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JCM Accepted Manuscript Posted Online 16 September 2015 J. Clin. Microbiol. doi:10.1128/JCM.01568-15 Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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JCM01568-15 Revised

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Metabolomic Profiling of Plasma from Patients with Tuberculosis Using

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Untargeted Mass Spectrometry Reveals Novel Biomarkers for Diagnosis

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Susanna K. P. Lau,a,b,c,d* Kim-Chung Lee,d Shirly O. T. Curreem,d Wang-Ngai Chow,d

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Kelvin K. W. To,a,b,c,d Ivan F. N. Hung,b,e Deborah T. Y. Ho,d Siddharth Sridhar,d Iris W.

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S. Li,d Vanessa S. Y. Ding,d Eleanor W. F. Koo,f Chi-Fong Wong,g Sidney Tam,h Ching-

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Wan Lam,h Kwok-Yung Yuen,a,b,c,d Patrick C. Y. Wooa,b,c,d *

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State Key Laboratory of Emerging Infectious Diseases,a Research Centre of Infection and

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Immunology,b Carol Yu Centre for Infection,c Department of Microbiology,d Department

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of Medicine,e The University of Hong Kong, Hong Kong; Department of Pathology,f

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Tuberculosis & Chest Unit,g Grantham Hospital, Hong Kong; Department of Pathology,h

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The University of Hong Kong, Hong Kong.

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Running title: Novel biomarkers for tuberculosis

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Keywords: metabolome, metabolomics, plasma, tuberculosis, mass spectrometry, novel,

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biomarkers, diagnosis

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*Corresponding author. Mailing address: Department of Microbiology, University

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Pathology Building, Queen Mary Hospital Compound, Pokfulam Road, Hong Kong.

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Phone: (852) 22554892. Fax: (852) 28551241. E-mail: [email protected] (SKP Lau);

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[email protected] (PCY Woo)

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ABSTRACT (246 words)

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Although tuberculosis (TB) is a re-emerging disease that affects developing

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countries and immunocompromised populations in developed countries, current

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diagnostic methods are far from optimal. Metabolomics is being increasingly used for

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studies on infectious diseases. We performed metabolome profiling of plasma samples to

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identify potential biomarkers for diagnosis of TB. We compared plasma metabolome

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profiles of TB patients (n=46) to those of community-acquired pneumonia (CAP) patients

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(n=30) and controls without active infection (n=30), using ultra-high-performance liquid

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chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry

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(UHPLC-ESI-QTOFMS). Using multi-variate and univariate analyses, four metabolites,

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12(R)-HETE, ceramide (d18:1/16:0), cholesterol sulphate and 4α-formyl-4β-methyl-5α-

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cholesta-8-en-3β-ol, with significantly higher levels in TB patients than CAP patients and

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controls, were identified. When compared between TB patients and controls, the four

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metabolites demonstrated AUC of 0.914, 0.912, 0.905 and 0.856, sensitivities of 84.8%,

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84.8%, 87.0% and 89.1%, specificities of 90.0%, 86.7%, 86.7% and 80.0%, and fold-

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changes of 4.19, 26.15, 6.09 and 1.83 respectively. When compared between TB and

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CAP patients, they demonstrated AUC of 0.793, 0.717, 0.802 and 0.894, sensitivities of

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89.1%, 71.7%, 80.4% and 84.8%, specificities of 63.3%, 66.7%, 70.0% and 83.3% and

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fold-changes of 4.69, 3.82, 3.75 and 2.16 respectively. 4α-formyl-4β-methyl-5α-cholesta-

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8-en-3β-ol combined with 12(R)-HETE or cholesterol sulphate offered ≥70% sensitivities

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and ≥90% specificities for differentiating TB patients from controls or CAP patients.

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These novel plasma biomarkers, especially 12(R)-HETE and 4α-formyl-4β-methyl-5α-

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cholesta-8-en-3β-ol, or their combinations, are potentially useful for rapid and non-

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invasive diagnosis of TB. The present findings may offer insights to the pathogenesis and

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host response in TB.

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INTRODUCTION

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Tuberculosis (TB) is a disease caused by the bacterium Mycobacterium tuberculosis

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(MTB). Although it is a well-known disease that has been around for much of human

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history, there are still millions of new TB cases per year worldwide and it remains a

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leading cause of deaths worldwide, especially in developing countries. Since the 1980s,

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TB has re-emerged as a result of the acquired immunodeficiency syndrome epidemic and

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increasing use of immunosuppressants. In recent years, a higher incidence of

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extrapulmonary disease in immunocompromised hosts and emergence of multidrug-

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resistant strains have further complicated diagnosis and treatment (1-3). Despite its

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medical importance, diagnosis of TB is still associated with many unresolved problems.

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The traditional gold standard is smear and culture for acid-fast bacilli from clinical

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specimens. Although culture offers higher sensitivity and specificity than smear, it is not

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useful for culture-negative cases especially in early, disseminated or extrapulmonary

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disease (4, 5). Moreover, it often takes two to six weeks before culture is positive and

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even longer for definitive species identification. While newer diagnostic modalities, such

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as adenosine deaminase levels in pleural fluid, lipoarabinomannan in urine, polymerase

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chain reaction (PCR) and Xpert MTB/RIF assays, have been developed (6-12), there are

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still limitations in terms of their sensitivities and/or specificities.

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Metabolomics is an emerging platform for studies of infectious diseases or

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pathogens (13-19). For TB, the technique has been applied on cultured isolates for

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differentiation from other Mycobacterium species and studies on the biology and

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virulence (14, 15, 20-22). For example, lipidomics studies have revealed novel

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metabolites potentially associated with growth and virulence of MTB (23, 24). We have

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also recently identified extracellular metabolites specific to MTB (25), supporting the

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potential of metabolomics in exploring novel biomarkers to better understand its biology

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and pathogenesis. On the other hand, metabolomics applied on direct patient samples

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may reveal specific diagnostic markers or monitor treatment response (26-29). It has been

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shown that volatile organic compounds (VOCs) in the urine of TB patients can be

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distinguished from those of healthy subjects (26). A study using nuclear magnetic

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resonance spectroscopy-based metabolomics on sera of TB patients demonstrated

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discrimination between patients and healthy controls (27). Another metabolomics study

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has identified several abundant metabolites, including two mycobacterium-derived cell

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wall glycolipids, in EDTA-plasma of TB patients compared to household contacts (28).

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However, patients with other bacterial infections were not included as controls in these

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studies, and thus, the potential of such metabolic profile for diagnosis of TB remains to

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be determined.

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To identify potential biomarkers for non-invasive diagnosis of TB, we applied

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state-of-the-art technology for metabolomics profiling of plasma samples from TB

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patients, using ultra-high-performance liquid chromatography-electrospray ionization-

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quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOFMS). Multi- and

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univariate statistical analyses were used to identify metabolites with significantly higher

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levels in plasma of TB patients than in plasma of patients with community-acquired

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pneumonia (CAP) or controls without active infection. The diagnostic performances of

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the identified biomarkers were assessed using whole-metabolome receiver operating

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characteristic curve (ROC) analysis.

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MATERIAL AND METHODS

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Patient and control samples. Clinical samples were collected from hospitalized adult

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patients (≥18 years of age) in Queen Mary Hospital, Hong Kong. A total of 46 plasma

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samples from 37 patients with newly-diagnosed TB, and 60 plasma samples from 30

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patients with CAP and 30 controls without active infection were recruited for UHPLC-

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QTOFMS analysis. Plasma samples from TB patients were collected before

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commencement of anti-mycobacterial treatment. Plasma samples collected at admission

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from CAP patients were used. This study was approved by the Institutional Review

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Board of the University of Hong Kong/Hospital Authority of Hong Kong West Cluster

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(reference number UW 13-265).

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Diagnostic criteria. The diagnosis of TB was based on compatible clinical

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features, together with the presence of the following microbiological evidence: (i)

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positive stain for acid-fast bacilli, (ii) positive culture for MTB, and/or (iii) positive PCR

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for MTB from clinical samples. The diagnosis of CAP was based on compatible clinical

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features and radiological evidence of lung infiltrates, with disease onset within 48 hours

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of hospital admission. Causative agents of CAP included Acinetobacter baumannii (n=1),

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Escherichia coli (n=2), Haemophilus influenzae (n=2), Klebisella pnuemoniae (n=1),

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Moraxella catarrhalis (n=1), Pseudomonas aeruginosa (n=1), Streptococcus pneumoniae

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(n=4) and influenza B virus (n=1), while the etiological agent was unknown for the

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remaining cases. Controls consisted of patients without any clinical evidence of active

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infection.

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Chemicals and reagents. LC-MS grade water, methanol and acetonitrile were

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purchased from J.T. Baker (Center Valley, PA, USA). HPLC-grade ethanol and acetone

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were purchased from Merck (Darmstadt, Germany). Formic acid was of ACS reagent

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grade from Sigma-Aldrich, (Saint Louis, MO, USA). Ceramide (d18:1/16:0) was

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purchased from Avanti Polar Lipid (Alabaster, Alabama, USA). 12(R)-HETE and

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Cholesterol sulphate standards were purchased from Cayman Chemical (Ann Arbor,

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Michigan, USA).

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Sample preparation. Blood samples were collected in heparin bottles,

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transferred immediately to the laboratory, and centrifuged at 3000 rpm at 4°C for 10 min

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to obtain the plasma fractions. For metabolomics analysis, 100 µl of plasma was thawed

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at 4oC and plasma proteins were precipitated with 400 µl of methanol/ethanol/acetone

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mixture at a ratio of 1:1:1 (v/v/v). The sample extract was vigorously vortexed for 1 min,

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and centrifuged at 14,000 rpm at 4oC for 10 min. The supernatant was collected for

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UHPLC-ESI-QTOFMS analysis. All specimens were immediately kept at -80oC until

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analysis and stored within one week. The thawed specimens were analyzed within 48 h in

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a random manner to prevent the batch effect.

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Untargeted metabolomics profiling of patient plasma using UHPLC-ESI-

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QTOFMS. The metabolomic profiling of plasma supernatants was performed using

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Agilent 1290 Infinity UHPLC (Agilent Technologies, Waldbronn, Germany) coupled

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with Agilent 6540 UHD Accurate-Mass QTOF system (Agilent Technologies, Santa

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Clara, CA, USA) accompanied with a MassHunter Workstation software for QTOF

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(version B.03.01 for Data Acquisition, Agilent Technologies, USA). Waters Acquity

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UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm) (Waters, Milford, MA, USA) was used

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for the separation with the injection volume of 5 μl. The column and autosampler

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temperature were maintained at 45°C and 10°C, respectively. The separation was

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performed at a flow rate of 0.4 mL/min under a gradient program in which mobile phase

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A was composed of LC-MS grade water containing 0.1% formic acid (v/v) and mobile

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phase B was composed of acetonitrile. The gradient program was applied as follows: t =

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0 min, 5% B; t = 0.5 min, 5% B; t = 7 min, 48% B; t = 20 min, 78% B; t = 27 min, 80%

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B; t = 31 min, 99.5% B; t = 36.5 min, 99.5% B; t = 36.51 min, 5% B. The stop time was

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40 min. The ESI mass spectra were acquired in both positive and negative ion modes

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using Agilent Jet Stream ESI source with capillary voltages at +3800 V and -3500 V,

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respectively. Other source conditions were kept constant in all the experiments as follow:

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gas temperature was kept constant at 300°C, drying gas (nitrogen) was set at the rate of 7

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L/min, and the pressure of nebulizer gas (nitrogen) was 40 psi. The sheath gas was kept at

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a flow rate of 10 L/min at a temperature of 350oC. The voltages of the Fragmentor,

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Skimmer 1, and OctopoleRFPeak were 135 V, 50 V and 500 V, respectively. The mass

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data were collected between m/z 80 and 1700 at the acquisition rate of 2 scans per

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second. Two reference masses at m/z 121.0509 (protonated molecular ion of C5H4N4) and

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m/z 922.0098 (protonated molecular ion of C18H18O6N3P3F24) for positive mode, and m/z

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119.0363 (deprotonated molecular ion of C5H4N4) and m/z 966.0007 (formate adduct of

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C18H18O6N3P3F24) for negative mode were used as constant mass correction during LC-

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MS run. Product ion scanning (PIS) experiments were performed with ACQUITY UPLC

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I-class system coupled with Waters Synapt G2-Si QTOF system(Waters, Milford, MA,

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USA) operating in both positive and negative electrospray ionization modes. The mass

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data were collected between m/z 50 and 1000 at the acquisition rate of 3 scans per second.

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The voltages of the capillary, sampling cone, and source offset were 3 kV, 30 V and 80 V,

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respectively. Other source conditions were kept as follow: source temperature at 120oC,

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desolvation temperature at 380oC, cone gas at 10 L/h, desolvation gas at 800 L/h and

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nebulizer gas at 6.5 bar. Intermittent injections of leucine enkephalin as lock masses with

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m/z 556.2771 at positive mode and m/z 554.2615 at negative mode at a concentration of

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400 pg/µL in 50% acetonitrile at a flow rate of 6 µl/min were used for accurate mass

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measurements. MS/MS analysis was performed using ultra-high purity argon at collision

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energy at 10, 20 or 40 eV to generate the best quality of MS/MS spectra for putative

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identification and structural elucidation of the significant metabolites.

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Data processing and statistical analysis. Multivariate and univariate analysis

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was performed to identify molecular features that discriminate TB patients from CAP

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patients and controls. Multivariate analysis was applied to a total of 106 LC-MS data of

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plasma samples from three groups (46, 30 and 30 samples from newly-diagnosed TB

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patients, CAP patients and controls without active infection respectively). The raw LC-

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MS data were converted into mzXML format using MSConvert (ProteoWizard) and

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subsequently

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(http://www.bioconductor.org/packages/2.8/bioc/html/xcms.html)

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(http://www.r-project.org/), which adopted different peak detection and alignment as well

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as data filtering with centWave algorithms. Data was further processed with

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normalization, scaling, filtering and statistical analysis using MetaboAnalyst 3.0

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(www.metaboanalyst.ca). The data were mean-centered, square root scaled and

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normalized such that the sum of squares for each chromatogram equaled on for statistical

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analysis (30). Insignificant features between TB patients and CAP patients or controls

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were filtered out using both uni- and multi-variate analyses. For multivariate analysis,

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principal component analysis (PCA) was performed for unsupervised analysis while

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partial-least squares discrimination analysis (PLS-DA) was performed for supervised

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analysis to identify features with discriminative power. A score plot was applied to

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reduce the dimensionality of the data for grouping of the samples, in which each point in

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the score point represented the individual sample and similar data sets exhibit clustering

processed

using

open-source

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XCMS operating

package in

R

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while different sets separate further apart. PLS-DA models were validated based on

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accuracy, multiple correlation coefficient (R2) and cross-validated R2 (Q2) in cross-

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validation. The significance of the biomarkers was ranked using the variable importance

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in projection (VIP) score (>1) from the PLS-DA model that was responsible for

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separation of TB patients from non-TB group, i.e. CAP patients and controls. Significant

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features were further identified by significance analysis of microarray (SAM), in which

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false discovery rate (FDR) was determined by running multiple tests on high-dimensional

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data that distinguish between TB and non-TB groups, with FDR < 0.05 considered to be

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significant (31, 32).

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For univariate analysis, statistical significance of features was determined among

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TB patients, CAP patients and controls without active infection using one-way ANOVA

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with Fisher’s post-hoc test by MetaboAnalyst 3.0. P0.85 for

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comparison between TB patients and controls without active infection were identified

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and ROC curve analysis for the identified metabolites was further performed to compare

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between TB and CAP patients. Box-whisker plots were generated and P values were

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calculated by the Student’s t test using Analyse-it software (Analyse-it Software, Leeds,

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United Kingdom).

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Metabolite identification. Features with significant differences were selected for

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PIS experiments. MS/MS spectra of the potential biomarkers and commercially available

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reference standards, including ceramide (d18:1/16:0), 12(R)-HETE and cholesterol

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sulphate, were processed using Waters MassLynx V4.1 software (Waters Corp, Milford,

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MA). Potential molecular formula based on the accurate mass and isotopic pattern

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recognitions of parent and fragment ions were generated. All putative identities were

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confirmed by matching with entries in the METLIN database (http://metlin.scripps.edu/),

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Human

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(http://www.massbank.jp/), LipidMaps (http://www.lipidmaps.org/) including Mtb Lipid

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database, KEGG database (http://www.genome.jp/kegg), MycoMass and MycoMap

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databases

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(http://www.brighamandwomens.org/research/depts/medicine/rheumatology/Labs/Moody

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/default.aspx) using exact molecular weights, nitrogen rule or MS/MS fragmentation

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pattern data and literature search. Efforts were made to distinguish metabolites from the

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other isobaric compounds whenever possible by its elution order and virtue of difference

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in fragmentation pattern corresponding to its structural characteristics. Putative identities

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of the biomarkers were confirmed by comparing their chromatographic retention times

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(RTs) and MS/MS spectra with those obtained from commercially available standards of

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ceramide (d18:1/16:0), 12(R)-HETE and cholesterol sulphate.

Metabolome

Database

(HMDB)

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(http://www.hmdb.ca/),

MassBank

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RESULTS

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Metabolomic profiling of plasma samples and omics-based statistical and

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bioinformatic analysis. The metabolomes of 106 plasma samples from the three groups

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(46 samples from newly-diagnosed TB patients, 30 samples from CAP patients and 30

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samples from controls without active infection) were characterized and compared. A total

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of 1626 and 1598 molecular features in positive and negative mode respectively were

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obtained and subjected to statistical analysis using MetaboAnalyst 3.0 (Supplementary

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Table 1 and 2). For multivariate analysis, PCA revealed that samples from TB patients

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and CAP patients were clustered separately. However, ambiguous separation was

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observed between TB patients and controls by this unsupervised method. Therefore,

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supervised method using PLS-DA was performed, which showed that TB group can be

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distinguished from non-TB group (CAP patients and controls), in both positive and

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negative mode data (Fig. 1A and 1B), with accuracies of 99% and 97.2%, multiple

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correlation coefficient of 92.5% and 88.6%, and cross-validated R2 of 82.7% and 77.4%,

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respectively. Significant features for the separation between TB and non-TB groups were

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ranked, yielding 242 and 243 features with VIP score >1 in positive and negative

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ionization modes respectively. SAM methods were also used to select the most

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discriminative features responsible for the separation between TB and non-TB groups,

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yielding 233 and 324 significant features with FDR 0.85, two each in positive and negative modes respectively, were

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identified, which exhibited up-regulation in TB patients compared to controls (Table 1).

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The first metabolite, with m/z 538.5190 and retention time (RT) at 33.02 min in

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positive mode, was identified as ceramide (d18:1/16:0) by the molecular formula and

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MS/MS fragmentation pattern corresponding to its structural characteristics in Human

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Metabolome Database (HMDB), and confirmed by matching RT and MS/MS spectrum

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with commercially available authentic chemical standard of ceramide (d18:1/16:0) (Fig.

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3A). The peaks at m/z 520.5091 and 502.4996 corresponded to fragments after neutral

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loss of one water molecule and two water molecules from the parent ion, respectively.

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The peak at m/z 264.2676 referred to the fragment after combined loss of two water

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molecules and hexadecanoyl group.

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The second metabolite, with m/z 429.3733 at RT 32.22 min in positive mode, was

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identified as 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol by the molecular formula and

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MS/MS fragmentation pattern corresponding to its characteristics in HMDB (Fig. 3B).

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The peak at m/z 411.3598 was the fragment after neutral loss of water molecule at C-3

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while the peak at m/z 303.2339 represented the fragment after cleavage of bond between

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C-17 and C-20 and loss of 4-β-methyl group. The peak at m/z 205.1228 was the fragment

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resulted from cleavage of bond between C-9 and C-11 and between C-8 and C-14, while

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the peak at m/z 165.0924 was the fragment resulted from cleavage of bond between C-9

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and C-10 and between C-7 and C-8, and loss of 4-β-methyl group. However, authentic

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chemical standard of 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol was not commercially

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available for comparison.

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The third metabolite, with m/z 319.2274 at RT 12.73 min in negative mode, was

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identified as 12-hydroxyeicosatetraenoic acid (12(R)-HETE) by matching the molecular

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formula and MS/MS spectra provided in METLIN and LipidMaps, and confirmed by

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matching RT and MS/MS spectrum using commercially available authentic chemical

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standard of 12(R)-HETE (Fig. 3C). The peaks at m/z 301.2181 and 257.2274 were

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fragments after neutral loss of one water molecule and combined loss of water molecule

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at C-12 and formate group respectively. The peak at m/z 179.1069 was the fragment

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resulted from cleavage of aliphatic group and neutral loss of water molecule at C-12.

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The fourth metabolite, with m/z 465.3059 at RT 33.0 min in negative mode, was

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identified as cholesterol sulphate by matching the molecular formula in HMDB, and

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confirmed by matching RT and MS/MS spectrum using commercially available authentic

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cholesterol sulphate (Fig. 3D). Two fragments, at m/z 79.9580 and 96.9594, referred to

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SO3- and HSO4- ions respectively.

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Diagnostic performance of metabolites. The AUC, sensitivity and specificity

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for ROC curves calculated at optimal cutoffs for the four metabolites were summarized in

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Table 2. Box-whisker plots were generated for the three groups (Fig. 4). Three

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metabolites, ceramide (d18:1/16:0), 12(R)-HETE and cholesterol sulphate, demonstrated

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AUC >0.9 when compared between TB patients and controls. 12(R)-HETE showed the

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largest AUC 0.914, 84.8% sensitivity and 90.0% specificity when compared between TB

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and controls; but showed relatively lower AUC 0.793, 89.1% sensitivity and 63.3%

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specificity when compared between TB and CAP patients. Ceramide (d18:1/16:0) and

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cholesterol sulphate demonstrated AUC of 0.912 and 0.905 respectively when compared

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between TB and controls, but also showed relatively lower AUC of 0.717 and 0.802

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respectively when compared between TB and CAP patients. In contrast, 4α-formyl-4β-

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methyl-5α-cholesta-8-en-3β-ol demonstrated better discrimination between TB and CAP

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patients with higher AUC 0.894, 84.8% sensitivity and 83.3% specificity, than between

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TB and controls.

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Using the same optimal cutoffs for each metabolite, we calculated the sensitivities

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and specificities of different combinations of two or three of the four metabolites for

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diagnosis of TB (Table 3). The specificities of combined metabolites were generally

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higher than those of individual metabolites, while the sensitivities of combined

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metabolites were generally lower than those of individual metabolites, for differentiating

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TB patients and controls or CAP patients. Two combinations, 4α-formyl-4β-methyl-5α-

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cholesta-8-en-3β-ol plus 12(R)-HETE, and 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol

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plus cholesterol sulphate, offered ≥70% sensitivities and ≥90% specificities for

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differentiating TB patients from controls and CAP patients.

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DISCUSSION

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Using metabolomics approach, we identified four novel biomarkers with significantly

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higher levels in plasma of TB patients. Among the four metabolites, 12(R)-HETE showed

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the largest AUC 0.914, with 84.8% sensitivity, 90.0% specificity and fold-change 4.19,

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when compared to controls. However, the AUC dropped to 0.793 with only 63.3%

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specificity when compared to CAP patients. The diagnostic performance of ceramide

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(d18:1/16:0) was also promising with AUC 0.912, 84.8% sensitivity, 86.7% specificity

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and fold-change 26.15 when compared to controls. However, the AUC dropped to 0.717

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with 71.7% sensitivity, 66.7% specificity and fold-change 3.82 when compared to CAP

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patients. Similarly, cholesterol sulphate showed a relatively high AUC 0.905, 87.0%

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sensitivity, 86.7% specificity and fold-change 6.09 when compared to controls. However,

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the AUC dropped to 0.802 with 80.4% sensitivity, 70% specificity and fold-change 3.75

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when compared to CAP patients. In contrast, 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-

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ol showed good diagnostic performance in distinguishing TB from both CAP patients and

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controls, with AUCs >0.85 and >80% sensitivities/specificities, though with fold-change

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only 2.16 and 1.83 respectively. Therefore, 12(R)-HETE, ceramide (d18:1/16:0) and

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cholesterol sulphate should offer better diagnostic performance than 4α-formyl-4β-

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methyl-5α-cholesta-8-en-3β-ol in differentiating between TB and controls. In particular,

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the >20 fold-change of ceramide (d18:1/16:0) suggests that it may be a promising

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candidate for diagnosis of TB. However, these biomarkers may also be elevated in CAP

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patients, though not to levels as high as in TB patients. On the other hand, 4α-formyl-4β-

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methyl-5α-cholesta-8-en-3β-ol may offer better discrimination between TB and CAP

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patients. When 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol is combined with 12(R)-

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HETE or cholesterol sulphate, the specificities were ≥90% for differentiating TB from

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both controls and CAP patients, although the sensitivities were