Published: May 27, 2011 r2011 American Chemical Society 5526 dx.doi.org/10.1021/ac200265g | Anal. Chem. 2011, 83, 5526–5534 ARTICLE pubs.acs.org/ac Use of Urine Volatile Organic Compounds To Discriminate Tuberculosis Patients from Healthy Subjects Khalid Muzaffar Banday, † Kishore Kumar Pasikanti, ‡ Eric Chun Yong Chan, ‡ Rupak Singla, § Kanury Venkata Subba Rao, † Virander Singh Chauhan,* ,|| and Ranjan Kumar Nanda* ,† † Immunology Group, International Center for Genetic Engineering and Biotechnology, New Delhi, India 110067 ‡ Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore 117543 § Department of Tuberculosis and Respiratory Diseases, Lala Ram Sarup Institute of Tuberculosis and Respiratory Diseases, New Delhi, India 110030 ) Malaria Group, International Center for Genetic Engineering and Biotechnology, New Delhi, India 110067 b S Supporting Information T uberculosis (TB) is an infectious disease caused by Myco- bacterium tuberculosis (M tb). TB is the topmost infectious disease with more than 10 million new cases and 3 million deaths reported each year. In addition, approximately one-third of the world population is infected with M tb (latent TB). Nearly 95% of all cases and 98% of deaths due to TB occur in developing countries. 1 The situation has become more alarming in recent years with the wide spread of HIV infection which decreases the immunity of the subjects and facilitates conversion of latent TB to active TB. 2 Improper disease management by misuse or incomplete drug treatment may result in the development of multiple drug resistance (MDR) and extensively drug-resistant (XDR) tuberculosis strains. 3 In developing countries, 25% of the avoidable death cases are contributed by TB. 4 Early diagnosis of TB can decrease its fatality rate and reduce further transmission of the disease. Current diagnosis of TB still relies on a 120 year old simple and inexpensive acid-fast bacillus (AFB) sputum test in spite of the requirement of high technical skills to perform the test. 5 Ser- ological tests are invasive, have low sensitivity in smear-negative patients and BCG vaccinated populations, and hold less promise in disease-endemic countries. 6 Furthermore, identification of MDR and XDR TB requires an expensive and sophisticated culture test which requires more than 2 weeks of analysis, a cost- intensive system, and trained manpower. 7,8 In these countries, the preferred choice of diagnosis would be a simple methodology which requires minimal resources, short analysis time, easy result interpretation, and minimal training requirements. Therefore, the development of a noninvasive method that can be used in endemic countries for the diagnosis of TB is pertinent. In this study, we investigated the applicability of urine as a matrix for the differentiation of TB patients from healthy controls using the global metabolic profiling approach. Volatile organic compounds (VOCs) present in human urine were profiled to investigate alteration in abundance of VOCs of TB patients in comparison to healthy controls. Examining the perturbations in metabolites provided an insight into the modification of the metabolic state of the host related to TB infection, and this might aid in the development of a diagnostic tool for disease identifica- tion. The workflow adopted in this metabolic profiling study is summarized in Figure 1. ’ EXPERIMENTAL SECTION Subject Recruitment. Informed consent was obtained from all study subjects after oral and written information related to the Received: February 2, 2011 Accepted: May 20, 2011 ABSTRACT: Development of noninvasive methods for tuber- culosis (TB) diagnosis, with the potential to be administered in field situations, remains as an unmet challenge. A wide array of molecules are present in urine and reflect the pathophysiologi- cal condition of a subject. With infection, an alteration in the molecular constituents is anticipated, characterization of which may form a basis for TB diagnosis. In the present study volatile organic compounds (VOCs) in human urine derived from TB patients and healthy controls were identified and quantified using headspace gas chromatography/mass spectrometry (GC/MS). We found significant (p < 0.05) increase in the abundance of o-xylene (6.37) and isopropyl acetate (2.07) and decreased level of 3-pentanol (0.59), dimethylstyrene (0.37), and cymol (0.42) in TB patients compared to controls. These markers could discriminate TB from healthy controls and related diseases like lung cancer and chronic obstructive pulmonary disorder. This study suggests a possibility of using urinary VOCs for the diagnosis of human TB.
Transcript
Published: May 27, 2011
r 2011 American Chemical Society 5526 dx.doi.org/10.1021/ac200265g |Anal. Chem. 2011, 83, 5526–5534
ARTICLE
pubs.acs.org/ac
Use of Urine Volatile Organic Compounds To DiscriminateTuberculosis Patients from Healthy SubjectsKhalid Muzaffar Banday,† Kishore Kumar Pasikanti,‡ Eric Chun Yong Chan,‡ Rupak Singla,§
†Immunology Group, International Center for Genetic Engineering and Biotechnology, New Delhi, India 110067‡Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore 117543§Department of Tuberculosis and Respiratory Diseases, Lala Ram Sarup Institute of Tuberculosis and Respiratory Diseases,New Delhi, India 110030
)Malaria Group, International Center for Genetic Engineering and Biotechnology, New Delhi, India 110067
bS Supporting Information
Tuberculosis (TB) is an infectious disease caused by Myco-bacterium tuberculosis (M tb). TB is the topmost infectious
disease with more than 10 million new cases and 3 million deathsreported each year. In addition, approximately one-third of theworld population is infected with M tb (latent TB). Nearly 95%of all cases and 98% of deaths due to TB occur in developingcountries.1 The situation has become more alarming in recentyears with the wide spread of HIV infection which decreases theimmunity of the subjects and facilitates conversion of latent TBto active TB.2 Improper disease management by misuse orincomplete drug treatment may result in the development ofmultiple drug resistance (MDR) and extensively drug-resistant(XDR) tuberculosis strains.3
In developing countries, 25% of the avoidable death cases arecontributed by TB.4 Early diagnosis of TB can decrease its fatalityrate and reduce further transmission of the disease. Currentdiagnosis of TB still relies on a 120 year old simple andinexpensive acid-fast bacillus (AFB) sputum test in spite of therequirement of high technical skills to perform the test.5 Ser-ological tests are invasive, have low sensitivity in smear-negativepatients and BCG vaccinated populations, and hold less promisein disease-endemic countries.6 Furthermore, identification ofMDR and XDR TB requires an expensive and sophisticatedculture test which requires more than 2 weeks of analysis, a cost-intensive system, and trained manpower.7,8 In these countries,
the preferred choice of diagnosis would be a simple methodologywhich requires minimal resources, short analysis time, easy resultinterpretation, and minimal training requirements. Therefore,the development of a noninvasive method that can be used inendemic countries for the diagnosis of TB is pertinent.
In this study, we investigated the applicability of urine as amatrix for the differentiation of TB patients from healthy controlsusing the global metabolic profiling approach. Volatile organiccompounds (VOCs) present in human urine were profiled toinvestigate alteration in abundance of VOCs of TB patients incomparison to healthy controls. Examining the perturbations inmetabolites provided an insight into the modification of themetabolic state of the host related to TB infection, and this mightaid in the development of a diagnostic tool for disease identifica-tion. The workflow adopted in this metabolic profiling study issummarized in Figure 1.
’EXPERIMENTAL SECTION
Subject Recruitment. Informed consent was obtained fromall study subjects after oral and written information related to the
Received: February 2, 2011Accepted: May 20, 2011
ABSTRACT: Development of noninvasive methods for tuber-culosis (TB) diagnosis, with the potential to be administered infield situations, remains as an unmet challenge. A wide array ofmolecules are present in urine and reflect the pathophysiologi-cal condition of a subject. With infection, an alteration in themolecular constituents is anticipated, characterization of whichmay form a basis for TB diagnosis. In the present study volatileorganic compounds (VOCs) in human urine derived from TBpatients and healthy controls were identified and quantifiedusing headspace gas chromatography/mass spectrometry (GC/MS). We found significant (p < 0.05) increase in the abundance ofo-xylene (6.37) and isopropyl acetate (2.07) and decreased level of 3-pentanol (0.59), dimethylstyrene (0.37), and cymol (0.42) inTB patients compared to controls. These markers could discriminate TB from healthy controls and related diseases like lung cancerand chronic obstructive pulmonary disorder. This study suggests a possibility of using urinary VOCs for the diagnosis of human TB.
project was provided. The subjects presenting with cough formore than 3 weeks with or without other constitutional symp-toms, which include expectoration, hemoptysis, breathlessness,fever, and weight loss, in the out ward patient department (OPD)of Lala Ram Sarup Institute of Tuberculosis and RespiratoryDiseases, New Delhi, India (LRS) were taken as TB suspects.The sputum samples were collected as spot specimens over aperiod of 3 days and stained by Ziehl�Neelsen stain. Onlysubjects who had never taken any anti-TB therapy prior, and atleast two of their sputum specimens if found positive for acid-faststain, were included as fresh TB cases in this study. Patients withother diseases or coinfection, HIV-positive subjects, infants up tothe age of 14 years, and pregnant womenwere excluded from thisstudy. A total of 117 fresh TB patients were recruited in ourstudy. Fresh patient cohorts under different treatment periodswere recruited in this study, early (1�3 months; n = 15) and latetreatment (4�7 months; n = 5) subjects. Urine samples fromhealthy controls (H) with no history of TB or other chronicdisease from the International Center for Genetic Engineeringand Biotechnology (ICGEB), New Delhi were collected in asimilar fashion {H (PPD �ve); n = 37}. Family member of theTB patients sharing the same premises and spending more than10 h every day with the index subject for the last 2 years with
purified protein derivative test (PPD)-positive status were in-cluded as healthy PPD-positive controls {H (PPDþve); n = 19}.The necessary medical check-up and tests of healthy PPD þveand PPD-negative (PPD �ve) subjects were carried out. Theclinical details of the recruited subjects from each group aresummarized in Table 1. Approvals from the ethical committee ofLRS hospital and ICGEB were taken and followed for collectionand handling of the clinical samples. As we recruited subjectsfrom more than one institution in a continuous basis it wasimpossible to get exactly matching age and gender populationsfor all study groups. Spot midstream urine samples from all therecruited subjects were collected from the respective institutions.Comparison of TB with Other Lung Diseases. Patients with
similar pulmonary diseases like lung cancer (n = 7) or chronicobstructive pulmonary disease (n = 5) were also recruited fromthe LRS hospital, New Delhi. These patients were thoroughlyassessed for their TB status by chest X-ray and PPD test. Thedemographic details are presented in Table 1.Sample Storage and Optimization of Sample Processing.
The collected samples were stored and transported at 4 �C to thesample bank at ICGEB. The samples were analyzed preferentiallyon the same day of sample collection, or else stored with theaddition of protease inhibitors (per 50 mL); 33 μL of 100 mM
Figure 1. Experimental workflow adopted in the urine volatile organic compounds study of tuberculosis patients and healthy controls using headspacegas chromatography mass spectrometry: /, subjects are a subset of the training set of discovery stage-II; 3M, 3 months of treatment; 7M, 7 months oftreatment; COPD, chronic obstructive pulmonary disease.
sodium azide, 500 μL of phenylmethylsulfonyl fluoride (PMSF;2%), and 1 μL of leupeptin (100 mM). The processed sampleswere stored at �80 �C for future analysis. The sample storageparameters like effect of addition of proteases, storage time period,and alteration of pH were optimized in detail before undertakingdata collection. Aliquots of a single subject urine sample at diffe-rent storage time periods (2 h to 30 days) from the time ofsample collection were carried out for gas chromatography/massspectrometry (GC/MS) data acquisition and analysis. Urine pHwas altered from 2 to 8 (normal urine pH ∼ 5.0) with theaddition of HCl (13 N) and NaOH (1 M). Optimization of thevolume of HCl to be added in the urine sample was carried out byadding different volumes of HCl (5, 10, 15, 20, 25, and50 μL/mL). GC/MS spectra were collected from each sampleafter randomizing the sample sets using MATLAB (R2008a,MathWorks, U.S.A.) to remove biasness and overfitting of thedata. The samples were coded prior to data acquisition, and theanalyst was blinded of the sample information.Method Reproducibility and Accuracy. The reproducibility
of the static headspace sampling GC/MSmethodwas establishedby injecting a healthy (PPD �ve, age 27, male) urine sample atthree different days (first, third, and fifth). Accuracy of themethod was determined by collecting GC/MS data of urinesamples belonging to the healthy group (PPD �ve; n = 5) andequal volume of a mixture of three standard solvents (tetrade-cane, pentadecane, and hexadecane at 0.218 g/L) five con-secutive times on the same day. The adopted GC/MS methodfor solvent data acquisition is described in the SupportingInformation.GC/MS Data Acquisition. Two discovery stages were em-
ployed in this study for the identification of potential urine VOCmarkers in TB patients. The head space and GC/MS instrumentparameters were standardized independently for the discoverystages (Agilent Technologies, U.S.A.). The detailed instrumentparameters are available in the Supporting Information. Aftercollection of initial data in discovery stage-I, machine parameterswere further optimized and applied in stage-II. A set of controlswere analyzed in the validation stage, where patients undergoingtreatment and patients suffering from other similar pulmonarydiseases (lung cancer, LC, and chronic obstructive pulmonarydiseases, COPD) were included along with fresh TB cases andhealthy PPD �ve subjects.Preprocessing of the Data Files. Two different preproces-
sing methods were employed for baseline correction, noisereduction, smoothing, replacement of missing values, and areacalculation in the two discovery stages.9�11 In the discoverystage-I, raw chromatograms (.d) from ChemStation (AgilentTechnologies, U.S.A.) were converted to NetCDF (.cdf) file, andwith the use of Shimadzu software (GCMS Solution, Shimadzu,Japan) peak detection and molecule identification were carriedout. In the stage-II, GC/MS data files obtained from ChemSta-tion were processed for baseline correction, noise reduction, andpeak picking using AMDIS.12 Deconvolution of individual datafiles was carried out using Spectconnect from the .elu filesobtained from AMDIS.13 The parameters of the Spectconnectelution threshold of 1 min, support threshold of g75% ofsamples, and similarity threshold of 80% were selected for groupcomparison. As the number of uploading sample files is restrictedto 10 per group we have run Spectconnect batchwise for theentire data set and then combined all the group-specific molec-ular information. After receiving the deconvoluted molecularmatrix from Spectconnect, we identified each peak from theT
individual data set using an off-line library search. A referencestandard library (NIST MS 2.0) comprising 209 311 spectra wasused to aid the identification of the GC-separated molecules. Thedata table was made ready using all the identified molecules asX- variables and their class belongingness as the Y-variable forfurther analysis.Data Analysis. In both discovery and validation stages, miss-
ing values in the data table were replaced with half a minimumvalue found in the data set and total area normalization wasperformed.10 The total area normalization for each sample wasperformed by dividing the integrated area of each analyte by thesum of total peak areas of analytes present in the sample, and thisdata table was exported to SIMCA-P 12.0.1 (Umetrics, U.S.A.)for multivariate statistical analysis. An unpaired t test wasemployed to the selected molecules to find out their significancefor disease discrimination.Multivariate Statistics. Chemometric data analysis was car-
ried out using the extracted molecular information. Principalcomponent analysis (PCA) was performed to verify the group-ing trends and outliers in the data. Outliers were eventuallyexcluded11 and were then visualized by scores and loading plots.The data were subjected to partial least-squares and discriminateanalysis (PLS-DA) and orthogonal partial least-squares anddiscriminate analysis (OPLS-DA), where a model was built andused to identify the putative marker metabolites with higherdiscriminatory power.14 Model validation was performed bypermutation tests with 999 iterations. These permutation testscompared the goodness of fit of several models based on therandomly selected permutation of the subsets of data of theY-observations, while keeping the X-matrix intact.15,16 A blindedset of samples (n = 38; age range, 17�39 years; male/female, 32/6)were used in the second PLS-DA model to calculate the diseasepredictability. For each variable the trade-off between the
sensitivity and specificity was summarized using the area underthe receiver operating characteristic curve (AUC) and calculatedusing the trapezoidal rule.17
Effect of Natural Variation (Age and Gender) on MarkerMolecules. The average age of TB patient samples used in thetest set was 27 years (age range: 17�39, n = 29). On this basis,the patient samples were grouped to two groups (17�27 years,n = 7; 28�39 years, n = 12). The patient test data set was alsogrouped based on their gender (male/female: 23/6). Theabundance of individual marker molecules was compared be-tween these groups, and p-value were calculated at the 95%confidence. Interday variations of marker molecules within thepatient groups were calculated using three patient samples run onthree consecutive days. Peak areas of the marker molecules weretaken for calculating relative standard deviation (% RSD).
’RESULTS
Using GC/MS coupled to a headspace sampler, urine VOCswere analyzed. Because of the diverse chemical nature of theanalytes present in urine which might influence the urinaryVOCs profile, storage and sample preparation conditions wereoptimized prior to GC/MS data acquisition. It was reported thatalteration of urine pH could influence the number of identifiablemolecular constituents.18 We found that acidification of theurine samples increased the number of identifiable metabolitesby ∼37% (Supporting Information Figure S-1) and addition of25 μL of HCl/mL of urine sample (pH = 3.0) yielded an opti-mum molecular information (Supporting Information Figure S-2).The total ion chromatogram (TIC) of urine sample did not showany significant differences with or without the addition ofprotease inhibitors in samples stored at �80 �C (SupportingInformation Figure S-3). Urinary VOCs profiles were not altered
Figure 2. (A) Total ion chromatogram showing a comparative metabolic profile of healthy controls [(H (PPDþve/�ve)] and TB patients (P) used inthe discovery stage-I. (B) OPLS-DA score plots (A = 2,N = 58, R2X = 0.462, R2Y = 0.674, andQ2 = 0.620) obtained from the comparative urine VOCsanalysis of TB and healthy controls. (C) Validation model scores using 999 random permutation tests not outperforming the original PLS-DA model.
up to 1 month when stored at�80 �C (Supporting InformationFigure S-4). The optimized storage conditions and preprocessingof urine samples were adopted for subsequent studies. On visualinspection, TICs of a healthy urine sample run on three differentdays (first, third, and fifth day) showed a high degree of similaritydepicting reproducibility of the adopted static head spacesampling GC/MS method (Supporting Information Figure S-5 ).Five healthy subject urine sample run on the same day confirmedinterindividual variability (Supporting Information Figure S-6A).A mixture of standard solvents run consecutively for five timesshowed similarity in TICs (RSD < 9%) and validates accuracy ofthe adopted method (Supporting Information Figure S-6B).
To analyze urine VOCs we followed two different discoverystages. In stage-I, urine samples from three groups, healthy PPDþve controls (n = 19), healthy PPD �ve controls (n = 11), andnew cases of TB patients (n = 30), were analyzed using GC/MSwith a modified split�splitless (SSL) injector. Visible differencesin the TICs were clearly observed between TB patients and bothgroups of control subjects (Figure 2A). On an average, 120 peakswere identified in each of the acidified protease inhibitor addedsamples. A total of 18 peaks were found to be present in most ofthe samples used in stage-I. Out of the 18 identified moleculestwo which were from column bleed were removed from the dataset before undertaking chemometric analysis. Comparison of theidentified metabolites between the healthy controls and patientgroups revealed complete class distinction (R2 = 0.772, Q2 =0.347, and principal component (PC) = 4) in PCA. The PLS-DAmodel contained four latent variables (LV), showing perfor-mance statistics of R2X = 0.728, R2Y = 0.706, and Q2 = 0.624.Application of PLS-DA and OPLS-DA resulted in clear
distinction between TB patients and both groups of healthycontrols (PPDþve andPPD�ve). TheOPLS-DAmodel showedperformance characteristics of A = 2, N = 58, R2X = 0.462, R2Y =0.674, andQ2 = 0.620 (Figure 2B). The model parameters for theexplained variation “R2” and the predictive capability “Q2” weresignificantly high indicating an excellent model. Five markermolecules selected based on their VIP (variable importance plot)score greater than 1.0 showed high discriminatory power for TBdiagnosis. Isopropyl acetate and o-xylene showed significantincrease in abundance (2.07- and 6.37-fold, respectively, p <0.05) in the urine of TB subjects. Molecules like cymol, 2,6-dimethystyrene, and 3-pentanol showed significant decrease inabundance (0.42, 0.37, 0.59, respectively, p < 0.05) in urine of TBpatients. Moreover, validation plot (Figure 2C) indicated that themodel was suitable and not due to chance correlation. Even thoughthe PPD þve and PPD �ve healthy controls represent a singleclass of noninfected subjects, a small class separation was observedwith some overlapping points. Overall, the two healthy controlgroups cluster distinctly from TB patients.
To assess whether the potential biomarkers generated fromthe adopted strategy could lead to differentiate TB patients fromnon-TB controls, we compared the molecular profiles obtainedfrom 113 new subjects out of which 17 non-TB subjects and 58TB patients were used as a second data set for marker discoveryand 38 subjects as the testing set. In stage-II, a total of 12molecules were identified in most subjects. PCA revealed thattwo patient subjects and one healthy subject were severe outliersand were excluded from further chemometric analysis (R2 =0.513, Q2 = 0.176, and PC = 2). Subsequent supervised multi-variate statistical analysis using PLS-DA demonstrated significant
Figure 3. (A) OPLS-DA score plots (A = 2,N = 75, R2X = 0.567, R2Y = 0.923, andQ2 = 0.870) obtained from the comparative urine VOCs analysis ofTB and healthy controls used in the discovery stage-II. (B) Validation plot obtained from 999 random permutation tests showing the robustness of theoriginal PLS-DA model. (C) Prediction of classification of blinded test subjects (T; n = 38) using the PLS-DA model. (D) Receiver operatingcharacteristic curve (ROC) calculated using the validated Y-predicted values obtained from the blinded test set. Diagnostic accuracy is calculated by thearea under curve (AUC). The AUC value for our blinded test set was 0.988.
discrimination between all the groups and revealed group-specific metabolic profiles. The PLS-DA model obtained fromthe discovery stage-II contained two latent variables, showingperformance statistics of R2X = 0.483, R2Y = 0.915, Q2 = 0.904,and LV = 2. The OPLS-DA showed performance statistics ofR2X = 0.567, R2Y = 0.923, Q2 = 0.870, and LV = 3 (Figure 3A).Metabolites with VIP value more than 1.0 were taken assignificant and identified as marker metabolites. The markermolecules and calculated fold change in TB patients were foundto be similar to the findings of stage-I. Isopropyl acetate ando-xylene showed significant increase (>2-fold, p < 0.05), andcymol, 2,6-dimethystyrene, and 3-pentanol showed significantdecrease in abundance in TB urine samples (<2-fold, p < 0.05)(Supporting Information Table S-1). The goodness of fit (R2
and Q2) of the original PLS-DA model and cluster of 999Y-permuted models demonstrated the validation of the originalmodel (Figure 3B). None of the random models outperformedthe initial models in terms of prediction. The model accuratelypredicted TB patients and non-TB control subjects from ablinded set of test subjects (n = 38) (Figure 3C). The AUCcalculated from the receiver operating characteristic curve(ROC) built from the Y-predicted values was 0.988, confirmingthe validity and robustness of the model (Figure 3D).
This demonstrated that the PLS-DA models built from twoseparate sample sets in the discovery stage possessed potentialdiscriminatory power for TB identification. The metabolicvariations observed between the non-TB and TB groups werecharacterized by the elevated levels of isopropyl acetate ando-xylene and decreased levels of cymol, 2,6-dimethylstyrene, and3-pentanol in the TB patient group. Essentially, similar resultswere obtained via the two different discovery strategies.
To investigate whether metabolic differences can be observedduring different stages of treatment, we included early (3 monthstreated; n= 15) and late treated subjects (7month treated; n= 5).A good separation was seen between the healthy PPD �ve(n = 15), untreated patients (n = 30), and patients undergoingearly treatment phase (Figure 4A). The patients and healthysubjects used in this comparative analysis were a subset of thetraining set used in the discovery stage-II. The class separationof the OPLS-DA model was found to be satisfactory (N = 65,R2X = 0.637, R2Y = 0.679, Q2 = 0.508, and LV = 3). In the latetreatment phase, patients showed a wide separation from theuntreated patients, while partial overlap is seen between subjectsundergoing early and late stages of treatment.
To determine whether other pulmonary diseases, such as LCand COPD have similar urine VOC profiles to TB, we comparedindividuals with other pulmonary disease conditions eitherCOPD (n = 5) or lung cancer (n = 8). OPLS-DA revealeddistinction between TB and either LC (R2X = 0.716,R2Y = 0.966,Q2 = 0.92, and LV = 3) (Figure 4B) or COPD (R2X = 0.609,R2Y = 0.967, Q2 = 0.926, and LV = 2) (Figure 4C).
Abundance of marker molecules did not show significantvariation with respect to their age or gender status (SupportingInformation Figures S-7 and S-8, respectively). The interdayvariation (RSD) of the marker molecules as calculated within thepatient group was found to be less than 6% (SupportingInformation Table S-2).
’DISCUSSION
The present study reports a standardized method of urinestorage, sample preparation, and parameters of headspace
Figure 4. (A) Urine metabolite profile (VOCs) of TB patients alters with treatment. OPLS-DA statistical analysis (N = 65, R2X = 0.637, R2Y = 0.679,Q2 = 0.508, LV = 3) comparing healthy control subjects (H (PPD�ve); n = 15) with TB patients (P; n = 30) and patients undergoing early (3M; 1�3months; n = 15) and late (7M; 4�7 months; n = 5) treatment periods. (B) OPLS-DA model comparing lung cancer (LC; n = 7) with performancecharacteristic of R2X = 0.716, R2Y = 0.966, Q2 = 0.92, and LV = 3. (C) OPLS-DA model comparing chronic obstructive pulmonary disease (COPD;n = 5) with performance characteristic of R2X = 0.609, R2Y = 0.967, Q2 = 0.94, and LV = 2.
GC/MS to identify urinary VOCs and provides a comparativecharacterization of humanTB. The conclusions were drawn fromthe study of (i) effect of protease inhibitors on urinary VOCsprofiles, (ii) effect of storage time ranging from 2 h to 30 daysat�80 �Con stability of urinary VOCs, (iii) effect of acidificationof urine samples for maximizing VOCs extraction, (iv) optimiza-tion of headspace parameters for identification of VOCs, (v) useof sample inlets (SSL) for sample introduction, (vi) preproces-sing methods of GC/MS spectra for multivariate data analysis,and (vi) application of PCA, PLS-DA, and OPLS-DA for markermetabolite discovery.
Sample preparation was optimized to minimize the stepsinvolved in sample processing and extraction of analytes fromhuman urine in order to avoid contamination. While addition ofprotease inhibitors and storage period up to 30 days did not alterthe volatile molecular profile, the acidification of urine samples(25 μL of 13 N HCl/mL of urine, pH = 3.0) influenced the totalnumber of identifiable VOCs and provided the optimum resolu-tion. The adopted method of head space sampling GC/MSanalysis was found to have high reproducibility and accuracy.
Subsequently, VOCs present in urine samples of 117 fresh TBpatients were compared with those of 56 non-TB controls using astatic headspace sampler coupled to GC/MS. The preprocessingof the GC/MS data followed by different software methodsyielded similar results in multivariate analysis and did not affectthe outcome of the experiments.
The robustness of the developed PLS-DAmodels was evidentfrom permutation test. Model validations were performed byresampling the model 999 times under the null hypothesis. TheR2 and Q2 values showed that none of the randomly permutedmodels outperformed the initial model. By comparing theurinary metabolites between non-TB and TB patients, isopropylacetate, o-xylene, cymol, 2,6-dimethylstyrene, and 3-pentanolwere found to show high discriminatory power in differentiatingTB patients from controls. The PPDþve and PPD�ve healthycontrols show some differences in the VOC profiles, which isevident from the PLS-DA data clustering. The differences are notas such prominent but could lead to the identification of latentTB-infected subjects with more in-depth metabolomic studies.The two healthy control groups have a very high discriminatingpower when compared to TB patients. Using a supplementalseries of clinically blinded samples we demonstrated excellentsensitivity and specificity in identifying TB infection. Ourresults indicate a high accuracy rate (98.8%) for this approach(Figure 3C).
Patients undergoing treatment showed a variation in the urineVOC profile in comparison to untreated patients and healthycontrols. The patients undergoing early and late treatment phasealso showed significant class separation, and this may be due tothe alteration in the overall metabolic activity induced by thedrugs used in therapy. Some subjects from the early treatmentperiod showed overlap with the TB subjects, and this vari-ability might be attributed to individual variation in their drugresponsiveness.
Comparison of the urinary metabolite profiles from patientswith TB and other lung infections revealed good separation.Although the information is from a low number of other diseasecontrol subjects, in principle it shows a difference in themolecular profile. Even though out of the seven, one of therecruited LC control subjects had a PPD þve status, it did notaffect the outcome of the prediction models. These observationsprovide an opportunity to go for a separate study by including
large number of subjects from LC, COPD, and other respiratorydiseases.
Natural variation like age and gender did not influence theabundance of identified marker molecules within the TB groupsignificantly. As sampling was carried out mostly during the earlyhours (9�12 a.m.) of the day, diurnal variation of markermolecules could not be established. Abundance of the markermolecules was found to have negligible interday and intersubjectvariation within the patient group.
VOCs had been shown to discriminate different pathogens inlaboratory culture conditions. For example, M tb when grown onsheep blood agar produced headspace VOCs such as methylphenylacetate, methyl p-anisate, methyl nicotinate, and o-phe-nylanisole that were specific to M tb but absent among severalother respiratory pathogens including Streptococcus pneumoniaeand Haemophilus influenza.19 Increase in the concentration of2-ethyl-1-hexanol and 2-methylpentane was reported in theheadspace GC analysis of cultured lung cancer cells whencompared with medium controls.20 Several studies have reportedVOCs from different sourcematerials like urine, breath, and stoolas potential biomarkers for several disease conditions.21�23
For example, alteration in the abundance of several metabolitesincluding 2-oxoglutarate and fumarate was reported in the urineof patients suffering from S. pneumoniae infection,22 and meta-bolites such as toluene, xylenes, and p-dichlorobenzene werereported as urine biomarkers for indoor air exposure to VOCs.23
Urinary 3-penten-2-one was identified as a useful biomarker ofincreased acetaldehyde during abnormal metabolic stress,24 andtrimethylamine and 4-heptanone were identified as the twometabolites of medical interest from the urine headspace analysisemploying an innovative multipurpose sampler.18 Certain im-mune system diseases like asthma and systemic lupus erythema-tosus, allograft rejections, and infectious diseases like malaria,leishmania, hepatitis, and cholera infection among others havebeen found to enhance the release of o-xylene. Using breath asthe source material, VOCs biomarkers were reported for differ-ent disease conditions including TB, chronic obstructive pul-monary disease, lung cancer, cystic fibrosis, and rheumatoidarthritis.25,26 It is noteworthy that styrene was identified as abiomarker in the breath of TB patients27 and a derivative of it,2,6-dimethylstyrene, has been characterized in the presentstudy from TB urine samples. Interestingly, stool also provideda VOC biomarker in characterizing patients infected with Vibriocholera.28
In the case of M tb, the use of urine as the biological matrix forbiomarker discovery holds special relevance. M tb infectioncomprising the latency, activation, and full-blown stages is likelyto exert differential metabolic effects on the host’s physiologicaland pathological status. Such metabolic perturbations may bemanifested as changes in the metabolic profiles of the biologicalmatrixes. Parida and Kaufmann indicated that metabolomicsprofiling could distinguish healthy latently infected individualsfrom patients with active TB.29 The marker metabolites couldeither be derived from host or pathogen origin andmay be causedby the strong metabolic influence of M tb on the infected tissues.Although five marker VOCs were identified in our study, theorigin of these metabolites could not be determined. Never-theless, they are possibly related as a consequence of TBinfection. The functional significance of the identified moleculeswas studied using Kyoto encyclopedia of genes and genomes(KEGG) analysis.30 It was found that 3-pentanol is involved inlipid metabolism and capectabine hydrolase reaction (R08220).
Toluene and o-xylene biodegradation are involved in variousmetabolic pathways including glycolysis, pyruvate metabolism,and glycerolipid metabolism (R05442). 2,6-Diethylstyrene wasfound to be involved in various metabolic as well as degradationpathways including glycolysis, pentose phosphate pathway, andpurine metabolism among others (R01120).
Urine as the molecular source for TB diagnosis has beeninvestigated, but most studies were focused on detection ofgenetic material specific to M tb.31 These PCR-based methodol-ogies shown varied sensitivity and specificity. The present studyis the first report describing the profiling of VOCs in urine of TBpatients for disease diagnosis and in principle can provide thetotal body burden of mycobacterial infection in any age group aswell as information on extrapulmonary TB, for which limiteddiagnostic methods are available. Although we described a single-step sample preparation leading to the analysis of VOCs usingGC/MS, the maintenance of a GC/MS system and training ofhighly skilled operators in hospitals would be cost- and resource-intensive particularly in developing countries. In this scenario,the real challenge after the discovery and validation stages of anyVOCs-based biomarker study is to transform the biomarker intoan inexpensive and easily administered diagnostic tool in theclinics or basic point of care. An automated headspace samplercoupled with multiple polymer sensors could respond to strain-specific VOC combinations to identify the type of bacteriagrowing in urine samples.32 A monitoring system based on themeasurement of NO in the breath of asthma patients usingcarbon nanotubes has been reported.33 A sensor based on goldnanoparticles could rapidly distinguish the breath of lung cancerpatients from healthy individuals.34 Electronic nose technologythat is based on use of sensor arrays combined with neuralnetwork classifiers is being developed for the measurement ofVOC produced in the headspace of cultured cells and can, inprinciple, be utilized for VOCs analysis from any source.35 Withthe use of one or more of these techniques, it may be possible todevelop a VOCs-specific sensor as a point-of-care facility todiagnose TB. In such case, the diagnostic method will be non-invasive, with added advantages such as long sample storagetime, noninfectious mode of sample handling, and reproducibleresults. This method also holds promise to be used for surveil-lance of treatment responsiveness and disease progression. Itcould be used to discover potential biomarkers from the VOCspresent in other biological fluids such as saliva, sputum, serum,and cerebrospinal fluid for other diseased conditions.
’CONCLUSION
We have demonstrated the potential of urinary VOCs as a TBdisease marker by applying an untargeted MS-based metabo-nomics approach using headspace as a reliable and powerfulsampling unit. Among numerous volatile molecules present inurine samples, the levels of five VOCs were found to besignificantly altered and together form a molecular signaturethat can accurately discriminate TB patients from non-TBindividuals. A major advantage of the proposed method is thenoninvasive nature of urine collection. Urine is a comparativelysafer matrix as compared to sputum and painless in collection ascompared to blood. An elucidation of the association betweenthe marker VOCs and TB could open new avenues in thediagnosis and screening of new TB cases in high-humidity andtemperate climates where the TB incidence remains very high.
’ASSOCIATED CONTENT
bS Supporting Information. Additional information asnoted in text. This material is available free of charge via theInternet at http://pubs.acs.org.
Financial assistance from the Department of Biotechnology,New Delhi, Government of India and International Center forGenetic Engineering and Biotechnology, New Delhi core fund isacknowledged. Participation of all the recruited subjects is highlyacknowledged. Dr. Neeta Singla, Dr. Namrata Hazarika, AnkurVarshney, and Ravinder are acknowledged for helping in subjectrecruitment and sample collection from hospital. We are thankfulto Dr. Dheeraj Kumar for useful discussion.
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