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Untargeted Lipidomics Reveals Differences in the Lipid Pattern among Clinical Isolates of Staphylococcus aureus Resistant and Sensitive to Antibiotics Weronika Hewelt-Belka,*,† Joanna Nakonieczna,‡ Mariusz Belka,§ Tomasz Bączek,§ Jacek Namieśnik,† and Agata Kot-Wasik† †

Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland ‡ Department of Biotechnology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Kładki 24, 80-822 Gdańsk, Poland § Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416 Gdańsk, Poland S Supporting Information *

ABSTRACT: Staphylococcus aureus resistance to antibiotics is a significant clinical problem worldwide. In this study, an untargeted lipidomics approach was used to compare the lipid fingerprints of S. aureus clinical isolates that are resistant and sensitive to antibiotics. High-performance liquid chromatography coupled with time-of-flight mass spectrometry was employed to rapidly and comprehensively analyze bacterial lipids. Chemometric and statistical analyses of the obtained lipid fingerprints revealed variations in several lipid groups between S. aureus strains resistant and sensitive to tested antibiotics including methicillin, gentamicin, ciprofloxacin, erythromycin, and fusidic acid. The levels of identified monoglycosyldiacylglycerol, phosphatidylglycerol, and diglycosyldiacylglycerol lipid groups were found to be upregulated in antibiotic-resistant S. aureus strains, whereas the levels of diacylglycerol lipid groups were downregulated. Differences in the lipid patterns between sensitive and resistant S. aureus strains suggest that antibiotic susceptibility may be associated with the lipid composition of bacterial cells. The lipids that were found to significantly differ between antibiotic-resistant and antibiotic-sensitive clinical isolates are involved in the biosynthesis of major S. aureus membrane lipids and lipoteichoic acid. This study indicates that S. aureus lipid biosynthesis pathways should be explored further to better understand the mechanism of antibiotic resistance in S. aureus strains. KEYWORDS: Staphylococcus aureus, lipidomics, antibiotic resistance, HPLC−MS

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INTRODUCTION Staphylococcus aureus is an aggressive pathogen responsible for a variety of diseases including life-threatening sepsis.1 Many strains of these bacteria are resistant to multiple classes of antibiotics, which is a substantial clinical problem in the treatment of S. aureus infections.2,3 Diseases caused by the methicillin-resistant strain of this pathogen are potentially more © XXXX American Chemical Society

life threatening and often require prolonged and expensive treatment including hospitalization. According to a report by the Centers for Disease Control and Prevention, 80 461 invasive methicillin-resistant S. aureus (MRSA) infections and Received: September 29, 2015

A

DOI: 10.1021/acs.jproteome.5b00915 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research Table 1. Clinical Properties of the Studied S. aureus Isolates antibiotic resistance/sensitivity statusa

a

ID

hospital unit

isolate origin

methicillin

gentamicin

ciprofloxacin

erythromycin

fusidic acid

3605S 1764/p 6452 9892 911/1 20932/Z 3941/S 473 6987 146 4091 984/K 1162/0 3805/S

laryngological ward external ward intensive care unit surgery ward intensive care unit gynecological ward neurological ward surgery ward orthopedic ward orthopedic ward orthopedic ward internal medicine ward intensive care unit surgery ward

ear ear nose postsurgery wound wound cervix nose wound wound wound wound blood bronchial tree wound

R R R R R R R S S S S S S S

S S R R R S S R S S S S S S

S R R R R S R R S S S S S S

S S R R R R R R S S S R S S

S S R R R S S S S S S S S S

R, S. aureus clinical isolate resistant to antibiotic; S, S. aureus clinical isolate sensitive to antibiotic.

11 285 related deaths occurred in the United States in 2011.4 The propensity of S. aureus to develop resistance to antibiotics has forced us to search for new targets for effective antimicrobial therapy. Multiple factors have long been recognized to contribute to the phenotype of antibiotic-resistant S. aureus.5 The currently available literature data show fundamental differences between antibiotic-resistant and antibiotic-sensitive S. aureus cells based on not only the presence of the mecA gene, which confers resistance to methicillin, but also on significant differences in the pathogenicity, virulence, and presence of various adhesins, toxins, and enzymes.6 In recent years, several studies have shown that the antibiotic resistance of S. aureus may be associated with changes in the lipid composition. Lysylated phosphatidylglycerols (Lys-PGs) may play a role in the resistance of S. aureus to cationic antimicrobial peptides (CAMPs)7 and the lipopeptide antibiotic daptomycin,8−10 and this effect is likely related to the decreased susceptibility of the membrane to these compounds because of the partial neutralization of the cellular membrane by the cationic headgroup of Lys-PG. Recent studies revealed that cardiolipines may also mediate resistance to daptomycin by preventing membrane translocation and permeabilization by this antibiotic.11 Differences in lipid composition between methicillin-resistant and methicillin-sensitive S. aureus were also described in our previous article.12 Despite these results, the roles of various bacterial lipids in the resistance to antibiotics remain poorly understood. The bacterial lipid composition varies by bacterial species,13 environmental condition, and growth phase.14,15 The S. aureus cell membrane primarily consists of phosphatidylglycerol (PG), lysyl-phosphatidylglycerol (Lys-PG), diglycosyldiacylglycerol (DGDG), cardiolipin (CL), and diacylglycerol (DG).16−18 The modulation of bacterial membrane properties allows the cell to adapt to changes in environmental conditions such as the temperature, osmolarity, or the presence of membranedamaging compounds. Changes in the type of fatty acid in a given set of membrane lipids19,20 or in the contents of particular lipid classes21,22 facilitate adaptation. The composition of membrane lipids determines the net charge of its surface and membrane-protein topology, which may consequently affect the life functions of the bacterial cell including the permeability of the membrane to various compounds.23−25

Thus, the lipid composition may be an important phenotypic marker of the susceptibility of S. aureus to antibiotics. Omics-based approaches that utilize hyphenated techniques, such as high-performance liquid chromatography coupled with mass spectrometry, and chemometric tools enable rapid and comprehensive analyses and the statistical comparison of data from biological samples.26−30 In our previous report,12 we described the development and validation of a comprehensive untargeted lipidomic workflow including sample preparation, liquid chromatography-quadrupole-time-of-flight mass spectrometry (LC-Q-TOF-MS) analysis, and data processing, designed to study the bacterial lipidome. The utility of the developed procedure was demonstrated by assessing differences among two reference strains of S. aureus, COL, and Newman. Herein, we present results of an untargeted lipidomic fingerprinting-based study involving the analysis of 14 phenotypically diversified clinical isolates to extract information related to their resistance to antibiotics. To date, this is the first study to show significant differences in the lipid fingerprints between MRSA and methicillin-sensitive S. aureus (MSSA) clinical isolates. We also demonstrate the relationship between the obtained lipid fingerprints and the resistance of. S. aureus strains to other antibiotics such as gentamicin, ciprofloxacin, fusidic acid, and erythromycin.

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EXPERIMENTAL SECTION

Samples

Bacterial Isolates. Fourteen clinical S. aureus strains (seven MRSA and seven MSSA) isolated at the Provincial Hospital in Gdansk from 1997−2010 were used in the analyses. The isolates were characterized by Gram staining and the ability to produce coagulase and clumping factor using Slidex Staph Plus (BioMerieux, Marcy-l’Étoile, France). Additionally, the species were identified using the biochemical identification system ID 32 Staph (BioMerieux, Marcy-l’Étoile, France). Resistance to methicillin was determined using a disc-diffusion method and the latex test, which detects PBP2a protein (Staphytect Plus, Oxoid Ltd., Basingstoke, UK). Susceptibility to fusidic acid, erythromycin, ciprofloxacin, and gentamicin was assessed using the VITEK 2 system, AST 603 cards (bioMérieux, Marcyl’Étoile, France). S. aureus ATCC 29213 served as the control strain, and the VITEK 2 minimum inhibitory concentration B


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Journal of Proteome Research

Technologies, Santa Clara, CA, USA). The mobile phase consisted of component A (5 mM ammonium formate in water/methanol (1:9 v/v)) and component B (5 mM ammonium formate in water/n-hexane/2-propanol (1:20:79 v/v/v)). The following gradient elution program was applied: 0% B from 0 to 10 min, 0−60% B from 10 to 28 min, 60−100% B from 28 to 32 min, and 100% B for 3 min thereafter. After 0.5 min, the eluent was then returned to 0% B, followed by a 10 min equilibration with this mobile phase composition prior to the next injection. The column temperature during the analyses was maintained at 45 °C. The flow rate of the mobile phase was 0.3 mL/min. Throughout the analysis, the samples were kept in an autosampler at 4 °C. The ESI source was operated in positive ion mode. The parameters were set to the following: the fragmentor voltage at 120 V, nebulizer gas at 35 psig, capillary voltage at 3500 V, drying gas flow rate at 10 L/min, and temperature at 300 °C. The data were collected in centroid and profile mode with a mass range of 100−1700 m/z using the high-resolution mode (4 GHz). The TOF was calibrated on a daily basis before the sample analysis and by the continuous introduction of calibration solutions, with reference masses at m/z 121.0509 (protonated purine), 149.02332 and 922.009798 [protonated hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazine or HP921], and 1221.990637 during the analysis run to ensure constant mass correction. Analytical batches consisted of clinical samples in random order. The QC samples were analyzed at the beginning and end of the batch and after every 4 injections throughout the run to assess technical variance as described previously.12 Data Processing and Chemometric Analysis. The RPLC-Q-TOF-MS raw data were processed with the MassHunter Workstation Qualitative Analysis software, version B.03.01 (Agilent Technologies, Santa Clara, CA, USA). Molecular feature extraction (MFE) was used to clean the raw data of background noise and signals from unrelated ions. The following MFE parameters were used for the untargeted analysis of the LC−MS data: extraction algorithm, small molecule; input data range, restricted retention time 1.3−26 min, restricted m/z 450−1700 m/z; ion species, + H; peak spacing tolerance, 0.0025 m/z plus 7.0 ppm; isotope model, common organic molecules; and charge state, 1−2. The background noise limit was set to 1000 counts. The obtained molecular features (MFs) are characterized by retention time, neutral mass, and peak volume, which is the size in [m/z ] × [retention time] × [abundance] and includes all isotope ion peaks associated with the molecular feature. Next, the sample was aligned to ensure that the same entity was marked as the same feature within each sample. The samples were aligned with the use of MassHunter Mass Profiler, version B.02.00 (Agilent Technologies, Santa Clara, CA, USA). The parameters applied for the alignment were 0.5% and 0.25 min for the retention time correction and 5 ppm for the retention time tolerance. Other alignment parameters were set to the following: number of ions, ≥2; charge state, any. A list of aligned molecular features was exported in .csv format. The MF filtering and normalization steps were applied prior to the statistical and chemometric analyses. The MF filtering was performed to reject irrelevant and nonrepeatable MFs. Data were filtered by selecting only the MFs that were present in at least 50 samples. Subsequently, the MF peak volumes were normalized to reduce intersample variance resulting from experimental and instrumental variability. The data were

(MIC) results were interpreted using the Advanced Expert System of VITEK 2 according to CLSI recommendations, excluding the MIC breakpoints for fusidic acid, for which EUCAST recommendations were applied (document V2.0, 2012, http://www.eucast.org). The strains were isolated from the following hospital units: Orthopedic Ward (three strains); Surgery Ward (three strains); Intensive Care Unit (three strains); Internal Medicine Ward (one strain); and Laryngological Ward, Gynecological Ward, Neurological Ward, and External Ward (one strain each). The clinical isolates used in this study and their characteristics are presented in Table 1. Bacterial Growth. S. aureus cells were incubated as previously described.12 Briefly, the bacteria were cultured in 50 mL of brain−heart infusion (BHI) medium (bioMerieux, Marcy-l’Étoile, France) for 23 h at 37 °C with aeration at 150 rpm. The cells were harvested by centrifugation (5 min, 7000g, 20 °C), washed twice with 0.78% NaCl, and further lyophilized for 23 h. Each isolate was independently cultured six times. All the cultures and extraction procedures of biological material were carried out in facility of biosafety containment level 2, that is, suitable for work involving agents of moderate potential hazard to personnel and the environment. All laboratory personnel have specific training in handling pathogenic agents and are supervised by scientists competent in handling infectious agents and associated procedures. All biological material is inactivated (autoclaved) before disposal. LC-Q-TOF-MS Lipid Fingerprinting

Eighty-four lipid extracts of S. aureus cells were analyzed using a lipid-fingerprinting approach based on previously developed LC-Q-TOF-MS methodology12 with slight modifications. Sample Preparation. Fifteen milligrams of lyophilized S. aureus cells was accurately weighed and dissolved in 500 μL of deionized water, and 450 μL of the sample was transferred to a borosilicate glass tube with a Teflon screw cap (Sigma-Aldrich, St. Louis, MO, USA). Subsequently, 1.9 mL of a chloroform/ methanol mixture (1:2 v/v) and 600 mg of glass beads (0.10− 0.11 mm diameter, Sartorius, Germany) were added to the solution, followed by vortexing for 5 min. Next, 625 μL of chloroform was added, followed by 10 s of vortexing, the addition of 625 μL of deionized water and another 60 s of vortexing. The sample was then centrifuged at 5000g for 10 min to separate polar and nonpolar species into two phases and to remove the lipid-free cell remains. Subsequently, the lower lipid-containing organic phase was gently aspirated with a glass Pasteur pipet, transferred to a clean glass tube, and analyzed using LC-Q-TOF-MS. The quality control (QC) samples were used as a quality-assurance strategy to assess the reproducibility of the extraction procedure and LC−MS system stability. The QC samples were prepared by pooling equal volumes of 12 samples (six samples of two different S. aureus strains), which resulted in seven QC samples. The QC samples were processed following the same analytical procedure used for the real samples. LC-Q-TOF-MS Analysis. The RP-LC-Q-TOF-MS analysis was performed on an Agilent 1290 LC system equipped with a binary pump, an online degasser, an autosampler, and a temperature-controlled column compartment coupled to a 6540 Q-TOF-MS with a Dual electrospray ionization (ESI) source (Agilent Technologies, Santa Clara, CA, USA). Crude lipid extract (0.5 μL) was injected into an Agilent ZORBAX SB-C-18 column (50 × 2.1 mm2, 1.8 μm, Agilent Technologies, Santa Clara, CA, USA) with a 0.2-μm in-line filter (Agilent C


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Figure 1. Scatter plot of scores obtained from OPLS-DA analysis of S. aureus lipid fingerprints. (A) Analysis of MRSA (red circles, n = 42) versus MSSA (gray circles, n = 39) clinical isolates, R2X = 0.73, R2Y = 0.958, Q2 = 0.899. (B) Analysis of S. aureus clinical isolates resistant (red circles, n = 24) and sensitive (gray circles, n = 57) to gentamicin, R2X = 0.624, R2Y = 0.96, Q2 = 0.944. (C) Analysis of S. aureus clinical isolates resistant (red circles, n = 36) and sensitive (gray circles, n = 45) to ciprofloxacin, R2X = 0.57, R2Y = 0.975, Q2 = 0.964. (D) Analysis of S. aureus clinical isolates resistant (red circles, n = 42) and sensitive (gray circles, n = 39) to erythromycin, R2X = 0.612, R2Y = 0.978, Q2 = 0.964. (E) Analysis of S. aureus clinical isolates resistant (red circles, n = 18) and sensitive (gray circles, n = 63) to fusidic acid, R2X = 0.531, R2Y = 0.945, Q2 = 0.927.

normalized using the MS all-signal approach: each MF peak volume was divided by the sum of all MF peak volumes that remained in the data set after filtering. Additionally, the % relative standard deviations (%RSDs) of the MF peak volumes in the QC samples were assessed to filter out MFs with peak

volume %RSDs exceeding 30%. The normalization and MF filtering were performed in Microsoft Excel 2010 (Microsoft, Redmond, WA, USA). Further comparisons between two groups of samples (extracts of S. aureus resistant and sensitive to antibiotics) were performed using univariate and multivariate D


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Table 2. Identified Lipid Groups That Significantly Differed between S. aureus Isolates That Were Resistant and Sensitive to Antibiotics (Data Shown for Methicillin; Please Refer to Table S-1 for Results Obtained for Other Antibiotics) methicillin lipid groupa DG (30:0) DG (30:0) DG (31:0) DG (31:0) DG (32:0) DG (34:0) DG (35:0) DG (36:0) DG (37:0) DGDG (29:0) DGDG (30:0) DGDG (30:0) DGDG (31:0) DGDG (31:0) DGDG (32:0) DGDG (32:0) DGDG (33:0) DGDG (33:0) DGDG (34:0) DGDG (35:0) Lys-PG (30:0) MGDG (31:0) MGDG (32:0) MGDG (32:0) MGDG (33:0) MGDG (35:0) PG (31:0) PG (32:0) PG (33:0) PG (35:0)

proposal ion [M + NH4]+ [M + K]+ [M + NH4]+ [M + K]+ [M + K]+ [M + NH4]+ [M + NH4]+ [M + NH4]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + NH4]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + H]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [M + Na]+ [DAG]+d [DAG]+ [DAG]+ [DAG]+

tR [min] 14.03 14.54 15.54 15.90 17.06 18.95 19.75 20.49 21.14 5.16 6.03 6.49 8.21 7.67 9.66 10.46 12.22 12.79 13.88 15.54 7.21 12.44 13.66 14.16 15.45 17.48 4.05 4.69 6.36 10.25

p-valueb

neutral mass 557.5017 578.4309 571.5172 592.4468 606.4624 613.5645 627.5802 641.5954 660.5660 872.5469 886.5628 886.5627 900.5786 900.5783 909.6387 914.5942 928.6095 928.6100 942.6255 956.6414 822.5730 738.5255 752.5411 752.5411 766.5569 794.5881 536.4803 550.4960 564.5115 592.5427

7.46 1.82 ns 2.53 ns ns ns ns 4.22 ns 2.78 ns ns 1.47 ns ns 5.91 1.06 1.15 1.61 ns 1.36 7.90 6.47 9.52 5.12 3.66 ns 2.54 1.64

−04

× 10 × 10−03 × 10−03

× 10−03 × 10−03

× 10−03

× × × ×

10−04 10−02 10−02 10−02

× × × × × ×

10−03 10−09 10−03 10−08 10−05 10−04

× 10−05 × 10−07

VIP score

percent change R vs Sc

1.39 1.90 0.08 1.76 1.51 0.46 0.52 0.07 1.65 0.22 0.17 0.38 0.29 0.83 1.06 0.25 1.37 0.87 1.27 1.00 0.94 1.79 2.06 1.38 2.09 1.93 1.48 1.29 1.71 2.06

−29.15 −30.84 −4.21 −20.22 −21.10 −9.42 10.16 −1.38 31.79 −0.63 5.44 −3.12 5.16 38.90 45.83 5.98 86.61 13.36 36.83 15.21 24.67 54.21 130.33 51.24 46.77 78.26 81.61 143.14 118.05 141.40

a DG, diacylglycerol; DGDG, diglycosyldiacylglycerol; MGDG; monoglycosyldiacylglycerol; PG, phosphatidylglycerol; Lys-PG, lysyl-phosphatidylglycerol. bP-value calculated using Mann−Whitney U test, p < 0.05; ns, not significant difference. cPercent change of S. aureus particular lipid content in antibiotic-resistant isolates versus S. aureus antibiotic-sensitive isolates. dFragment ion.

of their spectra for 0.3 min. The MS/MS analysis was performed in the 30−1700 m/z mass range. The collision energies were set to 35 and 50 V for two distinct experiments.

tools. The Mann−Whitney U-test was applied to identify significant differences (p < 0.05) between tested groups. An orthogonal partial least-squares discriminant analysis (OPLSDA) was used to select MFs that contributed most to the separation and discrimination between the analyzed groups. Significance tests for average and OPLS-DA were performed with the help of SIMCA (ver. 12, Umetrix, Umeå, Sweden) and Statistica (ver. 10, Statsoft, Tulsa, OK, USA). Box and whisker plots were drawn using BoxPlotR (a web-tool for the generation of box plots, http://boxplot.tyerslab.com/). Lipid Group Identification. The accurate masses of variables that significantly differed between resistant and sensitive strains of S. aureus were searched against the S. aureus lipid database.12 An automatic database search was performed using the MassHunter Qualitative Software with the following parameters: values to match, mass only; match tolerance, 5 ppm; charge carriers, H+, Na+, NH4+, K+; and charge state, 1−2. Additionally, the identities of compounds obtained from the database search were further confirmed with LC−MS/MS analysis. The chromatographic conditions of the experiments and the parameters of the ion source were identical to those in the LC−MS analysis. The MS/MS analyses were performed in auto MS/MS acquisition mode with instructions to select the two most abundant ions for fragmentation and further exclude

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RESULTS AND DISCUSSION In this study, we used our previously developed S. aureus lipidomics methodology to compare the lipid fingerprints of S. aureus clinical isolates that are resistant and sensitive to five antibiotics: methicillin, gentamicin, ciprofloxacin, fusidic acid, and erythromycin. We took advantage of an untargeted lipidomic approach to rapidly analyze 84 crude lipid extracts of 14 S. aureus strains: seven MRSA strains and seven MSSA strains. The characteristics of these strains, including their antibiotic susceptibility, are presented in Table 1. Each isolate was independently cultured six times and analyzed by LC-QTOF-MS after sample preparation, yielding 84 independent lipid profiles. Representative extracted compound chromatograms (ECCs) of each strain used in this study are presented in Figure S-1. The acquired raw chromatograms were then processed using a MFE algorithm and subsequently processed with the MassProfiler software to align MFs. The MFE algorithm was set to extract only protonated molecules as a feature. Thus, fragment ions and salt adducts of lipids were included in the obtained lipid fingerprints as distinct features. E


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changed and identified ions belong to five classes of lipids. In general, the levels of monoglycosyldiacyglycerols, phosphatidylglycerols, diglycosyldiacylglycerols, and lysyl-phosphatidylglycerols were elevated in the antibiotic-resistant S. aureus strains, whereas the levels of diacylglycerols were decreased in these strains. The levels of all detected monoglycosyldiacylglycerols lipids significantly increased in methicillin-, ciprofloxacin-, and erythromycin-resistant isolates of S. aureus compared with their sensitive counterparts. However, only two MDGD lipid groups significantly differed in gentamycin- and fusidic acid-resistant clinical isolates. Specifically, a percent change in the MGDG peak volumes exceeding 50% can be observed in resistant strains versus sensitive strains. Additionally, increased levels of phosphatidylglycerols are evident in strains resistant to all analyzed antibiotics. Conversely, the diacylglycerol content was significantly higher in sensitive isolates than in resistant isolates, irrespective of the analyzed antibiotic type. However, these differences are not as distinct as those observed in the MGDG or PG contents between antibiotic-resistant and antibiotic-sensitive isolates of S. aureus. Although the diacylglycerol content tends to be increased in sensitive strains, deviations from this general trend can be observed for the one lipid group significantly increased in methicillin-resistant clinical isolates [DG (37:0)] and two lipid groups significantly increased in fusidic-acid-resistant clinical isolates [DG (31:0) and DG (35:0)] compared with their sensitive counterparts. The levels of diglycosyldiacylglicerols were also higher in antibiotic-resistant strains than in antibioticsensitive strains. However, this difference was most pronounced between S aureus strains resistant and sensitive to erythromycin: almost all identified DGDG lipid groups significantly differed, except DGDG (32:0). To the best of our knowledge, this study is the first to employ an untargeted lipidomic approach to compare more than 80 lipid fingerprints of 14 S. aureus clinical isolates that are resistant and sensitive to methicillin. The chemometric tools facilitated the identification of significant differences in lipid profiles between S. aureus that are resistant and sensitive to not only methicillin, but also gentamycin, ciprofloxacin, erythromycin, and fusidic acid using the same LC−MS data. The LCQ-TOF-MS platform with the S. aureus lipid database used in this study facilitated the identification of major S. aureus lipid classes and consequently indicated biosynthesis pathways linked to the differential expression of lipids between antibiotic-resistant and antibiotic-sensitive S. aureus isolates. Differences in the lipid content between antibiotic-resistant and antibiotic-sensitive S. aureus strains have been previously identified. For example, increased levels of PG and decreased levels of CL and Lys-PG were observed in methicillin-sensitive strains compared with methicillin-resistant S. aureus.31 Another study showed that the expression of plasmid-mediated resistance to fusidic acid in an S. aureus strain was linked to changes in the molar ratio of PG to Lys-PG.32 Differences in the Lys-PG content were also demonstrated between S. aureus strains that are resistant and sensitive to daptomycin and gentamycin.9,10,25 Although the literature contains a large body of data/information describing the role of genes and proteins in the resistance of S. aureus to antibiotics, little is known about the role of lipids in this phenomenon. The differences in the lipid content between sensitive and resistant S. aureus strains observed in this study are interesting, especially given the possible impact of these differences on the mechanism of resistance to various antibiotics. None of the

The respective data matrices consisted of over 1700 MFs. The MFs were filtered by selecting only those features present in at least 50 of 84 samples, reducing the data set to 208 MFs. Subsequently, the peak volumes were normalized using the MS all-signal approach. After the normalization step, all MF peak volumes in the QC samples were characterized by %RSD values lower than 30%; therefore, all MFs remained in the data set. However, three samples of isolate no. 6987 were removed because of excessive missing values. After all preprocessing steps, the data set used for further multivariate analysis consisted of 208 features and 81 samples. To characterize lipids that differed between antibioticresistant and antibiotic-sensitive S. aureus clinical isolates, the untargeted lipidomic analysis was followed by an automated S. aureus lipid database search and LC−MS/MS experiments of crude lipid extracts of each strain. The MS/MS analyses were performed using the auto MS mode of fragmentation and two collision energy values (35 and 50 V) to obtain fragmentation spectra of all detected classes of lipids. The acquired spectra were used to confirm the identities of lipids identified by the automated S. aureus lipid database search, as described previously.12 This identification facilitated the assignment of the headgroup type, total fatty acyl carbon number, and total degree of fatty acyl unsaturation of the detected lipid groups. Exemplary MS/MS spectra and fragmentation patterns of the identified lipid classes are presented in Figure S-2. The main objective of this study was to identify differences in the lipid fingerprints of antibiotic-sensitive and antibioticresistant S. aureus clinical isolates. To this end, both uni- and multivariate approaches were applied. An OPLS-DA was used to select variables that contribute to differences between two groups, in this case, resistance or sensitivity to methicillin. Moreover, the susceptibility to other antibiotics was compared using the same LC−MS data. The OPLS-DA model obtained for the MSSA/MRSA isolates explained 73.0% of X matrix variance (R2X) and 95.8% of the discriminant variable (R2Y). The fraction of the total variation of X or Y that can be predicted by a component, as estimated by cross-validation, was 0.899 (Q2). As shown in Figure 1, panel A, circles representing particular MRSA and MSSA samples can be easily separated into two groups. All samples were correctly classified based on the obtained model, and the p value of Fisher’s test for discrimination was 4.9 × 10−24. Analogous OPLS-DA models were established for gentamicin (Figure 1B), ciprofloxacin (Figure 1C), erythromycin (Figure 1D), and fusidic acid (Figure 1E). The variance importance for the projection values (VIPs) were used to sort MFs according to their contribution to the model. On the basis of the VIP scores, only MFs with the highest importance and identified as a lipid groups were selected for further statistical assessment with the Mann−Whitney U test to determine the significance of the differences between resistant and sensitive isolates. Table 2 and Table S-1 present the identified lipid groups, which significantly differed between antibiotic-resistant and antibioticsensitive isolates, including their p values and the percent change in the average peak volume for resistant isolates relative to that of sensitive isolates, which was assigned a value of 100%. The mean peak volumes of the selected lipid groups were calculated for six biological replicates of each of the 14 strains and were used to establish their relative quantities. Comparing the lipid fingerprints of 14 S. aureus clinical isolates revealed differences in the lipid pattern among analyzed strains. As shown in Table 2 and Table S-1, the significantly F


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between the two groups of S. aureus strains are not only precursors of LTA, but also major components of the S. aureus bacterial membrane.16,17,36 Therefore, the observed differences in the composition and, consequently, in the architecture of the lipid membrane may affect the function of this cell structure. The balance in lipid composition is also crucial for proper membrane−protein topology, transport functions, DNA replication, and cell division.13 Changes in the permeability of the membrane resulting from modifications to its structure may reduce the entry of antibiotics into the cell.37 Accordingly, the regulation of the ratio of glycolipids (nonbilayer forming MGDG to bilayer-forming DGDG) has been speculated to be involved in determining the stability of the membrane.38 Thus, changes in the content of these lipids may affect the physical− chemical properties of the bilayer and its function. Changes in the composition of neutral lipids, such as MGDG, DGDG, and DG, and anionic lipids, such as PG, may also perturb the permeability of the cell to antibiotics by modifying the membrane surface charge. We observed significant differences in the relative abundance of one LysPG lipid group between strains sensitive and resistant to gentamicin (54.12% R vs S), ciprofloxacin (42.66% R vs S), and fusidic acid (88.40% R vs S). Interestingly, the levels of lipid groups significantly varied within each group of samples (in this case, sensitive and resistant groups), as evident in Figure 3 for MDGDs and methicillin resistance. The box and whisker plots compare the average and median normalized peak volumes and individual data points to determine significant differences in the MDGD content. The observed complex distribution of samples along the y-axis is related to phenotypic diversity among clinical isolates of S. aureus that are characterized as methicillin resistant or methicillin sensitive. Nevertheless, as described earlier, significant differences in the average peak volumes can be extracted from the studied data set, demonstrating the importance of studying larger sets of clinical isolates, not only reference strains.

selected antibiotics directly interacts with bacterial lipids, but the identified differences in lipid composition may reflect changes in the lipid biosynthesis and structure of the bacterial membrane in resistant strains. Thus, the susceptibility to an antibiotic may be independent of its mechanism of action. The classes of lipids that were significantly differentially expressed participate in several lipidomic pathways. Thus, the explanation of the mechanism underlying the observed differences is not straightforward. The obtained data indicate changes in the content of lipids that are major constituents of the S. aureus cell membrane, suggesting that the lipid membrane architecture differed between resistant and sensitive clinical isolates. Moreover, significant changes in lipids are associated with lipoteichoic acid (LTA) biosynthesis. Figure 2

Figure 2. General lipid biosynthetic pathway illustration of the lipidomic changes in antibiotic-resistant and antibiotic-sensitive in S. aureus indicating lipid level changes in antibiotic-resistant versus antibiotic-sensitive clinical isolates. The proposed pathway of S. aureus lipid biosynthesis shows the major S. aureus lipid classes, starting from phosphatidic acid (PA). The upward and downward arrows indicate differentially expressed (upregulated (↑) or downregulated (↓)) lipids between antibiotic-resistant and antibiotic-sensitive strains. The overall pathway analysis suggests changes in lipoteichoic acid (LTA) biosynthesis and membrane composition between antibiotic-resistant and antibiotic-sensitive S. aureus clinical isolates. The general lipid biosynthesis pathway is based on the KEGG metabolic pathways database (http://www.genome.jp/kegg/) and previous studies.33−35 ATP, adenosine triphosphate; CDP-DG, cytidine diphosphate diacylglycerol; CL, cardiolipin; CTP, cytidine triphosphate; DG, diacylglycerol; DGDG, diglycosyldiacylglycerol; MGDG, monoglycosyldiacylglycerol; G3P, glycerol-3-phosphate; LTA, lipoteichoic acid; Lys-PG, lysyl-phosphatidylglycerol; PG, phosphatidylglycerol; PG-P, phosphatidylglycerolphosphate; UDP-Glc, uridine diphosphate glucose.

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CONCLUSIONS The results presented in this paper reveal differences in the lipid profiles between antibiotic-resistant and antibioticsensitive clinical isolates of S. aureus. On the basis of the LCQ-TOF-MS untargeted lipidomic approach used in this study, the lipid composition of bacterial cells correlated with antibiotic resistance. Lipid fingerprints were assessed using a univariate Mann−Whitney test to determine the significance of the observed differences and a multivariate OPLS-DA. Some of the lipid groups significantly differed (p < 0.05) between resistant and sensitive isolates, with increases exceeding two-fold in the resistant counterpart. However, the observed high within-group variability precludes the differentiation of resistant and sensitive isolates based on a single lipid group. In this case, the multivariate OPLS-DA models facilitate extracting complex differences in lipid patterns. Moreover, the observed significant differences in the lipid content may reflect differences in lipid biosynthesis and membrane structure between antibioticresistant and antibiotic-sensitive S. aureus strains. This finding indicates that the composition of the cell membrane and its biophysical properties may mediate antibiotic sensitivity. Thus, the obtained results help to elucidate and further study the mechanisms of lipid metabolism in S. aureus strains resistant to antibiotics.

presents the lipidomic pathway of S. aureus membrane lipids and lipoteichoic acid biosynthesis33−35 and highlights lipidomic differences between S. aureus clinical isolates that are sensitive and resistant to the antibiotics observed in this study. Increased levels of LTA precursors, such as PGs, DGDGs, and MGDGs, and decreased levels of DGs in resistant S. aureus strains suggest altered LTA biosynthesis. Furthermore, the observed differences in the lipid content of these classes are common for all selected antibiotics. The lipid classes that significantly differed G


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Figure 3. Box and whisker plots of monoglycosyldiacylglycerols that significantly differed between MRSA and MSSA clinical isolates. The box and whisker plots compare the average and median normalized peak volumes and individual data points to identify significant differences (Mann− Whitney U test, p < 0.05) in MGDG contents. The observed complex distribution of samples along the y-axis is related to phenotypic diversity among methicillin-resistant and methicillin-sensitive clinical isolates of S. aureus. Center lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5-times the interquartile range from the 25th and 75th percentiles; outliers are represented by dots; crosses represent sample means; bars indicate 95% confidence intervals of the means; the width of the boxes is proportional to the square root of the sample size; and data points are plotted as open circles. (A) Box and whisker plot for the MGDG (31:0) lipid group, n = 42 for MRSA and n = 38 for MSSA. (B) Box and whisker plot for the MGDG (32:0) lipid group (retention time = 13.7 min), n = 42 for MRSA and n = 39 for MSSA. (C) Box and whisker plot for the MGDG (32:0) lipid group (retention time = 14.2 min), n = 41 for MRSA and n = 38 for MSSA. (D) Box and whisker plot for the MGDG (33:0) lipid group, n = 42 for MRSA and n = 39 for MSSA. (E) Box and whisker plot for the MGDG (35:0) lipid group, n = 42 for MRSA and n = 39 for MSSA.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00915. Representative extracted compound chromatograms obtained from the LC−MS-Q-TOF analysis of crude lipid extracts of S. aureus clinical isolates; identified lipid groups that significantly differed between S. aureus isolates that were resistant and sensitive to selected antibiotics; MS/MS spectra acquired in positive ionization mode and fragmentation patterns of the identified lipid classes (PDF)

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REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected], [email protected]. Phone: (+48 58) 347 18 33. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the National Science Centre, Poland, Grant No. 2014/13/N/ST4/03899. H


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