsa.net www.narsa.net 35 www.narsa.net www.narsa.net www.narsa.net www.narsa.net www.narsa.net www.narsa.net 4, 35 4 4 www.narsa.net 61, 35 62, 35 62, 35 www.narsa.net www.narsa.net, 58 63, 58 63 63 www.narsa.net www.narsa.net, 35 www.narsa.net www.narsa.net 64, 65 www.narsa.net 47 66 www.narsa.net www.narsa.net www.narsa.net www.narsa.net

CG (24) NG (72) CG (24) NG (72) NG (72) CG (24) CG (24) CG (24) CG (24) IC (48) NG (72) NG (72) IC (48) NG (72) IC (48) IC (48) NG (72) NG (72) CG (24) NG (72) NG (72) NG (72) CG (24) CG (24) CG (24) CG (24) IC (48) CG (24) IC (48) NG (72) IC (24) NG (72) CG (24) CG (24) CG (24) IC (48) CG (24) NG (72) CG (24) IC (48) CG (24) CG (24) CG (24)

239 5 239

5 5d 5 5 5 5d 5 8d 45d

45d 247 247d 5d 5 5 239d 239d 372d 372d 8d 5d 247d 247d 36d

strain references

35 35 35 52 54 56 54

This This This This This This This This This This This

study study study study study study study study study study study

a Etest (0.5 McFarland inoculum) results, as determined by NARSA (www.narsa.net) for VISA strains and by this study for in vitro mutants and selected MRSA strains: sensitive, MIC < 4; intermediate, 4 e MIC e 16; resistant, MIC > 16 mg/L. b Etest (2 McFarland inoculum) results, as determined by NARSA (www.narsa.net) for VISA strains and by this study for selected MRSA strains. c Agar screen test results as determined by NARSA (www.narsa.net); CG ) confluent growth, IC ) individual colonies, NG ) no growth. Incubation hours is in parentheses. d MLST (multilocus sequence typing) was either carried out or confirmed here. e U1, U2, and U3 refer to RFLP patterns that do not match any of those from ref 32, and the result for I was unclear.

Jose, CA) quadrupole ion trap mass spectrometer with a nanospray interface as described previously.29 Resulting MS/ MS spectra were interpreted using Mascot30 and searched 4692

Journal of Proteome Research • Vol. 6, No. 12, 2007

against eubacterial proteins in the UniRef100 database. The peak list was created automatically by Mascot Daemon (version 2.1 Matrix Science) (with extractmsn.exe), using a scan range

Vancomycin-Intermediate S. aureus of 4000-12 000 and mass range 700–3500. No scan grouping was allowed and precursor charge state was set to auto. Carbamidomethylation of cysteine and partial oxidation of methionine, 2 missed cleavages, and an error tolerance of 2.0 Da for peptides and 0.5 Da for fragments were considered in the search. A peptide was considered a good match if it produced a Mascot score greater than 53, the cutoff calculated by the software as indicating identity or extensive homology at p < 0.05. Two high-scoring peptides were required for a protein identification to be accepted, and identifications based on only 2 such peptides were confirmed by visual inspection of the associated spectra. Protein identification data is presented in Supplementary Table 1 in Supporting Information. Protein Digestion and iTRAQ Labeling. Duplicate protein aliquots of Mu50 and CMRSA-2 protein samples prepared for 2D gel electrophoresis (above) of 110 µg each were precipitated with the 2D Clean-Up Kit (Amersham). Each protein pellet was then solubilized in 0.5 M triethylammonium bicarbonate pH 8.5 supplemented with 0.1% (w/v) SDS. Protein cysteine residues were reduced with 5 mM TCEP for 1 h at 60 °C and then blocked with 8 mM methyl methane-thiosulfonate (MMTS) for 10 min at room temperature. Protein samples were digested with 5 µg of modified porcine trypsin (Promega) in the presence of 10 mM CaCl2 for 18 h at 37 °C. Final SDS concentration at this point was 0.05% (w/v). Efficiency of protein digestion was assessed by SDS-polyacrylamide gel electrophoresis using undigested and digested aliquots of proteins. Tryptic peptides from the different samples were each labeled with one of the four iTRAQ reagents (Mu50, 114 and 116; CMRSA-2, 115 and 117) according to the manufacturer’s protocol (Applied Biosystems). Labeled samples were then combined and dried in a vacuum concentrator. Isoelectric Focusing of iTRAQ-Labeled Peptides. Lyophilized iTRAQ-labeled peptides were resuspended in 325 µL of Milli-Q water containing 0.25% (v/v) ampholyte (Bio-Lyte 3/10 Ampholyte, Bio-Rad). The resulting solution was used to rehydrate an 18-cm IPG gel strip (pH 5-8) for 10 h. Conditions for isoelectric focusing of peptides were as follows: 250 V for 15 min, 10 000 V for 3 h, 10 000 V for 60 000 Vh and then a hold at 50 V until peptides were extracted. Excess overlaying oil was gently blotted away from the IPG strip, and the strip was cut into 36 pieces of 5 mm each. IPG strip pieces were transferred into a 96-well plate, and peptides were eluted from the gel pieces by two successive extractions (15 min each with shaking) that were subsequently pooled. The first extraction was in 100 µL of a 1% formic acid and 2% acetonitrile solution, and the second extraction was in 100 µL of 1% formic acid and 50% acetonitrile solution. Extracted peptides were then dried using a vacuum concentrator and resuspended in 25 µL of a 0.1% formic acid solution. Mass Spectrometry of iTRAQ-Labeled Peptides. For each analysis, 5 µL of the peptide solution was injected to the nano LC-ESI-MS/MS system. Mass spectrometry analyses were performed on QStar XL Hybrid ESI quadrupole time-of-flight tandem mass spectrometer (Applied Biosystems-MDS Sciex) interfaced with an integrated online capillary liquid chromatography system consisting of an autosampler, switching pump, and micro pump (LC Packings). Peptides were first trapped and concentrated on a 300 µm i.d. × 5 mm C18 reversed-phase precolumn (LC Packings) at a flow rate of 15 µL/min. The peptide mixture was then separated on a 75 µm i.d. × 10 cm BioBasic (New Objective) C18 reversed-phase capillary column at a flow rate of 200 nL/min. The gradient started at 98% buffer

research articles A (0.1% formic acid in water) and 2% buffer B (0.1% formic acid in acetonitrile) for 5 min. This was followed by an increase from 2% to 25% in buffer B over 85 min, then from 25% to 40% buffer B over 10 min, followed by 40-80% buffer B over 5 min The column was then washed with 80% buffer B for 5 min and equilibrated at 2% buffer B for 20 min. Eluted peptides were electrosprayed through a distal-coated silica tip (15 µm i.d.) (New Objective) with an ion spray voltage of 2800 V. Data acquisition on the mass spectrometer was in the positive ion mode within a mass range of 400 to 1600 m/z for the precursor ions for a 1 s period. Information-dependent acquisition (IDA) of MS/MS data were performed on the 3 most abundant peptides exceeding 15 counts with +2 to +4 charge states within a 100-2000 m/z value window. Time summation of MS/ MS events was set to 3 s. Fragmented target ions were dynamically excluded for 60 s with a 100 ppm mass tolerance. Data Analysis. For iTRAQ, raw data file (.wiff) processing, protein identification, protein quantification, and statistical analyses were performed using ProteinPilot v.1.0 software (Applied Biosystems, MDS-Sciex) running the Paragon algorithm.31 Searches were conducted against a subset of the UniRef100 protein database (version 8.0) containing only entries from Staphylococcus sp. (12 647 sequences). The search parameters allowed a peptide and fragment mass deviation of up to 0.2 Da and one missed trypsin cleavage. Cysteine modification by MMTS and oxidation of methionine, as well as iTRAQ labeling of the N-terminus of peptides and of the side chains of lysine and tyrosine residues, were allowed. Peak areas for each of the four reporter ions (m/z: 114, 115, 116, and 117) were corrected to account for isotopic overlap according to the manufacturer’s instructions. Protein identification and quantification results were calculated and viewed using ProteinPilot v.1.0. Protein iTRAQ ratios were corrected for experimental bias using the median average protein ratio as the correction factor. At this point, it became apparent that the 114 labeling reaction was poor (25% of the overall intensity of the other labels, Supplementary Table 2 in Supporting Information); therefore, the 116 Mu50 values were used for calculation of ratios with both CMRSA-2 samples (115 and 117). Only peptides above 80% confidence were used for identification and quantification, and only proteins including at least 1 peptide above 98% confidence were considered. Twelve peptides belonging to differentially expressed proteins scored below 90% confidence in Protein Pilot. Of these, 7 scored above 95% using Mascot (Matrix Science) and Scaffold (Proteome Software), including all 3 peptides from protein 318, leading us to retain this identification. The remaining 5 peptides were removed from the analysis. Quantification results were manually reviewed for all proteins found to be differentially expressed (iTRAQ ratio > 1.5 or < 0.66) and for those included in Table 2. Quantification data obtained from very low intensity spectra were removed from the protein ratio calculation. Proteins presented in Table 2 identified with less than 3 different peptides had their associated spectra inspected for manual validation of the peptide identification. Doubtful identifications were rejected. Protein identification data is presented in Supplementary Table 2 in Supporting Information. iTRAQ ratios presented in Table 2 represent protein ratios calculated by ProteinPilot based on the weighted average Log ratios of Journal of Proteome Research • Vol. 6, No. 12, 2007 4693

4694

accession

MS no.

b


Q6GG11 Q2YTE6 P63741 Q99W07 Q931P6

9 10 11 12 13

5921 5918

70 60

24 75 76

%cov

c

Q6GED5

Q931U5 Q5HCY1

A6TXU8

Q2YUM9

Q53707 Q6GK35 A6U3I1

P72373

A6U4P4 Q2FXT7 Q6GIU2

Q6GDA5

17

18 19

20

21 22 23 24 25

26

27 28 29

30

5998

5915

6002 5951

5917 5920

5945

71

81

22 34

59 26

54

e

396

2.4 × 10-2

231

379

117 454 408

194

102

65

19 164

266

301

43 35

378 363 294 476 367

36

22

46

42 17 8

33

20

31

36

39 23

26

18

60 43

28 14 11 23 55

33

26 24 46

248 54 176

%cov

c

-3.06

2.67

-3.32 0.60 1.08

1.75

2.73

2.43

2.24

0.97 1.08

0.97

0.75

-0.62 0.61

0.72 0.85 0.77 0.05 2.11

0.71

-3.06

2.82

putative transaldolase aconitate hydratase/citrate hydrolyase 2,3-bisphosphoglycerate-dependent phosphoglycerate mutase 2,3-bisphosphoglycerate-independent phosphoglycerate mutase citrate synthase II isocitrate dehydrogenase glycerol kinase alcohol dehydrogenase I acetate kinase, Mu50 homologue

acetate kinase, N315 homologue probable malate:quinone oxidoreductase 2 succinyl-CoA synthetase beta chain

gene name

CapG

MecA LytM

IsdA

VraD

Fe-regulated surface determinant protein A precursor

ABC transporter, ATP binding protein Preprotein translocase, YajC subunit PTS transport system, fructose-specific IIABC component ABC transporter, ATP-binding protein

penicillin binding protein 2A glycyl-glycine endopeptidase precursor transglycosylase-like precursor, similar to SceD UDP-N-acetyltalosamine 2-epimerase

MurA1 UDP-GlcNAc 1-carboxyvinyltransferase 1 GlmS glucosamine-fructose-6-phosphate aminotransferase hypothetical protein, similar to teichoic acid biosynthesis protein B SsaA2 Staphylococcal secretory antigen ssaA2 precursor Atl bifunctional autolysin IsaA immunodominant staphylococcal antigen A precursor CapO UDP-glucose/GDP-mannose dehydrogenase MurA2 UDP-GlcNAc 1-carboxyvinyltransferase 2

CitZ CitC GlpK Adh1 AckA

GpmI

Tal CitB GpmA

AckA Mqo2 SucC

gene

Transport: -3.43 StpC 0.79 YajC 1.54 FruA

2.04

2.40

2.22

2.06

0.93 1.02

0.92

0.70

-0.62 0.56

0.82 0.78 0.74 -0.03 2.03

0.68

-1.64 -0.81 -0.68

-1.69 -0.86 -0.65

ratio2

d

-2.52 -0.48 0.40

d

-2.48 -0.51 0.46

ratio1

iTRAQ data

55 65 30

f

29 13 318

prot no.

1.1 × 10-5 4.0 × 10-3

1.9 × 10-2 -

3.8 × 10-2

-

1.2 × 10-2 4.0 × 10-3 -

p-value

u MRSA -

4.5

2.7 4.3

2.7 u VISA

2.3

Other membrane/cell wall/secreted proteins: 31 Q99UX4

Q2G1B8

16

u VISA u VISA

-2.2 -1.8 u VISA

ratio

d

2D gel data

Cell surface structure metabolism: 14 Q5HEA0 15 Q6GES3

Q5HHP2

8

Carbohydrate metabolism: 1 Q99TF2 2 Q5HCU5 5914 3 Q5HGI7 5959 4 5946 5 Q99T88 6 Q6GH55 7 Q6GE17

line no.

Function:

Table 2. Proteins Differentially Expressed between CMRSA-2 and Mu50 Identified in This Studya

-1.21

-1.32 1.82 0.62

0.62

0.40 -2.21 0.87 1.57

1.36 1.43

-0.16 2.64

0.15

0.01

-0.45

M

2.98

1.47 1.67

0.41

0.46

0.63

P

2.79

0.92

0.44 -0.92

-0.82 -1.12 -0.52 -1.28 -1.23

-1.18

0.47 1.12

-1.23 1.58 -0.82

S

-2.17

-3.14

1.20

-1.54 2.26 3.39

Mc

comparison to previously published resultsg

research articles Drummelsmith et al.

P0A067 Q6GFL5

32 33 34 35

36 37

Q931I9

49

P0C1U6 Q5HHQ8 Q6GEV2

Q5HEP0

56 57 58

59

Fatty acid metabolism: 60 Q2YUK5 61 A6U199 62 Q2G1I3

UPI0000054751 Q7A601

54 55

Regulation: 50 Q2VG66 51 Q6GKJ3 52 Q6G5X1 53 Q6GFH6

Q2YUU9 Q5HHQ4 Q2YY09 UPI0000054388

5949 5950

5916

5999

5948

5964

6000 5960

Stress response: 43 A6U0B1 44 A5IUG5

45 46 47 48

6004

5961

MS no.

Q6G6M7 Q2YSM0 Q5HGV9

40 41 42

Cofactor biosynthesis: 38 Q5HIF5 39 Q931E6

accession

Q6GD14 Q5HJ91 UPI000005449E UPI00000547A8

line no.

Function:

Table 2 (Continued)

b

56 54

80

38

71

17

50 71

68

51

%cov

c

p-value

4.6 × 10-2

3.2 × 10-2

2.1 u VISA

2.0

8.0 × 10-4 -

3.3 × 10-2

u MRSA -

3.4

-1.5

e

2.0 × 10-3

u MRSA u MRSA -

1.9

u MRSA -

ratio

d

2D gel data

330

87 261 364

229 405

341 291 449

246 418 123

435 247

47 134

292 254

6 270 455 274

prot no.

f

ratio2

d

gene

0.75 1.00 1.98

VraR

0.69 SarA 0.99 HprK 1.84

-0.66 -0.64 CspC 0.63 0.67

(3R)-hydroxymyristoyl ACP dehydratase 3-oxoacyl-ACP reductase, putative hypothetical protein, acyl-CoA dehydrogenase domain

accessory gene regulator A HTH-type transcriptional regulator cold shock protein, DNA binding low molecular weight phosphotyrosine phosphatase cold-shock protein, DNA-binding possible rRNA SAM-dependent methyltransferase staphylococcal accessory regulator A HPr kinase/phosphorylase hypothetical protein, putative transcriptional regulator cell stress stimulon response regulator

GTP pyrophosphokinase hypothetical protein, SOD domain, JH1 homologue SodM superoxide dismutase [Mn/Fe] -0.62 -0.73 TrxB thioredoxin reductase 0.64 0.69 probable nitric oxide reductase 0.83 0.77 hypothetical protein, general stress protein-like protein hypothetical protein, SOD domain, Mu50 homologue

RelA

2.39 -0.76 -0.73 FabZ

87 24 2.01

55 11

gene name

IgG binding protein A secreted virulence factor hypothetical protein precursor probable transmembrane sulfatase precursor signal peptidase IB foldase precursor

pyridoxal biosynthesis lyase 3-methyl-2-oxobutanoate hydroxymethyltransferase -0.77 -0.76 PanE 2-dehydropantoate 2-reductase 0.80 0.77 PtpS 6-pyruvoyl tetrahydrobiopterin synthase CoaD Phosphopantetheine adenylyltransferase

1.25 SpsB 2.02 PrsA

-0.53 -0.53 PdxS -0.89 -0.81 PanB

1.40 2.17

-2.75 -2.80 Spa -1.13 -1.18 EsxA -0.71 -0.69 0.53 0.59

ratio1

d

AgrA 2.09 -1.65 -1.68 SarS 3.22 -1.30 -1.34 CspB 19 -0.75 -0.82

30 26 42

16 31

76 49

45 23

76 22 22 19

%cov

c

iTRAQ data

2.25

-0.51 0.05

-0.26

-0.19

-0.58 -1.22

0.09

0.20

-0.38

2.47 3.14

2.94

S

-1.40

P

-0.79

M

1.32

-2.07

1.68

-5.61

Mc


Vancomycin-Intermediate S. aureus

research articles

Journal of Proteome Research • Vol. 6, No. 12, 2007 4695

4696

accession

MS no.b


51

Q6GKT8

Q7A3W3

Q931N3

Q6G7P6

Q7A3J0

Other: 85

86

87

88

89

5953

44

63

Plasmid associated: 84 Q932M1 6001

5997

u VISA

53

2.1

-1.7

1.5

-1.8

28

Q5HIG4

-1.9 -1.8 -1.5

acid metabolism: Q5HIH5 5996 Q6GHN4 Q5HE88 6007

Nucleic 81 82 83

80

Proteolysis: 78 Q2FX14 79 Q6G9T5

26 20 10

7.7 × 10-5

1.0 × 10-3

6.0 × 10-3

-

8.0 × 10-3

0.035 1.9 × 10-5 1.0 × 10-3

139

138

445

221

97 116 68

180

322 260

41

39 353 1 9

biosynthesis: Q99U14 Q5HF94 Q2G0N1 5958 Q2YT47 5957 Q5HIC7 5963 Q99UK9

407

Protein 72 73 74 75 76 77

9.5 × 10-4

3.0 × 10-3 -

88 157 380

1.7

1.4 u VISA

acid metabolism: Q6GFX1 Q5HE87 Q6GC28

44

43 34

prot no.f

Amino 69 70 71

5956

6003 5955

p-valuee

u MRSA -

ratiod

388

Q9FD85 Q6GBP4 P65102

32

%covc

2D gel data

Urea metabolism: 67 Q5HDR7 68 Q6GEE4

64 65 66

Isoprenoid biosynthesis: 63 A6U473 5962

line no.

Function:

Table 2 (Continued)

28

31

22

40

43 31 58

26

2.62 20

34

50 2.04 76 60

25 27 27

22

31

%covc

1.01

0.96

1.21

-0.66

-0.65 1.17

-0.74

0.34 -0.88 -0.19

1.27

-0.70 0.92

0.73

-0.74 0.59 -0.09 -0.07

-0.93 -0.77 0.88

3.05

1.06

ratio2d

-0.61

0.27 -0.85 -0.18

1.33

-0.63 0.89

0.77

-0.70 0.57 -0.12 -0.06

-0.93 -0.77 0.82

3.77

0.84

ratio1d

iTRAQ data gene name

Prs PyrC Upp

FtsH

PepS

RpsA RplT FusA DnaK Tuf ProS

AroA GlyA MetB

UreE UreC

hypothetical protein, carbohydrate kinase domain hypothetical protein, Zn-containing alcohol dehydrogenase superfamily hypothetical protein, thioesterase superfamily hypothetical protein, putative S1 RNA binding domain hypothetical protein

replication-associated protein

ribose-phosphate pyrophosphokinase dihydroorotase uracil phosphoribosyltransferase

aminopeptidase hypothetical protein, similar to processing proteinase peptidase M41, FtsH

30S ribosomal protein S1 50S ribosomal protein L20 translation elongation factor G chaperone protein translation elongation factor Tu prolyl-tRNA synthetase

chorismate mutase serine hydroxymethyltransferase cystathionine gamma-synthase

urease accessory protein ureE urease alpha subunit

isopentenyl-diphosphate delta-isomerase, JH9 homologue MvaK1 mevalonate kinase MvaK2 phosphomevalonate kinase Fni isopentenyl-diphosphate delta-isomerase, Mu50 homologue

Fni

gene

-0.40

0.12

-0.30 -0.86 -0.76

1.27

0.19 0.11 -0.58 0.54 -0.08 0.65

0.51 -0.30

1.32

-0.32 0.18

S

-1.15

P

0.43

M

Mc


research articles Drummelsmith et al.

membrane-embedded lytic regulatory protein hypothetical protein UPF0135 protein hypothetical protein, DNA-binding domain 2.66 2.82 24 1.7 × 10-6 -

444

1.94 2.21 2.72 314

2.9 u VISA 49 56 Q931V5 P67273 Q7A4J4 91 92 93

5947 5952 Q2YYF6 90

a Proteins are defined by UniRef_100/UniParc accession number and grouped by known or predicted function (based on homology or sequence motifs). b MS no. refers to the mass spectrometry data set used for protein identification obtained from 2D gel spots (examples shown in Figure 1) as presented in Supplementary Table 1 in Supporting Information. c %cov, or percent coverage, refers to the percentage of the protein identified by mass spectrometry. d Ratio refers to the log2 of the expression ratio of the spot or protein in Mu50 versus CMRSA-2; positive values denote overexpression in Mu50, negative numbers represent overexpression in CMRSA-2. uMRSA indicates the spot was unique to CMRSA-2, whereas spots labeled uVISA were found only in gels containing Mu50 protein lysate. Ratio1 and ratio2 refer to iTRAQ experimental replicates 116/115 and 116/117, respectively. e As calculated by Progenesis software. f Prot no. refers to the mass spectrometry data set used for protein identification and quantitation obtained by iTRAQ analyses as presented in Supplementary Table 2 in Supporting Information. g Log2 expression ratios of resistant/sensitive from: S, Scherl et al.,25 membrane proteome by iTRAQ of 14–4/MGR3; P, Pieper et al.,24 proteome of VP32/P100; M, Mongodin et al.,23 microarray of VP32/P100; Mc, McAleese et al.,43 microarray of JH9/JH1.

Mc M P S ratio1d %covc ratiod %covc MS no.b accession line no.

Function:

Table 2 (Continued)

2D gel data

p-valuee

prot no.f

iTRAQ data

ratio2d

gene

gene name


Vancomycin-Intermediate S. aureus

research articles peptides displayed in Supplementary Table 2 in Supporting Information. Accessory Gene Regulator (agr) and Multilocus Sequence Typing. Genomic DNA suitable for PCR was prepared from S. aureus colonies picked from overnight tryptic soy agar plates using Instagene reagent (Bio-Rad) as suggested by the manufacturer except that 20 µg/mL lysostaphin (Sigma) was added to the Instagene mix and an incubation at 37 °C for 15 min was included after resuspension of the colony. agr typing was carried out as described by Papakyriacou et al.32 Multilocus sequence typing was carried out as described at www.mlst. net,33 and DNA sequencing was carried out by the Sequencing and Genotyping Platform of the Centre de recherche du Centre Hospitalier de l’Université Laval using the Big Dye sequencing kit and the ABI 3730xl DNA analyzer (Applied Biosystems). RNA Sample Preparation. Overnight cultures of strains were diluted 1/100 in tryptic soy broth containing either 0 (for all strains), 1 (for selected MRSA), or 3 (for selected VISA) mg/L Vn and grown to midlog phase. Cells from 1 mL culture were harvested and treated with RNAProtect (Qiagen). Cells were subsequently treated with lysostaphin for 30 min at 37 °C, and total RNA was isolated from each sample using the RNeasy Kit (Qiagen). Samples were then treated with Turbo DNA-free (Ambion, Austin, TX) to remove any contaminating genomic DNA. RNA quantity and quality was assessed using an Agilent Technologies 2100 Bioanalyzer and RNA 6000 Nano LabChip kit (Agilent, Mountain View, CA). Complementary DNA (cDNA) was generated from total RNA using a random primer hexamer following the protocol for Superscript II (Invitrogen, Carlsbad, CA). Real-Time Quantitative RT-PCR (qRT-PCR). Samples were run in triplicate and amplified in a 15 µL reaction containing 7.5 µL of 2× Universal PCR Master Mix (Applied Biosystems, Foster City, CA), 10 nM of Z-tailed forward primer, 100 nM of reverse primer, 100 nM of Amplifluor Uniprimer probe (Chemicon, Temecula, California), and 1 µL of cDNA target. Moreover, no-template controls were used as recommended. The mixture was incubated at 50 °C for 2 min, at 95 °C for 4 min, and then cycled at 95 °C for 15 s and at 55 °C for 40 s 55 times using the Applied Biosystems Prism 7900 Sequence Detector. Amplification efficiencies were validated and normalized to expression of the 16S rRNA gene as a standard, and quantity of target and standard genes were calculated according to a standard curve. Primers were designed using Primer Express 2.0 (Applied Biosystems), and their sequences were the following: for SAV2095, forward, 5-Ztail-GAAGTTGAAGCACCACAAAATGC-3, reverse, 5-TGTTGATGCTTGTGGTTGTTGAG-3; and for 16S rRNA, forward, 5-Ztail-TCGTGTCGTGAGATGTTGGG-3, reverse, 5-GCTTAAGGGTTGCGCTCGT-3. Amplicons were detected using the Amplifluor UniPrimer system where forward primers used contained the 5′ Z sequence ACTGAACCTGACCGTACA. Biological duplicates of most strains were tested (except in cases where the reverse transcription reaction appeared to have failed or insufficient RNA was extracted). Statistical analysis was carried out on log-transformed data using either Student’s t test or one-way analysis of variance followed by Tukey’s multiple comparison test in GraphPad Prism (v. 3.03). Homogeneity of variance and normal distribution (where n > 4) were verified. Journal of Proteome Research • Vol. 6, No. 12, 2007 4697

research articles

Drummelsmith et al.

Figure 1. Examples of portions of pH 4.5–5.5 Sypro Ruby-stained 2D gels of CMRSA-2 (Vn-sensitive) and Mu50 (Vn-resistant) protein lysates. Spots selected for MS/MS analysis are labelled as in Table 2.

Results and Discussion Comparative Proteomic Analyses of S. aureus Clinical Isolates CMRSA-2 and Mu50. To minimize variation unrelated to Vn resistance phenotype, a pair of clinical MRSA and VISA isolates of related genetic background were selected for analysis using MLST, which provides a high level of discrimination for determining the genetic relatedness of strains33 (see www. mlst.net, Table 1). CMRSA-2 is representative of an epidemic clonal type responsible for a significant number of Canadian MRSA infections,34 and is of the same type as the majority of VISA strains identified in the United States as well as the sequenced VISA type strain Mu50, type 5.35 Therefore, the strain pair CMRSA-2/Mu50 was selected for comparative proteomic analysis. Gels of overlapping pH ranges 4.5–5.5, 5.0–6.0, and 5.5–6.7 were generated using 4 independent biological samples for each strain (examples are shown in Figure 1) and analyzed using Progenesis PG240 software. Accounting for overlapping spots, approximately 1800 spots were reproducibly visualized in each strain. On the basis of a published estimate of 1.42 spots/ gene,36 this corresponds to 1268 ORFs, which would represent approximately 46% of the S. aureus predicted proteome. Overall, 104 spots were unique or at least 2-fold induced in CMRSA-2 (p < 0.05), and 98 in Mu50. Of these, 42 spots (based on spot intensity and fold-change) were subjected to MS/MS protein identification, and 36 returned acceptable identifications (see Experimental Section for criteria, Supplementary Table 1 in Supporting Information for MS/MS data). Occasionally, multiple valid identifications (in some cases 5 or 6) were obtained per spot. We carried forward only those secondary hits with Mascot scores >50% of that of the primary hit (Supplementary Table 1 in Supporting Information). For iTRAQ, two replicate samples of each strain were digested, labeled, and combined. Peptides were separated on IPG strips, and these were cut into 36 fractions from which peptides were eluted and subjected to LC-MS/MS. Data from all fractions were combined and analyzed, resulting in identifications of products from 467 ORFs with protein scores greater than or equal to 2, corresponding to p < 0.01, and a further 40 ORFs had scores greater than or equal to 1.4 (p < 0.05; see Supplementary Table 2 in Supporting Information for MS/MS data). This represents approximately 18% of the predicted proteome. Replicate MRSA samples showed very similar quantitation results overall, as depicted in Figure 2A, while comparison between MRSA and VISA strains identified a number 4698


Figure 2. Volcano plot representation of iTRAQ results. (A) Ratios of replicate samples of CMRSA-2 against p-value of ratio; (B) ratios of Mu50 versus CMRSA-2 against p-value of ratio. Ratios and p-values were calculated with ProteinPilot using the Paragon algorithm. Only proteins with multiple peptide measurements (and thus calculated p-values) are included. Dotted vertical lines indicate the fold-change threshold selected for inclusion in Table 2 of 1.5-fold, and the dotted horizontal line indicates the minimum p-value for inclusion in Table 2 of 0.05.

of differentially expressed proteins (Figure 2B). The qualities of the spectra associated with these identifications were manually verified, and after those based on poor-quality or lowintensity spectra were discarded, 63 proteins showing greater than 1.5-fold differences in expression between the two strains were retained. It is likely that several of these rejected identifications were valid (for example, the VraR protein was rejected in the iTRAQ experiment but was also clearly identified by 2D

Vancomycin-Intermediate S. aureus gel). Where a protein was identified by both iTRAQ and 2D gel (primary hit), the iTRAQ data was included in the table. In addition, all 8 secondary hits retained in our 2D gel analysis (Supplementary Table 1 in Supporting Information) were identified by iTRAQ and none were differentially expressed (Supplementary Table 2 in Supporting Information), leading us to remove them from further analysis. The combined results of the 2D gel and iTRAQ analyses are presented in Table 2. It has been shown that 2DE and MS-based methods have different limitations and, when used together, result in a much higher coverage of the proteome.37–39 This appeared to be the case in our hands, with 92 distinct proteins identified as differentially expressed overall, but only 14 identified by both approaches (although we did not identify 2D gel spots that were not altered in intensity). Of these, 6 proteins were found to be differentially expressed in the same direction (i.e., up- or downregulation) by both approaches (at least 2-fold by 2D gel and 1.5-fold by iTRAQ) (Table 2, lines 13, 21/22, 25, 26, 27, 89). There were 2 cases where proteins highlighted by 2DE were also found by iTRAQ, but were below the cutoff to be included independently (Table 2, lines 2 and 38), which could mean that the iTRAQ cutoff chosen was overly conservative. In 4 cases, the iTRAQ ratios were close to 1, but the 2D gel showed a significant difference (lines 12, 74, 75, 83), and in 2 cases, the direction of expression appeared to be opposite (Table 2, lines 3 and 81). These cases might be explained by sequence variation or post-translational modification, which could cause differences in 2D spot pattern not discernible by iTRAQ, but this hypothesis cannot be confirmed due to the limited number of 2D gel spots identified here. These cases could be interesting from a biological point of view, but require further study to clarify. In general, both techniques afforded biologically relevant data. Estimated coverage was higher using 2D gels, but iTRAQ analysis was much less labor-intensive. Of the proteins identified from 2D gels, only one (2.9% of the proteins identified by 2D gel) contains a putative TM segment as predicted by TMHMM.40 By comparison, 24 proteins containing predicted nonsignal peptide TM domains (predicted using TMHMM and SignalP v.3.041) were identified by iTRAQ, representing 4.8% of the total. Of these, 10 are predicted to contain between 2 and 9 TM segments, demonstrating that iTRAQ displayed a clear advantage in identifying proteins with putative TM domains. Comparison of ratios derived by iTRAQ and 2DE show a general trend whereby the difference measured by iTRAQ is smaller than that derived from 2DE, a phenomenon also observed by others,42 suggesting that iTRAQ ratios might be ‘compressed’. Overall, we estimate that we were able to visualize approximately 50% of the predicted proteome by analyzing whole-cell lysates. While this is a respectable level of coverage, fractionation prior to analysis would certainly increase this value and would allow lower-abundance proteins to be identified. Proteins Differentially Expressed between Mu50 and CMRSA-2. The combined results of the 2D gel and iTRAQ analyses are presented in Table 2, where proteins have been grouped by biological process or function where possible, based on sequence homology or the presence of particular protein domains. Proteins with roles in carbohydrate metabolism, peptidoglycan metabolism, stress response, and regulation were the most common, with many proteins of unknown function identified as well.

research articles Several comparative transcriptomic20–24,43 and a few proteomic24,25 analyses involving VISA strains have been carried out, using Mu50 or strains of similar genetic background. A number of the same genes/proteins were identified here, supporting the general relevance to Vn resistance of these findings (Table 2). Comparison with the results of McAleese (microarray data43) shows 10 genes in common, 9 showing similar expression trends, whereas comparison with Pieper (2D gel data24) shows 6 proteins in common, 5 with similar expression trend. We found many more protein identifications in common with the iTRAQ data of Scherl,25 most likely because this paper provided data for all proteins identified, not only differentially expressed ones. However, only 19 of 53 showed similar expression trend, which could be due to the use of different strains, different cellular fractions (membrane as opposed to whole-cell), or other factors. Many proteins involved in cell-wall biosynthesis were identified here, probably reflecting the increased cell-wall thickness displayed by Mu50. Recently, aconitase (CitB) enzymatic activity was found to decrease by approximately 2- to 4-fold upon selection of VISA strains for higher levels of resistance, and acetate catabolism was impaired in several VISA versus VSSA strains.44 Here, the relative amount of CitB was found to be lower in VISA versus MRSA (Table 2, line 6), suggesting a basis for this decreased activity. However, because we used related but nonisogenic strains in this study, the relevance of this finding will require further validation. Several proteins involved in stress response were seen, supporting the contention that at least some VISA strains are in a stressed state. The large number of proteins involved in transcriptional and potentially post-translational regulation, also seen in several studies, suggests that the Vn resistance phenotype is complex and affects many cellular processes. Identification of SAV2095 in 2D Gel Comparisons of CMRSA-2 and Mu50. Among the proteins identified in Table 2 was a highly induced spot (expression ratio in Mu50/ CMRSA-2 ) 19.5, p < 0.004, spot 5951 in Figure 1). Subsequent analysis by MS/MS indicated that this spot contained the protein SAV2095, a protein of unknown function, with a Mascot score of 591 and protein coverage of 34% (Table 2, line 25, MS no. 5951). This protein was also identified as being upregulated by iTRAQ (Table 2, line 25, Prot no. 194). SAV2095 carries a signal sequence and a signal peptidase site as well as a lytic transglycosylase (LT) domain, suggesting that it might be associated with the cell wall which is known to be thickened in VISA strains.14 Interestingly, this ORF was also identified by microarray as being upregulated in VISA strain JH9 versus its parent Vn-sensitive strain JH143 and by transcriptomic and proteomic analyses as being upregulated in highly resistant in vitro derivatives of Mu50 or HIP5827.23,24 On the basis, in part, of the ensemble of these findings, this gene was selected for further study as a potential biomarker. Relative Expression of SAV2095 in Clinical Isolates and in Vitro-Derived Mutants. While the clinical importance of VISA and heterogeneous VISA strains has been well-established, current methods to identify these strains lack sensitivity and/ or specificity19 and require long incubation times. Identification of a molecular biomarker could allow the development of a screening method allowing the rapid and accurate detection of these medically relevant strains. While many genes and proteins have been implicated in intermediate Vn resistance to date, few of these appear to be universal. For example, Journal of Proteome Research • Vol. 6, No. 12, 2007 4699

research articles

Figure 3. SAV2095 mRNA expression levels as measured by qRTPCR. Relative expression was normalized to 16S rRNA levels. Expression level units are arbitrary, with the lowest level of expression in each panel set to 1. Error bars represent the standard error of the mean (SEM) when duplicate samples were measured. (A) SAV2095 expression levels of various clinical isolates (as described in Table 1), visualized by agr type for a rough grouping by genetic background. ‘Potential hVISA’ refers to strains with Vn MIC < 4 mg/L by standard Etest but showing resistance by macromethod Etest as determined by NARSA (www.narsa.net). One sample each of Brazilian and BR5 strains failed, and error bars are too small to be seen for F-182, SA MER, LIM1, and HIP07930. (B) SAV2095 expression levels of series of in vitro-selected mutants (described in Table 1) from 4 different MRSA backgrounds. Vancomycin MIC, as measured by the standard Etest method, is listed in parentheses for each strain. Duplicate samples of CMRSA-1, C1V4, and C4V8 failed, while error bars for CMRSA-2, C2V24, and CMRSA-3 are too small to be seen. (C) The effects of growth in the presence of subinhibitory levels of Vn (1 mg/L for MRSA, 3 mg/L for VISA/hVISA) on SAV2095 expression levels in MRSA and genetically related VISA strains (related pairs are CMRSA-2/Mu50, CMRSA-3/BR5, and CMRSA-4/160013. Duplicate samples of CMRSA-4+Vn, Mu50+Vn, and BR5-Vn failed, while error bars on CMRSA-3+Vn are too small to be seen. 4700


Drummelsmith et al. expression levels of two genes able to alter Vn resistance, mprF and tcaA, were not correlated to resistance in multiple strains.45 All of the comparative studies relating SAV2095 induction to Vn resistance have been based on Mu50 or strains of highly related MLST types,35,46 but it was unknown whether this trend would hold over numerous MRSA and VISA strains of various genetic backgrounds. Through the Centre de recherche en Infectiologie and the Network on Antimicrobial Resistance in S. aureus (NARSA), we have access to over 2000 S. aureus isolates comprised mostly of MRSA strains and 43 VISA and hVISA strains, respectively. A number of these MRSA strains and all VISA strains were typed by agr typing using RFLP analysis (ref 32; Table 1). As expected from the literature,47–49 a majority of VISA (24/43) were of agr type II, but agr types I (13/43), Ia (2/43), and III (2/43) were also represented, with 2 strains showing banding patterns that could not be classified using this technique. Of the 18 MRSA strains typed by this approach, 3 were type I; 2 were type Ia; 4 were type II; 2 were type III; and 5, 1, and 1 each gave one of 3 distinct banding patterns. Sequencing will be required to determine the agr type of these unclassified strains. SAV2095 expression levels in the absence of Vn were measured by quantitative RT-PCR and normalized to 16S rRNA levels in 25 clinical S. aureus isolates of diverse genetic backgrounds, geographical origins, and levels of resistance (Table 1 and Figure 3A). Overall, SAV2095 was strongly induced in VISA versus MRSA strains with a high degree of significance (p < 0.001). Individually, all strains with Vn MIC g 4 mg/L by standard Etest showed higher expression levels than all MRSA tested. High levels of SAV2095 mRNA expression were seen in isolates of all agr types, suggesting that induction of this gene is a general trait of VISA strains and is independent of genetic background. Six strains tested in this study show Vn MIC < 4 mg/L using Clinical and Laboratory Standards Institute (CLSI)-approved methods, but appear to have intermediate levels of resistance by macromethod Etest using a 2 McFarland inoculum as reported by NARSA (www.narsa.net, Table 1). A MIC > 4 mg/L by macromethod Etest is indicative of hVISA/VISA. We tested SAV2095 levels in 6 ‘potential’ hVISA/VISA strains (CLSI Etest < 4 mg/L, macromethod Etest > 4 mg/L). Three of these strains (i.e., Mu3, SA MER, and 160013) showed levels of SAV2095 expression indistinguishable from those of more highly resistant strains, while the others (LIM1, LY-1999 0620–02, and HIP09662) did not. A recent study by Scherl et al.25 also found that several VISA strains, including LIM1 (NRS35) and LY-1999 0620–02 (NRS64), did not show higher levels of expression of particular gene products increased in some other VISA strains. Combining these findings with ours, it seems possible that LIM1 and LY1999 0620–02 might express resistance mechanisms distinct from those of other strains. It is also possible that the levels of SAV2095 are in fact increased relative to the parent strains of these isolates, given the variability in expression seen here among MRSA (Figure 3A), but these are unfortunately unavailable for comparison. The initial definition of some of these clinical isolates as Vnsensitive or -intermediate is complicated by the somewhat equivocal results obtained using standard testing methods (Table 1), as seen for several potential hVISA strains and CMRSA-1. By macromethod Etest, CMRSA-1 showed individual colony growth after 48 h incubation at 8 mg/L Vn (Table 1). Interestingly, CMRSA-1 also showed disproportionately high levels of SAV2095 expression with respect to the other MRSA

research articles

Vancomycin-Intermediate S. aureus strains (Figure 3A). It is possible that CMRSA-1 is a previously unrecognized hVISA strain. The macrodilution Etest has been shown to produce some false-positive results, even when a breakpoint of 8 mg/L Vn is used,19 and conventional Etest has poor sensitivity,50 making the actual Vn resistance status of these strains difficult to establish using CLSI-recommended diagnostic techniques. To further clarify the effects of resistance level on SAV2095 expression, levels of SAV2095 mRNA were determined for series of in vitro-selected strains derived from 4 distinct genetic backgrounds (Figure 3B). Expression level appeared to increase with increasing resistance in all cases, at least up to a point (no difference between strains with MICs of 10 and 16 mg/L in background CMRSA-1). Further increases in expression level were also seen between related clinical isolates with different levels of resistance (Figure 3A, LIM1 and LIM3, SA MER and SA MER-S20, LY-1999 0620–02 and -01). This suggests that monitoring SAV2095 expression over the course of treatment could also show correlations with the emergence of resistance. It is important to point out that samples for Figure 3A,B were grown in the absence of Vn, suggesting that these changes in expression are not drug-induced. We also found that growth in the presence of subinhibitory levels of Vn increased expression of SAV2095 in VISA strains of different backgrounds but not in MRSA (p < 0.05 in VISA; Figure 3C). This could indicate a potential inducible component in the regulation of this gene. The absence of any clear induction in sensitive strains suggests that this might be specific to VISA. It will be interesting to see whether this induction is seen in hVISA strains, as it could explain in part the ease with which these strains develop resistant subpopulations. This also suggests that the differences seen in Figure 3A could be amplified even further if samples were generated in the presence of Vn, and could present another characteristic that differentiates VISA from MRSA for diagnostic purposes. The results presented here suggest that SAV2095 might make a good biomarker for VISA and could be used to develop a rapid molecular test for the detection of these strains. To our knowledge, no other dependable molecular marker for VISA has been identified to date, and current methods of detection require several days. The role that SAV2095 may play in Vn resistance mechanism is unclear, and we are in the process of further characterizing this protein. Recently, this protein was found to play a role in nasal colonization, and its expression was shown to increase in the presence of high salt.51 Although studies applying global comparative approaches to the study of intermediate Vn resistance in S. aureus are scarce, they have opened up several avenues of investigation into what appear to be complex mechanisms of resistance and have now revealed a potential molecular biomarker that could allow the rapid and accurate identification and, consequently, the successful treatment of these clinically relevant strains. Abbreviations: CLSI, Clinical and Laboratory Standards Institute; hVISA, heterogeneous vancomycin-intermediate S. aureus; LT, lytic transglycosylase; MIC, minimum inhibitory concentration; MLST, multilocus sequence typing; MMTS, methyl methane-thiosulfonate; MRSA, methicillin-resistant S. aureus; NARSA, Network on Antimicrobial Resistance in S. aureus; OD, optical density; PG, peptidoglycan; RFLP, restriction fragment length polymorphism; TCEP, Tris-(2-carboxyethyl)phosphine; TSB, tryptic soy broth; VISA, vancomycinintermediate S. aureus; Vn, vancomycin.

Acknowledgment. The authors thank Gina Racine, Marthe Bernier, Ève Bérubé, Dr. Sylvie Bourassa, and Isabelle Kelly for their technical assistance, Dr. Marc Bergeron for help with statistical analyses, and Dr. Maurice Boissinot for MRSA strains. We are indebted to Dr. Arnaud Droit and Ken Sin Lo for their assistance in bioinformatic analyses. Clinical VISA isolates were obtained through the Network on Antimicrobial Resistance in S. aureus (NARSA) Program, supported under NIAID/NIH Contract No. N01-AI-95359. J.D. thanks the Centre de recherche du Centre Hospitalier Universitaire de Québec for its financial support of her research program. M.O. is a Burroughs Wellcome Fund Scholar in Molecular Parasitology and holds a Canada Research Chair in Antimicrobial Resistance. Supporting Information Available: Supplementary Table 1, MS data used for protein identification of 2D gel spots; Supplementary Table 2, MS data for peptides identified by iTRAQ. This information is available free of charge via the Internet at http://pubs.acs.org. References (1) Hiramatsu, K.; Hanaki, H.; Ino, T.; Yabuta, K.; Oguri, T.; Tenover, F. C. J. Antimicrob. Chemother. 1997, 40, 135–6. (2) Wong, S. S.; Ho, P. L.; Woo, P. C.; Yuen, K. Y. Clin. Infect. Dis. 1999, 29, 760–7. (3) Smith, T. L.; Pearson, M. L.; Wilcox, K. R.; Cruz, C.; Lancaster, M. V.; Robinson-Dunn, B.; Tenover, F. C.; Zervos, M. J.; Band, J. D.; White, E.; Jarvis, W. R. N. Engl. J. Med. 1999, 340, 493–501. (4) Ploy, M. C.; Grelaud, C.; Martin, C.; de Lumley, L.; Denis, F. Lancet 1998, 351, 1212. (5) Mainardi, J. L.; Shlaes, D. M.; Goering, R. V.; Shlaes, J. H.; Acar, J. F.; Goldstein, F. W. J. Infect. Dis. 1995, 171, 1646–50. (6) Howe, R. A.; Bowker, K. E.; Walsh, T. R.; Feest, T. G.; MacGowan, A. P. Lancet 1998, 351, 602. (7) Bobin-Dubreux, S.; Reverdy, M. E.; Nervi, C.; Rougier, M.; Bolmstrom, A.; Vandenesch, F.; Etienne, J. Antimicrob. Agents Chemother. 2001, 45, 349–52. (8) Palazzo, I. C.; Araujo, M. L.; Darini, A. L. J. Clin. Microbiol. 2005, 43, 179–85. (9) Pootoolal, J.; Neu, J.; Wright, G. D. Annu. Rev. Pharmacol. Toxicol. 2002, 42, 381–408. (10) Sievert, D. M.; Boulton, M. L.; Stoltman, G.; Johnson, D.; Stobierski, M. G.; Downes, F. P.; Somsel, P. A.; Rudrik, J. T.; Brown, W.; Hafeez, W.; Lundstrom, T.; Flanagan, E.; Johnson, R.; Mitchell, J. MMWR Morb. Mortal. Wkly. Rep. 2002, 51, 565–7. (11) Miller, D.; Urdaneta, V.; Weltman, A. MMWR Morb. Mortal. Wkly. Rep. 2002, 51, 902. (12) Chaterjee, A. N.; Perkins, H. R. Biochem. Biophys. Res. Commun. 1966, 24, 489–94. (13) Ishino, F.; Park, W.; Tomioka, S.; Tamaki, S.; Takase, I.; Kunugita, K.; Matsuzawa, H.; Asoh, S.; Ohta, T.; Spratt, B. G.; et al. J. Biol. Chem. 1986, 261, 7024–31. (14) Cui, L.; Ma, X.; Sato, K.; Okuma, K.; Tenover, F. C.; Mamizuka, E. M.; Gemmell, C. G.; Kim, M. N.; Ploy, M. C.; El-Solh, N.; Ferraz, V.; Hiramatsu, K. J. Clin. Microbiol. 2003, 41, 5–14. (15) Cui, L.; Iwamoto, A.; Lian, J. Q.; Neoh, H. M.; Maruyama, T.; Horikawa, Y.; Hiramatsu, K. Antimicrob. Agents Chemother. 2006, 50, 428–38. (16) Walsh, T. R.; Howe, R. A. Annu. Rev. Microbiol. 2002, 56, 657–75. (17) Hiramatsu, K.; Aritaka, N.; Hanaki, H.; Kawasaki, S.; Hosoda, Y.; Hori, S.; Fukuchi, Y.; Kobayashi, I. Lancet 1997, 350, 1670–3. (18) Wootton, M.; Howe, R. A.; Hillman, R.; Walsh, T. R.; Bennett, P. M.; MacGowan, A. P. J. Antimicrob. Chemother. 2001, 47, 399–403. (19) Wootton, M.; Macgowan, A. P.; Walsh, T. R.; Howe, R. A. J. Clin. Microbiol. 2007, 45, 329–32. (20) Cui, L.; Lian, J. Q.; Neoh, H. M.; Reyes, E.; Hiramatsu, K. Antimicrob. Agents Chemother. 2005, 49, 3404–13. (21) Kuroda, M.; Kuroda, H.; Oshima, T.; Takeuchi, F.; Mori, H.; Hiramatsu, K. Mol. Microbiol. 2003, 49, 807–21. (22) Kuroda, M.; Kuwahara-Arai, K.; Hiramatsu, K. Biochem. Biophys. Res. Commun. 2000, 269, 485–90. (23) Mongodin, E.; Finan, J.; Climo, M. W.; Rosato, A.; Gill, S.; Archer, G. L. J. Bacteriol. 2003, 185, 4638–43.

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research articles (24) Pieper, R.; Gatlin-Bunai, C. L.; Mongodin, E. F.; Parmar, P. P.; Huang, S. T.; Clark, D. J.; Fleischmann, R. D.; Gill, S. R.; Peterson, S. N. Proteomics 2006, 6, 4246–58. (25) Scherl, A.; Francois, P.; Charbonnier, Y.; Deshusses, J. M.; Koessler, T.; Huyghe, A.; Bento, M.; Stahl-Zeng, J.; Fischer, A.; Masselot, A.; Vaezzadeh, A.; Galle, F.; Renzoni, A.; Vaudaux, P.; Lew, D.; Zimmermann-Ivol, C. G.; Binz, P. A.; Sanchez, J. C.; Hochstrasser, D. F.; Schrenzel, J. BMC Genomics 2006, 7, 296. (26) Scherl, A.; Francois, P.; Bento, M.; Deshusses, J. M.; Charbonnier, Y.; Converset, V.; Huyghe, A.; Walter, N.; Hoogland, C.; Appel, R. D.; Sanchez, J. C.; Zimmermann-Ivol, C. G.; Corthals, G. L.; Hochstrasser, D. F.; Schrenzel, J. J. Microbiol. Methods 2005, 60, 247– 57. (27) Mulvey, M. R.; Chui, L.; Ismail, J.; Louie, L.; Murphy, C.; Chang, N.; Alfa, M. J. Clin. Microbiol. 2001, 39, 3481–5. (28) Boyle-Vavra, S.; Berke, S. K.; Lee, J. C.; Daum, R. S. Antimicrob. Agents Chemother. 2000, 44, 272–7. (29) Drummelsmith, J.; Brochu, V.; Girard, I.; Messier, N.; Ouellette, M. Mol. Cell. Proteomics 2003, 2, 146–55. (30) Eng, J. K.; McCormack, A. L.; Yates, I., J. R. J. Am. Soc. Mass Spectrom. 1994, 5, 976–89. (31) Shilov, I. V.; Seymour, S. L.; Patel, A. A.; Loboda, A.; Tang, W. H.; Keating, S. P.; Hunter, C. L.; Nuwaysir, L. M.; Schaeffer, D. A. Mol. Cell. Proteomics 2007, 6, 1638–55. (32) Papakyriacou, H.; Vaz, D.; Simor, A.; Louie, M.; McGavin, M. J. J. Infect. Dis. 2000, 181, 990–1000. (33) Enright, M. C.; Day, N. P.; Davies, C. E.; Peacock, S. J.; Spratt, B. G. J. Clin. Microbiol. 2000, 38, 1008–15. (34) Simor, A. E.; Ofner-Agostini, M.; Bryce, E.; McGeer, A.; Paton, S.; Mulvey, M. R. J. Infect. Dis. 2002, 186, 652–60. (35) McDougal, L. K.; Steward, C. D.; Killgore, G. E.; Chaitram, J. M.; McAllister, S. K.; Tenover, F. C. J. Clin. Microbiol. 2003, 41, 5113– 20. (36) Cordwell, S. J.; Larsen, M. R.; Cole, R. T.; Walsh, B. J. Microbiology 2002, 148, 2765–81. (37) Rabilloud, T. Proteomics 2002, 2, 3–10. (38) Kubota, K.; Wakabayashi, K.; Matsuoka, T. Proteomics 2003, 3, 616– 26. (39) Schmidt, F.; Donahoe, S.; Hagens, K.; Mattow, J.; Schaible, U. E.; Kaufmann, S. H.; Aebersold, R.; Jungblut, P. R. Mol. Cell. Proteomics 2004, 3, 24–42. (40) Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E. L. J. Mol. Biol. 2001, 305, 567–80. (41) Bendtsen, J. D.; Nielsen, H.; von Heijne, G.; Brunak, S. J. Mol. Biol. 2004, 340, 783–95. (42) Jiang, M.; Datta, K.; Walker, A.; Strahler, J.; Bagamasbad, P.; Andrews, P. C.; Maddock, J. R. J. Bacteriol. 2006, 188, 6757–70. (43) McAleese, F.; Wu, S. W.; Sieradzki, K.; Dunman, P.; Murphy, E.; Projan, S.; Tomasz, A. J. Bacteriol. 2006, 188, 1120–33. (44) Nelson, J. L.; Rice, K. C.; Slater, S. R.; Fox, P. M.; Archer, G. L.; Bayles, K. W.; Fey, P. D.; Kreiswirth, B. N.; Somerville, G. A. Antimicrob. Agents Chemother. 2007, 51, 616–22. (45) Wootton, M.; Macgowan, A. P.; Walsh, T. R. Biochim. Biophys. Acta 2005, 1726, 326–7.

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Drummelsmith et al. (46) Sieradzki, K.; Tomasz, A. J. Bacteriol. 2003, 185, 7103–10. (47) Sakoulas, G.; Eliopoulos, G. M.; Moellering, R. C., Jr.; Wennersten, C.; Venkataraman, L.; Novick, R. P.; Gold, H. S. Antimicrob. Agents Chemother. 2002, 46, 1492–502. (48) Sakoulas, G.; Eliopoulos, G. M.; Moellering, R. C., Jr.; Novick, R. P.; Venkataraman, L.; Wennersten, C.; DeGirolami, P. C.; Schwaber, M. J.; Gold, H. S. J. Infect. Dis. 2003, 187, 929–38. (49) Moise-Broder, P. A.; Sakoulas, G.; Eliopoulos, G. M.; Schentag, J. J.; Forrest, A.; Moellering, R. C., Jr Clin. Infect. Dis. 2004, 38, 1700–5. (50) Walsh, T. R.; Bolmstrom, A.; Qwarnstrom, A.; Ho, P.; Wootton, M.; Howe, R. A.; MacGowan, A. P.; Diekema, D. J. Clin. Microbiol. 2001, 39, 2439–44. (51) Stapleton, M. R.; Horsburgh, M. J.; Hayhurst, E. J.; Wright, L.; Jonsson, I. M.; Tarkowski, A.; Kokai-Kun, J. F.; Mond, J. J.; Foster, S. J. J. Bacteriol. 2007, 189, 7316–25. (52) Simor, A. E.; Ofner-Agostini, M.; Gravel, D.; Varia, M.; Paton, S.; McGeer, A.; Bryce, E.; Loeb, M.; Mulvey, M. Can. Commun. Dis. Rep. 2005, 31, 33–40. (53) de Lencastre, H.; Severina, E. P.; Milch, H.; Thege, M. K.; Tomasz, A. Clin. Microbiol. Infect. 1997, 3, 289–96. (54) Oliveira, D. C.; Tomasz, A.; de Lencastre, H. Microb. Drug Resist. 2001, 7, 349–61. (55) Kreiswirth, B. N.; Lutwick, S. M.; Chapnick, E. K.; Gradon, J. D.; Lutwick, L. I.; Sepkowitz, D. V.; Eisner, W.; Levi, M. H. Microb. Drug Resist. 1995, 1, 307–13. (56) Crisostomo, M. I.; Westh, H.; Tomasz, A.; Chung, M.; Oliveira, D. C.; de Lencastre, H. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 9865–70. (57) Aires de Sousa, M.; Sanches, I. S.; Ferro, M. L.; Vaz, M. J.; Saraiva, Z.; Tendeiro, T.; Serra, J.; de Lencastre, H. J. Clin. Microbiol. 1998, 36, 2590–6. (58) Bozdogan, B.; Ednie, L.; Credito, K.; Kosowska, K.; Appelbaum, P. C. Antimicrob. Agents Chemother. 2004, 48, 4762–5. (59) Tenover, F. C.; Lancaster, M. V.; Hill, B. C.; Steward, C. D.; Stocker, S. A.; Hancock, G. A.; O’Hara, C. M.; McAllister, S. K.; Clark, N. C.; Hiramatsu, K. J. Clin. Microbiol. 1998, 36, 1020–7. (60) Sieradzki, K.; Roberts, R. B.; Haber, S. W.; Tomasz, A. N. Engl. J. Med. 1999, 340, 517–23. (61) Kim, M. N.; Pai, C. H.; Woo, J. H.; Ryu, J. S.; Hiramatsu, K. J. Clin. Microbiol. 2000, 38, 3879–81. (62) Hageman, J. C.; Pegues, D. A.; Jepson, C.; Bell, R. L.; Guinan, M.; Ward, K. W.; Cohen, M. D.; Hindler, J. A.; Tenover, F. C.; McAllister, S. K.; Kellum, M. E.; Fridkin, S. K. Emerging Infect. Dis. 2001, 7, 1023–5. (63) Elhag, K. M.; Al Jardani, A. K.; Al Yaqubi, F. M.; Mohsin, N. Clin. Microbiol. Infect. 2000, 6, 173–4. (64) Boyle-Vavra, S.; Carey, R. B.; Daum, R. S. J. Antimicrob. Chemother. 2001, 48, 617–25. (65) Khurshid, M. A.; Chou, T.; Carey, R.; Larsen, R.; Conover, C.; Bornstein, S. L. Morb. Mortal. Wkly. Rep. 2000, 48, 1165–7. (66) Denis, O.; Nonhoff, C.; Byl, B.; Knoop, C.; Bobin-Dubreux, S.; Struelens, M. J. J. Antimicrob. Chemother. 2002, 50, 383–91.

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Comparative Proteomics Analyses Reveal a Potential Biomarker for

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