Investigation of Gender-Specific Exhaled Breath Volatome in Humans

Dec 18, 2013 - Lawrence A. Adutwum , A. Paulina de la Mata , Heather D. Bean , Jane E. Hill , James J. Harynuk. Analytical and Bioanalytical Chemistry...
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Investigation of Gender-Specific Exhaled Breath Volatome in Humans by GCxGC-TOF-MS Mrinal Kumar Das, Subasa Chandra Bishwal, Aleena Das, Deepti Dabral, Ankur Varshney, Vinod Kumar Badireddy, and Ranjan Nanda* Immunology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India S Supporting Information *

ABSTRACT: Exploring gender-specific metabolic differences in biofluids provides a basic understanding of the physiological and metabolic phenotype of healthy subjects. Many reports have shown gender-specific metabolome profiles in the urine and serum of healthy subjects; however, limited studies focusing on exhaled human breath are available in the literature. In this study, we profiled the exhaled breath (∼450 mL) volatile organic compounds (VOCs) of 47 healthy volunteers (age: 19−47; 23 male (M) and 24 female (F)) using a multidimensional gas chromatography and mass spectrometry and employed chemometric analysis to identify gender-specific VOCs. Eleven exhaled breath VOCs were identified from both uni and multivariate analysis from a training set (M = 15, F = 15) that could differentiate the genders within a healthy population. A partial least-squares discriminate analysis (PLS-DA) model built using these putative markers showed high accuracy in predicting (area under the receiver operating characteristic curve >0.9) a hold out/test sample set (n = 17). The outcomes of this report open up new avenues to undertake larger studies to elucidate the association of exhaled breath metabolites with gender-specific disease phenotypes and pharmacokinetics in the future. genders; however, contradictory findings on acetone and isoprene were reported from different studies in healthy subjects.13−15 To the best of our knowledge, a comprehensive study to assess gender-specific exhaled breath profile is not reported so far. One of the critical factors in comparative metabolomic-based biomarker studies is material normalization. Creatinine concentration or total area normalization methods are employed commonly in urine-based biomarker research. Many normalization methods have been used by researchers before in breath metabolomics data analysis.11,16,17 However, in breath marker molecule identification, the lack of a standardized normalization method for sample collection is an inherent limitation.18 In this report, we studied whether breath volatome of a healthy human control population comprises useful genderspecific VOCs. Breath samples collected from healthy subjects using an easy-to-use sampling method and multidimensional gas chromatography and mass spectrometry with standard chemoinformatic methods were employed to compare the exhaled breath profiles of male and female healthy study subjects. A comparative study using reported normalization methods including a new one using sum of abundances of identified common molecules was adopted to understand the role of these methods in breath metabolomics data analysis.

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ender dimorphism in humans is reflected in physical, physiological, genetic, and metabolic levels. It is a crucial factor for clinical as well as basic scientific research.1,2 Further understanding metabolic differences between genders may provide new insight into clinical research, including pharmacokinetics, pharmacodynamics, disease diagnosis, and so forth. Metabolite profiling from urine and plasma/serum using key technologies has provided encouraging information on genderspecific differences.1,3−5 Even differences in gut microflora, altered drug pharmacokinetics, and associations between metabolic and genetic biomarkers were associated with gender dimorphism.6,7 Although in very low abundance, in general, exhaled breath contains a plethora (∼3500) of volatile organic compounds (VOCs) which are derived from both endogenous metabolic activities and exogenous inhalation of surrounding air.8,9 Therefore, gender-specific metabolic differences may also be reflected in the volatome profile of exhaled breath isolated from healthy subjects. Recently, exhaled breath as an alternate noninvasive matrix for understanding the phenotype of subjects is gaining attention. Recent advancements in high-resolution multidimensional gas chromatography and time-of-flight mass spectroscopy (GCxGC-TOF-MS) for separation and identification of molecules present in very low abundance (picogram to femtogram levels) has accelerated these studies.10 Several reports have shown gender-specific differences in the abundance of specific molecules (e.g., isopropanol, hydrogen sulfide, and methanethiol in exhaled breath).11,12 Molecular abundances of alkane did not show significant changes between © 2013 American Chemical Society

Received: November 2, 2013 Accepted: December 18, 2013 Published: December 18, 2013 1229

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× 0.1 mm × 0.1 μm) were used as primary and secondary column, respectively. The temperature of the primary oven was raised from 35 to 200 °C at a ramp of 10 °C/min with 1 min hold time after reaching a final temperature of 200 °C. The temperature of the modulator and secondary oven were set at 30 and a 5 °C offset to the primary oven, respectively. The temperature of the detector transfer line was set at 225 °C for the entire run time. During data acquisition, the energy of the electron ionization (EI) mode was set at 70 eV, and a mass range of 45 to 450 m/z was recorded with a scan speed of 200 Hz. The ion source temperature was set at 250 °C, and detection voltage was at 1450 V. Data acquisition steps and GCxGC-TOF-MS parameters were controlled by ChromaTOF software (version 4.50.8.0, Leco Corporation, U.S.A.). Data acquisitions of collected samples were completed within 1 week of sample collection. Method Sensitivity and Precision. Limit of detection (LOD) and limit of qualification (LOQ) of the adopted method used for breath molecular profiling were calculated by introducing 1 μL of a 10-fold diluted standard mixture (isothermal column (ITC) mix: 10 molecule, Restek Co, U.S.A.) using a syringe (Gerstel, Australia) in a blank glass tube to the TDS-GCxGC-TOF-MS system for five times. Relative standard deviations (RSD) of peak areas for both primary and secondary retention times were calculated considering the results obtained from these five runs. Data Processing. Baseline correction and peak picking of all acquired data files were carried out using ChromaTOF software. Tentative identification of the molecules was carried out by library search consisting of mainlib (2 12 961 EI MS spectra) and replib (30 932 EI MS spectra). Additional information like peak area, signal-to-noise ratio (S/N), % area, retention index (RI), and chemical abstracts service (CAS) number were generated for peak table preparation. Peaks with at least signal-to-noise (S/N) of more than 250 (S/ N > 50 for sub peak) and library spectra similarity index of more than 600 were selected for molecule name assignment. Molecules identified by library search were confirmed by matching their retention indices (RI) with reported ones from the literature and database (National Institute of Standards and Technologies (NIST) and Pherobase). Those peaks not qualifying by these stringent criteria were considered as not detected or absent in the study samples. Peak Alignment Using Statistical Compare of Chroma TOF for Analysis. All processed data files (n = 47) were first aligned on the basis of retention times and mass spectral matching using statistical compare feature of ChromaTOF. Maximum retention time difference was set at 0.2 s and maximum number of modulation period apart was set at “1” for matching the peaks. For mass spectral match minimum spectral similarity among aligned molecules was set at 600. Molecules present in all observations belonging to both study groups were selected as common molecules. A compound table was prepared using selected molecules (n = 129) present in at least 50% of samples within one of the study groups and exported as a .csv file from ChromaTOF for metadata preparation. Normalization of Metadata. A comprehensive comparison of different normalization factors as calculated from (a) TA: total area of all the variables present in the observation;16 (b) TI: area under total ion current;17 (c) TM: total area of median values of all the variables;19 and (d) SC: sum of common molecules identified in this study (n = 41) and their

Altered molecular profiles of exhaled breath isolated from both genders might reflect gender-specific metabolism.

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EXPERIMENTAL SECTION Subject Recruitment. Healthy volunteers from the International Center for Genetic Engineering and Biotechnology (ICGEB), New Delhi, were recruited, after receiving their written informed consent, following the approved and recommended procedures of the Institutional Review Board (IRB) and the Ethical Committee (EC). A subset of samples collected for an ongoing project approved by the IRB and EC of the institute have been used in this study. Breath Sample Collection. Each volunteer was advised to avoid consuming any food or beverages at least 3 h before sample collection and was equilibrated for at least 30 min in an isolated room prior to breath sample collection. All sample collection was completed within a week (precisely 3 days in mid June 2013) to minimize the variation contributed by surrounding air composition. Subjects with no past history of respiratory or metabolic diseases and not undertaking any kind of medication were recruited in this study. The demographic details of all study subjects as used in the study are presented in Supporting Information Table S-1. A volume of ∼450 mL of exhaled alveolar breath air samples was collected using a simple, easy-to-use Bio-VOC (Bio-VOC sampler, Markes International Limited, U.K.) apparatus following the recommended procedures as explained by the suppliers. Subjects were asked to exhale a single slow vital capacity breath into the Bio-VOC, so that the initial volume will be passed through the sampler opening and only the last ∼150 mL of breath samples will be trapped. The collected air was immediately transferred to a thermal desorption tube (TDT) (Carbotrap 300 (C300), Gerstel, Germany) at a rate of ∼50 mL/min manually (using a stop watch for ∼3 min for transferring) following the direction of sampling to capture VOCs present in the trapped air. This procedure was repeated thrice to collect a total of ∼450 mL (∼150 mL × 3) of breath samples. Each TDTs were re/conditioned for 6 h at 350 °C with a continuous flow of helium at 2.8 bar and stored at room temperature in an airtight screw-capped container before air sample collection. TDTs with samples were coded (tube no.: date of data acquisition as DDMMYYYY) to minimize bias during data acquisition and stored at 4 °C until taken for data acquisition using a thermal desorption system coupled to multidimensional gas chromatography and time-of-flight mass spectrometry (TDS-GCxGC-TOF-MS). TDT Desorption and GCxGC-TOF-MS Data Acquisition. TDTs containing samples were brought from 4 °C to ambient temperature (∼22 °C) for at least 30−60 min, before loading in autosampler tray (TDSA2; Gerstel, Germany) connected to a thermal desorption system (TDS3; Gerstel, Germany) following the direction of the matrix toward the TDS for data acquisition. Helium was used as the carrier gas at a constant flow of 1 mL/min during entire data acquisition steps. Desorption of TDTs were carried out in TDS3 at 310 °C for 3 min. Desorbed VOCs from TDTs were trapped in a cool injection system (CIS4; Gerstel, Germany) at −35 °C for 3 min. These trapped molecules were released from CIS at a temperature gradient of −35 to 350 °C with a ramp of 12 °C/s to a primary column. Steps from sample loading to cool injection were controlled through Maestro software (version 1; Gerstel, Germany). RTX-1 (100% dimethyl siloxane, 30 m × 0.25 mm × 0.25 μm) and RTX-wax (Poly Ethylene Glycol, 2 m 1230

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with the selected putative markers (n = 11) as variables, and two ROC plots were made to calculate the area under curve (AUC). PLS-DA algorithm with two latent variables was selected for carrying out ROC analysis.

importance in the breath sample to sample normalization was studied. According to the adopted normalization methods, a total of four data matrices were prepared in excel (Microsoft Office Excel 2007) and saved as .csv format to upload in Metaboanalyst (version 2.0) web server for multivariate analysis.20 Multivariate Analysis of Data Matrices to Compare Normalization Methods. Missing values were imputed by replacing with half of the minimum value from the entire matrix, g-log transformed and auto- (mean centered and divided by standard deviation of each variable) or pareto scaled (mean centered and divided by square root of standard deviation of each variable) to get a near Gaussian distribution. Five separate partial least-squares discriminate analysis (PLSDA) models were generated using normalized data obtained from four methods and one without any normalization for a comparative study. Uni and Multivariate Analysis for Putative Marker Identification. The original data set with 47 subjects was split randomly into a training set of 30 (male: n = 15 and female: n = 15) and the hold out set with remaining subjects 17 (male: n = 8, female: n = 9) for prediction using an online resource (Research Randomizer version 4.0, www.randomizer.org). A training set (30 × 129) normalized with SC method was used for PLS-DA model building and to identify important molecules that have high discriminatory power. A permutation test was performed with 100 iterations to validate model performance, and molecules with variable importance parameter (VIP > 1.5) were selected as important features.21 All chemometric analysis was carried out using a web-based resource (i.e., Metaboanalyst).20 Untransformed training data matrix was used for univariate data analysis and molecules that showed at least 2-fold increase or decrease in abundance at p = 0.05 (Wilcoxon Mann− Whitney test) between the study groups (i.e., male and female) were selected as important molecules. A consensus list of molecules from both multivariate (VIP > 1.5) and univariate (fold change: FC of 2 at p < 0.05) analysis were selected as putative markers. Furthermore to understand the degree of association between the identified putative marker molecules, without losing overall predictive accuracy, a Pearson correlation analysis was carried out. The resulting matrix consisting of only the identified putative marker molecules and their abundances in the training set study samples (n = 30) were selected for an unsupervised two-dimensional hierarchical clustering to explore the pattern that may exist in the study groups. Parameters like Euclidean distance as the distance metric and Ward’s linkage method to link the groups were used in hierarchical clustering for both observations as well as variables. Effect of body mass index (BMI in kg/m2) and age (in years) on the abundances of identified putative markers was analyzed using the nonparametric Wilcoxon Mann−Whitney test. BMI of 25 kg/m2 was used as cut off and selected as two groups (low and high). Age groups were selected based on a cutoff of 30 years. Receiver Operating Characteristic Curve (ROC) Analysis. For receiver operating characteristic curve (ROC) analysis, a web-based resource ROCCET (ROC Curve Explorer and Tester, http://www.roccet.ca), was used.22 Monte Carlo cross validation (MCCV) with multiple iterations was used to compute the area under curve (AUC) with confidence intervals of the ROC. ROC tester feature of ROCCET was employed to feed both the cross-validated/ training set (n = 30) and the hold out set (n = 17) data sets

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RESULTS Sensitivity and Precision of Adopted Analytical Method. The calculated LOD, LOQ, and relative standard deviation (RSD) of peak areas of the optimized GCxGC-TOFMS method as obtained from the standard molecule mix are presented in Table 1. The LOD and LOQs were in picogram Table 1. Sensitivity and Precision of the Analytical Method Employed in the Exhaled Breath profiling studya molecule 2,6dimethylaniline 1,2-hexanediol decane undecane dodecane tridecane nonanal 2,6dichlorophenol naphthalene 1-octanol

LOD (in pg)

LOQ (in pg)

RT RSD (in seconds)

area RSD (%)

139

458

0.0

14.3

114 66 57 85 41 86 189

376 217 188 280 135 283 623

0.2 0.1 0.0 0.0 0.0 0.1 0.0

18.7 11.7 22.1 19.6 9.3 3.6 27.9

182 90

600 297

0.0 0.0

21.5 13.2

a

LOD: limit of detection; LOQ: limit of quantification; RT: primary retention time; RSD: relative standard deviation; pg: picogram.

range, and the RSD of peak areas of standard molecules were calculated to be less than 27.9%. Minor shifts in primary and secondary retention times (∼2−10 s) were observed during all sample runs (n = 47). Molecular Profiling of Breath Air Samples. Exhaled breath (∼450 mL) collected from healthy subjects (age: 19−47, male: 23 and female: 24) by using Bio-VOC breath collection device resulted in the identification of 1379 molecules (S/N > 250 and similarity index >600) in at least one of the exhaled breath samples out of the entire study population (n = 47). At least 129 molecules were identified to be minimally present in 50% of subjects in one of the study groups and selected for further analysis. Qualitative and quantitative variations in the exhaled breath molecular profile were observed between male and female samples. Approximately 31% of the entire identified molecules (41 out of 129) were found to be consistently present in breath of all study subjects (100%, n = 47) and are presented in Table 2 with abundance details. Although five of these common molecules do show significant variations in abundances between these study groups (Wilcoxon Mann− Whitney test p < 0.05), the FC were less than two and hence not selected as important features for further analysis. Role of Normalization Method in Gender-Based Classification. We adopted four normalization methods (TA, TI, TM, and SC), and the variations of normalization factors in all study subjects were similar for TA, TM, and SC (Figure 1A). However, the variation using the TI normalization factor was different from the rest of the adopted methods (Figure 1A). Five generated PLS-DA models (including data matrix without normalization) using the metadata (47 × 129) normalized by various methods showed two major clusters (Figure 1B). The top two components resulted from these 1231

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Table 2. List of Common Molecules (41) As Identified from the Complete Data Set (n = 47) and Mean Abundance (With Standard Deviation: SD) In Each Study Group (Female: 24 and Male: 23)a female

male

molecules

mean

SD

mean

SD

1,4-cyclohexadiene, 1-methyl1-decene 1-heptene 1-octene 1-phenyl-5-methylheptane 2-butene 2-ethylacrolein 2-hexene, 5,5-dimethyl-, (z)2-propenal 2-propenenitrile 2-propyl-1-pentanol 2-undecanethiol, 2-methyl3-dodecene, (z)5-tridecene, (e)-* 7-tetradecene, (z)-* benzene benzene, (1,3-dimethylbutyl)benzene, 1,3-dichlorobenzene, 1,3-dimethylbenzene, 1-ethyl-2-methylbenzene, butylbenzene, heptyl-* benzene, hexylbenzene, pentyl-* benzene, propylbiphenyl butanal butyl isocyanatoacetate cyclohexene cyclopentene, 3-methylcyclopropane, 1-ethyl-2-methyl-, ciscyclopropane, ethylidenedimethylamine ethanol furan furan, 3-methylhexane, 2,4-dimethyl-* hexane, 3-methylnaphthalene acetone trichloromethane

26 046 167 887 156 819 431 511 103 621 847 863 87 109 89 423 715 490 448 423 67 364 120 012 144 551 142 238 270 346 4 923 212 43 893 87 946 506 407 100 510 289 441 86 972 133 652 215 369 467 351 233 722 112 473 109 040 43 232 46 664 209 054 471 322 221 775 523 568 65 870 25 990 528 032 90 499 285 356 655 146 400 600

15 476 69 298 116 333 328 252 39 636 550 279 95 345 42 246 807 216 461 643 59 726 79 825 38 583 50 356 98 151 2 194 269 22 926 136 162 442 886 33 315 122 765 33 544 54 326 60 352 200 666 69 562 89 084 94 729 16 805 31 368 131 446 396 495 131 692 1 117 494 44 873 17 400 439 196 44 771 90 383 843 787 819 774

24 040 147 057 166 196 346 863 95 041 758 196 61 200 88 673 716 451 589 427 42 979 81 070 129 615 113 335 215 698 4 380 648 43 406 77 653 333 745 93 602 224 970 65 390 120 802 173 886 412 059 223 800 86 233 140 487 40 486 32 733 198 990 522 268 199 779 157 047 62 351 18 490 310 268 102 002 248 775 406 675 742 817

9 908 63 026 91 071 215 489 43 961 338 122 44 540 38 715 979 755 903 554 36 139 54 858 56 732 40 853 68 682 1 840 183 21 824 79 139 79 129 33 970 111 587 31 035 55 838 73 867 171 888 97 716 79 537 112 159 21 261 15 483 114 727 359 534 91 334 128 473 60 961 10 824 161 517 65 746 111 462 460 226 1 944 044

a Out of these 41 molecules, five molecules showed significant differences (p < 0.05) in abundance between male and female study groups and marked with asterisk.

decrease in abundance between two study groups at statistically significant levels (p < 0.05), and these molecules were selected as important features. However, eleven molecules were found to be common in both uni and multivariate analysis and were considered as putative marker molecules (Figure 2B). Increased mean abundance of molecules like (E)-1-phenyl-1-butene; 1hexene, 4-methyl-; 2-butene, 2-methyl-; benzene, 2-propenyl-; butanal, 3-methyl-; oxalic acid, allyl dodecyl ester; oxirane, (1methylbutyl)-; pentane, 2-cyclopropyl-; (5.60, 6.50, only present in female, 4.40, 3.54, 13.41, 6.71, 5.51, respectively, at p < 0.05) and decreased abundance of 1-propene, 2-methyl-; methylal; propane, 1-chloro-2-methyl- (0.28, 0.08, and 0.20, respectively, at p < 0.05) were observed in the exhaled breath of females with respect to male study groups. More importantly,

PLS-DA models explained the similar total variance (26.1− 27.9), and the permutation statistics varied from 0.22 to 0.31 (at p < 0.01) for 100 permutation tests. Metadata normalized by the SC normalization method showed the lowest permutation result and variation within groups was minimum, and hence it was selected for further analysis. Gender-Specific Molecule Identification. From a separate PLS-DA analysis of the training data set (n = 30; M/F: 15/15) and using the sum of common molecules (SC) as a normalization factor, there resulted a set of 18 molecules having a VIP score of more than 1.5 (Figure 2A). Using the nonparametric univariate data analysis (Wilcoxon Mann− Whitney test) of the training data set, there resulted a list of 22 molecules that showed at least either a 2-fold increase or 1232

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Figure 1. Role of normalization methods on breath volatome data analysis. (A) Variation of different normalization factors (■:TI; red :TA; blue ▼:SC; and green ▲ : TM) used for normalizing the entire breath data (129 metabolites) collected from all study subjects (n = 47, M: male and F: female). Similar normalization factors resulted from all methods except from the TI method. (B) Gender-based class separation as observed from the score plot of PLS-DA obtained from exhaled breath profile not normalized (NN), normalized by using total ion current (TI), normalized by using total area (TA), normalized by total median (TM) and normalized by sum of common molecules (SC). It may be noted that similar variance levels were explained by top two components of all PLS-DA models.

we observed 2-butene, 2-methyl- to be present in 71% of total females (17/24), but it was absent in all male samples (0/23) of the entire study set. A certain degree of correlation between these sets of putative marker molecules was observed, and two combinations involving three molecules (1-hexene, 4-methyl-; 2-butene, 2methyl- and pentane, 2-cyclopropyl-) showed a correlation coefficient of higher than 0.7 (0.83 between 1-hexene 4-methyl-

and 2-butene, 2-methyl-; 0.78 between 1-hexene 4-methyl- and pentane, 2-cyclopropyl-) (Figure 2C). Although 1-hexene, 4 methyl- and 2-butene, 2-methyl- showed high correlation, removal of any one of them resulted in significant loss in grouping pattern, and the set of eleven molecules was found to be minimum for differentiating both study groups. An unsupervised, two-dimensional hierarchical clustering of the training set samples with selected eleven putative marker 1233

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Figure 2. Signature of eleven putative marker molecules showed high gender specificity. (A) Score plot of partial least-squares discriminate analysis (PLS-DA) obtained from comparative exhaled breath VOCs analysis of male (blue ●, n = 15) and female (red ■, n = 15) study subjects used in the training set (n = 30). Top two components of PLS-DA model explained ∼25.1% variance and showed gender-specific clusters. (B) Venn Diagram showing the number of important molecules from different analyses (i.e., fold change, Wilcoxon Mann−Whitney test, and PLS-DA VIP score. (C) Correlation plot among the identified 11 putative marker molecules. Increased intensities of brown and blue color boxes represent high positive or negative correlation, respectively. (D) Two-dimensional hierarchical clustering of the identified marker molecules presented in column and individual subjects used in training set presented in rows. Green and red represents female and male subjects, respectively. Brighter shades of brown reflect higher abundance, and light blue represents very low or absent. M1: 1-hexene, 4-methyl-; M2: 2-butene, 2-methyl-; M3: pentane, 2cyclopropyl-; M4: (E)-1-phenyl-1-butene; M5: oxalic acid, allyl dodecyl ester; M6: oxirane, (1-methylbutyl)-; M7: benzene, 2−1propenyl-; M8: butanal, 3-methyl-; M9: 1-propene, 2-methyl-; M10: propane,1-chloro-2-methyl-; and M11: methylal.

molecules showed two major clusters of subject groups and one misclassified subject (Figure 2D). When age and BMI were considered significant difference in abundance of one putative marker molecule was observed in >30 years of age group (Supporting Information Figure S-1). In case of two BMI groups insignificant differences in abundances of putative marker molecules were observed (Supporting Information Figure S-2). ROC Analysis. The cross validated/training set showed an AUC of 0.98 (CI: 0.9−1.0 at 95% significance), and the hold out or test set showed an AUC of 1.0 on ROC analysis (Figure 3). The predictive accuracy of ROC for the test data set (n = 17) was calculated to be 0.941 (16/17). Discussion. Molecular profiling of urine and plasma using metabolomic approaches employing nuclear magnetic resonance spectroscopy (NMR) and liquid chromatography and

mass spectroscopy (LC-MS) to understand the gender-specific biochemical differences (or dimorphisim) in mice or human have been attempted by several researchers.1,4 Researchers have focused on the alteration induced by diurnal physiological variation, dietary, culture habits, gender, age, BMI, and microbiome on the metabolome in urine and plasma isolated from healthy mice or humans.3,7,23,24 NMR and LC-MS-based studies specifically identify a nonvolatile class of molecules, mainly amino acids, creatine, esters, amines, acylcarnitines, phosphatidylcholines, lysophosphatidyylcholines, sphingomyelins, hexose, and steroids.1,3,4,24 However, limited information on gender-specific global molecular profiling of exhaled breath is available in the literature. The present study may be the first report to profile VOCs in exhaled breath of healthy subjects with a focus to explore gender-specific differences. 1234

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were common (>90%), whereas in our study, we identified 68 molecules to be present in more than 90% of the entire Indian study population.25 A set of 129 molecules were identified in at least 50% of either of these study groups (n = 47) after peak alignment, retention indices matching, and manual inspection. Although these molecules are tentatively identified based on library search and RI match, further confirmation and reliable absolute quantification could be attempted using external standards in a large-scale study. Normalization of Breath Samples. The lack of a universally accepted normalization method is one of the major challenges in human exhaled breath profiling studies.18,31 Metabolomic studies involving other biological matrices like urine use creatinine concentration as a normalization factor to minimize the effect of dilution.32 However, multiple normalization methods were used by breath researchers for reporting breath biomarker studies.16,19 Although we have selected the SC method for normalization for further analysis, it should be noted that it was a comparative study, and the ability of a normalization method to separate classes may not be considered as a criteria to comment on its suitability. Kischkel et al.11 showed that body surface area may also be used as a normalization method; however, it did not alter the statistical findings, and this is also supported by findings of Cope et al.33 Therefore, we did not include it in our method standardization. Gender-Specific Variation. As in other biological matrices, the breath volatome might also be influenced by natural variations like age and gender and external factors like environment, nutrition, or medication. Gender-specific variation in physiologic and metabolic status of subjects like hormonal profile, serum, and urine metabolites are wellknown.3,7,23,24 We used a comprehensive global profiling approach to explore the degree of difference that occurs and to identify important molecules that could be useful as putative biomarker to distinguish breath volatome of male and female. A PLS-DA model built from the training data set (30 × 129) showed robustness as evident from the permutation test results. Comparison of the exhaled breath metabolites between male and female healthy subjects identified a set of 11 molecules (VIP > 1.5 and FC > 2.0 at p < 0.05) that has high discriminatory power to differentiate the genders of the study subjects. The PLS-DA model predicted a hold out set (n = 17) with high accuracy (0.941). However, we need to be cautious of the low sample size used in this report, and more studies including a larger sample size and from diverse geographical and ethnic background will be more informative. Additionally, Sinues et al. reported the presence of physiological diurnal variations in exhaled breath molecular profiles of healthy subjects.34 It would be useful to explore how gender dimorphism may influence diurnal variation. These differences in breath profiles may not be surprising as the exhaled breath of male and female releases with physiological differences (i.e., abdominal and thoracic part of the lungs).35 Effect of lung tissues and metabolism may also alter the exhaled breath metabolite profile. Limited information about the possible link between the putative markers and metabolic state of the subjects are available in the literature. One of the marker molecules (butanal, 3-methyl- or isovaleraldehyde) is known to be a degradation product of the amino acid isoleuciene.36 Staphylococcus aureus, which is commonly found in the human respiratory tract is also reported to release this molecule.37 Like the differences in gut microbiota as reported earlier, gender-specific differences in microbiome

Figure 3. Receiver operating characteristic curve (ROC) plotted using a web-based tool ROCCET as obtained from the training (or cross validated: CV, blue line) and hold out test sets (pink line) using eleven selected putative marker molecules. Diagnostic accuracy is calculated by the area under curve (AUC) of ROC plots.

Like other biological matrices, exhaled breath contains a plethora of molecules that represents metabolic phenotype of a subject.18,25 In this GC-MS-based metabolomics analysis, the exhaled breath profile of healthy Indian population was investigated. An easy-to-use method for breath sample collection (i.e., Bio-VOC) that has been used in multiple reports was employed for breath sample collection.26,27 Few breath collection devices with CO2 sensors for alveolar breath sampling are available. Due to logistic challenges we used BioVOC for all breath sample collection. Improved efficiency of modified multibed matrices like Carbotrap 300 containing thermal desorption tubes (TDTs) were used to trap VOCs with varied class from ∼450 mL of breath samples. We observed that 450 mL of exhaled alveolar breath provides sufficient molecular details from a healthy subject. Background subtraction of these breath samples by corresponding room air VOCs was not employed in this study due to the limitation of losing genderassociated differential absorption as well as degradation of exogenous molecules. Moreover, background room air subtraction may also increase the statistical error of the assay.28 We employed a TDS coupled to a GCxGC-TOF-MS for global profiling of exhaled breath samples collected from healthy subjects. The sensitivity and precision of the adopted method was calculated using a standard molecular mix, and the findings corroborate with previous reports.17,29 As GCxGCTOF-MS provides major advantages of separation efficiency of molecules with close boiling points by adding additional dimension of separation based on polarity to identify more molecules than conventional one-dimensional GC-MS25, it was therefore selected in this study. Limited findings on breath profiling using multidimensiosnal GC-TOF-MS are available in literature.25,30 In this study, we identified 1379 molecules in exhaled breath of at least one out of 47 Indian healthy subjects, which corroborates the findings of Phillip et al. (2013) from 34 American subjects.25 Phillips et al. reported that 91 molecules 1235

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present in the respiratory tract may be contributing such differences.23 Two of the markers molecules (E)-1-phenyl-1butene and benzene, 2-propenyl- belong to the allyl benzene group, and this class of compounds is released from spices used in Indian cuisine.38 The allyl benzene group of molecules are metabolized inside human body by P450 enzymes.39 Earlier reports explained gender-specific microsomal P450 isoforms in rat and human.40 Therefore, if these gender-specific enzyme isoforms lead to differential metabolism of allyl benzene in human that may contribute to differential abundance in exhaled breath, this aspect is an important matter for further investigation. Further, 2 out of these 11 identified molecules, 1-hexene, 4-methyl- and 1-propene, 2-methyl-, were reported as markers for tuberculosis41 and liver cirrhosis,42 respectively. Use of VOCs for identification of different disease conditions either from breath or urine is taking momentum and in principle could also be useful in elucidating the gender-specific metabolism in healthy and diseased groups.25,43 Advantages of a noninvasive mode of sample collection, no restriction in volume, and times of collection make breath as a matrix that is very useful for monitoring the altered metabolic conditions of healthy or diseased individuals as well as subjects undertaking therapeutic interventions to understand their status after medication. Differences in pharmacokinetics between genders are well-known. The adopted method presented in this study could also be useful in understanding many systemic diseases specific to different genders like breast cancer44 or for elucidating gender-specific pathophysiological differences relevant to diseases like pulmonary infection45 and obesity.46 Conclusions. In this study, we analyzed if the sum of abundances of common molecules identified in all study samples could be useful as an alternate normalization method during breath biomarker discover studies. Exhaled breath molecular profiles of healthy subjects show gender-specific variation. A set of 11 putative markers could effectively be useful in differentiating these study groups with high accuracy. These findings suggest that breath metabolites could also be useful for a deeper understanding of the metabolic phenotype of healthy subjects that needs further validation in a larger population representing diverse geographical and ethnic backgrounds.

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REFERENCES

(1) Mittelstrass, K.; Ried, J. S.; Yu, Z.; Krumsiek, J.; Gieger, C.; Prehn, C.; Roemisch-Margl, W.; Polonikov, A.; Peters, A.; Theis, F. J.; Meitinger, T.; Kronenberg, F.; Weidinger, S.; Wichmann, H. E.; Suhre, K.; Wang-Sattler, R.; Adamski, J.; Illig, T. PLoS Genet. 2011, 7, e1002215. (2) Kim, A. M.; Tingen, C. M.; Woodruff, T. K. Nature 2010, 465, 688−689. (3) Slupsky, C. M.; Rankin, K. N.; Wagner, J.; Fu, H.; Chang, D.; Weljie, A. M.; Saude, E. J.; Lix, B.; Adamko, D. J.; Shah, S.; Greiner, R.; Sykes, B. D.; Marrie, T. J. Anal. Chem. 2007, 79, 6995−7004. (4) Kochhar, S.; Jacobs, D. M.; Ramadan, Z.; Berruex, F.; Fuerholz, A.; Fay, L. B. Anal. Biochem. 2006, 352, 274−281. (5) Yang, X.; Schadt, E. E.; Wang, S.; Wang, H.; Arnold, A. P.; Ingram-Drake, L.; Drake, T. A.; Lusis, A. J. Genome Res. 2006, 16, 995−1004. (6) Tanaka, E. J. Clin. Pharm. Ther. 1999, 24, 339−346. (7) Markle, J. G.; Frank, D. N.; Mortin-Toth, S.; Robertson, C. E.; Feazel, L. M.; Rolle-Kampczyk, U.; von Bergen, M.; McCoy, K. D.; Macpherson, A. J.; Danska, J. S. Science 2013, 339, 1084−1088. (8) Popov, T. A. Ann. Allergy, Asthma, Immunol. 2011, 106, 451−457. (9) Pleil, J. D.; Stiegel, M. A.; Risby, T. H. J Breath Res 2011, 7, 017107. (10) Libardoni, M.; Stevens, P. T.; Waite, J. H.; Sacks, R. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2006, 842, 13−21. (11) Kischkel, S.; Miekisch, W.; Sawacki, A.; Straker, E. M.; Trefz, P.; Amann, A.; Schubert, J. K. Clin. Chim. Acta 2010, 411, 1637−1644. (12) Snel, J.; Burgering, M.; Smit, B.; Noordman, W.; Tangerman, A.; Winkel, E. G.; Kleerebezem, M. Arch. Oral Biol. 2011, 56, 29−34. (13) Schwarz, K.; Filipiak, W.; Amann, A. J. Breath Res. 2009, 3, 027002. (14) Kushch, I.; Arendacka, B.; Stolc, S.; Mochalski, P.; Filipiak, W.; Schwarz, K.; Schwentner, L.; Schmid, A.; Dzien, A.; Lechleitner, M.; Witkovsky, V.; Miekisch, W.; Schubert, J.; Unterkofler, K.; Amann, A. Clin. Chem. Lab. Med. 2008, 46, 1011−1018. (15) Phillips, M.; Greenberg, J.; Cataneo, R. N. Free Radical Res. 2000, 33, 57−63. (16) Van Berkel, J. J.; Dallinga, J. W.; Moller, G. M.; Godschalk, R. W.; Moonen, E. J.; Wouters, E. F.; Van Schooten, F. J. Respir. Med 2010, 104, 557−563. (17) Gaspar, E. M.; Lucena, A. F.; Duro da Costa, J.; Chaves das Neves, H. J. Chromatogr. A 2009, 1216, 2749−2756. (18) Miekisch, W.; Schubert, J. K.; Noeldge-Schomburg, G. F. Clin. Chim. Acta 2004, 347, 25−39. (19) Martinez-Lozano Sinues, P.; Kohler, M.; Zenobi, R. PLoS One 2013, 8, e59909. (20) Xia, J.; Wishart, D. S. Nat. Protoc. 2011, 6, 743−760. (21) Huang, M.; Liang, Q.; Li, P.; Xia, J.; Wang, Y.; Hu, P.; Jiang, Z.; He, Y.; Pang, L.; Han, L.; Luo, G. Mol. Biosyst. 2013, 9, 2134−2141. (22) Xia, J.; Broadhurst, D. I.; Wilson, M.; Wishart, D. S. Metabolomics 2013, 9, 280−299. (23) Li, M.; Wang, B.; Zhang, M.; Rantalainen, M.; Wang, S.; Zhou, H.; Zhang, Y.; Shen, J.; Pang, X.; Wei, H.; Chen, Y.; Lu, H.; Zuo, J.; Su, M.; Qiu, Y.; Jia, W.; Xiao, C.; Smith, L. M.; Yang, S.; Holmes, E.; Tang, H.; Zhao, G.; Nicholson, J. K.; Li, L.; Zhao, L. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 2117−2122. (24) Lenz, E. M.; Bright, J.; Wilson, I. D.; Hughes, A.; Morrisson, J.; Lindberg, H.; Lockton, A. J. Pharm. Biomed. Anal. 2004, 36, 841−849. (25) Phillips, M.; Cataneo, R. N.; Chaturvedi, A.; Kaplan, P. D.; Libardoni, M.; Mundada, M.; Patel, U.; Zhang, X. PLoS One 2013, 8, e75274. (26) Poli, D.; Carbognani, P.; Corradi, M.; Goldoni, M.; Acampa, O.; Balbi, B.; Bianchi, L.; Rusca, M.; Mutti, A. Respir. Res. 2005, 6, 71. (27) Mazzone, P. J. J. Breath Res. 2012, 6, 027106. (28) Phillips, M. In Disease Markers in Exhaled breath: Basic Mechanisms and Clinical Applications; Marczin, N., Kharitonov, S., Yacoub, M., Barnes, P. J., Eds.; Marcel Decker: New York, 2005; pp 201−212.

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*E-mail: [email protected]. Fax: +91-11-26742316. Tel.: +9111-26741358 −426. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to the volunteers from the International Center for Genetic Engineering and Biotechnology, New Delhi, who participated in this study. Financial assistance through Grand Challenges Explorations Initiatives from the Bill and Melinda Gates Foundation and Grand Challenges Canada and core fund of International Center for Genetic Engineering and Biotechnology, New Delhi, are acknowledged. 1236

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(29) Ribes, A.; Carrera, G.; Gallego, E.; Roca, X.; Berenguer, M. A.; Guardino, X. J. Chromatogr. A 2007, 1140, 44−55. (30) Caldeira, M.; Perestrelo, R.; Barros, A. S.; Bilelo, M. J.; Morete, A.; Camara, J. S.; Rocha, S. M. J. Chromatogr. A 2012, 1254, 87−97. (31) Solga, S. F.; Risby, T. H. IEEE Sens. J. 2010, 10, 7−9. (32) Waikar, S. S.; Sabbisetti, V. S.; Bonventre, J. V. Kidney Int. 2010, 78, 486−494. (33) Cope, K. A.; Watson, M. T.; Foster, W. M.; Sehnert, S. S.; Risby, T. H. J. Appl. Physiol. (1985) 2004, 96, 1371−1379. (34) Sinues, P. M.; Kohler, M.; Zenobi, R. Anal. Chem. 2013, 85, 369−373. (35) Kaneko, H.; Horie, J. Respir. Care 2012, 57, 1442−1451. (36) Al Mardini, H.; O’Brien, C. J.; Bartlett, K.; Williams, R.; Record, C. O. Clin. Chim. Acta 1987, 165, 61−71. (37) Filipiak, W.; Sponring, A.; Baur, M. M.; Filipiak, A.; Ager, C.; Wiesenhofer, H.; Nagl, M.; Troppmair, J.; Amann, A. BMC Microbiol. 2012, 12, 113. (38) Parthasarathy, V. A., Chempakam, B., Zachariah, T. J. Chemistry of Spices, 1st ed.; CAB International: Oxfordshire, U.K., 2008. (39) Liebler, D. C.; Guengerich, F. P. Biochemistry 1983, 22, 5482− 5489. (40) Parkinson, A.; Mudra, D. R.; Johnson, C.; Dwyer, A.; Carroll, K. M. Toxicol. Appl. Pharmacol. 2004, 199, 193−209. (41) Phillips, M.; Basa-Dalay, V.; Bothamley, G.; Cataneo, R. N.; Lam, P. K.; Natividad, M. P.; Schmitt, P.; Wai, J. Tuberculosis (Edinb) 2010, 90, 145−151. (42) Dadamio, J.; Van den Velde, S.; Laleman, W.; Van Hee, P.; Coucke, W.; Nevens, F.; Quirynen, M. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2012, 905, 17−22. (43) Banday, K. M.; Pasikanti, K. K.; Chan, E. C.; Singla, R.; Rao, K. V.; Chauhan, V. S.; Nanda, R. K. Anal. Chem. 2011, 83, 5526−5534. (44) Phillips, M.; Cataneo, R. N.; Ditkoff, B. A.; Fisher, P.; Greenberg, J.; Gunawardena, R.; Kwon, C. S.; Tietje, O.; Wong, C. Breast Cancer Res. Treat. 2006, 99, 19−21. (45) Casimir, G. J.; Lefevre, N.; Corazza, F.; Duchateau, J. Biol. Sex Differ. 2013, 4, 16. (46) Kanter, R.; Caballero, B. Adv. Nutr. 2012, 3, 491−498.

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NOTE ADDED AFTER ASAP PUBLICATION This paper published ASAP on December 27, 2013. Additional revisions were made to the text and the revised version was reposted on December 30, 2013.

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