Characterising Tuberculosis and Treatment Failure Thereof Using Metabolomics

Characterising tuberculosis and treatment failure thereof using metabolomics

L Luies

21637156

Thesis submitted for the degree Philosophiae Doctor in Biochemistry at the Potchefstroom Campus of the North-West University

Promoter: Prof Du Toit Loots

April 2017

"In science one tries to tell people, in such a way as to be understood by everyone, something that no one ever knew before."

Paul Dirac

ACKNOWLEDGEMENTS

The contributions of the following institutions and individuals made towards the successful completion of this study are hereby acknowledged:

 NRF/DST Centre of Excellence in Biomedical Tuberculosis Research, Faculty of Health Sciences, University of Stellenbosch, South Africa, for providing the urine samples used in this investigation.

 The National Research Foundation (NRF) of South Africa, Technology Innovation Agency (TIA) and North-West University (NWU) for the research grants provided.

 Prof. Du Toit Loots, my study supervisor, and other members of the NWU Biochemistry Department (especially Prof. Japie Mienie, Dr. Ilse du Preez, Mrs. Mari van Reenen, Mrs. Derylize Beukes-Maasdorp and Dr. Zander Lindeque), for their guidance, expertise, insights, advice and patience throughout the years.

 My friends, for the moments of laughter and stress relief, which has kept me sane.

 My dearest parents, Leonard and Sandra, for their unending love, prayers, mental encouragement and financial support during my studies.

 My husband, Juan, for all your love, support and encouragement, every step of the way.

“I can do everything though Christ who gives me strength.” Philippians 4:13

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... i SUMMARY ...... vi LIST OF TABLES ...... viii LIST OF FIGURES ...... x LIST OF SYMBOLS AND ABBREVIATIONS ...... xii

CHAPTER 1: PREFACE ...... 1 1.1 BACKGROUND AND MOTIVATION ...... 1 1.2 AIMS AND OBJECTIVES OF THIS STUDY ...... 2 1.2.1 Aims ...... 2 1.2.2 Objectives ...... 3 1.3 STRUCTURE OF THESIS AND RESEARCH OUTPUTS ...... 3 1.4 AUTHOR CONTRIBUTIONS ...... 5 1.5 REFERENCES ...... 7

CHAPTER 2: LITERATURE REVIEW ...... 8 2.1 METABOLOMICS ...... 8 2.2 M. TUBERCULOSIS BACTERIOLOGY AND PATHOPHYSIOLOGY...... 11 2.2.1 The M. tuberculosis cell wall ...... 11 2.2.2 Transmission and pathogenicity ...... 13 2.3 TUBERCULOSIS DIAGNOSTICS ...... 16 2.3.1 Diagnosing latent M. tuberculosis infection ...... 16 2.3.1.1 Tuberculin skin test ...... 16 2.3.1.2 Interferon gamma release assays ...... 17 2.3.2 Diagnosing active M. tuberculosis infection ...... 18 2.3.2.1 Microscopy smear techniques ...... 18 2.3.2.2 Bacteriological cultures...... 18 2.3.2.3 Nucleic acid amplification techniques ...... 19 2.3.2.4 Serological (immunological) methods ...... 20 2.3.2.5 Phage-based assay ...... 21 2.3.3 Metabolomics biomarkers for tuberculosis diagnostics ...... 21 2.3.3.1 Metabolomics biomarkers detected using Mycobacterium cultures ...... 22 2.3.3.2 Metabolomics biomarkers detected using sputum ...... 26 2.3.3.3 Metabolomics biomarkers detected using blood and tissue ...... 28

2.3.3.4 Metabolomics biomarkers detected using urine ...... 32 2.3.3.5 Metabolomics biomarkers detected using breath ...... 33 2.3.3.6 Diagnostic validity of the metabolomics tuberculosis biomarkers identified to date ...... 35 2.3.3.7 From benchtop to clinical application: Future prospects and challenges of metabolomics biomarkers ...... 38 2.4 TUBERCULOSIS TREATMENT ...... 39 2.4.1 Isoniazid ...... 40 2.4.2 Rifampicin ...... 41 2.4.3 Pyrazinamide ...... 42 2.4.4 Ethambutol ...... 43 2.4.5 Using metabolomics to better explain anti-tuberculosis drug action and drug metabolism ...... 44 2.5 TUBERCULOSIS TREATMENT FAILURE...... 46 2.5.1 Variable individual metabolism ...... 47 2.5.1.1 Xenobiotic metabolism ...... 47 2.5.1.2 Drug malabsorption ...... 49 2.5.1.3 Drug-drug interactions ...... 50 2.5.2 Drug-resistance and pathogenicity by the infectious organism ...... 53 2.5.3 Non-adherence due to the associated side-effects...... 57 2.6 PREDICTING TUBERCULOSIS TREATMENT OUTCOME ...... 59 2.7 REFERENCES ...... 62

CHAPTER 3: METABOLOMICS METHODOLOGY AND REPEATABILITY ...... 75 3.1 INTRODUCTION ...... 75 3.2 EXPERIMENTAL DESIGN ...... 76 3.3 MATERIALS AND METHODS ...... 77 3.3.1 Urine sample collection and storage ...... 77 3.3.1.1 Quality control samples ...... 78 3.3.2 Reagents and chemicals ...... 78 3.3.3 Organic acid extraction procedure and derivatisation ...... 79 3.3.4 GCxGC-TOFMS analyses ...... 79 3.3.5 Data processing ...... 80 3.3.6 Batch effect ...... 80 3.4 RESULTS AND DISCUSSION ...... 82 3.4.1 Repeatability ...... 82 3.5 CONCLUSION ...... 84 3.6 REFERENCES ...... 84

iii

CHAPTER 4: ADAPTATIONS OF MAN AND MICROBE IN ORDER TO OUTCOMPETE AND SURVIVE ...... 85 4.1 ABSTRACT...... 85 4.2 INTRODUCTION ...... 86 4.3 MATERIALS AND METHODS ...... 87 4.3.1 Urine sample collection and storage ...... 87 4.3.2 Sample analysis ...... 88 4.3.3 Statistical data analyses...... 88 4.4 RESULTS AND DISCUSSION ...... 89 4.5 CONCLUSION ...... 95 4.6 REFERENCES ...... 95

CHAPTER 5: URINARY METABOLITE MARKERS CHARACTERISING TUBERCULOSIS TREATMENT FAILURE ...... 99 5.1 ABSTRACT...... 99 5.2 INTRODUCTION ...... 100 5.3 METHODS ...... 101 5.3.1 Clinical samples ...... 101 5.3.2 Sample analysis ...... 101 5.3.3 Statistical data analyses...... 102 5.4 RESULTS AND DISCUSSION ...... 102 5.5 CONCLUSION ...... 110 5.6 REFERENCES ...... 110

CHAPTER 6: PREDICTING TUBERCULOSIS TREATMENT OUTCOME USING METABOLOMICS ...... 114 6.1 ABSTRACT...... 114 6.2 INTRODUCTION ...... 115 6.3 MATERIALS AND METHODS ...... 116 6.3.1 Clinical samples ...... 116 6.3.2 Sample analysis ...... 117 6.3.3 Statistical data analyses...... 117 6.4 RESULTS AND DISCUSSION ...... 118 6.5 CONCLUSION ...... 126 6.6 REFERENCES ...... 127

CHAPTER 7: FINAL CONCLUSIONS ...... 130 7.1 CONCLUDING SUMMARY ...... 130 7.2 FUTURE RESEARCH PROSPECTS ...... 131

APPENDIX A ...... 133 APPENDIX B ...... 138 APPENDIX C ...... 141 APPENDIX D ...... 142

SUMMARY

Tuberculosis (TB), a highly contagious bacterial disease caused by Mycobacterium tuberculosis, is considered the leading cause of death globally from a single bacterial pathogen. The latest reports indicate 10.4 million new TB cases are diagnosed globally per annum, of which approximately only 5.7 million actually receive treatment, resulting in an estimated 1.8 million deaths. This high TB prevalence may be ascribed to a number of factors, including, amongst others, untimely and inaccurate diagnostics, inadequate treatment regimens, drug-resistant M. tuberculosis strains, human immunodeficiency virus (HIV) co-infection, and inadequate knowledge of the TB disease in general.

This study is novel in the sense that it used a metabolomics research approach to identify new metabolite markers from patient-collected urine, for better characterising/understanding the TB disease state and treatment failure thereof.

Using a validated urinary organic acid extraction and a two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) metabolomics approach, we were able to differentiate a culture-confirmed active TB-positive group and TB-negative healthy control group, based on their detected metabolite differences, utilising a variety of multi- and univariate statistical methods. We identified the most significant urinary TB metabolite markers contributing to these differences, which shed light on previously unknown mechanisms/adaptations of the host in response to M. tuberculosis and other host–pathogen interactions. The most significant of these were the TB-induced changes resulting in an abnormal host fatty acid and amino acid metabolism, mediated through changes in interferon gamma and possibly insulin. This also explains some of the symptoms associated with TB and provides clues to better treatment approaches.

Thereafter, the same approach was used to compare TB-positive patients with an unsuccessful treatment outcome to those successfully treated. Differentiation of the groups was achieved using the urine samples collected from these patients at time of diagnosis, i.e. before any treatment was administered. The identified urinary biomarkers were then used to better understand the underlying biology related to TB treatment failure. The most significant observations were the elevated levels of those metabolites associated with a gut microbiome imbalance, which has been shown to alter an individual’s response to anti-TB drugs and also negatively influence their immune function, contributing to an unsuccessful treatment outcome. Another interesting observation was those metabolites traditionally used for diagnosing inborn abnormalities in any of the three enzymes of the mitochondrial trifunctional protein complex in the treatment failure group. Since L-carnitine and various short-chain

fatty acids are also reduced in these individuals, and are well-known for their antimycobacterial properties, this metabolic profile may explain an additional mechanism responsible for these individuals having an increased disease severity and/or a poor response to TB treatment.

Considering the possible significance of these findings from a diagnostic perspective, the GCxGC-TOFMS data generated from the aforementioned treatment outcome experiment was reanalysed using a univariate statistical approach, in order to find possible diagnostic markers for predicting treatment outcome, utilising urine collected at time of diagnosis. Using a logistic regression model, two predictors, i.e. 3,5-dihydroxybenzoic acid and 3-(4-hydroxy- 3-methoxyphenyl)propionic acid, displayed the capacity to predict an unsuccessful treatment with an area under the receiver operating characteristic curve value of 0.94, and a leave-oneout cross-validation value of 0.89, indicating high sensitivity and specificity. Furthermore, these two identified predictors are also associated with an imbalance in gut microbiota, confirming the previously proposed mechanisms related to treatment failure in these individuals.

Considering the results, this study not only proved the capability of a metabolomics research approach to identify new metabolite markers which could be used towards better understanding TB, and treatment failure thereof, but also possibly diagnostically for predicting treatment outcome of first-line anti-TB drugs at time of diagnosis. Furthermore, the fact that these markers can be detected from patient-collected urine, as opposed to sputum, has additional benefits for both research and diagnostic applications, considering the ease by which such samples can be obtained, with very little discomfort to the individual.

Key words: host–pathogen interactions; metabolomics; prediction; treatment failure; tuberculosis; urine

vii

LIST OF TABLES

CHAPTER 1:

Table 1-1: The research team...... 5

CHAPTER 2:

Table 2-1: A summary of the advantages and disadvantages of the common techniques and tools used in metabolomics...... 10

Table 2-2: Tuberculosis biomarkers identified in culture using a metabolomics research approach...... 25

Table 2-3: Tuberculosis biomarkers identified in human sputum using a metabolomics research approach...... 28

Table 2-4: Tuberculosis biomarkers identified in blood and tissue using a metabolomics research approach...... 30

Table 2-5: Tuberculosis biomarkers identified in human urine using a metabolomics research approach...... 33

Table 2-6: Tuberculosis biomarkers identified in human breath using a metabolomics research approach...... 35

Table 2-7: A summary of the general metabolites of first-line anti-TB drugs and their associated side-effects ...... 58

Table 2-8: Treatment outcome for tuberculosis patients on first-line medication, as defined by the World Health Organization ...... 60

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CHAPTER 4:

Table 4-1: The 12 urinary metabolite markers that best explain the variation detected in the urine samples of the TB-negative healthy control and TB-positive patients, ranked alphabetically...... 90

CHAPTER 5:

Table 5-1: The 50 urinary metabolite markers identified at time of diagnosis that best explain the variation detected between the successful and unsuccessful treatment outcome groups...... 104

CHAPTER 6:

Table 6-1: The 18 univariate urine metabolite markers considered for potentially predicting treatment outcome at time of diagnosis...... 120

Table 6-2: The maximum likelihood parameter estimates and odds ratios...... 122

APPENDIX A:

Table A-1: A summary of those compounds detected as biomarkers for TB, by more than one research group, in more than one analytical sample media...... 133

LIST OF FIGURES

CHAPTER 2:

Figure 2-1: The cell wall composition of M. tuberculosis...... 12

Figure 2-2: The transmission and pathogenicity of tuberculosis...... 15

CHAPTER 3:

Figure 3-1: Experimental design...... 76

Figure 3-2: Batch correction ...... 81

Figure 3-3: Distribution of the coefficient of variation (CV) values for the analytical machine (GCxGC-TOFMS) and analyst/extraction methodology repeatability...... 83

CHAPTER 4:

Figure 4-1: Summary of the experimental design...... 87

Figure 4-2: Principle component analysis (PCA) differentiation of GCxGC-TOFMS organic acid analysis data, of urine samples collected from active TB-positive and TB- negative patients...... 89

Figure 4-3: Venn-diagram indicating compound selection using a multi-statistical approach ...... 90

Figure 4-4: Altered host metabolome induced by M. tuberculosis ...... 94

CHAPTER 5:

Figure 5-1: Principal components analysis (PCA) scores plots of principal component 1 versus principal component 2 of the successful and unsuccessful treatment outcome groups ...... 103

Figure 5-2: Amino acid metabolism of the identified urinary metabolite markers...... 107

Figure 5-3: Fatty acid oxidation of the identified urinary metabolite markers, which are either increased (↑) or decreased (↓) in the treatment failure group comparatively. .. 109

CHAPTER 6:

Figure 6-1: Venn-diagram indicating the multi-selection approach used to select the most significant metabolite markers for predicting treatment failure...... 118

Figure 6-2: Volcano plot of the log10 scaled Mann-Whitney P-values against the log2 scaled fold change values...... 119

Figure 6-3: Box-like plots of the top four fold change metabolites, demonstrating that not all compounds are necessarily good predictors...... 121

Figure 6-4: Receiver operating characteristic (ROC) curve of the final logistic regression model...... 123

Figure 6-5: Box-like plots of the two identified predictors, with the accompanying chemical structures...... 124

Figure 6-6: Bivariate scatter plot of the raw data generated for 3,5-dihydroxybenzoic acid versus 3-(4-hydroxy-3-methoxyphenyl)propionic acid ...... 124

APPENDIX C:

Figure A-1: Principal components analysis (PCA) scores plots of principal component 1 versus principal component 2 of the successful and unsuccessful treatment outcome groups, at (a) time of diagnosis, (b) week 1, (c) week 2, (d) week 4 of treatment and (e) two weeks after treatment completion (week 26), subsequent to an organic acid extraction and GCxGC-TOFMS analysis...... 141

LIST OF SYMBOLS AND ABBREVIATIONS

Abbreviation Meaning Abbreviation Meaning

2MG 2-C-Methylglycerol 5-OH-PA 5-Hydroxypyrazinoic acid

ABC ATP-binding cassette ACAA1 3-Ketoacyl-CoA thiolase

AcHZ Monoacetylhydrazine AcINH N1-Acetyl-N2-isonicotinylhydrazide

ACOX 1 Acyl-CoA oxidase 1 ACOX 2 Acyl-CoA oxidase 2

ACP Acyl carrier protein ADR Adverse drug reaction

ARV Antiretroviral ATD Automated thermal desorption

ATDH Anti-tuberculosis drug-induced ATP Adenosine triphosphate hepatotoxicity AUC Area under the curve BCAA Branched chain amino acid

BCG Bacille Calmette-Guerin BH4 Tetrahydrobiopterin

BMI Body mass index bp Base pair

BSTFA Bis(trimethylsilyl)-trifluoroacetamide CACT Carnitine-acylcarnitine translocase

CDC Centres for Disease Control and CE Capillary electrophoresis Prevention CFP Culture filtrate protein CNS Central nervous system

CoA Coenzyme A COPD Chronic obstructive pulmonary disease CPT-1 Carnitine palmitoyltransferase 1 CPT-2 Carnitine palmitoyltransferase 2

CSF Cerebrospinal fluid CV Coefficient of variation

CYP450 Cytochrome P450 DFA Discriminant function analysis

DHPS Dihydropteroate synthase DiAcHZ Diacetylhydrazine

DNA Deoxyribonucleic acid DOTS Directly Observed Therapy Short- course D-PBE Peroxisomal bifunctional enzyme DPG Diphosphatidylglycerol

EDA 2,2’-(Ethylenediimino)-di-butyric ELISA Enzyme-linked immunosorbent acid assay EMB Ethambutol EN Electronic nose

ESAT Early secreted antigenic target ESI Electrospray ionisation

etc. Et cetera FDA Food and Drug Administration

GABA Gamma-aminobutyric acid GATB Global Alliance for Tuberculosis Drug Development (Continues on next page)

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(Continues from previous page)

Abbreviation Meaning Abbreviation Meaning

GC Gas chromatography GDH Glutamate dehydrogenase

GSTM1 Glutathione S-transferase Mu 1 H2O2 Hydrogen peroxide

HCl Hydrochloric acid HIV Human immunodeficiency virus

HPLC High-performance liquid HZ Hydrazine chromatography IDO1 Indoleamine 2,3-dioxygenase 1 IFN-γ Interferon gamma

IGRA Interferon-gamma release assay INA Isonicotinic acid

INH Isoniazid/isonicotinylhydrazide INH-KA Isoniazid-ketoglutaric acid

INH-PA Isoniazid hydrazones with pyruvic KAT Kynurenine aminotransferase acid LAM Lipoarabinomannan LC Liquid chromatography

LCFA Long-chain fatty acid LM Lipomannan

M. tuberculosis Mycobacterium tuberculosis M/SCHAD Medium/short chain hydroxyacyl- CoA dehydrogenase MADD Multiple acyl-CoA dehydrogenase MAPc Mycolyl arabinogalactan- deficiency peptidogalactan complex MCAD Medium chain acyl-CoA MCT Medium chain 3-ketoacyl-CoA dehydrogenase thiolase MDR-TB Multidrug-resistant tuberculosis MEP 2-C-Methyl-D-erythritol-4-phosphate

MIC Minimum inhibitory concentration MS Mass spectrometry

MS/MS Tandem mass spectrometry MTP Mitochondrial trifunctional protein

Na2SO4 Anhydrous sodium sulphate NAA Nucleic acid amplification

NAD Nicotinamide adenine dinucleotide NADH Nicotinamide adenine dinucleotide (NAD) + hydrogen (H) NAT 2 N-acetyltransferase 2 NMR Nuclear magnetic resonance

NRF National Research Foundation NWU North-West University

OPLS-DA Orthogonal partial least-squares PA Pyrazinoic acid discriminant analysis PABA p-Aminobenzoate PAH Phenylalanine hydroxylase

PAS Para-aminosalicylic acid PC Principal component

PCA Principle component analysis PCR Polymerase chain reaction

PDIM Phthiocerol dimycocerosate PE Phosphatidylethanolamine

PHYH Phytanoyl-CoA hydroxylase PI Phosphatidylinositol

PIM Phosphatidylinositol mannoside pistanalDH Pristanaldehyde dehydrogenase

(Continues on next page)

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(Continues from previous page)

Abbreviation Meaning Abbreviation Meaning

PKU Phenylketonuria PLIEM Potchefstroom Laboratory for Inborn Errors of Metabolism PLS-DA Partial least squares discriminant PMN Polymorphonuclear analysis PPD Purified protein derivative PZA Pyrazinamide

QC Quality control QC–CV Quality control-coefficient of variation Q-TOFMS Quadruple time-of-flight mass RIF Rifampicin spectrometry RNA Ribonucleic acid ROC Receiver operating characteristic

RT Retention time S/N Signal to noise ratio

SCAD Short chain acyl-CoA SCFA Short-chain fatty acid dehydrogenase SCT Short chain 3-ketoacyl-CoA thiolase SCYD Short chain enoyl-CoA hydratase

SPME Solid phase micro-extraction STARD Standards for the Reporting of Diagnostic Accuracy Studies TAT Tyrosine aminotransferase TB Tuberculosis

TBSA Tuberculostearic acid TCA Tricarboxylic acid

TDM Trehalose 6,6' dimycolate TDO Tryptophan dioxygenase

TDR-TB Totally drug-resistant tuberculosis TIA Technology Innovation Agency

TL Translocase TMCS Trimethylchlorosilane

TNF-α Tumour necrosis factor alpha TOFMS Time-of-flight mass spectrometry

TST Tuberculin skin test U(H)PLC Ultra-(high) performance liquid chromatography UV Ultra-violet VIP Variables’ influence on the projection VLCAD Very-long-chain acyl-coenzyme A VOC Volatile organic compound dehydrogenase WHO World Health Organisation XDR-TB Extensively-drug-resistant tuberculosis XME Xenobiotic-metabolising enzyme Xpert GeneXpert MTB/RIF

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Symbols:

Symbol Meaning Symbol Meaning

˂ Smaller than ↑ Increased

> Greater than ↓ Decreased

α Alpha β Beta

°C Degrees Celsius g Gram

m Metre m/z Mass to charge ratio

mg/g Milligram per gram mL Millilitre

mL/min Millilitre per minute mmol/L Millimole per litre

μg/mL Microgram per millilitre μL Microliter

μL/μmol Microliter per micromole μm Micrometre

xv Chapter 1 CHAPTER 1: PREFACE

1.1 BACKGROUND AND MOTIVATION

Tuberculosis (TB) remains the world’s foremost cause of death from a single bacterial agent, which is alarming as it is considered curable. Nearly 10.4 million new TB cases are reported per annum, resulting in approximately 1.8 million deaths globally. Additionally, TB is considered the leading cause of death in patients co-infected with the human immunodeficiency virus (HIV), contributing to approximately 0.4 million of the reported global TB deaths in 2015, with Africa accounting for the majority of these (± 84%). In South Africa, approximately 454 000 people were infected with TB in 2015, resulting in 73 000 deaths. In the same year, 910 124 people who were newly enrolled in HIV-care globally were also started on TB-preventive therapy, with South Africa being the country with the highest global prevalence of this (45% of the total cases) (World Health Organization, 2016).

Accessibility of treatment is another concern. In 2015, of the approximately 580 000 cases of multi-drug-resistant (MDR)-TB reported, only 125 000 (20%) actually received treatment, contributing to an estimated 250 000 deaths. Furthermore, an estimated 9.5% of these MDR cases were diagnosed with extensively-drug-resistant TB (XDR-TB) (World Health Organization, 2016). In 2007, the first cases of totally-drug-resistant TB (TDR-TB) were reported in India, Iran and Italy, with South Africa becoming the fourth country reporting the emergence of this virtually untreatable strain (World Health Organization, 2012). The latest treatment outcome data indicate a treatment success rate of 83% for fully drug-susceptible TB, 52% for MDR-TB and 28% for XDR-TB (World Health Organization, 2016). Treatment failure or an unsuccessful treatment outcome is an additional concern, and although it has been strongly associated with a number of factors, including (a) irregular or inadequate anti- TB drug supplies to rural areas in third-world countries, (b) poor patient TB-education, (c) poor socio-economic circumstances (such as poverty, malnutrition and overcrowding), (d) the prolonged treatment duration, (e) treatment non-adherence, (f) drug-resistance by the infectious organism, and (g) HIV co-infection, there are various biological/biochemical factors associated with this which are not yet well described or unknown (De Villiers & Loots, 2013).

Despite the major discovery in 1882 by Robert Koch, that the causal agent of TB is M. tuberculosis (Daniel, 2005), and all the genomics, transcriptomics and proteomics data collected on this organism to date, as well as the vaccination, diagnostic and treatment approaches developed since, TB is still considered a major health problem globally. Considering this, we still have a lot to learn about this infectious organism and the host–

1 Chapter 1 microbe adaptations and interactions for the development of improved diagnostic and treatment strategies. An alternative research approach such as metabolomics, considering its capability for new biomarker identification and hypotheses generation, may serve well towards achieving these goals. To date, the North-West University’s Research Focus Area: Human Metabolomics, has used this research strategy to better understand TB and to define the functionality of unknown mycobacterial genes (Loots et al., 2013; Loots et al., 2016), to better describe M. tuberculosis virulence (Meissner-Roloff et al., 2012), drug-resistance (Du Preez & Loots, 2012; Loots, 2014; Loots, 2015), host and microbe interactions (Du Preez & Loots, 2013b; Luies & Loots, 2016), and also to identify or characterise various Mycobacterium species for possible diagnostic applications (Du Preez & Loots, 2013a; Olivier & Loots, 2012a; Olivier & Loots, 2012b). Despite these recent applications of metabolomics with a view towards a better understanding of this disease and related topics, little research has been done using metabolomics for identifying urinary biomarkers of TB, which would serve to possibly better describe host adaptations, or host–microbe interactions. Furthermore, very little is known about the biology behind treatment failure, and the possible prediction of this phenomenon.

1.2 AIMS AND OBJECTIVES OF THIS STUDY

1.2.1 Aims

The aims of this study are to use a GCxGC-TOFMS metabolomics approach to compare and differentiate the urine organic acid profiles of:

1. Culture-confirmed active TB-positive patients (n=38) and TB-negative healthy controls (n=30), to identify characteristic urinary metabolites, occurring as a result of the host– pathogen interactions and adaptations, for better characterising TB.

2. Culture-confirmed active TB-positive patients with a successful (n=27) and unsuccessful (n=11) treatment outcome as early as possible during the treatment regimen, and identify those metabolite markers which would (a) better characterise and explain the biological mechanisms related to TB treatment failure, and to (b) possibly be used diagnostically for predicting treatment failure.

2 Chapter 1

1.2.2 Objectives

In view of the above mentioned aims, the objectives of this study are to:

1. Determine and validate the analytical repeatability of the methodology (analyst/ extraction repeatability) and that of the GCxGC-TOFMS analytical apparatus used (machine repeatability), for analysing samples in the context of a metabolomics research application.

2. Apply the validated methodology (mentioned in Objective 1) to extract all samples.

3. Utilise various multi- and univariate statistical approaches to identify metabolite markers for better characterising the TB disease state (Aim 1), treatment failure (Aim 2a), as well as to be used diagnostically for predicting treatment failure (Aim 2b).

1.3 STRUCTURE OF THESIS AND RESEARCH OUTPUTS

This thesis is written specifically to comply with the requirements of the North-West University, Potchefstroom Campus, South Africa, for the completion of the degree Philosophiae Doctor (Biochemistry) in article format. Thus, each chapter will have its own introduction, materials and methods, results, discussion, conclusion and reference sections, relevant to that chapter. Additionally, a comprehensive literature review (Chapter 2) and conclusion (Chapter 7) are also added in accordance to these guidelines.

Chapter 1 (the current chapter) gives a brief background and motivation for the study, as well as the aims and objectives. Also included in this chapter are the basic layout/structure of the thesis, and the research outputs/publications which emanated from this study.

Chapter 2 provides a literature review of TB in general and all other related aspects of this disease, relevant to this investigation. Parts of this chapter are published/submitted in three separate review papers (see Appendix D):

 De Villiers, L. & Loots, DT. (2013). Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4): 306-317.  Du Preez, I., Luies, L. & Loots, DT. (2017). Metabolomics biomarkers for tuberculosis diagnostics: Current status and future objectives. Biomarkers in Medicine, 11 (2): 179- 194.  Luies, L., Du Preez, I. & Loots, DT. (2017). Improved tuberculosis treatment strategies using metabolomics. Submitted for publication to Biomarkers in Medicine (Manuscript number: BMM-2017-0141).

3 Chapter 1

Chapter 3 describes the experimental design for the study, including the patient sample collection approach, as well as the metabolomics methodology used and validation thereof.

In Chapter 4, the methodology and data validated in Chapter 3 were used to compare and differentiate culture-confirmed active TB-positive (n=38) and TB-negative healthy control (n=30) groups from one another, based on the differences in their urinary metabolite profiles. Various multi- and univariate statistical analyses were used to identify which metabolite markers contributed most to the variation seen between the compared groups. These markers were then interpreted, considering their role in known metabolic pathways and biological mechanisms in both man and microbe, and subsequently new host–microbe interactions and adaptations better characterising TB were identified and discussed. This chapter was published in Metabolomics (see Appendix D):

 Luies, L. & Loots, DT. (2016). Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3): 1-9.

In Chapter 5, the methodology and data validated in Chapter 3 were used to differentiate TB-positive individuals with a successful (n=27) and unsuccessful (n=11) treatment outcome, on the basis of the metabolome differences detected between these two groups, from urine collected at various intervals throughout the course of treatment, i.e. from time of diagnosis, during treatment and two weeks after treatment completion. Multi- and univariate statistics were used to identify which metabolite markers differed most between the groups, which when described in the context of known metabolic pathways, better explains the underlying biological mechanisms of treatment failure. This chapter has been submitted for publication in Metabolomics (see Appendix D):

 Luies, L., Mienie, J., Motshwane, C., Ronacher, K., Walzl, G. & Loots, DT. (2017). Urinary metabolite markers characterising tuberculosis treatment failure. Submitted for publication in Metabolomics (Manuscript number: MEBO-D-17-00069).

In Chapter 6, we re-examined the data generated from the pre-treatment urine samples, which showed differentiation between the successfully cured and treatment failure groups (Chapter 5), from a purely univariate perspective, for the purpose of identifying markers, which when used in a logistic regression model can potentially serve as diagnostic predictors for TB treatment failure. This chapter has been submitted for publication in Biomarkers in Medicine (see Appendix D):

 Luies, L., Van Reenen, M., Ronacher, K., Walzl, G. & Loots, DT. (2017). Predicting tuberculosis treatment outcome using metabolomics. Submitted for publication to Biomarkers in Medicine (Manuscript number: BMM-2017-0133).

4 Chapter 1

Chapter 7 is a comprehensive summary of all the results and conclusions, in the context of the aims originally set out in this investigation, in addition to future recommendations, which could be considered in light of the current findings.

1.4 AUTHOR CONTRIBUTIONS

The primary author/investigator of this thesis is Laneke Luies (neé de Villiers). The contributions of the co-authors, co-workers, and collaborators made towards this work, are summarised in Table 1-1.

The following is a statement from the primary investigator and supervisor, confirming their individual roles in the study and giving their permission that the data generated and conclusions made may form part of this thesis: I declare that my role in this study, as indicated in Table 1-1, is a representation of my actual contribution, and I hereby give my consent that this work may be published as part of the Ph.D. thesis of Laneke Luies.

Prof. Du Toit Loots Laneke Luies

Table 1-1: The research team.

Co-author Co-worker Collaborator Contribution

Responsible, together with the study leader, for the conceptualising, planning, Laneke Luies execution, data analyses, (B.Sc. Hons. Biochemistry) and writing of the thesis, publications, and all other documentation associated with this study

Study leader: Conceptualised, co- ordinated and supervised Prof. Du Toit Loots all aspects of the study, including the study design, (Ph.D. Biochemistry) planning, execution, writing of the thesis, publications, and all other documentation associated with this study

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5 Chapter 1

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Co-author Co-worker Collaborator Contribution

Co-author on two review papers, and responsible, together with the other co- Dr. Ilse du Preez authors, for developing

(Ph.D. Biochemistry) and conceptualising the review topic, working on data acquisition and drafting the article

Co-author on a paper, Prof. Japie Mienie where he assisted with

(Ph.D. Biochemistry) data interpretation and drafting the article

Co-author on a paper, Mrs. Mari van Reenen where she assisted with statistical data analyses (M.Sc. Statistics) and interpretation and drafting the article

Mrs. Derylize Beukes- Assisted with sample analyses as officially Maasdorp appointed laboratory (B.Sc. Biochemistry) manager

Christinah Motshwane Performed the LC-MS sample analyses, used in (B.Sc. Hons. Biochemistry) Chapter 5

Determined the creatinine AMPATH Laboratories values of all collected urine samples

Profs. Gerhard Walzl and Katharina Ronacher at the DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/MRC Centre Provided the patient urine for Molecular and samples used in this Cellular Biology, metabolomics study Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University (Tygerberg)

6 Chapter 1

1.5 REFERENCES

Daniel, T. 2005. Robert Koch and the pathogenesis of tuberculosis [Founders of Our Knowledge]. The International Journal of Tuberculosis and Lung Disease, 9(11):1181-1182. De Villiers, L. & Loots, D.T. 2013. Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4):306-317. Du Preez, I. & Loots, D.T. 2012. Altered fatty acid metabolism due to rifampicin-resistance conferring mutations in the rpoB gene of Mycobacterium tuberculosis: Mapping the potential of pharmacometabolomics for global health and personalized medicine. OMICS: A Journal of Integrative Biology, 16(11):596-603. Du Preez, I. & Loots, D.T. 2013a. Detection limit for differentiating between various Mycobacterium species and Pseudomonas aeruginosa using gas chromatography-mass spectrometry (GC-MS) metabolomics: A comparison of two extraction methods. African Journal of Microbiology Research, 7(9):797-801. Du Preez, I. & Loots, D.T. 2013b. New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. Tuberculosis, 93(3):330-337. Loots, D.T. 2014. An altered Mycobacterium tuberculosis metabolome induced by katG mutations resulting in isoniazid resistance. Antimicrobial Agents and Chemotherapy, 58(4):2144-2149. Loots, D.T. 2015. New insights into the survival mechanisms of rifampicin-resistant Mycobacterium tuberculosis. The Journal of Antimicrobial Chemotherapy, 71(3):655-660. Loots, D.T., Meissner-Roloff, R.J., Newton-Foot, M. & Van Pittius, N.C.G. 2013. A metabolomics approach exploring the function of the ESX-3 type VII secretion system of M. smegmatis. Metabolomics, 9(3):631-641. Loots, D.T., Swanepoel, C.C., Newton-Foot, M. & van Pittius, N.C.G. 2016. A metabolomics investigation of the function of the ESX-1 gene cluster in mycobacteria. Microbial Pathogenesis, 100:268-275. Luies, L. & Loots, D. 2016. Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3):1-9. Meissner-Roloff, R.J., Koekemoer, G., Warren, R.M. & Loots, D.T. 2012. A metabolomics investigation of a hyper-and hypo-virulent phenotype of Beijing lineage M. tuberculosis. Metabolomics, 8(6):1194-1203. Olivier, I. & Loots, D.T. 2012a. A comparison of two extraction methods for differentiating and characterising various Mycobacterium species and Pseudomonas aeruginosa using GC-MS metabolomics. African Journal of Microbiology Research, 6(13):3159-3172. Olivier, I. & Loots, D.T. 2012b. A metabolomics approach to characterise and identify various Mycobacterium species. Journal of Microbiological Methods, 88(3):419-426. World Health Organization. 2012. Global tuberculosis control 2012. Geneva, Switzerland (WHO Press). World Health Organization. 2016. Global tuberculosis report 2016. Geneva, Switzerland (WHO Press).

7 Chapter 2 CHAPTER 2: LITERATURE REVIEW

Parts of this chapter are in various stages of publication:

 De Villiers, L. & Loots, DT. (2013). Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4): 306-317.  Du Preez, I., Luies, L. & Loots, DT. (2017). Metabolomics biomarkers for tuberculosis diagnostics: Current status and future objectives. Biomarkers in Medicine, 11 (2): 179-194.  Luies, L., Du Preez, I. & Loots, DT. (2017). The role of metabolomics in tuberculosis treatment research. Submitted for publication to Biomarkers in Medicine (Manuscript number: BMM-2017-0141).

Since this thesis focuses on identifying new metabolite markers better describing the active TB disease state from a metabolomics perspective, in addition to using the same approach for better describing and predicting treatment failure, the majority of the literature described in Chapter 2 will be in accordance with this. However, for the sake of presenting a holistic perspective of TB, a short description of the bacteriology and pathophysiology of the disease will also be given in section 2.2.

2.1 METABOLOMICS

One of the newcomers to the “omics” revolution, metabolomics, can be defined as the unbiased identification and quantification of all the intra- and extra-cellular metabolites (small molecule intermediates and products of metabolism) present in a biological system, using highly selective and sensitive analytical techniques (Dunn et al., 2005; Orešič, 2009; Van der Werf et al., 2007), in conjunction with biostatistical and mathematical analyses for identifying new metabolite markers (Olivier & Loots, 2011).

Metabolomics is the holistic study of an organisms’ metabolism, and is considered an important addition to systems biology when the data generated are interpreted together with that generated using genomics, transcriptomics and proteomics (Chen et al., 2007). The application of metabolomics for biomarker discovery, is based on the principle that an external stimulus, such as TB disease or infection, an anti-TB drug, or a mutation resulting in drug-resistance, may disrupt normal metabolism, altering the overall physiological status of 8 Chapter 2 an organism or host, and these metabolic changes are specific to the perturbation investigated and not due to an overall state of inflammation or unrelated disease processes (Chen et al., 2007; Kaddurah-Daouk et al., 2008). Thus, an individual’s metabolic state is a representation of their overall physiological status. Considering this, the new metabolite markers identified due to the perturbation not only allow us to better understand the underlying disease mechanisms, but can also be used diagnostically and/or to predict individual/organism drug responses (Kaddurah-Daouk et al., 2008). In the context of TB, M. tuberculosis disrupts the host’s normal metabolism, initiating metabolite changes that can be visualised as metabolic biosignatures or metabolite patterns (Parida & Kaufmann, 2010; Weckwerth & Morgenthal, 2005). Metabolomics analyses the end-products of these perturbations present in blood, sputum and/or urine (or any other fluid or tissue for that matter), and identifies metabolites that are both endogenous and exogenous to the perturbation, capturing information with regards to the mechanisms of disease or drug action (Schoeman & Loots, 2011).

Various chromatographic instruments are used as tools in metabolomics, e.g. gas chromatography (GC), liquid chromatography (LC), capillary electrophoresis (CE) and nuclear magnetic resonance (NMR) (Kaddurah-Daouk et al., 2008), for the detection of these alterations. Usually, these instruments are combined with mass spectrometry (MS) for compound detection, of which a variety are available, including the quadrupole MS, ion trap MS and the time-of-flight MS (TOFMS), each of which have their own advantages and disadvantages pertaining to sensitivity, specificity and molecular preferences (Weckwerth & Morgenthal, 2005). The type of analytical instrumentation selected for a metabolomics experiment would dependent on the goal(s) of the study, and the type of molecule or metabolite pathways one would expect to have changed, or would be interested in investigating (Weckwerth & Morgenthal, 2005), in addition to the various advantages and disadvantages of the respective instrumentation (see Table 2-1). However, as per definition, since metabolomics is the “study of all the metabolites present in a biological system/sample”, many approaches have been developed in order to analyse the entire metabolome, or as much of it as possible, using one or more analytical methods (Du Preez & Loots, 2012; Du Preez & Loots, 2013b; Loots et al., 2005; Schoeman & Loots, 2011). In both of the aforementioned instances, as part and parcel of true metabolomics research, multivariate statistical analysis, such as principle component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or orthogonal partial least-squares discriminant analysis (OPLS-DA), are employed to extract information from the large metabolite datasets generated, in order to identify possible biomarkers (Chen et al., 2007; Halouska et al., 2007; Powers, 2009).

9 Chapter 2 Table 2-1: A summary of the advantages and disadvantages of the common techniques and tools used in metabolomics (Aligent Technologies, 2007; Lei et al., 2011; Shulaev, 2006; Wallace et al., 2010; Want et al., 2007).

Advantages Disadvantages/Limitations

 Good combination of sensitivity and  Susceptible to ion suppression or Direct selectivity enhancement MS  Highly specific chemical information  Data interpretation can be challenging (accurate mass, isotope distribution  Inability to differentiate isomers patterns, characteristic fragment ions)  Limited to volatile, thermally stable, and energetically stable compounds  Suited for volatile and non-volatile (after  Requires additional sample preparation, derivatisation) compound analyses such as derivatisation, as this approach  Affordable, with relatively low running costs depends on the analytes being volatile and  High sensitivity, mass resolution and thermally stable, and few metabolites meet accuracy this requirement in their natural state  Good dynamic range  Less amenable to large, highly polar GC-MS  Compound identification using mass metabolites (poor volatility) spectral library matching  Co-eluting analytes in single dimension GC  Provides additional and orthogonal data (i.e. (corrected for using GCxGC) retention time/factor/index)  Careful attention required for splitless  Reproducible chromatographic separations injections  Identification of stereoisomers possible  Slower scan rates/speed, unless coupled  Shorter run times with TOFMS  Lower bleed (thinner films)  Lower mass accuracy, unless coupled with TOFMS detectors

 Applicable for targeted and non-targeted metabolomics  Relatively low reagent cost  Lower chromatographic resolution  High analytical sensitivity, specificity and compared to GC-MS coverage depth  Comparatively higher running costs  Able to analyse a wide range of compound  Electrospray ionisation (ESI) can suffer from LC-MS classes ionisation suppression  Ideal for highly polar and ionic compounds/  Retention time shifts are known to occur metabolites  Higher signal to noise (S/N) ratios  Requires minimum sample preparation compared to GC-MS  Low matrix effects and interferences  Low sample throughput  The application of both positive and negative ionisation, hence more comprehensive metabolome coverage

 Applicable for targeted and non-targeted  Lower sensitivity compared to other metabolomics techniques  Ideal for highly polar and ionic compounds/  Poor reproducibility CE-MS metabolites  Electrochemical reactions of metabolites  Fast, relatively affordable, and highly  May lack the necessary robustness efficient separation technique  Least suitable for analysing complex  Typically coupled with TOFMS for fast biological samples acquisition rates

NMR-  Highly selective  Lower sensitivity compared to other MS  Non-destructive to sample material techniques  Metabolite structural elucidation

10 Chapter 2 A number of metabolomics studies have already been done to date, identifying TB biomarkers which can be used for various applications, including improved diagnostics (as will be described in detail in section 2.3.3), and expanding on the existing knowledge of the biology of the causative pathogen (Rhee et al., 2011), as well as to better explain various underlying disease mechanisms, including those related to the aforementioned drug-resistant M. tuberculosis strains (Du Preez & Loots, 2012; Loots, 2014; Loots, 2015), identifying new virulence factors (Meissner-Roloff et al., 2012), and contributing to a better understanding of anti-TB drug mechanisms (Halouska et al., 2012; Halouska et al., 2013) and side-effects in the host (Loots et al., 2005). What is of even greater interest currently is that metabolomics has allowed for a better understanding of the adaptations of M. tuberculosis to the host defence and vice versa (Du Preez & Loots, 2013b). This has shed light on never before identified metabolic pathways in both man and M. tuberculosis, which in time will undoubtedly contribute to improved treatment strategies, and ultimately assist in curbing this pandemic.

2.2 M. TUBERCULOSIS BACTERIOLOGY AND PATHOPHYSIOLOGY

M. tuberculosis, the highly infectious causative pathogen of TB, was first identified in 1882 by Robert Koch. These non-motile, rod-shaped, non-spore-forming, aerobic bacteria are facultative intracellular parasites, characterised with a slow growth rate. Mycobacteria usually measure 0.5 x 3 μm and are classified as acid-fast bacilli, due to fact that their cell walls are impermeable to certain dyes and stains (Knechel, 2009; Todar, 2009).

2.2.1 The M. tuberculosis cell wall

The cell wall composition of M. tuberculosis is considered unique among prokaryotes and crucial to its survival, virulence and growth. The cell wall consists of three layers, namely the (a) capsule (an outer impermeable coating), (b) mycolyl arabinogalactan-peptidogalactan complex (MAPc), and (c) inner cell membrane (see Figure 2-1). It is mainly composed of a variety of complex lipids (over 60%), which are largely considered toxic to eukaryotic cells. These cell wall lipids provide an extraordinary lipid barrier, which gives these bacteria a number of unique properties, including impermeability to stains and dyes, as well as resistance to host defence mechanisms, anti-TB drugs, acidic and alkaline compounds, osmotic lysis and lethal oxidations, allowing for its survival inside the host macrophages (Brennan, 2003; Knechel, 2009; Todar, 2009).

11 Chapter 2

Figure 2-1: The cell wall composition of M. tuberculosis. The cell wall consists of multiple, complex layers, each contributing to M. tuberculosis virulence and its survival within the host. The well- developed cell wall consists of three layers, namely the capsule or outer impermeable coating, mycolyl arabinogalactan-peptidogalactan complex (MAPc), and the inner cell membrane, mostly composed of phospholipids.

The inner cell membrane contains primarily polar phospholipids, including phosphatidylethanolamine (PE), phosphatidylinositol (PI) and diphosphatidylglycerol (DPG), which form the basis of the membrane bilayer. M. tuberculosis has a family of four phosphatidylinositol mannosides (PIMs), which function to reinforce the cell membrane, adding additional coverage to ensure impermeability (Minnikin et al., 2015). A peptidoglycan polymer, just outside the phospholipid inner cell membrane, is covalently attached to heteropolysaccharide arabinogalactan, which in turn is esterified to long-chain mycolic acids. These mycolic acids are complex β-hydroxylated α-alkyl-branched very long chain fatty acids (strong hydrophobes) with approximately 70–90 carbons and contain various diverse functional groups. These cell wall components form part and parcel of what is known as the MAPc, which functions by conferring rigidity to the cell wall as well as resistance and impermeability to various medicinal interventions (Brennan, 2003; Grzegorzewicz et al., 2016; Knechel, 2009; Minnikin et al., 2015; Todar, 2009). Cord factors, or trehalose dimycolates (TDMs), are composed of a trehalose sugar and disaccharide, esterified to two mycolic acid residues, and occur abundantly in the outer cell wall of virulent M. tuberculosis strains, allowing for these cells to grow in slender cords. This is also known to contribute to M. tuberculosis virulence, since it is toxic to mammalian cells, inhibits polymorphonuclear (PMN) leukocyte migration, induces granulomatous reactions, and furthermore protects the pathogen against the host defence mechanisms by preventing phagolysosomal fusion.

12 Chapter 2 However, its main role is to act as an intermediate for the transfer of mycolic acids onto arabinosyl units in the cell envelope (Brennan, 2003; Minnikin et al., 2015; Todar, 2009). Wax-D, on the other hand, is one of the major constituents found within the cell envelope, and may be characterised as an autolytic product of the cell wall’s MAPc. It is thought to contribute to M. tuberculosis adjuvant activity and, hence, is necessary for cell wall growth regulation — since fractions of wax-D serve as building blocks from which the cell wall polymers are constructed (Kawabata et al., 1998; Todar, 2009).

The outer layer consists of free mycolic acids, polypeptides and complex surface (glycol)lipids, interspersed with proteins, phthiocerol-containing lipids (e.g. phthiocerol dimycocerosate), lipomannan (LM), and lipoarabinomannan (LAM) (Brennan, 2003). LM and LAM are well-known mycobacterial glycolipids with long mannose polymer skeletons. LAM in particular contains arabinose-mannose disaccharide subunit repeats, and is considered a carbohydrate structural antigen, that is associated with pathogenic functionality, crucial to M. tuberculosis survival within host macrophages (Knechel, 2009), and functions as a potent inhibitor of interferon gamma-mediated activation of host murine macrophages. Furthermore, LAM scavenges oxygen radicals and inhibits host protein kinase C, thereby down-regulating the host immune response against this pathogen (Todar, 2009). Phthiocerol dimycocerosate (PDIM), on the other hand, is a highly apolar component of the M. tuberculosis cell wall, resulting in its wax-like characteristics, and consists of a long chain β-diol (phthiocerol moiety), esterified with two mycocerosic acids. Previous studies have also linked PDIM with M. tuberculosis virulence (Brennan, 2003).

2.2.2 Transmission and pathogenicity

TB is considered to be highly infectious, and the M. tuberculosis pathogen is transmitted from an individual with an active TB disease state, through small infectious aerosol droplets (typically 1–5 μm in diameter), by means of coughing, sneezing or talking/singing. Transmission is influenced by various factors, including the number of bacilli contained in these infectious droplets, its virulence, exposure to ultra-violet (UV) light, and ventilation of the environment where infection occurs. M. tuberculosis usually manifests in the lungs, and this is referred to as pulmonary TB (Knechel, 2009). However, extra-pulmonary TB may also occur, in which case M. tuberculosis disseminates and infects other parts of the human body, causing clinical manifestations (in order of prevalence) in the pleura (pleural TB) (Porcel, 2009), lymph nodes (TB lymphadenitis) (Golden & Vikram, 2005), urinary tract and reproductive system (genitourinary TB), bones and joints (osteal/skeletal TB) (Sharma & Bhatia, 2004), central nervous system (CNS/meningeal TB) (Golden and Vikram 2005), abdominal organs (including gastrointestinal tract, peritoneum, omentum, mesentery, liver, spleen and pancreas) (peritoneal TB) (Sharma & Bhatia, 2004), and various other organs.

13 Chapter 2 The pathogenicity of M. tuberculosis depends its ability to secrete virulence factors that are displayed on the bacterial cell surface (as previously discussed). As illustrated in Figure 2-2, the M. tuberculosis-containing droplets travel through the respiratory tract, where the majority of the bacilli become trapped by mucus-secreting goblet cells, that latter of which are tasked with blocking entry and/or removing foreign entities. However, this mucociliary system first- line defence can sometimes be bypassed by these droplets, allowing them to reach the upper, aerated parts of the lungs (pulmonary TB), where the bacilli may undergo rapid replication. At this point during infection, the host’s second-line/innate immune mechanisms may be recruited, by which the host macrophages engulf the infecting bacilli and attempt to destroy these using various proteolytic enzymes and cytokines, including tumour necrosis factor alpha (TNF-α) and interferon gamma (IFN-γ). This signals T-lymphocyte transfer to the site of infection, initiating a cell-mediated immune response, which may either eliminate the infecting organism or result in a granuloma formation, the latter of which is a well-known pathological occurrence characterising TB (Knechel, 2009; Philips & Ernst, 2012). A granuloma contains the bacteria (known as Ghon’s focus) (Behr & Waters, 2014) and is defined as an amorphous mass of macrophages, monocytes and neutrophils, located in the lung, and functions to restrict the replication and spread of the infecting mycobacteria. However, after macrophage internalisation and granuloma formation, M. tuberculosis may avoid death by modulating the host immune system and blocking phagolysosomal fusion, creating a hospitable niche within these phagosomes for the bacilli to persist in a non- or slowly-replicating state, where they may survive for decades (Knechel, 2009; Nunes-Alves et al., 2014; Pai et al., 2016; Philips & Ernst, 2012; Warner, 2014). Considering this, although the host immune response may be unable to eradicate M. tuberculosis, a fully immune- competent host can suppress the infection indefinitely. This asymptomatic and non- infectious state is referred to as latent TB. Current reports indicate that one-third of the global population is infected with latent TB. The majority of these individuals (±90%) never manifest any signs of disease, however, when the immune system becomes compromised, for instance during HIV co-infection, the granuloma becomes caseous, loses its rigid integrity and ruptures, releasing the bacteria, which develops into active TB (the symptomatic and highly infectious state of the disease). Although HIV co-infection is considered the primary cause of active TB, other conditions, such as malnutrition, renal failure, uncontrolled diabetes mellitus, sepsis, chemotherapy, uncontrolled alcohol use, smoking and organ transplants, may also trigger the disease conversion from latent to active TB (Behr & Waters, 2014; Fennelly & Jones-López, 2015; Knechel, 2009; Pai et al., 2016).

The clinical symptoms associated with pulmonary TB are often non-specific, but may include constant coughing with mucus (lasting three weeks or longer), pleurisy, haemoptysis, dyspnoea, wheezing, weakness and/or progressive fatigue, weight-loss, no or loss of appetite, chills/fever, and night sweats (Bakhsi, 2006; Leung, 1999; Pai et al., 2016). In

14 Chapter 2 approximately 5% of all adult active TB cases, clinical symptoms are absent; while up to 60% of all paediatric pulmonary TB cases are asymptomatic (Pineda et al., 1993).

Figure 2-2: The transmission and pathogenicity of tuberculosis. (a) TB transmission occurs from host-to-host by means of coughing, sneezing or talking/singing. (b) The majority of M. tuberculosis- containing droplets are trapped and excreted by mucus-secreting goblet cells; however, some may still reach the lungs, initiating infection. (c) The invading bacteria are engulfed by host alveolar macrophages as part of the innate immune response, which results in the release of proteolytic enzymes and cytokines. (d) This in turn attracts T-lymphocytes to the site of infection, initiating cell- mediated immunity and necrotic granuloma formation, limiting the spread of the mycobacteria. (e) A fully immune-competent host can suppress the infection and keep it in its asymptomatic and non- infectious state (latent TB). (f) However, when the immune system becomes compromised, the granuloma becomes caseous, loses its rigid integrity and ruptures, releasing the bacteria, which develops into active disease. Adapted from Nunes-Alves et al. (2014).

15 Chapter 2

2.3 TUBERCULOSIS DIAGNOSTICS

A definitive TB diagnostic test functions on the basis of directly detecting M. tuberculosis, or one or more specific biomarkers of the organism present in a diagnostic specimen (e.g. blood, urine or sputum) (Knechel, 2009). Most tests and techniques used for diagnosing TB are surprisingly inexpensive, however, they vary largely with regards to their speed, sensitivity and specificity (Frieden & Driver, 2003).

2.3.1 Diagnosing latent M. tuberculosis infection

The current diagnosis of latent TB relies primarily on the detection of the host immune response to the M. tuberculosis infection. Two methods based on this principle, the tuberculin skin test (TST) and the interferon-gamma release assay (IGRA), are currently the only methods recommended by the World Health Organisation (WHO) for the detection of M. tuberculosis infection (World Health Organization, 2006). Although easy to perform, these tests have a number of limitations, since false-positive results can occur in individuals who were previously vaccinated or previously infected with M. tuberculosis, and false-negative results are common in patients with a compromised immune system, such as that caused by HIV (Ferrara et al., 2006; Pai et al., 2006a; Pouchot et al., 1997).

In the light of the current investigation, when used for predicting treatment outcome or monitoring treatment progression, IGRAs were shown to be the more accurate indicator of progression to active TB over time compared to the TST (Kik et al., 2010). However, it is important to note that individuals who are able to eliminate the infection may still have a positive TST or IGRA result due to memory T-lymphocyte responses, hence these tests have a low predictive/prognostic value (Pai et al., 2016).

2.3.1.1 Tuberculin skin test

The tuberculin skin test (TST) uses purified protein derivative (PPD), also known as tuberculin, a combination of proteins obtained from heat-killed M. tuberculosis, M. bovis and other environmental mycobacteria, which reduces the specificity of the TST. This test is based on an acquired, delayed-type hypersensitivity reaction in M. tuberculosis-infected individuals, when PPD is intra-cutaneously injected into the ventral forearm. T-cells that are produced due to a M. tuberculosis infection migrate to the site of injection where they release lymphokines, resulting in a thickening of the skin, which can be measured by a trained health-care professional, 48–72 hours after injection (González-Martín et al., 2010; Olivier & Loots, 2011).

16 Chapter 2 The TST, however, cannot distinguish between latent or active infection, and will result in a positive diagnostic outcome in both instances. Another disadvantage is that false-positive results are also prevalent in individuals who previously received the Bacille Calmette-Guerin (BCG) vaccination, those who are infected with different Mycobacterium species other than M. tuberculosis, and those individuals who have previously had TB. On the other hand, false-negative results may also occur in immune-compromised individuals (i.e. patients with HIV, those who recently had an organ transplant, or those treated with corticosteroids or chemotherapy), or due to other bacterial, fungal or viral infections, chronic renal failure, severe malnutrition, lymphoid organ disease, and in infants and the elderly. Other limitations include administration/technical mistakes, reading errors (which is subjective to interpretation), the need for a second visit to read the test results, and a lack of privacy. Thus, the reported sensitivity and specificity of this technique varies (González-Martín et al., 2010; Mazurek et al., 2001; Olivier & Loots, 2011).

2.3.1.2 Interferon gamma release assays

Another approach for detecting latent TB is the whole-blood cytokine detection assay. This test is based on the in vitro detection of a cell-mediated host immune reaction, resulting in the release of cytokines (IFN-γ in particular), in response to the antigens presented during M. tuberculosis infection (Mazurek et al., 2001). Early IGRAs used PPD as the preferred antigen, however, these were replaced by those using alternative antigens more specific to M. tuberculosis, such as early secreted antigenic target (ESAT)-6, culture filtrate protein (CFP)-10 and TB7.7 (Rv2654). These tests are based on the principle of an enzyme-linked immunosorbent assay (ELISA) to assess an individual’s acquired immunological response to ESAT-6 and CFP-10, based on the amount of IFN-γ released. Currently, there are two commercially available IGRAs named Quanti-FERON®-TB Gold In-Tube, using an ELISA- based method, and the T-SPOT-TB, based on the ELISPOT technique (González-Martín et al., 2010; Mazurek et al., 2001; Olivier & Loots, 2011).

A number of comparative studies show that IGRAs have a high specificity (>95%), but a reduced sensitivity (75–97%) when compared to the TST. However, IGRAs are significantly more specific in the vaccinated population when compared to that of TST. Furthermore, IGRAs have additional operational advantages, including (a) an ability to generate a diagnostic result immediately (point-of-care), (b) it being a single-step procedure, (c) ease of standardisation and implementation, as well as (d) various technical, logistic, and other cost advantages (González-Martín et al., 2010; Mazurek et al., 2001). Despite these however, this approach is not without its disadvantages, which includes follow-up testing in order to determine if the patient does in fact have active TB, which is also the case for TST (Mazurek et al., 2001).

17 Chapter 2 2.3.2 Diagnosing active M. tuberculosis infection

2.3.2.1 Microscopy smear techniques

Smear microscopy, as first demonstrated by Robert Koch in 1882, is currently the most commonly used method for diagnosing active TB. This technique is based on the acid-fast staining of mycobacteria after treatment with an acid-alcohol solution. M. tuberculosis is classified as a gram positive, acid-fast bacterium, a characteristic attributed to its high cell wall lipid content. The presence of mycolic acids in the cell wall prevents the binding of conventional dyes, and hence it is necessary to drive dyes into the cells using heat and phenol (known as the Ziehl-Neelsen method). During sputum smear microscopy, sputum is smeared onto a slide, stained with dye, dried using heat, and treated with acid-alcohol, after which all the acid-fast bacteria present will colour red and be visible under a microscope (Knechel, 2009; Konstantinos, 2010; Willey, 2008). Despite the fact that it is a low-cost, fast (<2 hours) and simple to use method, it requires a high number of bacilli (5 000–10 000 bacteria/mL sample) before a definitive diagnosis can be made, resulting in a sensitivity of only 62%. A further disadvantage is that it cannot distinguish between various Mycobacterium species, nor can it detect if they are drug-resistant (Dhingra et al., 2003).

Apart from its role in diagnosing TB, this technique can be also used for monitoring treatment efficacy and progression, since the bacteria should visibly decrease with each smear done on successively collected follow-up samples, during the treatment duration, if treatment is successful. To date, several studies have reported that a positive sputum microscopy during the second month of treatment is an indicator of treatment failure; however, conversion may take some time, depending on the initial bacterial load (Bernabe-Ortiz et al., 2011; González- Martín et al., 2010).

2.3.2.2 Bacteriological cultures

Bacteriological culture is based on the observation of growth in culture media of M. tuberculosis harvested from patient sputum. It is considered the gold standard for TB diagnostics since it has a reported sensitivity and specificity of almost 100%, and only requires as little as 10–100 bacteria/mL sample. This technique can be used to detect drug- resistance when antibiotics are added to the culture media, however, this method is time- consuming, considering the slow growth rates of mycobacteria, thereby delaying treatment onset (Moore & Curry, 1995). Either solid or liquid media are used to grow cultures, each of which has its own advantages and disadvantages. Solid media can be purchased at lower costs, but requires 2–6 weeks of incubation before macroscopically visible growth can be seen. Although liquid culture media methods are more costly, they are considerably faster

18 Chapter 2 (2–4 weeks before growth is visible) and more sensitive. A disadvantage of this method however, is the false-positive culture results which may occur due to bacilli transfer from TB-positive to TB-negative samples during laboratory handling, despite rigorous anti- contamination procedures (Olivier & Loots, 2011).

Currently, although the radiometric BACTEC 460TB system remains the preferred method for diagnosing TB (Cruciani et al., 2004), the non-radiometric, fully automated BACTEC 960/MGIT system is also widely used (Trivedi, 2015). The BACTEC 460TB and BACTEC 960/MGIT systems have a reported sensitivity and specificity for detecting mycobacteria of 81.5% and 99.6% and 85.8% and 99.9%, respectively. Additionally, when combined with solid media (such as the traditional Löwenstein–Jensen medium), the sensitivities of these two systems increase to approximately 87.7% and 89.7%, respectively (Cruciani et al., 2004; Lee et al., 2010; Zhao et al., 2014). Furthermore, the above-mentioned systems can not only distinguish between various Mycobacterium species (Olivier & Loots, 2011), but also detect drug resistance. Numerous studies investigated the accuracy of these two methods when testing susceptibility to first-line anti-TB drugs, and reported a level of agreement between results of approximately 97.2% with concordance values for isoniazid, rifampicin and ethambutol of 96.3%, 98.8% and 98.8%, respectively (Garrigó et al., 2007; Piersimoni et al., 2013).

Bacteriological cultures are also the preferred method for following treatment efficacy and progression. For this purpose, patient samples are collected bimonthly at every follow-up visit, at 15 days, and again at 30 days after treatment onset, provided that a sputum sample can be obtained from the lower respiratory tract. A favourable treatment outcome may be predicted by a negative M. tuberculosis culture within two months of commencing treatment (Antoine et al., 2007; González-Martín et al., 2010).

2.3.2.3 Nucleic acid amplification techniques

Molecular methods, such as nucleic acid amplification (NAA), also referred to as direct amplification, are based on the amplification of nucleic acid regions specific to the M. tuberculosis complex, and can hence distinguish between M. tuberculosis and various other Mycobacterium species. This approach is used as a follow-up to confirm sputum smear positive or culture results, and shows a 100% specificity and sensitivity compared to smear microscopy. Although this method has the capacity to outperform the current gold standard with regards to diagnostic turnover time and improve diagnostic certainty, it is far less sensitive (Knechel, 2009; Konstantinos, 2010; Pai et al., 2003) when compared to bacteriological cultures.

19 Chapter 2 The GeneXpert MTB/RIF (Xpert) assay is currently the fastest NAA TB diagnostic technique commercially available, reportedly able to obtain a diagnostic result within 90 minutes. It has been developed and packaged into a nearly fully automated cartridge-based diagnostic test, able to simultaneously identify M. tuberculosis and detect rifampicin-resistance. The Xpert assay purifies and concentrates M. tuberculosis bacilli from collected patient samples and utilises a hemi-nested real-time polymerase chain reaction (PCR) to detect and amplify clinically relevant M. tuberculosis-specific deoxyribonucleic acid (DNA) sequences, as well as mutations on the rpoB gene conferring rifampicin-resistance. This method has a reported sensitivity of 77.3% in smear-negative TB cases, and 98.2% in smear-positive TB cases (Boehme et al., 2011; Hillemann et al., 2011). Disadvantages, however, include various operational issues, the high cost and requirement for a stable flow of electricity, annual calibration, low limits for detecting rifampicin-resistance, and that the operating and storage temperatures may not exceed 30°C (Boehme et al., 2011; Small & Pai, 2010).

Since this assay is able to quantify bacterial load, it can potentially predict treatment response. However, the Centres for Disease Control and Prevention (CDC) does not recommend the use of NAA tests such as the Xpert assay to monitor treatment response, since dead bacilli persist in the host for quite some time after, making it impossible for this assay to distinguish between the active disease and successfully cured disease (Blakemore et al., 2011; Pai et al., 2003; Shenai et al., 2016).

2.3.2.4 Serological (immunological) methods

Serological (or immunological) methods function by detecting host antibodies and immune complexes produced in response to M. tuberculosis antigens. Despite a lack of accuracy and the inadequate sensitivity and specificity, this method remains popular in developing countries, mainly due to its speed, technological simplicity, low costs and modest training requirements (Steingart et al., 2011). However, using serological methods for diagnosing TB is challenging due to the various stages associated with M. tuberculosis infection (exposure, latent/active infection and severe disease), each having their own antibody patterns. Additionally, cross-contamination from environmental mycobacteria can cause false-positive results and serological tests are unable to distinguish between latent and active TB or between different Mycobacterium species (Olivier & Loots, 2011; Pai et al., 2006b).

Considering all the disadvantages and limitations of serological methods, the WHO strongly recommends not using these methods to diagnose TB. Also considering that there is no evidence that serological methods can be used to monitor response to treatment, these tests are not recommended for use to monitor treatment progression (World Health Organization, 2011).

20 Chapter 2 2.3.2.5 Phage-based assay

The phage-based assay allows for the rapid detection of TB in individuals with no previous treatment history, giving results within 48 hours. During this technique, clinical samples are infected with mycobacteriophages (viruses infecting mycobacteria), after which a solution is added to destroy all phages that have not infected the bacteria. The remaining phages replicate, are released during cell lysis, and amplified. The phages can then be visualised as clear areas in a lawn of host cells, which indicate the presence of M. tuberculosis. The FASTPlaqueTB assay is currently the only commercially available phage assay for detecting TB, and has a reported sensitivity of 75% and specificity of 98% when compared to smears, and 97% specificity when compared to cultures. Considering this, as well as its speed and specificity for M. tuberculosis, this test could possibly show promise for use in countries with a high TB prevalence, especially when other diagnostic tests are not suitable (Albert et al., 2002; Tortoli & Palomino, 2007).

Considering its mode of action, the phage-based assay could potentially be used as a fairly handy tool to follow treatment progression, especially in combination with other techniques. However, a major disadvantage to this is that this test requires approximately a five times larger volume of patient sample as compared to that required for direct microscopy and culture methods (Tortoli & Palomino, 2007).

2.3.3 Metabolomics biomarkers for tuberculosis diagnostics

Considering the above-mentioned advantages and disadvantages of current TB diagnostic techniques, no single test meets all the specifications of sensitivity, specificity, speed, safety, robustness, training simplicity, and cost. Thus, there is a need for new TB diagnostic techniques, which will overcome these limitations and can easily be implemented in low-income, high-burden counties. One of the first steps towards achieving this goal is to address this problem from a different perspective and to identify new TB biomarkers, which will lead to the development of improved diagnostic strategies.

Although numerous single biomarkers have been implemented successfully for use in the diagnosis and prognosis of various disease states, the emergence of the more recent “omics” technologies has unleashed the possibility of using biosignatures, which are profiles of combined biomarkers, towards this end. These biosignatures are especially useful when individual biomarkers do not exist or cannot be identified. The term “omics” is used to describe research models aimed at acquiring large-scale data from each sample in a sample group, for the purpose of identifying disease biomarkers and/or elucidating novel functional or pathological mechanisms (Wheelock et al., 2013). These datasets can comprise of a

21 Chapter 2 collection of genes (genomics), products of gene expression (transcriptomics), proteins (proteomics), or metabolites (metabolomics). Due to the largely untargeted means by which these datasets are usually generated, they may contain hundreds or even thousands of variables, and therefore, biomarkers and/or biosignatures are mined from the data using advanced biostatistical approaches. The validation and clinical performance of these biosignatures are evaluated by reporting the sensitivity, specificity and receiver operator characteristic (ROC) curves, amongst others, according to the Standards for the Reporting of Diagnostic Accuracy Studies (STARD) guidelines (Bossuyt et al., 2003; Xia et al., 2013).

To date, several metabolomics studies have been done with the aim of identifying specific single metabolite biomarkers and/or biosignatures which can be used in either the initial screening or subsequent speciation phase of TB diagnostics. As indicated in Tables 2-2 to 2-6, these approaches include the use of various sample matrices which were analysed using a variety of analytical approaches. The identified biomarkers not only include the well-known TB biomarkers; tuberculostearic acid (TBSA), branched chain fatty acids, and other cell wall components, but also novel compounds, never before associated with TB infection. Some of the compounds identified as potential biomarkers were detected in more than one study and in different sample matrices (indicated in bold text in Tables 2-2 to 2-6), the most significant of which will be discussed below.

2.3.3.1 Metabolomics biomarkers detected using Mycobacterium cultures

Although the metabolite profiles of the various TB-causing mycobacteria grown in vitro differs to some extent from that grown in vivo, some researchers still prefer to use bacteriological cultures in the initial stages of diagnostic biomarker identification. Biomarkers identified in this manner are indicative of those organism-specific compounds which may be present in more complex patient-collected diagnostic samples, such as sputum or blood, and this pre-identification may assist in their detection in these patient samples where they may occur in diluted quantities and/or be masked by the matrix background.

Pavlou et al. (2004) used a metabolomics approach to distinguish between M. tuberculosis, M. avium, P. aeruginosa and a mixed infection (M. tuberculosis and M. scrofulaceum). They achieved a 96% prediction value when analysing the headspace above the cultured samples of the aforementioned infectious Mycobacterium species, using an electronic nose (EN). The underlying principle of the method is as follows: the EN contains 14 conducting-polymer sensor arrays which interact with the volatile compounds liberated in the headspace of the cultured Mycobacterium samples. This interaction results in a change in electrical resistance, producing characteristic signals of multiple sensor parameters. These variables collected for each culture were then used to construct a data matrix which was statistically

22 Chapter 2 analysed via discriminant function analysis (DFA) to differentiate the sample groups. When this methodology was applied to 46 patient sputum samples, it successfully differentiated sputum containing M. tuberculosis, M. avium, P. aeruginosa, a mixed infection and eight control samples (TB-negative patients admitted to the clinic) (Pavlou et al., 2004). The method was then further validated using 330 TB-positive and TB-negative patient collected sputum samples, and correctly identified TB with a sensitivity of 89% and specificity of 91%, at a detection limit of 1 x 104 bacteria/mL sputum (Fend et al., 2006). When investigating the potential clinical application of this EN methodology, two different off-the-shelf EN devices were tested in a real-life clinical setting. The first device could diagnose TB with a sensitivity of 68%, specificity of 69% and accuracy of 69%, while the second showed a sensitivity of 75%, specificity of 67% and accuracy of 69%. Although these results show promise, they do suggest that the EN technology is not yet sensitive, specific or accurate enough for clinical application towards TB diagnostics (Kolk et al., 2010). Furthermore, the chemical characteristics and identities of the signals detected cannot be characterised or quantified as yet and, therefore, the characteristic compounds causing these signals were not identified.

Mycobacterial cell wall components, specifically the characteristic mycolic acids, potentially serve as good indicators for the presence of this pathogen in a biological sample. Numerous studies have focused on the presence or absence, as well as the ratios, of these compounds, as a means to characterise, identify or differentiate the various infectious Mycobacterium species from one another, for application to TB diagnostics (Butler et al., 1991; Freeman et al., 1994; Glickman et al., 1994). To date, the Sherlock™ Mycobacteria ID system (MYCO-LCS) is the only commercially available high-performance liquid chromatography (HPLC) based, pattern recognition method for mycobacterial speciation, and functions by identifying variations in the infecting mycobacteria’s mycolic acid content. This method claims to have the ability to identify and differentiate 25 mycobacteria species and 35 non-mycobacteria species from pure culture, in less than 30 minutes, at a cost of approximately $6 USD per analysis (Midi Inc, 2015). In 2012, Olivier and Loots applied a similar approach, identifying the characteristic lipid profiles of M. tuberculosis, M. bovis, M. kansasii and M. avium, using a GC-MS metabolomics research methodology, to fully automate this classification model. Twelve lipid biomarkers were identified (Table 2-2) and used to build a multivariate discriminant model, which could correctly assign unknown samples to their respective species groups with probabilities ranging from 72 to 100%. Although this method requires 16 hours to achieve a diagnostic result, only 1 x 103 cultured cells were needed for correct classification, making it more sensitive than the previously described HPLC approach (Olivier & Loots, 2012b). The research team however, later improved on this method by implementing the use of a mixer mill to assist in the extraction of these biomarkers, resulting in a five hour turnaround time and the need for smaller patient sample and solvent volumes. A further development was the application of an alternative

23 Chapter 2 extraction approach, allowing for the detection of metabolites belonging to not only the lipid markers described, but also other compounds including amino acids, alcohols, organic acids, monosaccharides, alkenes, alkanes, purines, pyrimidines, etc. Nineteen metabolites were identified as biomarkers for differentiating M. tuberculosis, M. avium, M. bovis, M. kansasii and P. aeruginosa (Table 2-2). Indole-acetic acid, cadaverine, purine, putrescine, and two unknown compounds with molar masses of 343 and 373 were detected exclusively in P. aeruginosa, whereas inositol and myo-inositol were characteristic of the Mycobacterium species investigated. Succinic acid and an unknown compound with a mass of 268 were uniquely identified in M. kansasii, and another unknown compound with a mass of 541 was detected exclusively in M. tuberculosis (Olivier & Loots, 2012a). In addition to the capacity of the extraction method to identify a greater variety of metabolites, it further reduced the solvent volumes required, and improved the detection limit to only 250 bacterial cells per analysis (Du Preez & Loots, 2013a).

More recently, Lau et al. (2015) described an optimised ultra-high performance liquid chromatography–electrospray ionisation–quadruple time-of-flight mass spectrometry (UHPLC-ESI-Q-TOFMS) metabolomics approach to differentiate various Mycobacterium species from one another, by analysing their excreted metabolite profiles. Although the main aim of this investigation was to differentiate M. tuberculosis and non-tuberculous mycobacteria (NTM), clear separation between all species was achieved when the collected data were analysed using PCA and PLS-DA. Of the 24 biomarkers identified (metabolites detected in significantly higher concentrations in M. tuberculosis as compared to the other NTM strains), only seven could be annotated via MS/MS database matching (Table 2-2) (Lau et al., 2015).

Furthermore, in the context of the rising incidence of drug-resistance, a successful TB diagnostic method should also be able to identify whether the infected M. tuberculosis strain is fully drug-susceptible or drug-resistant to either first- or second-line drugs. Although no metabolomics study to date has identified biomarkers for TB drug-susceptibility testing per se, differentiation of various drug-resistant strains from drug-susceptible parent strains has been demonstrated using the differences in the metabolite profiles of these organisms. In two related studies, GC-MS metabolomics were used to compare the fatty acid metabolome (Du Preez & Loots, 2012) and total metabolome (Loots, 2015) profiles of two rpoB mutant M. tuberculosis strains (S522L and S531L) to that of a fully drug-susceptible wild-type parent strain, to better characterise rifampicin-resistance. In each study, biomarkers for rifampicin- resistance were identified and used to elucidate previously unknown biological mechanisms related to the survival and adaptation of these mutant strains. Contributing to this, Lahiri et al. (2016) used a LC-MS based organism-wide screen to compare approximately 10 000 cell wall lipids occurring in rifampicin-resistant mutants, in order to identify the lipid alterations

24 Chapter 2 induced by various rpoB mutations. Their results indicated altered mycobactin siderophores and acylated sulfoglycolipid concentrations, both of which are essential for bacterial growth. This study provided further evidence of characteristic cell wall lipid remodelling in rifampicin- resistant M. tuberculosis strains, and additionally suggests new diagnostic and therapeutic targets against drug-resistant strains of M. tuberculosis (Lahiri et al., 2016). In a similar fashion, Loots (2014) investigated mono-resistance to isoniazid (resulting from mutations in the katG gene) in M. tuberculosis. From the differentiating metabolites identified, it was shown that the isoniazid-resistant strains are more susceptible to oxidative stress, and a survival adaptation to this is to increase the uptake and utilisation of alkanes and fatty acids as a carbon/energy source, and to synthesise antioxidant compounds, i.e. ascorbic acid, and its oxidation via an ascorbate degradation pathway (Loots, 2014). Although the diagnostic potential of the identified biomarkers were not investigated, these studies show that metabolomics can be implemented towards the identification of characteristic drug-resistant biomarkers.

Although the methods described above all required a time-consuming culturing step, these attempts at improved TB diagnostics using bacterial cultures prove the capacity of metabolomics for detecting new diagnostic biomarkers. Ideally, a TB diagnostic method, including the initial screening and speciation steps, should be done directly from the collected clinical samples (preferably collected in a non-invasive manner) to shorten the delay between disease presentation and treatment onset. The true clinical application of these biomarkers can therefore only be determined when investigating their diagnostic value in patient- collected samples, as was done by Syhre et al. (2009) and Phillips et al. (2010) (see section 2.3.3.5).

Table 2-2: Tuberculosis biomarkers identified in culture using a metabolomics research approach.

Analytical Sample cohort Biomarkers identified Reference apparatus

1-methyl-naphthalene; 3-heptanone; methylcyclododecane; 2,2,4,6,6-pentamethyl-heptane; Phillips et al. M. tuberculosis ATD-GC-MS trans-anti-1-methyldecahydronaphthalene; 2-hexene; (2007) 1-methyl-4-(1-ethylethyl)-benzene; 1,4-dimethyl- cyclohexane; 3,5-dimethylamphetamine; 3-methyl-butanal

M. tuberculosis M. bovis M. bovis BCG Syhre and methyl phenylacetate; methyl p-anisate; methyl M. avium complex SPME GC-MS Chambers nicotinate; o-phenylanisole M. fortuitum (2008) M. chelonae M. abscessus

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Analytical Sample cohort Biomarkers identified Reference apparatus

M. tuberculosis isopropyl-tetradecanoic acid; 9-hexadecenoic acid; M. avium TBSA; 10-heptadecenoic acid; heptadecanoic acid; Olivier and GC-MS M. bovis 11-eicosanoic acid; 13-docosenoic acid; tricosane; Loots (2012a) M. kansasii C32 mycocerosic acid; 15-tetracosenoic acid TBSA; docosanoic acid; 2-octyl-cyclopropaneoctanoic acid; heptadecanoic acid; 13-docosenoic acid; M. tuberculosis 15-tetracosenoic acid; 2,4-dimethyltetradecanoic acid; 9-hexadecenoic acid; eicosanoic acid; tetradecanoic M. avium acid; octacosanoic acid; pentacosanoic acid; 2-hexyl- Olivier and M. bovis GC-MS cyclopropaneoctanoic acid; purine; 9-octadecenoic acid; Loots (2012b) M. kansasii indole-acetic acid; 11-eicosanoic acid; citric acid; P. aeruginosa cadaverine; inositol; 5'-adenylic acid; decanoic acid; 9-hexadecenoic acid (trans); myo-inositol; succinic acid; putrescine; erythritol; valeric acid

M. tuberculosis M. avium complex dexpanthenol; Val-His-Glu-His; 1-tuberculosinyladenosine; M. bovis (BCG) UHPLC-ESI- 1-tuberculosinyl2’-deoxyadenosine; 1-tuberculosinyl Lau et al. M. chelonae QTOFMS derivative; 1-tuberculosinyl-O-acetyladenosine; (2015) M. fortuitum phosphatidylglycerol M. kansasii

Note: Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Abbreviations: GC-MS, gas chromatography mass spectrometry; ATD, automated thermal desorption; SPME, Solid phase microextraction; ESI, electrospray ionisation; TOFMS, time-of-flight mass spectrometry; UHPLC-MS/MS, ultra-high performance liquid chromatography-tandem mass spectrometry.

2.3.3.2 Metabolomics biomarkers detected using sputum

The most commonly used sample matrix for diagnosing pulmonary TB to date is sputum. This mucus-like biofluid originates directly from the area of infection, i.e. the airways of the lungs, and is highly populated with M. tuberculosis in TB-positive patients. A metabolic profile of a TB patient’s sputum would contain Mycobacterium-specific metabolites, due to the physical presence of the organism in this sample, and also various altered, disease- induced host metabolites, thereby expanding the possibility of identifying diagnostic biomarkers. In addition to diagnostics, these biomarkers could also be implemented towards the elucidation of disease mechanisms, seeing that they are a true reflection of adaptations to the metabolome of M. tuberculosis due to in vivo growth and the host response to infection/disease. Despite this, however, very few metabolomics studies have been focused on biomarker identification for improved TB diagnostics using patient-collected sputum. The reason for this is most likely linked to the complexities in using this sample matrix, such as its viscosity and uneven consistency, and also the likelihood of possible TB infection when handling these samples in a standard analytical laboratory not equipped for these purposes. The aforementioned viscosity and uneven consistency of sputum bring about the need for additional, time-consuming sample pre-processing steps before metabolomics analyses can

26 Chapter 2 commence. These methods were not always standardised, and even after successfully applying the necessary extraction procedures and analyses of the samples, the metabolic profiles obtained are complex and contain many compounds from a variety of compound classes. Hence, comprehensive statistical analyses to sift through the masses of data are required to identify potential biomarkers (Du Preez & Loots, 2013b).

In 2012, specifically for metabolomics TB biomarker identification, Schoeman et al. developed a new sputum pre-processing method, in conjunction with a global metabolite extraction approach and GCxGC-TOFMS analysis, to differentiate sputum spiked with M. tuberculosis bacilli and control sputum (not spiked). The compounds identified as markers mainly represented those associated with the cell wall of M. tuberculosis (Table 2-3) (Schoeman et al., 2012). This method was applied to differentiate culture-confirmed TB- positive (n=34) and TB-negative (n=61) patient sputum, and the 22 compounds (Table 2-3) best explaining the variation between the groups, were once again identified. Various new disease mechanisms were described, including the presence of a citramalate cycle in M. tuberculosis, and the interaction of this cycle with an upregulated glyoxylate cycle and increased fatty acid oxidation during in vivo growth in the human host. Furthermore, these biomarkers also shed light on an additional process by which the host produces hydrogen peroxide (H2O2) via glucose oxidation, as a means to eliminate the infected bacteria. The elevated concentrations of various neurotransmitters associated with the TB-infection provided added information explaining many of the clinical symptoms that TB patients experience (Du Preez & Loots, 2013b). Although the objective of this study was to better characterise TB, the differentiation of the groups based on underlying metabolite differences suggest the potential for this type of metabolomics approach to be used for diagnostic purposes.

Although not using a traditional metabolomics approach, research groups have also investigated the possible use of individual sputum metabolites, such as the aforementioned TBSA, for diagnosing TB. In 2009, Cha et al. indicated that TBSA can be detected directly from patient sputum samples using solid-phase micro-extraction (SPME) and post- derivatisation coupled to GC-MS. The group reported that this procedure is more sensitive than smear microscopy and requires only five hours per analysis, thereby proving that the use of single metabolite biomarkers can be useful in the initial screening phase of TB diagnostics. However, similar to a positive smear microscopy result, the detection of TBSA in these samples is due to the presence of the mycolic acids, a common component in the cell walls of all mycobacteria, and can therefore not be used for speciation or the detection of drug-resistance (Cha et al., 2009).

27 Chapter 2 Table 2-3: Tuberculosis biomarkers identified in human sputum using a metabolomics research approach.

Analytical Sample cohort Biomarkers identified Reference apparatus

nonadecanoic acid; TBSA; hexacosanoic acid; acetohydroxamic acid; myo-inositol; eicosanoic acid á-D-glucopyranoside; D-glycero-L-manno-heptonic acid; palmitoleic acid; furan; tetracosanoic acid; arachidonic 6 x TB- spiked with acid; octadecanoic acid; D-glucose; propane; GCxGC- Schoeman et M. tuberculosis heptadecanoic acid; docosanoic acid; 9-octadecenoic TOFMS al. (2012) 6 x TB- (Not spiked) acid; D-mannose; D-galactose; D-glucosamine; glycerol; uridine; D-fructose; 2-O-glycerol-à-D-galactopyranoside; cadaverine; á-D-galactopyranoside; á-D-xylopyranose; arabinofuranose; D-erythro-pentitol; L-threonine; á-D-galactofuranose; phenylethanolamine

à-D-glucopyranose-2-acetylamino-2-deoxy; à-D-glucopyranoside; á-L-mannopyranose; N-acetyl- glucosamine; à-D-mannopyranoside; D-galactose-6-deoxy; à-D-galactopyranose; D-glucosamine; D-citramalic acid; 61 x TB+ GCxGC- Du Preez and methyl-17-methyl-octadecanoic acid; D-gluconic acid; TOFMS Loots (2013b) 34 x TB- d-lactone; 10-heptadecenoic acid; 9-octadecenoic acid; nonadecanoic acid; 2-deoxy-D-erythro-pentitol; glutaric acid; sebacic acid; ethane; butanal; g-aminobutyric acid; 3,4-dihydroxybutanoic acid; normetanephrine

Note: Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Abbreviations: GC-MS, gas chromatography mass spectrometry; TOFMS, time-of-flight mass spectrometry; TB+, positively diagnosed for active TB via culture; TB-, patients portraying TB-related symptoms, but tested negative for TB via culture.

2.3.3.3 Metabolomics biomarkers detected using blood and tissue

Blood samples (plasma and serum) are regarded as being comparatively more homogeneous in composition, less viscose and easier to process than sputum. Furthermore, although very low concentrations of potential metabolites originating directly from the infectious bacteria may be detected in the blood, it reflects those metabolites altered in the host due to the infection, and is therefore better suited to identify host pathological and defence mechanisms (Weiner et al., 2012), to examine disease progression (Somashekar et al., 2012), and/or for diagnostic and therapeutic monitoring purposes (Zhou et al., 2013).

When analysing serum samples collected from healthy controls, individuals with latent M. tuberculosis infection, and patients with active TB, using GC-MS and UHPLC-MS/MS, Weiner et al. (2012) were able to detect alterations in amino acids, lipids and nucleotides, due to the anti-inflammatory changes that typically occur during TB disease progression. The altered metabolite profiles also revealed reduced phospholipase activity, increased indoleamine 2,3-dioxygenase 1 (IDO1) activity, an increased abundance of adenosine metabolic products, and indicators of fibrotic lesions in those patients with active TB, comparatively. Additionally, 20 metabolites with potential diagnostic value were identified

28 Chapter 2 (Table 2-4) (Weiner et al., 2012). Similarly, using serum-based NMR metabolomics, Zhou et al. (2013) identified 17 metabolites with possible diagnostic value (Table 2-4), in addition to better describing the TB disease state in terms of an observed increased glycolysis, lipid degradation, nucleotide biosynthesis, energy consumption, and a modified protein metabolism in TB patients. Expanding on this, they also investigated the metabolite changes induced by other disease states, including diabetes, malignancy and community-acquired pneumonia, and subsequently indicated that each of the changes to the metabolome induced by these perturbations is, in fact, disease specific (Zhou et al., 2013). With the main aim of identifying TB diagnostic biomarkers, Feng et al. (2015) also included patients with diseases other than TB in their patient cohort. The serum metabolome profiles of patients with active TB, chronic obstructive pulmonary disease (COPD), pulmonitis, bronchiectasis, lung cancer and healthy controls, were compared using UHPLC-MS analyses. By employing OPLS-DA, they were able to differentiate TB patients from healthy controls and patients with other lung conditions, and identified a TB-specific serum biosignature. A set of 12 metabolites, mostly fatty acids, amino acids and lipids (Table 2-4), were identified as biomarkers for active TB, and a combination of four of these compounds (i.e. lysophosphatidylcholine (18:0), behenic acid, threoninyl--glutamate and presqualene diphosphate) allowed for discrimination between active TB and control samples with an area under the curve (AUC) value of 0.99 (Feng et al., 2015).

With the aim of explaining pathogen-induced changes in the host, in addition to using serum, Shin et al. (2011) compared the spleen, lung, and liver tissue metabolome data from M. tuberculosis-infected and healthy control mice using 1H NMR. Based on the metabolite profiles, clear differentiation between TB-positive and control mice were evident for all tissue and serum samples collected, and the most characteristic biomarkers were identified (Table 2-4). These compounds indicated that precursors of membrane phospholipids, i.e. phosphocholine and phosphoethanolamine, as well as the oxidative stress response, glycolysis, amino acid metabolism and nucleotide metabolism, are altered due to the TB disease state (Shin et al., 2011). Similarly, Somashekar et al. (2012) applied a 1H NMR metabolomics approach using lung and serum samples collected from guinea pigs, 30 and 60 days after infection, with the aim of studying the effects of disease progression on the host metabolome. Sixteen lung metabolites were identified as TB biomarkers (Table 2-4), four of which (aspartate, glutathione, betaine and trimethylamine N-oxide) were uniquely detected in the active TB group as a result of increased oxidative stress and glutaminolysis in the lung lesions. Furthermore, several other metabolites, including lactate, choline- containing compounds, ethanolamine, phosphocreatine, nicotinamide and glutamate, were detected in reduced amounts in the serum of the infected animals comparatively, whereas formate and acetate were significantly increased after infection. The authors concluded that the metabolite variation detected in both lung tissue and serum indicate metabolic changes

29 Chapter 2 associated with necrotic disease processes, including anaerobic glycolysis, glutaminolysis, and gluconeogenesis (Somashekar et al., 2012).

In an attempt to find possible diagnostic markers for active TB, not influenced by anti-TB drug administration, Che et al. (2013) used a GC-TOFMS metabolomics approach to compare serum samples collected from healthy controls and TB patients, before and after anti-TB treatment. They indicated that cholesterol, galactose and malonic acid were significantly elevated in the TB patient samples, whereas phenylalanine, glycerol 3-phospate, ornithine, inositol, lactic acid and 5-oxoproline were comparatively downregulated. Of these nine potential TB biomarkers, 5-oxoproline was determined to remain unaffected in the TB-positive patient sample group despite commencing treatment using first-line anti-TB drugs. Hence, this biomarker was validated using a bigger sample cohort. The importance of this study is that it indicates the capacity for metabolomics to identify useful biomarkers for early prediction of treatment outcome, and for possible use in new anti-TB drug development (Che et al., 2013).

Table 2-4: Tuberculosis biomarkers identified in blood and tissue using a metabolomics research approach.

Sample Analytical Sample cohort Biomarkers identified Reference matrix apparatus

aspartate; glutathione; betaine; Lung Tissue 40 x TB+ trimethylamine N-oxide; lactate; alanine; Somashekar HRMAS NMR (Guinea pigs) 10 x HC acetate; creatine; phosphocholine; et al. (2012) myo-inositol; glycerophosphocholine

AMP; creatine; lactate; glucose; NAD+; NADP+; oxaloacetate; fumarate; succinate; alanine; aspartate; glutamate; leucine; lysine; isoleucine; Lung Tissue 10 x TB+ Shin et al. 1H NMR phenylalanine; tyrosine; glutamine; (Mice) (2011) 10 x HC betaine; phosphocholine; niacinamide; phosphoethanolamine; acetaldehyde; taurine; UDP-glucose; uracil; uridine; xanthine; glutathione; itaconic acid

AMP; lactate; NADP+; oxaloacetate; fumarate; succinate; alanine; leucine; Spleen tissue 10 x TB+ Shin et al. 1H NMR isoleucine; tyrosine; glutamine; (Mice) (2011) 10 x HC phosphocholine; niacinamide; uridine; glutathione

AMP; creatine; lactate; glycogen; glucose; NADP+; oxaloacetate; succinate; uracil; Liver tissue 10 x TB+ aspartate; glutamate; leucine; lysine; Shin et al. 1H NMR (Mice) 10 x HC isoleucine; uridine; phenylalanine; (2011) tyrosine; glutamine; phosphocholine; niacinamide; taurine; UDP-glucose

Serum 10 x TB+ glucose; leucine; isoleucine; Shin et al. 1H NMR (Mice) 10 x HC phenylalanine; formate (2011)

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Sample Analytical Sample cohort Biomarkers identified Reference matrix apparatus

lactate; choline-containing compounds; Serum 40 x TB+ Somashekar HRMAS NMR ethanolamine; phosphocreatine; (Guinea pigs) et al. (2012) 10 x HC nicotinamide; glutamate; formate; acetate

10 x TB+ Serum 6 x TB+ (before and Che et al. GC-TOFMS 5-oxoproline (Human) after treatment) (2013) 10 x TB-

histidine; cysteine; threonine; citrulline; UHPLC-MS/MS cysteine-gluathione disulphide; urea; (positive mode) 44 x TB+ N-acetylneuraminate; glycocholenate sulphate; inosine; tryptophan; mannose; Serum 46 x latent TB+ Weiner et al. UHPLC-MS/MS 3-carboxy-4-methyl-5-propyl-2- (Human) (asymptomatic) (2012) (negative mode) furanpropanoate; phenylalanine; 46 x healthy controls pyroglutamine; taurocholenate sulphate; glutamine; octadecanedioate; gamma- GC-MS glutamylglutamine; glycylvaline; asparate

1-methylhistidine; acetoacetate; acetone; glutamate; glutamine; isoleucine; lactate; Serum 38 x TB+ lysine; glycine; nicotinate; phenylalanine; Zhou et al. 1H NMR (Human) 39 x healthy controls pyruvate; tyrosine; alanine; formate; (2013) glycerolphosphocholine; low-density lipoproteins

120 x TB+ hexadecanoic acid; lysoPC (C16:0); lysoPC 105 x healthy controls (C18:0); 3D, 7D, 11D-phytanic acid; Serum 51 x lung cancer docosanoic acid; phytal; threoninyl-γ- Feng et al. UHPLC-MS (Human) 45 x pneumonia glutamate; kynurenine; quinolinic acid; (2015) presqualene diphosphate; 28 x COPD lysoPC (P-18:1 (9Z)); lysoPC (P-16:0) 22 x bronchiectasis

Serum 20 x TB+ 99 biomarkers identified - related to Zhong et al. LC-MS glycerophospholipid and arachidonic acid (2016) (Human) 18 x healthy controls metabolism 61 biomarkers identified, including anti-TB drugs, glutamate, choline derivatives, Plasma 17 x TB+ M. tuberculosis-derived cell wall glycolipids Frediani et al. LC-MS (Human) 17 x healthy controls (trehalose-6-mycolate and (2014) phosphatidylinositol), lipid mediators of inflammation

Note: Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Abbreviations: GC-MS, gas chromatography mass spectrometry; LC-MS, liquid chromatography mass spectrometry; TOFMS, time-of- flight mass spectrometry; HRMAS, high resolution magic angle spinning; 1H NMR, nuclear magnetic resonance; UHPLC-MS/MS, ultra- high performance liquid chromatography-tandem mass spectrometry; TB+, positively diagnosed for active TB via culture; TB-, patients portraying TB-related symptoms, but tested negative for TB via culture; Healthy control, healthy with no TB-related symptoms and tested negative for TB via culture.

In some instances, due to the complexity of the blood metabolome and the increasing sensitivity of some of the analytical equipment used for the analysis thereof, an extremely large number of biomarkers can potentially be identified. In two such studies using LC-MS, Frediani et al. (2014) and Zhong et al. (2016) identified 99 and 61 biomarkers respectively when comparing the blood metabolomes of TB-positive patients and healthy controls

31 Chapter 2 (Frediani et al., 2014; Zhong et al., 2016). Although the disease-induced alterations to the host’s metabolic pathways were discussed in these studies (Table 2-4), neither investigated the potential diagnostic capacity of the identified biomarkers.

From a diagnostic perspective, although standardised protocols are available for the collection of blood samples, these techniques are considered invasive, and in many patients the fear of needles can induce a stress response, altering the metabolic profile and inducing many of the markers previously described for the TB disease state (Dunn et al., 2005).

2.3.3.4 Metabolomics biomarkers detected using urine

Similar to blood, little or no mycobacteria are expected to be present in the urine of TB patients, and therefore, very low concentrations of those metabolites associated with or unique to M. tuberculosis would likely be detected. However, urine collected from TB patients would contain higher concentrations of the altered host-derived metabolites induced by the TB disease state, when compared to that of other patient sample matrixes. This may be considered an additional advantage for biomarker identification for better disease characterisation and diagnostic applications, and therefore many metabolomics research investigations have been done using urine as the preferred sample matrix.

Banday et al. (2011) analysed the volatile organic compounds (VOCs) in patient-collected urine for the purpose of discriminating TB patients from healthy controls, and from patients with other diseases such as COPD and lung cancer, using headspace GC-MS. They found significantly elevated levels of o-xylene and isopropyl acetate, accompanied by reduced levels of 3-pentanol, dimethylstyrene and cymol in the TB-positive patient group comparatively (Table 2-5) (Banday et al., 2011). Similarly, Luies and Loots (2016) used a GCxGC-TOFMS approach to investigate the urinary TB metabolite markers induced by infection-related adaptations of the host metabolome and/or host–pathogen interactions. Clear PCA differentiation was achieved between the TB-positive and healthy control group based on their respective metabolite profiles. The identified metabolite biomarkers (Table 2-5) indicated TB-induced changes resulting in abnormal host fatty acid and amino acid metabolism (phenylalanine in particular), mediated through changes to IFN-γ and possibly insulin (Luies & Loots, 2016). These studies demonstrate the potential of metabolomics to identify TB biomarkers using patient-collected urine.

In addition to disease-induced metabolic changes, urine also directly reflects metabolites excreted by the liver and kidneys, and therefore provides additional information regarding changes to an individual’s metabolome as a result of a toxic insult. To this end, Das et al. (2015) identified unique urinary TB biomarkers and determined the effects of TB treatment on the levels of these compounds. By comparing the GC-MS analysed urine metabolic 32 Chapter 2 profiles of TB-positive patients to that of TB-negative individuals (with TB-like symptoms), they detected 42 features with potential diagnostic value, of which six could accurately be annotated compound names (Table 2-5). Most of the metabolites were related to the phenylalanine metabolic pathway (similar to what was observed by Luies and Loots (2016)), and when using them to plot a ROC curve, the AUC was calculated as 0.85. The influence of first-line TB treatment on these metabolic profiles was investigated by analysing samples obtained from TB patients at various time points during the treatment regimen. A treatment dependent trend was observed, and those patients considered successfully cured after treatment completion, showed a metabolic profile similar to that of healthy controls (Das et al., 2015). This study indicates that in addition to pre-treatment diagnostics, urinary biomarkers also have the capacity to monitor disease progression and to potentially monitor treatment response and predict treatment outcome. This information would be useful in new TB drug trials, since these markers could serve as surrogate endpoints for treatment outcome, long before the six month treatment regimen is completed.

Table 2-5: Tuberculosis biomarkers identified in human urine using a metabolomics research approach.

Analytical Sample cohort Biomarkers identified Reference apparatus

117 x TB+ 37 x healthy controls o-xylene; isopropyl acetate; 3-pentanol; Banday et al. GC-MS 7 x COPD dimethylstyrene; cymol (2011) 5 x other pulmonary diseases

21 x TB+ lactic acid; norepinephrine; hydroquinone; malic acid; Das et al. GC-MS 21 x TB- 4-hydroxybenzoic acid; glucose* (2015)

2-C-methylglycerol; 2-octenoic acid; 5-hydroxyhexanoic acid; 5-hydroxyhydantoin; glycerol monostearate; indole-3-carboxylic acid; kynurenic acid; L-rhamnulose; 30 x healthy controls GCxGC- Luies and Loots oxalic acid; phenylacetic acid; quinolinic acid; ribitol; TOFMS (2016) 46 x TB+ homovanillic acid; 5-hydroxyindoleacetic acid; tryptophan; phenyllactic acid; N-acetyltyrosine (indicating elevated tyrosine)

*Of the 42 biomarkers, the identities of only these six compounds were confirmed. Note: Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Abbreviations: COPD, chronic obstructive pulmonary disease; GC-MS, gas chromatography mass spectrometry; TOFMS, time-of-flight mass spectrometry; TB+, positively diagnosed for active TB via culture; TB-, patients portraying TB-related symptoms, but tested negative for TB via culture; Healthy control, healthy with no TB-related symptoms and tested negative for TB via culture.

2.3.3.5 Metabolomics biomarkers detected using breath

Due to the recent advancements in breath collection techniques, this sample matrix has become an attractive option for diagnostic biomarker identification for TB. Breath originates

33 Chapter 2 directly from the infected lung, and would be expected to contain the characteristic host and bacterial VOCs. Additionally, it can be collected in a non-invasive manner (Scott-Thomas et al., 2013), even from children and HIV-positive individuals, where sputum collection is problematic.

Using an automated thermal desorption (ATD) GC-MS, a set of 130 compounds (mostly alkanes, naphthalene and benzene derivatives) were uniquely detected in the headspace of M. tuberculosis cultures, compared to those produced in the headspace of sterile culture media. In a pilot study, the group applied this knowledge to patient-collected breath samples, and successfully differentiated healthy controls (n=59) from hospitalised patients with a suspicion of pulmonary TB, with a 100% sensitivity and specificity. Using only the most abundant compounds detected in the headspace of the M. tuberculosis cultures (Table 2-6) as a diagnostic biosignature, they were also able to classify patients with negative (n=19) and positive (n=23) sputum culture results, with a specificity of 100% and sensitivity of 82.6% (Phillips et al., 2007). This approach was then applied to a larger sample cohort of 226 patients and successfully identified the TB-positive patients (those for whom the sputum culture, smear, and chest radiograph were all positive) with a sensitivity of 84% and specificity of 64.7% (Phillips et al., 2010).

Syhre and Chambers (2008) analysed the compounds in the headspace of cultures of various Mycobacterium species (M. tuberculosis, M. fortuitum, M. bovis, M. bovis BCG, M. abscessus, and M. avium complex) using SPME and GC-MS. The aim of their investigation was not to identify biomarkers for speciating infection, but rather to identify specific markers which can differentiate M. tuberculosis and M. bovis from all other mycobacteria, for later application to patient breath sample diagnostics. Two compounds, methyl p-anisate and methyl nicotinate, were identified exclusively in M. tuberculosis and M. bovis, whereas methyl phenylacetate was identified as a common biomarker in M. tuberculosis, M. bovis and M. avium complex (Table 2-6) (Syhre & Chambers, 2008). An in vivo validation for methyl nicotinate as a TB-specific biomarker in patient-collected breath showed significant differences in the concentration when comparing TB-positive patients to healthy controls, proving that it may potentially be valuable in a clinical environment (Syhre et al., 2009). However, a true validation of this would need to be done in breath collected from TB-positive patients and clinically sick controls (those with TB-like symptoms, but testing negative for TB via culture).

In a related study, Kolk et al. (2012) later also compared the GC-MS profiles of breath samples collected from 50 TB-positive patients and 50 hospitalised TB-negative patients. Seven VOCs (Table 2-6), including one unknown compound, were identified as potential biomarkers and were used to build a classification model which could predict TB with a sensitivity of 72%, specificity of 86% and accuracy of 79%. The model was further validated

34 Chapter 2 using a different set of samples (21 TB-positive and 50 hospitalised TB-negative), and correctly classified TB with a sensitivity of 62%, specificity of 84% and accuracy of 77% (Kolk et al., 2012).

Considering the above mentioned evidence, breath biomarkers show huge potential for their application in a non-invasive TB diagnostic method, however, further validation is necessary in order to determine which of these compounds are present due to a general disease state, oxidative stress or other external confounders such as smoking or diet, and which of these are, in fact, truly TB-specific.

Table 2-6: Tuberculosis biomarkers identified in human breath using a metabolomics research approach.

Analytical Sample cohort Biomarkers identified Reference apparatus

10 x TB+ Syhre et al. SPME GC-MS methyl nicotinate 10 x healthy controls (2009)

1,3-dimethyl-trans-cyclohexane; 1,4-dichloro-benzene; 1,4-dimethyl-cyclohexane; 2-butyl-1-octanol; 1-methyl-naphthalene; camphene; 4-methyl-decane; 3-ethyl-2-methyl-heptane; 2,6-dimethyl- 23 x TB+ octane; 1,2,3,4-tetramethyl-benzene; 3,6,6-trimethyl- Phillips et al. 19 x TB- ATD-GC-MS bicyclo-3,1,1-hept-2-ene; 1-ethyl-4-methyl-trans- (2007) 59 x healthy controls cyclohexane; l-beta-pinene; ethyl-benzene; methyl- benzene; propyl-benzene; 3-methyl-heptane; 2-methoxy-2- me-propane; 1-octene; cyclohexane; heptanal; 2,4-dimethyl-heptane; 4-methyl-heptane; nonanal; 2-methyl-pentane; styrene; tridecane

3-(1-methylethyl)-oxetane; 4-methyl-dodecane; hexyl-cyclohexane; Bis-(3,5,5-trimethylhexyl) phthalate; 30 x TB+ Phillips et al. ATD-GC-MS 1,3,5-trimethyl-benzene; 3,7-dimethyl-decane; tridecane; (2010) 196 x TB- 4,6,8-trimethyl-1-nonene; 5-ethyl-2-methyl-heptane; 4-methyl-1-hexene

50 x TB+ dodecane; 3-heptafluorobutyroxypentadecane; octanol; Kolk et al. GC-MS 50 x TB- 5-hexenoic acid; 2-ethyl-1-hexanol (2012)

Note: Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Abbreviations: GC-MS, gas chromatography mass spectrometry; ATD, automated thermal desorption; SPME, Solid phase microextraction; TB-, patients portraying TB-related symptoms, but tested negative for TB via culture; Healthy control, healthy with no TB-related symptoms and tested negative for TB via culture.

2.3.3.6 Diagnostic validity of the metabolomics tuberculosis biomarkers identified to date

A more comprehensive summary of the 49 metabolite biomarkers detected in more than one study (those markers indicated in bold text in Tables 2-2 to 2-6) is given in Appendix A of this thesis (see Table A-1). The biomarkers identified from cultured Mycobacterium (9-hexadecenoic acid, TBSA, heptadecanoic acid, 11-eicosenoic acid, 13-docosenoic acid,

35 Chapter 2 15-tetracosenoic acid, eicosanoic acid and cadaverine) were all detected more than once in different studies conducted by the same research group (Olivier & Loots, 2012a; Olivier & Loots, 2012b; Schoeman et al., 2012), aimed at differentiating various infectious Mycobacterium species from one another. These eight compounds are associated with the unique cell wall structure of these organisms, which is already well-known to vary between Mycobacterium species, therefore these markers would be expected to have diagnostic value, especially for speciation (Midi Inc, 2015). Additionally, 10-heptadecenoic acid, 9-octadecenoic acid, nonadecanoic acid, á-D-glucopyranoside and D-glucosamine were detected more than once as biomarkers for cultured M. tuberculosis (Olivier & Loots, 2012b; Schoeman et al., 2012), and were confirmed when comparing TB-positive and TB-negative patient sputum (Du Preez & Loots, 2013b). These biomarkers are also associated with the unique cell wall of M. tuberculosis and therefore show promise for possible use in new TB diagnostic methods using patient-collected sputum. An additional 14 compounds characterising TB were repeatedly detected in different tissue samples (liver, spleen and lung tissue) (Table 2-4). Interestingly, both myo-inositol and uridine were confirmed as characteristic compounds in TB-positive tissue from mice and guinea pigs (Shin et al., 2011; Somashekar et al., 2012), and were also identified as biomarkers in experiments using cultured M. tuberculosis (Olivier & Loots, 2012b; Schoeman et al., 2012). The use of tissue samples in a clinical environment is, however, impractical, and the presence of these biomarkers in human blood or urine, for instance, needs to be validated before evaluating their diagnostic capacity. Considering this, eight compounds identified in TB-positive host tissue were in fact also identified as TB biomarkers in serum and urine samples collected from TB-positive patients (Luies & Loots, 2016; Weiner et al., 2012; Zhou et al., 2013). Of these, lysine, isoleucine, phenylalanine and tyrosine were detected in elevated concentrations in all of the TB-positive sample groups analysed, irrespective of whether tissue, serum or urine was used, which serves as confirmation for their potential use as TB biomarkers. However, discrepancies in the concentrations of many other metabolite markers do leave room for debate as to their potential application for these purposes. Lactate and glutamate, for example, were seen to accumulate in various TB-positive host tissue (Shin et al., 2011) and TB-positive patient serum (Zhou et al., 2013) samples, but were found to be reduced in serum collected from TB-infected guinea pigs (Somashekar et al., 2012). Additionally, glutamine was detected in reduced amounts in TB-positive patient serum by one study group (Weiner et al., 2012), and then later in an elevated abundance by a different group, both using patient-collected serum (Zhou et al., 2013). Similarly, elevated levels of formate were identified in the serum collected from M. tuberculosis-infected mice (Shin et al., 2011) and guinea pigs (Somashekar et al., 2012), however, this metabolite was indicated to be reduced in serum collected from TB-positive patients when compared to healthy controls (Zhou et al., 2013). However, this may be due to the challenge posed by the fact that experimental models allow for very tight control of time (post infection), whereas clinical TB 36 Chapter 2 manifests at an indeterminate stage post infection. Thus, this could simply represent different disease stages, which are themselves dynamic.

1-Methyl-naphthalene, 1,4-dimethyl-cyclohexane (Phillips et al., 2007) and methyl nicotinate (Syhre & Chambers, 2008; Syhre et al., 2009) were identified as characteristic markers when using headspace analyses of M. tuberculosis cultures, and were also confirmed as biomarkers from TB-positive patient-collected breath samples. The use of methyl nicotinate as a possible TB diagnostic marker was further validated in a follow-up study using a larger sample cohort of collected breath samples from TB-positive and TB-negative patients (Syhre et al., 2009). Although this compound is used in a niacin test to distinguish cultured M. tuberculosis from other related species, its quantities in patient-collected breath may be influenced by other confounding factors such as smoking, chewing betel nuts and a suppressed immune system (for example in HIV-positive patients) (Syhre et al., 2009). Another TB breath biomarker, tridecane, was verified using two independent sample cohorts (Phillips et al., 2010). However, since this marker is thought to originate from increased oxidative stress in TB patients (Phillips et al., 2010), it would most probably not be specific to a TB disease state, since elevated oxidative stress is a common occurrence in most diseases. Similarly, the biomarker quinolinic acid, identified in TB patient-collected serum (Feng et al., 2015) and urine (Luies & Loots, 2016), originates from the kynurenine pathway, induced by several pro-inflammatory cytokines and chemokines involved in immune reactions related to a number of disease states, and is thus probably also not specific enough for TB.

The challenges experienced to date for identifying a single, disease-specific metabolite for diagnostic application, could potentially be solved by using a combination of metabolites/ biomarkers simultaneously. Docosanoic acid (behenic acid), for example, was detected as one of the most abundant fatty acids in M. tuberculosis (Olivier & Loots, 2012b), but its diagnostic capacity using TB-positive patient samples was rather poor. However, when used in combination with three other compounds (lysophosphatidylcholine (18:0) threoninyl-γ- glutamate, and presqualene diphosphate), it could differentiate TB patients from healthy controls as well as patients with other lung diseases with an AUC of 0.99 (Feng et al., 2015).

Regardless of the aim of the study, all biomarkers or biosignatures identified should be validated both statistically and biologically (Broadhurst & Kell, 2006). Although all of the above mentioned metabolomics investigations identified potentially valuable biomarkers, the majority of these were used to better characterise the disease and the pathogen-induced metabolic changes in the host. For this reason, only four of the 21 studies described in this section, and summarised in Tables 2-2 to 2-6, actually validated the identified biomarkers for their potential diagnostic applications, using either an independent test or validation sample set. Another point to note is that many of the TB characterisation and diagnostic studies

37 Chapter 2 described above used rather strict inclusion and exclusion criteria for sample selection. Although this serves well to eliminate variation in the sample cohort to better characterise the disease mechanisms, it could potentially bias the outcome towards better diagnostic outcomes, which would not necessarily be attained in a true clinical setting. Additionally, only two of the diagnostic studies described (Banday et al., 2011; Feng et al., 2015), compared the identified TB biomarkers to those identified in patients with related diseases, such as COPD and lung cancer, while only one other research group (Weiner et al., 2012) included asymptomatic individuals with latent TB infection. In the immediate future, it would be beneficial to see increased efforts towards validating these newly identified biomarkers for use in a clinical context, especially towards improved TB diagnostics.

2.3.3.7 From benchtop to clinical application: Future prospects and challenges of metabolomics biomarkers

Considering the above, over the last ten years, metabolomics has contributed exponentially to the number of new TB biomarkers identified, which have not only shed light on new disease mechanisms but also improved TB diagnostics. Ideally, a TB diagnostic method should be sensitive, specific, fast, inexpensive, and easy to use without extensive training or clinical facilities. Such a method should be able to accurately diagnose TB, and be capable of identifying the species of Mycobacterium causing the disease, while also indicating whether or not it is drug-resistant. The method should additionally be able to predict and/or monitor treatment outcome since this would determine the most appropriate treatment approach. Although these ideal diagnostic requirements seem impossible considering the limitations of current TB diagnostic approaches, with the recent advancements in metabolomics and the new TB biomarkers identified to date, it may be achievable. Improvements to the sensitivities and specificities of these approaches can easily be attained using other detection technologies or targeted analytical approaches on the same metabolomics MS-based equipment. The analyses times can also be significantly reduced to only a few minutes, or even seconds, when searching for the identified biomarkers in a targeted manner. Furthermore, metabolomics has identified characteristic biomarkers of active TB using patient-collected sputum, blood, urine and breath (hence the application of these to less-invasive diagnostics), and also for speciation, characterising drug-resistance, and predicting/monitoring treatment outcome. The latter is not only important in the sense that one could potentially predict a patient’s response to first-line treatment at time of diagnosis (i.e. before the medication is administrated) or well before the six month treatment regimen is completed, but these markers could accelerate the time-consuming process of new TB medication efficacy testing, and elucidate their mechanisms of action.

38 Chapter 2 The major limitations of the ideal diagnostic requirements we propose above regarding the current metabolomics strategies, would be that a metabolomics approach typically uses expensive analytical equipment, operated by highly trained staff, in a laboratory environment. One should, however, bear in mind that the general idea behind metabolomics is to use these high-end analytical apparatus only for the initial biomarker(s) identification and validation phase. Once these markers have been identified, other technologies (like nanotechnology, or other detection technologies) can be used to develop strategies to increase the sensitivity and specificity of detection and to develop an inexpensive kit for rapid point-of-care diagnostics, with minimal training to the user. Alternatively, kits can also be developed for high-throughput laboratory based TB diagnostics on 96 well plates, using inexpensive laboratory equipment, such as a basic spectrophotometer. Another limitation is that no research group to date has reported a single, unique biomarker present in only TB-positive patients (with the exception of the well-known TBSA). That is not to say that such biomarkers have not in fact been identified, and/or are being developed for diagnostic applications. Using a biosignature rather than a single characteristic marker to diagnose a disease does complicate the matter to a certain extent, however, with the aid of statistical or mathematical prediction models, or pattern recognition software, this can easily be overcome and the diagnosis automated.

2.4 TUBERCULOSIS TREATMENT

The Bacille Calmette-Guerin (BCG) strain, an avirulent M. bovis strain, is currently used to manufacture the only TB vaccine available for TB prevention. Administration of this vaccine shortly after birth is mandatory in high-incidence TB countries, and is largely considered effective (Delogu & Fadda, 2009), although this is a huge point of debate, since it is reportedly only effective in a mere 50% of all adult cases (González-Martín et al., 2010). Currently, sixteen new vaccines for TB prevention are in clinical trials, with a few approaching or currently in proof-of-concept field studies, with numerous others in various stages of preclinical development. However, the results of the phase III trials will only be known within a few years (World Health Organization, 2015b).

Anti-TB drugs are categorised as either first-line or second-line drugs. First-line drugs are typically used to treat drug-susceptible TB, and include isoniazid (INH), rifampicin (RIF), pyrazinamide (PZA) and ethambutol (EMB), used in accordance with the Directly Observed Therapy Short-course (DOTS) program, as recommended by the WHO. The DOTS treatment program is a recommended six month therapy regimen, consisting of an initial two month (intensive) treatment phase using a combination of INH, RIF, PZA and EMB, followed by a four month maintenance phase, consisting of only INH and RIF (Olivier & Loots, 2011;

39 Chapter 2 Raviglione et al., 1992). Second-line drugs, on the other hand, are typically only used to treat drug-resistant M. tuberculosis strains, and include the fluoroquinolones, thioamides, aminoglycosides, polypeptides, D-cycloserine, terizidone and aminosalicylic acid (Caminero et al., 2010; Parida & Kaufmann, 2010).

When the DOTS program is fully adhered to, it has a high successful treatment rate in patients with drug-susceptible TB (World Health Organization, 2010). Despite this, however, almost 15% of the 10.4 million new TB cases reported annually, are treated unsuccessfully (World Health Organization, 2015a). Several factors, including varying individual metabolism and HIV co-infection, have been associated with unsuccessful treatment outcomes. Unsuccessful treatment, and the consequent development of drug-resistant TB, can also be ascribed to disruptions and/or early termination of this first-line treatment by the patient due to the severe side-effects associated with these drugs, or perhaps as a result of the limited access to the medical facilities where the drugs are administered in many third world countries (De Villiers & Loots, 2013). For MDR-TB cases, defined as resistance to both RIF and INH, the WHO recommends the addition of the more expensive and more toxic second- line drugs to this DOTS program, thereby extending the total therapy time to 24 months (known as DOTS plus) (World Health Organization, 2010). However, despite these treatment recommendations, globally only 125 000 of the 580 000 (20%) patients with confirmed MDR-TB actually received treatment in 2015, and of these, only 52% were reportedly treated successfully (World Health Organization, 2016). For the sake of brevity, only the first-line drugs will be discussed in this thesis, considering its relevance to the current investigation searching for metabolomics biomarkers related to explaining and predicting first-line treatment failure mechanisms in Chapters 5 and 6, respectively.

2.4.1 Isoniazid

Isoniazid (INH), also known as isonicotinylhydrazide, is a nicotinamide analogue with a structure comprising a pyridine ring and hydrazine group. This drug has been used since 1952 and has a minimum inhibitory concentration (MIC) of 0.02–0.20 μg/mL. Its mode of action is bactericidal on rapidly growing bacilli, with a diminished effect on slow- and intermittently growing bacilli (Arbex et al., 2010). The mechanism of action of INH action is based on the production of various highly reactive compounds, including reactive oxygen species, reactive organic species, and certain electrophilic species, which act on numerous targets in M. tuberculosis (Jena et al., 2014). INH penetrates host cells and passively diffuses across the M. tuberculosis membrane. Since it is a prodrug, it requires catalytic oxidative activation by the M. tuberculosis catalase-peroxidase enzyme, encoded by the M. tuberculosis KatG gene, using nicotinamide adenine dinucleotide (NAD), manganese ions and oxygen as cofactors in this reaction. INH activation produces organic free radicals and

40 Chapter 2 oxygen-derived free radicals, namely superoxide, hydrogen peroxide, peroxynitrite, and an isonicotinoyl radical or anion (Fernandes et al., 2017), the latter of which inhibits the FASII NADH-dependent 2-transenoyl acyl carrier protein (ACP) reductase (InhA). Inhibition of this enzymatic complex results in long-chain fatty acid accumulation, thereby inhibiting mycolic acid biosynthesis, which leads to DNA damage and M. tuberculosis death (Jena et al., 2014; Timmins & Deretic, 2006; Vilcheze & Jacobs, 2007). Additionally, the various highly reactive compounds and active metabolites of INH may inhibit other vital M. tuberculosis cellular pathways, including NAD metabolism, as well as nucleic acid and phospholipid biosynthesis (Arbex et al., 2010; Caminero et al., 2010; Chetty et al., 2016; Kolyva & Karakousis, 2012). A variety of mutations (insertions, deletions and point mutations) in the KatG gene and the intergenic region mabA-inhA of M. tuberculosis results in INH-resistance (50–80%), decreasing INH activity and preventing conversion into its active metabolite. Although many known mutations may result in catalase-peroxidase inactivation, Ser315Thr is the most commonly reported to date (Arbex et al., 2010; Caminero et al., 2010; Zhang & Yew, 2015).

In the patient, INH is metabolised in the liver by N-acetyltransferase 2 (NAT 2) acetylation, producing acetylisoniazid. This metabolite may be hydrolysed into two products namely; isonicotinic acid and acetylhydrazine, the latter of which is further hydrolysed into hydrazine, or acetylated into diacetylhydrazine (Tostmann et al., 2008a). The half-life of INH varies between 1–5 hours (depending on the genetic characteristic of each patient), after which most of the drug (70–96%) is excreted by the kidneys, predominantly producing inactive metabolites, and a small portion excreted in faecal matter (Arbex et al., 2010). Various adverse side-effects are associated with INH ingestion, including anti-TB drug-induced hepatotoxicity (ATDH)/hepatitis, rash, increased urination, severe dizziness/nausea, vomiting, fever, headache, drug-induced lupus erythematous, high anion gap metabolic acidosis, seizures (status epilepticus), mood changes (confusion, psychosis, depression), and pyridoxine (vitamin B6) depletion (resulting in CNS, peripheral neuropathy, effects and sideroblastic anaemia) (De Villiers & Loots, 2013).

2.4.2 Rifampicin

Rifampicin (RIF), a lipophilic ansamycin with a MIC for M. tuberculosis of 0.05–0.50 μg/mL, has been used for treating TB since 1966. RIF, like INH, also has bactericidal activity, and is considered the most important of all anti-TB drugs due to its ability to kill metabolically active M. tuberculosis, as well as those in the stationary phase (Arbex et al., 2010). Due to its lipophilic profile, RIF can easily diffuse across the M. tuberculosis cell membranes (Kolyva & Karakousis, 2012), after which it binds with high affinity to the β-subunit of DNA-dependent ribonucleic acid (RNA) polymerase, encoded by rpoB, inhibiting gene transcription of the infecting mycobacteria, preventing messenger RNA transcription and protein synthesis,

41 Chapter 2 resulting in cell death (Ahmad & Mokaddas, 2009; Arbex et al., 2010; Chetty et al., 2016). Resistance to RIF occurs mainly due to single point mutations in the 81-bp “hotspot” region of the rpoB gene (predominantly in codons 531, 526 and 516), which results in the ineffective binding of the drug to this region. Since mono-resistance to RIF is rare (90% of all RIF isolates are also resistant to INH), RIF-resistance has been used as a surrogate marker for detecting MDR-TB (Ahmad & Mokaddas, 2009; Arbex et al., 2010; Chetty et al., 2016; Kolyva & Karakousis, 2012; Zhang & Yew, 2015).

In the patient, approximately 85% of RIF is metabolised by the microsomal enzymes of the liver cytochrome P450 (CYP450) system, producing desacetylrifampicin, which in turn undergoes hydrolysis to produce 3-formyl rifampicin. RIF has a half-life of 2–4 hours, after which it is excreted via the biliary tract (60–65%), with about 6–15% excreted in its unmetabolised form, which may be reabsorbed in the intestine, and an even smaller portion of the unmetabolised RIF excreted in the urine (Arbex et al., 2010; Tostmann et al., 2008a). Various adverse side-effects are associated with RIF, including ATDH/hepatitis, upregulation of hormonal hepatic metabolism (decreasing hormone levels), flushing, pruritus, rash, breathlessness, cramps, diarrhoea, nausea, vomiting, fever, headache, arthralgia, malaise, dysphoria, immunological reactions, and colour change in bodily fluid (urine, tears, sweat) (De Villiers & Loots, 2013).

2.4.3 Pyrazinamide

Pyrazinamide (PZA), or pyrazoic acid amide, is an amide derivative of pyrazine-2-carboxylic acid and a nicotinamide analogue with a similar molecular structure to that of INH. This drug has been used to treat TB since 1952 and has a MIC for M. tuberculosis of 6.25–50.0 μg/mL and functions at a pH of 5.5. PZA has a potent sterilising effect with a unique ability to target and eliminate semi-dormant bacilli populations that reside in acidic environments, i.e. within macrophages and other sites of acute inflammation (Arbex et al., 2010; Kolyva & Karakousis, 2012). Although the mechanism of action of PZA is not yet fully understood, it is proposed to enter M. tuberculosis via an adenosine triphosphate (ATP)-dependent transport system, and is then converted to its active form, pyrazinoic acid, via the bacterial enzyme pyrazinamidase, encoded for by pncA. This metabolite accumulates in the bacterial cytoplasm due to an inefficient efflux system unique to M. tuberculosis, decreasing the intracellular pH of the infecting bacteria, thereby inactivating fatty acid synthase I, inhibiting mycolic acid and cell wall structure biosynthesis, reducing the pathogen’s membrane potential, inhibiting its ATP synthesis and ultimately resulting in bacterial death (Ahmad & Mokaddas, 2009; Arbex et al., 2010; Kolyva & Karakousis, 2012; Zhang & Yew, 2015). In 2011, Shi et al. identified M. tuberculosis RpsA (ribosomal protein S1) as the target of pyrazinoic acid, which inhibits trans-translation, a crucial process for freeing rare ribosomes

42 Chapter 2 in non-replicating bacilli (Shi et al., 2011). M. tuberculosis PZA-resistance is due to mutations in its pncA gene, encoding for the enzyme pyrazinamidase, which is responsible for converting PZA into its active form. Most of these mutations have been reported to occur in the 561-bp and 82-bp genome regions (Arbex et al., 2010; Kolyva & Karakousis, 2012). Additionally, Gopal et al. (2016) suggested that PZA-resistance may be caused by two distinct mechanisms, namely (a) mutations in aspartate decarboxylate panD that prevent coenzyme A depletion, and (b) the loss of virulence factor synthesis due to mutations in the phthiocerol dimycocerosate (PDIM) genes; mas (mycocerosic acid synthase) and ppsA-E (phenolthiocerol synthesis type I polyketide synthases) (Gopal et al., 2016).

In the patient, PZA is easily absorbed and distributed throughout the body, following oral administration. PZA is metabolised in the liver, and has a half-life of 9–10 hours, after which it is excreted in the urine through the glomerulus (70%, of which 3% is in its unmetabolised form) (Arbex et al., 2010). Various adverse side-effects are associated with PZA, including ATDH/hepatitis, pruritus (skin irritation), rash and/or urticarial (hives), dysuria (painful urination), interstitial nephritis (kidney inflammation), nausea, vomiting, fever, arthralgia (due to decreased uric acid secretion), malaise, and sideroblastic anaemia (De Villiers & Loots, 2013).

2.4.4 Ethambutol

Ethambutol (EMB), also known as dextro-2,2-(ethylenediimino)-di-1-butanol, has been used since 1966 as an anti-TB drug due to its bacteriostatic effect mainly on rapidly growing intra- and extracellular bacilli, with an MIC for M. tuberculosis of 1–5 μg/mL (Arbex et al., 2010; Kolyva & Karakousis, 2012). EMB interferes with the biosynthesis of arabinogalactan, the primary polysaccharide in the mycobacterial cell wall, via inhibition of three homologous, membrane-associated arabinosyl-transferase enzymes (encoded for by the embCAB genes), which mediate the polymerisation of arabinose into arabinogalactan (Ahmad & Mokaddas, 2009; Arbex et al., 2010). Furthermore, EMB has also been reported to inhibit RNA metabolism, mycolic acid transfer into the cell wall, phospholipid synthesis, and spermidine biosynthesis (Kolyva & Karakousis, 2012). In vitro resistance to EMB develops slowly and is usually associated with point mutations in the embCAB operon, with mutations in embB (in codons 306, 406 and 497) being most prevalent. Previous studies have linked the EmbA and EmbB proteins with the formation of the proper terminal hexa-arabinofuranoside motif during arabinogalactan synthesis, while EmbC is associated with lipoarabinomannan synthesis (Ahmad & Mokaddas, 2009; Kolyva & Karakousis, 2012; Zhang & Yew, 2015).

Following oral administration, 75–80% of the EMB is absorbed in the gastrointestinal tract of the patient and distributed throughout the body (with the exception of the cerebrospinal fluid

43 Chapter 2 (CSF) in patients without meningitis). Approximately 8–15% of the total dose of EMB administered is metabolised (oxidised) in the liver to form an aldehyde intermediate, which is then converted into a hydrophilic dicarboxylic acid; 2,2’-(ethylenediimino)-di-butyric acid. The half-life of EMB is 3–4 hours, after which the majority of the drug (50–80%) is excreted in the urine, and approximately 20% excreted in faecal matter (Arbex et al., 2010). Various adverse side-effects are associated with EMB, including ATDH/hepatitis, milk skin reaction, retrobulbar/optic neuritis, red-green colour blindness, vertical nystagmus, hyperuricaemia, arthralgia, and peripheral neuropathy (De Villiers & Loots, 2013).

2.4.5 Using metabolomics to better explain anti-tuberculosis drug action and drug metabolism

Although the DOTS and DOTS plus TB treatment regimens are considered effective when fully adhered to by the patient, the exact mechanisms of action and xenobiotic metabolism of each of these drugs, in addition to the molecular mechanisms responsible for mycobacterial dormancy (suspending or temporarily stopping all physiological functions, i.e. latent infection), persistence (inability rid the host of metabolically active bacteria despite treatment) and drug-resistance, remain poorly understood (Lipworth et al., 2016; Van den Boogaard et al., 2009). By using a global metabolomics approach, hundreds of different compounds can be analyzed and identified simultaneously, allowing for the discovery of new enzyme activities and metabolic pathways (Prosser et al., 2014). A number of research groups to date have applied this approach towards a better understanding of first/second-line TB drug action and metabolism, as well as to investigate the therapeutic characteristics of new TB drug candidates, with the hope that such insights may eventually lead to the development of more effective, less toxic, shortened treatment regimes.

Li et al. (2011) applied an ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOFMS) metabolomics approach to characterize INH metabolism in humans. When comparing the urinary metabolite profiles obtained from healthy controls to those from INH-administered healthy volunteers, eight known and seven new INH-derived metabolites were identified, the latter of which comprised of two new hydroxylated INH metabolites (2-oxo-1,2-dihydro-pyridine-4-carbohydrazine and isoniazid N-oxide) and five new hydrazones (INH conjugated to 4-methyl-2-oxopentanoic acid and/or methyl-2- oxopentanoic acid; INH conjugated to 2-oxohexanedioic acid; INH conjugated to 3-(4- hydroxyphenyl)-2-oxopropanoic acid; INH conjugated to 2-oxo-3-phenylpropanoic acid; and INH conjugated to 3-(1H-indol-3-yl)-2-oxopropanoic acid). The hydrazones are intermediates of the leucine and/or isoleucine, lysine, tyrosine, tryptophan and phenylalanine metabolic pathways, suggesting a disruption in the essential amino acid metabolism as an additional mechanism by which INH inhibits M. tuberculosis growth (Li et al., 2011). Similarly,

44 Chapter 2 Nandakumar et al. (2014) used LC-TOFMS metabolic profiling to identify metabolic changes in replicating M. tuberculosis populations induced by a sub-lethal dose of INH, RIF and streptomycin, respectively. By applying unsupervised hierarchical clustering analysis, a subset of drug-induced metabolome changes common to all three antibiotics was identified. These changes represented the tricarboxylic acid (TCA) cycle, glyoxylate metabolism and biosynthetic pathways of various amino acids. With regards to the TCA intermediates, pyruvate, succinate, fumarate and malate levels were seen to be elevated subsequent to drug exposure, whereas a-ketoglutarate levels was reduced, and citrate/isocitrate levels remained unchanged. This result suggests that despite the previously known diverse bacterial targets of these anti-TB drugs, all three also result in the activation of isocitrate lyases (ICLs). These findings were further substantiated by fact that the ICL-deficient M. tuberculosis strains are significantly more sensitive to these anti-TB drugs. Additionally, this increased susceptibility could be chemically restored using antioxidants, confirming that the observed TCA changes are indicative of an adaptive respiratory slowdown caused by an antibiotic-triggered increase in the production of respiratory-derived reactive oxygen intermediates. This metabolomics approach therefore identified a new mechanism by which first-line TB drugs may act on M. tuberculosis, thereby suggesting an alternative strategy which may be exploited in the development of more effective anti-TB drugs (Nandakumar et al., 2014).

With application of metabolomics to investigating second-line TB drug action, Halouska et al., (2007) used a NMR metabolomics approach to identify biomarkers which may explain the mechanisms by which D-cycloserine (DCS), a MDR anti-TB drug, functions. Although DCS was previously reported to inhibit both D-alanine racemase (Alr) and D-alanine-D-alanine ligase (Ddl) activity in vitro, its in vivo activity against M. tuberculosis was not yet confirmed. Therefore, they compared the NMR metabolite profiles from a wild-type M. smegmatis strain to that of an alr null mutant, its complemented derivatives, and two DCS-resistant mutant strains. The metabolite changes identified when comparing the complemented derivatives, indicated that a second D-alanine pathway exists in the alr null mutants, which appears to persist after exposure to DCS. PCA differentiation of the alr null mutant and its complemented derivatives was, however, lost after DCS exposure, which suggests that the multiple DCS targets (Alr and Ddl) determined in vitro, are also present in vivo. Clustering of the DCS-resistant strains with the complemented derivative strains suggests that DCS treatment also results in inhibition of the cell wall enzymes, with minimal anti-microbial impact however, and that Ddl appears to be the primary in vivo lethal target of DCS (Halouska et al., 2007). In a follow-up metabolomics study, the same group confirmed these in vivo drug targets by indicating that cell growth is inhibited when D-alanyl-D-alanine production is halted (Halouska et al., 2013). Prosser and De Carvalho (2013) later confirmed these results using a more direct ex vivo stable isotope metabolomics approach, identifying Ddl as a possible

45 Chapter 2 target for future drug development initiatives against M. tuberculosis (Prosser & de Carvalho, 2013).

Although there are currently a number of new potential anti-TB drugs undergoing phase II and III preclinical trials, delamanid and bedaquiline are the only newly approved anti-TB drugs in over 50 years. These drugs, however, are currently only used for treating adults with MDR-TB when no other alternatives show effect (Zumla et al., 2013). Considering the urgent need for new anti-TB drugs and alternative TB treatment approaches, De Carvalho et al. (2011) used a targeted LC-MS metabolic profiling method to investigate the potential mechanisms of drug action of nitazoxanide (NTZ) and tizoxanide (TIZ) (two broad spectrum anti-parasitic drugs) against M. tuberculosis. The results of their metabolomics investigation proved that both compounds are capable of penetrating and accumulating in M. tuberculosis cells, and act against replicating and non-replicating populations by disrupting the membrane potential and pH homeostasis. This mechanism of action is identical to that of an antimycobacterial drug with a similar chemical structure, niclosamide (NCS), which is an oxidative phosphorylation uncoupler. This study shows how metabolomics can be used in the development of more potent and bioavailable anti-TB drug analogues of NTZ and/or TIZ (De Carvalho et al., 2011), and similarly to both Halouska et al., (2007) and Prosser and De Carvalho (2013), they proved the capability for using metabolomics to successfully elucidate previously unknown mechanisms of drug action.

A comprehensive review of this topic and how metabolomics has and can be used towards improved TB treatment strategies was written by the author, and attached in Appendix D. One of the objectives of this study, however, was to use metabolomics to identify biomarkers better describing and predicting treatment failure, hence a comprehensive background to this will be given in the next section of this literature chapter.

2.5 TUBERCULOSIS TREATMENT FAILURE

TB treatment failure can be defined as the occurrence of persistently positive sputum smears despite treatment (Mukherjee et al., 2004). Treatment failure may be attributed to a long list of factors, including: (a) irregular or inadequate anti-TB drug supplies by health providers, (b) a lack of patient TB-education, (c) poverty and poor life quality (further resulting in transport problems to the sites of treatment administration), as well as (d) long treatment duration. Additionally, and more relevant to this investigation, are the various biochemically related factors contributing to a poor treatment outcome, including: (a) variable individual metabolism of the anti-TB drugs (including variation in xenobiotic metabolism, drug absorption and drug-drug interactions), (b) drug-resistance and pathogenicity by the infectious organism, and (c) non-adherence to the treatment program, especially due to the 46 Chapter 2 associated side-effects (Adhvaryu & Vakharia, 2011; Du Preez & Loots, 2012; Mukonzo, 2011; Quy et al., 2003; Wares et al., 2003). These proposed mechanisms are, however, not yet fully understood. Considering this, new research approaches, such as metabolomics, are currently being used to increase our understanding of the above mentioned biochemical mechanisms related to treatment failure, and could contribute significantly towards improved TB control, and will be discussed in greater detail below.

2.5.1 Variable individual metabolism

2.5.1.1 Xenobiotic metabolism

Xenobiotic metabolism refers to a natural response by which the body modifies a foreign compound (such as drugs) by enzymatic/biochemical reactions (biotransformation), in order to detoxify the body, by eventually eliminating these exogenous substances. During this process, many drugs may, however, be converted to their active states. Furthermore, the duration and intensity of drug action may also be influenced by the rate of its metabolism/biotransformation, which may vary between individuals. These metabolised compounds also serve as “signals” for various pathophysiological outcomes (Chasseaud & Hawkins, 1990; Chen et al., 2007; Liska, 1998), such as explaining or predicting a response to the medication and of treatment outcome. Consequently, identifying xenobiotic metabolites and their related metabolic pathways are considered essential to understanding drug action and their associated side-effects. Variability in individual xenobiotic metabolism has remained a major challenge for pharmacology research. It is important to understand why individual drug responses differ, and to what extent this variability is responsible for the differences we observe in therapeutic efficacy and tendencies towards adverse reactions (Lu, 1998). To date, research has shown that individual variability of xenobiotic metabolism and their associated side-effects are influenced by a number of factors, including genetics, race, ethnicity, gender and external stimuli such as diet, alcohol consumption, drug-drug interactions, etc. (Matthews, 1995).

The current knowledge pool pertaining to the functionality of anti-TB drugs is considered insufficient, as their exact mechanisms of action, especially in the human host, and on the bacteria, are still largely unclear (Böttger, 2011; Olivier & Loots, 2011). This is most probably a major contributor to the lack of development of new anti-TB drugs over the last five decades (only two new drugs have been approved by the Food and Drug Administration (FDA) since 1962), and the rising threat of drug-resistant strains. Thus, it is clear that new strategies are urgently required for better understanding the current in vivo host metabolism, anti-TB drug mechanisms and drug-drug interactions (Halouska et al., 2012), which when elucidated, would undoubtedly lead to improved patient prognosis, as measures could then

47 Chapter 2 be put into place, using the knowledge gained in order to prevent or lower the prevalence of treatment failure. To date, a number of examples of this already exists, where metabolomics is not only being used for the identification of new metabolite markers and altered patient metabolite profiles, shedding light on new mechanisms of TB disease and anti-TB drug action, but when combined with other “omics” approaches, allows for a more in-depth and holistic understanding of the research question (Olivier & Loots, 2011). This approach could potentially also be used for identifying markers which could predict individual drug response phenotypes (pharmacometabolomics), as well as in diagnostic applications for the early prediction of treatment failure.

Three approaches are suggested, focusing on individual xenobiotic metabolism research, aimed at resolving practical issues in drug metabolism and toxicology. Firstly, metabolomics could typically be used to better elucidate drug metabolism by identifying metabolites differentiating two groups, one receiving the xenobiotic and another not, after which the biological samples (urine, blood, tissue, saliva, faeces, etc.) are collected for comparison of the resulting difference in the metabolite profiles using a selected analytical method. When comparing these groups, in theory, all the additional compounds or alterations to the metabolite profile in the xenobiotic group in comparison with the control, would be present because of the exogenous compound ingested/injected (Chen et al., 2007). These differences can broadly be visualised using a multivariate, unsupervised scores plot (such a PCA) and then, if a natural differentiation is achieved, those compounds/xenobiotic metabolites best explaining or contributing to the differences, identified using a variety of supervised multivariate (such as PLS-DA) in conjunction with other univariate methods (such as t-test and effect sizes) (Loots et al., 2013). For example, Loots et al. (2005) conducted a rat-model metabolomics study, based on a similar study design, in order to evaluate the effect of combined anti-TB drug (Rifater) therapy, on the free radical and organic acid profiles of Sprague-Dawley rats, to get a better understanding of their impact on normal metabolism and potentially better explain their associated side-effects. These metabolic effects were also compared to an additional treatment group receiving a combined treatment of Rifater and melatonin. In their study, they determined that the combined anti-TB drug Rifater results in oxidative stress and a metabolite profile associated with individuals suffering from a multiple acyl-CoA dehydrogenase deficiency (MADD), and eventually linked this to the drug causing inhibition of the electron transport flavoproteins, in addition to an explanation of a number of side-effects, which could be corrected for by the co-administration of melatonin (Loots et al., 2005). This approach may however be limiting if the differences detected are due to both exogenous and endogenous xenobiotic metabolites, as was the case in Loots et al.’s study. One is, however, able to sift out these differences using libraries specific towards identifying either the drug metabolites, or the altered natural metabolome of the organism. The advantage of this approach is that it may potentially describe alternative pathways

48 Chapter 2 influenced by the drugs, better explaining the side-effects. Considering this, Chen et al. (2007) proposed a stable isotope-labelled xenobiotic treatment group comparative to an unlabelled group, as an alternative to this approach, and in so doing, one would be able to track the drug specific metabolites and their associated metabolism and also elucidate which metabolites are directly linked to the xenobiotic metabolism, and which are due to secondary toxicity (Chen et al., 2007; VandenHeuvel, 1986). Lastly, Chen et al. (2007) propose a study design to identify human xenobiotic-metabolising enzyme (XME) polymorphisms responsible for adverse drug reactions (ADRs), which can be used for investigating anti-TB drug side- effects, or an individual’s response to anti-TB drugs. ADR may also occur due to various factors relating to a patient’s general health status, various environmental contaminants and as a result of drug-drug interactions with other co-administered drugs requiring the same enzyme systems for metabolic processing (Chen et al., 2007). In these cases, ADR may largely be caused by genetic polymorphisms of XMEs, or other factors influencing enzymatic activities or their expression, such as an individual’s general health, environmental contaminant exposure, cultural and/or genetic factors, and the previously mentioned drug- drug interactions. If ADRs are observed in a patient group, suspected to be due to XME polymorphisms, an investigation into the metabolite profiles and pharmacokinetics would be of great importance, and could potentially identify the metabolic pathways affected and pinpoint the genetic factors causing these ADRs. This in turn may lead to the necessary adaptations to the medication and ultimately individualised treatment procedures (Chen et al., 2007; Evans & Relling, 1999; Matthews, 1995).

2.5.1.2 Drug malabsorption

Although rare, malabsorption of anti-TB drugs may also lead to a poor treatment outcome. Probable reasons for malabsorption, although poorly characterised, include comorbidities of TB with HIV/AIDS infection, hypoalbuminemia in the malnourished, infectious gastroenteritis, gastric achlorydria, gastrointestinal and/or malabsorption diseases, thyroid and end-stage liver or renal diseases. Although either a single or combination of these factors may contribute to drug malabsorption, HIV/AIDS infection is considered by far the most common risk factor associated with malabsorption of anti-TB drugs (Bento et al., 2010; Gurumurthy et al., 2004).

It has previously been reported in the literature that HIV/AIDS infection may lead to anti-TB drug malabsorption and acquired drug-resistance (Gurumurthy et al., 2004; Wells et al., 2007). This was based on the observation that 68% of all patients with reduced serum RIF and INH levels were co-infected with HIV (Bento et al., 2010; Wells et al., 2007). As both RIF and INH reach maximum serum concentrations 2 hours after oral administration, Bento et al. (2010) proposed taking two measurements; 2 hours and 6 hours after administration, in

49 Chapter 2 order to determine if a patient has malabsorption (indicated by persistently low concentrations) or delayed absorption (slowly increase concentrations) (Bento et al., 2010), for the purpose of adapting the treatment protocols. However, this approach may be costly and have significant health policy implications. Alternatively, it was proposed that urinary RIF and INH levels could be monitored, providing important information related to drug absorption. Considering this, Gurumurthy et al. (2004) investigated the absorption of RIF and INH in patients with HIV/AIDS and diarrhoea, TB/HIV co-infection, pulmonary TB alone, and healthy subjects, by analysing the urinary excretion of these drugs and the associated metabolites. Their investigation indicated that TB-positive patients, without an underlying disease or illnesses, absorb anti-TB drugs reliably, while those with gastrointestinal disorders or HIV/AIDS infection have less optimal absorption (Gurumurthy et al., 2004).

Although no metabolomics investigations on this topic have been done to date, this research approach could be used/contribute to it in the following manner. A comparison of the metabolomes of those individuals grouped as good absorbers vs. poor absorbers, using blood/urine collected at time of diagnosis (prior to commencing treatment), may better explain the underlying mechanisms associated with treatment outcome, be it by underlying disease mechanisms or genetic factors. This information could be used to better explain this phenomenon, or alternatively be used diagnostically to identify malabsorbers prior to treatment, so that the necessary adaptations can be made, i.e. personalised treatment approaches. Similarly, these groups can be compared during treatment using a number of methodological approaches and possibly lead to a better understanding of potentially varying drug metabolism between these individuals.

2.5.1.3 Drug-drug interactions

Drug-drug interactions are especially prevalent in TB patients co-infected with HIV/AIDS, as INH and RIF in particular have inauspicious drug interactions with many of the antiretroviral drugs used to treat HIV/AIDS (Adhvaryu & Vakharia, 2011). These unwanted drug-drug interactions (inhibition or induction) are thought to result in (a) lowered efficacy of co-administered drugs, leading to a poor treatment outcome and drug-resistance by the organism, and also (b) increased risk of toxicity and hence, treatment interruptions (Dean et al., 2002), also leading to drug-resistance.

These drug-drug interactions are thought to occur due to these different medications all being metabolised by the same CYP450 enzyme system (Wong et al., 2013). The CYP450 enzymes refer to a group of important XMEs, and are especially important for the metabolic activation and metabolism of several drugs. INH is known to permanently inhibit several of the CYP450 enzymes, and this state persists until new CYP450 enzymes are synthesised.

50 Chapter 2 Side-effects due to INH drug interactions may include an increased risk of hepatotoxicity, hypertension, flushing and tachycardia. Excess catecholamine (a dopamine precursor), and elevated levels of other medications, may also be present due to inefficient metabolism of these drugs when INH is present (Cheung & Gonzalez, 2008; King & Brucker, 2010).

On the other hand, an increased hepatic blood flow or CYP450 synthesis, leads to enzyme induction, as is the case with RIF administration (Michalets, 1998), where this reaction is mediated through the activation of the pregnane-X receptor by RIF (Ma et al., 2010), reducing the therapeutic effects of numerous other co-ingested medications metabolised through this enzyme system. This may further lead to complicated health consequences when RIF is taken in combination with other common drugs, such as oral contraceptives, warfarin (Coumadin) or sulfonylureas. Furthermore, RIF is a known inducer of cytochrome P450 (CYP) 3A, reflected by independent regulation of CYP3A expression at various sites, with large inter-individual variation observed (Gorski et al., 2003). Gorski et al. (2003) investigated CYP3A susceptibility to RIF induction, as a function of age and gender, with midazolam, a CYP3A substrate. RIF significantly increased both the systemic and oral clearance of midazolam and reduced the oral midazolam availability by 88%. Interestingly, gender also had a significant effect on induction of oral midazolam clearance, as this was found to be greater in males when compared to females, largely due to the fact that these gender-related differences in response to CYP3A inducers are substrate-dependent (Gorski et al., 2003). RIF related drug-drug interactions are also reported to lead to an increased risk of aplastic anaemia and hypoglycaemia. Lowered levels of other medications in the blood are commonly reported, thus dosages of these may need to be increased to avoid lowered efficacy when given in combination to RIF (King & Brucker, 2010; Michalets, 1998). For example, verapamil is shown to be decreased 10–20 fold when co-administered with RIF. On the other hand, however, recent studies have shown the potential of verapamil to be used as an effective efflux inhibitor in many organisms, including M. tuberculosis, thereby reducing the duration of anti-TB treatment and the emergence of drug-resistance (Gupta et al., 2013). These drug-drug interactions may persist, depending either on the half-life of the inducer or the rate of enzyme degradation and new enzyme synthesis. Considering the short half-life of RIF and the 1–6 day turnover time of CYP450 enzymes, RIF elimination is more rapid than that of excess CYP450, thus interaction duration would be dependent on enzyme turnover (Michalets, 1998).

Bedaquiline (TMC207), a diarylquinoline, was recently introduced as a new anti-TB drug for treating pulmonary MDR-TB in adults. Bedaquiline has specific ATP synthase inhibition activity against M. tuberculosis, affecting the energy production required for cell viability, acting as a bactericidal and sterilising agent against M. tuberculosis and various other mycobacterial species, including, amongst others, the M. avium complex, M. chelonae and

51 Chapter 2 M. kansasii (Palomino & Martin, 2013). Based on clinical evaluations of its safety, tolerance and efficacy, the process for approving bedaquiline for treating pulmonary MDR-TB in adults has been accelerated (Matteelli et al., 2010; Palomino & Martin, 2013). Bedaquiline’s long half-life and unique mechanism of action potentially makes it suitable for intermittent administration, which is a major advantage compared with RIF, for instance. However, one of the major concerns of this drug is its drug-drug interactions with RIF, as co-administration with RIF has been shown to halve the maximum bedaquiline concentration. As bedaquiline is metabolised by CYP3A4, producing the less-active N-monodesmethyl bedaquiline, any CYP3A4 inducer, such as RIF, will affect the final concentration of bedaquiline (Palomino & Martin, 2013). Another concern is the possible drug-drug interactions between bedaquiline and antiretroviral agents (ARV’s), when treating TB/HIV co-infected patients. As current studies devoted to the use of bedaquiline in these cases are still underway, no clinically significant findings have been suggested to date (Matteelli et al., 2010; Palomino & Martin, 2013).

As anti-TB drug-induced toxicity may have a major influence on other medications used simultaneously, the need to predict ADRs as early as possible during the drug treatment regimen is crucial. Metabolomics can provide the means for a global overview of individual variation and the prediction of potential drug-drug interactions, via analyses of urinary metabolites after drug administration, which can serve as a signature for organ-specific drug toxicity, general activity or inactivation (Chen et al., 2007; Clayton et al., 2006; Idle & Gonzalez, 2007). Metabolomic profiling has already been shown to predict drug responses in animal models (Clayton et al., 2006), however, as xenobiotic metabolism differs between different species, particularly due to molecular variation in receptors and XMEs, it presents a problem when it comes to extrapolating data obtained from rodent models (especially rats and mice) to humans (Gonzalez & Yu, 2006; Idle & Gonzalez, 2007). Thus, more reliable in vivo systems, such as humanised mouse models, need to be generated in order to study and potentially predict human xenobiotic responses (Gonzalez & Yu, 2006). Currently, the development of animal models as a tool to simulate human xenobiotic metabolism is expanding rapidly (Tateno et al., 2004). Considering this, humanised transgenic mice can be generated by introducing a specific human gene into the rodent genome, of which many genetically-modified mouse model systems for several important nuclear receptors (including AhR, PXR, CAR) have been established to date, which not only offer a better animal system for studying the influence of drug-drug interactions on xenobiotic metabolism, and the consequent prediction of human xenobiotic responses, but also facilitates the elucidation of the related underlying drug mechanisms (Chen et al., 2007; Gonzalez & Yu, 2006). An additional example of this is the Kramnik mouse model (mice harboring the C3HeB/FeJ genotype), in which necrotic lung lesions are generated. Following M. tuberculosis infection, animals may develop necrotic lung lesions, where drug-resistant bacilli are typically present,

52 Chapter 2 persisting in an extracellular micro-environment within these lesions (Driver et al., 2012; Kaushal, 2012). Using this model, Driver et al. (2012) examined the efficacy of drug treatment by comparing the formation of colliquative necrotic lesions in Kramnik mice to that of BALB/c mice, with non-necrotic lesions, following aerosol challenge. This was accomplished by infecting both the Kramnik and BALB/c mice with M. tuberculosis bacilli, followed by 7–8 weeks of monotherapy using drugs with varying modes of action. Drug efficacy was quantified by determining the bacterial load, and disease progression visualised via staining methods. In the Kramnik mice, results indicated the formation of fibrous encapsulated lung lesions with central colliquative necrosis having abundant extracellular bacilli, in the late stages of infection, while the BALB/c mice formed non-necrotic lesions, primarily infected with intracellular bacilli. Furthermore, the Kramnik mice had indications of hypoxia within the necrotic lesions, and significantly more anti-TB drug-resistant colonies, especially for PZA, compared to the BALB/c mice (Driver et al., 2012). As the Kramnik mouse model is considered a reliable representation of human TB, these results not only indicate the suitability of the Kramnik mouse model for testing anti-TB drug efficacy, especially for drug-resistant M. tuberculosis strains, allowing for validation of possible candidate anti-TB drugs and vaccine selection, but also the capacity to develop a TB mouse model with a disease state more closely representing that of human TB. This model is advantageous when wanting to elucidate the mechanisms of M. tuberculosis pathogenesis (Driver et al., 2012; Kaushal, 2012), and could also potentially also be used in various metabolomics investigations.

To date, xenobiotic receptor-mouse models have also been developed to closely resemble the human metabolome, and have been used to better elucidate the metabolic pathways leading to drug toxicity and drug-drug interactions, aiding in the identification of potential drug biomarkers in humans. In these instances, the unique combination of humanised mouse models subjected to metabolomic analysis, in conjunction with multivariate analysis, is providing answers to key questions related to human health and disease questions (Chen et al., 2007; Cheung & Gonzalez, 2008; Idle & Gonzalez, 2007). Most ideal, however, is the use of metabolomics to investigate these potential drug-drug interactions, using clinical samples of patients already being treated for one or more co-infections. This will allow for the identification of drug-drug interaction markers relating to drug efficacy and/or side-effects in humans.

2.5.2 Drug-resistance and pathogenicity by the infectious organism

As previously mentioned, the DOTS program is based on administering four of the first-line anti-TB drugs, namely INH, RIF, PZA and EMB, simultaneously. To summarise briefly, INH has high bactericidal activity and is used to efficiently and specifically inhibit active growing

53 Chapter 2 M. tuberculosis (Carlton & Kreutzberg, 1966). RIF is considered a powerful sterilising drug, responsible for eliminating active and semi-dormant M. tuberculosis from the host (Ahmad & Mokaddas, 2009), as its primary mechanism of action is transcription inhibition (Ahmad & Mokaddas, 2009; Gumbo et al., 2009). PZA is also classified as a sterilising drug and functions by eliminating semi-dormant M. tuberculosis bacilli located within host macrophages (Ahmad & Mokaddas, 2009; Gumbo et al., 2009). EMB is classified as a bacteriostatic agent and typically adds additional coverage to reduce the development of drug-resistance (Ahmad & Mokaddas, 2009; King & Brucker, 2010), and is considered effective against both intra- and extracellular M. tuberculosis bacteria (Olivier & Loots, 2011).

Considering the limited information surrounding mechanisms by which drug-resistance occur, one of the few metabolomics investigations published regarding drug-resistance to anti-TB drugs was done in 2012, by Du Preez and Loots. Using GC-MS, they investigated an altered fatty acid metabolome of two rpoB mutant M. tuberculosis strains by comparison to that of an isogenic drug-sensitive wild-type parent strain, in order to characterise RIF-resistance due to mutations in the rpoB gene. Metabolite markers best describing the variation were identified using multivariate statistical approaches, namely PCA and PLS-DA. This study was the first to identify a link between an altered fatty acid metabolism and RIF-resistance, rpoB mutations, and the β-subunit of RNA polymerase, present in M. tuberculosis. In short, they determined that the mutation in the rpoB gene of M. tuberculosis, resulting in RIF-resistance, influences mycolic acid synthesis by disruption of the mRNA:NTP (ribonucleoside 5’-triphosphotases) equilibrium. This was confirmed by the lack of detection of 10-methyl branched chain fatty acids in the RIF-resistant mutants, accompanied by the accumulation of their fatty acid substrates. Additionally, these mutants displayed greater dependence on fatty acids and acetate as altered energy sources, and the resulting upregulation of the glyoxylic acid cycle. This study not only indicated the potential of pharmacometabolomics for the identification of metabolites associated with drug-resistance, but also mapped its potential for global health and personalised medicine (Du Preez & Loots, 2012). In view of the above, the methodology described by Du Preez and Loots (2012), could furthermore be applied to investigating the mechanisms of drug-resistance of M. tuberculosis to the other first-line, or even second-line drugs. The elucidation of xenobiotic-related metabolites in these organisms, as opposed to those previously described for in the host, by comparison of drug- sensitive and drug-resistant strains, with low doses of these drugs in the growth media, could provide further clues pertaining to the biotransformation of xenobiotics in these organisms, and shed light on additional metabolic pathways (and ultimately genes) involved in drug- resistance.

In the following year, Chakraborty et al. (2013) conducted a metabolomics approach proving that para-aminosalicylic acid (PAS) does not inhibit M. tuberculosis growth by its inhibiting

54 Chapter 2 action on dihydropteroate synthase (DHPS). PAS was previously thought to act as a competitive inhibitor of DHPS by acting as an alternative substrate, in competition with p-aminobenzoate (PABA), in folate metabolism. They additionally indicated that other sulfonamides are ineffective against M. tuberculosis due to inactivation by bacterial metabolism, rather than inadequate uptake. As new approaches are needed to improve our understanding of drug action and its efficacy for drug development, this discovery highlights that catalysis by an enzyme, rather than the inhibition thereof, could possibly be the key to new drug development (Chakraborty et al., 2013).

Furthermore, from a growth and pathogenesis perspective, bacteria are known to utilise catabolite repression in order to maximise growth, via consumption of host carbon substrates, in a preferred sequence. A metabolomics study conducted by de Carvalho et al. (2010) revealed that M. tuberculosis is capable of compartmentalised/simultaneous co-catabolism of multiple carbon sources/substrates, enhancing its growth. These carbon sources can be catabolised through the glycolytic, tricarboxylic acid, and/or pentose phosphate pathways, a phenomenon related to a previously undescribed bacterial metabolic network, and possibly linked to the pathogenicity of M. tuberculosis. Thus, using metabolomics, new knowledge was obtained, reshaping our basic understanding of M. tuberculosis and its ability to adapt to the host for survival (De Carvalho et al., 2010).

Additionally, a GCxGC-TOFMS metabolomics study conducted by Meissner-Roloff et al. (2012) compared the metabolomes of a hyper- and hypo-virulent Beijing M. tuberculosis strain, attempting to characterise the varying degrees of virulence within the M. tuberculosis species, and consequently identified metabolite markers best differentiating these two strains. This pilot study was the first of its kind to identify metabolites associated with an increased metabolic activity, cell wall synthesis, replication rates, and an altered anti-oxidant mechanism, which is thought to result in the increased pathogenicity and survival of this hyper-virulent strain of M. tuberculosis (Meissner-Roloff et al., 2012).

In light of the above, a better understanding of the metabolome of the infectious organism, be it drug-sensitive or drug-resistant, in response to a drug, may potentially lead to improved treatment approaches, and to either preventing drug-resistance or to the development of alternative drugs for treating the disease (Chen et al., 2007). With the emergence of MDR-, XDR-, and more recently even TDR-TB, the robustness of our approaches to discover and develop new, more effective anti-TB drugs is questionable. The discovery and development of new anti-TB drugs are largely dependent on our understanding of the complex host- pathogen interactions (Duncan, 2003), xenobiotic metabolism by both the host and bacteria, the specific drug action, their side-effects, etc.

55 Chapter 2 Furthermore, another shortcoming is that of translating basic research into drug discovery programs. To date, various genetic tools have been developed for manipulating M. tuberculosis to identify specific drug targets, but few candidates have emerged. Once validated drug targets are identified, lead compounds acting against these targets should be established, in order for medicinal chemistry to utilise these leads in the development of new drugs. Additionally, mycobacterial persistence/resistance models need be characterised for rapid analyses of compounds with potent sterilising activity against M. tuberculosis. Finally, the identification of metabolite markers providing an early indication or prediction of treatment outcome could facilitate clinical trials tremendously (Duncan, 2003).

Unfortunately, drug discovery and development is a lengthy process, especially in the case of those developed against M. tuberculosis, and resources remain limiting. Although the estimated cost of drug development may vary, it is estimated that the full development of a new anti-TB drug, could cost up to approximately 100 million US dollars. In 2000, The Global Alliance for TB Drug Development (GATB) was established, in order to address these issues. The aims of this alliance is to overcome natural barriers in the field of TB drug development, by forming partnerships between various diverse organisations, such as academic institutions, the pharmaceutical industry, government research laboratories, and non-governmental organisations (Duncan, 2003). The objectives of the GATB include: (a) reducing the current duration and complexity of treatment and/or the doses needed to cure active, drug-sensitive TB, (b) improving the efficacy of MDR-TB treatment, (c) reducing the treatment period of latent TB infection, and (d) simplifying the treatment of TB/HIV co- infected patients, by lowering the incidence of drug-drug interactions, especially those associated with ARV’s (Duncan, 2003; Ginsberg & Spigelman, 2007). These will not only result in reduced side-effects, better patient adherence and lower the costs of delivering DOTS, but will also ultimately have a major impact on treatment outcome. The challenges for reaching these goals include: (a) elucidating the biological mechanisms of mycobacterial resistance and latency, (b) discovery and development of new anti-TB drugs with novel mechanisms of action, (c) developing and validating accurate animal models that can reliably be translated to humans, (d) developing and validating metabolite markers for predicting treatment outcome, possibly reducing clinical trial duration, (e) identifying optimum drug combinations, and (f) the means to conduct clinical trials in high-burden TB countries (Ginsberg & Spigelman, 2007). Thus, in order to reach these GATB goals, a significantly increase in effort and resources is required at every stage of the drug discovery and development process. Furthermore, optimisation of these programs will ensure that valuable research is not wasted and potential new anti-TB drugs missed, due to attrition in the development process (Duncan, 2003).

56 Chapter 2 2.5.3 Non-adherence due to the associated side-effects

According to numerous studies, reasons for non-adherence of patients to the anti-TB treatment program includes, amongst others, drug-related side-effects (especially nausea, vomiting, giddiness), initial relief from TB symptoms, alcohol consumption, work or domestic related problems, being too ill to get to treatment points, or the stigma associated with having TB (Jaggarajamma et al., 2007). However, the anti-TB drug side-effects are most often associated with non-adherence, leading to treatment failure and drug-resistance (Duncan, 2003), and thus this aspect of non-adherence is of biological mechanistic relevance, topical for this thesis.

According to a 2008 WHO bulletin, the side-effects associated with anti-TB drug use include hepatotoxicity/hepatitis (liver inflammation), jaundice, exanthema (rash), dyspepsia (indigestion) and arthralgia (joint pain), and are considered the major factors leading to non- adherence. According to this bulletin, 23% of all TB patients terminated or defaulted treatment due to one or more of these side-effects. Furthermore, up to 86% of all patients may develop these side-effects, especially those co-infected with HIV or with a history of hepatitis (Awofeso, 2008). TB treatment regimens require a high (>90%) compliance rate in order to achieve a successful treatment outcome (Amuha et al., 2009). Amuha et al. (2008) conducted a study aimed at determining the prevalence and factors associated with non-adherence to anti-TB drugs. An interviewer administered questionnaire was used for qualitative data collection, and revealed that most patients tend to terminate treatment when they start to feel better, or due to the severe side-effects and pill burden, as these drugs may be considered by the patient to be too many to take at once (Amuha et al., 2009). Although the aim of TB control is that trained health staff is able to recognise and manage these side-effects, it would be best if these could be eliminated entirely. Of further concern, however, are the additional expenses which may be incurred by supplying complementary medication to alleviate these associated side-effects (Awofeso, 2008).

To the best of our knowledge, only one such metabolomics study has been done to date. As described earlier, in 2005, Loots et al. conducted a metabolomics investigation into the side- effects associated with the oral administration of the combined anti-TB drug Rifater, and alleviating them using combination therapy with melatonin, in a Sprague-Dawley rat model. Rifater was seen to not only result in increased free radicals, but also induce increased organic acid levels characteristic of a MADD metabolic profile, which includes elevated levels of isovalerylglycine, ethylmalonic acid, butyrylglycine, 2-methylbutyrylglycine and suberic acid. On the basis of this, the similarity in symptoms, and the known mechanism by which this abnormal metabolite profile exists in MADD, a better understanding of how these anti-TB drugs cause these associated side-effects was proposed: by inhibition of the electron

57 Chapter 2 transport flavoproteins. Therefore, melatonin, a well-known antioxidant previously proposed to increase the efficacy of INH, was investigated as a means of alleviating these symptoms, when administered in combination to these drugs. The result was alleviation of both the oxidative stress and MADD metabolite profile (Loots et al., 2005).

Despite the limited metabolomics data, a number of studies have been done investigating the general metabolites of first-line anti-TB drugs formed in the host, and their associated side-effects. These are summarised in Table 2-7.

Table 2-7: A summary of the general metabolites of first-line anti-TB drugs and their associated side-effects.

Anti-TB drug Associated metabolites Associated side-effects (Year discovered)  Hepatotoxic: ATDH/hepatitis Ammonia;  Cutaneous: rash Diacetylhydrazine (DiAcHZ),  Nephrology: increased urination Hydrazine (HZ);  Abdominal: severe dizziness/nausea, Isoniazid (INH) (1952): vomiting Hydrazones with pyruvic (INH-PA) and  Flu-like symptoms: fever, headache (Arbex et al., 2010; Ohno et ketoglutaric acids (INH-KA); al., 2000; Roy et al., 2008;  Other: drug-induced lupus erythematous, Isonicotinic acid (INA); Tostmann et al., 2008a; high anion gap metabolic acidosis, Tostmann et al., 2008b) Monoacetylhydrazine (AcHZ); seizures (status epilepticus), mood N1-Acetyl-N2-isonicotinylhydrazide changes (confusion, psychosis, (AcINH); depression), pyridoxine (vitamin B6) depletion (resulting in central nervous Oxidising free radicals system (CNS), peripheral neuropathy, effects and sideroblastic anaemia)  Hepatotoxic: ATDH/hepatitis  Cutaneous: pruritus (skin irritation), rash and/or urticarial (hives) Pyrazinamide (PZA) (1952): Pyrazinoic acid (PA);  Nephrology: dysuria (painful urination), (Arbex et al., 2010; Heifets et interstitial nephritis (kidney inflammation) al., 1989; Olivier & Loots, 5-Hydroxypyrazinoic acid (5-OH-PA)  Abdominal: nausea, vomiting 2011; Tostmann et al., 2008a)  Flu-like symptoms: fever, arthralgia (due to decreased uric acid secretion), malaise  Other: sideroblastic anaemia  Hepatotoxic: ATDH/hepatitis  Cutaneous: milk skin reaction Ethambutol (EMB) (1966): 2,2’-(Ethylenediimino)-di-butyraldehyde;  Optical: retrobulbar/optic neuritis, red- green colour blindness, (Arbex et al., 2010; Becker et 2,2’-(Ethylenediimino)-di-butyric acid vertical nystagmus al., 2008) (EDA)  Nephrology: hyperuricaemia  Flu-like symptoms: arthralgia  Other: peripheral neuropathy  Hepatotoxic: ATDH/hepatitis, upregulation of hormonal hepatic metabolism (decreasing hormone levels)  Cutaneous: flushing, pruritus, rash Rifampicin (RIF) (1966):  Respiratory: breathlessness (Arbex et al., 2010; Ohno et Desacetylrifampicin;  Abdominal: cramps, diarrhea, nausea, al., 2000; Tostmann et al., 3-Formyl rifampicin vomiting 2008a)  Flu-like symptoms: fever, headache, arthralgia, malaise, dysphoria  Other: immunological reactions, colour change in bodily fluid (urine, tears, sweat) 58 Chapter 2 From the literature, however, very little information exist linking metabolism of drugs and the resulting metabolites to the aforementioned side-effects, except for that of the hepatotoxicity which is relatively well described. All four first-line drugs used as part of the DOTS program (EMB to a lesser extent) are associated with anti-TB drug-induced hepatotoxicity (ATDH). ATDH generally occurs within the first two months of treatment, and is thought to be dose- dependent. Hence, decreasing the drug dosage may lighten the symptoms. Although the occurrence of ATDH varies in different populations, studies have shown the most common factors to determine a predisposition towards this include genetics, advanced age, gender, malnutrition, alcoholism, hepatitis B or C, and HIV/AIDS infection, which may in turn lead to increased morbidity, mortality, non-adherence, treatment failure/relapse and the consequential development of MDR-TB. Genetics is considered the most common cause of ATDH and polymorphisms in various drug metabolising loci, such as N-acetyl transferase 2 (NAT 2), cytochrome P450 oxidase (CYP2E1) and glutathione S-transferase Mu 1 (GSTM1), contributes to the inter-individual variations and varying pharmacological responses to these drugs (Arbex et al., 2010; Roy et al., 2008). Although the exact mechanism of ATDH remains unknown, the toxic metabolites associated with the first-line anti-TB drugs play a crucial role in ATDH development (Tostmann et al., 2008a), as these drugs are metabolised by the liver. INH, RIF and PZA (especially when combined) are potentially hepatotoxic drugs and mostly associated with an increased risk of developing ATDH if the patient is unable to clear the toxic metabolites formed in liver due to any of the aforementioned predispositions. Ammonia, diacetylhydrazine (DiAcHZ), hydrazine (HZ), hydrazones with pyruvic (INH-PA) and ketoglutaric acids (INH-KA), isonicotinic acid (INA), monoacetylhydrazine (AcHZ), N1-acetyl-N2-isonicotinylhydrazide (AcINH) and oxidising free radicals, are the toxic metabolites produced when INH is metabolised, either via NAT 2 or hydrolysis (Ohno et al., 2000; Roy et al., 2008; Tostmann et al., 2008b). These reactions may further be facilitated with the co-administration of RIF, via its induction of the hepatic microsomal enzymes of CYP450. Furthermore, as RIF competes with bilirubin absorption and conjugation for inhibition of glucuronosyltransferase, jaundice and increased serum bilirubin and/or hepatic enzyme levels may occur (Arbex et al., 2010). Additionally, pyrazinoic acid (PA) and 5-hydroxypyrazinoic acid (5-OH-PA), toxic metabolites associated with PZA metabolism, have also recently been implicated to be responsible for PZA-induced hepatotoxicity (Shih et al., 2013).

2.6 PREDICTING TUBERCULOSIS TREATMENT OUTCOME

The WHO has compiled a specific list defining possible treatment outcomes for TB patients using first-line medication, i.e. individuals with drug-susceptible TB (see Table 2-8) (World Health Organization, 2014).

59 Chapter 2 Table 2-8: Treatment outcome for tuberculosis patients on first-line medication, as defined by the World Health Organization (World Health Organization, 2014).

Treatment Outcome Definition

A pulmonary TB patient with bacteriologically confirmed TB at the Cured beginning of treatment who was smear- or culture-negative in the last month of treatment and on at least one previous occasion.

A TB patient who completed treatment without evidence of failure, but with no record to show that sputum smear or culture results in the last Treatment completed month of treatment and on at least one previous occasion were negative, either because tests were not done or because results are unavailable.

A TB patient whose sputum smear or culture is positive at month 5 or Treatment failed later during treatment.

A TB patient who dies for any reason before starting or during the Died course of treatment.

A TB patient who did not start treatment or whose treatment was Lost to follow-up (defaulter) interrupted for 2 consecutive months or more.

A TB patient who was previously treated and declared cured or treatment completed at the end of their most recent course of Relapse treatment, and is now diagnosed with a recurrent episode of TB (either a true relapse or a new episode of TB caused by reinfection).

Treatment success The sum of cured and treatment completed.

Current TB control aims to prevent TB morbidity and mortality, as well as anti-TB drug- resistance via the early diagnosis of active TB and the effective treatment thereof (Antoine et al., 2007). TB treatment is considered effective when the patient is completely cured from the active disease state (thus preventing transfer of the infectious organism to other healthy individuals) and also from latent M. tuberculosis infection (preventing relapse). Developing an effective method to monitor treatment progression and/or accurately predict treatment outcome or potential relapse, well before the end of the treatment regimen, would therefore be regarded as a major breakthrough since alternative treatment approaches can be considered earlier. Considering this, various metabolomics studies have been done to identify biomarkers for this purpose.

60 Chapter 2 Mahapatra et al. (2014) used LC-MS metabolomics to identify metabolic biosignatures characterising treatment outcome using patient-collected urine. By comparing the metabolite profiles of urine collected at time of diagnosis (before treatment) to those collected one month after treatment has commenced, they were able to identify 45 metabolites as potential biomarkers differentiating these sample groups. After reducing these metabolites to a set of six statistically significant molecular features, they were able to classify the urine as either baseline or one month treatment samples, with an error rate of 11.8% (Mahapatra et al., 2014). In future, these metabolites may serve as biomarkers for evaluating drug response, which could potentially accelerate anti-TB drug clinical trials. In the following year, Das et al. (2015) investigated the altered urinary metabolite profiles of active TB-positive patients on first-line anti-TB drugs by comparing samples collected at various time points during the treatment regimen, with those collected from healthy controls. A time-dependent treatment trend was observed, and the successfully cured patients were shown to have a metabolic profile similar to that of asymptomatic healthy controls. Although this study indicates the potential of metabolomics towards broadening our understanding of TB treatment progression, the main aim of the study was to identify diagnostic biomarkers and, therefore, no metabolite biomarkers explaining this trend were actually identified or discussed (Das et al., 2015). The same group also investigated individual variation of anti-TB drug metabolism, which can be associated with treatment outcome, drug-related side-effects and the development of drug-resistance. They applied an untargeted GC-TOFMS metabolomics approach and analysed urine samples collected from newly diagnosed TB patients at various intervals (i.e. 2h, 6h, 12h, 24h, 36h and 48h) after first-line oral anti-TB drug administration. The majority of the first-line drugs (INH, RIF and PZA) administered was detected, as well their known metabolites. This metabolomics study not only allowed for a comprehensive description of the drug metabolism phenotype associated with first-line anti-TB drugs (by interpretation of the variation in drug abundance and their known metabolites), but also identified a previously unknown drug metabolite of EMB (Das et al., 2016). In a similar study, Tientcheu et al. (2015) compared the transcriptomic and metabolic profiles of patients with either a M. africanum or M. tuberculosis-infection after receiving the same anti-TB treatment, in order to identify host biomarkers associated with lineages-specific pathogenesis and treatment response. The serum metabolic profiles showed a decline in pro-inflammatory metabolites after treatment, which was more pronounced in the M. tuberculosis-infected patients, comparatively. They concluded that differences in the transcriptomic and serum metabolic profiles associated with these two lineages may be an indication of intrinsic host factors associated with susceptibility to TB and/or efficacy of standard anti-TB treatment (Tientcheu et al., 2015).

61 Chapter 2 Over the past few years, progress has also been made in the development and application of electronic nose (sensor array) technologies toward metabolomics research. An electronic nose contains conducting-polymer sensor arrays that interact with volatile compounds, resulting in electrical resistance producing characteristic signals of multiple sensor parameters (Pavlou et al., 2004; Wilson, 2016). In 2016, Zetola et al. applied this technology towards the development of improved TB diagnostics and monitoring TB treatment efficacy. Breath samples were collected from healthy, HIV-uninfected controls and pulmonary TB-positive patients, of which 61% were co-infected with HIV, at time of diagnosis and at various intervals during the treatment regimen. The electronic nose signals obtained from the VOCs in these breath samples indicated that the device had a sensitivity of 94.1% and a specificity of 90.0% for differentiating TB-positive patients (before treatment onset) and healthy controls. Changes in these breath signals were also observed longitudinally (at 0, 2, 7, 14, and 30 days following TB treatment onset), indicating its potential application for monitoring treatment progression (Zetola et al., 2016).

Considering the above, metabolomics can be used as an extremely powerful approach to identify markers predicting or monitoring treatment outcome, which can also be used as surrogate endpoints for evaluating the efficacy of new anti-TB drugs during clinical trials.

To date, however, no metabolomics studies have been done using patient urine, collected over the entire six month DOTS treatment regimen, comparing those who were eventually cured to those whose treatment failed.

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Van den Boogaard, J., Kibiki, G.S., Kisanga, E.R., Boeree, M.J. & Aarnoutse, R.E. 2009. New drugs against tuberculosis: Problems, progress, and evaluation of agents in clinical development. Antimicrobial Agents and Chemotherapy, 53(3):849-862. Van der Werf, M.J., Overkamp, K.M., Muilwijk, B., Coulier, L. & Hankemeier, T. 2007. Microbial metabolomics: Toward a platform with full metabolome coverage. Analytical Biochemistry, 370(1):17-25. VandenHeuvel, W.J.A. 1986. Drug metabolite identification: Stable isotope methods. The Journal of Clinical Pharmacology, 26(6):427-434. Vilcheze, C. & Jacobs, J., William R. 2007. The mechanism of isoniazid killing: clarity through the scope of genetics. Annual Review of Microbiology, 61:35-50. Wallace, A.M., Gibson, S., De La Hunty, A., Lamberg-Allardt, C. & Ashwell, M. 2010. Measurement of 25-hydroxyvitamin D in the clinical laboratory: Current procedures, performance characteristics and limitations. Steroids, 75(7):477-488. Want, E.J., Nordström, A., Morita, H. & Siuzdak, G. 2007. From exogenous to endogenous: The inevitable imprint of mass spectrometry in metabolomics. Journal of Proteome Research, 6(2):459- 468. Wares, D.F., Singh, S., Acharya, A.K. & Dangi, R. 2003. Non-adherence to tuberculosis treatment in the eastern Tarai of Nepal. The International Journal of Tuberculosis and Lung Disease, 7(4):327- 335. Warner, D.F. 2014. Mycobacterium tuberculosis metabolism. Cold Spring Harbor perspectives in medicine, 5(4):10.1101/cshperspect.a021121. Weckwerth, W. & Morgenthal, K. 2005. Metabolomics: From pattern recognition to biological interpretation. Drug Discovery Today, 10(22):1551-1558. Weiner, J., Parida, S.K., Maertzdorf, J., Black, G.F., Repsilber, D., Telaar, A., Mohney, R.P., Arndt- Sullivan, C., Ganoza, C.A. & Fae, K.C. 2012. Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients. PloS one, 7(7):e40221. Wells, C.D., Cegielski, J.P., Nelson, L.J., Laserson, K.F., Holtz, T.H., Finlay, A., Castro, K.G. & Weyer, K. 2007. HIV infection and multidrug-resistant tuberculosis—the perfect storm. Journal of Infectious Diseases, 196(Supplement 1):S86-S107. Wheelock, C.E., Goss, V.M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P.J., Snowden, S., Burg, D., D'Amico, A., Horvath, I., Chaiboonchoe, A., Ahmed, H., Ballereau, S., Rossios, C., Chung, K.F., Montuschi, P., Fowler, S.J., Adcock, I.M., Postle, A.D., Dahlen, S.E., Rowe, A., Sterk, P.J., Auffray, C., Djukanovic, R. & Group, U.B.S. 2013. Application of 'omics technologies to biomarker discovery in inflammatory lung diseases. The European Respiratory Journal, 42(3):802-825. Willey, J.M. 2008. Prescott, Harley, and Klein's Microbiology-7th international ed./Joanne M. Willey, Linda M. Sherwood, Christopher J. Woolverton: New York [etc.]: McGraw-Hill Higher Education. Wilson, A.D. 2016. Biomarker metabolite signatures pave the way for electronic-nose applications in early clinical disease diagnoses. Current Metabolomics, 4(2):12. Wong, E.B., Cohen, K.A. & Bishai, W.R. 2013. Rising to the challenge: New therapies for tuberculosis. Trends in Microbiology, 21(9):493-501.

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74 Chapter 3 CHAPTER 3: METABOLOMICS METHODOLOGY AND REPEATABILITY

3.1 INTRODUCTION

To date, the majority of research explaining M. tuberculosis virulence and growth mechanisms was generated from experiments using M. tuberculosis cultures. It has, however, been well established that various growth and virulence mechanisms are only activated once in the host’s growth milieu and/or challenged by the host’s immune system. Considering this, host–microbe interactions and adaptations has become a new research interest over the last 5–10 years, and studying these organisms isolated from patient sputum, or studying the metabolome from patient-collected sputum, blood or urine, has become an increasingly important research approach towards better understanding these mechanisms. Similar to blood, little or no mycobacteria are expected to be present in the urine of TB patients and, therefore, very low concentrations of those metabolites associated with or unique to M. tuberculosis would likely be detected. However, urine collected from TB patients would contain higher concentrations of the altered host-derived metabolites induced by the TB disease state, when compared to that of other patient sample matrixes. In addition to disease-induced metabolic changes, urine also directly reflects metabolites excreted by the liver and kidneys, and therefore provides additional information regarding changes to an individual’s metabolome as a result of a toxic insult by the infectious pathogen or treatment thereof. Urine sample collection is also considered non-invasive, with larger amounts of sample material readily available, which can be collected and analysed with far lower chance of transferring disease, and easily transported and stored.

Since TB metabolomics using patient-collected urine is a new approach, a basic method validation of the experimental procedures and the data generated for later metabolomics biomarker identification are required, and is described in this chapter.

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3.2 EXPERIMENTAL DESIGN

Figure 3-1: Experimental design. A schematic representation of the experimental design of this metabolomics investigation, aimed at determining repeatability and identifying urinary metabolite markers better characterising TB (Aim 1), treatment failure thereof (Aim 2a), and also for the early prediction of an unsuccessful treatment outcome (Aim 2b).

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The broad outline of this experiment can be divided into three separate sections, as shown in Figure 3-1, indicating the experimental design proposed to answer the aims and objectives of this investigation.

For this study, urine samples from 38 culture-confirmed active pulmonary TB patients were collected at baseline (time of diagnosis), during the course of treatment (weeks 1, 2 and 4) and two weeks after treatment completion (week 26) (n=210), as well as from a healthy TB-negative group of individuals (n=30) from the same community (more detail on these samples will be given in the next section). A quality control (QC) sample stock solution was compiled by combining equal volumes of all collected urine samples, and aliquots of this were used as identical QC samples throughout the analysis. Since a total of 210 participant samples were collected, these were divided into smaller batches (n=18) for analyses. Samples were randomly assigned to these batches and included one QC aliquot per batch. Each batch of samples were analysed by performing a creatinine determination, organic acid extraction and derivatisation, followed by GCxGC-TOFMS analyses.

In order to achieve the first objective, the repeatability of the analyst/methodology and that of the GCxGC-TOFMS apparatus was evaluated using data collected from the QC samples, as shown in Figure 3-1 (indicated in red). For determining the analyst/methodology repeatability, the GCxGC-TOFMS data of all the QC sample aliquots (n=18) across all sample batches were statistically compared. The repeatability of the analytical apparatus was determined by comparing the GCxGC-TOFMS data collected from a randomly selected QC aliquot, injected and analysed repeatedly (n=10).

Once the GCxGC-TOFMS data was validated and its accuracy determined for metabolomics biomarker identification, the data was processed and cleaned prior to statistical analyses, and the metabolite markers were identified by comparing the appropriate groups to one another, as stipulated in the aims for this study (also indicated in green and purple in Figure 3-1).

3.3 MATERIALS AND METHODS

3.3.1 Urine sample collection and storage

This study was conducted according to the Declaration of Helsinki and International Conference on Harmonisation Guidelines. The necessary ethical approval for this investigation was obtained from the Ethics Committee of the North-West University, South Africa (reference number NWU-00127-11-A1), Stellenbosch University Health Research Ethics Committee (reference number 99/039) and Cape Town City Health. All participants

77 Chapter 3 gave written informed consent for study participation and HIV testing. Furthermore, samples were anonymised. The clinical information received with regards to the urine samples included age, gender, body mass index (BMI), and treatment outcome. Additionally, information regarding the bacteriological cultures at the time of diagnosis and at the end of the treatment programme (week 26) was also obtained.

All urine samples were collected in standard urine collection vials from individuals in the same geographical area, after which they were immediately placed on ice, transported to the laboratory, and frozen at -20°C until analysis. These samples were collected from 30 TB- negative healthy controls and 38 culture-confirmed active TB-positive South African patients (19 males and 19 females, between the age of 17 and 58) at baseline (time of diagnosis, thus prior to initiation of treatment), during the course of treatment with the DOTS strategy (weeks 1, 2 and 4) and two weeks after treatment completion (week 26). All TB-positive patients included in this study had drug-susceptible TB, were HIV-seronegative, not pregnant, and with no other diseases (including diabetes, malignancy, lung cancer, chronic bronchitis and sarcoidosis). Furthermore, the TB-positive samples were divided into successful (n=27) and unsuccessful (n=11) treatment outcome groups, which was determined by bacteriological culture at 26 weeks (two weeks after treatment).

3.3.1.1 Quality control samples

The data generated from the quality control (QC) samples not only allow for the monitoring of the analytical procedure, but also allow for data validation and the correction of any potential batch effects which may have occurred during sample analysis. A single pooled QC sample was compiled by combining 0.2 mL from each of the collected urine samples in a new, clean vial and mixed. From this pooled QC sample stock solution, smaller QC aliquots of 1 mL were prepared and stored at -20°C until analysis. One QC aliquot was extracted, derivatised, and analysed with each batch and injected three times, i.e. in the beginning, middle and at the end of each batch. As it is necessary to equilibrate the machine, the first QC sample was injected five times prior to any analysis.

3.3.2 Reagents and chemicals

Ethyl acetate and diethyl ether were from Honeywell International Inc. (Muskegon, MI, USA). These organic solvents were ultra-pure Burdick and Jackson brands. Hydrochloric acid

(HCl), pyridine, anhydrous sodium sulphate (Na2SO4) and trimethylchlorosilane (TMCS) were from Merck (Darmstadt, Germany). 3-Phenylbutyric acid, hexane and bis(trimethylsilyl)- trifluoroacetamide (BSTFA) were from Sigma-Aldrich (St. Louis, MO, USA).

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3.3.3 Organic acid extraction procedure and derivatisation

Creatinine values for all urine samples, including QC samples, were determined prior to an organic acid extraction, using a creatinine enzyme kit (Thermo Scientific; reference number 981845) and analysed on an Indiko Clinical Analyser, Type 863 (Thermo Scientific). These creatinine values are used to normalise metabolite concentrations, as well as to determine the volume of urine, internal standard, BSTFA, TMCS and pyridine used for each extraction. For acidification, 5N HCl (six drops) was added to the determined amount of urine, followed by the addition of the internal standard, 3-phenylbutyric acid (25 µmol/mg creatinine). Thereafter, ethyl acetate (6 mL) was added, followed by mixing and centrifugation (3000 rpm for 3 minutes) to achieve solvent phase separation, after which the organic phase was aspirated into a clean tube. Diethyl ether (3 mL) was then added to the remaining aqueous phase, again followed by mixing, centrifugation and aspirating the organic solvent into the tube containing the previously collected phase. Hereafter, Na2SO4 (approximately 3 g) was added to remove any water still present in the sample, followed by a brief vortex and centrifugation. The organic phase was then decanted from the pellet and evaporated to complete dryness under a stream of nitrogen at 37°C. This was followed by a 60 minute derivatisation period at 60°C with BSTFA (22.6 μL/μmol creatinine), TMCS (4.5 μL/μmol creatinine) and pyridine (4.5 μL/μmol creatinine).

3.3.4 GCxGC-TOFMS analyses

Each derivatised sample was transferred to a 1.5 mL GC-MS sample vial, capped and placed into the auto-sampler tray of a Gerstel Multi-Purpose Sampler (Gerstel GmbH and co. KG, Mülheim an der Ruhr, Germany), coupled to a Pegasus 4D GCxGC-TOFMS (LECO Africa (Pty) Ltd, Johannesburg, South Africa), equipped with an Agilent 7890A gas chromatograph (Agilent, Atlanta, USA), and a TOFMS (LECO Africa). All samples (1 μL) were injected in random sequence, along with QC samples, at regular intervals throughout the analytical run, using a 1:12 split ratio. Compound separation was achieved using a Restek Rxi-5Sil MS primary capillary column (30 m, 0.25 μm film thickness and 250 μm internal diameter), and a Rxi-17 secondary capillary column (0.9 m, 0.1 μm film thickness and 100 μm internal diameter). Cryomodulation was achieved with a hot pulse of nitrogen gas for 0.7 s, every 3 s. The injector temperature was maintained at a constant 280°C for the entire run and ultra-high purity helium was used as the carrier gas at a constant flow of 1 mL/minute. The temperature program for the primary column was 55°C for 1 minute, after which it was increased at a rate of 7°C/minute to a temperature of 285°C, at which it was maintained for a further 4 minutes, followed by a temperature increase of 20°C/minute, to a final temperature of 305°C, where it was maintained for a minute. The secondary oven’s

79 Chapter 3 temperature ramp was programed identically to that of the primary column, with the exception of a +5°C deviation at all of the time points. The filament was switched off for the first 8 minutes of each sample run, as this period was considered to correspond to a solvent delay during which no mass spectra were recorded. However, this 8 minute interval was included on the first column’s time axis to reflect accurate retention times. The transfer line temperature was held at a constant 270°C and the ion source temperature at a constant 200°C for the entire run. The detector voltage was 1600 V and the filament bias -70 eV. Mass spectra were collected over the range 50–600 m/z at an acquisition rate of 200 spectra per second.

3.3.5 Data processing

Mass spectral deconvolution, peak identification/annotation, and peak alignment was conducted using ChromaTOF software (version 4.32), from the LECO Corporation. Mass spectral deconvolution is used to reveal peaks with mixed mass spectra of co-eluting compounds and assigns an accurate mass spectrum to each individual analyte. Deconvolution was performed at a signal-to-noise (S/N) ratio of 300, with a minimum of three apexing peaks. Peak alignment functions by using the retention times and mass spectra to align peaks between a variety of samples, compiling a data matrix that contains the relative concentrations of all detected compounds, from all samples analysed, aligned with one another. Hence, possible retention time shifts were corrected across all samples by aligning identical mass spectra displaying similar retention times. Peaks were identified by comparison of their characteristic mass fragmentation patterns and retention times with various libraries compiled from previously injected standards.

3.3.6 Batch effect

A batch effect refers to a gradual change in the accuracy or precision of the results, which may exist due to non-biological systematic differences that occur due to small changes or variation during the extraction and analytical procedures. This effect has been well documented in large-scale metabolomic studies. Although the necessary precautions should always be taken to minimise possible batch effects, these may be inevitable, and thus need to be corrected for. Considering this, a drift correction step was applied, using quantile equating, to correct for any potential batch effects (Wang et al., 2012). As shown in Figure 3-2, using the data collected from the QC samples analysed, a PCA (of which a brief description is given in section 4.1.3) was done both before and after batch correction, to visualise whether or not this was successful. According to this illustration, a batch effect is evident (Figure 3-2a), and after correction of the entire dataset (i.e. QC, patient and control

80 Chapter 3 samples), better overlap between the batches occur (Figure 3-2b). Therefore, any differentiation of the patient/control groups investigated from this point onwards, and the metabolite makers identified, can be ascribed to variation due to a biological effect and not a batch effect.

(a)

(b)

Figure 3-2: Batch correction. Two dimensional principle component analysis (PCA) scores plot of the GCxGC-TOFMS quality control data (a) before and (b) after a correcting the observed batch effect, using quantile equating.

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3.4 RESULTS AND DISCUSSION

3.4.1 Repeatability

In order to achieve the first objective (section 1.2.2), the repeatability of the GCxGC-TOFMS apparatus used (analytical machine repeatability) and that of the methodology (analyst/extraction methodology repeatability), was determined.

The analytical machine repeatability was determined and validated by extracting and derivatising one randomly selected QC sample (as described in section 3.3.3), and injecting and analysing this sample repeatedly (n=10) using a GCxGC-TOFMS (see section 3.3.4). The data were processed (as described in section 3.3.5) and, using this dataset of ten injection repeats, a coefficient of variation (CV) value was determined for all detected compounds as well as for ten compounds occurring in the highest concentrations throughout the chromatographic run. Similarly, the repeatability of the analyst/extraction methodology was also determined. However, in this instance, all of the QC samples (n=18) were separately extracted, injected and analysed in triplicate, using the GCxGC-TOFMS, over 18 days (one sample per day) followed by data processing. Once again, the CV values for all the detected compounds, and for the same ten compounds occurring in the highest concentrations throughout the total chromatographic run, were determined.

The CV value is a parameter for measuring repeatability, and can be mathematically defined as CV = (s ÷ x̄) x 100, where “s” is the compound standard deviation and “x̄” is the compound average. The CV value of a compound directly correlates with its detected concentration, thus a compound detected in high concentrations will inherently have a low CV value, and vice versa. The FDA recommends that the CV value be below 20% for GC-MS analyses (Food and Drug Administration, 2001), however, as per definition, metabolomics considers all compounds in an unbiased manner, and should therefore, in theory, also be unbiased towards compounds occurring in lower concentrations. For this reason, a hypothetical CV cut-off value of 50% was shown to be optimal for metabolomics investigations, in order to include compounds occurring in lower concentrations, in the final data matrix, for answering the biological questions (Schoeman et al., 2012).

When considered from this perspective, 97.44% of all the detected compounds from the ten QC sample repeats fell below the hypothetical 50% CV value. Interestingly, 78.77% of all CV values were below 20%, further confirming the excellent GCxGC-TOFMS repeatability and its application for metabolomics investigations. Similarly, when evaluating the repeatability of the analyst/extraction methodology using the same approach, 91.61% of all detected compounds fell below the hypothetical CV cut-off of 50%, however far fewer of these had a

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CV below 20%, which can be expected since each step of the extraction procedure could be a possible source for variation. Figure 3-3 graphically depicts the distribution of the CV values for all the detected compounds, following an organic acid extraction, derivatisation and GCxGC-TOFMS analyses, for both the analytical machine and analyst/extraction methodology repeatability.

110

100

90 80 70 60 50 40 30

20 % x below CV withMetabolites% 10 0 0 10 20 30 40 50 60 70 80 Coefficient of variation (x)

Machine repeatability Analyst repeatability

Figure 3-3: Distribution of the coefficient of variation (CV) values for the analytical machine (GCxGC- TOFMS) and analyst/extraction methodology repeatability.

As previously mentioned, since the concentration of a compound can influence its CV, for the purpose of showing the repeatability of this approach from a more traditional perspective, the ten compounds occurring in the highest concentrations throughout the chromatographic run with their respective relative concentrations and CV values are given in Table 3-1. From this it can be seen that all of the compounds fell below the FDA’s recommended 20% cut-off value when considering the analytical machine and analyst/extraction methodology repeatability.

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Table 3-1: Average concentration and coefficient of variation (CV) values, calculated for the ten most abundant compounds throughout the chromatographic run.

Average Analyst/ concentration Machine extraction Metabolite name (mg/g creatinine) repeatability methodology (standard error of (CV value) repeatability (CV value) the mean) (R*,R*)-2,3-Dihydroxybutanoic acid 59.978 (1.293) 1.62% 12.41% 1-Propene-1,2,3-tricarboxylic acid 203.971 (3.359) 4.89% 19.13% 2-Hydroxy-1,2,3-Propanetricarboxylic acid 238.054 (5.371) 2.67% 14.95% 2-Methyl-2-hydroxypropanoic acid 77.834 (1.946) 3.86% 17.17% 4-Hydroxybenzeneacetic acid 190.744 (2.708) 2.88% 15.41% Butanedioic acid 146.171 (3.248) 1.22% 10.84% Citramalic acid 4.283 (0.672) 1.17% 17.52% Ethanedioic acid 92.393 (4.974) 5.15% 19.98% Hydroxyacetic acid 94.050 (2.306) 4.66% 18.70% N-Benzoylglycine 932.037 (13.99) 2.82% 15.11%

3.5 CONCLUSION

After a batch correction step using quantile equating, the generated GCxGC-TOFMS data showed excellent batch overlap of the data generated from the QC samples and, furthermore, acceptable repeatability, validating the methodology and data for use in further metabolomics investigations and addressing the proposed aims of this study (see section 1.2.1).

3.6 REFERENCES

Food and Drug Administration. Services, U.S.D.O.H.a.H. 2001. Guidance for industry: Bioanalytical method validation. Rockville: Center for Drug Evaluation and Research. 25 pages. Schoeman, J.C., Du Preez, I. & Loots, D.T. 2012. A comparison of four sputum pre-extraction preparation methods for identifying and characterising M. tuberculosis using GCxGC-TOFMS metabolomics. Journal of Microbiological Methods, 91:301-311. Wang, S.-Y., Kuo, C.-H. & Tseng, Y.J. 2012. Batch normalizer: A fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Analytical Chemistry, 85(2):1037-1046.

84 Chapter 4 CHAPTER 4: ADAPTATIONS OF MAN AND MICROBE IN ORDER TO OUTCOMPETE AND SURVIVE

This chapter has been published:

 Luies, L. & Loots, DT. (2016). Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3), 1-9.

4.1 ABSTRACT

Despite numerous research efforts to control TB, it is still regarded as a global pandemic. It is clear that the infectious agent responsible and its associated disease mechanisms remain poorly understood. Alternative research strategies are therefore urgently needed to better characterise active-TB, especially the adaptations of the host and microbe as they compete to survive. Using a GCxGC-TOFMS metabolomics approach, we identified new urinary TB metabolite markers induced by adaptations of the host metabolome and/or host–pathogen interactions. The most significant of these were the TB-induced changes resulting in abnormal host fatty acid and amino acid metabolism, in particular to tryptophan, phenylalanine and tyrosine, inducing a metabolite profile similar to that of patients suffering from phenylketonuria, mediated through changes to IFN-γ and possibly insulin. This also explains some of the symptoms associated with TB and provides clues to better treatment approaches.

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4.2 INTRODUCTION

TB, a highly contagious bacterial disease caused by M. tuberculosis, is considered the leading cause of death globally from a single bacterial pathogen. This disease affects approximately one-third of the world’s population, either in its active or latent form. The latest reports indicate 9 million new TB cases per annum, resulting in an estimated 1.8 million deaths (3 800 deaths a day) (World Health Organization, 2014).

TB transmission occurs through small infectious aerosol droplets that contain live M. tuberculosis bacilli. When inhaled into the lungs, they undergo rapid replication, before being engulfed by host macrophages. Following macrophage internalisation, M. tuberculosis avoids death by blocking phagolysosomal fusion, allowing the bacilli to persist in a non- or slowly-replicating state within the relatively hospitable niche of these phagosomes, where they can survive for decades (Philips & Ernst, 2012; Warner, 2014). Thus, although the host immune response is unable to eradicate M. tuberculosis, a fully immune-competent host can suppress the infection. This asymptomatic and non-infectious state is referred to as latent TB. However, when the immune system becomes compromised, latent TB develops into active TB — the symptomatic and highly infectious state of the disease. Common symptoms associated with active pulmonary TB include a bad cough with mucus, pleurisy, haemoptysis, dyspnoea, wheezing, weakness/fatigue, weight-loss, loss of appetite, chills/fever, and night sweats (Long et al., 2002).

Despite numerous research efforts to date, since the discovery of M. tuberculosis in 1882, it is clear that this pathogen and its underlying disease mechanisms remain poorly understood. Thus, alternative research approaches, giving a different perspective of the disease, are urgently needed to better characterise TB, especially those investigating host–microbe interactions and each of their adaptations, as they compete with each other in order to survive. To this end, metabolomics has the potential to identify new disease markers, describing previously unidentified disease mechanisms, adaptations of the pathogen to environmental changes and how these react to antimicrobial treatments (Drapal et al., 2014). “Metabolomics” refers to the unbiased identification and quantification of all metabolites present in a biological system, utilising highly sensitive and selective analytical methods, in conjunction with biostatistics for metabolite marker selection. This “omics” approach allows for a holistic view of a patient’s metabolic state/metabolome, detecting alterations due to a specific perturbation, such as TB (De Villiers & Loots, 2013). Using this approach on sputum collected from TB patients, Du Preez and Loots (2013) were able to identify new metabolites from both M. tuberculosis and the infected host, thereby revealing previously unknown metabolic pathways and adaptations of each. These included: (a) the presence of a

86 Chapter 4 citramalate cycle in M. tuberculosis; (b) interaction of this cycle with an upregulated glyoxylate cycle; and (c) increased utilisation of fatty acids and glutamate as additional carbon sources by M. tuberculosis during pulmonary infection. Adaptations of the human host included: (a) an alternative mechanism for H2O2 synthesis via glucose oxidation, in order to counteract the bacterial infection more efficiently; (b) inhibition of oxidative phosphorylation due to pronounced oxidative stress; and (c) elevated concentrations of various neurotransmitters and other metabolites related to some of the symptoms associated with TB (Du Preez & Loots, 2013).

In the current investigation, we applied a GCxGC-TOFMS metabolomics approach, to compare and differentiate culture-confirmed active TB-positive (n=38) and TB-negative healthy control (n=30) groups, based on the comparative detected changes to their urinary metabolomes. Apart from the fact that urine from TB patients is readily available, easily stored and can be collected by non-invasive techniques, it serves as an ideal matrix for identifying metabolites related to an altered TB host metabolome, since it contains metabolic signatures of many pathways that may change as a result of infection or disease.

4.3 MATERIALS AND METHODS

A summary of the experimental design is given in Figure 4-1, and described in detail below.

Figure 4-1: Summary of the experimental design. Subsequent to sample collection, an organic acid extraction and derivatisation of each sample extract was performed, followed by GCxGC-TOFMS analysis, data processing, statistical data clean-up and biostatistics. Hereafter metabolite markers were selected on the basis of various predefined statistical “cut offs”.

4.3.1 Urine sample collection and storage

Urine samples were collected in standard vials, from 30 TB-negative healthy controls and 38 culture-confirmed active TB-positive patients from the same geographical area, prior to any treatment, after which they were placed on ice, immediately transported to the laboratory, and frozen at -20°C until analysis.

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4.3.2 Sample analysis

Creatinine values for all urine samples were determined using a creatinine enzyme kit (Thermo Scientific; reference number 981845) and analysed on an Indiko Clinical Analyser, Type 863 (Thermo Scientific). The creatinine values are used to normalise metabolite concentrations, and to determine the volume of urine, internal standard, BSTFA, TMCS and pyridine needed for each extraction. An organic acid extraction of the collected patient urine samples were performed, analysed on a GCxGC-TOFMS and processed following the methods described in sections 3.3.3, 3.3.4 and 3.3.5, respectively.

4.3.3 Statistical data analyses

Prior to statistical data analyses, a standard metabolomics data clean-up procedure was applied. All compounds were normalised relative to the internal standard by calculating the relative concentration of each. Following this, variables showing no variation between the groups were removed, and a data filter was applied to each variable to eliminate those with more than 50% zero values in each group (Smuts et al., 2013). Using the QC samples, quantile equating was applied to correct for any batch effects (Wang et al., 2012). Hereafter, a 50% QC–CV filter was applied. Lastly, as most zero values present in the dataset were due to low abundance rather than being absent, they were replaced with a value calculated as half of the smallest value (detection limit) present in the dataset (Schoeman et al., 2012).

Various multivariate (PCA; PLS-DA) and univariate (effect sizes; unpaired t-test) statistical analyses were applied using MetaboAnalyst, a metabolomics web-based server based on the statistical package “R” (version 2.10.0). PCA is an unsupervised method used to determine whether or not a natural grouping or differentiation exists between sample groups. PCA is based on a mathematical procedure that transforms possibly related variables (metabolites) into a smaller number of unrelated variables, known as the principal components (PCs). PC 1 accounts for the most variance in the data and each following PC (PC 2, PC 3, etc.) accounts for the next highest variance of the remaining data. Using one PC per axis, a PCA is visualised as a scores plot, and the variables/metabolites best describing this differentiation are ranked according to their respective modelling powers (Olivier & Loots, 2012). PLS-DA, on the other hand, is a supervised method used to determine group membership of an individual sample, based on its metabolic profile. It utilises multivariate regression techniques to extract, via linear combination of original variables (X), the information that can predict the class or group membership (Y). Thus, it identifies those variables/metabolites that best characterise the differentiated sample groups. Each metabolite is ranked according to the variables’ influence on the projection (VIP)

88 Chapter 4 parameter, which directly corresponds to its importance (Du Preez & Loots, 2013). Effect sizes are used to compare each variable individually between the groups, without taking sample size into account, and indicate practical/clinical significance. For this parametric dataset, an effect size >0.5 indicates a moderate effect. The unpaired t-test establishes statistical significance (P-value) by determining whether or not the averages of two groups differ. Conventionally, a P-value <0.05 is considered significant.

4.4 RESULTS AND DISCUSSION

Figure 4-2 shows clear PCA differentiation of the TB-positive and TB-negative healthy control sample groups on the basis of the GCxGC-TOFMS metabolomics data collected. The total amount of variance explained by the first two principal components (PCs) (R2X cum) was 71.0%, of which PC1 accounted for 62.5%, and PC2 accounted for 8.5%. Hereafter, a PLS-DA model was built and showed a modelling parameter R2Y (cum) of 93.6%, indicative of the total explained variation of the response Y, and a Q2 (cum), corresponding to the cross-validated variation accounted for by the response Y, of 81.0%.

Figure 4-2: Principle component analysis (PCA) differentiation of GCxGC-TOFMS organic acid analysis data, of urine samples collected from active TB-positive and TB-negative patients. A plot of principle component 1 versus principle component 2 of the TB-negative healthy control (denoted by C) and active TB-positive patient (denoted by P) groups, showing clear differentiation of these two groups as a result of variation in their metabolite profiles. The variances accounted for are indicated in parenthesis.

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As indicated in Figure 4-3, 12 compounds with a PCA modelling power >0.5 (Brereton, 2003), a PLS-DA VIP >1.0 (Smuts et al., 2013), an effect size >0.5, and a t-test with a P-value <0.05 (Ellis & Steyn, 2003) were selected as the metabolite markers that best explained the variation between the analysed groups (listed in Table 4-1). Box-and-whisker plots of these 12 compounds are provided in Appendix B. This multi-statistical approach is based on the assumption that the different statistical tests correct/compensate for each other’s flaws with the elimination of false-positive compounds (Venter et al., 2015).

Figure 4-3: Venn-diagram indicating compound selection using a multi-statistical approach. A total of 12 compounds were identified as most significant for describing the variation between the TB-negative healthy control and TB-positive patient groups.

Table 4-1: The 12 urinary metabolite markers that best explain the variation detected in the urine samples of the TB-negative healthy control and TB-positive patients, ranked alphabetically.

TB-negative Active TB- healthy controls: positive patients: Effect Average Average PCA PLS-DA t-test Metabolite name concentration concentration sizes (Power) (VIP) (P-value) (mg/g creatinine) (mg/g creatinine) (d-value) (standard error of (standard error of the mean) the mean) 2-C-Methylglycerol 0.071 (0.020) 0.727 (0.084) 0.666 2.432 1.319 <0.001 2-Octenoic acid 0.011 (0.007) 0.114 (0.015) 0.568 2.366 1.150 <0.001 5-Hydroxyhexanoic acid 0.045 (0.021) 0.594 (0.127) 0.534 2.312 0.730 <0.001 5-Hydroxyhydantoin 0.049 (0.019) 0.287 (0.045) 0.522 1.961 0.902 <0.001 Glycerol monostearate 0 (0) 1.079 (0.247) 0.688 3.804 0.737 <0.001 Indole-3-carboxylic acid 0.137 (0.026) 0.929 (0.130) 0.642 1.048 1.032 <0.001 Kynurenic acid 46.055 (0.812) 23.312 (3.872) 0.701 1.017 0.993 <0.001 L-Rhamnulose 0.028 (0.021) 0.302 (0.041) 0.718 2.838 1.146 <0.001 Oxalic acid 0.030 (0.009) 0.143 (0.024) 0.821 2.147 0.813 <0.001 Phenylacetic acid 0.214 (0.034) 1.137 (0.114) 0.719 1.028 1.372 <0.001 Quinolinic acid 0.615 (0.046) 6.422 (1.042) 0.743 1.151 0.942 <0.001 Ribitol 1.089 (0.276) 4.768 (0.745) 0.663 2.694 0.838 <0.001

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Most of the identified metabolites can be explained by changes in the host metabolome in response to TB, supporting previously speculated mechanisms and shedding light on hitherto unknown mechanisms related to M. tuberculosis pathogenesis, and host–microbe interactions and adaptations, as will be described below.

M. tuberculosis infection results in a host immune response, signalling macrophages to engulf the invading bacteria. Accompanying this is an inflammatory response resulting in the release of tumour necrosis factor alpha (TNF-α) and IFN-γ (Philips & Ernst, 2012), the latter of which has been shown to elevate indoleamine 2,3-dioxygenase 1 (IDO1), which in turn is also associated with the prognosis of various pathological conditions, including TB (Suzuki et al., 2012). This IDO1-upregulating host response is thought to deplete the bacterial environment of tryptophan, an amino acid essential to M. tuberculosis growth (Warner, 2014), and also expected to result in alterations in the downstream metabolites in this pathway (see Figure 4-4). Quinolinic acid, for instance, a neurotoxic metabolite formed via tryptophan catabolism, has been linked to inflammatory diseases, and is known to be elevated through IDO1 activation (Heyes et al., 1992). A study by Asp et al. (2011) indicated that TNF-α and IFN-γ reduced the levels of kynurenic acid (a neuroprotective agent), by inhibiting kynurenine aminotransferases (KATs) synthesis (Asp et al., 2011). For these reasons, one would expect elevations in quinolinic acid and reductions in kynurenic acid in the TB-infected host, which is what we see from our metabolomics data. Additionally, indole- 3-carboxylic acid, the dehydrogenation product of tryptophan (Lübbe et al., 1983), was elevated approximately nine times in the TB-positive group, which may serve as an additional means by which the host attempts to reduce the much-needed tryptophan for M. tuberculosis survival. Interestingly, although not detected as one of the 12 metabolite markers using the multi-statistical method described, tryptophan was found to be elevated in the TB-positive group (0.667 vs. 0.096 mmol/L; P=0.041), either because M. tuberculosis can synthesise tryptophan de novo (Warner, 2014), or from another phenomenon explained below.

Another important observation is the presence of more than five times the normal levels of phenylacetic acid detected in the urine of TB-positive patients. Phenyllactic acid, although not identified as a metabolite marker, was also significantly elevated in the TB patients (4.107 vs. 0.009 mmol/L; P<0.0001). Elevations in these two metabolites are indicative of phenylalanine accumulation in the TB-positive patients, similarly to what is seen in phenylketonuria (PKU) patients. Normally, phenylalanine is metabolised to tyrosine via phenylalanine hydroxylase (PAH), using tetrahydrobiopterin (BH4) as a cofactor. If PAH or

BH4 are deficient for some reason, phenylalanine will accumulate and undergo transamination to produce phenylpyruvic acid, which is then either oxidised to phenylacetic acid or reduced to phenyllactic acid (Puri, 2006). Since the usual tryptophan

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(5-hydroxyindole-acetic acid (0.155 vs. 0.917 mmol/L; P>0.05)), and tyrosine (N-acetyltyrosine (0.014 vs. 0.117 mmol/L; P<0.0001); homovanillic acid (7.356 vs. 19.642 mmol/L; P<0.0001)) catabolism metabolites were elevated in the TB-positive group — and considering that both of these amino acid catabolic pathways require BH4 — a respective catabolic enzyme defect or BH4 deficiency can be ruled out. Elevated concentrations of these amino acids are more likely due to compromised insulin production, which further supports earlier evidence for the association between TB and diabetes (Dooley & Chaisson, 2009). This is further substantiated by the recently described detection of D-gluconic acid δ-lactone in the sputum of TB-positive patients, thought to be induced by elevated glucose levels, in addition to elevated levels of normetanephine, a metabolite of norepinephrine, associated with this response and also formed from tyrosine (Du Preez & Loots, 2013). Insulin not only influences carbohydrate but also amino acid and protein metabolism, as a means to preserve amino acids for other essential anabolic processes. Since tryptophan and phenylalanine, as well as tyrosine to a certain extent, are essential amino acids, their concentrations in the human systemic circulation is dependent on protein consumption and uptake from the portal circulation system. These amino acids can also be released from tissue reservoirs or circulating proteins, which is typically what happens during wasting, a well-known occurrence in patients suffering from active TB, and also linked to reduced insulin secretion. The quantities of dietary phenylalanine, tyrosine and tryptophan entering systemic circulation are regulated by their catabolic hepatic enzymes, namely PAH, tyrosine aminotransferase (TAT), and tryptophan dioxygenase (TDO), respectively, which are induced by various factors, including reduced insulin. Although recent developments have shed light on the association between compromised insulin secretion and altered amino acid metabolism, the exact mechanism by which this occurs remains unknown (Adams, 2011; Cansev & Wurtman, 2007). In a previous metabolomics investigation, in which insulin- sensitive and insulin-resistant subjects were compared, both elevated phenylalanine and tyrosine concentrations were factors differentiating these groups (Tai et al., 2010) and serves as further evidence for the role of reduced insulin in the elevation of these amino acids. Moreover, reduced insulin secretion also results in free fatty acid oxidation in vivo (Adams, 2011). When the body uses fat as the primary energy source, it is broken down and released into the bloodstream, resulting in increased urinary fatty acids and their breakdown products, as was detected in our study. These fatty acids and breakdown products included glycerol monostearate, 2-octenoic acid and 5-hydroxyhexanoic acid. This could potentially explain the weight-loss associated with TB if these fatty acids are sourced from the body’s fat stores (Kairamkonda et al., 2003; Niu et al., 2012). This host response may additionally be driven by various bacterial mechanisms. Upon infection, M. tuberculosis initially utilises host glucose and triacylglycerides as primary carbon sources, under aerobic conditions. However, with increased IFN-γ production, the glucose-deficient macrophages become

92 Chapter 4 hypoxic, and access to iron and its usual carbon sources become limited, forcing M. tuberculosis to induce an iron-scavenging reaction and utilise host cholesterol and fatty acids exclusively as the primary carbon sources. Hence, M. tuberculosis is able to adapt its metabolic activity in order to survive and persist within its human host, as well as to maximise energy production from alternative nutrient sources (Philips & Ernst, 2012; Rhee et al., 2011; Singh et al., 2012). M. tuberculosis can also co-catabolise many carbon sources simultaneously via compartmentalisation using multiple pathways (De Carvalho et al., 2010).

The elevated 5-hydroxyhydantoin detected in the TB-positive group is associated with DNA damage induced by oxidative stress, a common occurrence in TB patients, primarily due to

H2O2 production by the macrophages in an attempt to overcome the bacterial invasion (Du Preez & Loots, 2013). The ascorbic acid (vitamin C) oxidation product, oxalic acid (Robitaille et al., 2009), was observed in elevated concentrations in the TB-positive group. This is most likely due to ascorbic acid intake by the TB patients as this vitamin is not naturally synthesised by humans. Apart from this, foodborne moulds of Aspergillus species proliferate in the gastrointestinal tract of the host, as a secondary condition to the primary TB disease state (Singh & Toskes, 2004) and are also known to produce oxalic acid (Alam et al., 2002).

The ribitol, L-rhamnulose and 2-C-methylglycerol (2MG) detected in elevated concentrations in the TB patients, are most likely derived directly from M. tuberculosis, as these are components of its cell walls (Mikusova et al., 1996; Silhavy et al., 2010; Velagapudi et al., 2010). Furthermore, 2MG is also an intermediate of the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway, unique to M. tuberculosis, enabling this pathogen to synthesise essential metabolites derived from isoprenoid compounds, and is considered a target for the development of new anti-TB drugs, since homologous enzymes are absent in humans (Eoh et al., 2007; Kholodar & Murkin, 2013).

Finally, previous studies have proposed that the nutritional imbalance (resulting in wasting) caused by M. tuberculosis trigger autophagy, a homeostatic intracellular degradation process targeting intracellular M. tuberculosis. During this process, macromolecules and organelles are fused with lysosomes and degraded, in order to sustain cellular anabolic processes (Choi et al., 2013; Deretic & Levine, 2009; Goletti et al., 2013; Gutierrez et al., 2004). Once these macromolecules have been degraded, monomeric units, such as amino acids, fatty acids and DNA components, are either exported, excreted or reused (Goletti et al., 2013; Mizushima, 2007). Various studies have also shown that reduced insulin secretion (Mao et al., 2011) or increased glucagon (Deter & De Duve, 1967) may induce autophagy as an additional host response to TB, contributing to the elevated amino acids, fatty acids, DNA breakdown products, and bacterial components detected in the TB patients.

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Apart from the novel contribution of this discovery towards better understanding host–microbe interactions/adaptations, these markers may help explain some of the symptoms associated with TB, including neurological abnormalities (since quinolinic acid is a N-methyl-D-aspartate receptor agonist) (Heyes et al., 1992), vomiting, nausea, diarrhoea, drowsiness, fatigue, loss of appetite and weight-loss (associated with elevated levels of phenylacetic acid) (Sherwin & Kennard, 1919). Co-administration of anti-TB drugs with melatonin may assist in reducing elevated quinolinic acid (Cabrera et al., 2000), since melatonin has also been shown to lower a number of toxic metabolites associated with the side-effects of anti-TB drugs and also increase its efficacy for eliminating M. tuberculosis

(Loots et al., 2005). Furthermore, BH4 is given as a treatment for PKU patients in an attempt to reduce phenylacetic acid (McInnes et al., 1984), hence co-administration of anti-TB drugs with this merits consideration. Lastly, considering the association between TB and diabetes, co-administration of anti-TB drugs with antidiabetic drugs, such as metformin, could also be investigated. Singhal et al. (2014) showed how treatment of TB patients with metformin reduced intracellular M. tuberculosis growth by inducing reactive oxygen species production, facilitated phagolysosomal fusion by enhancing the host immune response, reduced the TB-induced tissue pathology and inflammation, decreased TB severity, increased conventional anti-TB drug efficacy, improved clinical outcome, and reduced the incidence of latent TB (Singhal et al., 2014).

Figure 4-4: Altered host metabolome induced by M. tuberculosis. A schematic representation of the metabolite markers (and their associated metabolism) identified in the urine of TB-positive patients, which are either increased (↑) or decreased (↓) relative to the healthy TB-negative group. Metabolites hypothesised/previously indicated in the literature to be elevated, are indicated with open arrows.

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4.5 CONCLUSION

Metabolomics is considered a multistage process, often aimed at collecting data for the purpose of formulating new hypotheses and elucidating previously unknown disease mechanisms and/or host–microbe interactions and adaptations. This metabolomics study compared a healthy TB-negative group to that of an active TB-positive patient group, to identify the host and microbe interactions and adaptations, in response to TB infection. The 12 identified urinary metabolites, best explaining the differences occurring between these groups, could directly be linked to an abnormal host fatty acid and amino acid metabolism, especially those affecting tryptophan, phenylalanine and tyrosine, and inducing a metabolite profile similar to that of a PKU patient. This is due to changes to IFN-γ and possibly insulin, and may also explain some of the TB-associated symptoms, providing clues to better treatment approaches.

4.6 REFERENCES

Adams, S.H. 2011. Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Advances in Nutrition (Bethesda, Md.), 2(6):445-456. Alam, S., Khalil, S., Ayub, N. & Rashid, M. 2002. In vitro solubilization of inorganic phosphate by phosphate solubilizing microorganisms (PSM) from maize rhizosphere. International Journal of Agriculture and Biology, 4(4):454-458. Asp, L., Johansson, A.S., Mann, A., Owe-Larsson, B., Urbanska, E.M., Kocki, T., Kegel, M., Engberg, G., Lundkvist, G.B. & Karlsson, H. 2011. Effects of pro-inflammatory cytokines on expression of kynurenine pathway enzymes in human dermal fibroblasts. Journal of Inflammation (London, England), 8:25-9255-9258-9225. Brereton, R.G. 2003. Chemometrics: Data analysis for the laboratory and chemical plant. West Sussex, England: John Wiley & Sons. Cabrera, J., Reiter, R.J., Tan, D.-X., Qi, W., Sainz, R.M., Mayo, J.C., Garcia, J.J., Kim, S.J. & El- Sokkary, G. 2000. Melatonin reduces oxidative neurotoxicity due to quinolinic acid: In vitro and in vivo findings. Neuropharmacology, 39(3):507-514. Cansev, M. & Wurtman, R.J. 2007. Aromatic amino acids in the brain. Handbook of neurochemistry and molecular neurobiology. Springer. p. 59-97). Choi, A.M.K., Ryter, S.W. & Levine, B. 2013. Autophagy in human health and disease. New England Journal of Medicine, 368(7):651-662. De Carvalho, L.P.S., Fischer, S.M., Marrero, J., Nathan, C., Ehrt, S. & Rhee, K.Y. 2010. Metabolomics of Mycobacterium tuberculosis reveals compartmentalized co-catabolism of carbon substrates. Chemistry & Biology, 17(10):1122-1131. De Villiers, L. & Loots, D.T. 2013. Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4):306-317.

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Suzuki, Y., Suda, T., Asada, K., Miwa, S., Suzuki, M., Fujie, M., Furuhashi, K., Nakamura, Y., Inui, N., Shirai, T., Hayakawa, H., Nakamura, H. & Chida, K. 2012. Serum indoleamine 2,3-dioxygenase activity predicts prognosis of pulmonary tuberculosis. Clinical and Vaccine Immunology: Cvi, 19(3):436-442. Tai, E.S., Tan, M.L.S., Stevens, R.D., Low, Y.L., Muehlbauer, M.J., Goh, D.L.M., Ilkayeva, O.R., Wenner, B.R., Bain, J.R. & Lee, J.J.M. 2010. Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia, 53(4):757- 767. Velagapudi, V.R., Hezaveh, R., Reigstad, C.S., Gopalacharyulu, P., Yetukuri, L., Islam, S., Felin, J., Perkins, R., Boren, J., Oresic, M. & Backhed, F. 2010. The gut microbiota modulates host energy and lipid metabolism in mice. Journal of Lipid Research, 51(5):1101-1112. Venter, L., Lindeque, Z., Van Rensburg, P.J., Van Der Westhuizen, F., Smuts, I. & Louw, R. 2015. Untargeted urine metabolomics reveals a biosignature for muscle respiratory chain deficiencies. Metabolomics, 11(1):111-121. Wang, S.-Y., Kuo, C.-H. & Tseng, Y.J. 2012. Batch normalizer: A fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Analytical Chemistry, 85(2):1037-1046. Warner, D.F. 2014. Mycobacterium tuberculosis metabolism. Cold Spring Harbor perspectives in medicine, 5(4):10.1101/cshperspect.a021121. World Health Organization. 2014. Tuberculosis: WHO global tuberculosis report 2014. Geneva, Switzerland (WHO Press).

98 Chapter 5 CHAPTER 5: URINARY METABOLITE MARKERS CHARACTERISING TUBERCULOSIS TREATMENT FAILURE

This chapter has been submitted for publication:

 Luies, L., Mienie, J., Motshwane, C., Ronacher, K., Walzl, G. & Loots, DT. (2017). Urinary metabolite markers characterising tuberculosis treatment failure. Submitted for publication to Metabolomics (Manuscript number: MEBO-D-17-00069).

5.1 ABSTRACT

Considering that approximately 15% of the nine million new TB cases reported per annum are not treated successfully, new, distinctive and specific biomarkers are needed to better characterise the biological basis of a poor treatment outcome. Urine samples from 36 active pulmonary TB patients were collected at baseline (time of diagnosis), during treatment (weeks 1, 2 and 4) and two weeks after treatment completion (week 26). These samples were divided into successful (cured) and unsuccessful (failed) treatment outcome groups and analysed using a GCxGC-TOFMS metabolomics research approach. The metabolite data collected showed clear differentiation of the cured and failed treatment outcome groups using the samples collected at the time of diagnosis, i.e. before any treatment was administered. The treatment failure group was characterised by an imbalanced gut microbiome, in addition to elevated levels of metabolites associated with abnormalities in the long-chain fatty acid β-oxidation pathway, accompanied by reduced L-carnitine and short-chain fatty acids, indicative of a mitochondrial trifunctional protein defect in particular. Furthermore, an altered amino acid metabolism was also observed in these patients, which confirms previous findings and associations to increased IFN-γ due to the host’s immune response to M. tuberculosis and a compromised insulin secretion.

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5.2 INTRODUCTION

TB remains the world’s foremost cause of death from a single bacterial agent, which is alarming as it is considered curable. Nearly 10.4 million new TB cases are reported per annum, of which almost 15% are not treated successfully, resulting in approximately 1.8 million deaths globally (3 800 deaths a day) (World Health Organization, 2016). TB treatment failure may be attributed to, amongst others: (a) irregular or inadequate anti-TB drug supplies to rural areas and third-world countries, (b) poor patient TB-education, (c) poor socio-economic circumstances, (d) the prolonged treatment duration, (e) treatment non- adherence, (f) anti-TB drug-resistance, as well as (g) various biological/biochemical factors (De Villiers & Loots, 2013).

The WHO recommends a six-month multi-drug treatment regimen, in which a combination of four drugs (INH, RIF, PZA and EMB) is used for treating active TB. In patients with drug- susceptible TB, this regimen reportedly has a 1–4% failure rate, and only 7% of those patients with a successful treatment outcome relapse within 24 months (Dye et al., 2005). When considering that every active TB patient can potentially infect an additional 10–15 individuals per annum (World Health Organization, 2016), it becomes evident that TB treatment failure and relapse are important considerations in achieving the millennium goals pertaining to the eradication of TB. Despite the many TB research efforts to date, the biological mechanisms associated with anti-TB drug response remains poorly understood. It is also unclear if certain patients have more efficient mechanisms for eliminating this disease and responding to the treatment thereof, and to what extent lifestyle and environmental factors may contribute to this (De Villiers & Loots, 2013).

Early disease diagnosis and effective treatment protocols are the two primary objectives of TB control, aimed at reducing mortality and morbidity while also preventing the development of drug-resistance (Antoine et al., 2007). The means to accurately predict prognosis from monitoring disease progression early in the treatment regimen would be regarded as a major breakthrough. This would instruct alternative treatment approaches in a timely fashion in order to reduce treatment failure and development of drug-resistance (Horne et al., 2010; Walzl et al., 2008). Considering this, there is a need for new, sensitive and specific biomarkers, not only for use in the early prediction of treatment failure, but also for better characterising and explaining the underlying mechanisms related to this occurrence, so that alternative treatment approaches can be developed. Once identified, these biomarkers need to be sufficiently validated for use as surrogate endpoints of treatment failure, early in the treatment regimen, ideally even before treatment begins. To date, no such urinary biomarkers have been identified with absolute certainty, hence the aim of this study was to

100 Chapter 5 use a GCxGC-TOFMS metabolomics approach to identify biomarkers differentiating individuals with a successful (n=27) and unsuccessful (n=11) treatment outcome, as early as possible during the treatment regimen, which would also better characterise and explain the biological mechanisms related to TB treatment failure. Urine was selected as the preferred sample for addressing the above mentioned aim, since large quantities can easily be obtained and less complex sample preparation is required for analyses, as compared to sputum for instance. Furthermore, in recent years, the need for a holistic approach to metabolism has led to the development of urinary metabolomics for biomarker discovery in various diseases (Mahapatra et al., 2014; Ryan et al., 2011).

5.3 METHODS

5.3.1 Clinical samples

Anonymised archived urine samples were procured from the Faculty of Medicine and Health Sciences, NRF/DST Centre of Excellence for Biomedical Research, at the Stellenbosch University/MRC Centre for TB Research, from where they were transported to the North-West University (NWU), Human Metabolomics: Infectious Disease Laboratory, for metabolomic analysis. These samples were selected from a prospective observational cohort study of individuals with active pulmonary TB, diagnosed using smear microscopy and bacteriological cultures (Hesseling et al., 2010). From these original samples, all treatment failure patients were included and matched by age, gender and extent of disease on chest X-rays to cured patients. Hence, the samples included were from 38 culture-confirmed active TB-positive South African patients (19 males and 19 females, between the age of 17 and 58) at baseline (time of diagnosis, thus prior to initiation of treatment), during the course of treatment with the DOTS strategy (weeks 1, 2 and 4) and two weeks after treatment completion (week 26). These patients had drug-susceptible TB, were HIV-seronegative, not pregnant, and with no other diseases (including diabetes, malignancy, lung cancer, chronic bronchitis and sarcoidosis). The samples were divided into successful (n=27) and unsuccessful (n=11) treatment outcome groups. According to the WHO, treatment failure can be defined as a patient whose sputum smear or culture is positive at month five or later during treatment (World Health Organization, 2014).

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5.3.2 Sample analysis

Creatinine values for all urine samples, including QC samples, were determined using a creatinine enzyme kit (Thermo Scientific; reference number 981845) and analysed on an Indiko Clinical Analyser, Type 863 (Thermo Scientific). These creatinine values are used to normalise metabolite concentrations, and to determine the volume of urine, internal standard, BSTFA, TMCS and pyridine needed for each extraction and derivatisation. Organic acid extractions of the patient-collected urine samples were performed, analysed on a Pegasus 4D GCxGC-TOFMS (LECO Africa (Pty) Ltd, Johannesburg, South Africa) along with the necessary QC samples, and processed (as described in sections 3.3.3, 3.3.4 and 3.3.5, respectively) to identify all compounds.

5.3.3 Statistical data analyses

A standard metabolomics data clean-up procedure was applied before statistical data analyses. All metabolites were normalised relative to the internal standard by calculating the relative concentration of each, and a 50% zero filter was applied to each variable (Smuts et al., 2013) to eliminate those compounds with more than 50% zero values within both groups. Quantile equating was applied to correct for any batch effects (Wang et al., 2012), followed by a 50% QC–CV filter. Lastly, all zero values were replaced with a value calculated as half of the lowest detected value present in the entire dataset, as these may be due to low abundance rather than being absent (Schoeman et al., 2012). GCxGC-TOFMS analysis yielded 782 compounds of which only 241 remained for further statistical analyses after the above mentioned data clean-up were completed.

The data was analysed via a variety of multi- and univariate statistical methods, using MetaboAnalyst (based on the statistical package “R”; version 2.10.0), which included PCA (Wold et al., 1987), PLS-DA (Westerhuis et al., 2008), fold change (Du Preez & Loots, 2013) and Mann-Whitney test (Pallant, 2001).

5.4 RESULTS AND DISCUSSION

Using the GCxGC-TOFMS data generated, a PCA was done to determine at which time point individuals who responded to the TB treatment can be differentiated from those who were not cured from TB. The PCA scores plots show clear differentiation of the successful and unsuccessful treatment outcome groups, using the urine collected at time of diagnosis and again at week 26 (Figure 5-1, a and b respectively), the latter of which can be expected

102 Chapter 5 since the cured individuals are TB culture negative, and those with a poor treatment outcome are still TB culture positive at this point in time (Luies & Loots, 2016). No PCA differentiation was achieved for the other time intervals investigated (see Appendix C), most likely due to the effects of the anti-TB medication on the human metabolome, masking any underlying differences which occur in the metabolism initially differentiating the successful and unsuccessful treatment outcome groups at time of diagnosis. For the differentiation achieved at time of diagnosis (Figure 5-1a), the total amount of variance explained by the first two PCs (R2X cum) was 93.4%, of which PC1 accounted for 91.8%, and PC2 accounted for 1.6%. For the differentiation achieved at week 26 (Figure 5-1b), the total amount of variance explained by the first two PCs (R2X cum) was 91.9%, of which PC1 accounted for 90.2% and PC2 accounted for 1.7%.

(a) (b)

Figure 5-1: Principal components analysis (PCA) scores plots of principal component 1 versus principal component 2 of the successful and unsuccessful treatment outcome groups. Clear differentiation of these two groups was achieved at (a) time of diagnosis and (b) week 26, due to variation in their underlying metabolite profiles. The variances accounted for are indicated in parenthesis.

Since the aim of this study was to identify biomarkers for early prediction of treatment response and explain the mechanisms associated with treatment failure, only the PCA differentiation achieved at time of diagnosis was of further interest in this investigation. The separation of the groups at week 26 are mostly due to metabolic changes since one group is TB-positive and the other TB-negative, which was already investigated on a previous occasion (Luies & Loots, 2016). Considering the above, a PLS-DA model was built for the time of diagnosis data and showed a modelling parameter R2Y (cum) of 93.48%, indicative of the total explained variation of the response Y.

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Clear separation of the successful and unsuccessful treatment outcome groups is remarkable considering the possible predictive value that these may have for identifying individuals who will or will not respond to conventional treatment, even before treatment commences. However, before this biosignature or the individual metabolite markers can be utilised for this purpose, further validation is needed using new subjects to ensure these findings can be generalised. In the context of this study, the biosignature/metabolite markers were used to better describe the biological mechanisms as to why certain individuals with drug-sensitive TB are not successfully treated. Those compounds with a PLS-DA VIP >1.0 (Smuts et al., 2013), or a fold change >|2|, or a Mann-Whitney with a P-value <0.05 (Du Preez & Loots, 2013), were selected as the metabolite markers that best explained the variation between the analysed groups. Of the 72 characteristic metabolite markers selected, 50 could be annotated using libraries compiled from previously injected standards, and are listed in Table 5-1.

Table 5-1: The 50 urinary metabolite markers identified at time of diagnosis that best explain the variation detected between the successful and unsuccessful treatment outcome groups.

Successful Unsuccessful treatment treatment outcome: outcome: Fold Mann- Average Average PLS-DA change Whitney Metabolite name concentration concentration (VIP) (absolute test (mg/g creatinine) (mg/g creatinine) value) (P-value) (standard error (standard error of the mean) of the mean) Amino acid metabolites: 2-Ketovaleric acid 2.093 (0.428) 3.799 (1.302) 1.30 1.82 0.1318 2-Methyl-3-hydroxybutyric acid 3.777 (0.489) 5.487 (0.708) 2.30 1.45 0.0273 2-Piperidinecarboxylic acid (Pipecolic acid) 0.893 (0.230) 0.428 (0.104) 1.24 2.09 0.0197 3-Hydroxy-3-methylglutaric acid 9.437 (0.456) 12.179 (0.945) 1.08 1.29 0.0077 3-Hydroxyvaleric acid 7.691 (1.341) 8.896 (1.861) 1.05 1.16 0.3301 4-Methylcatechol 6.916 (1.150) 10.299 (1.854) 1.36 1.49 0.1022 Glyceric acid 0.268 (0.098) 0.356 (0.091) 1.14 1.33 0.0062 Methylsuccinic acid 3.912 (0.448) 6.581 (1.020) 2.60 1.68 0.0030 N-Tiglylglycine 0.834 (0.247) 1.567 (0.371) 1.91 1.88 0.0094 o-Hydroxyphenylacetic acid 3.093 (0.384) 5.293 (0.602) 2.50 1.71 0.0030 Oxalic acid 14.250 (1.308) 19.933 (2.116) 2.29 1.40 0.0191 Phenylacetic acid 0.305 (0.157) 0.903 (0.273) 2.14 2.97 0.0306 Quinolinic acid 5.311 (0.854) 9.448 (1.403) 5.50 1.78 0.0040 Vanillylmandelic acid 17.088 (0.878) 22.269 (1.830) 1.27 1.30 0.0040 M. tuberculosis cell wall components: Ribitol 1.910 (0.627) 2.455 (0.523) 1.84 1.29 0.0708

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Gut microbiota imbalance metabolites: 2,3-Butanediol 17.831 (4.351) 16.301 (5.137) 3.65 1.09 0.4450 3-(3-Hydroxyphenyl)-3-hydroxypropionic acid 20.030 (3.029) 28.087 (4.093) 5.40 1.40 0.0553 3-Hydroxy-3-(4-hydroxy-3-methoxyphenyl) 2.912 (0.575) 8.366 (2.431) 6.45 2.87 0.0474 propionic acid 3-Hydroxyhippuric acid 17.580 (1.716) 23.662 (2.525) 2.37 1.35 0.0289 4-Hydroxybenzoic acid 4.642 (0.663) 9.966 (1.754) 4.92 2.15 0.0027 4-Hydroxyphenyllactic acid 5.695 (1.526) 6.724 (1.720) 1.21 1.18 0.1485 5-Hydroxymethyl-2-furoic acid 17.563 (4.279) 21.994 (6.053) 4.46 1.25 0.1795 Benzoic acid 0.936 (0.275) 1.367 (0.251) 1.56 1.46 0.0229 cis-4-Hydroxycyclohexanecarboxylic acid 1.428 (0.610) 0.666 (0.190) 0.87 2.15 0.0306 Citramalic acid 8.700 (1.608) 10.742 (3.124) 1.66 1.23 0.4550 Furan-2,5-dicarboxylic acid 8.755 (2.540) 10.156 (3.446) 1.14 1.16 0.3032 Furoylglycine 7.466 (1.443) 10.421 (2.241) 3.39 1.40 0.1115 Vanillic acid 7.731 (0.876) 11.249 (1.230) 2.23 1.46 0.0082 DNA damage and oxidative stress markers: 2-Deoxyribonic acid 0.477 (0.113) 1.080 (0.245) 1.85 2.27 0.0169 Parabanic acid 2.984 (0.799) 3.584 (0.865) 2.68 1.20 0.0474 TCA and GABA metabolites: 3,4-Dihydroxybutyric acid 9.160 (0.825) 11.730 (0.89) 1.03 1.28 0.0108 4-Hydroxybutyric acid 0.305 (0.097) 0.544 (0.158) 1.68 1.79 0.0088 Citric acid 2.730 (0.838) 3.526 (1.619) 1.34 1.29 0.2691 Malic acid 4.579 (2.712) 5.085 (2.668) 3.13 1.11 0.4351 Dicarboxylic acids and 3-hydroxy fatty acids: 2,3-Dihydroxypentonic acid 4.632 (1.255) 6.118 (2.265) 2.56 1.32 0.3485 3,5-Dihydroxypentonic acid 3.956 (0.469) 5.698 (0.790) 1.29 1.44 0.0743 3-Hydroxydodecanedioic acid 4.946 (1.659) 2.313 (1.075) 2.27 2.14 0.2000 3-Hydroxysebacic acid 1.499 (0.386) 3.716 (1.140) 2.36 2.48 0.0582 3-Ketosebacic acid 2.419 (0.535) 6.451 (1.560) 6.96 2.67 0.0324 3-Methylhexanoic acid (3-Methyladipic acid) 7.723 (0.800) 12.631 (1.637) 3.30 1.64 0.0058 cis,cis-4,7-Decadiene-1,10-dioic acid 9.523 (1.259) 12.113 (1.359) 1.19 1.27 0.0743 cis-4-Decene-1,10-dioic acid 1.403 (0.316) 2.561 (0.649) 1.74 1.83 0.0499 Heptanedioic acid (Pimelic acid) 2.895 (0.434) 4.967 (0.684) 2.32 1.72 0.0024 Hexanedioic acid (Adipic acid) 0.713 (0.192) 1.369 (0.443) 1.38 1.92 0.0816 Nonanedioic acid (Azelaic acid) 4.596 (0.918) 6.465 (1.271) 2.01 1.41 0.0894 Octanedioic acid (Suberic acid) 3.452 (0.595) 4.577 (0.394) 1.68 1.33 0.0169 Other: 1,2,3-Trihydroxybutane 1.962 (0.540) 2.842 (1.415) 1.22 1.45 0.4950 2-Methyl-1,2-dihydroxypropane 5.360 (1.768) 6.456 (2.985) 2.78 1.20 0.3672 4-Pentenoic acid 5.860 (0.898) 8.201 (1.289) 1.86 1.40 0.0611 Glucuronic acid 5.923 (0.442) 8.307 (0.757) 1.22 1.40 0.0062

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When considering the metabolite markers best differentiating the successful and unsuccessful treatment outcome patient groups, the first important observation is the elevated levels of the metabolites associated with tryptophan (quinolinic acid (Heyes et al., 1992)), phenylalanine (phenylacetic acid (Puri, 2006), o-hydroxyphenylacetic acid (Taniguchi & Armstrong, 1963)), and tyrosine (4-methylcatechol (Li et al., 2007), vanillylmandelic acid (Eisenhofer et al., 2004)) metabolism (see Figure 5-2). The same metabolic flux and the resulting accumulation of these amino acids, were also previously reported by Luies and Loots (2016) in TB-positive patient urine, and explained by elevations in IFN-γ due to the host’s immune response to M. tuberculosis and a compromised insulin secretion by the host (Luies & Loots, 2016). Furthermore, markers directly associated with the presence of M. tuberculosis (ribitol (Silhavy et al., 2010)), a gut microbiota imbalance (2,3-butanediol (Hong, 2011), 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (Shaw, 2013), 3-hydroxy-3-(4- hydroxy-3-methoxyphenyl)propionic acid, 3-hydroxyhippuric acid (Gonthier et al., 2003), 4-hydroxybenzoic acid (Russell et al., 2013; Tomás‐Barberán & Clifford, 2000), 4-hydroxyphenyllactic acid (Russell et al., 2013), 5-hydroxymethyl-2-furoic acid (Jellum et al., 1973), benzoic acid (Russell et al., 2013), cis-4-hydroxycyclohexanecarboxylic acid (Kronick et al., 1983), citramalic acid (Du Preez & Loots, 2013), furan-2,5-dicarboxylic acid (Jellum et al., 1973; Pettersen & Jellum, 1972), furoylglycine (Pettersen & Jellum, 1972), vanillic acid (Tomás‐Barberán & Clifford, 2000)), and DNA damage (2-deoxyribonic acid (Zhou & Greenberg, 2012)) due to oxidative stress (parabanic acid (Marklund et al., 2000)), were also detected and correlate with those markers previously characterising TB, as described by Luies and Loots (2016). Considering the fact that these markers are not only indicative of a general TB disease state (Luies & Loots, 2016), but also more pronounced in the treatment failure group in this study, may indicate that these individuals who are unsuccessfully treated have an increased disease severity due to a microbiota imbalance and/or underlying host abnormality, which will be discussed in greater detail below.

The highly significant evidence from the almost three times elevated 3-hydroxy-3-(4-hydroxy- 3methoxyphenyl)propionic acid and more than two times lower cis-4-hydroxycyclohexanecarboxylic acid in the treatment failure group, strongly testifies towards an imbalance in gut microbiota in the unsuccessfully treated patients, which is associated with a weaker immune system and inability to fight disease. Both the innate and adaptive immune response systems have evolved to rely on microbiota interactions, which not only promote immune cell maturation but also influence the normal development of immune functions (Clemente et al., 2012). Additionally, gut microbiota is also well-known to interact with various drugs, influencing many factors relating to their absorption and plasma concentrations, and hence is considered an important contributor to xenobiotic/drug bioavailability and toxicity (Gonzalez et al., 2011).

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Figure 5-2: Amino acid metabolism of the identified urinary metabolite markers. These markers are indicated as either increased (↑) or decreased (↓) in the treatment failure group comparatively.

Furthermore, altered levels of various dicarboxylic acids, acylcarnitines and 3-hydroxy fatty acids were detected in the urine of the treatment failure group. Lipid molecules are increasingly recognised as having the potential to shape the immune response to infectious pathogens (Kaushal, 2012) since these are important bioactive mediators of cellular activity during pathophysiological processes, and affect various activities including cell apoptosis, monocyte adhesion, platelet aggregation, and regulation of immune responses (Hasanally et al., 2014). Interestingly, however, the presence of these metabolites in urine is also used to distinguish between individuals with inherited defects of the following long-chain fatty acid (LCFA) transport-associated enzymes: (a) carnitine palmitoyltransferase 1 (CPT-1), (b) carnitine palmitoyltransferase 2 (CPT-2), (c) translocase (TL), (d) very-long-chain acyl- coenzyme A (CoA) dehydrogenase (VLCAD) or (e) any of the three enzymes making up the mitochondrial trifunctional protein (MTP); long chain 2-enoyl-Co A hydratase, long-chain 3-hydroxyacyl-CoA dehydrogenase and 3-ketoacyl-CoA thiolase (Garg & Dasouki, 2006; Sim et al., 2002). A CPT-1 deficiency results in reduced C16–C18 acylcarnitines and increased free carnitine, a CPT-2, TL or VLCAD deficiency results in both elevated long chain acylcarnitines and dicarboxylic acids with reduced free carnitine, and a MTP deficiency (on any of its three associated enzymes) results in elevated long chain acylcarnitines, dicarboxylic acids and 3-hydroxy fatty acids with reduced free carnitine (Garg & Dasouki, 2006). Since the organic acid extraction method used in this study was not suited for the extraction or detection of acylcarnitines, hence their absence in Table 5-1, we did a further ultra-performance liquid chromatography (UPLC) (Van Aardt et al., 2016) urinary

107 Chapter 5 acylcarnitine analysis (Venter et al., 2015), and found significantly elevated palmitoylcarnitine

(C16) in the urine of the treatment failure group comparatively (136.19 vs. 58.22 mmol/L; P=0.0095). Additionally, although not statistically significant, the total short chain acylcarnitines were comparatively decreased in the treatment failure group (8.77 vs. 2.75 mmol/L; P=0.2298). Free carnitine (C0) was also decreased (0.64 vs. 2.58 mmol/L; P=0.4329), as expected in such deficiencies, because these remain bound to the LCFAs and are unable to cross the mitochondrial membrane. Considering this diagnostic metabolite profile, a MTP deficiency or abnormality is suggested to occur in the treatment failure group (see Figure 5-3). MTP mutations are estimated at a prevalence of approximately 1:75 000 (Garg & Dasouki, 2006), and according to unpublished results of the Potchefstroom Laboratory for Inborn Errors of Metabolism (PLIEM), may be as high as 1 in 100 for a mutation in any of the seven enzyme/protein systems mentioned above. Additional evidence for this, is the serum carnitine deficiencies previously reported in 47.7% of all TB-positive patients (Hatamkhani et al., 2014), which are also associated with these carnitine transporters (Flanagan et al., 2010). Furthermore, L-carnitine has been shown to play a significant role in T-cell-dependent antibacterial activity in the host, and enhance the immune response via reduced macrophage and lymphocyte malfunction in TB patients (Jirillo et al., 1993; Jirillo et al., 1991), hence a deficiency in L-carnitine, due to an underlying enzyme/protein system deficiency, may explain why these individuals did not respond to treatment. Confirmation of a reduced capacity for mitochondrial β-oxidation due to such a deficiency, is the elevated concentrations of fatty acids with odd numbered carbons, such as azelaic acid and heptanedioic (pimelic) acid in the treatment failure group, due to the above mentioned accumulating fatty acids undergoing peroxisomal α-oxidation prior to peroxisomal β-oxidation (Figure 5-3) (Van Veldhoven, 2010).

Accumulation of fatty acyl-CoA derivatives, due to the defective LCFA transport, can also contribute to a compromised insulin secretion (Luies & Loots, 2016). Long-chain fatty acyl-CoA’s are strong inhibitors of glutamate dehydrogenase (GDH) (Lai et al., 1994), and since this enzyme is responsible for the conversion of glutamate to α-ketoglutarate, and an elevated flux of the TCA cycle, eventually resulting in insulin secretion (see Figure 5-3) (Newsholme et al., 2006), suggests an additional means by which insulin secretion may be compromised in the treatment failure group. Further confirmation of this is the elevated levels of 3,4-dihydroxybutyric acid and 4-hydroxybutyric acid, indicating an increased metabolic flux in the gamma-aminobutyric acid (GABA) shunt pathway (Shinka et al., 2002), in the opposite direction of the glutamate to α-ketoglutarate reaction previously mentioned (see Figure 5-2). Compromised insulin secretion has also been shown to result in an accumulation of various branched chain amino acids (BCAAs) (Lu et al., 2013), which explains the elevated leucine (3-hydroxyvaleric acid, 3-hydroxy-3-methylglutaric acid) and

108 Chapter 5 isoleucine (2-methyl-3-hydroxybutyric acid, methylsuccinic acid, N-tiglylglycine) metabolite intermediates observed in the treatment failure group comparatively.

Figure 5-3: Fatty acid oxidation of the identified urinary metabolite markers, which are either increased (↑) or decreased (↓) in the treatment failure group comparatively. Abbreviations: CPT I, carnitine palmitoyltranferase 1; CPT II, carnitine palmitoyltransferase 2; CACT, carnitine-acylcarnitine translocase; VLCAD, very long chain acyl-CoA dehydrogenase; MCAD, medium chain acyl-CoA dehydrogenase; SCAD, short chain acyl-CoA dehydrogenase; M/SCHAD, medium/short chain hydroxyacyl-CoA dehydrogenase; MTP, mitochondrial trifunctional protein; SCYD, short chain enoyl- CoA hydratase; MCT, medium chain 3-ketoacyl-CoA thiolase; SCT, short chain 3-ketoacyl-CoA thiolase; ACOX 1, acyl-CoA oxidase 1; ACOX 2, acyl-CoA oxidase 2; D-PBE, peroxisomal bifunctional enzyme; ACAA1, 3-ketoacyl-CoA thiolase; PHYH, Phytanoyl-CoA hydroxylase; HPLC2, Unknown; pistanalDH, pristanaldehyde dehydrogenase.

Whatever the proposed mechanism, the patients in the treatment failure group show a reduced capacity to transport LCFA into the mitochondria for metabolism into short-chain fatty acids (SCFAs), which are well-known for their antimicrobial effects (i.e. increase the fluidity of the bacterial cell wall which negatively influence cell wall integrity (Royce et al., 2013), and induce host defence peptide LL-37 (Mily et al., 2015)). Considering this, apart from the reduced L-carnitine detected in the treatment failure group, the reduced SCFAs may be an additional explanation as to why these individuals may not be responding to the treatment and why the metabolite markers suggest elevated disease severity.

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5.5 CONCLUSION

This study highlights the capacity of metabolomics to identify markers which predict a poor response to treatment, and also better characterise or propose previously unknown mechanisms resulting in TB treatment failure. The most significant observations in this metabolomics study were the elevated levels of those metabolites associated with an imbalance in the gut microbiome. Since this influence xenobiotic uptake and toxicity, the synchronous use of probiotics for optimising the microbiome during first-line anti-TB treatment, may improve treatment outcome, and could be a topic of further investigation. Furthermore, it may be interesting to compare these patients to a cohort of healthy humans with a normal intestinal biosis in order to firmly allocate the microbiota dysbiosis to the treatment failure cohort. Additionally, similar experimental undertakings with a larger sample cohort could certainly support this hypothesis. Also considering the fact that no PCA differentiation was achieved for the other time intervals investigated (see Appendix C), it would be interesting to learn whether the microbiota balance in the two comparative groups become equally disturbed or equally restored during treatment. The issue of whether the metabolites analysed in the urine of these patients are due to human host metabolism or whether they originate from the commensal intestinal bacteria can be addressed in future by experiments with germ-free and conventionalised M. tuberculosis-infected laboratory animals. Finally, another interesting observation was those metabolites associated with abnormalities in any of the three enzymes of the MTP complex in the treatment failure group. Since L-carnitine and SCFAs are also reduced in these individuals, and well-known for their anti-mycobacterial properties, this metabolic pathway may explain why these individuals have an increased disease severity and/or a poor response to TB treatment.

5.6 REFERENCES

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Dye, C., Watt, C.J., Bleed, D.M., Hosseini, S.M. & Raviglione, M.C. 2005. Evolution of tuberculosis control and prospects for reducing tuberculosis incidence, prevalence, and deaths globally. Jama, 293(22):2767-2775. Eisenhofer, G., Kopin, I.J. & Goldstein, D.S. 2004. Catecholamine metabolism: A contemporary view with implications for physiology and medicine. Pharmacological Reviews, 56(3):331-349. Flanagan, J.L., Simmons, P.A., Vehige, J., Willcox, M.D. & Garrett, Q. 2010. Role of carnitine in disease. Nutrition & Metabolism, 7(1):1. Garg, U. & Dasouki, M. 2006. Expanded newborn screening of inherited metabolic disorders by tandem mass spectrometry: Clinical and laboratory aspects. Clinical Biochemistry, 39(4):315-332. Gonthier, M.-P., Cheynier, V., Donovan, J.L., Manach, C., Morand, C., Mila, I., Lapierre, C., Rémésy, C. & Scalbert, A. 2003. Microbial aromatic acid metabolites formed in the gut account for a major fraction of the polyphenols excreted in urine of rats fed red wine polyphenols. The Journal of Nutrition, 133(2):461-467. Gonzalez, A., Stombaugh, J., Lozupone, C., Turnbaugh, P.J., Gordon, J.I. & Knight, R. 2011. The mind-body-microbial continuum. Dialogues in Clinical Neuroscience, 13(1):55. Hasanally, D., Chaudhary, R. & Ravandi, A. 2014. Role of phospholipases and oxidized phospholipids in inflammation. Phospholipases in health and disease. Springer. p. 55-72). Hatamkhani, S., Khalili, H., Karimzadeh, I., Dashti‐Khavidaki, S., Abdollahi, A. & Jafari, S. 2014. Carnitine for prevention of antituberculosis drug‐induced hepatotoxicity: A randomized, clinical trial. Journal of Gastroenterology and Hepatology, 29(5):997-1004. Hesseling, A., Walzl, G., Enarson, D., Carroll, N., Duncan, K., Lukey, P., Lombard, C., Donald, P., Lawrence, K. & Gie, R. 2010. Baseline sputum time to detection predicts month two culture conversion and relapse in non-HIV-infected patients. The International Journal of Tuberculosis and Lung Disease, 14(5):560-570. Heyes, M.P., Saito, K. & Markey, S.P. 1992. Human macrophages convert L-tryptophan into the neurotoxin quinolinic acid. Biochemical Journal, 283:633-635. Hong, Y.S. 2011. NMR‐based metabolomics in wine science. Magnetic Resonance in Chemistry, 49(S1):S13-S21. Horne, D.J., Royce, S.E., Gooze, L., Narita, M., Hopewell, P.C., Nahid, P. & Steingart, K.R. 2010. Sputum monitoring during tuberculosis treatment for predicting outcome: systematic review and meta-analysis. The Lancet Infectious Diseases, 10(6):387-394. Jellum, E., Børresen, H.C. & Eldjarn, L. 1973. The presence of furan derivatives in patients receiving fructose-containing solutions intravenously. Clinica Chimica Acta, 47(2):191-201. Jirillo, E., Altamura, M., Marcuccio, C., Tortorella, C., De Simone, C. & Antonaci, S. 1993. Immunological responses in patients with tuberculosis and in vivo effects of acetyl-L-carnitine oral administration. Mediators of Inflammation, 2(7):S17-S20. Jirillo, E., Altamura, M., Munno, I., Pellegrino, N., Sabato, R., Fabio, S.D. & Simone, C.D. 1991. Effects of acetyl-L-carnitine oral administration on lymphocyte antibacterial activity and TNF-α levels in patients with active pulmonary tuberculosis. A randomized double blind versus placebo study. Immunopharmacology and Immunotoxicology, 13(1-2):135-146. Kaushal, D. 2012. Eicosanoids, prostaglandins, and the progression of tuberculosis. Journal of Infectious Diseases:jis611.

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Kronick, J., Mamer, O., Montgomery, J. & Scriver, C. 1983. Apparent gastrointestinal origin of cis-4- hydroxycyclohexanecarboxylic acid. Clinica Chimica Acta, 132(2):205-208. Lai, J.C., Liang, B.B., Zhai, S., Jarvi, E.J. & Lu, D.R. 1994. Brain mitochondrial citrate synthase and glutamate dehydrogenase: differential inhibition by fatty acyl coenzyme A derivatives. Metabolic Brain Disease, 9(2):143-152. Li, G., Zhang, H., Sader, F., Vadhavkar, N. & Njus, D. 2007. Oxidation of 4-methylcatechol: implications for the oxidation of catecholamines. Biochemistry, 46(23):6978-6983. Lu, J., Xie, G., Jia, W. & Jia, W. 2013. Insulin resistance and the metabolism of branched-chain amino acids. Frontiers of Medicine, 7(1):53-59. Luies, L. & Loots, D. 2016. Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3):1-9. Mahapatra, S., Hess, A.M., Johnson, J.L., Eisenach, K.D., DeGroote, M.A., Gitta, P., Joloba, M.L., Kaplan, G., Walzl, G. & Boom, W.H. 2014. A metabolic biosignature of early response to antituberculosis treatment. BMC Infectious Diseases, 14(1):53. Marklund, N., Östman, B., Nalmo, L., Persson, L. & Hillered, L. 2000. Hypoxanthine, uric acid and allantoin as indicators of in vivo free radical reactions. Description of a HPLC method and human brain microdialysis data. Acta Neurochirurgica, 142(10):1135-1142. Mily, A., Rekha, R.S., Kamal, S.M., Arifuzzaman, A.S.M., Rahim, Z., Khan, L., Haq, M.A., Zaman, K., Bergman, P. & Brighenti, S. 2015. Significant effects of oral phenylbutyrate and vitamin D3 adjunctive therapy in pulmonary tuberculosis: A randomized controlled trial. PloS one, 10(9):e0138340. Newsholme, P., Brennan, L. & Bender, K. 2006. Amino acid metabolism, β-cell function, and diabetes. Diabetes, 55(Supplement 2):S39-S47. Pallant, J. 2001. Manual SPSS Survival: A step by step guide to data analysis using SPSS (pp. 302): Open University Press, McGraw-Hill Education, Philadelphia, USA. Pettersen, J.E. & Jellum, E. 1972. The identification and metabolic origin of 2-furoylglycine and 2, 5- furandicarboxylic acid in human urine. Clinica Chimica Acta, 41:199-207. Puri, D. 2006. Textbook of Medical Biochemistry: Clinical cases, Chapter 13.2. Textbook of medical biochemistry. New-Delhi, India: Elsevier. p. 376). Royce, L.A., Liu, P., Stebbins, M.J., Hanson, B.C. & Jarboe, L.R. 2013. The damaging effects of short chain fatty acids on Escherichia coli membranes. Applied Microbiology and Biotechnology, 97(18):8317-8327. Russell, W.R., Hoyles, L., Flint, H.J. & Dumas, M.-E. 2013. Colonic bacterial metabolites and human health. Current Opinion in Microbiology, 16(3):246-254. Ryan, D., Robards, K., Prenzler, P.D. & Kendall, M. 2011. Recent and potential developments in the analysis of urine: A review. Analytica Chimica Acta, 684(1):17-29. Schoeman, J.C., Du Preez, I. & Loots, D.T. 2012. A comparison of four sputum pre-extraction preparation methods for identifying and characterising M. tuberculosis using GCxGC-TOFMS metabolomics. Journal of Microbiological Methods, 91:301-311. Shaw, W. 2013. Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from patients with autism and schizophrenia. Nutritional Neuroscience.

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Shinka, T., Inoue, Y., Ohse, M., Ito, A., Ohfu, M., Hirose, S. & Kuhara, T. 2002. Rapid and sensitive detection of urinary 4-hydroxybutyric acid and its related compounds by gas chromatography–mass spectrometry in a patient with succinic semialdehyde dehydrogenase deficiency. Journal of Chromatography B, 776(1):57-63. Silhavy, T.J., Kahne, D. & Walker, S. 2010. The bacterial cell envelope. Cold spring harbor perspectives in biology, 2(5):a000414. Sim, K.G., Hammond, J. & Wilcken, B. 2002. Strategies for the diagnosis of mitochondrial fatty acid β-oxidation disorders. Clinica Chimica Acta, 323(1):37-58. Smuts, I., Van Der Westhuizen, F.H., Louw, R., Mienie, L.J., Engelke, U.F.H., Wevers, R.A., Mason, S., Koekemoer, G. & Reinecke, C.J. 2013. Disclosure of a putative biosignature for respiratory chain disorders through a metabolomics approach. Metabolomics, 9(2):379-391. Taniguchi, K. & Armstrong, M.D. 1963. The enzymatic formation of o-hydroxyphenylacetic acid. The Journal of Biological Chemistry, 238:4091-4097. Tomás‐Barberán, F.A. & Clifford, M.N. 2000. Dietary hydroxybenzoic acid derivatives–nature, occurrence and dietary burden. Journal of the Science of Food and Agriculture, 80(7):1024-1032. Van Aardt, W.J., le Roux, J.M., Lindeque, J.Z., Mason, S. & Louw, R. 2016. The effect of temperature on the respiration and metabolism of the African burrowing scorpion (Opistophthalmus latimanus). Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, 20:50- 56. Van Veldhoven, P.P. 2010. Biochemistry and genetics of inherited disorders of peroxisomal fatty acid metabolism. Journal of Lipid Research, 51(10):2863-2895. Venter, L., Lindeque, Z., Van Rensburg, P.J., Van Der Westhuizen, F., Smuts, I. & Louw, R. 2015. Untargeted urine metabolomics reveals a biosignature for muscle respiratory chain deficiencies. Metabolomics, 11(1):111-121. Walzl, G., Ronacher, K., Siawaya, J.F.D. & Dockrell, H.M. 2008. Biomarkers for TB treatment response: Challenges and future strategies. Journal of Infection, 57(2):103-109. Wang, S.-Y., Kuo, C.-H. & Tseng, Y.J. 2012. Batch normalizer: A fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Analytical Chemistry, 85(2):1037-1046. Westerhuis, J.A., Hoefsloot, H.C.J., Smit, S., Vis, D.J., Smilde, A.K., van Velzen, E.J.J., van Duijnhoven, J.P.M. & van Dorsten, F.A. 2008. Assessment of PLSDA cross validation. Metabolomics, 4(1):81-89. Wold, S., Esbensen, K. & Geladi, P. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1):37-52. World Health Organization. 2014. Definitions and reporting framework for tuberculosis – 2014 revision. Geneva, Switzerland (WHO Press). World Health Organization. 2016. Global tuberculosis report 2016. Geneva, Switzerland (WHO Press). Zhou, C. & Greenberg, M.M. 2012. Histone-catalyzed cleavage of nucleosomal DNA containing 2- deoxyribonolactone. Journal of the American Chemical Society, 134(19):8090-8093.

113 Chapter 6 CHAPTER 6: PREDICTING TUBERCULOSIS TREATMENT OUTCOME USING METABOLOMICS

This chapter has been submitted for publication:

6.1 ABSTRACT

Predicting a poor treatment outcome would offer significant benefits for patient care and for new drug development. Urine samples from TB-positive patients with a successful and unsuccessful treatment outcome were collected at baseline and analyzed. The identified metabolites were used in a forward logistic regression model, which achieved a ROC AUC of 0.94 (95% CI 0.84–1) and cross-validated well in a leave-one-out context, with an AUC of 0.89 (95% CI 0.7–1). Two possible predictors were identified, which are associated with a gut microbiota imbalance. Our findings show the capacity of metabolomics to predict treatment failure at time of diagnosis, which potentially offers significant benefits for use in new drug development clinical trials and individualized patient care.

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6.2 INTRODUCTION

The latest reports indicate 10.4 million individuals are infected with M. tuberculosis globally, resulting in an estimated 1.8 million deaths from TB, annually. Clearly, current strategies to control this pandemic are inadequate, especially in high-burden countries with limited resources, where the treatment target goals, as recommended by the WHO, have not yet been met (World Health Organization, 2015).

Currently, the most effective anti-TB treatment strategy, DOTS, consists of an initial two month (intensive) phase using a combination of INH, RIF, PZA and EMB, followed by a four month (maintenance) phase, consisting of only INH and RIF (Olivier & Loots, 2011). The DOTS strategy, when fully adhered to, has a relatively high success rate in patients with drug-susceptible TB (World Health Organization, 2015). TB treatment failure using this approach, however, still occurs with drug-sensitive TB, and is defined as the occurrence of persistently positive sputum smears or cultures at the end of treatment (Mukherjee et al., 2004). An additional concern is that these individuals, who are not treated successfully, remain infectious for prolonged periods of time, and continue to transmit the disease to others. Improved treatment outcomes and timely intervention is a priority for TB control programs and requires the identification of those factors associated with treatment failure (Alobu et al., 2014). Previous studies suggest the following risk factors for drug-susceptible TB treatment failure and relapse: (a) a less than 5% gain in bodyweight (Krapp et al., 2008); (b) age; (c) HIV co-infection; (d) diabetes type II, (e) illiteracy; (f) alcoholism and (g) prior TB treatment (Albuquerque et al., 2007). The most prominent contributor to this occurrence, however, is a slow patient response to treatment. It is also important to note that a number of novel immunological biomarkers (i.e cytokines and chemokines) have been proposed for monitoring TB treatment response and predicting treatment outcome (Walzl et al., 2008), including those directly associated with M. tuberculosis, such as lipoarabinomannan (Lawn et al., 2017; Lawn et al., 2012) or those associated with the overall inflammatory response, such as induced protein (IP)-10 (Cannas et al., 2010; Petrone et al., 2015; Petrone et al., 2016).

Two diagnostic approaches are frequently used to detect active TB and monitor treatment outcome, namely sputum smear microscopy and bacteriological cultures, the latter of which is considered the gold standard. Response to treatment is usually determined based on sputum culture conversion after two months of treatment (Horne et al., 2010). However, a period of two months or more before the first indications of treatment efficacy can be determined, is considered too long, since the bacteria have had the opportunity to adapt and create drug-resistant strains (Baumann et al., 2012; Siawaya et al., 2008), and also

115 Chapter 6 considering that these patients may still transfer the disease to others during this time. Additionally, these treatment failure predictive methods require frequent laboratory monitoring, which is not only time consuming, but also costly. Thus, there is an urgent need to search for alternative approaches or biomarkers which can be used for the early detection of treatment failure (Namukwaya et al., 2011).

Over the last 10 years, metabolomics has proved to be a very effective tool to search for and identify new diagnostic and prognostic biomarkers and/or disease risk factors (Dunn et al., 2012). Metabolomics is defined as the unbiased identification and quantification of all intermediates of metabolism in a biological system, using specialised analytical techniques, in conjunction with biostatistical and mathematical analysis (De Villiers & Loots, 2013). In 2012, Olivier and Loots applied a GC-MS metabolomics approach to characterise and differentiate various infectious Mycobacterium species (i.e. M. tuberculosis, M. bovis, M. kansasii and M. avium) and Pseudomonas aeruginosa based on their characteristic lipid profiles. Twelve lipid biomarkers were identified and used to build a multivariate discriminant model, which could correctly assign unknown samples to their respective species groups with probabilities ranging from 72 to 100% (Olivier & Loots, 2012). Che et al. (2013) used a similar approach to compare serum samples collected from healthy controls and TB patients, before and after TB therapy, to find diagnostic markers for active TB, not influenced by anti- TB therapy. They indicated nine potential diagnostic TB biomarkers, one of which (i.e. 5-oxoproline) remained unaffected by first-line TB therapy (Che et al., 2013). These studies indicate the capacity of metabolomics to not only differentiate M. tuberculosis from other Mycobacterium species, but also to identify useful biomarkers that can be used for the early prediction of treatment outcome, and for possible use in new anti-TB drug development. Considering this, we used an untargeted GCxGC-TOFMS urinary metabolomics approach to differentiate TB-positive patients with a successful (n=27) and unsuccessful (n=11) treatment outcome at time of diagnosis, and identified those metabolite markers which best differentiate the groups, in order to build a logistic regression model to possibly predict treatment failure before first-line anti-TB drug administration commences

6.3 MATERIALS AND METHODS

6.3.1 Clinical samples

116 Chapter 6 for metabolomics analysis. These samples were part of a prospective observational cohort study of individuals with active pulmonary TB, diagnosed with M. tuberculosis infection using smear microscopy and bacteriological cultures and strain typing (Hesseling et al., 2010). Representation of different M. tuberculosis strains was not significantly different between cured and failed participants. The samples were not collected at a specific time of day in order to ensure robustness of any identified metabolites. The samples were not collected at a specific time of day in order to ensure robustness of any identified metabolites. From the original samples, all treatment failure cases were included and matched by age, gender and extent of disease on chest X-rays to cured cases. Thus, the samples included were from 38 culture-confirmed active TB-positive South African patients (19 males and 19 females, aged between 17 and 58) at baseline (time of diagnosis, thus prior to initiation of treatment). These patients had drug-susceptible TB, were HIV-seronegative, not pregnant, and had no other diseases (i.e. diabetes, malignancy, lung cancer, chronic bronchitis and sarcoidosis), nor were they receiving any other medication, including antibiotics. These samples were divided into successful (n=27) and unsuccessful (n=11) treatment outcome groups.

6.3.2 Sample analysis

A creatinine value was determined for each urine sample collected, followed by an organic acid extraction and derivatisation (as described in section 3.3.3). Hereafter, the samples were analysed by randomly injecting each sample, along with the necessary QC samples, into a Pegasus 4D GCxGC-TOFMS and processed in order to identify compounds by comparison of their mass spectra to libraries generated from previously injected standards, as described in sections 3.3.4 and 3.3.5, respectively.

6.3.3 Statistical data analyses

It is standard practise in metabolomics investigations to perform various data clean-up procedures, prior to statistical data analyses (Van den Berg et al., 2006). The concentrations of all the identified metabolites were calculated relative to the internal standard and expressed in relation to the creatinine value. A 50% zero filter was applied to each variable (Smuts et al., 2013) to eliminate compounds with more than 50% zero values in both groups. Any batch effects were corrected for using quantile equating (Wang et al., 2012), followed by a 50% quality control–coefficient of variation (QC–CV) filter. Finally, in order to select the most significant metabolite markers, various univariate statistical analyses (i.e. fold change, Mann-Whitney test and effect sizes) were applied to the untransformed data using MATLAB with Statistics and PLS Toolbox Release (2012; The MathWorks Inc., Natick, MA, USA) and SAS (2015; The SAS System for Windows Release 9.3 TS Level 1M0 Copyright© by SAS

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Institute Inc., Cary, NC, USA). Fold change compares the absolute value changes of specific variables across two groups, and fold changes greater than two are considered significant (Du Preez & Loots, 2013). The Mann-Whitney test is considered the non-parametric alternative to the t-test, when assessing independent samples. This test determines significance by comparing the medians of variables across two groups, where a P-value below 0.05 is considered significant (Pallant, 2001). Effect sizes indicate practical significance by comparing variables individually between groups. For this non-parametric dataset, effect sizes were based on the associated Mann-Whitney z-values and an effect size above 0.3 (which indicates a moderate effect) were deemed practically relevant (Ellis & Steyn, 2003).

6.4 RESULTS AND DISCUSSION

As indicated in Figure 6-1, compounds with an absolute fold change exceeding two, a Mann- Whitney P-value of 0.05 or lower and an effect size larger than 0.3 were selected as the metabolite markers most capable of predicting treatment failure, and are listed in Table 6-1 (n=18). This multi-selection approach is based on the assumption that these different statistical methods compensate for each other’s limitations and should reduce the rate of false discovery of unimportant compounds (Luies & Loots, 2016). A volcano plot was used to compare the size of the fold change to the statistical significance level by plotting the log10 scaled Mann-Whitney P-values against the log2 scaled fold change values (see Figure 6-2).

Figure 6-1: Venn-diagram indicating the multi-selection approach used to select the most significant metabolite markers for predicting treatment failure.

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Figure 6-2: Volcano plot of the log10 scaled Mann-Whitney P-values against the log2 scaled fold change values.

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size

0.41

0.429 0.368 0.383 0.414 0.573 0.497 0.296 0.391 0.323 0.338 0.429 0.315 0.402 0.535 0.323 0.323 0.387

Effect

Mann

Whitney

(ω value)

value)

test

0.02 0.05 0.03 0.04

0.008 0.016 0.011 0.001 0.003 0.015 0.036 0.008 0.013 0.001 0.036 0.036 0.011 0.016

Mann

Whitney

(

2.46

Fold

2.008 2.334 2.367 2.251 3.085 2.046 2.591 5.832 4.969 2.251 2.201 2.838 2.943 2.038 2.172

value)

11.171 94.334

change

(absolute

)

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

(n=

Samples detected

(1.714)

Average

(Standard

deviation)

0.248 (0.21)

35.154 (35.4) 1.364 (1.018) 2.374 (1.655) 3.554 (3.813) 2.649 (2.383) 1.759 1.926 (1.077) 2.234 (2.357) 0.967 (0.858) 0.347 (0.232) 16.9 (13.627) 2.874 (2.568)

Unsuccessful

concentration

5.458 (13.715)

31.249 (36.752) 22.472 (21.333) 12.211 (36.959)

(mg/g creatinine)

289.043 (416.387)

treatmentoutcome:

)

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

(n=

95.20% 95.20% 95.20% 90.50% 95.20% 95.20% 95.20%

Samples detected

potentially predicting treatment outcome at time of diagnosis. time of at outcome treatment predicting potentially

Average

(Standard

deviation)

0.576 (0.6)

Successful

0.489 (0.35) 1.054 (0.49) 0.45 (0.725) 0.43 (0.364)

13.388 (6.72) 7.285 (4.019) 1.737 (0.779) 1.077 (1.799) 0.679 (0.706) 0.158 (0.179) 0.087 (0.096) 8.294 (7.618) 0.129 (0.112) 1.324 (0.511)

concentration

17.509 (12.067) 11.231 (39.786)

(mg/g reatinine)

98.212 (168.565)

treatmentoutcome:

(s)

1489,1.7 520,1.65

time (RT)

1396,1.59 1573,1.52 1018,2.12 1021,1.69 1573,1.64 1720,1.78 1444,1.48 1642,1.97 1612,1.71 2128,2.53 1363,1.58 1648,1.77 1753,1.59 2050,1.84 2101,1.85 2116,2.05

Retention

lactone

1,4

enzenepropanoic acid enzenepropanoic

ethylhexyl ester

dioic acid dioic

ropanetricarboxylic acid ropanetricarboxylic

Metabolite name

methoxyphenyl)propionic acid methoxyphenyl)propionic

ydroxyoxy

1,10

h -

methylphenol

The 18 univariate urine metabolite markers considered for for considered markers metabolite urine 18 univariate The - -

exanedioic acid

5 4

1,2,3 h

- -

Tetrahydroxypentanoic acid Tetrahydroxypentanoic

Decene

Hydroxy -

- Butylhydroquinone

Dihydroxybenzoic acid Dihydroxybenzoic

Hydroxy Methoxy acid Octenoic (4 Hydroxy Methoxy

------

2,3,4,5 2 2 2 3,5 3 3 3 cis Phthalicacid mono tert Unknown1 Unknown2 Unknown3 Unknown4 Unknown5 Unknown6 Unknown7

Table 6 Table

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The predictive ability of these compounds (Table 6-1) were compared and combined by performing forward, backward and stepwise selection logistic regression. Therefore, all metabolites in Table 6-1 were given an equal opportunity to form part of the final model since all were provided to the logistic regression procedure. The procedure then considers various models, but penalizes models with higher numbers of predictors. Metabolites are added (forward), removed (backward) or both (stepwise) until a model is found which performs the best in terms of prediction and number of predictors. The final model was based on forward selection and was chosen as it showed the most predictive potential (i.e. had the highest area under the receiver operating characteristic) — it should be noted however that not all compounds with a high fold change or low Mann-Whitney p-value are necessarily good predictors (see Figure 6-3). The model contained two metabolite markers as potential predictors of treatment outcome, namely 3,5-dihydroxybenzoic acid and 3-(4-hydroxy-3- methoxyphenyl)propionic acid. The model showed acceptable fit (goodness-of-fit) with a non-significant Hosmer-Lemeshow statistic (P=0.444), as well as a reasonable relationship between treatment outcome and the above predictors with a maximum rescaled R-squared statistics of 0.75. Both predictors made significant contributions to the prediction, as indicated by the Wald criterion reported in Table 6-2.

Figure 6-3: Box-like plots of the top four fold change metabolites, demonstrating that not all compounds are necessarily good predictors.

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Table 6-2: The maximum likelihood parameter estimates and odds ratios.

Wald Chi- 95% CI for Odds Ratio 95% CI for Parameter Estimate P-value square Estimates (unit=1) Odds Ratio

3,5-Dihydroxybenzoic acid 3.24 5.71 0.017 1.2 – 6.8 25.6 3.2 – 886.1

3-(4-Hydroxy-3-methoxyphenyl) 0.29 3.53 0.06 0.1 – 0.7 1.3 1.1 – 2.0 propionic acid

The odds ratio, reported for each of the possible predictors in Table 6-2, is a measure of the association between a specific feature (i.e. metabolite) and a treatment outcome (i.e. failure to respond to treatment) (Field, 2013). Thus, the odds ratio represents the increased or decreased probability that a patient will not be cured successfully (treatment failure) after anti-TB drug treatment, for every unit increase or decrease in the metabolite measured. An odds ratio of greater than 20 is considered significant for a diagnostic biomarker (Dunn et al., 2012). An increase of one unit in the levels of 3,5-dihydroxybenzoic acid and 3-(4-hydroxy-3- methoxyphenyl)propionic acid are associated with an increased risk of treatment failure, i.e. the odds of treatment failure increase by 25.6 and 1.3, respectively, for each one unit increase. Although the odds ratio associated with 3-(4-hydroxy-3-methoxyphenyl)propionic acid are not indicative of a biomarker, the combination of multiple “weak” metabolite markers into a single model may also provide a high level of discrimination (Dunn et al., 2012). The large confidence intervals (CIs) are due, as least in part, to the small groups used here and the relatively large variation within these groups. As with all newly developed models, further validation of these possible predictors is an absolute requirement prior to application.

The predictive ability of the model was assessed based on the receiver operating characteristic (ROC) curve, as shown in Figure 6-4. The model achieved an area under the ROC (AUC) value of 0.94 (95% CI 0.84 – 1) and cross-validated well in a leave-one-out context, with an AUC equal to 0.89 (95% CI 0.7 – 1). The predictive ability of a model is established if it achieves an AUC of 0.9 or higher (Dunn et al., 2012), however, to truly validate the model will require a larger, independent sample cohort. Therefore, we focused our attention on interpreting the two possible predictors in the model.

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0.9

0.8

0.7

0.6

0.5 Sensitivity 0.4

0.3 ROC (AUC=0.94) 0.2 CV LOO ROC (AUC=0.89) 0.1

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1-Specificity

Figure 6-4: Receiver operating characteristic (ROC) curve of the final logistic regression model. The black line represents the ROC when all samples are used to access sensitivity and specificity of the model, while the red line represents the ROC when cross-validated using a leave-one-out approach.

The box-like plots of these two possible predictors (and their chemical structures), as illustrated in Figure 6-5, indicate the individual variation in the data. These plots were generated using notBoxPlot.m, a MATLAB function developed by Rob Campbell (http://www.mathworks.com/ matlabcentral/fileexchange/26508-raacampbell13-notboxplot). The raw data is scattered over a 95% confidence interval (red box) and one standard deviation (blue box) of the mean (red line). The individual observations (indicated by the grey dots) show a much larger variation in the unsuccessful treatment outcome group comparatively, as well as some overlap. It is therefore evident from these graphs that neither predictor will perform well in isolation. We also include a bivariate scatter plot (see Figure 6- 6) to graphically illustrate the combined predictive ability of 3,5-dihydroxybenzoic acid and 3- (4-hydroxy-3-methoxyphenyl) propionic acid to discriminate between individuals with a successful and unsuccessful treatment outcome.

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Figure 6-5: Box-like plots of the two identified possible predictors, with the accompanying chemical structures.

Figure 6-6: Bivariate scatter plot of the raw data generated for 3,5-dihydroxybenzoic acid versus 3-(4- hydroxy-3-methoxyphenyl)propionic acid. The line indicates clear separation between the two comparative groups based on the two metabolites selected as predictors.

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When considering these two possible predictors from a biological mechanistic perspective, both 3,5-dihydroxybenzoic acid (α-resorcylic acid) (Liu et al., 2012) and 3-(4-hydroxy-3- methoxyphenyl)propionic acid (dihydroferulic acid) (Rechner et al., 2001) are compounds with aromatic rings associated with a microbial imbalance (Liu et al., 2012; Rechner et al., 2001), and also with a TB disease state (Luies & Loots, 2016; Singh & Toskes, 2004). Hence, an altered microbiome in the treatment failure group may be contributing to the lacking efficacy of the anti-TB drugs (Estudante et al., 2013). These results also confirm previous findings by Luies et al., (2017), who identified a number of metabolites related to a microbial imbalance, using a combination of multivariate and univariate statistical approaches, which characterised treatment failure to first-line drugs (Luies et al., 2017). Since the innate and adaptive immune response systems rely on various microbiota interactions to promote immune cell maturation and function, a microbial imbalance results in a weakened immune system and an inability to fight disease (Clemente et al., 2012). Additionally, the gut microbiota is considered an important contributor to xenobiotic/drug bioavailability and toxicity since it interacts with various drugs, influencing factors relating to their absorption, distribution and secretion, and hence plasma concentrations (Gonzalez et al., 2011).

Compounds with aromatic rings (Estudante et al., 2013; Vasiliou et al., 2009; Warren et al., 1975), including the majority of the anti-TB drugs used in this investigation (INH, RIF and PZA), and their phase II metabolites, are transported across cell membranes and eliminated via the ATP-binding cassette (ABC)-type multidrug transporters, which are highly expressed in human enterocytes of the liver, intestine, blood-brain barrier, blood-testis barrier, placenta and kidneys (Louw et al., 2009; Vasiliou et al., 2009) as well as on the cell surface of various intersinal microbiota (Mercado-Lubo, 2010). If for whatever reason (such as a microbial imbalance for instance), there is an overexpression/activity of these proteins on the host’s cell surfaces, it would lead to an elevated excretion of these aromatics, explaining the elevated concentrations of the 3,5-dihydroxybenzoic acid and 3-(4-hydroxy-3- methoxyphenyl)propionic acid in the urine of the treatment failure patient group, suggesting that these aromatic anti-TB drugs would have suffered the same fate, possibly explaining treatment failure.

The genome of M. tuberculosis also contains 2.5% ABC transporters, which may be classified as either importers or exporters, determined by the direction of translocation of substrates (Louw et al., 2009). The known mechanisms of drug-resistance to first-line anti- TB drugs, can be summarized as follows: INH-resistance is due to mutations in the katG and inhA genes; RIF-resistance results from alterations or point mutations in the rpoB gene of RNA polymerase; PZA-resistance may result from various mutations in the pncA, rpsA and

125 Chapter 6 panD genes; and EMB-resistance is mainly due to mutations on the embB gene (Zhang & Yew, 2015). However, these mutations do not explain the mechanism of drug-resistance in all clinical isolates, suggesting that additional, unknown mechanisms may also be at play, such as the production of enzymes/proteins which modify/degrade drugs resulting in inactivation, cell wall impermeability and efflux mechanisms. Although such individual mechanisms may not result in clinical resistance, the combinations of these may confer high- level resistance, which may help explain the variation in resistance seen in M. tuberculosis isolates (Louw et al., 2009).

Although, the scope of this investigation was only to identify metabolite markers and test their capacity for predicting treatment failure, using patient-collected urine samples, and not to elucidate the possible mechanisms for treatment failure, the above mentioned hypotheses are interesting topics for future investigation, and also serve as further validation for their possible use for predicting a failed treatment outcome to first-line anti-TB drugs. Furthermore, it may be interesting to compare these patients to healthy humans with a normal intestinal biosis in order to firmly allocate the microbiota dysbiosis to the treatment failure cohort. Additionally, the issue of whether the metabolites analysed in the urine of these patients are due to human host metabolism or whether they originate from the commensal intestinal bacteria can be addressed in future by experiments with germ-free and conventionalised M. tuberculosis-infected laboratory animals.

6.5 CONCLUSION

We were able to identify two urinary metabolite markers which possibly predict treatment failure of TB patients to first-line anti-TB drugs, before treatment onset. When these possible predictors were combined into a logistic regression model, they performed well with regards to sensitivity and specificity using leave-one-out cross-validation. It is important to note that the purpose of this model was to identify a combination of variables with high discriminatory ability containing as few variables as needed to achieve this, for potential diagnostic purposes. We propose that these two possible predictors warrant further investigation and validation using a bigger sample cohort. Once validated, the development of a prediction rule or predictive model can commence. Nonetheless, our findings show the capacity of metabolomics to identify patients at greater risk of a poor treatment outcome, at time of diagnosis, which potentially offers significant benefits for use in new drug development clinical trials and an individualized approach to patient care.

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6.6 REFERENCES

Albuquerque, M.d.F.P.M., Ximenes, R.A.d.A., Lucena-Silva, N., Souza, W.V.d., Dantas, A.T., Dantas, O.M.S. & Rodrigues, L.C. 2007. Factors associated with treatment failure, dropout, and death in a cohort of tuberculosis patients in Recife, Pernambuco State, Brazil. Cadernos de Saúde Pública, 23(7):1573-1582. Alobu, I., Oshi, D.C., Oshi, S.N. & Ukwaja, K.N. 2014. Profile and determinants of treatment failure among smear-positive pulmonary tuberculosis patients in Ebonyi, Southeastern Nigeria. International Journal of Mycobacteriology, 3(2):127-131. Baumann, R., Kaempfer, S., Chegou, N.N., Nene, N.F., Veenstra, H., Spallek, R., Bolliger, C.T., Lukey, P.T., Van Helden, P.D. & Singh, M. 2012. Serodiagnostic markers for the prediction of the outcome of intensive phase tuberculosis therapy. Tuberculosis, 93:239-245. Cannas, A., Calvo, L., Chiacchio, T., Cuzzi, G., Vanini, V., Lauria, F.N., Pucci, L., Girardi, E. & Goletti, D. 2010. IP-10 detection in urine is associated with lung diseases. Bmc Infectious Diseases, 10(1):333. Che, N., Cheng, J., Li, H., Zhang, Z., Zhang, X., Ding, Z., Dong, F. & Li, C. 2013. Decreased serum 5-oxoproline in TB patients is associated with pathological damage of the lung. Clinica Chimica Acta, 423:5-9. Clemente, J.C., Ursell, L.K., Parfrey, L.W. & Knight, R. 2012. The impact of the gut microbiota on human health: an integrative view. Cell, 148(6):1258-1270. De Villiers, L. & Loots, D.T. 2013. Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4):306-317. Du Preez, I. & Loots, D.T. 2013. New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. Tuberculosis, 93(3):330-337. Dunn, W.B., Wilson, I.D., Nicholls, A.W. & Broadhurst, D. 2012. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18):2249-2264. Ellis, S.M. & Steyn, H.S. 2003. Practical significance (effect sizes) versus or in combination with statistical significance (p-values): Research note. Management Dynamics: Journal of the Southern African Institute for Management Scientists, 12(4):p. 51-53. Estudante, M., Morais, J.G., Soveral, G. & Benet, L.Z. 2013. Intestinal drug transporters: an overview. Advanced Drug Delivery Reviews, 65(10):1340-1356. Field, A. 2013. Discovering statistics using SPSS statistics. 3. London: Sage Publications Ltd. Gonzalez, A., Stombaugh, J., Lozupone, C., Turnbaugh, P.J., Gordon, J.I. & Knight, R. 2011. The mind-body-microbial continuum. Dialogues in Clinical Neuroscience, 13(1):55. Hesseling, A., Walzl, G., Enarson, D., Carroll, N., Duncan, K., Lukey, P., Lombard, C., Donald, P., Lawrence, K. & Gie, R. 2010. Baseline sputum time to detection predicts month two culture conversion and relapse in non-HIV-infected patients. The International Journal of Tuberculosis and Lung Disease, 14(5):560-570.

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Horne, D.J., Royce, S.E., Gooze, L., Narita, M., Hopewell, P.C., Nahid, P. & Steingart, K.R. 2010. Sputum monitoring during tuberculosis treatment for predicting outcome: systematic review and meta-analysis. The Lancet Infectious Diseases, 10(6):387-394. Krapp, F., Veliz, J., Cornejo, E., Gotuzzo, E. & Seas, C. 2008. Bodyweight gain to predict treatment outcome in patients with pulmonary tuberculosis in Peru. The International Journal of Tuberculosis and Lung Disease, 12(10):1153-1159. Lawn, S.D., Kerkhoff, A.D., Burton, R., Schutz, C., Boulle, A., Vogt, M., Gupta-Wright, A., Nicol, M.P. & Meintjes, G. 2017. Diagnostic accuracy, incremental yield and prognostic value of Determine TB- LAM for routine diagnostic testing for tuberculosis in HIV-infected patients requiring acute hospital admission in South Africa: a prospective cohort. BMC Medicine, 15(1):67. Lawn, S.D., Kerkhoff, A.D., Vogt, M. & Wood, R. 2012. Diagnostic accuracy of a low-cost, urine antigen, point-of-care screening assay for HIV-associated pulmonary tuberculosis before antiretroviral therapy: a descriptive study. Lancet Infectious Diseases, 12(3):201-209. Liu, C., Kuei, C., Zhu, J., Yu, J., Zhang, L., Shih, A., Mirzadegan, T., Shelton, J., Sutton, S. & Connelly, M.A. 2012. 3,5-Dihydroxybenzoic acid, a specific agonist for hydroxycarboxylic acid 1, inhibits lipolysis in adipocytes. Journal of Pharmacology and Experimental Therapeutics, 341(3):794-801. Louw, G., Warren, R., Van Pittius, N.G., McEvoy, C., Van Helden, P. & Victor, T. 2009. A balancing act: efflux/influx in mycobacterial drug resistance. Antimicrobial Agents and Chemotherapy, 53(8):3181-3189. Luies, L. & Loots, D. 2016. Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3):1-9. Luies, L., Mienie, J., Motshwane, C., Ronacher, K., Walzl, G. & Loots, D. Biochemistry. 2017. Urinary metabolite markers characterizing tuberculosis treatment failure (Unpublished manuscript). Mercado-Lubo, R. 2010. The interaction of gut microbes with host ABC transporters. Gut Microbes, 1(5):301-306. Mukherjee, J.S., Rich, M.L., Socci, A.R., Joseph, J.K., Virú, F.A., Shin, S.S., Furin, J.J., Becerra, M.C., Barry, D.J. & Kim, J.Y. 2004. Programmes and principles in treatment of multidrug-resistant tuberculosis. The Lancet, 363(9407):474-481. Namukwaya, E., Nakwagala, F., Mulekya, F., Mayanja-Kizza, H. & Mugerwa, R. 2011. Predictors of treatment failure among pulmonary tuberculosis patients in Mulago hospital, Uganda. African Health Sciences, 11(3):105-111. Olivier, I. & Loots, D.T. 2011. An overview of tuberculosis treatments and diagnostics. What role could metabolomics play. Journal of Cell and Tissue Research, 11(1):2655-2671. Olivier, I. & Loots, D.T. 2012. A comparison of two extraction methods for differentiating and characterising various Mycobacterium species and Pseudomonas aeruginosa using GC-MS metabolomics. African Journal of Microbiology Research, 6(13):3159-3172. Pallant, J. 2001. Manual SPSS Survival: A step by step guide to data analysis using SPSS (pp. 302): Open University Press, McGraw-Hill Education, Philadelphia, USA. Petrone, L., Cannas, A., Aloi, F., Nsubuga, M., Sserumkuma, J., Nazziwa, R.A., Jugheli, L., Lukindo, T., Girardi, E. & Reither, K. 2015. Blood or urine IP-10 cannot discriminate between active

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tuberculosis and respiratory diseases different from tuberculosis in children. BioMedical Research International, 2015. Petrone, L., Cannas, A., Vanini, V., Cuzzi, G., Aloi, F., Nsubuga, M., Sserunkuma, J., Nazziwa, R.A., Jugheli, L., Lukindo, T., Girardi, E., Antinori, A., Pucci, L., Reither, K. & Goletti, D. 2016. Blood and urine inducible protein 10 as potential markers of disease activity. International Journal of Tuberculosis and Lung Disease, 20(11):1554-1561. Rechner, A.R., Spencer, J.P.E., Kuhnle, G., Hahn, U. & Rice-Evans, C.A. 2001. Novel biomarkers of the metabolism of caffeic acid derivatives in vivo. Free Radical Biology and Medicine, 30(11):1213- 1222. Siawaya, J.F.D., Bapela, N.B., Ronacher, K., Veenstra, H., Kidd, M., Gie, R., Beyers, N., van Helden, P. & Walzl, G. 2008. Immune parameters as markers of tuberculosis extent of disease and early prediction of anti-tuberculosis chemotherapy response. Journal of Infection, 56(5):340-347. Singh, V.V. & Toskes, P.P. 2004. Small bowel bacterial overgrowth: Presentation, diagnosis, and treatment. Current Treatment Options in Gastroenterology, 7(1):19-28. Smuts, I., Van Der Westhuizen, F.H., Louw, R., Mienie, L.J., Engelke, U.F.H., Wevers, R.A., Mason, S., Koekemoer, G. & Reinecke, C.J. 2013. Disclosure of a putative biosignature for respiratory chain disorders through a metabolomics approach. Metabolomics, 9(2):379-391. Van den Berg, R.A., Hoefsloot, H.C., Westerhuis, J.A., Smilde, A.K. & Van der Werf, M.J. 2006. Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7(1):1. Vasiliou, V., Vasiliou, K. & Nebert, D.W. 2009. Human ATP-binding cassette (ABC) transporter family. Human Genomics, 3(3):281. Walzl, G., Ronacher, K., Djoba Siawaya, J.F. & Dockrell, H.M. 2008. Biomarkers for TB treatment response: challenges and future strategies. Journal of Infection, 57(2):103-109. Wang, X., Zhang, A. & Sun, H. 2012. Urine metabolomics. Clinica Chimica Acta, 414:65-69. Warren, G., Houslay, M. & Metcalfe, J. 1975. Cholesterol is excluded from the phospholipid annulus surrounding an active calcium transport protein. Nature, 255:684-687. World Health Organization. 2015. Global tuberculosis report 2015. Geneva, Switzerland (WHO Press). Zhang, Y. & Yew, W. 2015. Mechanisms of drug resistance in Mycobacterium tuberculosis: Update 2015. The International Journal of Tuberculosis and Lung Disease, 19(11):1276-1289.

129 Chapter 7 CHAPTER 7: FINAL CONCLUSIONS

Since the results were already discussed extensively in the previous chapters, this chapter will only summarise the main findings, in the context of the global investigation. Additional future recommendations/research prospects potentially emanating from this research investigation will also be discussed.

7.1 CONCLUDING SUMMARY

Despite the fervent research efforts to date focussing on the eradication of TB since the discovery of the causal agent in 1882 by Robert Koch, this disease still remains a global pandemic. One of the latest additions to the “omics” revolution, metabolomics, has allowed for the identification of new TB biomarkers, which has led to an exponential growth in the knowledge of TB disease mechanisms, as well as possible diagnostics. This research approach was also applied here to identify new TB biomarkers using patient-collected urine, and served well in explaining new host response mechanisms to active TB, as well as characterising and predicting treatment failure to first line anti-TB drugs.

Although only a few mycobacterial metabolites are expected to be present in the urine of TB-positive patients, it is considered a good sample matrix for identifying unique metabolites characterising the effects of TB on the host, i.e. host reaction and adaptations to the microbe, or altered host metabolome due to the disease. The fact that urine from TB patients can be easily collected, transported, stored, and carries low risk for infection, are additional advantages to using this sample matrix for research and possible diagnostic applications.

Before the data could be used, however, we needed to evaluate the repeatability of the analytical apparatus, in addition to that of the analyst/extraction methodology, to validate the generated data for use in the metabolomics applications pertaining to this study. After the necessary batch correction step was done on the entire GCxGC-TOFMS dataset, using the analysed QC samples, it was considered to be repeatable and reliable for metabolomics biomarker identification and the biological interpretation thereof, in the context of the aims of the study.

Metabolomics comparisons of a healthy TB-negative control group with an active TB-positive patient group led to the identification of new TB-associated urinary metabolite markers. These markers indicated that M. tuberculosis/TB induces an abnormal host fatty acid and amino acid profile (affecting tryptophan, phenylalanine and tyrosine in particular), most likely

130 Chapter 7 due to the host IFN-γ response and possibly a compromised insulin production in these patients, substantiated by prior findings of our research team and others. These results also gave further clues explaining some of the TB-associated symptoms, providing suggestions regarding improved treatment approaches.

Next, we investigated the underlying mechanisms of TB treatment failure by comparing the urinary metabolome of TB-positive patients who received first-line anti-TB drugs and were successfully cured, to those with an unsuccessful treatment outcome, at baseline (time of diagnosis, thus prior to initiation of treatment), during the course of DOTS (i.e. weeks 1, 2 and 4) and two weeks post-treatment completion (week 26). The metabolite profiles of these TB-positive individuals showed clear differentiation of the groups at time of diagnosis. The treatment failure group was characterised by metabolite markers indicative of an imbalance in their gut microbiome, in addition to elevated levels of those metabolites associated with abnormalities in the long-chain fatty acid β-oxidation pathway, accompanied by reduced L-carnitine and SCFAs, indicative of a possible MTP protein defect in particular. The altered amino acid metabolites also observed in the treatment failure patients, confirms our previous findings and associations of treatment failure with an increased IFN-γ host immune response to M. tuberculosis, and also possibly compromised insulin secretion.

Since these results showed that a metabolomics approach can differentiate active TB-positive individuals based on treatment outcome, at baseline, this data was reanalysed using a univariate statistical approach. Two metabolites were identified as potential predictors of an unsuccessful treatment outcome, and when combined in a forward selection logistic regression model, performed well with regards to both sensitivity and specificity when using a leave-one-out cross-validation. Interestingly, these two predictors are also well- known to be associated with a gut microbiome imbalance, further substantiating this mechanism as the major contributor to an unsuccessful treatment outcome.

7.2 FUTURE RESEARCH PROSPECTS

As suggested in Chapter 4, co-administration of anti-TB drugs with melatonin may assist in reducing the elevated quinolinic acid, reduce a number of toxic metabolites associated with the side-effects of anti-TB drugs, and also increase its efficacy in eliminating M. tuberculosis.

Furthermore, BH4 is given as a treatment for PKU patients in an attempt to reduce phenylacetic acid; hence co-administration of anti-TB drugs with this also merits consideration and further research. Lastly, considering the association between TB and diabetes, co-administration of anti-TB drugs with antidiabetic drugs, such as metformin, could also be investigated.

131 Chapter 7

Additionally, since the results obtained in Chapters 5 and 6 suggest an imbalance in the gut microbiome, the synchronous use of probiotics for optimising the microbiome during first-line anti-TB treatment may improve treatment outcome, and could be a topic of further investigation. It may also be interesting to compare these patients to a cohort of healthy humans with a normal intestinal biosis in order to firmly allocate the microbiota dysbiosis to the treatment failure cohort. The issue of whether the metabolites analysed in the urine of these patients are due to human host metabolism or whether they originate from the commensal intestinal bacteria can be addressed in future by experiments with germ-free and conventionalised M. tuberculosis-infected laboratory animals.

Considering the data already made available in this study, the same metabolomics biomarker identification statistics methodology could also be used to compare the data generated pre- and post-treatment administration in order to identify markers (a) characterising the host response to treatment, (b) elucidating the mechanism(s) of drug action/side-effects, and (c) identifying inter-individual variations in the host response to treatment and mechanisms of drug action from a metabolomics perspective, and therefore generate data better describing the underlying cause of successful or unsuccessful treatment outcomes.

Additionally, given the longitudinal nature of this investigation when comparing the urinary metabolomes of the successful and unsuccessful treatment outcomes patient groups at various time intervals, it may be interesting to explore the metabolic changes in these patients over time, to determine the capacity of these markers for monitoring treatment response.

132

Table A-1: A summary of those compounds detected as biomarkers for TB, by more than one research group, in more than one analytical sample media.

Biomarker Sample media Sample cohort Corresponding reference

Biomarkers detected by the same research group in pilot and follow-up validation study (same sample media) (n=9) Olivier and Loots (2012a) 11-Eicosenoic acid Culture Mycobacterium spp. Olivier and Loots (2012b) Olivier and Loots (2012a) 13-Docosenoic acid Culture Mycobacterium spp. Olivier and Loots (2012b) Olivier and Loots (2012a) 15-Tetracosenoic acid Culture Mycobacterium spp. Olivier and Loots (2012b)

Olivier and Loots (2012a) APPENDIX 9-Hexadecenoic acid Culture Mycobacterium spp. Olivier and Loots (2012b) Culture Mycobacterium spp. Olivier and Loots (2012a) Cadaverine Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) Culture Mycobacterium spp. Olivier and Loots (2012a) Eicosanoic acid

A Sputum spiked with culture M. tuberculosis Schoeman et al. (2012)

133 Culture Mycobacterium spp. Olivier and Loots (2012a) Heptadecanoic acid Olivier and Loots (2012b) Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) TB+, TB-, HC Phillips et al. (2007) Tridecane Breath

TB+, TB- Phillips et al. (2010) A Culture Mycobacterium spp. Olivier and Loots (2012a) Tuberculostearic acid Olivier and Loots (2012b) Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) Biomarkers detected by the same research group in pilot and follow-up validation study (different sample media) (n=6) Culture Mycobacterium spp. Olivier and Loots (2012b) 10-Heptadecenoic acid Sputum TB+, TB- Du Preez and Loots (2013) Culture Mycobacterium spp. Olivier and Loots (2012a) 9-Octadecenoic acid Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) Sputum TB+, TB- Du Preez and Loots (2013) Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) alpha-D-glucopyranoside Sputum TB+, TB- Du Preez and Loots (2013) Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) D-glucosamine Sputum TB+, TB- Du Preez and Loots (2013) Culture Mycobacterium spp. Syhre and Chambers (2008) Methyl nicotinate Breath TB+, HC Syhre et al. (2009) Sputum spiked with culture M. tuberculosis Schoeman et al. (2012) Nonadecanoic acid Sputum TB+, TB- Du Preez and Loots (2013)

Biomarkers detected in the same study, but in different sample media (n=13) Culture M. tuberculosis 1,4-Dimethyl-cyclohexane Phillips et al. (2007) Breath TB+, TB-, HC Culture M. tuberculosis 1-Methyl-naphthalene Phillips et al. (2007) Breath TB+, TB-, HC Acetate Lung tissue (Guinea pigs), Serum (Guinea pigs) TB+, HC Somashekar et al. (2012) AMP Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Fumarate Lung tissue (Mice), Spleen tissue (Mice) TB+, HC Shin et al. (2011) Leucine Lung tissue (Mice), Spleen tissue (Mice),Liver tissue (Mice), Serum (Mice) TB+, HC Shin et al. (2011) Nadp+ Lung tissue (Mice), Spleen tissue (Mice) TB+, HC Shin et al. (2011) Niacinamide Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Oxaloacetate Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Succinate Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Taurine Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) UDP-glucose Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011)

A Uracil Lung tissue (Mice),Liver tissue (Mice) TB+, HC Shin et al. (2011)

134 Biomarkers detected by different research groups using the same sample media (n=2) Lung tissue (Mice) TB+, HC Shin et al. (2011) Betaine Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Mice) TB+, HC Shin et al. (2011) Formate Serum (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Human) TB+, HC Zhou et al. (2013) Biomarkers detected by different study groups using different sample media (n=19) Lung tissue (Mice), Spleen tissue (Mice) TB+, HC Shin et al. (2011) Alanine Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Human) TB+, HC Zhou et al. (2013) Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Aspartate Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Creatine Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Sputum spiked with culture TB+, TB- Schoeman et al. (2012) D-mannose Serum (Human) TB+, TB-, HC Weiner et al. (2012) Culture Mycobacterium spp. Olivier and Loots (2012a) Docosanoic acid Serum (Human) TB+, HC, OD Feng et al. (2015) Lung tissue (Mice), Liver tissue (Mice), Serum (Mice) TB+, HC Shin et al. (2011) Glucose Urine TB+, TB- Das et al. (2015)

Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Glutamate Serum (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Human) TB+, HC Zhou et al. (2013) Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Glutamine Serum (Human) TB+, HC Zhou et al. (2013) Serum (Human) TB+, TB-, HC Weiner et al. (2012) Lung tissue (Mice), Spleen tissue (Mice) TB+, HC Shin et al. (2011) Glutathione Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice), Serum (Mice) TB+, HC Shin et al. (2011) Isoleucine Serum (Human) TB+, HC Zhou et al. (2013) Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Lactate Lung tissue (Guinea pigs), Serum (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Human) TB+ HC Zhou et al. (2013) Sputum spiked with culture TB+, TB- Schoeman et al. (2012) L-threonine Serum (Human) TB+, TB-, HC Weiner et al. (2012) Lung tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Lysine Serum (Human) TB+, HC Zhou et al. (2013) Culture Mycobacterium spp. Olivier and Loots (2012a) Myo-inositol Lung tissue (Guinea pigs) TB+, TB- Somashekar et al. (2012)

A Lung tissue (Mice), Liver tissue (Mice), Serum (Mice) TB+, HC Shin et al. (2011)

135 Serum (Human) TB+, HC Zhou et al. (2013) Phenylalanine Serum (Human) TB+, TB-, HC Weiner et al. (2012) Urine TB+, HC Luies and Loots (2016) Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Phosphocholine Lung tissue (Guinea pigs) TB+, HC Somashekar et al. (2012) Serum (Human) TB+, HC, OD Feng et al. (2015) Quinolinic acid Urine TB+, TB- Luies and Loots (2016) Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011) Tyrosine Serum (Human) TB+, HC Zhou et al. (2013) Urine TB+, HC Luies and Loots (2016) Sputum spiked with culture TB+, TB- Schoeman et al. (2012) Uridine Lung tissue (Mice), Spleen tissue (Mice), Liver tissue (Mice) TB+, HC Shin et al. (2011)

Abbreviations: TB+: positively diagnosed for active TB via culture; TB-: patients portraying TB-related symptoms, but tested negative for TB via culture; Healthy control: HC, healthy control with no TB-related symptoms and tested negative for TB via culture, OD, other, related diseases Bold = increased abundance in TB+ group as compared to TB- and/or HC Italics = decreased abundance in TB+ group as compared to TB- and/or HC Normal text = concentration variations between different Mycobacterium species

References:

Das, M.K., Bishwal, S.C., Das, A., Dabral, D., Badireddy, V.K., Pandit, B., Varghese, G.M. & Nanda, R.K. 2015. Deregulated tyrosine–phenylalanine metabolism in pulmonary tuberculosis patients. Journal of Proteome Research, 14(4):1947-1956. Du Preez, I. & Loots, D.T. 2013b. New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa. Tuberculosis, 93(3):330-337. Feng, S., Du, Y.Q., Zhang, L., Zhang, L., Feng, R.R. & Liu, S.Y. 2015. Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: Application in a pilot study to discriminate patients with tuberculosis. Chinese Medical Journal, 128(2):159-168. Luies, L. & Loots, D. 2016. Tuberculosis metabolomics reveals adaptations of man and microbe in

A order to outcompete and survive. Metabolomics, 12(3):1-9.

136 Olivier, I. & Loots, D.T. 2012a. A comparison of two extraction methods for differentiating and

characterising various Mycobacterium species and Pseudomonas aeruginosa using GC-MS metabolomics. African Journal of Microbiology Research, 6(13):3159-3172. Olivier, I. & Loots, D.T. 2012b. A metabolomics approach to characterise and identify various Mycobacterium species. Journal of Microbiological Methods, 88(3):419-426. Phillips, M., Basa-Dalay, V., Bothamley, G., Cataneo, R.N., Lam, P.K., Natividad, M.P.R., Schmitt, P. & Wai, J. 2010. Breath biomarkers of active pulmonary tuberculosis. Tuberculosis, 90(2):145- 151. Phillips, M., Cataneo, R.N., Condos, R., Erickson, G.A.R., Greenberg, J., La Bombardi, V., Munawar, M.I. & Tietje, O. 2007. Volatile biomarkers of pulmonary tuberculosis in the breath. Tuberculosis, 87(1):44-52. Schoeman, J.C., Du Preez, I. & Loots, D.T. 2012. A comparison of four sputum pre-extraction preparation methods for identifying and characterising M. tuberculosis using GCxGC-TOFMS metabolomics. Journal of Microbiological Methods, 91:301-311. Shin, J.-H., Yang, J.-Y., Jeon, B.-Y., Yoon, Y.J., Cho, S.-N., Kang, Y.-H., Ryu, D.H. & Hwang, G.-S. 2011. 1H NMR-based metabolomic profiling in mice infected with Mycobacterium tuberculosis. Journal of Proteome Research, 10(5):2238-2247. Somashekar, B.S., Amin, A.G., Tripathi, P., MacKinnon, N., Rithner, C.D., Shanley, C.A., Basaraba, R., Henao-Tamayo, M., Kato-Maeda, M. & Ramamoorthy, A. 2012. Metabolomic signatures in guinea pigs infected with epidemic-associated W-Beijing strains of Mycobacterium tuberculosis. Journal of Proteome Research, 11(10):4873-4884. Syhre, M. & Chambers, S.T. 2008. The scent of Mycobacterium tuberculosis. Tuberculosis, 88(4):317-323. Syhre, M., Manning, L., Phuanukoonnon, S., Harino, P. & Chambers, S.T. 2009. The scent of Mycobacterium tuberculosis–part II breath. Tuberculosis, 89(4):263-266. Weiner, J., Parida, S.K., Maertzdorf, J., Black, G.F., Repsilber, D., Telaar, A., Mohney, R.P., Arndt- Sullivan, C., Ganoza, C.A. & Fae, K.C. 2012. Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients. PloS one, 7(7):e40221.

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Zhou, A., Ni, J., Xu, Z., Wang, Y., Lu, S., Sha, W., Karakousis, P.C. & Yao, Y.-F. 2013. Application of 1H NMR spectroscopy-based metabolomics to sera of tuberculosis patients. Journal of Proteome Research, 12(10):4642-4649.

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APPENDIX B

Box-and-whisker plots of the 12 compounds that best explained the variation between the TB-negative healthy control and TB-positive patients, selected on the basis of a PCA modelling power >0.5, a PLS-DA VIP >1.0, an effect size >0.5, and a P-value <0.05. These plots illustrate the data distribution around the median (thick horizontal line in centre of the box), the 25th and 75th percentiles/quartiles (bottom and top of the box, respectively), as well as the 5th and 95th percentiles (ends of the whiskers).

5-Hydroxyhexanoic acid Phenylacetic acid

2-Octenoic acid 2-C-Methylglycerol

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5-Hydroxyhydantoin Oxalic acid

L-Rhamnulose Quinolinic acid

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Ribitol Indole-3-carboxylic acid

Kynurenic acid Glycerol monostearate

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APPENDIX C

(a) (b)

(e) Figure A-1: Principal components analysis (PCA) scores plots of principal component 1 versus principal component 2 of the successful and unsuccessful treatment outcome groups, at (a) time of diagnosis, (b) week 1, (c) week 2, (d) week 4 of treatment and (e) two weeks after treatment completion (week 26), subsequent to an organic acid extraction and GCxGC-TOFMS analysis. The explained variances are indicated in parenthesis.

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APPENDIX D

The following is a list of all publications originating from this metabolomics investigation:

 De Villiers, L. & Loots, DT. (2013). Using metabolomics for elucidating the mechanisms related to tuberculosis treatment failure. Current Metabolomics, 1(4): 306-317.

 Du Preez, I., Luies, L. & Loots, DT. (2017). Metabolomics biomarkers for tuberculosis diagnostics: Current status and future objectives. Biomarkers in Medicine, 11 (2): 179-194.

 Luies, L., Du Preez, I. & Loots, DT. (2017). The role of metabolomics in tuberculosis treatment research. Submitted for publication to Biomarkers in Medicine (Manuscript number: BMM-2017-0141).

 Luies, L. & Loots, DT. (2016). Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics, 12(3): 1-9.

A142 Send Orders for Reprints to [email protected] 306 Current Metabolomics, 2013, 1, 306-317 Using Metabolomics for Elucidating the Mechanisms Related to Tubercu- losis Treatment Failure

Laneke de Villiers and Du Toit Loots*

School for Physical and Chemical Sciences, Centre for Human Metabonomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom, 2531, South Africa

Abstract: Tuberculosis (TB), caused by Mycobacterium tuberculosis is a highly infectious disease, responsible for 1.4 million deaths annually. Unfortunately, the latest reports of treatment outcomes for patients using the current anti-TB drugs are disappointing, considering the high treatment failure rates. This has been ascribed to a number of factors, including, amongst others: (1) variable individual metabolism (including xenobiotic metabolism, drug malabsorption and drug- drug interactions), (2) drug resistance by the infectious organism and (3) non-adherence to the treatment program, especially due to the associated drug side-effects. Despite this however, only two new drugs have been approved by the FDA since 1962. Thus, it is clear that new strategies are needed for the better elucidation of in vivo anti-TB drug mechanisms and their associated side-effects, and the mechanisms by which the infectious organism develops drug resistance. To this end, the relatively new research approach termed ‘metabolomics’, shows promising results through its capacity for identifying new drug markers, or metabolic pathways related to these contributing factors. Metabolomics refers to the unbiased identification and quantification of all metabolites (products of bio-molecular processes) present in a biological system, using highly selective and sensitive analytical methods. The application of metabolomics, for biomarker discovery, is based on the principle that an external stimulus, such as TB disease or infection, an anti-TB drug, or a mutation resulting in drug resistance, may disrupt normal metabolism, altering the overall physiological status of an organism or host, and these metabolic changes are specific to the perturbation investigated and not due to overall inflammation or disease process. Analyses of these altered metabolic pathways, may subsequently shed new light on the mechanisms associated with the causes of treatment failure, and ultimately lead to new treatment strategies which may most likely be aimed at targeting specific metabolic pathways in M. tuberculosis, and/or the genes/proteins associated with these. Keywords: Drug-drug interactions, drug mechanisms, metabolomics, treatment failure, tuberculosis, xenobiotic metabolism.

INTRODUCTION pyrazinamide (PZA) and ethambutol (EMB), followed by a four month maintenance phase, consisting of only INH and Tuberculosis (TB), caused by Mycobacterium tuberculo- RIF [1, 6]. The aim of TB treatment is to cure the patient, sis (M. tuberculosis), is considered the world’s foremost prevent death, cease transmission and prevent drug resis- cause of death from a single infectious agent, which is tance [7]. alarming as it is widely believed to be curable [1]. TB infects approximately one-third of the world’s population, either in TB treatment failure can be defined as the occurrence its active (symptomatic) or the latent (asymptomatic) state of persistently positive sputum smears despite treatment [2, 3], and is responsible for as many as 1.4 million deaths [8]. Treatment failure may be attributed to a long list of annually [4]. Furthermore, the ever increasing TB prevalence factors, including: (1) irregular or inadequate anti-TB reported, especially for African countries, have been attrib- drug supplies by health providers, (2) a lack of patient uted to, amongst others, the increased incidence of HIV, TB-education, (3) poverty and poor life quality (further poor treatment compliance resulting in anti-TB drug resis- resulting in transport problems to the sites of treatment tance and poor socio-economic conditions (poverty, malnu- administration), as well as (4) long treatment duration. trition and overcrowding) [5]. Subsequently, the DOTS (Di- Additionally, and more relevant to this review, are the rectly Observed Therapy, Short course) treatment program various biochemically related factors contributing to poor was introduced in an attempt to reduce the global TB epi- treatment outcome, including: (1) variable individual me- demic. DOTS is the recommended 6 month therapy regimen, tabolism of the anti-TB drugs (including variation in consisting of an initial 2 month (intensive) treatment phase xenobiotic metabolism, drug absorption and drug-drug using a combination of isoniazid (INH), rifampicin (RIF), interactions), (2) drug resistance and pathogenicity by the infectious organism, and (3) non-adherence to the treatment program, especially due to the associated side- *Address correspondence to this author at the School for Physical and Chemical Sciences, Centre for Human Metabonomics, North-West Univer- effects [9-13]. These proposed mechanisms, are however sity (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom, not fully understood. Considering this, new research ap- South Africa, 2531; Tel: +27 (0) 18 299 1818; Fax: +27 (0) 18 299 1823; proaches, such as metabolomics, are currently being used E-mail: [email protected] to increase our understanding of the above mentioned

2213-2368/13 $58.00+.00 © 2013 Bentham Science Publishers Metabolomics and TB Treatment Failure Current Metabolomics, 2013, Vol. 1, No. 4 307 biochemical mechanisms related to treatment failure, Considering this, metabolomics could serve as a valu- and could contribute significantly towards improved TB able tool for better understanding the proposed causes of control. treatment failure, from a biochemical perspective, considering the contributory causes, and will subsequently be dis- Metabolomics can be defined as the unbiased identification and quantification of all metabolites present in a bio- cussed under the following headings in the remainder of this review. logical system, using highly selective and sensitive analytical methods [14-16], in conjunction with bioinformatics, for identifying new metabolite markers [1]. Metabolomics 1. VARIABLE INDIVIDUAL METABOLISM is the holistic study of an organisms’ metabolism, and gives 1.1. Xenobiotic Metabolism an overview of the complete metabolome, resulting from a specific perturbation, including a patho-physiological Xenobiotic metabolism refers to a natural response by stimulus or genetic alteration [17], and is considered an which the body modifies a foreign compound (such as important addition to systems biology, when the data gen- drugs) by enzymatic/biochemical reactions (biotransforma- erated is interpreted together with that generated using ge- tion), in order to detoxify the body, by eventually eliminat- nomics, transcriptomics and proteomics [18]. Metabolom- ing these exogenous substances. During this process, many ics is based on the concept that an individual’s metabolic drugs may however be converted to their active states. Fur- state is a representation of the overall physiological status. thermore, the duration and intensity of drug action may Therefore, it is possible to better understand the mecha- also be influenced by the rate of its metabo- nisms of disease, identify diagnostic biomarkers, and pre- lism/biotransformation, which may vary between individu- dict individual/organism drug responses, using this research als. These metabolized compounds may also serve as “sig- approach [17], on the basis of the new metabolite markers nals” for various patho-physiological outcomes [18, 32, identified due to the perturbation. When seen from this per- 33], such as explaining or predicting a response to the spective, TB disrupts the host metabolism, initiating me- medication and of treatment outcome. Consequently, iden- tabolite changes which can be visualized as metabolic bio- tifying xenobiotic metabolites and their related metabolic signatures or metabolite patterns [19, 20]. Metabolomics pathways are considered essential to understanding drug subsequently analyzes the end-products of these perturba- action and their associated side-effects. Variability in indi- tions present in blood, sputum and/or urine (or any other vidual xenobiotic metabolism has remained a major chal- fluid or tissue for that matter), and identifies metabolites lenge for pharmacology research. It is important to under- that are both endogenous and exogenous to the perturba- stand why individual drug responses differ, and to what tion, capturing information with regards to the mechanisms extent this variability is responsible for the differences we of disease or drug action [21]. observe in therapeutic efficacy and tendencies towards adverse reactions [34]. To date, research has shown that indi- Various chromatographic instruments are used as tools vidual variability of xenobiotic metabolism and their asso- in metabolomics, e.g. gas chromatography (GC), liquid ciated side-effects are influenced by a number of factors, chromatography (LC), capillary electrophoresis (CE) and including genetics, race, ethnicity, gender and external nuclear magnetic resonance (NMR) [17] for detecting these stimuli such as diet, alcohol consumption, drug-drug inter- alterations. Usually, these instruments are combined with actions, etc. [35]. mass spectrometry (MS), of which a variety are available, including the quadrupole MS, ion trap MS and the time of Considering this, the current knowledge pool pertaining flight MS (TOFMS), each of which having their own ad- to the functionality of anti-TB drugs is considered insuffi- vantages and disadvantages pertaining to sensitivity, speci- cient, as their exact mechanisms of action, especially in the ficity and molecular preferences [19]. By combining these human host, and on the bacteria, are still largely unclear [1, instruments, the best possible results and reliable metabo- 36]. This is most probably a major contributor to the lack lite profiles are acquired [16, 17]. The choice of the ana- of development of new anti-TB drugs over the last five lytical instrumentation used would be dictated by the decades (only two new drugs have been approved by the goal(s) of the study and the type of molecule or metabolite Food and Drug Administration (FDA) since 1962), and the pathways one would expect to show the most variation rising threat of drug-resistant strains. Considering this, it is [19], also considering the advantages and disadvantages of clear that new strategies are urgently required for better these different metabolomic approaches for these applica- understanding the current in vivo host metabolism, anti-TB tions (see Table 1). However, as per definition, metabolom- drug mechanisms and drug-drug interactions [25], which ics is the “study of all metabolites in a biological system”, when elucidated, would undoubtedly lead to improved pa- many approaches have been developed in order to analyze tient prognosis, as measures could then be put into place, the entire metabolome, or as much of this as possible, using using the knowledge gained to preventing or lowering the one or more analytical methods [13, 22-24]. In both of the prevalence of treatment failure. To date, a number of ex- aforementioned instances, and part and parcel of true me- amples of this already exists, where metabolomics is not tabolomics research, multivariate statistical analysis, such only being used for the identification of new metabolite as principle component analysis (PCA), partial least markers and altered patient metabolite profiles, shedding squares discriminant analysis (PLS-DA) or orthogonal par- light on new mechanisms of TB disease and anti-TB drug tial least-squares discriminant analysis (OPLSDA), are em- action, but when combined with other ‘omics’ approaches, ployed to extract information from the large metabolite allows for a more in-depth and holistic understanding of the datasets generated, in order to identify possible biomarkers research question [1]. This approach, could potentially also [18, 25, 26]. be used for identifying markers which could be used to 308 Current Metabolomics, 2013, Vol. 1, No. 4 de Villiers and Loots

Table 1. A summary of the advantages and disadvantages of the common techniques and tools used in metabolomics [27-31].

Advantages Disadvantages/Limitations

• Good combination of sensitivity and selectivity • Susceptible to ion suppression or enhancement Direct MS • Highly specific chemical information (accurate mass, isotope • Data interpretation can be challenging distribution patterns, characteristic fragment ions) • Inability to differentiate isomers • Suited for volatile and non-volatile (after derivatization) com- • Limited to volatile, thermally stable, and energetically sta- pound analyses ble compounds • Affordable, with relatively low running costs • Requires additional sample preparation, such as derivatiza- • High sensitivity, mass resolution and accuracy tion, as this approach depends on the analytes being volatile and thermally stable, and few metabolites meet this re- • Good dynamic range quirement in their natural state • Compound identification using mass spectral library matching • Less amenable to large, highly polar metabolites (poor Provides additional and orthogonal data (i.e. retention GC-MS • volatility) time/factor/index) • Co-eluting analytes in single dimension GC (corrected for Reproducible chromatographic separations • using GCxGC) • Identification of stereoisomers possible • Careful attention required for splitless injections • Shorter run times • Slower scan rates/speed, unless coupled with TOFMS • Lower bleed (thinner films) • Lower mass accuracy, unless coupled with TOFMS detectors • Applicable for targeted and non-targeted metabolomics • Lower chromatographic resolution compared to GC-MS • Relatively low reagent cost • Comparatively higher running costs • High analytical sensitivity, specificity and coverage depth • Electrospray ionization (ESI) can suffer from ionization • Able to analyze a wide range of compound classes suppression LC-MS • Ideal for highly polar and ionic compounds/metabolites • Retention time shifts are known to occur • Requires minimum sample preparation • Higher signal to noise (S/N) ratios compared to GC-MS • Low matrix effects and interferences • Low sample throughput • The application of both positive and negative ionization, hence more comprehensive metabolome coverage

• Applicable for targeted and non-targeted metabolomics • Lower sensitivity compared to other techniques • Ideal for highly polar and ionic compounds/metabolites • Poor reproducibility CE-MS • Fast, relatively affordable, and highly efficient separation • Electrochemical reactions of metabolites technique • May lack the necessary robustness • Typically coupled with TOFMS for fast acquisition rates • Least suitable for analyzing complex biological samples • Highly selective • Lower sensitivity compared to other techniques NMR-MS • Non-destructive to sample material • Metabolite structural elucidation

predict individual drug response phenotypes (pharmaco- alterations to the metabolite profile in the xenobiotic group metabolomics), and subsequently, in diagnostic applica- in comparison to the control, would be because of the ex- tions for the early prediction of treatment failure. ogenous compound ingested/injected [18]. Subsequently, Considering this, three approaches are suggested, focus- these differences can broadly be visualized using a multivariate, unsupervised scores plot (such a PCA) and then, if ing on individual xenobiotic metabolism research, aimed at a natural differentiation if achieved, those com- resolving practical issues in drug metabolism and toxicol- pounds/xenobiotic metabolites best explaining or contribut- ogy. Firstly, metabolomics could typically be used to better ing to the differences, identified using a variety of super- elucidate drug metabolism, by identifying metabolites dif- vised multivariate (such as PLS-DA) in conjunction with ferentiating between two groups, one receiving the xenobiotic and another not, after which the biological samples other univariate methods (t-test and effect sizes) [37]. For example, Loots et al. (2005) conducted a rat-model me- (urine, blood, tissue, saliva, feces, etc.) are collected for tabolomics study, based on a similar study design, in order comparison of the resulting difference in the metabolite to evaluate the effect of combined anti-TB drug (Rifater) profiles using a selected analytical method. When compar- therapy, on the free radical and organic acid profiles of ing these groups, in theory, all the additional compounds or Metabolomics and TB Treatment Failure Current Metabolomics, 2013, Vol. 1, No. 4 309

Sprague-Dawley rats, in order to get a better understanding diseases, thyroid and end-stage liver or renal diseases. Al- of their impact on normal metabolism and potentially better though either a single or combination of these factors may explain their associated side-effects. Additionally, these contribute to drug malabsorption, HIV/AIDS infection is metabolic effects where compared to an additional treat- considered by far the most common risk factor associated ment group receiving a combined treatment of Rifater and with malabsorption of anti-TB drugs [40, 41]. melatonin. In their study, they determined that the com- It has previously been reported in the literature, that bined anti-TB drug Rifater results in oxidative stress and a HIV/AIDS infection may lead to anti-TB drug malabsorp- metabolite profile associated with individuals suffering tion and acquired drug resistance [40, 42]. This was based from a multiple acyl-CoA dehydrogenase deficiency on the observation that 68% of all patients with reduced (MADD), and eventually linked this to the drug causing serum RIF and INH levels, were co-infected with HIV [41, inhibition of the electron transport flavoproteins, in addi- 42]. As both RIF and INH reach maximum serum concen- tion to an explanation of a number of side-effects, which trations 2h after oral administration, Bento et al. (2010) could be corrected for by the co-administration of mela- proposed taking two measurements; 2h and 6h after ad- tonin [22]. This approach may however be limiting if the ministration, in order to determine if a patient has malab- differences detected are due to both exogenous and en- sorption (indicated by persistently low concentrations) or dogenous xenobiotic metabolites, as was the case in Loots delayed absorption (slowly increase concentrations) [41], et al.’s study. One is, however, able to sift out these differ- for the purpose of adapting the treatment protocols. How- ences using libraries specific towards identifying either the ever, this approach may be costly and have significant drug metabolites, or the altered natural metabolome of the health policy implications. Alternatively, it was proposed organism. The advantages of this approach is that it may that urinary RIF and INH levels could be monitored, pro- potentially describe alternative pathways influenced by the viding important information related to drug absorption drugs, better explaining the side-effects, which will be dis- [40]. Considering this, Gurumurthy et al. (2004) investi- cussed in section 3 of this review. Considering this, Chen gated the absorption of RIF and INH in patients with et al. (2007) proposed a stable isotope-labeled xenobiotic HIV/AIDS and diarrhea, HIV/AIDS and TB co-infection, treatment group comparative to an unlabeled group, as an pulmonary TB alone, and healthy subjects, by analyzing the alternative to this approach, and in so doing, one would be urinary excretion of these drugs and the associated metabo- able to track the drug specific metabolites and their associ- lites. Their investigation indicated that TB positive pa- ated metabolism and also elucidate which metabolites are tients, without an underlying disease or illnesses, absorb directly linked to the xenobiotic metabolism, and which are anti-TB drugs reliably, while those with gastrointestinal due to secondary toxicity [18, 38]. Lastly, Chen et al. disorders or HIV/AIDS infection have less optimal absorp- (2007) propose a study design to identify human xenobi- tion [40]. otic-metabolizing enzyme (XME) polymorphisms responsible for adverse drug reactions (ADRs), which can be used Although no metabolomics investigations on this topic for investigating anti-TB drug side-effects, or an individ- have been done to date, this research approach could be ual’s response to anti-TB drugs. ADR may also occur due used/contribute to this in the following manner. A compari- to various factors relating to a patient’s general health son of the metabolomes of those individuals grouped as good status, various environmental contaminants and as a result absorbers vs. poor absorbers, using blood/urine collected at of drug-drug interactions with other co-administered time of diagnosis (prior to commencing treatment), may bet- drugs requiring the same enzyme systems for metabolic ter explain the underlying mechanisms associated with processing [18]. In these cases, ADR may largely be treatment outcome, be it by underlying disease mechanisms caused by genetic polymorphisms of XMEs, or other fac- or genetic factors. This information could be used to better tors influencing enzymatic activities or their expression, explain this phenomenon, or alternatively be used diagnosti- such as an individual’s general health, environmental con- cally to identify malabsorpers prior to treatment, so that the taminant exposure, cultural and/or genetic factors, and the necessary adaptions can be made i.e. personalized treatment previously mentioned drug-drug interactions. If ADRs are approaches. Similarly, these groups can be compared during observed in a patient group, suspected to be due to XME treatment, by one or more of the methodological approaches polymorphisms, an investigation into the metabolite pro- discussed in the previous section, and potentially lead to a files and pharmacokinetics would be of great importance, better understanding of potentially varying drug metabolism and could potentially identify the metabolic pathways between these individuals. affected and pinpoint the genetic factors causing these ADRs. This in turn may lead to the necessary adaptions to 1.3. Drug-drug Interactions the medication and ultimately individualized treatment Drug-drug interactions are especially prevalent in TB procedures [18, 35, 39]. patients co-infected with HIV/AIDS, as INH and RIF in particular, have inauspicious drug interactions with many of the 1.2. Drug Malabsorption antiretroviral drugs used to treat HIV/AIDS [11]. These un- Although rare, malabsorption of anti-TB drugs may also wanted drug-drug interactions (inhibition or induction), are lead to a poor treatment outcome. Probable reasons for thought to result in (1) lowered efficacy of co-administered malabsorption, although poorly characterized, include co- drugs, leading to a poor treatment outcome and drug resis- morbidities of TB with HIV/AIDS infection, hypoalbu- tance by the organism, and also (2) increased risk of toxicity minemia in the malnourished, infectious gastroenteritis, and hence, treatment interruptions [43], also leading to drug gastric achlorydria, gastrointestinal and/or malabsorption resistance. 310 Current Metabolomics, 2013, Vol. 1, No. 4 de Villiers and Loots

These drug-drug interactions are thought to occur due to the M. avium complex, M. chelonae and M. kansasii [51]. these different medications, all being metabolized by the Based on clinical evaluations of its safety, tolerance and effi- same cytochrome P450 (CYP450) enzyme system [44]. The cacy, the process for approving bedaquiline for treating pul- CYP450 enzymes, refer to a group of important XMEs, and monary MDR-TB in adults, has been accelerated [51, 52]. are especially important for the metabolic activation and Bedaquiline’s long half-life and unique mechanism of action, metabolism of several drugs. INH is known to permanently potentially makes it suitable for intermittent administration, inhibit several of the CYP450 enzymes, and this state per- which is a major advantage compared with RIF for instance. sists until new CYP450 enzymes are synthesized. Side- However, one of the major concerns of this drug is its drug- effects due to INH drug interactions may include an in- drug interactions with RIF, as co-administration with RIF creased risk of hepatotoxicity, hypertension, flushing and has been shown to half the maximum bedaquiline concentra- tachycardia. Excess catecholamine (a dopamine precursor) tion. As bedaquiline is metabolized by CYP3A4, producing and elevated levels of other medications, may also be present the less-active N-monodesmethyl bedaquiline, any CYP3A4 due to inefficient metabolism of these drugs when INH is inducer, such as RIF, will affect the final concentration of present [45, 46]. bedaquiline [51]. Another concern is the possible drug-drug interactions between bedaquiline and antiretroviral agents On the other hand, an increased hepatic blood flow or (ARV's), when treating TB-HIV/AIDS co-infected patients. CYP450 synthesis, leads to enzyme induction, as is the case with RIF administration [47], where this reaction is mediated As current studies devoted to the use of bedaquiline in these cases are still underway, no clinical significant findings have through the activation of the pregnane-X receptor by RIF been suggested to date [51, 52]. [48], reducing the therapeutic effects of numerous other co- ingested medications metabolized through this enzyme sys- As anti-TB drug induced toxicity may have a major tem. This may further lead to complicated health conse- influence on other medications used simultaneously, the quences when RIF is taken in combination with other com- need to predict ADRs as early as possible during the drug mon drugs, such as oral contraceptives, warfarin (Coumadin) treatment regimen is crucial. Metabolomics can provide the or sulfonylureas. Furthermore, RIF is a known inducer of means for a global overview of individual variation and the cytochrome P450 (CYP) 3A, reflected by independent regu- subsequent prediction of potential drug-drug interactions, lation of CYP3A expression at various sites, with large inter- via analysis of urinary metabolites after drug administra- individual variation observed [49]. Gorski et al. (2003) in- tion, which can serve as a signature for organ-specific drug vestigated CYP3A susceptibility to RIF induction, as a func- toxicity, general activity or inactivation [18, 53, 54]. Me- tion of age and gender, with midazolam, a CYP3A substrate. tabolomic profiling has already been shown to predict drug RIF significantly increased both the systemic and oral clear- responses in animal models [53], however, as xenobiotic ance of midazolam and reduced the oral midazolam avail- metabolism differs between different species, particularly ability by 88%. Interestingly, gender also had a significant due to molecular variation in receptors and XMEs, it pre- effect on induction of oral midazolam clearance, as this was sents a problem when it comes to extrapolating data ob- found to be greater in males when compared to females, tained from rodent models (especially rats and mice) to largely due to the fact that these gender-related differences in humans [54, 55]. Thus, more reliable in vivo systems, such response to CYP3A inducers are substrate-dependent [49]. as humanized mouse models, need to be generated in order RIF related drug-drug interactions are also reported to lead to to study and potentially predict human xenobiotic re- an increased risk of aplastic anaemia and hypoglycaemia. sponses [55]. Currently, the development of animal models Lowered levels of other medications in the blood are com- as a tool to simulate human xenobiotic metabolism is ex- monly reported, thus dosages of these may need to be in- panding rapidly [56, 57]. Considering this, humanized creased to avoid lowered efficacy when given in combina- transgenic mice can be generated by introducing a specific tion to RIF [46, 47]. For example, verapamil is shown to be human gene into the rodent genome, of which many geneti- decreased 10-20 fold when co-administered with RIF. On the cally-modified mouse model systems for several important other hand however, recent studies have shown the potential nuclear receptors (including AhR, PXR, CAR) have been of verapamil to be used as an effective efflux inhibitor in established to date, which not only offer a better animal many organisms, including M. tuberculosis, subsequently system for studying the influence of drug-drug interactions reducing the duration of anti-TB treatment and the emer- on xenobiotic metabolism, and the consequent prediction of gence of drug resistance [50]. These drug-drug interactions human xenobiotic responses, but also facilitates the eluci- may persist, depending either on the half-life of the inducer dation of the related underlying drug mechanisms [18, 55]. or the rate of enzyme degradation and new enzyme synthe- An additional example of this is the Kramnik mouse model sis. Considering the short half-life of RIF and the 1-6 day (mice harboring the C3HeB/FeJ genotype), in which ne- turnover time of CYP450 enzymes, RIF elimination is more crotic lung lesions are generated. Following M. tuberculo- rapid than that of excess CYP450, thus interaction duration sis infection, animals may develop necrotic lung lesions, would be dependent on enzyme turnover [47]. where drug-resistant bacilli are typically present, persisting in an extracellular micro-environment within these lesions Bedaquiline (TMC207), a diarylquinoline, was recently [58, 59]. Using this model, Driver et al. (2012) examined introduced as a new anti-TB drug for treating pulmonary the efficacy of drug treatment, by comparing the formation MDR-TB in adults. Bedaquiline has specific ATP synthase inhibition activity against M. tuberculosis, affecting the en- of colliquative necrotic lesions in Kramnik mice, to that of BALB/c mice, with non-necrotic lesions, following aerosol ergy production required for cell viability, acting as a bacte- challenge. This was accomplished by infecting both the ricidal and sterilizing agent against M. tuberculosis and vari- Kramnik and BALB/c mice with M. tuberculosis bacilli, ous other mycobacterial species, including, amongst others, Metabolomics and TB Treatment Failure Current Metabolomics, 2013, Vol. 1, No. 4 311 followed by 7 to 8 weeks of monotherapy, using drugs with Considering the limited information surrounding varying modes of action. Drug efficacy was quantified by mechanisms by which drug resistance occurs, one of the determining the bacterial load, and disease progression few metabolomics investigations published regarding drug visualized via staining methods. In the Kramnik mice, resistance to anti-TB drugs was done in 2012, by du Preez results indicated the formation of fibrous encapsulated and Loots. Using GC-MS, they investigated an altered fatty lung lesions with central colliquative necrosis having acid metabolome of two rpoB mutant M. tuberculosis abundant extracellular bacilli, in the late stages of infec- strains, by comparison to that of an isogenic drug sensitive tion, while the BALB/c mice formed non-necrotic lesions, wild-type parent strain, in order to characterize RIF- primarily infected with intracellular bacilli. Furthermore, resistance, due to mutations in the rpoB gene. Metabolite the Kramnik mice had indications of hypoxia within the markers best describing the variation were identified using necrotic lesions, and significantly more anti-TB drug- multivariate statistical approaches, namely PCA and PLS- resistant colonies, especially for PZA, compared to the DA. This study was the first to identify a link between an BALB/c mice [58]. As the Kramnik mouse model is con- altered fatty acid metabolism and RIF-resistance, rpoB mu- sidered a reliable representation of human TB, these re- tations, and the β -subunit of RNA polymerase, present in sults not only indicate the suitability of the Kramnik M. tuberculosis. In short, they determined that the mutation mouse model for testing anti-TB drug efficacy, especially in the rpoB gene of M. tuberculosis, resulting in RIF- for drug-resistant M. tuberculosis strains, allowing for resistance, also influences mycolic acid synthesis by validation of possible candidate anti-TB drugs and vac- disruption of the mRNA:NTP (ribonucleoside 5’- cine selection, but also the capacity to develop a TB triphosphotases) equilibrium. 10-methyl branched chain mouse model with a disease state more closely represent- fatty acids in the RIF. This was confirmed by the lack of ing that of human TB. This model is not only advanta- detection of 10 methyl branched chain fatty acids in the geous when wanting to elucidate the mechanisms of RIF-resistant mutants, accompanied by the accumulation of M. tuberculosis pathogenesis [58, 59], but could poten- their fatty acid substrates. Additionally, these mutants tially also be used in various metabolomics investigations. showed greater dependence on fatty acids and acetate, as altered energy sources, and subsequent up-regulation of the To date, xenobiotic receptor-mouse models have also glyoxylic acid cycle. This study not only indicated the po- been developed to closely resemble the human metabolome, tential of pharmaco-metabolomics for the identification of and have subsequently been used to better elucidate the metabolites associated with drug resistance, but also metabolic pathways leading to drug toxicity and drug-drug interactions, aiding in the identification of potential drug mapped its potential for global health and personalized medicine [13]. In view of the above, the methodology de- biomarkers in humans. In these instances, the unique combi- scribed by du Preez and Loots (2012), could furthermore be nation of humanized mouse models subjected to me- applied to investigating the mechanisms of drug resistance tabolomic analysis, in conjunction with multivariate analysis, of M. tuberculosis to the other first-line, or even second- is providing answers to key questions related to human line drugs. The elucidation of xenobiotic related metabo- health and disease questions [18, 45, 54]. Most ideally however, is the use of metabolomics to investigate these potential lites in these organisms, as opposed to that previously described for in the host, by comparison of drug-sensitive and drug-drug interactions, using clinical samples of patients drug-resistant strains, with low doses of these drugs in the already being treated for one or more co-infections. This will growth media, could additionally provide further clues per- allow for the identification of drug-drug interaction markers taining to the biotransformation of xenobiotics in these or- relating to drug efficacy and/or side-effects in humans. ganisms, and shed light on additional metabolic pathways (and ultimately genes) involved in drug resistance. 2. DRUG RESISTANCE AND PATHOGENICITY of THE INFECTIOUS ORGANISM In the following year, Chakraborty et al. (2013) conducted a metabolomics approach proving that para- As previously mentioned, the DOTS program is based on aminosalicylic acid (PAS) does not inhibit M. tuberculosis administering four of the first-line anti-TB drugs, namely growth by its inhibiting action on dihydropteroate synthase INH, RIF, PZA and EMB, simultaneously. As an introduc- (DHPS). PAS was previously thought to act as a competitive tion to this section, we thought to give a brief overview on inhibitor of DHPS by acting as an alternative substrate, in drug action first. As several reviews have been written on the competition with p-aminobenzoate (PABA), in folate me- topic, we will focus on the metabolomics investigations done tabolism. They additionally indicated that other sulfona- to date. However, to summarize briefly, INH has high bacte- mides are ineffective against M. tuberculosis due to inactiva- ricidal activity and is used to efficiently and specifically in- tion by bacterial metabolism, rather than inadequate uptake. hibit active growing M. tuberculosis [60]. RIF is considered As new approaches are needed to improve our understanding a powerful sterilizing drug, responsible for eliminating active of drug action and its efficacy for drug development, this and semi-dormant M. tuberculosis from the host [61], as its discovery highlights that catalysis by an enzyme, rather than primary mechanism of action is transcription inhibition [61, the inhibition thereof, could possibly be the key to new drug 62]. PZA, is also classified as a sterilizing drug and functions development [63]. by eliminating semi-dormant M. tuberculosis bacilli located within hosts macrophages [61, 62], and EMB, is classified as Furthermore, from a growth and pathogenesis perspec- a bacteriostatic agent and typically adds additional coverage tive, bacteria are known to utilize catabolite repression in to reduce the chance of developing drug resistance [46, 61], order to maximize growth, via consumption of host carbon considered effective against both intra- and extracellular M. substrates, in a preferred sequence. Considering this, a me- tuberculosis bacteria [1]. tabolomics study conducted by de Carvalho et al. (2010), 312 Current Metabolomics, 2013, Vol. 1, No. 4 de Villiers and Loots revealed that M. tuberculosis is capable of compartmental- development, by forming partnerships between various di- ized/simultaneous co-catabolism of multiple carbon verse organizations, such as academic institutions, the phar- sources/substrates, enhancing its growth. These carbon maceutical industry, government research laboratories, and sources can be catabolized through the glycolytic, tricarbox- non-governmental organizations [66]. The objectives of the ylic acid, and/or pentose phosphate pathways, a phenomenon GATB include (1) reducing the current duration and com- related to a previously undescribed bacterial metabolic net- plexity of treatment and/or the doses needed to cure active, work, and possibly linked to the pathogenicity of M. tuber- drug-sensitive TB, (2) improving the efficacy of MDR-TB culosis. Thus, using metabolomics, new knowledge was ob- treatment, (3) reducing the treatment period of latent TB tained, reshaping our basic understanding of M. tuberculosis infection, and (4) simplifying the treatment of TB- and its ability to adapt to the host for survival [64]. HIV/AIDS co-infected patients, by lowering the incidence of Additionally, a GCxGC-TOFMS metabolomics study drug-drug interactions, especially those especially those associated with ARV's [66, 67]. These will not only result in conducted by Meissner-Roloff et al. (2012), compared reduced side-effects, better patient adherence and lower the the metabolomes of a hyper- and hypo-virulent Beijing costs of delivering DOTS, but will also ultimately have a M. tuberculosis strain, attempting to characterise the varying major impact on treatment outcome. The challenges for degrees of virulence within the M. tuberculosis species, and reaching these goals include (1) elucidating the biological consequently identified metabolite markers best differentiating between these two strains. This pilot study was the first mechanisms of mycobacterial resistance and latency, (2) discovery and development of new anti-TB drugs with novel of its kind to identify metabolites associated with an in- mechanisms of action, (3) developing and validating accu- creased metabolic activity, cell wall synthesis, replication rate animal models that can reliably be translated to humans, rates, and an altered anti-oxidant mechanism, which is (4) developing and validating metabolite markers for predict- thought to result in the increased pathogenicity and survival ing treatment outcome, subsequently reducing clinical trial of this hyper-virulent strain of M. tuberculosis [65]. duration, (5) identifying optimum drug combinations, and In the light of the above, a better understanding of the (6) the means to conduct clinical trials in high burden TB metabolome of the infectious organism, being it drug sensi- countries [67]. Thus, in order to reach these GATB goals, a tive or drug-resistant, in response to a drug, may potentially significantly increased effort and resources is required at every lead to improved treatment approaches, and to either pre- stage of the drug discovery and development process. Fur- venting drug resistance or to the development of alternative thermore, optimization of these programs will ensure that drugs for treating the disease [18]. With the emergence of valuable research is not wasted and potential new anti-TB multi-drug-resistant (MDR), extensively-drug-resistant drugs missed, due to attrition in the development process [66]. (XDR), and more recently even totally-drug-resistant (TDR) TB, the robustness of our approaches to discover and de- 3. NON-ADHERENCE, ESPECIALLY DUE TO ASSO- velop new, more effective anti-TB drugs, are questionable. CIATED SIDE-EFFECTS The discovery and development of new anti-TB drugs are largely dependent on our understanding of the complex host- According to numerous studies, reasons for non- pathogen-interactions [66], xenobiotic metabolism by both adherence of patients to the anti-TB treatment program in- the host and bacteria, the specific drug action, their side- cludes, amongst others, drug related side-effects (especially effects, etc. nausea, vomiting, giddiness), initial relief from TB- symptoms, alcohol consumption, work or domestic related Furthermore, another shortcoming, is translating basic problems, too ill to get to treatment points, or the stigma research into drug discovery programs. To date, various ge- associated with having TB [68]. However, the anti-TB drug netic tools have been developed for manipulating side-effects are most often associated with non-adherence, M. tuberculosis in order to identify specific drug targets, but leading to treatment failure and drug resistance [66], and few candidates have emerged. Once validated drug targets thus this aspect of non-adherence is of biological mechanis- are identified, lead compounds acting against these targets tic relevance, topical for this review. should be established, in order for medicinal chemistry to use these leads for developing new drugs. Additionally, my- According to a 2008 World Health Organization (WHO) cobacterial persistence/resistance models need be character- bulletin, the side-effects associated with anti-TB drug use ized for rapid analyses of compounds with potent sterilizing include hepatotoxicity/hepatitis (liver inflammation), jaun- activity against M. tuberculosis. Finally, the identification of dice, exanthema (rash), dyspepsia (indigestion) and arthral- metabolite markers providing an early indication or predic- gia (joint pain), and are considered the major factors leading tion of treatment outcome could facilitate clinical trials tre- to non-adherence. According to this bulletin, 23% of all TB mendously [66]. patients terminated or defaulted treatment due to one or more of these side-effects. Furthermore, up to 86% of all patients Unfortunately, drug discovery and development is a may develop these side-effects, especially those co-infected lengthy process, especially in the case of those developed with HIV/AIDS or those with a history of hepatitis [69]. TB against M. tuberculosis, and resources remain limiting. Al- treatment regimens require a high (>90%) compliance rate in though the estimated cost of drug development may vary, it order to achieve a successful treatment outcome [70]. Amuha is estimated that the full development of a new anti-TB drug, et al. (2008) conducted a study, aimed at determining the could cost up to approximately $100M. In 2000, The Global prevalence and factors associated with non-adherence to Alliance for TB Drug Development (GATB) was estab- anti-TB drugs. An interviewer administered questionnaire lished, in order to address these issues. The aims of this alli- was used for qualitative data collection, and revealed that ance is to overcome natural barriers in the field of TB drug most patients tend to terminate treatment when they start to Metabolomics and TB Treatment Failure Current Metabolomics, 2013, Vol. 1, No. 4 313 feel better, or due to the severe side-effects and pill burden, includes elevated levels of isovalerylglycine, ethylmalonic as these drugs may be considered by the patient to be too acid, butyrylglycine, 2-methylbutyrylglycine and suberic many to take at once [70]. Although the aim of TB control is acid. On the basis of this, the similarity in symptoms, and the that trained health staff be able to recognize and manage known mechanism by which this abnormal metabolite pro- these side-effects, it would serve best if these could be file exists in MADD, a better understanding of how these eliminated entirely. Further concern however is the addi- anti-TB drugs cause these associated side-effects was pro- tional expenses which may incur by supplying additional posed: by inhibition of the electron transport flavoproteins. complementary medication to alleviate these associated side- Subsequently, melatonin, a well-known antioxidant previ- effects [69]. ously proposed to increase the efficacy of INH, was investigated as a means of alleviating these symptoms, when ad- To the best of our knowledge, only one such metabolomics study has been done to date. As described earlier, in ministered in combination to these drugs. The result was alleviation of both the oxidative stress and MADD metabo- 2005, Loots et al. conducted a metabolomics investigation lite profile [22]. into the side-effects associated with the oral administration of the combined anti-TB drug Rifater, and alleviating this Despite the limited metabolomics data, a number of stud- using combination therapy with melatonin, in a Sprague- ies have been done investigating the general metabolites of Dawley rat model. Rifater was seen to not only result in in- first-line anti-TB drugs, formed in the host and their associ- creased free radicals, but also induce increased organic acid ated side-effects. These are summarized in (Table 2). levels characteristic of a MADD metabolic profile, which

Table 2. A summary of the general metabolites of first-line anti-tb drugs, and their associated side-effects.

Anti-TB Drug (Year Discovered) Associated Metabolites Associated Side-Effects

Ammonia; • Hepatotoxic: ATDH/hepatitis Diacetylhydrazine (DiAcHZ), • Cutaneous: rash Hydrazine (HZ); • Nephrology: increased urination Hydrazones with pyruvic (INH-PA) and • Abdominal: severe dizziness/nausea, vomiting Isoniazid (INH) (1952): ketoglutaric acids (INH-KA); • Flu-like symptoms: fever, headache [72-75, 77] Isonicotinic acid (INA); • Other: drug-induced lupus erythematous, high anion gap metabolic acidosis, seizures (status epilepticus), mood changes (con- Monoacetylhydrazine (AcHZ); fusion, psychosis, depression), pyridoxine (vitamin B6) depletion N1-Acetyl-N2-isonicotinylhydrazide (resulting in central nervous system (CNS), peripheral neuropa- (AcINH); thy, effects and sideroblastic anaemia) Oxidizing free radicals • Hepatotoxic: ATDH/hepatitis • Cutaneous: pruritus (skin irritation), rash and/or urticarial (hives) • Nephrology: dysuria (painful urination), interstitial nephritis Pyrazinamide (PZA) (1952): Pyrazinoic acid (PA); (kidney inflammation) [1, 73, 75, 78] 5-Hydroxypyrazinoic acid (5-OH-PA) • Abdominal: nausea, vomiting • Flu-like symptoms: fever, arthralgia (due to decreased uric acid secretion), malaise • Other: sideroblastic anaemia • Hepatotoxic: ATDH/hepatitis • Cutaneous: milk skin reaction 2,2’-(Ethylenediimino)-di-butyraldehyde; Optical: retrobulbar/optic neuritis, red-green color blindness, ver- Ethambutol (EMB) (1966): • 2,2’-(Ethylenediimino)-di-butyric acid tical nystagmus [71, 75] (EDA) • Nephrology: hyperuricaemia • Flu-like symptoms: arthralgia • Other: peripheral neuropathy • Hepatotoxic: ATDH/hepatitis, upregulation of hormonal hepatic metabolism (decreasing hormone levels) • Cutaneous: flushing, pruritus, rash Respiratory: breathlessness Rifampicin (RIF) (1966): Desacetylrifampicin; • • Abdominal: cramps, diarrhea, nausea, vomiting [72, 73, 75] 3-Formyl rifampicin • Flu-like symptoms: fever, headache, arthralgia, malaise, dysphoria • Other: immunological reactions, color change in bodily fluid (urine, tears, sweat)

314 Current Metabolomics, 2013, Vol. 1, No. 4 de Villiers and Loots

From the literature however, very little information exists and genetic variance [53]. Predicting which TB patients linking metabolism of drugs and the resulting metabolites to would or would not respond to a specific treatment regimen the above mentioned side-effects, except for that of the hepa- early on, such that changes can be made to the regimen, is totoxicity which is rather well described. All four first line likely to result in a shortened therapy, improved patient ad- drugs used as part of the DOTS program (EMB to a lesser herence and a favorable clinical outcome. Selecting a per- extent), are associated with anti-TB drug induced hepatotox- sonalized treatment regimen on the basis of individual pa- icity (ATDH). ATDH generally occurs within the first two tient characteristics, may ultimately improve drug efficacy months of treatment, and it thought to be dose-dependent. and limit side-effects. Thus, implementing a monitoring sys- Hence, decreasing the drug dosage may lighten the symp- tem by measuring surrogate biomarkers before and during toms. Although the occurrence of ATDH varies in different treatment, could have a significant impact on the global bur- populations, studies have shown the most common factors to den of this disease, especially in under-developed countries, determine a predisposition towards this is genetics, advanced were DOTS is already common practice [80]. age, gender, malnutrition, alcoholism, hepatitis B or C, and Although we do not yet fully understand the exact HIV/AIDS infection, which may in turn lead to increased mechanisms of TB disease and its full effect on the human morbidity, mortality, non-adherence, treatment fail- host, the full mechanisms and side-effects of the current anti- ure/relapse and the consequential development of MDR- TB drugs, and drug resistance by the organism, elucidating TB. Genetics is considered the most common cause of these from a metabolomics perspective will undoubtedly ATDH and polymorphisms in various drug metabolizing contribute to the existing knowledge pool, generated to date loci, such as N-acetyl transferase 2 (NAT 2), cytochrome using genomics, proteomics and transcriptomics. Further P450 oxidase (CYP2E1) and glutathione S-transferase studies aimed at elucidating TB disease and drug metabolite (GSTM1), contributes to the inter-individual variations and pathways, through the identification of new metabolite varying pharmacological responses to these drugs [75, 77]. markers, especially those related to treatment monitoring or Although the exact mechanism of ATDH remains un- outcome, will certainly be of tremendous value. These mark- known, the toxic metabolites associated with the first-line ers may ultimately provide crucial information and clues anti-TB drugs play a crucial role in ATDH development towards the understanding of the exact varying effect of [73], as these drugs are metabolized by the liver. INH, RIF M. tuberculosis on the human host, and the subsequent host and PZA (especially when combined) are potentially hepa- adaptions, in addition to individual variations to drug me- totoxic drugs and mostly associated with an increased risk tabolism and side-effects, which may in turn lead to a better of developing ATDH, if the patient is unable to clear the understanding of treatment failure, and subsequently meas- toxic metabolites formed in liver due to any of the afore- ures for preventing this. Considering this, metabolomics is mentioned predispositions. Ammonia, diacetylhydrazine already showing promise for new and valuable scientific (DiAcHZ), hydrazine (HZ), hydrazones with pyruvic (INH- discoveries related to this. PA) and ketoglutaric acids (INH-KA), isonicotinic acid (INA), monoacetylhydrazine (AcHZ), N1-acetyl-N2- isonicotinylhydrazide (AcINH) and oxidizing free radicals, CONFLICT OF INTEREST are the toxic metabolites produced when INH is metabolized, The authors confirm that this article content has no con- either via NAT2 or hydrolysis [72, 74, 77]. These reactions flict of interest. may further be facilitated with the co-administration of RIF, via its induction of the hepatic microsomal enzymes ACKNOWLEDGEMENTS of CYP450. Furthermore, as RIF competes with bilirubin absorption and conjugation, for inhibition of glucuronosyl- Declared none. transferase, jaundice and increased serum bilirubin and/or hepatic enzyme levels may occur [75]. Additionally, pyraz- ABBREVIATIONS inoic acid (PA) and 5-hydroxypyrazinoic acid (5-OH-PA), CE = Capillary electrophoresis toxic metabolites associated with PZA metabolization, have also recently been implicated to be responsible for PZA- CNS = Central nervous system induced hepatotoxicity [76]. CYP450 = Cytochrome P450

4. CONCLUSIONS DiAcHZ = Diacetylhydrazine DHPS = dihydropteroate synthase Olivier & Loots (2012) used a GC-MS metabolomics approach, in combination with multivariate statistical data DOTS = Directly Observed Therapy, Short analyses, to characterize and identify various Mycobacterium course species. Based a combination of 12 metabolites, a discrimi- ESI = Electrospray ionization nant classification model, based on Bayes' theorem, for dis- tinguishing between various TB causing and non-TB Myco- EMB = Ethambutol bacterium species, was developed [79]. Considering this, XDR = Extensively-drug-resistant metabolomics shows potential for identifying surrogate markers which could be used to predict and/or monitor TB FDA = Food and Drug Administration treatment outcome, and allow for personalized treatment GC = Gas chromatography approaches. These surrogate markers may be influenced by any of the factors described above, including environmental HIV = Human immunodeficiency virus Metabolomics and TB Treatment Failure Current Metabolomics, 2013, Vol. 1, No. 4 315

HZ = Hydrazine [7] Frieden, T. R.; Driver, C. R. Tuberculosis control: Past 10 years and future progress. Tuberculosis, 2003, 83(1), 82-85. INH-KA = Hydrazones with ketoglutaric acids [8] Mukherjee, J. S.; Rich, M. L.; Socci, A. R.; Joseph, J. K.; Virú, F. A.; Shin, S. S.; Furin, J. J.; Becerra, M. C.; Barry, D.J.; Kim, J. Y. INH-PA = Hydrazones with pyruvic acid Programmes and principles in treatment of multidrug-resistant tu- INH = Isoniazid berculosis. Lancet, 2004, 363(9407), 474-481. [9] Quy, H.; Lan, N.; Borgdorff, M.; Grosset, J.; Linh, P.; Tung, L.; INA = Isonicotinic acid Van Soolingen, D.; Raviglione, M.; Co, N.; Broekmans, J. Drug resistance among failure and relapse cases of tuberculosis: is the LC = Liquid chromatography standard re-treatment regimen adequate? Int. J. Tuberculosis Lung Dis., 2003, 7(7), 631-636. MS = Mass spectrometry [10] Wares, D.; Singh, S.; Acharya, A.; Dangi, R. Non-adherence to tuberculosis treatment in the eastern Tarai of Nepal. Int. J. 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Measuring the metabolome: Current analytical technologies. Analyst, 2005, 130(5), criminant analysis 606-625. PABA = p-aminobenzoate [15] van der Werf, Mariët J; Overkamp, K. M.; Muilwijk, B.; Coulier, L.; Hankemeier, T. Microbial metabolomics: Toward a platform PAS = Para-aminosalicylic acid with full metabolome coverage. Anal. Biochem., 2007, 370(1), 17- 25. PLS-DA = Partial least squares discriminant [16] Orešič, M. Metabolomics, a novel tool for studies of nutrition, analysis metabolism and lipid dysfunction. Nutrit., Metabol. Cardiovasc. Dis., 2009, 19(11), 816-824. PCA = Principle component analysis [17] Kaddurah-Daouk, R.; Kristal, B. S.; Weinshilboum, R. M. Me- tabolomics: A global biochemical approach to drug response and PZA = Pyrazinamide disease. Annu. Rev. Pharmacol. Toxicol., 2008, 48, 653-683. [18] Chen, C.; Gonzalez, F. J.; Idle, J. R. LC-MS-based metabolomics PA = Pyrazinoic acid in drug metabolism. Drug Metab. Rev., 2007, 39(2-3), 581-597. 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Received: August 26, 2013 Revised: October 28, 2013 Accepted: November 12, 2013

Review

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11 Review 2017/01/30 Metabolomics biomarkers for tuberculosis diagnostics: current status and future objectives

Biomark. Med. Numerous studies have contributed to our current understanding of the complex Ilse du Preez*,1, Laneke Luies1 biology of pulmonary tuberculosis and subsequently provided solutions to its control & Du Toit Loots1 or eradication. Metabolomics, a newcomer to the Omics research domain, has 1School for Physical & Chemical Sciences, Human Metabolomics, significantly contributed to this understanding by identifying biomarkers originating North-West University (Potchefstroom from the disease-associated metabolome adaptations of both the microbe and host. Campus), Private Bag x6001, Box 269, These biomarkers have shed light on previously unknown disease mechanisms, many Potchefstroom, South Africa, 2531 of which have been implemented toward the development of improved diagnostic *Author for correspondence: strategies. In this review, we will discuss the role that metabolomics has played in [email protected] tuberculosis research to date, with a specific focus on new biomarker identification, and how these have contributed to improved disease characterization and diagnostics, and their potential clinical applications.

First draft submitted: 13 October 2016; Accepted for publication: 18 November 2016; Published online: 18 January 2017

Keywords: biomarkers • blood and tissue • breath • culture • diagnostics • metabolites • metabolomics • sputum • tuberculosis • urine

Pulmonary tuberculosis (TB), a mostly cur- in patients with a compromised immune able disease caused by Mycobacterium tuber- system, such as that caused by the HIV [3–5]. culosis, has reportedly infected approximately Smear microscopy is currently the most com- a third (1.9 billion) of the world’s popula- monly used method for diagnosing active tion, either in its active (symptomatic) or TB. Despite the fact that it is a low-cost, latent (asymptomatic) form. This disease quick and simple method, it has a sensitivity 2 results in the death of 1.5 million individuals of only 62% and cannot distinguish between per annum, ranking it the world’s foremost various Mycobacterium species, nor can it cause of death from a single infectious bacte- detect drug resistance [6]. The current gold rial agent [1] . The current diagnosis of latent standard for diagnosing active TB is bacte- 2017 TB relies primarily on the detection of the riological culture. The latter is based on the host immune response to the M. tuberculosis observation of growth of M. tuberculosis har- infection. Two methods based on this prin- vested from patient sputum, and the method ciple, the tuberculin skin test and the IFN-γ has a reported sensitivity and specificity of release assay, are currently the only methods almost 100%, and can be used to detect drug recommended by the WHO for the detection resistance. This method is, however, time- of M. tuberculosis infection [2]. Although easy consuming, considering the slow growth to perform, these tests have a number of limi- rates of mycobacteria, and even the most tations, since false-positive results can occur advanced culture systems such as MB/BacT, in individuals who were previously vacci- take approximately 2 weeks for a diagnosis, nated or previously infected with M. tuber- thereby delaying treatment onset [7]. More culosis, and false-negative results are common recently, newer technologies such as nucleic part of

acid amplification techniques, phage assays and sero- of analytical techniques, in combination with various logical tests, have also been implemented in the clinical computational, statistical and mathematical analyses. environment. Although these tests have the capacity to The metabolome can be defined as the collection of all outperform the gold standard with regards to diagnos- the metabolites, which are small molecular compounds tic turnover time, they are far less sensitive [8–10]. Fur- (<1500 Da) participating in the metabolic reactions thermore, the implementation of these tests, especially necessary for normal cell function, growth and mainte- in low-income countries, is limited due to their high nance [14] . Since the metabolome is the ultimate down- costs and the requirement for expensive infrastructure stream product of the genome, transcriptome and pro- and highly trained personnel. teome, a disturbance at any of these levels due to, for When considering the above argument in the light example, a pathological stimulus, will alter the metabo- of the most recent TB prevalence statistics, it is clear lome with regards to the presence or absence of specific that TB disease control needs to be re-evaluated and metabolites and/or their quantities [15] . The identifica- improved upon. One of the first steps toward achiev- tion of these changes forms the basis for metabolomics ing this goal is to address this problem from a differ- biomarker discovery. Over the past few years, metabo- ent perspective and to identify new TB biomarkers, lomics has been used to identify potential diagnostic which will not only better characterize the disease, but biomarkers for a variety of disease states, including TB, also lead to the development of improved diagnostic the latter of which is the focus of this review. strategies. In this review, we discuss the metabolomicsbased TB biomarkers identified to date and critically Existing metabolomics biomarkers with evaluate the methodology used, and each biomarker potential TB diagnostic capacity in the context of their possible application toward TB To date, several metabolomics studies have been done diagnostics. with the aim of identifying specific single metabolite biomarkers and/or biosignatures, which can be used The metabolomics research approach in either the initial screening or subsequent speciation Although numerous single biomarkers have been phase of TB diagnostics. As indicated in Tables 1–5, implemented successfully for use in the diagnosis and these approaches include the use of various sample prognosis of various disease states, the emergence of matrices, which were analyzed using a variety of ana- the more recent Omics technologies has unleashed the lytical approaches. The identified biomarkers not only possibility of using biosignatures, which are profiles include the well-known TB biomarkers, tuberculo- of combined biomarkers, toward this end. These bio- stearic acid (TBSA), branched chain fatty acids and signatures are especially useful when individual bio- other cell wall components, but also novel compounds, markers do not exist or cannot be identified. The term never before associated with TB infection. Some of Omics is used to describe research models aimed at the compounds identified as potential biomarkers acquiring large-scale data from each sample in a sample were detected in more than one study and in different group, for the purpose of identifying disease biomark- sample matrices (indicated in bold text in Tables 1–5), ers and/or elucidating novel functional or pathological the most significant of which will be discussed in the mechanisms [11] . These datasets can comprise of a col- subsequent sections of this review. lection of genes (genomics), products of gene expression (transcriptomics), proteins (proteomics) or metab- Biomarkers detected using Mycobacterium olites (metabolomics). Due to the largely untargeted cultures means by which these datasets are usually generated, Although the metabolite profiles of the various TB- they may contain hundreds or even thousands of vari- causing mycobacteria grown in vitro differ to some ables, and therefore, biomarkers and/or biosignatures extent from that grown in vivo, some researchers still are mined from the data using advanced biostatistical prefer to use bacteriological cultures in the initial stages approaches. The validation and clinical performance of diagnostic biomarker identification. Biomarkers of these biosignatures are evaluated by reporting the identified in this manner are indicative of those organ- sensitivity, specificity and receiver operator character- ism-specific compounds that may be present in more istic curves, among others, according to the Standards complex patient-collected diagnostic samples, such as for the Reporting of Diagnostic Accuracy Studies sputum or blood, and this pre-identification may assist guidelines [12,13]. in their detection in these patient samples where they One of the newcomers to the Omics revolution, may occur in diluted quantities and/or be masked by metabolomics, is defined as the nonbiased identifica- the matrix background. tion and quantification of the complete metabolome Pavlou et al. (2004) used a metabolomics approach of a specific biological system, using an assortment to distinguish between M. tuberculosis, Mycobacterium

10.2217/bmm-2016-0287 Biomark. Med. (Epub ahead of print) future science group Metabolomics biomarkers for tuberculosis diagnostics: current status & future objectives Review

Table 1. Tuberculosis biomarkers identified in culture, using a metabolomics research approach. Sample cohort Analytical apparatus Biomarkers identified Study (year) Ref.

Mycobacterium tuberculosis ATD-GC–MS 1-methyl-naphthalene; Phillips et al. [16] 3-heptanone; methylcyclododecane; (2007) 2,2,4,6,6-pentamethyl-heptane; trans- anti-1-methyldecahydronaphthalene; 2-hexene;1-methyl-4-(1-ethylethyl)- benzene; 1,4-dimethyl-cyclohexane; 3,5-dimethylamphetamine; 3-methyl-butanal

M. tuberculosis SPME GC–MS Methyl phenylacetate; methyl p-anisate; Syhre and [17] Mycobacterium bovis methyl nicotinate; o-phenylanisole Chambers M. bovis (BCG) (2008) Mycobacterium avium complex Mycobacterium fortuitum Mycobacterium chelonae Mycobacterium abscessus

M. tuberculosis GC–MS Isopropyl-tetradecanoic acid; 9-hexadecenoic Olivier and [18] M. avium acid; TBSA; 10-heptadecenoic acid; Loots (2012) M. bovis heptadecanoic acid;11-eicosanoic acid; Mycobacterium kansasii 13-docosenoic acid; tricosane; C32 mycocerosic acid; 15-tetracosenoic acid

M. tuberculosis GC–MS TBSA; docosanoic acid; 2-octyl- Olivier and [19] M. avium cyclopropaneoctanoic acid; heptadecanoic Loots (2012) M. bovis acid; 13-docosenoic acid;15-tetracosenoic M. kansasii acid; 2,4-dimethyltetradecanoic acid;9- Pseudomonas aeruginosa hexadecenoic acid; eicosanoic acid; tetradecanoic acid; octacosanoic acid; pentacosanoic acid; 2-hexyl- cyclopropaneoctanoic acid; purine; 9-octadecenoic acid; indole-acetic acid; 11-eicosenoic acid; citric acid; cadaverine; inositol; 5´-adenylic acid; decanoic acid;9- hexadecenoic acid (trans); myo-inositol; succinic acid; putrescine; erythritol; valeric acid

M. tuberculosis UHPLC-ESI-QTOFMS Dexpanthenol; Val-His-Glu-His; Lau et al. (2015) [20] M. avium complex 1-tuberculosinyladenosine; 1-tuberculosinyl2’- M. bovis (BCG) deoxyadenosine; 1-tuberculosinyl derivative; M. chelonae 1-tuberculosinyl-O-acetyladenosine; M. fortuitum phosphatidylglycerol M. kansasii Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. ATD: Automated thermal desorption; BCG: Bacillus Calmette–Guerin; ESI-QTOFMS: Electrospray ionisation quadrupole time-of-flight mass spectrometry; SPME: Solid-phase microextraction; TB: Tuberculosis; TBSA: Tuberculostearic acid; TOFMS: Time-of-flight mass spectrometry; UHPLC–MS/MS: Ultrahigh performance liquid chromatography–tandem mass spectrometry. avium, Pseudomonas aeruginosa and a mixed infection change in electrical resistance, producing characteristic (M. tuberculosis and Mycobacterium scrofulaceum). signals of multiple sensor parameters. These variables They achieved a 96% prediction value when analyzing collected for each culture were then used to construct the headspace above the cultured samples of the afore- a data matrix which was statistically analyzed via dis- mentioned infectious Mycobacterium species, using an criminant function analysis to differentiate the sample electronic nose (EN). The underlying principle of the groups. When this methodology was applied to 46 method is as follows: the EN contains 14 conducting- patient sputum samples, it successfully differentiated polymer sensor arrays, which interact with the volatile sputum containing M. tuberculosis, M. avium, P. aeru- compounds liberated in the headspace of the cultured ginosa, a mixed infection and eight control samples Mycobacterium samples. This interaction results in a (TB-negative patients admitted to the clinic) [37]. The

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method was then further validated using 330 TB-pos- LCS [MIDI Inc., DE, USA]) is the only commercially itive and -negative patient-collected sputum samples, available HPLC-based, pattern recognition method for and correctly identified TB with a sensitivity of 89% mycobacterial speciation, and functions by identify- and specificity of 91%, at a detection limit of 1 × 104 ing variations in the infected mycobacteria’s mycolic bacteria per ml sputum [38]. When investigating the acid content. This method claims to have the ability to potential clinical application of this EN methodology, identify and differentiate 25 mycobacteria species and two different off-the-shelf EN devices were tested in 35 nonmycobacteria species from pure culture, in <30 a real-life clinical setting. The first device could diag- min, at a cost of approximately US$6 per analysis [43]. nose TB with a sensitivity of 68%, specificity of 69% In 2012, Olivier and Loots applied a similar approach, and accuracy of 69%, while the second showed a sensi- identifying the characteristic lipid profiles of M. tuber- tivity of 75%, specificity of 67% and accuracy of 69%. culosis, Mycobacterium bovis, Mycobacterium kansasii and Although these results show promise, they do suggest M. avium, using a gas chromatograpy–mass spectrom- that the EN technology is not yet sensitive, specific or etry (GC–MS) metabolomics research methodology, accurate enough for clinical application toward TB to fully automate this classification model. Twelve lipid diagnostics [39]. Furthermore, the chemical character- biomarkers were identified(Table 1) and used to build a istics and identities of the signals detected cannot be multivariate discriminant model, which could correctly characterized or quantified as yet, and therefore, the assign unknown samples to their respective species characteristic compounds causing these signals were groups with probabilities ranging from 72 to 100%. not identified. Although this method requires 16 h to achieve a diag- Mycobacterial cell wall components, specifically the nostic result, only 1 × 103 cultured cells were needed for characteristic mycolic acids, potentially serve as good correct classification, making it more sensitive than the indicators for the presence of this pathogen in a biologi- previously described HPLC approach [19] . The research cal sample. Various studies have focused on the pres- team, however, later improved on this method by imple- ence or absence and the ratios of these compounds, as menting the use of a mixer mill to assist in the extrac- a means to characterize, identify or differentiate the tion of these biomarkers, resulting in a 5 h turnaround various infectious Mycobacterium species from one time and the need for smaller patient sample and solvent another, for application to TB diagnostics [40–42]. To volumes. A further development was the application of date, the Sherlock™ Mycobacteria ID system (MYCO- an alternative extraction approach, allowing for the

Table 2. Tuberculosis biomarkers identified in human sputum, using a metabolomics research approach. Sample cohort Analytical Biomarkers identified Study Ref. apparatus (year)

6 × TB- spiked with GCxGC- Nonadecanoic acid; TBSA; hexacosanoic acid; Schoeman [21] Mycobacterium TOFMS acetohydroxamic acid; myo-inositol; eicosanoic acid;á- et al. (2012) tuberculosis D-glucopyranoside; d -glycero- l -manno-heptanoic 6 × TB- (not spiked) acid; palmitoleic acid; furan; tetracosanoic acid; arachidonic acid; octadecanoic acid; d -glucose; propane; heptadecanoic acid; docosanoic acid; 9-octadecenoic acid; d -mannose; d -galactose; d -glucosamine; glycerol; uridine; d -fructose; 2-O-glycerol-à- d -galactopyranoside; cadaverine; á- d -galactopyranoside; á- d -xylopyranose; arabinofuranose; d -erythro-pentitol; l -threonine;á-d- galactofuranose; phenylethanolamine

61 × TB+ GCxGC- à- d -glucopyranose-2-acetylamino-2-deoxy;à- d -glucopyranoside; Du Preez [22] 34 × TB- TOFMS á- l -mannopyranose; N-acetyl-glucosamine; and Loots à- d -mannopyranoside; d -galactose-6-deoxy; (2013) à- d -galactopyranose; d -glucosamine; d -citramalic acid; methyl- 17-methyl-octadecanoic acid; d -gluconic acid;δ-lactone; 10-heptadecenoic acid; 9-octadecenoic acid; nonadecanoic acid; 2-deoxy- d -erythro-pentitol; glutaric acid; sebacic acid; ethane; butanal; γ-aminobutyric acid; 3,4-dihydroxybutanoic acid; normetanephrine Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. TB: Tuberculosis; TB+: Positively diagnosed for active TB via culture; TB-: Patients portraying TB-related symptoms, but tested negative for TB via culture; TBSA: Tuberculostearic acid; TOFMS: Time-of-flight mass spectrometry.

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Table 3. Tuberculosis biomarkers identified in blood and tissue, using a metabolomics research approach. Sample Sample cohort Analytical Biomarkers identified Study (year) Ref. matrix apparatus

Lung tissue 40 × TB+ HRMAS NMR Aspartate; glutathione; betaine; Somashekar et al. [23] (guinea pigs) 10 × healthy trimethylamine N-oxide; lactate; alanine; (2012) controls acetate; creatine; phosphocholine;myo- inositol; glycerophosphocholine

Lung tissue 10 × TB+ 1H NMR AMP; creatine; lactate; glucose; NAD+; Shin et al. (2011) [24] (mice) 10 × healthy NADP+; oxaloacetate; fumarate; succinate; controls alanine; aspartate; glutamate; leucine; lysine; isoleucine; phenylalanine; tyrosine; glutamine; betaine; phosphocholine; niacinamide; phosphoethanolamine; acetaldehyde; taurine; UDP-glucose; uracil; uridine; xanthine; glutathione; itaconic acid

Spleen tissue 10 × TB+ 1H NMR AMP; lactate; NADP+; oxaloacetate; Shin et al. (2011) [24] (mice) 10 × healthy fumarate; succinate; alanine; leucine; controls isoleucine; tyrosine; glutamine; phosphocholine; niacinamide; uridine; glutathione

Liver tissue 10 × TB+ 1H NMR AMP; creatine; lactate; glycogen; glucose; Shin et al. (2011) [24] (mice) 10 × healthy NADP+; oxaloacetate; succinate; aspartate; controls glutamate; leucine; lysine; isoleucine; uridine; phenylalanine; tyrosine; glutamine; phosphocholine; niacinamide; taurine; UDP- glucose; uracil

Serum 10 × TB+ 1H NMR Glucose; leucine; isoleucine; phenylalanine; Shin et al. (2011) [24] (mice) 10 × healthy formate controls

Serum 40 × TB+ HRMAS NMR Lactate; choline-containing compounds; Somashekar et al. [23] (guinea pigs) 10 × healthy ethanolamine; phosphocreatine; (2012) controls nicotinamide; glutamate; formate; acetate

Serum 10 × TB+ GC-TOFMS 5-oxoproline Che et al. (2013) [25] (human) 6 × TB+ (before and after treatment) 10 × TB-

Serum 44 × TB+ UPLC–MS/MS Histidine; cysteine; threonine; citrulline; Weiner et al. [26] (human) 46 × latent TB+ (positive mode) cysteine-glutathione disulfide; urea; N- (2012) (asymptomatic) UPLC–MS/ acetylneuraminate; glycocholenate sulfate; 46 × healthy MS (negative inosine; tryptophan; mannose;3-carboxy- controls mode) 4-methyl-5-propyl-2-furanpropanoate; GC–MS phenylalanine; pyroglutamine; taurocholenate sulfate; glutamine; octadecanedioate; γ-glutamylglutamine; glycylvaline; asparate Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Healthy control: Healthy with no TB-related symptoms and tested negative for TB via culture; HRMAS: High-resolution magic angle spinning; NAD+: Nicotinamide adenine dinucleotide; NADP+: Nicotinamide adenine dinucleotide phosphate; TB: Tuberculosis; TB+: Positively diagnosed for active TB via culture; TB-: Patients portraying TB-related symptoms, but tested negative for TB via culture; TOFMS: Time-of-flight mass spectrometry; UHPLC–MS/MS: Ultrahigh performance liquid chromatography-tandem mass spectrometry.

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Table 3. Tuberculosis biomarkers identified in blood and tissue, using a metabolomics research approach (cont.). Sample Sample cohort Analytical Biomarkers identified Study (year) Ref. matrix apparatus

Serum 38 × TB+ 39 × 1H NMR 1-methylhistidine; acetoacetate; acetone; Zhou et al. (2013) [27] (human) healthy controls glutamate; glutamine; isoleucine; lactate; lysine; glycine; nicotinate; phenylalanine; pyruvate; tyrosine; alanine; formate; glycerolphosphocholine; low-density lipoproteins

Serum 120 × TB+ UPLC–MS Hexadecanoic acid; lysoPC (C16:0); lysoPC Feng et al. (2015) [28] (human) 105 × healthy (C18:0); 3D, 7D, 11D-phytanic acid; controls docosanoic acid; phytal; threoninyl-γ- 51 × lung cancer glutamate; kynurenine; quinolinic acid; 45 × pneumonia presqualene diphosphate; 28 × COPD lysoPC (P-18:1 (9Z)); lysoPC (P-16:0) 22 × bronchiectasis

Serum 20 × TB+ LC–MS 99 biomarkers identified – related to Zhong et al. (2016) [29] (human) 18 × healthy glycerophospholipid and arachidonic acid controls metabolism

Plasma 17 × TB+ LC–MS 61 biomarkers identified, including anti- Frediani et al. [30] (human) 17 × healthy TB drugs, glutamate, choline derivatives, (2014) controls Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol), lipid mediators of inflammation Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. Healthy control: Healthy with no TB-related symptoms and tested negative for TB via culture; HRMAS: High-resolution magic angle spinning; NAD+: Nicotinamide adenine dinucleotide; NADP+: Nicotinamide adenine dinucleotide phosphate; TB: Tuberculosis; TB+: Positively diagnosed for active TB via culture; TB-: Patients portraying TB-related symptoms, but tested negative for TB via culture; TOFMS: Time-of-flight mass spectrometry; UHPLC–MS/MS: Ultrahigh performance liquid chromatography-tandem mass spectrometry.

detection of metabolites belonging to not only the lipid various Mycobacterium species from one another, by markers described, but also other compounds including analyzing their excreted metabolite profiles. Although amino acids, alcohols, organic acids, monosaccharides, the main aim of this investigation was to differenti- alkenes, alkanes, purines, pyrimidines, etc. Nineteen ate M. tuberculosis and nontuberculous mycobacteria, metabolites were identified as biomarkers for differen- clear separation between all species was achieved when tiating M. tuberculosis, M. avium, M. bovis, M. kansasii the collected data were analyzed using principle com- and P. aeruginosa (Table 1). Indole-acetic acid, cadaver- ponent analysis and partial least squares-discriminant ine, purine, putrescine and two unknown compounds analysis. Of the 24 biomarkers identified (metabo- with masses of 343 and 373 were detected exclusively lites detected in significantly higher concentrations in P. aeruginosa, whereas inositol and myo-inositol were in M. tuberculosis as compared with the other nontu- characteristic of the Mycobacterium species investigated. berculous mycobacteria strains), only seven could be Succinic acid and an unknown compound with a mass annotated via MS/MS database matching (Table 1) [20]. of 268 were uniquely identified inM. kansasii, and Furthermore, in the context of the rising incidence another unknown compound with a mass of 541 was of drug resistance, a successful TB diagnostic method detected exclusively in M. tuberculosis [18]. In addition should also be able to identify whether the infected to the capacity of the extraction method to identify a M. tuberculosis strain is fully drug susceptible or drug greater variety of metabolites, it further reduced the resistant to either first- or second-line drugs. Although solvent volumes required, and improved the detection no metabolomics study to date has identified biomark- limit to only 250 bacterial cells per analysis [44]. ers for TB drug-susceptibility testing per se, differentia- More recently, Lau et al. (2015) described an opti- tion of various drug-resistant strains from drug-suscep- mized ultrahigh performance liquid chromatogra- tible parent strains has been demonstrated using the phy-ESI-quadruple time-of-flight mass spectrometry differences in the metabolite profiles of these organ- (Q-TOFMS) metabolomics approach to differentiate isms. In two related studies, GC–MS metabolomics

10.2217/bmm-2016-0287 Biomark. Med. (Epub ahead of print) future science group Metabolomics biomarkers for tuberculosis diagnostics: current status & future objectives Review

were used to compare the fatty acid metabolome [45] tion and treatment onset. The true clinical application and total metabolome [46] profiles of tworpoB mutant of these biomarkers can therefore only be determined M. tuberculosis strains (S522L and S531L) to that of a when investigating their diagnostic value in patient- fully drug-susceptible wild-type parent strain, to bet- collected samples, as was done by Syhre et al. [34] and ter characterize rifampicin resistance. In each study, Phillips et al. [35] (see the ‘Biomarkers detected using biomarkers for rifampicin resistance were identified breath’ subsection). and used to elucidate previously unknown biological mechanisms related to the survival and adaptation of Biomarkers detected using sputum these mutant strains. In a similar fashion, the group The most commonly used sample matrix for diagnos- also investigated monoresistance to isoniazid (resulting ing pulmonary TB to date is sputum. This mucus-like from mutations in the katG gene) in M. tuberculosis. biofluid originates directly from the area of infection, From the differentiating metabolites identified, it was that is, the airways of the lungs, and is highly popu- shown that the isoniazid-resistant strains are more sus- lated with M. tuberculosis in TB-positive patients. A ceptible to oxidative stress, and a subsequent survival metabolic profile of a TB patient’s sputum would adaptation to this is to increase the uptake and utili- subsequently contain Mycobacterium-specific metabo- zation of alkanes and fatty acids as a carbon/energy lites, due to the physical presence of the organism in source, and to synthesize antioxidant compounds, this sample, and also various altered, disease-induced that is, ascorbic acid, and its oxidation via an ascor- host metabolites, thereby expanding the possibility of bate degradation pathway [47]. Although the diagnostic identifying diagnostic biomarkers. In addition to diag- potentials of the identified biomarkers were not inves- nostics, these biomarkers could also be implemented tigated, these studies show that metabolomics can be toward the elucidation of disease mechanisms, seeing implemented toward the identification of characteristic that they are a true reflection of adaptations to the drug-resistant biomarkers. metabolome of M. tuberculosis due to in vivo growth Although the methods described above all required and the host response to infection/disease. Despite a time-consuming culturing step, these attempts at this, however, very few metabolomics studies have improved TB diagnostics using bacterial cultures been focused on biomarker identification for improved prove the capacity of metabolomics for detecting TB diagnostics using patient-collected sputum. The new diagnostic biomarkers. Ideally, a TB diagnostic reason for this is most likely linked to the complexities method, including the initial screening and specia- in using this sample matrix, such as its viscosity and tion steps, should be done directly from the collected uneven consistency, and also the likelihood of possible clinical samples (preferably collected in a noninvasive TB infection when handling these samples in a stan- manner) to shorten the delay between disease presenta- dard analytical laboratory not equipped for these pur-

Table 4. Tuberculosis biomarkers identified in human urine, using a metabolomics research approach.

Sample cohort Analytical Biomarkers identified Study (year) Ref. apparatus

117 × TB+ GC–MS o-xylene; isopropyl acetate; 3-pentanol; Banday et al. (2011) [31] 37 × healthy controls dimethylstyrene; cymol 7 × COPD 5 × other pulmonary diseases

21 × TB+ GC–MS Lactic acid; norepinephrine; hydroquinone;4- Das et al. (2015) [32] 21 × TB- hydroxybenzoic acid; glucose†

30 × healthy controls GCxGC- 2-C-methylglycerol; 2-octenoic acid; Luies and Loots [33] 46 × TB+ TOFMS 5-hydroxyhexanoic acid; 5-hydroxyhydantoin; (2016) glycerol monostearate;indole-3-carboxylic acid; kynurenic acid; l -rhamnulose; oxalic acid; phenylacetic acid; quinolinic acid; ribitol; homovanillic acid; 5-hydroxyindoleacetic acid; tryptophan; phenyllactic acid; N-acetyltyrosine (indicating elevated tyrosine) Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. †Of the 42 biomarkers, the identities of only these six compounds were confirmed. COPD: Chronic obstructive pulmonary disease; Healthy control: Healthy with no TB-related symptoms and tested negative for TB via culture; TB: Tuberculosis; TB+: Positively diagnosed for active TB via culture; TB-: Patients portraying TB-related symptoms, but tested negative for TB via culture; TOFMS: Time-of-flight mass spectrometry.

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Table 5. TB biomarkers identified in human breath using a metabolomics research approach. Sample cohort Analytical Biomarkers identified Study (year) Ref. apparatus

10 × TB+ SPME GC–MS Methyl nicotinate Syhre et al. (2009) [34] 10 × healthy controls

23 × TB+ ATD-GC–MS 1,3-dimethyl-trans-cyclohexane;1,4-dichloro-benzene; Phillips et al. (2007) [16] 19 × TB- 1,4-dimethyl-cyclohexane;2-butyl-1-octanol; 1-methyl- 59 × healthy naphthalene; camphene; controls 4-methyl-decane; 3-ethyl-2-methyl-heptane; 2,6-dimethyl-octane; 1,2,3,4-tetramethyl-benzene; 3,6,6-trimethyl-bicyclo-3,1,1-hept-2-ene; 1-ethyl-4- methyl-trans-cyclohexane; l-β-pinene; ethyl-benzene; methyl-benzene; propyl-benzene; 3-methyl-heptane; 2-methoxy-2-me-propane; 1-octene; cyclohexane; heptanal;2,4-dimethyl-heptane; 4-methyl-heptane; nonanal;2-methyl-pentane; styrene; tridecane

30 × TB+ ATD-GC–MS 3-(1-methylethyl)-oxetane; 4-methyl-dodecane; hexyl- Phillips et al. (2010) [35] 196 × TB- cyclohexane; Bis-(3,5,5-trimethylhexyl) phthalate; 1,3,5-trimethyl-benzene; 3,7-dimethyl-decane; tridecane; 4,6,8-trimethyl-1-nonene; 5-ethyl-2-methyl- heptane;4-methyl-1-hexene

50 × TB+ GC–MS Dodecane; 3-heptafluorobutyroxypentadecane; octanol; Kolk et al. (2012) [36] 50 × TB- 5-hexenoic acid; 2-ethyl-1-hexanol Compounds in bold have been identified as potential TB biomarkers in more than one metabolomics study. ATD: Automated thermal desorption; GC–MS: Gas chromatography–mass spectrometry; Healthy control: Healthy with no TB-related symptoms and tested negative for TB via culture; SPME: Solid-phase microextraction; TB: Tuberculosis; TB+: Positively diagnosed for active TB via culture; TB-: Patients portraying TB-related symptoms, but tested negative for TB via culture.

poses. The aforementioned viscosity and uneven con- and the interaction of this cycle with an upregulated sistency of sputum bring about the need for additional, glyoxylate cycle and increased fatty acid oxidation dur- time-consuming sample preprocessing steps before ing in vivo growth in the human host. Furthermore, metabolomics analyses can commence. These methods these biomarkers also shed light on an additional were not always standardized, and even after success- process by which the host produces hydrogen perox- fully applying the necessary extraction procedures and ide via glucose oxidation, as a means to eliminate the analyses of the samples, the metabolic profiles obtained infected bacteria. The elevated concentrations of vari- are complex and contain many compounds from a various neurotransmitters associated with the TB infec- ety of compound classes. Subsequently comprehensive tion provided added information explaining many of statistical analyses to sift through the masses of data, the clinical symptoms that TB patients experience [22]. are required to identify potential biomarkers [22]. Although the objective of this study was to better char- In 2012, specifically for metabolomics TB bio- acterize TB, the differentiation of the groups based on marker identification, Schoeman et al. developed a underlying metabolite differences suggests the poten- new sputum preprocessing method, in conjunction tial for this type of metabolomics approach to be used with a global metabolite extraction approach and for diagnostic purposes. GC×GC-TOFMS analysis, to differentiate sputum Although not using a traditional metabolomics spiked with M. tuberculosis bacilli and control spu- approach, research groups have also investigated the tum (not spiked). The compounds identified as mark- possible use of individual sputum metabolites, such ers mainly represented those associated with the cell as the aforementioned TBSA, for diagnosing TB. In wall of M. tuberculosis (Table 2) [21] . This method was 2009, Cha et al. indicated that TBSA can be detected applied to differentiate culture-confirmed TB-positive directly from patient sputum samples using solid- (n = 34) and -negative (n = 61) patient sputum, and the phase microextraction and postderivatization coupled 22 compounds (Table 2), best explaining the variation to GC–MS. The group reported that this procedure between the groups, were once again identified. Vari- is more sensitive than smear microscopy and requires ous new disease mechanisms were described, including only 5 h per analysis, thereby proving that the use of the presence of a citramalate cycle in M. tuberculosis, single metabolite biomarkers can be useful in the ini-

10.2217/bmm-2016-0287 Biomark. Med. (Epub ahead of print) future science group Metabolomics biomarkers for tuberculosis diagnostics: current status & future objectives Review tial screening phase of TB diagnostics. However, simi- and patients with other lung conditions, and subse- lar to a positive smear microscopy result, the detection quently identified a TB-specific serum biosignature. A of TBSA in these samples is due to the presence of the set of 12 metabolites, mostly fatty acids, amino acids mycolic acids, a common component in the cell walls and lipids (Table 3), were identified as biomarkers for of all mycobacteria, and can therefore not be used for active TB, and a combination of four of these com- speciation or the detection of drug resistance [48]. pounds (lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate and presqualene diphosphate) Biomarkers detected using blood & tissue allowed for discrimination between active TB and Blood samples (plasma and serum) are regarded as control samples with an AUC value of 0.991 [28]. being comparatively more homogeneous in composi- With the aim of explaining pathogen-induced tion, less viscose and easier to process than sputum. changes in the host, in addition to using serum, Furthermore, although very low concentrations of Shin et al. (2011) compared the spleen, lung and liver potential metabolites originating directly from the tissue metabolome data, from M. tuberculosis-infected infectious bacteria may be detected in the blood, it and healthy control mice, using 1H NMR. Based on reflects those metabolites altered in the host due to the the metabolite profiles, clear differentiation between infection, and is subsequently better suited to iden- TB-positive and control mice was evident for all tis- tify host pathological and defense mechanisms [26], to sue and serum samples collected, and the most char- examine disease progression [23], and/or for diagnostic acteristic biomarkers were identified (Table 3). These and therapeutic monitoring purposes [27]. compounds indicated that precursors of membrane When analyzing serum samples collected from phospholipids, that is, phosphocholine and phospho- healthy controls, individuals with latent M. tuberculosis ethanolamine, as well as the oxidative stress response, infection, and patients with active TB, using GC–MS glycolysis, amino acid metabolism and nucleotide and UPLC–MS/MS, Weiner et al. (2012) were able metabolism, are altered due to the TB disease state [24]. to detect alterations in amino acids, lipids and nucleo- Similarly, Somashekar et al. (2012) applied a 1H NMR tides, due to the anti-inflammatory changes that typi- metabolomics approach using lung and serum samples cally occur during TB disease progression. The altered collected from guinea pigs, 30 and 60 days after infec- metabolite profiles also revealed reduced phospholi- tion, with the aim of studying the effects of disease pro- pase activity, increased indoleamine 2,3-dioxygenase gression on the host metabolome. Sixteen lung metab- 1 activity, an increased abundance of adenosine meta- olites were identified as TB biomarkers (Table 3), four bolic products and indicators of fibrotic lesions in those of which (aspartate, glutathione, betaine and trimeth- patients with active TB, comparatively. Additionally, ylamine N-oxide) were uniquely detected in the active 20 metabolites with potential diagnostic value were TB group as a result of increased oxidative stress and identified (Table 3) [26]. Similarly, using serum-based glutaminolysis in the lung lesions. Furthermore, sev- NMR metabolomics, Zhou et al. (2013) identified 17 eral other metabolites, including lactate, choline-con- metabolites with possible diagnostic value (Table 3), in taining compounds, ethanolamine, phosphocreatine, addition to better describing the TB disease state in nicotinamide and glutamate, were detected in reduced terms of an observed increased glycolysis, lipid degra- amounts in the serum of the infected animals compar- dation, nucleotide biosynthesis, energy consumption atively, whereas formate and acetate were significantly and a modified protein metabolism in TB patients. increased after infection. The authors concluded that Expanding on this, they also investigated the metabo- the metabolite variation detected in both lung tissue lite changes induced by other disease states, including and serum indicates metabolic changes associated diabetes, malignancy and community-acquired pneu- with necrotic disease processes, including anaerobic monia, and subsequently indicated that each of the glycolysis, glutaminolysis, and gluconeogenesis [23]. changes to the metabolome induced by these perturba- In an attempt to find possible diagnostic mark- tions is, in fact, disease specific [27]. With the main aim ers for active TB, not influenced by anti-TB drug of identifying TB diagnostic biomarkers, Feng et al. administration, Che et al. (2013) used a GC-TOFMS (2015) also included patients with diseases other than metabolomics approach to compare serum samples TB in their patient cohort. The serum metabolome collected from healthy controls and TB patients, profiles of patients with active TB, chronic obstructive before and after anti-TB treatment. They indicated pulmonary disorder (COPD), pulmonitis, bronchiec- that cholesterol, galactose and malonic acid were sig- tasis, lung cancer and healthy controls, were compared nificantly elevated in the TB patient samples, whereas using UPLC–MS analyses. By employing orthogonal phenylalanine, glycerol 3-phosphate, ornithine, ino- partial least squares-discriminant analysis, they were sitol, lactic acid and 5-oxoproline were comparatively able to differentiate TB patients from healthy controls downregulated. Of these nine potential TB biomark-

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ers, 5-oxoproline was determined to remain unaf- Similarly, Luies and Loots (2016) used a GC×GC- fected in the TB-positive patient sample group despite TOFMS approach to investigate the urinary TB commencing treatment using first-line anti-TB drugs. metabolite markers induced by infection-related Hence, this biomarker was validated using a bigger adaptations of the host metabolome and/or host– sample cohort. The importance of this study is that pathogen interactions. Clear principle component it indicates the capacity for metabolomics to identify analysis differentiation was achieved between the useful biomarkers for early prediction of treatment TB-positive and healthy control group based on their outcome, and for possible use in new anti-TB drug respective metabolite profiles. The identified metab- development [25]. olite biomarkers (Table 4) indicated TB-induced In some instances, due to the complexity of the changes resulting in abnormal host fatty acid and blood metabolome and the increasing sensitivity of amino acid metabolism (phenylalanine in particu- some of the analytical equipment used for the analy- lar), mediated through changes to IFN-γ and possibly sis thereof, an extremely large number of biomark- insulin [33]. These studies demonstrate the potential ers can potentially be identified. In two such studies of metabolomics to identify TB biomarkers using using LC–MS, Frediani et al. (2014) and Zhong et al. patient-collected urine. (2016) identified 99 and 61 biomarkers, respectively, In addition to disease-induced metabolic changes, when comparing the blood metabolomes of TB-pos- urine also directly reflects metabolites excreted by the itive patients and healthy controls [29,30]. Although liver and kidneys, and therefore provides additional the disease-induced alterations to the host’s metabolic information regarding changes to an individual’s pathways were discussed in these studies (Table 3), nei- metabolome as a result of a toxic insult. To this end, ther investigated the potential diagnostic capacity of Das et al. (2015) identified unique urinary TB bio- the identified iomarkers.b markers and determined the effects of TB treatment From a diagnostic perspective, although standard- on the levels of these compounds. By comparing the ized protocols are available for the collection of blood GC–MS analyzed urine metabolic profiles of TB- samples, these techniques are considered invasive, and positive patients to that of TB-negative individuals in many patients, the fear of needles can induce a stress (with TB-like symptoms), they detected 42 features response, altering the metabolic profile and inducing with potential diagnostic value, of which six could many of the markers previously described for the TB accurately be annotated compound names (Table 4). disease state [14] . Most of the metabolites were related to the phenylalanine metabolic pathway (similar as to what was seen Biomarkers detected using urine by Luies and Loots [2016]), and when using them to Similar to blood, little or no mycobacteria are expected plot a receiver operator characteristic curve, the AUC to be present in the urine of TB patients, and therefore, was calculated as 0.85. The influence of first-line TB very low concentrations of those metabolites associ- treatment on these metabolic profiles was investigated ated with or unique to M. tuberculosis would likely be by analyzing samples obtained from TB patients at detected. However, urine collected from TB patients various time points during the treatment regimen. A would contain higher concentrations of the altered treatment-dependent trend was observed, and those host-derived metabolites induced by the TB disease patients considered successfully cured after treatment state, when compared with that of other patient sam- completion, showed a metabolic profile similar to that ple matrixes. This may be considered an additional of healthy controls [32]. This study indicates that in advantage for biomarker identification for better dis- addition to pretreatment diagnostics, urinary biomark- ease characterization and diagnostic applications, and ers also have the capacity to monitor disease progres- therefore many metabolomics research investigations sion and to potentially monitor treatment response and have been done using urine as the preferred sample predict treatment outcome. This information would matrix. be useful in new TB drug trials, since these markers Banday et al. (2011) analyzed the volatile organic could serve as surrogate end points for treatment out- compounds (VOCs) in patient-collected urine for the come, long before the 6-month treatment regimen is purpose of discriminating TB patients from healthy completed. controls, and from patients with other diseases such as COPD and lung cancer, using headspace GC–MS. Biomarkers detected using breath They found significantly elevated levels of o-xylene Due to the recent advancements in breath collection and isopropyl acetate, accompanied by reduced lev- techniques, this sample matrix has become an attrac- els of 3-pentanol, dimethylstyrene and cymol in the tive option for diagnostic biomarker identification for TB-positive patient group comparatively (Table 4) [31] . TB. Breath originates directly from the infected lung,

10.2217/bmm-2016-0287 Biomark. Med. (Epub ahead of print) future science group Metabolomics biomarkers for tuberculosis diagnostics: current status & future objectives Review and would be expected to contain the characteristic one unknown compound, were identified as poten- host and bacterial VOCs. Additionally, it can be col- tial biomarkers and were used to build a classification lected in a noninvasive manner, even from children and model that could predict TB with a sensitivity of 72%, HIV-positive individuals, where sputum collection is specificity of 86% and accuracy of 79%. The model problematic. was further validated using a different set of samples Using an automated thermal desorption GC–MS, (21 TB positive and 50 hospitalized TB negative), a set of 130 compounds (mostly alkanes, naphthalene and correctly classified TB with a sensitivity of 62%, and benzene derivatives) was uniquely detected in specificity of 84% and accuracy of 77% [36]. the headspace of M. tuberculosis cultures, compared Considering the abovementioned evidence, breath with that produced in the headspace of sterile culture biomarkers show huge potential for their application in media. In a pilot study, the group applied this knowl- a noninvasive TB diagnostic method, however, further edge to patient-collected breath samples, and success- validation is necessary in order to determine which of fully differentiated healthy controls (n = 59) from hos- these compounds are present due to a general disease pitalized patients with a suspicion of pulmonary TB, state, oxidative stress or other external confounders with a 100% sensitivity and specificity. Using only the such as smoking or diet, and which of these are, in most abundant compounds detected in the headspace fact, truly TB specific. of the M. tuberculosis cultures (Table 5) as a diagnostic biosignature, they were also able to classify patients Diagnostic validity of the TB biomarkers with negative (n = 19) and positive (n = 23) sputum identified to date culture results, with a specificity of 100% and sensitiv- A more comprehensive summary of the 49 metabo- ity of 82.6% [16] . This approach was then applied to a lite biomarkers detected in more than one study larger sample cohort of 226 patients and successfully (those markers indicated in bold text in Tables 1–5) identified the TB-positive patients (those for whom the is given in the supplementary section of this review sputum culture, smear and chest radiograph were all (Supplementary Table 1). Considering the biomarkers positive) with a sensitivity of 84% and specificity of identified from culturedMycobacterium : 9-hexadec- 64.7% [35]. enoic acid, TBSA, heptadecanoic acid, 11-eicosenoic Syhre and Chambers (2008) analyzed the com- acid, 13-docosenoic acid, 15-tetracosenoic acid, eico- pounds in the headspace of cultures of various Myco- sanoic acid and cadaverine, were all detected more bacterium species (M. tuberculosis, M. fortuitum, M. than once in different studies conducted by the bovis, M. bovis BCG, M. abscessus and M. avium com- same research group [18–19,21], aimed at differentiat- plex) using solid-phase microextraction and GC–MS. ing various infectious Mycobacterium species from The aim of their investigation was not to identify bio- one another. These eight compounds are associated markers for speciating infection, but rather to identify with the unique cell wall structure of these organ- specific markers that can differentiateM. tuberculosis isms, which is already well-known to vary between and M. bovis from all other mycobacteria, for later Mycobacterium species, and subsequently these application to patient breath sample diagnostics. Two markers would be expected to have diagnostic value, compounds, methyl p-anisate and methyl nicotinate, especially for speciation [43]. Additionally, 10-hep- were identified exclusively inM. tuberculosis and M. tadecenoic acid, 9-octadecenoic acid, nonadecanoic bovis, whereas methyl phenylacetate was identified as acid, á-D-glucopyranoside and d -glucosamine were a common biomarker in M. tuberculosis, M. bovis and detected more than once as biomarkers for cultured M. avium complex (Table 5) [17] . An in vivo valida- M. tuberculosis [19,21], and were confirmed when com- tion for methyl nicotinate as a TB-specific biomarker paring TB-positive and -negative patient sputum [22]. in patient-collected breath, showed significant differ- These biomarkers are also associated with the unique ences in the concentration when comparing TB-pos- cell wall of M. tuberculosis and therefore show prom- itive patients to healthy controls, proving that it may ise for possible use in new TB diagnostic methods potentially be valuable in a clinical environment [34]. using patient-collected sputum. An additional 14 However, a true validation of this would need to be compounds characterizing TB were repeatedly done in breath collected from TB-positive patients and detected in different tissue samples (liver, spleen and clinically sick controls (those with TB-like symptoms, lung tissue) (Table 3). Interestingly, both myo-inosi- but testing negative for TB via culture). tol and uridine were confirmed as characteristic com- In a related study, Kolk et al. (2012), later also compounds in TB-positive tissue from mice and guinea pared the GC–MS profiles of breath samples collected pigs [23,24], and were also identified as biomarkers from 50 TB-positive patients and 50 hospitalized TB- in experiments using cultured M. tuberculosis [19,21] . negative patients. Seven VOCs (Table 5), including The use of tissue samples in a clinical environment

future science group www.futuremedicine.com 10.2217/bmm-2016-0287 Review Preez, Luies & Loots

is, however, impractical, and the presence of these urine [33], originates from the kynurenine pathway, biomarkers in human blood or urine, for instance, induced by several proinflammatory cytokines, and needs to be validated before considering their diag- chemokines involved immune reactions related to a nostic capacity. Considering this, eight compounds number of disease states, and thus, probably also not identified in TB-positive host tissue were in fact specific enough for TB. also identified as TB biomarkers in serum and urine The challenges experienced to date for identify- samples collected from TB-positive patients [26,27,33]. ing a single, disease-specific metabolite for diag- Of these, lysine, isoleucine, phenylalanine and tyro- nostic application, could potentially be solved by sine were detected in elevated concentrations in all of using a combination of metabolites/biomarkers the TB-positive sample groups analyzed, irrespective simultaneously. Docosanoic acid (behenic acid), for of whether tissue, serum or urine was used, which example, was detected as one of the most abundant serves as confirmation for their potential use as TB fatty acids in M. tuberculosis [19], but its diagnos- biomarkers. However, discrepancies in the concen- tic capacity using TB-positive patient samples was trations of many other metabolite markers do leave rather poor. However, when used in combination room for debate, for their potential application for with three other compounds (lysophosphatidylcho- these purposes. Lactate and glutamate, for example, line (18:0) threoninyl-γ-glutamate and presqualene were seen to accumulate in various TB-positive diphosphate), it could differentiate TB patients from host tissue [24] and TB-positive patient serum [27] healthy controls as well as patients with other lung samples, but were found to be reduced in serum col- diseases with an AUC of 0.991 [28]. lected from TB-infected guinea pigs [23]. Addition- Regardless of the aim of the study, all biomarkers ally, glutamine, was detected in reduced amounts in or biosignatures identified should be validated both TB-positive patient serum by one study group [26], statistically and biologically [49]. Although all of the and then later in an elevated abundance by a dif- abovementioned metabolomics investigations identi- ferent group, both using patient-collected serum [27]. fied potentially valuable biomarkers, the majority of Similarly, elevated levels of formate were identified these were used to better characterize the disease and in the serum collected from M. tuberculosis-infected the pathogen-induced metabolic changes in the host. mice [24] and guinea pigs [23], however, this metabo- For this reason, only 4 of the 21 studies described in lite was indicated to be reduced in serum collected this review, and summarized in Tables 1–5, actually from TB-positive patients when compared with validated the identified biomarkers for their potential healthy controls [27]. diagnostic applications, using either an independent 1-methyl-naphthalene, 1,4-dimethyl-cyclohex- test or validation sample set. Another point to note is ane [16] and methyl nicotinate [17,34] were identified that many of the TB characterization and diagnostic as characteristic markers when using headspace studies described above, used rather strict inclusion analyses of M. tuberculosis cultures, and were also and exclusion criteria for sample selection. Although confirmed as biomarkers from TB-positive patient- this serves well to eliminate variation in the sample collected breath samples. The use of methyl nicotin- cohort to better characterize the disease mechanisms, ate as a possible TB diagnostic marker was further it could potentially bias the diagnostic outcome and validated in a follow-up study using a larger sample would not necessarily be attained in a true clinical cohort of collected breath samples from TB-positive setting. Additionally, only two of the diagnostic and -negative patients [34]. Although this compound studies described [28,31] compared the identified TB is used in a niacin test to distinguish cultured M. biomarkers to those identified in patients with related tuberculosis from other related species, its quanti- diseases, such as COPD and lung cancer, while only ties in patient-collected breath may be influenced by one other research group [26] included asymptomatic other confounding factors such as smoking, chew- individuals with latent TB infection. Considering ing betel nuts and a suppressed immune system (for this, in the immediate future, it would be beneficial example in HIV-positive patients) [34]. Another TB to see increased efforts toward validating these newly breath biomarker, tridecane, was verified using two identified biomarkers for use in a clinical context, independent sample cohorts [35]. However, since this especially with regards to improved TB diagnostics. marker is thought to originate from increased oxidative stress in TB patients [35], it would most probably Future perspective & challenges: from not be specific to a TB disease state, since elevated benchtop to clinical application oxidative stress is a common occurrence in most Considering the above, over the last 10 years, metab- diseases. Similarly, the biomarker quinolinic acid, olomics has contributed exponentially to the num- identified in TB patient-collected serum [28] and ber of new TB biomarkers identified, which have not

10.2217/bmm-2016-0287 Biomark. Med. (Epub ahead of print) future science group Metabolomics biomarkers for tuberculosis diagnostics: current status & future objectives Review only shed light on new disease mechanisms but also 96-well plates, using inexpensive laboratory equip- improved TB diagnostics. Ideally, a TB diagnostic ment, such as a basic spectrophotometer. Another method should be sensitive, specific, fast, inexpen- limitation is that no research group to date has sive and easy to use without extensive training or reported a single, unique biomarker present in only clinical facilities. Such a method should be able to TB-positive patients (with the exception of the well- accurately diagnose TB, and be capable of identi- known TBSA). That is not to say that such biomark- fying the species of Mycobacterium causing the dis- ers have not in fact been identified, and/or are being ease, while also indicating whether or not it is drug developed for diagnostic applications. Using a bio- resistant. The method should additionally be able signature rather than a single characteristic marker to predict and/or monitor treatment outcome since to diagnose a disease, does complicate the matter to a this would determine the most appropriate treatment certain extent, however, with the aid of statistical or approach [50]. Although these idealistic diagnostic mathematical prediction models, or pattern recogni- requirements seem impossible considering the limitation software, this can easily be overcome and the tions of current TB diagnostic approaches, with the diagnosis automated. recent advancements in metabolomics and the new TB biomarkers identified to date, it may be achiev- Conclusion able. Improvements to the sensitivities and specifici- The recent application of metabolomics for TB ties of these approaches can easily be attained using research has led to an exponential increased number other detection technologies or targeted analytical of new TB biomarkers identified over the last decade. approaches on the same metabolomics MS-based These biomarkers have shed light on new mecha- equipment. The analyses times can also be signifi- nisms related to the biology of the causative patho- cantly reduced to only a few minutes, or even sec- gen, M. tuberculosis, as well as to various underlying onds, when searching for the identified biomarkers disease mechanisms and adaptations, such as drug in a targeted manner. Furthermore, metabolomics resistance, host–microbe interactions and treatment has identified characteristic biomarkers of active TB failure. Additionally, these compounds have also using patient-collected sputum, blood, urine and contributed toward the development of new diag- breath (hence the application of these to less invasive nostic procedures for active and latent TB, pathogen diagnostics), and also for speciation, characterizing speciation and predicting treatment outcome, using drug resistance and predicting/monitoring treatment patient samples collected via less invasive sampling outcome. The latter is not only important in the techniques. Considering this, the new TB biomark- sense that one could potentially predict a patient’s ers identified using metabolomics have not only eluci- response to first-line treatment at time of diagnosis dated previously unknown disease mechanisms, but (i.e., before the medication is administrated) or well also have the potential to contribute to all aspects of before the 6-month treatment regimen is completed, TB clinical care. However, increased efforts toward but these markers could accelerate the time-consum- validating these newly identified biomarkers for use ing process of new TB medication efficacy testing, in a clinical context, especially toward improved and elucidate their mechanisms of action. diagnostics and TB treatment strategies, are of the The major limitations considering the idealistic utmost importance. diagnostic requirements we propose above regarding the current metabolomics strategies, would be that a Disclosure metabolomics approach typically uses expensive ana- The opinions expressed and conclusions derived are those of lytical equipment, operated by highly trained staff, the authors and are not necessarily those of the National Re- in a laboratory environment. One should, however, search Foundation. bear in mind that the general idea behind metabolomics is to use these high-end analytical apparatus Financial & competing interests disclosure only for the initial biomarker(s) identification and The financial assistance of the National Research Foundation validation phase. Once these markers have been iden- of South Africa for this research is gratefully acknowledged tified, other technologies (like nanotechnology, or (UID: 95269 and 94217). The authors have no other relevant other detection technologies) can be used to develop affiliations or financial involvement with any organization or strategies to increase the sensitivity and specificity of entity with a financial interest in or financial conflict with the detection and to develop an inexpensive kit for rapid subject matter or materials discussed in the manuscript apart point-of-care diagnostics, with minimal training to from those disclosed. the user. Alternatively, kits can also be developed for No writing assistance was utilized in the production of this high-throughput laboratory-based TB diagnostics on manuscript.

future science group www.futuremedicine.com 10.2217/bmm-2016-0287 Review Preez, Luies & Loots

Executive summary • Current tuberculosis (TB) diagnostic strategies fail since there is still no method with the necessary sensitivity, specificity, speed, affordability, accuracy and ease to use without extensive training or clinical facilities, which can determine which species of Mycobacterium is causing the disease, whether or not it is drug resistant and with the ability to predict treatment outcome. • Over the past 10 years, metabolomics has contributed exponentially to the number of new TB biomarkers, which have shed light on new disease mechanisms and also improved TB diagnostics using patient-collected sputum, blood, urine and breath (hence the application of these to less invasive diagnostics). • These biomarkers can be used together with other technologies (like nanotechnology or other detection technologies) to not only increase the sensitivity and specificity of detection, but also to develop inexpensive kits for application to rapid point-of-care diagnostics, or alternatively high-throughput spectrophotometer- based TB diagnostic methods using 96-well plates. • In the immediate future, however, it would be beneficial to see increased efforts toward validating these newly identified biomarkers for use in a clinical context.

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The role of metabolomics in tuberculosis treatment research

For Review Only Journal: Biomarkers in Medicine

Manuscript ID BMM-2017-0141.R1

Manuscript Type: Review

Keywords: Metabolomics, Treatment biomarkers, Tuberculosis

https://mc04.manuscriptcentral.com/fm-bmm Page 1 of 35 Biomarkers in Medicine

1 2 3 1 The role of metabolomics in tuberculosis treatment research 4 5 6 7 2 Abstract 8 9 10 11 3 Despite the fact that tuberculosis (TB) is a curable disease, it still results in approximately 1.8 million 12 13 4 deaths annually. Various inadequacies in the current TB treatment strategies are major contributors to 14 15 5 this high disease prevalence, including the long duration of therapy, the severe sideeffects associated 16 17 6 with TB drugs, treatment failure due to drugresistance, posttreatment disease relapse, and HIV co 18 For Review Only 19 7 infection. In this review, we describe how metabolomics has contributed towards better 20 21 8 explaining/elucidating the mechanisms of drug action/metabolism, drug toxicity, and microbial drug 22 23 9 resistance, and how metabolite biomarkers may serve as prognostic indicators for predicting treatment 24 25 10 outcome as well as for the development of new TB drugs. We also discuss possible future 26 27 11 contributions that metabolomics can make toward more efficient, less toxic TB treatment strategies. 28 29 30 12 Keywords: Biomarkers; Metabolomics; Treatment; Tuberculosis 31 32 33 13 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm1 Biomarkers in Medicine Page 2 of 35

1 2 3 4 14 1. Introduction 5 6 7 15 Despite the fact that tuberculosis (TB) has been studied for decades, using various research 8 9 16 approaches, including genomics, transcriptomics and proteomics, this disease is still regarded as the 10 11 17 foremost cause of death from a single infectious bacterial agent, with a global annual mortality rate of 12 13 18 1.8 million individuals [1] . 14 15 16 19 Currently, the World Health Organization (WHO) recommends the Directly Observed Therapy Short 17 18 20 course (DOTS) strategyFor for treating Review fully drugsusceptible TB.Only This program consists of a six month 19 20 21 therapeutic regimen, starting with an initial two month (intensive) treatment phase, using a 21 22 22 combination of isoniazid (INH), rifampicin (RIF), pyrazinamide (PZA) and ethambutol (EMB), 23 24 23 followed by a four month (maintenance) phase, using only INH and RIF. Additionally, each dose of 25 26 24 27 TB drugs is consumed under the supervision of a trained medical professional (direct observation), in 28 25 29 an attempt to improve therapeutic adherence [2] . When the DOTS program is fully adhered to, it has 30 26 31 a high cure rate in patients with drugsusceptible TB [3] , however, almost 15% of the 10.4 million 32 27 33 new TB cases reported annually, are still treated unsuccessfully [1] . Several factors, including 34 28 35 varying individual metabolism, human immunodeficiency virus (HIV) coinfection, disruptions and/or 36 37 29 premature termination of this firstline treatment approach (which are mostly attributed to the drug 38 39 30 related sideeffects), and/or limited access to the medical facilities where these drugs are 40 41 31 administered, have been associated with an unsuccessful treatment outcome and drugresistance [4] . 42 43 32 44 For treating multidrugresistant (MDR)TB (defined as resistance to both RIF and INH), the WHO 45 33 46 recommends the addition of expensive and more toxic secondline drugs to the DOTS program, 47 48 34 thereby extending the total therapeutic duration to 24 months (known as DOTS plus) [3] . However, 49 50 35 despite these treatment recommendations, globally only 125 000 (20%) of the 580 000 patients with 51 52 36 confirmed MDRTB, actually received treatment in 2015, and of these individuals, only 52% were 53 54 37 reportedly treated successfully [5] . 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm2 Page 3 of 35 Biomarkers in Medicine

1 2 3 38 An essential process towards TB eradication, would be to reduce the number of failed treatment 4 5 39 outcome cases, and also to prevent the development and spread of drugresistant TB. The first step 6 7 40 towards achieving these objectives would be to get a better understanding of the underlying 8 9 41 mechanisms of current TB drugs and their related sideeffects, in addition to elucidating the 10 11 42 mechanisms of drugresistance and the varying individual drug responses. These new insights could 12 13 43 potentially contribute to the development of new, less toxic and more efficient TB drugs, requiring a 14 15 44 shorter treatment duration, and resulting in fewer drugresistant cases. In this review, we will discuss 16 17 45 the contributions that metabolomics, and the identification of new metabolite biomarkers using this 18 For Review Only 19 46 research approach, have made towards achieving these goals. 20 21 22 23 47 2. Using metabolomics towards improving tuberculosis treatment strategies 24 25 26 48 27 Metabolomics can be defined as the comprehensive identification and quantification of all small intra 28 49 29 and extracellular metabolites (<1500 Da) present in a biological system at a specific point in time, 30 50 31 using highly selective and sensitive analytical methods, in conjunction with bioinformatics. The 32 51 33 application of metabolomics for biomarker discovery is based on the identification of alterations to 34 52 35 the specific metabolome being investigated, due to an external stimulus, such as a disease state or 36 37 53 xenobiotics, or as a result of changes to the genome originating from mutations or other genetic 38 39 54 variation [6, 7] . However, it is important to consider both the advantages and limitations of the 40 41 55 analytical instrumentation/techniques used for metabolomics applications, which have been 42 43 56 summarized by De Villiers and Loots (2013). Briefly, gas chromatography mass spectrometry (GC 44 45 57 MS) is relatively affordable with high sensitivity and a good dynamic range, but requires lengthy 46 47 58 sample preparation, especially when analyzing nonvolatile compounds (requiring derivatization). 48 49 59 Liquid chromatography mass spectrometry (LCMS) is ideal for highly polar and ionic compounds 50 51 60 and offers a comprehensive metabolome coverage when being applied in both positive and negative 52 53 61 ionization modes, however, it has comparatively lower chromatographic resolution and higher signal 54 55 62 tonoise ratios when compared to that of GCMS. Capillary electrophoresis mass spectrometry (CE 56 57 63 MS), on the other hand, is fast, affordable, and highly efficient for analyzing polar and ionic 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm3 Biomarkers in Medicine Page 4 of 35

1 2 3 64 compounds, but has poor a sensitivity and reproducibility when compared to the other techniques 4 5 65 described. Lastly, although nuclear magnetic resonance (NMR) is considered highly selective and 6 7 66 does not result in sample destruction during analysis, it is has a far lower sensitivity when compared 8 9 67 to the aforementioned techniques [4] . 10 11 12 68 With regards to its applications to TB treatment strategies, metabolomics has successfully identified 13 14 69 new metabolite biomarkers contributing to (a) an improved understanding of the mechanisms of TB 15 16 70 drug action [8] , (b) their sideeffects [9] , and (c) mechanisms by which TB drugresistance occur [10] . 17 18 71 Furthermore, metabolomicsFor has alsoReview contributed to the identification Only of biomarkers which may be 19 20 72 used as prognostic indicators for predicting treatment outcome [11] , and/or towards the development 21 22 73 or clinical testing of new TB drugs [12] . These metabolomics contributions, and the identified 23 24 74 biomarkers, will be discussed in greater detail in the remainder of this review. 25 26 27 28 75 2.1 Metabolome variations related to tuberculosis drug action and drug metabolism 29 30 31 76 Although the DOTS and DOTS plus TB treatment regimens are considered effective when fully 32 33 77 adhered to by the patient, the exact mechanisms of action and xenobiotic metabolism of each these 34 35 78 drugs, in addition to the molecular mechanisms responsible for mycobacterial dormancy (suspending 36 37 79 38 or temporarily stopping all physiological functions, i.e. latent infection), persistence (inability rid the 39 80 40 host of metabolically active bacteria despite treatment) and drugresistance, remain poorly understood 41 81 42 [13, 14] . By using a global metabolomics approach, hundreds of different compounds can be 43 82 44 analyzed and identified simultaneously, allowing for the discovery of new enzyme activities and 45 83 46 metabolic pathways [15]. As summarized in Table 1, a number of research groups to date have 47 48 84 applied this approach towards a better understanding of first/secondline TB drug action and 49 50 85 metabolism, as well as to investigate the therapeutic characteristics of new TB drug candidates, with 51 52 86 the hope that such insights may eventually lead to the development of more effective, less toxic, 53 54 87 shortened treatment regimes. 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm4 Page 5 of 35 Biomarkers in Medicine

1 2 3 88 Li et al. (2011) applied an ultraperformance liquid chromatography timeofflight mass spectrometry 4 5 89 (UPLCTOFMS) metabolomics approach, to characterize INH metabolism in humans. When 6 7 90 comparing the urinary metabolite profiles obtained from healthy controls to those from INH 8 9 91 administered healthy volunteers, eight known and seven new INHderived metabolites, the latter of 10 11 92 which comprised of two new hydroxylated INH metabolites (2oxo1,2dihydropyridine4 12 13 93 carbohydrazine and isoniazid Noxide) and five new hydrazones (INH conjugated to 4methyl2 14 15 94 oxopentanoic acid and/or methyl2oxopentanoic acid; INH conjugated to 2oxohexanedioic acid; 16 17 95 INH conjugated to 3(4hydroxyphenyl)2oxopropanoic acid; INH conjugated to 2oxo3 18 For Review Only 19 96 phenylpropanoic acid; and INH conjugated to 3(1Hindol3yl)2oxopropanoic acid). The 20 21 97 hydrazones are intermediates of the leucine and/or isoleucine, lysine, tyrosine, tryptophan and 22 23 98 phenylalanine metabolic pathways, suggesting a disruption in the essential amino acid metabolism as 24 25 99 an additional mechanism by which INH inhibits M. tuberculosis growth [16] . Similarly, Nandakumar 26 27 100 et al. (2014) used LCTOFMS metabolic profiling to identify metabolic changes in replicating 28 29 101 M. tuberculosis populations induced by a sublethal dose of INH, RIF and streptomycin, respectively. 30 31 102 By applying unsupervised hierarchical clustering analysis, a subset of druginduced metabolome 32 33 103 changes common to all three antibiotics was identified. These changes represented the tricarboxylic 34 35 104 36 acid (TCA) cycle, glyoxylate metabolism and biosynthetic pathways of various amino acids. With 37 105 38 regards to the TCA intermediates, pyruvate, succinate, fumarate and malate levels were seen to be 39 106 40 elevated subsequent to drug exposure, whereas aketoglutarate levels reduced, and citrate/isocitrate 41 107 42 levels remained unchanged. This result suggests, that despite the previously known diverse bacterial 43 108 44 targets of these TB drugs, all three also result in the activation of isocitrate lyases (ICLs). These 45 46 109 findings were further substantiated by fact that the ICLdeficient M. tuberculosis strains are 47 48 110 significantly more sensitive to these TB drugs. Additionally, this increased susceptibility could be 49 50 111 chemically restored using antioxidants, confirming that the observed TCA changes are indicative of 51 52 112 an adaptive respiratory slowdown caused by an antibiotictriggered increase in the production of 53 54 113 respiratoryderived reactive oxygen intermediates. This metabolomics approach therefore identified a 55 56 114 new mechanism by which firstline TB drugs may act on M. tuberculosis , thereby suggesting an 57 58 115 alternative strategy which may be exploited in the development of more effective TB drugs [17] . 59 60 https://mc04.manuscriptcentral.com/fm-bmm5 Biomarkers in Medicine Page 6 of 35

1 2 3 116 With application of metabolomics to investigating secondline TB drug action, Halouska et al. , (2007) 4 5 117 used a NMR metabolomics approach to identify biomarkers which may explain the mechanisms by 6 7 118 which Dcycloserine (DCS), a MDRTB drug, functions. Although DCS was previously reported to 8 9 119 inhibit both Dalanine racemase (Alr) and DalanineDalanine ligase (Ddl) activity in vitro , its in vivo 10 11 120 activity against M. tuberculosis was not yet confirmed. Therefore, they compared the NMR 12 13 121 metabolite profiles from a wildtype M. smegmatis strain to that of an alr null mutant, its 14 15 122 complemented derivatives, and two DCSresistant mutant strains. The metabolite changes identified 16 17 123 when comparing the complemented derivatives, indicated that a second Dalanine pathway exists in 18 For Review Only 19 124 the alr null mutants, which appears to persist after exposure to DCS. Principle component analysis 20 21 125 (PCA) differentiation of the alr null mutant and its complemented derivatives was, however, lost after 22 23 126 DCS exposure, which suggests that the multiple DCS targets (Alr and Ddl) determined in vitro, are 24 25 127 also present in vivo . Clustering of the DCSresistant strains with the complemented derivative strains 26 27 128 suggests that DCS treatment also results in inhibition of the cell wall enzymes, with minimal anti 28 29 129 microbial impact however, and that Ddl appears to be the primary in vivo lethal target of DCS [8] . In 30 31 130 a followup metabolomics study, the same group confirmed these in vivo drug targets by indicating 32 33 131 cell growth is inhibited when DalanylDalanine production is halted [18] . Prosser and De Carvalho 34 35 132 36 (2013) later confirmed these results using a more direct ex vivo stable isotope metabolomics approach, 37 133 38 identifying Ddl as a possible target for future drug development initiatives against M. tuberculosis 39 134 40 [19] . 41 42 135 Although there are currently a number of new potential TB drugs undergoing phase II and III 43 44 136 preclinical trials, delamanid and bedaquiline are the only newly approved TB drugs in over 50 years. 45 46 137 47 These drugs, however, are currently only used for treating adults with MDRTB when no other 48 138 49 alternatives show effect [20] . Considering the urgent need for new TB drugs and alternative TB 50 139 51 treatment approaches, De Carvalho et al . (2011) used a targeted LCMS metabolic profiling method 52 140 53 to investigate the potential mechanisms of drug action of nitazoxanide (NTZ) and tizoxanide (TIZ) 54 141 55 (two broad spectrum antiparasitic drugs) against M. tuberculosis . The results of their metabolomics 56 57 142 investigation proved that both compounds are capable of penetrating and accumulating in 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm6 Page 7 of 35 Biomarkers in Medicine

1 2 3 143 M. tuberculosis cells, and act against replicating and nonreplicating populations by disrupting the 4 5 144 membrane potential and pH homeostasis. This mechanism of action is identical to that of an anti 6 7 145 mycobacterial drug with a similar chemical structure, niclosamide (NCS), which is an oxidative 8 9 146 phosphorylation uncoupler. This study shows how metabolomics can be used in the development of 10 11 147 more potent and bioavailable TB drug analogues of NTZ and/or TIZ [21] , and similarly to both 12 13 148 Halouska et al. , (2007) [8] and Prosser and De Carvalho (2013) [19] , the capability for using 14 15 149 metabolomics to successfully elucidate previously unknown mechanisms of drug action. 16 17 18 For Review Only 19 150 2.2 Drug-induced changes to the host metabolome suggesting mechanisms of toxicity 20 21 22 151 One of the major obstacles in the control of pulmonary TB is the rising incidence of drugresistant 23 24 152 M. tuberculosis strains, an occurrence that can largely be attributed to patient nonadherence as a 25 26 153 27 result of the sideeffects associated with these drugs. These adverse drug reactions (ADRs) including, 28 154 29 but not limited to, hepatotoxicity/hepatitis, jaundice, hypoglycemia, rashes, myocardial damage, 30 155 31 neurological complications, respiratory complications, indigestion, joint pain and acidosis [4, 9] , have 32 156 33 been shown to contribute to approximately 30% of all TB treatment failures [26] . The ability to 34 157 35 predict and/or prevent ADRs would undoubtedly improve patient adherence. 36 37 158 38 To date, several metabolomics studies have focused on characterizing the altered TB druginduced 39 159 40 metabolome, for the purpose of elucidating mechanisms related to TB drug toxicity (Table 2). With 41 160 42 the aim of elucidating mechanisms related to INHinduced hepatotoxicity, Bando et al . (2011) applied 43 161 44 a GCMS based metabolomics approach to investigate changes in the plasma and urine metabolite 45 162 46 profiles of rats subsequent to oral hydrazine (an INH metabolite) administration (with a dosage of 120 47 48 163 and 240 mg/kg). Various metabolite alterations associated with histopathological changes, including 49 50 164 fatty acid degradation and glycogen accumulation, were identified. These alterations included a dose 51 52 165 dependent elevation in the amino acid precursors of glutathione (cysteine, glutamate and glycine), 5 53 54 166 oxoproline (a product of glutathione metabolism) and ascorbate. TCA cycle intermediates were 55 56 167 reduced in the hydrazine treated rats, whereas urea cycle metabolites and other amino acids were 57 58 168 found to be elevated. Collectively, the metabolite changes detected in this study proved the 59 60 https://mc04.manuscriptcentral.com/fm-bmm7 Biomarkers in Medicine Page 8 of 35

1 2 3 169 importance of oxidative stress and glutathione consumption in the etiology of hydrazineinduced 4 5 170 hepatotoxicity [27] , something which can potentially be utilized for pharmacology and toxicology 6 7 171 screening purposes. Using a similar approach, Rawat et al . (2017) investigated the toxicity index for 8 9 172 PZA and its metabolic products, pyrazinoic acid (PA) and 5hydroxy pyrazinoic acid (5OHPA), in 10 11 173 rat serum samples, using a 1H NMR metabolomics approach. A list of 19 metabolites were identified 12 13 174 as being perturbed in the samples collected from the treated rats. Comparatively, elevated 14 15 175 concentrations of the inflammatory associated metabolites: choline/ glycerylphosphorylcholine , N 16 17 176 acetyl glycoproteins and Oacetyl glycoproteins, were detected in the treated rat samples, 18 For Review Only 19 177 accompanied by reduced lipid levels due to pronounced drug induced oxidative stress and consequent 20 21 178 lipid peroxidation. Furthermore, the elevated levels of glucose and lactate in the treated group 22 23 179 indicated a disturbance in glucose metabolism and a reduced aerobic glycolytic activity, characteristic 24 25 180 of liver toxicity. Furthermore, reduced levels of glucogenic and ketogenic amino acids (glutamate, 26 27 181 alanine, valine and leucine) were detected in the treated rats, substantiating their role as key energy 28 29 182 metabolites during conditions of oxidative stress. These perturbations were found to be more 30 31 183 significant in the serum of those rats dosed with 5OHPA and PA, indicating that PA and 5OHPA are 32 33 184 more hepatotoxic than the PZA drug itself, by inducing liver injury via oxidative stress and 34 35 185 36 inflammation [28] . The above mentioned studies, prove the capacity of metabolomics to elucidate the 37 186 38 mechanisms of toxicity induced by TB drugs individually administered. However, since TB drugs are 39 187 40 rarely administrated individually, Loots et al. (2005) used a GCMS metabolomics approach to 41 188 42 investigate the effects of the combined TB drug, Rifater, on the metabolome of SpragueDawley rats. 43 189 44 Their results indicated that Rifater administration increased oxidative stress and induced a metabolic 45 46 190 profile resembling that of a multiple acylCoA dehydrogenase deficiency (MADD) in humans (Table 47 48 191 2). The metabolic biomarkers identified suggest that Rifater treatment inhibits the functioning of the 49 50 192 electron transport chain flavoproteins, and could be associated with many of ADRs reported for 51 52 193 Rifater. Furthermore, the observed MADD metabolite profile and the associated oxidative stress 53 54 194 could be alleviated by the coadministration of melatonin [9] , a potent antioxidant. Considering this, 55 56 195 Ozkan et al . (2012) measured the plasma melatonin and urinary 6hydroxymelatonin sulphate levels 57 58 196 in 31 patients with pulmonary TB as compared to that of 31 healthy controls. They also proved that 59 60 https://mc04.manuscriptcentral.com/fm-bmm8 Page 9 of 35 Biomarkers in Medicine

1 2 3 197 supplementation with melatonin during the course of TB treatment, reduces the severity of the side 4 5 198 effects [29] . However, to date, current treatment protocols have not been adapted to include the use 6 7 199 of melatonin, possibly since its effective dosage and possible sideeffects, particularly during long 8 9 200 term use, have as yet not been adequately evaluated. Additionally, melatonin manufacturing 10 11 201 protocols are as yet not standardized, hence the dosage per tablet may vary and/or may also contain 12 13 202 harmful substances [30] . 14 15 16 17 203 2.3 Metabolome variations related to TB drug-resistance 18 For Review Only 19 20 204 Drugresistance in M. tuberculosis can be classified as either originating from a genetic or phenotypic 21 22 205 occurrence. Genetic derived drugresistance occurs as a result of mutations in the chromosomal genes 23 24 206 of the pathogen, resulting in MDR or extensively drugresistant (XDR) M. tuberculosis strains. 25 26 207 27 Phenotypic derived drugresistance, on the other hand, occurs as a result of epigenetic alterations in 28 208 29 gene expression and protein modification, resulting in a M. tuberculosis strain with increased 30 209 31 tolerance to TB drugs, especially in nonreplicating bacteria (requiring prolonged treatment) [39] . 32 210 33 Considering the rising incidence of drugresistance, especially in many third world countries, it is 34 211 35 important to better understand the molecular mechanisms leading to the associated phenotype, in 36 37 212 order to successfully diagnose and treat individuals infected with these drugresistant M. tuberculosis 38 39 213 strains, or to prevent the occurrence thereof. In 2012, Du Preez and Loots used GCMS metabolomics 40 41 214 to compare the fatty acid metabolomes of two rpoB mutant M. tuberculosis strains (S522L and 42 43 215 S531L) to that of a fully drugsusceptible wildtype parent strain, in order to better characterize RIF 44 45 216 resistance. Their results indicated a decreased synthesis of 10methyl branchedchain fatty acids and 46 47 217 various cell wall lipids, and an increased dependency for shortchain fatty acids as an alternative 48 49 218 carbon source in the drugresistant strains. The more clinically prevalent rpoB S531L mutants 50 51 219 additionally showed an increased capacity for using these alternative energy sources, compared to the 52 53 220 less frequently occurring rpoB S522L mutants. This study was the first of its kind to link an altered 54 55 221 fatty acid metabolism with RIFresistance in M. tuberculosis [10] (Table 3). In a followup 56 57 222 investigation, Loots (2016) indicated a total depletion of aconitic acid, suggesting a shift in aconitase 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm9 Biomarkers in Medicine Page 10 of 35

1 2 3 223 functionality towards mRNA binding and stability (as a survival response to the primary mutation 4 5 224 causing drugresistance) and away from energy production and growth, explaining the reduced 6 7 225 viability of these RIFresistant strains. This explained and substantiated the previously observed 8 9 226 increased dependency on alternative energy sources as a survival mechanism. A number of other 10 11 227 metabolic changes observed confirmed an additional survival response to the mutation via 12 13 228 maintaining/remodeling the cell wall [40] . Contributing to this, Lahiri et al . (2016) used a LCMS 14 15 229 based organismwide screen to compare approximately 10 000 cell wall lipids occurring in RIF 16 17 230 resistant mutants, in order to identify the lipid alterations induced by various rpoB mutations. Their 18 For Review Only 19 231 results indicated altered mycobactin siderophores and acylated sulfoglycolipid concentrations, both of 20 21 232 which are essential for bacterial growth. This study provided further evidence of characteristic cell 22 23 233 wall lipid remodeling in RIFresistant M. tuberculosis strains first proposed by Loots (2015), and 24 25 234 additionally suggests new diagnostic and therapeutic targets against drugresistant strains of M. 26 27 235 tuberculosis [41] . Loots (2014) also investigated monoresistance to INH (resulting from mutations 28 29 236 in the katG gene) in M. tuberculosis , using GCxGCTOFMS metabolomics, and compared the 30 31 237 metabolite profiles of two INHresistant strains (H15 and H71) to that of a wildtype parent strain. 32 33 238 Results indicated that the INHresistant strains were more susceptible to oxidative stress, and adapted 34 35 239 36 to this via the upregulation of those mechanisms involved in the uptake and utilization of alkanes and 37 240 38 fatty acids as an additional carbon/energy source. Additionally, the synthesis of compounds 39 241 40 associated with reducing oxidative stress — which included an ascorbic acid degradation pathway, 41 242 42 never before associated with this infectious pathogen — was also upregulated [42] . 43 44 243 As previously mentioned, M. tuberculosis drugresistance develops largely from patient non 45 46 244 47 adherence, however, new evidence suggests that the causal genetic mutations in M. tuberculosis may 48 245 49 also result from pharmacokineticpharmacodynamic variability associated with the induction of the 50 246 51 mycobacterial drug efflux pump [43] . Therefore, although significant progress has been made in the 52 247 53 understanding of drugresistance in M. tuberculosis to date, many contributing factors are still largely 54 248 55 unknown, with many new discoveries still to be made. In future, this will undoubtedly contribute 56 57 249 towards the development of more efficient treatment strategies against this organism. 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm10 Page 11 of 35 Biomarkers in Medicine

1 2 3 250 2.4 Metabolomics investigations aimed towards better monitoring of TB treatment efficiency 4 5 251 and predicting treatment outcome 6 7 8 252 9 Currently, smear microscopy, is the most widely used method for monitoring TB treatment efficacy. 10 253 11 This method for detecting the presence of M. tuberculosis in patient collected sputum, functions on 12 254 13 the basis of the ability of these acidfast mycobacteria for retaining a dye subsequent to exposure with 14 15 255 an acidalcohol solution [44] . During a successful TB treatment protocol, a visible decrease in the 16 17 256 bacterial load is expected when applying this method to subsequent patient collected sputum samples, 18 For Review Only 19 257 and a positive sputum smear during the second month of treatment is indicative of possible treatment 20 21 258 failure. However, a period of two months before the first signs of treatment failure is still considered 22 23 259 too timely, since the bacteria may have developed drug resistance during this time [45, 46] . 24 25 260 Additionally, although this method is fast (requiring less than two hours) and easy to perform [44] , the 26 27 261 color reaction is based on the characteristic mycolic acids present in the cell walls of mycobacteria, 28 29 262 and since these fatty acids persist even when the bacteria die, false positive results are a frequent 30 31 263 anomaly [47] . Furthermore, numerous quantitative studies have indicated that this approach is rather 32 33 264 insensitive for the detection of acid fast mycobacteria, (requiring 5 000–10 000 bacteria/mL to be 34 35 265 present in the patient collected sputum) [48] , the latter resulting in a false negative result. Alternative 36 37 266 approaches for monitoring or predicting treatment outcome includes monitoring patient mass over the 38 39 267 duration of the treatment regimen, and a less than 5% body weight gain after the first month of 40 41 268 treatment may also be indicative of a possible negative treatment outcome [45, 49] . Although patient 42 43 269 mass monitoring is a simple and inexpensive approach for predicting treatment outcome, it is rather 44 45 270 unspecific and influenced by numerous confounders including socioeconomic status, HIV co 46 47 271 infection, nutritional habits and stress, to name but a few. Walzl et al. (2008) reviewed a number of 48 49 272 novel immunological biomarkers in serum for monitoring TB treatment response and predicting 50 51 273 treatment outcome [50] . Although these biomarkers showed promise for monitoring treatment 52 53 274 response, none of these have been validated as yet, or even considered for clinical trial evaluation, due 54 55 275 to the costs, complexed logistics and special field site characteristics that would be required. 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm11 Biomarkers in Medicine Page 12 of 35

1 2 3 276 Despite these efforts, a fast, simple and accurate approach for the early detection/prediction of 4 5 277 treatment failure, especially in highrisk patients, does as yet not exist, and this is one of the major 6 7 278 contributors to the resulting extended therapeutic duration on inefficient TB medication and the 8 9 279 development of drugresistant TB strains [51] . 10 11 12 280 An effective method for accurately monitoring treatment progression and/or accurately predicting 13 14 281 treatment outcome or potential relapse, well before the end of the treatment regimen or even before 15 16 282 therapy with standard TB drugs commences, would be regarded as a major breakthrough, since 17 18 283 alternative treatmentFor approaches Review can be considered at an Only earlier stage. Considering this, various 19 20 284 metabolomics studies have been done for the purpose of identifying the metabolome variation and 21 22 285 associated biomarkers which could be accurately used for this purpose. 23 24 25 286 Mahapatra et al. (2014) used LCMS metabolomics to identify metabolic variations characterizing 26 27 287 treatment outcome using patientcollected urine. By comparing the metabolite profiles of urine 28 29 288 collected at time of diagnosis (before treatment) to those collected one month after treatment had 30 31 289 commenced, they identified 45 metabolites differentiating these sample groups. After reducing these 32 33 290 metabolites to a set of six statistically significant molecular features, they were able to classify the 34 35 291 urine as either baseline or one month treatment samples, with an error rate of 11.8% [12] . In future, 36 37 292 these metabolites may serve as biomarkers for evaluating drug response, which could potentially 38 39 293 accelerate TB drug clinical trials. In the following year, Das et al. (2015) investigated the altered 40 41 294 urinary metabolite profiles of active TBpositive patients on firstline TB drugs by comparing samples 42 43 295 collected at various time points during the treatment regimen, with those collected from healthy 44 45 296 controls. A timedependent treatment trend was observed, and the successfully cured patients were 46 47 297 shown to have a metabolic profile similar to that of asymptomatic healthy controls. Although this 48 49 298 study indicates the potential use of metabolomics towards broadening our understanding of TB 50 51 299 treatment progression, the main aim of the study was to identify diagnostic biomarkers and, therefore, 52 53 300 no metabolites explaining this trend were actually identified or discussed [52] . The same group also 54 55 301 investigated the individual variation of TB drug metabolism, which can be associated with treatment 56 57 302 58 outcome, drugrelated sideeffects and the development of drugresistance. They applied an 59 60 https://mc04.manuscriptcentral.com/fm-bmm12 Page 13 of 35 Biomarkers in Medicine

1 2 3 303 untargeted GCTOFMS metabolomics approach and analyzed urine samples collected from newly 4 5 304 diagnosed TB patients at various intervals (i.e. 2h, 6h, 12h, 24h, 36h and 48h) after firstline oral TB 6 7 305 drug administration. The majority of the firstline drugs (INH, RIF and PZA) administered were 8 9 306 detected, as well their known metabolites. This metabolomics study not only allowed for a 10 11 307 comprehensive description of the drug metabolism phenotype associated with firstline TB drugs (by 12 13 308 interpretation of the variation in drug abundance and their known metabolites), but also identified a 14 15 309 previously unknown drug metabolite of EMB [25] . Using a GCxGCTOFMS metabolomics 16 17 310 approach, Luies and Loots (2017, unpublished results) were able to differentiate the urinary 18 For Review Only 19 311 metabolite profiles of active TBpositive patients with a successful and unsuccessful treatment 20 21 312 outcome at time of diagnosis (i.e. before treatment onset). The major metabolite variations identified 22 23 313 suggested that a gut microbiome imbalance predicts and explains why certain individuals have an 24 25 314 unsuccessful treatment outcome [53] . Additionally, these patients presented with elevated levels of 26 27 315 those metabolites associated with abnormalities in the longchain fatty acid βoxidation pathway, 28 29 316 accompanied by reduced Lcarnitine and shortchain fatty acids, which is indicative of a possible 30 31 317 mitochondrial trifunctional protein (MTP) defect. Abnormalities to the amino acid profile of the 32 33 318 unsuccessful treatment outcome group also indicated compromised insulin secretion [54] . In a similar 34 35 319 36 study, Tientcheu et al. (2015) compared the transcriptomic and metabolic profiles of patients with 37 320 38 either a M. africanum or M. tuberculosis infection after receiving the same TB treatment, in order to 39 321 40 identify host variations associated with lineagespecific pathogenesis and treatment response. The 41 322 42 serum metabolic profiles showed a decline in proinflammatory metabolites after treatment, which 43 323 44 was more pronounced in the M. tuberculosis infected patients, comparatively. They concluded that 45 46 324 differences in the transcriptomic and serum metabolic profiles associated with these two lineages may 47 48 325 be an indication of intrinsic host factors associated with susceptibility to TB and/or efficacy of 49 50 326 standard TB treatment [55] . 51 52 327 53 Over the past few years, progress has also been made in the development and application of electronic 54 328 55 nose (sensor array) technologies toward metabolomics research. An electronic nose contains 56 57 329 conductingpolymer sensor arrays that interact with volatile compounds, resulting in electrical 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm13 Biomarkers in Medicine Page 14 of 35

1 2 3 330 resistance producing characteristic signals of multiple sensor parameters [56, 57] . In 2016, Zetola 4 5 331 et al. applied this technology towards the development of improved TB diagnostics and monitoring 6 7 332 TB treatment efficacy. Breath samples were collected from healthy, HIVuninfected controls and 8 9 333 pulmonary TBpositive patients, of which 61% were coinfected with HIV, at time of diagnosis and at 10 11 334 various intervals during the treatment regimen. The electronic nose signals obtained from the volatile 12 13 335 organic compounds in these breath samples indicated that the device had a sensitivity of 94.1% and a 14 15 336 specificity of 90.0% for differentiating TBpositive patients (before treatment onset) and healthy 16 17 337 controls. Changes in these breath signals were also observed longitudinally (at 0, 2, 7, 14, and 30 18 For Review Only 19 338 days following TB treatment onset), indicating its potential application for monitoring treatment 20 21 339 progression [58] . 22 23 24 340 Considering the above, metabolomics has proved to be a useful tool for identify metabolite variations 25 26 341 characterizing treatment efficiency and outcome. These variations could, in future, be implemented 27 28 342 towards the development of a targeted diagnostic/prognostic approach, by specifically searching for 29 30 343 characteristic metabolite signatures using MS based analytical equipment. The application of such a 31 32 344 method to patient collected urine for example, would be considered advantageous since large volumes 33 34 345 are readily available via noninvasive sampling, and minimal sample preparation is required, reducing 35 36 346 the analysis time to merely a few minutes. Furthermore, the sensitivity/or limits of detection using 37 38 347 such approaches is as little as 250 bacterial cells per sample [59] , which is a vast improvement on the 39 40 348 detection limits and poor sensitivities of currently used methods as describe above. A possible 41 42 349 disadvantage of a metabolomics based approach however, would be the high cost of the analytical 43 44 350 equipment used. However, the idea behind biomarker identification using these specialized analytical 45 46 351 47 techniques are for the purpose of developing inexpensive, easytouse, color reaction kits, using 48 352 49 nanotechnology, for example, for later clinical applications. 50 51 52 353 2.5 The application of metabolomics in new anti-tuberculosis drug clinical trials 53 54 55 56 354 The identification of new drug related biomarkers is an important facet of new drug discovery, since 57 58 355 these can potentially explain drug targets and mechanisms, drugdrug interactions, drugrelated 59 60 https://mc04.manuscriptcentral.com/fm-bmm14 Page 15 of 35 Biomarkers in Medicine

1 2 3 356 immune reactions, doseresponse relationships, and toxicity, well before the resulting changes to the 4 5 357 phenotype occurs [60] . When considering clinical drug trials, these biomarkers (including those 6 7 358 identified via metabolomics studies) can be applied to (a) identify and monitor drug delivery to 8 9 359 targets, (b) elucidate and predict the associated pathophysiological mechanisms of disease and drug 10 11 360 interactions, and (c) determine and monitor clinical effects. The prioritization of rational drug 12 13 361 discovery resources can be facilitated by evaluating biomarkers in these three ways early in the drug 14 15 362 development process, which enables early proofofconcept studies for novel therapeutic targets [60] . 16 17 18 363 To this end, metabolicFor profiling Review (metabolomics) is an efficientOnly tool to identify biomarkers for 19 20 364 elucidating both the in vitro and in vivo activities of metabolic drug targets, and also to identify the 21 22 365 metabolic pathways influenced and their associated enzymes, revealing additional drug targets [61] . 23 24 366 As reviewed by Prosser et al . (2014) and Jansen and Rhee (2017), metabolomics can also be used 25 26 367 towards the discovery of novel metabolic functions/pathways, and additionally the identification of 27 28 368 previously unknown drug enzyme targets and the influenced metabolic pathways [15, 61] . Such 29 30 369 metabolomics studies typically compare the metabolomes of a patient group receiving the drug from 31 32 370 those receiving a placebo. The detected altered metabolite profile, when interpreted in the context of 33 34 371 known metabolism and metabolic defects, can be used to predict drug action and possible toxicities. 35 36 372 Alternatively, one can use metabolomicsbased methods to track drugspecific metabolites and their 37 38 373 associated metabolism by using a stable isotopelabeled xenobiotic treatment group in comparison to 39 40 374 an unlabeled group [4, 6] . 41 42 43 375 The ideal TB drug, or combinations thereof, should be fast acting, effective against drugsusceptible 44 45 376 and drugresistant strains, and have antimycobacterial activity against both replicating and non 46 47 377 replicating mycobacteria populations, with little or no sideeffects. Since no such drug has been 48 49 378 discovered or developed for TB to date, new compounds for this purpose are constantly being 50 51 379 investigated. Currently, the efficacy of new candidate TB drugs are evaluated by determining the 52 53 380 degree of growth inhibition of M. tuberculosis in lung homogenates, a process requiring 54 55 381 approximately three weeks of incubation [11] . Although qualitative analysis of tuberculostearic acid 56 57 382 58 (TBSA) in sputum samples has been investigated for diagnosing pulmonary TB, no attempts for using 59 60 https://mc04.manuscriptcentral.com/fm-bmm15 Biomarkers in Medicine Page 16 of 35

1 2 3 383 TBSA as a quantitative biomarker for bacterial growth have been reported. To this end, Cai et al. 4 5 384 (2013) used a GCMS/MS approach to quantify the derivatized methyl ester of TBSA produced 6 7 385 during M. tuberculosis growth in (a) an axenic medium, (b) macrophage cell (J774) cultures, and (c) 8 9 386 the lungs of interferongamma knockout mice, with and without exposure to various TB drugs. In 10 11 387 both the macrophage and knockout mice experiments, TBSA could be positively associated with the 12 13 388 number of colony forming units in the M. tuberculosis cultures. It was also evident that TBSA 14 15 389 persists once synthesized and does not accumulate further after the inhibition of bacterial replication, 16 17 390 thereby validating the use of TBSA as a quantitative biomarker for M. tuberculosis growth. However, 18 For Review Only 19 391 the poor stability of TBSA in nonviable bacteria limits its use to drug studies measuring growth 20 21 392 inhibition. Thus, the authors suggested using TBSA as a bacteriostatic biomarker, and not as a 22 23 393 bactericidal biomarker. This group also investigated the use of hexacosanoic acid as a secondary 24 25 394 biomarker to TBSA, for detecting M. tuberculosis in sputum samples. They reported a similar 26 27 395 response to TB drugs as that seen with TBSA; however, hexacosanoic acid concentrations were 28 29 396 approximately 100fold lower and provided no additional advantages [11] . This study proved the 30 31 397 potential of using a single M. tuberculosis metabolite for monitoring the efficacy of TB drugs. 32 33 398 However, complete metabolite biosignatures, from data obtained using one or more analytical 34 35 399 36 platform(s), are typically preferred for such applications due to the comprehensive amounts of data 37 400 38 generated, which in turn can be used to not only evaluate drug efficacy, but also drug mechanisms 39 401 40 [62] . 41 42 43 402 3. Future perspectives of metabolomics for the development of improved anti-tuberculosis 44 45 403 treatment strategies 46 47 48 49 404 As discussed above, metabolomics can be considered a powerful tool for identifying previously 50 51 405 unknown metabolite variations characterizing a xenobiotic perturbation, which in turn can be used for 52 53 406 evaluating drug mechanisms, drugrelated sideeffects, drugresistance, treatment progression and 54 55 407 disease relapse, and also prompt assessment of the efficacy and safety of new TB drug candidates. 56 57 408 Although the rapid development of new TB drug candidates will undoubtedly bring about numerous 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm16 Page 17 of 35 Biomarkers in Medicine

1 2 3 409 advantages to the general TB population and society as a whole, it is important to realize that 4 5 410 individual variation in drug response, as is the case for many other xenobiotics for that matter, also 6 7 411 exists. Many studies have indicated that interindividual variation in the healthy human metabolome 8 9 412 can directly be associated with an individual’s genetic makeup, age, gender and race, as well as to 10 11 413 various environmental factors such as an individual’s social habits, diet, stress levels, and lifestyle 12 13 414 [63] . By including these known confounders into a metabolomics experiment, the metabolite 14 15 415 variations characterizing and explaining individual variation and the associated drugrelated side 16 17 416 effects or a lack of treatment response, can be identified and better explained, allowing for 18 For Review Only 19 417 personalized treatment approaches [64] . This in turn could also lead to a shorter treatment duration 20 21 418 for firstline TB drugs (currently six months), further lowering the risk for developing late onset drug 22 23 419 sideeffects. These metabolite variations could additionally be used diagnostically for predicting 24 25 420 treatment outcome/response, perhaps even before treatment onset [7, 65] , and also identify those 26 27 421 individuals who would potentially relapse. In practice, however, the identification of such metabolite 28 29 422 variations would require very large sample cohorts, including patient and control groups, representing 30 31 423 all possible environmental and genetic variation. Achieving this however, would most likely require a 32 33 424 global initiative to firstly standardize all metabolomics procedures used for biomarker identification, 34 35 425 36 including all analytical approaches (such as extraction methods, quantification procedures and the 37 426 38 quality control samples used), and statistical analysis, which may affect results. The metabolomics 39 427 40 data generated by all platforms globally should then be made available publically, enabling 41 428 42 researchers to validate biomarkers identified by different research groups using diverse sample 43 429 44 cohorts. This is something the international metabolomics community is slowly adopting, through the 45 46 430 initiatives of the “International Metabolomics Society”. 47 48 49 431 4. Concluding remarks 50 51 52 53 432 Although the use of metabolomics towards the identification of biomarkers aimed at improved TB 54 55 433 treatment strategies could be considered a relatively new research domain, significant progress has 56 57 434 been made towards the discovery of numerous metabolites better explaining previously unknown 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm17 Biomarkers in Medicine Page 18 of 35

1 2 3 435 mechanism s of drug action, toxicity, treatment progression, relapse and drugresistance. The future 4 5 436 identification and validation of these biomarkers may pave the way toward more effective, less toxic 6 7 437 and less expensive TB drugs, which could be administered for shorter treatment periods, with a 8 9 438 reduced incidence of an unsuccessful treatment outcome and/or relapse. 10 11 12 13 439 5. Executive summary 14 15 16 440 • Abstract: Current TB treatment strategies need to be improved due to the many limitations 17 18 441 associated withFor these, including Review the long duration of therapy,Only severe sideeffects associated with 19 20 442 TB drugs, occurrence of treatment failure (due to patient genetics or drugresistance), post 21 22 443 treatment disease relapse, and human immunodeficiency virus (HIV) coinfection. 23 24 444 25 • Conclusion: Using a metabolomics research approach, numerous new metabolite biomarkers 26 445 27 have been identified for better explaining previously unknown mechanism of drug action, 28 446 29 toxicity, treatment progression, relapse and drugresistance. 30 31 447 • Conclusion: The validation of these biomarkers could potentially contribute to the development 32 33 448 of new, more effective, less toxic and less expensive TB drugs, leading to shorter treatment 34 35 449 duration and fewer drugresistant cases. 36 37 38 450 39 6. Financial disclosure/Acknowledgements 40 41 42 451 The authors declare that there are no conflicts of interest, and that this manuscript, and the work 43 44 452 described therein, is unpublished and has not been submitted for publication elsewhere. 45 46 47 453 The financial assistance of the National Research Foundation (NRF) of South Africa for this research 48 49 454 is gratefully acknowledged (UID: 95269). The opinions expressed and conclusions derived are those 50 51 455 of the authors and are not necessarily those of the NRF. 52 53 54 55 456 7. References 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm18 Page 19 of 35 Biomarkers in Medicine

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1 2 3 522 28. Rawat A, Chaturvedi S, Singh A et al . Metabolomics approach discriminates toxicity index of 4 523 pyrazinamide and its metabolic products, pyrazinoic acid and 5hydroxy pyrazinoic acid . Hum. Exp. 5 524 6 Toxicol. 0960327117705426 (2017). 7 8 525 29. Ozkan E, Yaman H, Cakir E et al . Plasma melatonin and urinary 6hydroxymelatonin levels in patients 9 526 with pulmonary tuberculosis . Inflammation 35(4), 14291434 (2012). 10 11 527 30. World Health Organization. Mode of travel: Health considerations . International Travel and Health 12 13 528 (2010). 14 15 529 31. Sumner SJ, Burgess JP, Snyder RW, Popp JA, Fennell TR. Metabolomics of urine for the assessment of 16 530 microvesicular lipid accumulation in the liver following isoniazid exposure . Metabolomics 6(2), 238249 17 18 531 (2010). For Review Only 19 20 532 32. Cunningham K, Claus SP, Lindon JC et al . Pharmacometabonomic characterization of xenobiotic and 21 533 endogenous metabolic phenotypes that account for interindividual variation in isoniazidinduced 22 23 534 toxicological response . J. Proteome Res. 11(9), 46304642 (2012). 24 25 535 33. Cheng J, Krausz KW, Li F, Ma X, Gonzalez FJ. CYP2E1dependent elevation of serum cholesterol, 26 536 triglycerides, and hepatic bile acids by isoniazid . Toxicol. Appl. Pharmacol. 266(2), 245253 (2013). 27 28 537 29 34. Church RJ, Wu H, Mosedale M et al . A systems biology approach utilizing a mouse diversity panel 30 538 identifies genetic differences influencing isoniazidinduced microvesicular steatosis . Toxicol. Sci. 140(2), 31 539 481492 (2014). 32 33 540 35. Nicholls AW, Holmes E, Lindon JC et al . Metabonomic investigations into hydrazine toxicity in the rat . 34 35 541 Chem. Res. Toxicol. 14(8), 975987 (2001). 36 37 542 36. Bollard ME, Keun HC, Beckonert O et al . Comparative metabonomics of differential hydrazine toxicity 38 543 in the rat and mouse . Toxicol. Appl. Pharmacol. 204(2), 135151 (2005). 39 40 544 41 37. Garrod S, Bollard ME, Nicholls AW et al . Integrated metabonomic analysis of the multiorgan effects of 42 545 hydrazine toxicity in the rat . Chem. Res. Toxicol. 18(2), 115122 (2005). 43 44 546 38. Zhao H, Si ZH, Li MH et al . Pyrazinamideinduced hepatotoxicity and gender differences in rats as 45 547 46 revealed by a 1 H NMR based metabolomics approach . Toxicology Research 6(1), 1729 (2017). 47 48 548 39. Zhang Y, Yew W. Mechanisms of drug resistance in Mycobacterium tuberculosis : Update 2015 . The 49 549 International Journal of Tuberculosis and Lung Disease 19(11), 12761289 (2015). 50 51 550 40. Loots DT. New insights into the survival mechanisms of rifampicinresistant Mycobacterium 52 53 551 tuberculosis. J. Antimicrob. Chemother. 71(3), 655660 (2015). 54 55 552 41. Lahiri N, Shah RR, Layre E et al . Rifampin Resistance Mutations Are Associated with Broad Chemical 56 553 Remodeling of Mycobacterium tuberculosis . J. Biol. Chem. 291(27), 1424814256 (2016). 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm21 Biomarkers in Medicine Page 22 of 35

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1 2 3 586 56. Pavlou AK, Magan N, Jones JM, Brown J, Klatser P, Turner APF. Detection of Mycobacterium 4 587 tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network 5 588 6 system . Biosens. Bioelectron. 20(3), 538544 (2004). 7 8 589 57. Wilson AD. Biomarker metabolite signatures pave the way for electronicnose applications in early 9 590 clinical disease diagnoses . Curr. Metabolomics 4(2), 12 (2016). 10 11 591 58. Zetola NM, Modongo C, Matsiri O et al . Diagnosis of pulmonary tuberculosis and assessment of 12 13 592 treatment response through analyses of volatile compound patterns in exhaled breath samples . J. Infect. 14 593 (2016). 15 16 594 59. Du Preez I, Loots DT. Detection limit for differentiating between various Mycobacterium species and 17 18 595 Pseudomonas aeruginosaFor using Review gas chromatographymass Only spectrometry (GCMS) metabolomics: A 19 596 comparison of two extraction methods . Afr. J. Microbiol. Res. 7(9), 797801 (2013). 20 21 597 60. Parida SK, Kaufmann SHE. The quest for biomarkers in tuberculosis . Drug Discov. Today 15(3), 148 22 23 598 157 (2010). 24 25 599 61. Jansen RS, Rhee KY. Emerging Approaches to Tuberculosis Drug Development: At Home in the 26 600 Metabolome . Trends Pharmacol. Sci. (2017). 27 28 601 29 62. Jacobsen M, Mattow J, Repsilber D, Kaufmann SH. Novel strategies to identify biomarkers in 30 602 tuberculosis . Biol. Chem. 389(5), 487495 (2008). 31 32 603 63. Koen N, Du Preez I. Chapter Three: Metabolomics and personalized medicine . Adv. Protein Chem. 33 604 Struct. Biol. 102 5378 (2016). 34 35 605 36 64. Kumar B, Prakash A, Ruhela RK, Medhi B. Potential of metabolomics in preclinical and clinical drug 37 606 development . Pharmacol. Rep. 66(6), 956963 (2014). 38 39 607 65. Kaddurah ‐Daouk R, Weinshilboum RM. Pharmacometabolomics: implications for clinical pharmacology 40 608 41 and systems pharmacology . Clin. Pharmacol. Ther. 95(2), 154167 (2014). 42 43 609 44 45 610 Papers of special note have been highlighted as: * of interest or ** of considerable interest. 46 47 611 *Halouska et al., 2007 48 49 50 612 In this study, nuclear magnetic resonance (NMR) metabolomics was used to investigate the 51 52 613 mechanism of action of Dcycloserine (DCS), a drug currently used to treat multidrugresistant TB 53 54 614 (MDRTB). The results showed that that DalanineDalanine ligase (Ddl) is the in vivo lethal 55 56 615 target of DCS. 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm23 Biomarkers in Medicine Page 24 of 35

1 2 3 616 *Loots et al., 2005 4 5 6 617 Here, metabolomics was used to indicate that a combined TB drug, Rifater, induced a metabolic 7 8 618 profile resembling that of multiple acylCoA dehydrogenase deficiency (MADD) in Sprague 9 10 619 Dawley rats, thereby explaining a number of drugrelated sideeffects, which could be alleviated 11 12 620 by the coadministration of melatonin. 13 14 15 621 16 17 622 18 *Du Preez and Loots,For 2012 Review Only 19 20 623 The groups applied GCMS metabolomics to link rifampicin resistance in M. tuberculosis to an 21 22 624 altered fatty acid metabolism. 23 24 25 625 **Luies and Loots, 2017a 26 27 28 626 In this study, GCxGCTOFMS acquired urinary metabolome profiles were used to describe novel 29 30 627 mechanisms related to TB treatment failure, even before treatment onset, unlocking the possibility 31 32 628 of developing an early prognostic method for TB treatment outcome. 33 34 35 629 * Tientcheu et al., 2015 36 37 38 630 The group compared the serum transcriptomic and metabolic profiles of M. africanum to that of 39 40 631 M. tuberculosis infected patients after receiving the same TB treatment, and identified host 41 42 632 biomarkers associated with lineagesspecific pathogenesis and treatment response, which may be 43 44 633 an indication of intrinsic host factors associated with susceptibility to TB and/or efficacy of 45 46 634 standard TB treatment. 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm24 Page 25 of 35 Biomarkers in Medicine

1 2 3 4 5 Table 1: A summary of various metabolomics applications investigating mechanisms of TB drug 6 7 action and drug metabolism. 8 9 Drug/drug Number of Sample Analytical Research model Reference 10 metabolite samples matrix apparatus 11 DCS M. smegmatis : - 12 Wild-type 12 13 TAM23 12 14 TAM23 pTAMU3 12 GPM16 12 15 GPM14 12 Cultures 1H NMR [8] 16 Wild-type with DCS 12 17 TAM23 with DCS 12 18 TAM23For pTAMU3 Review with DCS 12 Only 19 GPM16 with DCS 12 GPM14 with DCS 12 20 Controls M. smegmatis 40 21 Amiodarone 10 22 Amiodarone 10 23 Ampicillin 10 24 Chloramphenicol 10 Chlorprothixene 10 25 Ciproflaxacin 10 26 DCS 10 Cultures 1H NMR [22] 27 EMB 10 28 Ethionamide 10 29 Gentamicin 10 INH 10 30 Kanamycin 10 31 RIF 10 32 Streptomycin 10 33 Vancomycin 10 DCS M. smegmatis 6 34 Cultures 1H NMR [18] M. tuberculosis 6 35 DCS M. tuberculosis - Cultures LC-MS [19] 36 INH TB+ human patients 8 Urine 37 TB+ mice 8 Urine LC-MS [23] 38 M. tuberculosis - Cultures 39 Controls M. tuberculosis INH 40 - Cultures LC-MS [17] RIF 41 Streptomycin 42 UPLC- INH Healthy humans 6 Urine [16] 43 TOFMS 44 RIF Healthy humans 12 Urine UPLC-QTOF [24] 45 Controls Healthy humans 10 46 DOTS TB+ humans 20 Urine GC-TOFMS [25] 47 48 EMB Healthy humans 2 49 Abbreviations: DCS, D-cycloserine; TAM23, D-alanine racemase null mutant; TAM23 pTAMU3, TAM23 complemented with wild-type alr gene; GPM16, DCS-resistant mutant; GPM14, D-alanine racemase overproducing mutant; LCS, L-Cycloserine; 1H NMR, nuclear magnetic 50 resonance; LC-MS, liquid chromatography mass spectrometry; UPLC, ultra-performance liquid chromatography; TOFMS, time-of-flight 51 mass spectrometry; QTOF, quadrupole time-of-flight; GC, gas chromatography; TB+, tuberculosis positive; INH, isoniazid; RIF, 52 rifampicin; EMB, ethambutol; DOTS, directly observed therapy short-course; TB+, tuberculosis positive. 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 26 of 35

1 2 3 4 5 Table 2: A summary of various metabolomics applications investigating TB drug side-effects. 6 7 Drug/drug Number of Sample Analytical Side-effect Research model Reference 8 metabolite samples matrix apparatus 9 Hepatotoxicity INH Rats 12 Urine 1H NMR [31] 10 (Hepatic MVLA) 1 11 INH Neurotoxicity Rats 30 Urine H NMR [32] 12 Wild-type mice 7 INH Hepatotoxicity Urine LC-MS [33] 13 Cyp2e1-null mice 7 14 Hepatotoxicity INH Mice (34 strains) 272 Liver 1H NMR [34] 15 (Steatosis) Hepatotoxicity 16 Hydrazine Urine (overall Rats 20 1H NMR [35] 17 (INH metabolite) Plasma metabolic effect) 18 ForHepatotoxicity ReviewRats 30Only Hydrazine 19 (overall Urine 1H NMR [36] (INH metabolite) 20 metabolic effect) Mice 24 Kidney 21 Hydrazine Hepatotoxicity Rats 18 Liver 1H NMR [37] 22 (INH metabolite) Neurotoxicity 23 Brain Urine Hydrazine 24 Hepatotoxicity Rats 24 Plasma GC-MS [27] (INH metabolite) 25 Liver 26 PZA Hepatotoxicity Rats 24 Serum 1H NMR [28] Rats (Female) 30 27 Liver PZA Hepatotoxicity 1H NMR [38] 28 Serum 29 Rats (Male) 30 Rifater Oxidative stress Rats 6 Urine GC-MS [9] 30 (combination therapy) MADD profile 31 Abbreviations: INH, isoniazid; PZA, pyrazinamide; MVLA, microvesicular lipid accumulation; LC-MS, liquid chromatography mass 32 spectrometry; 1H NMR, nuclear magnetic resonance; GC-MS, gas chromatography mass spectrometry; MADD, multiple acyl-CoA dehydrogenase deficiency; TB+, tuberculosis positive; HPLC, high performance liquid chromatography; ELISA, enzyme-linked 33 immunosorbent assay. 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm Page 27 of 35 Biomarkers in Medicine

1 2 3 4 5 Table 3: A summary of various metabolomics applications investigating M. tuberculosis drug 6 7 resistance mechanism to TB drugs. 8 9 Drug/drug Resistance M. tuberculosis Analytical apparatus Ref. 10 metabolite conferring mutation mutation 11 Deletion of codon 315 INH katG GCxGC-TOFMS [42] 12 W321F S531L 13 RIF rpoB GC-MS [10] 14 S522L S531L RIF rpoB GCxGC-TOFMS [40] 15 S522L 16 H526Y UHPLC-ESI-Q- 17 RIF rpoB S531L [41] TOFMS 18 For ReviewQ513E Only 19 Abbreviations: INH, isoniazid; RIF, rifampicin; GCxGC-TOFMS, two dimensional gas chromatography time-of-flight mass spectrometry; GC-MS, gas chromatography mass spectrometry; UHPLC-ESI-Q-TOFMS, ultra-high performance liquid chromatography–electrospray 20 ionization–quadruple time-of-flight mass spectrometry. 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 https://mc04.manuscriptcentral.com/fm-bmm Metabolomics (2016) 12:40 DOI 10.1007/s11306-016-0969-x

ORIGINAL ARTICLE

Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive

1 1 Laneke Luier • Du Toit Loots

Received: 7 September 2015 / Accepted: 10 December 2015 Ó Springer Science+Business Media New York 2016

Abstract Despite numerous research efforts to control 1 Introduction tuberculosis, it is still regarded as a global pandemic. It is clear that the infectious agent responsible and its associated Tuberculosis (TB), a highly contagious bacterial disease disease mechanisms remain poorly understood. Alternative caused by Mycobacterium tuberculosis (M. tuberculosis), research strategies are therefore urgently needed to better is considered the leading cause of death globally from a characterize active-TB, especially the adaptations of the single bacterial pathogen. This disease affects approxi- host and microbe as they compete to survive. Using a mately one-third of the world’s population, either in its GCxGC-TOFMS metabolomics approach, we identified active or latent form. The latest reports indicate 9 million new urinary TB metabolite markers induced by adaptations new TB cases per annum, resulting in an estimated 1.5 of the host metabolome and/or host-pathogen interactions. million deaths (3800 deaths a day) (World Health Orga- The most significant of these were the TB-induced changes nization, 2014). resulting in abnormal host fatty acid and amino acid TB transmission occurs through small infectious aerosol metabolism, in particular to tryptophan, phenylalanine and droplets that contain live M. tuberculosis bacilli. When tyrosine, inducing a metabolite profile similar to that of inhaled into the lungs, they undergo rapid replication, patients suffering from phenylketonuria, mediated through before being engulfed by host macrophages. Following changes to INF-c and possibly insulin. This subsequently macrophage internalization, M. tuberculosis avoids death also explains some of the symptoms associated with TB by blocking phagolysosomal fusion, allowing the bacilli to and provides clues to better treatment approaches. persist in a non- or slowly-replicating state within the relatively hospitable niche of these phagosomes, where they Keywords Tuberculosis Á Urinary metabolomics Á can survive for decades (Philips and Ernst 2012, Warner Metabolite markers Á Host-pathogen Á Adaptations Á 2014). Thus, although the host immune response is unable Interactions to eradicate M. tuberculosis, a fully immune-competent host can suppress the infection. This asymptomatic and non-infectious state is referred to as latent TB. However, Electronic supplementary material The online version of this when the immune system becomes compromized, latent article (doi:10.1007/s11306-016-0969-x) contains supplementary TB develops into active TB—the symptomatic and highly material, which is available to authorized users. infectious state of the disease. Common symptoms asso- & Du Toit Loots ciated with active pulmonary TB include a bad cough with [email protected] mucus, pleurisy, haemoptysis, dyspnoea, wheezing, weak- Laneke Luier ness/fatigue, weight-loss, loss of appetite, chills/fever, and [email protected] night sweats (Long et al. 2002). Despite numerous research efforts to date, since the 1 Human Metabolomics, School for Physical and Chemical discovery of M. tuberculosis in 1882, it is clear that this Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Box 269, Potchefstroom 2531, South pathogen and its underlying disease mechanisms remain Africa poorly understood. Thus, alternative research approaches, 123 40 Page 2 of 9 L. Luier, D. T. Loots giving a different perspective of the disease, are urgently 2.1 Urine sample collection and storage needed to better characterize TB, especially those investigating host-microbe interactions and each of their adapta- Urine samples were collected in standard vials, from tions, as they compete with each other in order to survive. 30 TB-negative healthy controls and 46 culture-confirmed To this end, metabolomics has the potential to identify new active TB-positive patients from the same geographical disease markers, describing previously unidentified disease area, prior to any treatment, after which they were placed mechanisms, adaptations of the pathogen to environmental on ice, immediately transported to the lab, and frozen at changes and how these react to antimicrobial treatments -20 °C until analysis. (Drapal et al. 2014). ‘Metabolomics’ refers to the unbiased identification and quantification of all metabolites present 2.2 Reagents and chemicals in a biological system, utilizing highly sensitive and selective analytical methods, in conjunction with bio- Ethyl acetate and diethyl ether were from Honeywell statistics for metabolite marker selection. This ‘omics’ International Inc. (Muskegon, MI, USA). These organic approach allows for a holistic view of a patient’s metabolic solvents were ultra-pure Burdick and Jackson brands. state/metabolome, detecting alterations due to a specific Hydrochloric acid (HCl), pyridine, anhydrous sodium sul- perturbation, such as TB (De Villiers and Loots 2013). phate (Na2SO4) and trimethylchlorosilane (TMCS) were Using this approach on sputum collected from TB patients, from Merck (Darmstadt, Germany). 3-Phenylbutyric acid, Du Preez and Loots (2013) were able to identify new hexane and bis(trimethylsilyl)-trifluoroacetamide (BSTFA) metabolites from both M. tuberculosis and the infected were from Sigma-Aldrich (St. Louis, MO, USA). host, thereby revealing previously unknown metabolic pathways and adaptations of each. These included the: 2.3 Organic acid extraction procedure (a) presence of a citramalate cycle in M. tuberculosis; and derivatization (b) interaction of this cycle with an upregulated glyoxylate cycle; and (c) increased utilization of fatty acids and glu- Creatinine values for all urine samples were determined prior tamate as additional carbon sources by M. tuberculosis to organic acid extraction, using a creatinine enzyme kit during pulmonary infection. Adaptations of the human host (Thermo Scientific; reference number 981845) and subse- included: (a) an alternative mechanism for hydrogen per- quent analysis on an Indiko Clinical Analyser, Type 863 oxide (H2O2) synthesis via glucose oxidation, in order to (Thermo Scientific). These creatinine values are used to nor- counteract the bacterial infection more efficiently; (b) sub- malize metabolite concentrations, as well as to determine the sequent inhibition of oxidative phosphorylation due to volume of urine, internal standard, BSTFA, TMCS and pyr- pronounced oxidative stress; and (c) elevated concentra- idine used for each extraction. 5 N HCl (six drops) was added tions of various neurotransmitters and other metabolites to the determined amount of urine, followed by the addition of related to some of the symptoms associated with TB (Du the internal standard, 3-phenylbutyric acid (25 lmol/mg Preez and Loots 2013). creatinine). Thereafter, ethyl acetate (6 mL) was added, fol- In the current investigation, we applied a two-dimen- lowed by mixing and centrifugation (3000 rpm for 3 min) to sional gas chromatography time-of-flight mass spectrome- achieve solvent phase separation, after which the organic try (GCxGC-TOFMS) metabolomics approach, to compare phase was aspirated into a clean tube. Diethyl ether (3 mL) and differentiate culture-confirmed active TB-positive was then added to the remaining aqueous phase, again fol- (n = 46) and TB-negative healthy control (n = 30) groups, lowed by mixing, centrifugation and aspirating the organic based on the comparative detected changes to their urinary solvent into the tube containing the previously collected metabolomes. Apart from the fact that urine from TB phase. Hereafter, Na2SO4 (approximately 3 g) was added to patients is readily available, easily stored and can be col- remove any water still present in the sample, followed by a lected by non-invasive techniques, it serves as an ideal brief vortex and centrifugation. The organic phase was then matrix for identifying metabolites related to an altered TB decanted from the pellet and evaporated to complete dryness host metabolome, since it contains metabolic signatures of under a stream of nitrogen at 37 °C. This was followed by a many pathways that may change as a result of infection or 60 min derivatization period at 60 °C with BSTFA (22.6 lL/ disease. lmol creatinine), TMCS (4.5 lL/lmol creatinine) and pyridine (4.5 lL/lmol creatinine) (Loots et al. 2004).

2 Materials and methods 2.4 GCxGC-TOFMS analyses

A summary of the experimental design is given in Fig. 1, Each derivatized sample was transferred to a 1.5 mL GC– and described in detail below. MS sample vial, capped and placed into the auto-sampler 123 Tuberculosis metabolomics reveals adaptations of man and microbe… Page 3 of 9 40

Fig. 1 Summary of the experimental design. Subsequent to sample analysis, data processing, statistical data clean-up and biostatistics. collection, an organic acid extraction and derivatization of each Hereafter biomarkers were selected on the basis of various predefined sample extract was performed, followed by GCxGC-TOFMS statistical ‘‘cut offs’’ tray of a Gerstel Multi-Purpose Sampler (Gerstel GmbH and performed at a signal to noise ratio of 300, with a minimum Co. KG, Mu¨lheim an der Ruhr, Germany), coupled to a of 3 apexing peaks, and any possible retention time shifts Pegasus 4D GCxGC-TOFMS (LECO Africa (Pty) Ltd, were corrected across all samples by aligning identical Johannesburg, South Africa), equipped with an Agilent mass spectra displaying similar retention times. Peaks were 7890A gas chromatograph (Agilent, Atlanta, USA), and a identified by comparison of their characteristic mass frag- TOFMS (LECO Africa). All samples (1 lL) were injected in mentation patterns and retention times with libraries random sequence, along with quality control (QC) samples, compiled from previously injected standards. at regular intervals throughout the analytical run, using a 1:12 split ratio. Compound separation was achieved using a 2.6 Statistical data analyses Restek Rxi-5Sil MS primary capillary column (30 m, 0.25 lm film thickness and 250 lm internal diameter), and a Prior to statistical data analysis, a standard metabolomics Rxi-17 secondary capillary column (0.9 m, 0.1 lm film data clean-up procedure was applied. All compounds were thickness and 100 lm internal diameter). Cryomodulation normalized relative to the internal standard by calculating was achieved with a hot pulse of nitrogen gas for 0.7 s, every the relative concentration of each. Following this, variables 3 s. The injector temperature was maintained at a constant showing no variation between the groups were removed, 280 °C for the entire run, and ultra-high-purity helium was and a data filter was applied to each variable to eliminate used as the carrier gas at a constant flow of 1 mL/min. The those with more than 50 % zero values in each group temperature program for the primary column was 55 °C for (Smuts et al. 2013). Using the QC samples, quantile 1 min, after which it was increased at a rate of 7 °C/min to a equating was applied to correct for any batch effects (Wang temperature of 285 °C, at which it was maintained for a et al. 2012). Hereafter, a 50 % QC coefficient of variation further 4 min, followed by a temperature increase of 20 °C/ (CV) filter was applied. Lastly, as most zero values present min, to a final temperature of 305 °C, where it was main- in the dataset were due to low abundance rather than being tained for a further 1 min. The secondary oven’s temperature absent, they were replaced with a value calculated as half ramp was programmed identically to that of the primary of the smallest value (detection limit) present in the dataset column, with a ?5 °C deviation at all of the time points. The (Schoeman et al. 2012). filament was switched off for the first 8 min of each sample Various multivariate (principal components analysis run, as this period was considered to correspond to a solvent (PCA); partial least squares–discriminant analysis (PLS- delay during which no mass spectra were recorded. How- DA)) and univariate (effect sizes; unpaired t test) biosta- ever, this 8 min interval was included on the first column’s tistical analyses were applied using MetaboAnalyst, a time axis to reflect accurate retention times. The transfer line metabolomics web-based server based on the statistical temperature was held at a constant 270 °C and the ion source package ‘‘R’’ (version 2.10.0). PCA is an unsupervised temperature at a constant 200 °C for the entire run. The method used to determine whether or not a natural detector voltage was 1600 V and the filament bias -70 eV. grouping or differentiation exists between sample groups. Mass spectra were collected over the range 50–600 m/z at an The variables/metabolites best describing this differentia- acquisition rate of 200 spectra/s. tion are ranked according to their respective modelling powers. PLS-DA, on the other hand, is a supervised 2.5 Data processing method used to determine group membership of an individual sample, and identifies those variables/metabolites Mass spectral deconvolution, peak alignment and peak that best characterize the differentiated sample groups. identification/annotation was conducted using ChromaTOF Each metabolite is ranked according to the variables’ software (version 4.32). Mass spectral deconvolution was influence on the projection (VIP) parameter, which directly

123 40 Page 4 of 9 L. Luier, D. T. Loots corresponds to its importance (Du Preez and Loots 2013). As indicated in Fig. 3, 12 compounds with a PCA Effect sizes are used to compare each variable individually modelling power [0.5 (Brereton 2003), a PLS-DA between the groups, without taking sample size into VIP [ 1.0 (Smuts et al. 2013), an effect size [0.5, and a account, and indicates practical/clinical significance. For t test with a P value \0.05 (Ellis and Steyn 2003), were this parametric dataset, an effect size [0.5 indicates a selected as the metabolite markers that best explained the moderate effect. The unpaired t-test establishes statistical variation between the analysed groups (listed in Table 1). significance (P-value) by determining whether or not the Box-and-whisker plots of these 12 compounds are provided averages of two groups differ. Conventionally, a P-value in the supplementary information. This multi-statistical \0.05 is considered significant (Ellis and Steyn 2003). approach is based on the assumption that the different statistical tests correct/compensate for each other’s flaws with the elimination of false-positive compounds (Venter 3 Results and discussion et al. 2015). Most of the identified metabolites can be explained by Figure 2 shows clear PCA differentiation of the TB-posi- changes in the host metabolome in response to TB, sup- tive and TB-negative healthy control sample groups on the porting previously speculated mechanisms and shedding basis of the GCxGC-TOFMS metabolomics data collected. light on hitherto unknown mechanisms related to M. tu- The total amount of variance explained by the first two berculosis pathogenesis, and host-microbe interactions and principal components (PCs) (R2X cum) was 71.0 %, of adaptations, as will be described below. which PC1 accounted for 62.5 %, and PC2 accounted for M. tuberculosis infection results in a host immune 8.5 %. A PLS-DA model was subsequently built and response, signalling macrophages to engulf the invading showed a modelling parameter R2Y (cum) of 93.6 %, bacteria. Accompanying this is an inflammatory response indicative of the total explained variation of the response resulting in the release of tumor necrosis factor alpha Y, and a Q2 (cum), corresponding to the cross-validated (TNF-a) and interferon gamma (IFN-c) (Philips and Ernst variation accounted for by the response Y, of 81.0 %. 2012), the latter of which has been shown to elevate

Fig. 2 PCA differentiation of GCxGC-TOFMS organic acid analyzed data, of urine samples collected from active TB- positive and TB-negative patients. A plot of PC1 versus PC2 of the TB-negative healthy control (denoted by C) and active TB-positive patient (denoted by P) groups, showing clear differentiation of these two groups as a result of variation in their metabolite proﬁles. The variances accounted for are indicated in parenthesis

123 Tuberculosis metabolomics reveals adaptations of man and microbe… Page 5 of 9 40

Fig. 3 Venn diagram indicating compound selection using a multi-statistical approach. A total of 12 compounds were identiﬁed as most signiﬁcant for describing the variation between the TB-negative healthy control and TB-positive patient groups

Table 1 The 12 urinary metabolite markers that best explain the variation detected in the urine samples of the TB-negative healthy control and TB-positive patients, ranked according to the retention times Metabolite name Retention TB-negative healthy controls: Active TB-positive: PCA PLS- Effect t-test (Chemspider ID) time (RT) Average concentration (mg/g Average concentration (mg/g (Power) DA sizes (d- (P- (s) creatinine) (standard error of creatinine) (standard error of (VIP) value) value) the mean) the mean)

5-Hydroxyhexanoic 780,0.17 0.045 (0.021) 0.594 (0.127) 0.534 2.312 0.730 \0.001 acid (21230835) Phenylacetic acid 1107,2.31 0.214 (0.034) 1.137 (0.114) 0.719 1.028 1.372 \0.001 (10181341) 2-Octenoic acid 1134,2.35 0.011 (0.007) 0.114 (0.015) 0.568 2.366 1.150 \0.001 (4445840) 2-C-Methylglycerol 1300,0.23 0.071 (0.020) 0.727 (0.084) 0.666 2.432 1.319 \0.001 (19304) 5-Hydroxyhydantoin 1373,6.24 0.049 (0.019) 0.287 (0.045) 0.522 1.961 0.902 \0.001 (3369395) Oxalic acid (946) 1416,1.34 0.030 (0.009) 0.143 (0.024) 0.821 2.147 0.813 \0.001 L-Rhamnulose 1575,0.29 0.028 (0.021) 0.302 (0.041) 0.718 2.838 1.146 \0.001 (388459) Quinolinic acid 1590,1.92 0.615 (0.046) 6.422 (1.042) 0.743 1.151 0.942 \0.001 (1037) Ribitol (10254628) 1743,1.28 1.089 (0.276) 4.768 (0.745) 0.663 2.694 0.838 \0.001 Indole-3-carboxylic 1854,6.66 0.137 (0.026) 0.929 (0.130) 0.642 1.048 1.032 \0.001 acid (13985315) Kynurenic acid 1889,6.12 46.055 (0.812) 23.312 (3.872) 0.701 1.017 0.993 \0.001 (3712) Glycerol 2392,8.16 0 (0) 1.079 (0.247) 0.688 3.804 0.737 \0.001 monostearate (71407) indoleamine 2,3-dioxygenase 1 (IDO1), which in turn is M. tuberculosis growth (Warner 2014), and also expected also associated with the prognosis of various pathological to result in alterations in the downstream metabolites in this conditions, including TB (Suzuki et al. 2012). This IDO1- pathway (see Fig. 4). Quinolinic acid, for instance, a neu- upregulating host response is thought to deplete the bac- rotoxic metabolite formed via tryptophan catabolism, has terial environment of tryptophan, an amino acid essential to been linked to inﬂammatory diseases, and is known to be

123 40 Page 6 of 9 L. Luier, D. T. Loots

Fig. 4 Altered host metabolome induced by M. tuberculosis.A healthy TB-negative group, indicated by :; respectively. Metabolites schematic representation of the metabolite markers (and their hypothesized/previously indicated in the literature to be elevated, are associated metabolism), identified in the urine of TB-positive indicated with open arrows patients, which are either increased or decreased relative to the elevated through IDO1 activation (Heyes et al. 1992). A Another important observation is the presence of more study by Asp et al. (2011) indicated that TNF-a and IFN-c than five times the normal levels of phenylacetic acid reduced the levels of kynurenic acid (a neuroprotective detected in the urine of TB-positive patients. Phenyllactic agent), by inhibiting kynurenine aminotransferases (KATs) acid, although not identified as a metabolite marker, was synthesis (Asp et al. 2011). For these reasons, one would also significantly elevated in the TB patients (4.107 vs. expect elevations in quinolinic acid and reductions in 0.009 mmol/L; P \ 0.0001). Elevations in these two kynurenic acid in the TB-infected host, which is what we metabolites are indicative of phenylalanine accumulation in see from our metabolomics data. Additionally, indole-3- the TB-positive patients, similarly to what is seen in carboxylic acid, the dehydrogenation product of tryptophan phenylketonuria (PKU) patients. Normally, phenylalanine (Lu¨bbe et al. 1983), was elevated approximately nine times is metabolized to tyrosine via phenylalanine hydroxylase in the TB-positive group, which may serve as an additional (PAH), using tetrahydrobiopterin (BH4) as a cofactor. If means by which the host attempts to reduce the much- PAH or BH4 are deficient for whatever reason, phenylala- needed tryptophan for M. tuberculosis survival. Interest- nine will accumulate and undergo transamination to pro- ingly, although not detected as one of the 12 metabolite duce phenylpyruvic acid, which is then either oxidized to markers using the multi-statistical method described, phenylacetic acid or reduced to phenyllactic acid (Puri, tryptophan was found to be elevated in the TB-positive 2006). Since the usual tryptophan [5-hydroxyindole-acetic group (0.667 vs. 0.096 mmol/L; P = 0.041), either because acid (0.155 vs. 0.917 mmol/L; P [ 0.05)], and tyrosine [N- M. tuberculosis can synthesize tryptophan de novo (War- acetyltyrosine (0.014 vs. 0.117 mmol/L; P \ 0.0001); ner 2014), or from another phenomenon explained below. homovanillic acid (7.356 vs. 19.642 mmol/L; P \ 0.0001)] 123 Tuberculosis metabolomics reveals adaptations of man and microbe… Page 7 of 9 40 catabolism metabolites were elevated in the TB-positive triacylglycerides as primary carbon sources, under aerobic group—and considering that both of these amino acid conditions. However, with increased IFN-c production, the catabolic pathways require BH4—a respective catabolic glucose-deficient macrophages become hypoxic, and access enzyme defect or BH4 deficiency can be ruled out. Elevated to iron and its usual carbon sources become limited, forcing concentrations of these amino acids are more likely due to M. tuberculosis to induce an iron-scavenging reaction and compromised insulin production, which further supports utilize host cholesterol and fatty acids exclusively as the earlier evidence for the association between TB and dia- primary carbon sources. Hence, M. tuberculosis is able to betes (Dooley and Chaisson 2009). This is further sub- adapt its metabolic activity in order to survive and persist stantiated by the recently described detection of D-gluconic within its human host, as well as to maximize energy pro- acid d-lactone in the sputum of TB-positive patients, duction from alternative nutrient sources (Philips and Ernst thought to be induced by elevated glucose levels, in addition 2012; Rhee et al. 2011; Singh et al. 2012). M. tuberculosis to elevated levels of normetanephine, a metabolite of can also co-catabolize many carbon sources simultaneously norepinephrine, associated with this response and also via compartmentalization using multiple pathways (De formed from tyrosine (Du Preez and Loots 2013). Insulin Carvalho et al. 2010). not only influences carbohydrate but also amino acid and The elevated 5-hydroxyhydantoin detected in the TB- protein metabolism, as a means to preserve amino acids for positive group is associated with DNA damage induced by other essential anabolic processes. Since tryptophan and oxidative stress, a common occurrence in TB patients, phenylalanine, as well as tyrosine to a certain extent, are primarily due to H2O2 production by the macrophages in an essential amino acids, their concentrations in the human attempt to overcome the bacterial invasion (Du Preez and systemic circulation is dependent on protein consumption Loots 2013). The ascorbic acid (vitamin C) oxidation and uptake from the portal circulation system. These amino product, oxalic acid (Robitaille et al. 2009), was observed acids can also be released from tissue reservoirs or circu- in elevated concentrations in the TB-positive group. This is lating proteins, which is typically what happens during most likely due to ascorbic acid intake by the TB patients wasting, a well-known occurrence in patients suffering as this vitamin is not naturally synthesized by humans. from active TB, and also linked to reduced insulin secretion. Apart from this, foodborne moulds of Aspergillus species The quantities of dietary phenylalanine, tyrosine and tryp- proliferate in the gastrointestinal tract of the host, as a tophan entering systemic circulation are regulated by their secondary condition to the primary TB disease state (Singh catabolic hepatic enzymes, namely PAH, tyrosine amino- and Toskes 2004) and are also known to produce oxalic transferase (TAT), and tryptophan dioxygenase (TDO), acid (Alam et al. 2002). respectively, which are induced by various factors, includ- The ribitol, L-rhamnulose and 2-C-methylglycerol ing reduced insulin. Although recent developments have (2MG) detected in elevated concentrations in the TB shed light on the association between compromized insulin patients, are most likely derived directly from M. tuber- secretion and altered amino acid metabolism, the exact culosis, as these are components of its cell walls (Mikusova mechanism by which this occurs remains unknown (Adams et al. 1996; Silhavy et al. 2010; Velagapudi et al. 2010). 2011; Cansev and Wurtman 2007). In a previous metabo- Furthermore, 2MG is also an intermediate of the 2-C- lomics investigation, in which insulin-sensitive and insulin- methyl-D-erythritol-4-phosphate (MEP) pathway, unique to resistant subjects were compared, both elevated pheny- M. tuberculosis, enabling this pathogen to synthesize lalanine and tyrosine concentrations were factors differen- essential metabolites derived from isoprenoid compounds, tiating these groups (Tai et al. 2010), and serves as further and subsequently considered a target for the development evidence for the role of reduced insulin in the elevation of of new anti-TB drugs, since homologous enzymes are these amino acids. Moreover, reduced insulin secretion also absent in humans (Eoh et al. 2007; Kholodar and Murkin results in free fatty acid oxidation in vivo (Adams 2011). 2013). When the body uses fat as the primary energy source, it is Finally, previous studies have proposed that the nutri- broken down and released into the bloodstream, resulting in tional imbalance (resulting in wasting) caused by M. tu- increased urinary fatty acids and their breakdown products, berculosis trigger autophagy, a homeostatic intracellular as was detected in our study. These fatty acids and break- degradation process targeting intracellular M. tuberculosis. down products included glycerol monostearate, 2-octenoic During this process, macromolecules and organelles are acid and 5-hydroxyhexanoic acid. This could potentially fused with lysosomes and degraded, in order to sustain explain the weight-loss associated with TB if these fatty cellular anabolic processes (Choi et al. 2013; Deretic and acids are sourced from the body’s fat stores (Kairamkonda Levine 2009; Goletti et al. 2013; Gutierrez et al. 2004). et al. 2003; Niu et al. 2012). This host response may addi- Once these macromolecules have been degraded, mono- tionally be driven by various bacterial mechanisms. Upon meric units, such as amino acids, fatty acids and DNA infection, M. tuberculosis initially utilizes host glucose and components, are either exported, excreted or reused 123 40 Page 8 of 9 L. Luier, D. T. Loots

(Goletti et al. 2013; Mizushima 2007). Various studies This is due to changes to INF-c and possibly insulin, and may have also shown that reduced insulin secretion (Mao et al. also explain some of the TB-associated symptoms, providing 2011) or increased glucagon (Deter and De Duve 1967) clues to better treatment approaches. may induce autophagy as an additional host response to TB, contributing to the elevated amino acids, fatty acids, DNA breakdown products, and bacterial components Author contribution Both authors contributed equally to this work: detected in the TB patients. DTL conceptionalized the study design, and LDV did the data Apart from the novel contribution of this discovery acquisition/analysis. LDV worked on data interpretation, and drafted towards better understanding host-microbe interactions/ the article, and DTL revised it critically for important intellectual adaptations, these markers may help explain some of the content. Both authors approved the final version to be submitted. symptoms associated with TB, including neurological Compliance with Ethical standards abnormalities (since quinolinic acid is a N-methyl-D-aspartate receptor agonist) (Heyes et al. 1992), vomiting, nausea, Conflict of interest The authors declare that there are no conflicts diarrhea, drowsiness, fatigue, loss of appetite and weight- of interest, and that this manuscript, and the work described therein, is unpublished and has not been submitted for publication elsewhere. loss (associated with elevated levels of phenylacetic acid) (Sherwin and Kennard 1919). Co-administration of anti-TB Ethical approval Ethical approval for this investigation, carried out drugs with melatonin may assist in reducing elevated according to the Declaration of Helsinki and International Conference quinolinic acid (Cabrera et al. 2000), since melatonin has of Harmonization guidelines, was obtained from the Ethics Com- mittee of the North-West University, South Africa (Number NWU- also been shown to lower a number of toxic metabolites 00127-11-A1). All participants gave written informed consent for associated with the side-effects of anti-TB drugs and also collection of urine and its use as described. increase its efficacy for eliminating M. tuberculosis (Loots et al. 2005). Furthermore, BH4 is given as a treatment for PKU patients in an attempt to reduce phenylacetic acid References (McInnes et al. 1984), hence co-administration of anti-TB drugs with this merits consideration. Lastly, considering the Adams, S. H. (2011). 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Effect of tuberculosis report 2014, 1–2. ginseng polysaccharide on the urinary excretion of type 2

123 Metabolomics

URINARY METABOLITE MARKERS CHARACTERIZING TUBERCULOSIS TREATMENT FAILURE --Manuscript Draft--

Manuscript Number: MEBO-D-17-00069 Full Title: URINARY METABOLITE MARKERS CHARACTERIZING TUBERCULOSIS TREATMENT FAILURE Article Type: Original Article Keywords: Biomarkers; M. tuberculosis; Metabolomics; Treatment failure; Tuberculosis; Urine Corresponding Author: Laneke de Villiers, B.Sc North-West University Potchefstroom, North-West SOUTH AFRICA Corresponding Author Secondary Information: Corresponding Author's Institution: North-West University Corresponding Author's Secondary Institution: First Author: Laneke de Villiers, B.Sc First Author Secondary Information: Order of Authors: Laneke de Villiers, B.Sc Japie Mienie, Ph.D. Christinah Motshwane, B.Sc. Katharina Ronacher, Ph.D. Gerhard Walzl, Ph.D. Du Toit Loots, Ph.D. Order of Authors Secondary Information: Funding Information: Abstract: Background: Considering that approximately 15% of the nine million new tuberculosis (TB) cases reported per annum are not treated successfully, new, distinctive and specific biomarkers are needed to better characterize the biological basis of a poor treatment outcome.

Methods: Urine samples from 41 active pulmonary TB patients were collected at baseline (time of diagnosis), during treatment (weeks 1, 2 and 4) and two weeks after treatment completion (week 26). These samples were divided into successful (cured) and unsuccessful (failed) treatment outcome groups and analyzed using a GCxGC- TOFMS metabolomics research approach.

Results: The metabolite data collected showed clear differentiation of the cured and failed treatment outcome groups using the samples collected at the time of diagnosis, i.e. before any treatment was administered.

Conclusions: The treatment failure group was characterized by an imbalanced gut microbiome, in addition to elevated levels of metabolites associated with abnormalities in the long-chain fatty acid β-oxidation pathway, accompanied by reduced L-carnitine and short-chain fatty acids, indicative of a mitochondrial trifunctional protein defect in particular. Furthermore, an altered amino acid metabolism was also observed in these patients, which confirms previous findings and associations to increased interferon gamma due to the host's immune response to M. tuberculosis and a compromised insulin secretion.

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1 URINARY METABOLITE MARKERS CHARACTERIZING TUBERCULOSIS 2 3 TREATMENT FAILURE 4 5 6 7 RUNNING TITLE: Characterizing TB treatment failure 8 9 10 Laneke Luies1, Japie Mienie1, Christinah Motshwane1, Katharina Ronacher2,3, Gerhard Walzl2 and 11 12 Du Toit Loots1* 13 14 15 1School for Physical and Chemical Sciences, Human Metabolomics, North-West University (Potchefstroom 16 17 Campus), Private Bag x6001, Box 269, Potchefstroom, South Africa, 2531. 2DST/NRF Centre of Excellence for 18 19 Biomedical Tuberculosis Research/MRC Centre for Molecular and Cellular Biology, Division of Molecular 20 21 Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, 22 23 South Africa, 7505. 3Mater Medical Research, The University of Queensland, Brisbane, Australia 24 25 26 27 28 29 30 31 32 CORRESPONDING AUTHOR 33 34 35 Du Toit Loots; E-mail: [email protected]; Address: North-West University, Potchefstroom 36 37 Campus, Building F3: Biochemistry, 11 Hoffman Street, Potchefstroom, South Africa, 2531. 38 39 40 To avoid confusion, please note that the author’s first name is ‘Du Toit’ and last name is ‘Loots’ 41 42 43 44 45 46 47 WORD COUNT: Abstract: 199 48 49 50 Text: 2 746 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 65 1 1 ABSTRACT 2 3 4 5 2 Background: Considering that approximately 15% of the nine million new tuberculosis (TB) cases 6 7 3 reported per annum are not treated successfully, new, distinctive and specific biomarkers are needed to 8 9 4 better characterize the biological basis of a poor treatment outcome. 10 11 12 13 14 5 Methods: Urine samples from 41 active pulmonary TB patients were collected at baseline (time of 15 16 6 diagnosis), during treatment (weeks 1, 2 and 4) and two weeks after treatment completion (week 26). 17 18 7 These samples were divided into successful (cured) and unsuccessful (failed) treatment outcome 19 20 8 groups and analyzed using a GCxGC-TOFMS metabolomics research approach. 21 22 23 24 9 Results: The metabolite data collected showed clear differentiation of the cured and failed treatment 25 26 27 10 outcome groups using the samples collected at the time of diagnosis, i.e. before any treatment was 28 29 11 administered. 30 31 32 33 12 Conclusions: The treatment failure group was characterized by an imbalanced gut microbiome, in 34 35 13 addition to elevated levels of metabolites associated with abnormalities in the long-chain fatty acid β- 36 37 38 14 oxidation pathway, accompanied by reduced L-carnitine and short-chain fatty acids, indicative of a 39 40 15 mitochondrial trifunctional protein defect in particular. Furthermore, an altered amino acid 41 42 16 metabolism was also observed in these patients, which confirms previous findings and associations to 43 44 17 increased interferon gamma due to the host’s immune response to M. tuberculosis and a compromised 45 46 18 insulin secretion. 47 48 49 50 19 KEY WORDS: Biomarkers; M. tuberculosis; Metabolomics; Treatment failure; Tuberculosis; Urine. 51 52 53 20 54 55 56 57 58 59 60 61 62 63 64 2 65 1 21 1. INTRODUCTION 2 3 4 5 22 Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains the world’s foremost cause of 6 7 23 death from a single bacterial agent, which is alarming as it is considered curable. Nearly 10.4 million 8 9 24 new TB cases are reported per annum, of which almost 15% are not treated successfully, resulting in 10 11 12 25 approximately 1.5 million deaths globally (3 800 deaths a day) (World Health Organization 2016). 13 14 26 TB treatment failure may be attributed to, amongst others: (a) irregular or inadequate anti-TB drug 15 16 27 supplies to rural areas and third-world countries, (b) poor patient TB-education, (c) poor socio- 17 18 28 economic circumstances, (d) the prolonged treatment duration, (e) treatment non-adherence, (f) anti- 19 20 21 29 TB drug resistance, as well as (g) various biological/biochemical factors (De Villiers and Loots 2013). 22 23 24 30 The World Health Organization (WHO) recommends a six-month multi-drug treatment regimen, in 25 26 31 which a combination of four drugs (isoniazid (INH), rifampicin (RIF), pyrazinamide (PZA) and 27 28 32 ethambutol (EMB)) are used for treating active TB. In patients with drug-susceptible TB, this regimen 29 30 33 reportedly has a 1–4% failure rate, and only 7% of those patients with a successful treatment outcome 31 32 33 34 relapse within 24 months (Dye et al. 2005). When considering that every active TB patient can 34 35 35 potentially infect an additional 10–15 individuals per annum (World Health Organization 2016), it 36 37 36 becomes evident that TB treatment failure and relapse are important considerations in achieving the 38 39 37 millennium goals pertaining to the eradication of TB. Despite the many TB research efforts to date, 40 41 42 38 the biological mechanisms associated with anti-TB drug response remains poorly understood. It is 43 44 39 also unclear if certain patients have more efficient mechanisms for eliminating this disease and 45 46 40 responding to the treatment thereof, and to what extent lifestyle and environmental factors may 47 48 41 contribute to this (De Villiers and Loots 2013). 49 50 51 42 Early disease diagnosis and effective treatment protocols are the two primary objectives of TB control, 52 53 54 43 aimed at reducing mortality and morbidity while also preventing the development of drug resistance 55 56 44 (Antoine et al. 2007). Thus, monitoring treatment progression and having the means to accurately 57 58 45 predict treatment outcome, well before the six-month treatment regimen is completed, would be 59 60 46 regarded as a major breakthrough, as poor treatment outcomes could be identified early, and 61 62 63 64 3 65 47 alternative treatment approaches implemented, ultimately reducing non-adherence, drug resistance and 1 2 48 treatment failure (Walzl et al. 2008; Horne et al. 2010). Considering this, there is a need for new, 3 4 49 sensitive and specific biomarkers, not only for use in the early prediction of treatment failure, but also 5 6 50 for better characterizing and explaining the underlying mechanisms related to this occurrence, so that 7 8 9 51 alternative treatment approaches can be developed. Once identified, these biomarkers need to be 10 11 52 sufficiently validated for use as surrogate endpoints of treatment failure, early in the treatment 12 13 53 regimen, ideally even before treatment begins. To date, no such urinary biomarkers have been 14 15 54 identified with absolute certainty, hence the aim of this study was to use a two-dimensional gas 16 17 18 55 chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) metabolomics approach to 19 20 56 identify biomarkers differentiating individuals with a successful (n = 26) and unsuccessful (n = 15) 21 22 57 treatment outcome, as early as possible during the treatment regimen, which would also better 23 24 58 characterize and explain the biological mechanisms related to TB treatment failure. Urine was 25 26 59 selected as the preferred sample for addressing the above mentioned aim, since large quantities can 27 28 29 60 easily be obtained and less complex sample preparation is required for analyses, as compared to 30 31 61 sputum for instance. Furthermore, in recent years, the need for a holistic approach to metabolism has 32 33 62 led to the development of urinary metabolomics for biomarker discovery in various diseases (Ryan et 34 35 63 al. 2011; Mahapatra et al. 2014). 36 37 38 39 40 64 2. METHODS 41 42 43 44 65 2.1 Clinical samples 45 46 47 48 66 Anonymised archived urine samples were procured from the Faculty of Medicine and Health Sciences, 49 50 67 NRF/DST Centre of Excellence for Biomedical Research, at the Stellenbosch University/MRC Centre 51 52 68 for TB Research, from where they were transported to the North-West University (NWU), Human 53 54 55 69 Metabolomics: Infectious Disease Laboratory, for metabolomic analysis. These samples were selected 56 57 70 from a prospective observational cohort study of individuals with active pulmonary TB, diagnosed 58 59 71 using smear microscopy and bacteriological cultures (Hesseling et al. 2010). From these original 60 61 62 63 64 4 65 72 samples, all treatment failure patients were included and matched by age, gender and extent of disease 1 2 73 on chest X-rays to cured patients. Hence, the samples included were from 41 culture-confirmed active 3 4 74 TB-positive South African patients (22 males and 19 females, between the age of 17 and 58) at 5 6 75 baseline (time of diagnosis, thus prior to initiation of treatment), during the course of treatment with 7 8 9 76 the directly observed treatment short-course (DOTS) strategy (weeks 1, 2 and 4) and two weeks after 10 11 77 treatment completion (week 26). These patients had drug-susceptible TB, were human 12 13 78 immunodeficiency virus (HIV)-seronegative, not pregnant, and with no other diseases (including 14 15 79 diabetes, malignancy, lung cancer, chronic bronchitis and sarcoidosis). The samples were divided into 16 17 18 80 successful (n=26) and unsuccessful (n=15) treatment outcome groups. According to the WHO, 19 20 81 treatment failure can be defined as a patient whose sputum smear or culture is positive at month five or 21 22 82 later during treatment (World Health Organization 2014). 23 24 25 26 83 2.2 Sample analysis 27 28 29 30 84 Creatinine values for all urine samples, including quality control (QC) samples, were determined using 31 32 33 85 a creatinine enzyme kit (Thermo Scientific; reference number 981845) and analyzed on an Indiko 34 35 86 Clinical Analyzer, Type 863 (Thermo Scientific). These creatinine values are used to normalize 36 37 87 metabolite concentrations, and to determine the volume of urine, internal standard, bis-(trimethylsilyl)- 38 39 88 trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS) and pyridine needed for each extraction 40 41 42 89 and derivatization. An organic acid extraction of the collected patient urine samples were performed, 43 44 90 analyzed on a Pegasus 4D GCxGC-TOFMS (LECO Africa (Pty) Ltd, Johannesburg, South Africa) 45 46 91 along with the necessary QC samples, and processed to identify all compounds, following the methods 47 48 92 described in Luies and Loots (2016) (Luies and Loots 2016). 49 50 51 52 93 2.3 Statistical data analysis 53 54 55 56 57 94 A standard metabolomics data clean-up procedure was applied before statistical data analysis. All 58 59 95 metabolites were normalized relative to the internal standard by calculating the relative concentration 60 61 62 63 64 5 65 96 of each, and a 50% zero filter was applied to each variable (Smuts et al. 2013) to eliminate those 1 2 97 compounds with more than 50% zero values within both groups. Quantile equating was applied to 3 4 98 correct for any batch effects (Wang et al. 2012), followed by a 50% quality control–coefficient of 5 6 99 variation (QC–CV) filter. Lastly, all zero values were replaced with a value calculated as half of the 7 8 9 100 lowest detected value present in the entire dataset, as these may be due to low abundance rather than 10 11 101 being absent (Schoeman et al. 2012). GCxGC-TOFMS analysis yielded 782 compounds of which 12 13 102 only 241 remained for further statistical analysis after the above mentioned data clean-up were 14 15 103 completed. The data were analyzed via a variety of multi- and univariate statistical methods, using 16 17 18 104 MetaboAnalyst (based on the statistical package “R”; version 2.10.0), which included principal 19 20 105 components analysis (PCA) (Wold et al. 1987), partial least squares–discriminant analysis (PLS–DA) 21 22 106 (Westerhuis et al. 2008), fold change (Du Preez and Loots 2013) and Mann-Whitney test (Pallant 23 24 107 2001). 25 26 27 28 108 3. RESULTS AND DISCUSSION 29 30 31 32 33 109 Using the GCxGC-TOFMS data generated, a PCA was done to determine at which time point 34 35 110 individuals who responded to the TB treatment can be differentiated from those who were not cured 36 37 111 from TB. The PCA scores plots show clear differentiation of the successful and unsuccessful 38 39 112 treatment outcome groups, using the urine collected at time of diagnosis and again at week 26 40 41 42 113 (Figure 1, a and b respectively), the latter of which can be expected since the cured individuals are TB 43 44 114 culture negative, and those with a poor treatment outcome are still TB culture positive at this point in 45 46 115 time (Luies and Loots 2016). No PCA differentiation was achieved for the other time intervals 47 48 116 investigated (see supplementary material), most likely due to the effects of the anti-TB medication on 49 50 117 the human metabolome, masking any underlying differences which occur in the metabolism initially 51 52 53 118 differentiating the successful and unsuccessful treatment outcome groups at time of diagnosis. For the 54 55 119 differentiation achieved at time of diagnosis (Figure 1a), the total amount of variance explained by the 56 57 120 first two principal components (PCs) (R2X cum) was 93.4%, of which PC1 accounted for 91.8%, and 58 59 121 PC2 accounted for 1.6%. For the differentiation achieved at week 26 (Figure 1b), the total amount of 60 61 62 63 64 6 65 122 variance explained by the first two PCs (R2X cum) was 91.9%, of which PC1 accounted for 90.2% and 1 2 123 PC2 accounted for 1.7%. 3 4 5 124 Since the aim of this study was to identify biomarkers for the early prediction of treatment response 6 7 125 and explain the mechanisms associated with treatment failure, only the PCA differentiation achieved 8 9 10 126 at time of diagnosis was of further interest in this investigation. The separation of the groups at 11 12 127 week 26 are mostly due to metabolic changes since one group is TB-positive and the other TB- 13 14 128 negative, which was already investigated on a previous occasion (Luies and Loots 2016). Considering 15 16 129 the above, a PLS–DA model was built for the time of diagnosis data and showed a modeling 17 18 130 parameter R2Y (cum) of 93.48%, indicative of the total explained variation of the response Y. 19 20 21 22 131 Clear separation of the successful and unsuccessful treatment outcome groups is remarkable 23 24 132 considering the possible predictive value that these may have for identifying individuals who will or 25 26 133 will not respond to conventional treatment, even before treatment commences. However, before this 27 28 134 biosignature or the individual metabolite markers can be utilized for this purpose, further validation is 29 30 135 needed using new subjects to ensure these findings can be generalized. In the context of this study, the 31 32 33 136 biosignature/metabolite markers were used to better describe the biological mechanisms as to why 34 35 137 certain individuals with drug-sensitive TB are not successfully treated. Subsequently, those 36 37 138 compounds with a PLS–DA variable influence on the projection (VIP) >1.0 (Smuts et al. 2013), or a 38 39 139 fold change >|2|, or a Mann-Whitney with a P-value <0.05 (Du Preez and Loots 2013), were selected 40 41 42 140 as the metabolite markers that best explained the variation between the analyzed groups. Of these 72 43 44 141 characteristic metabolite markers, 50 could be annotated using libraries compiled from previously 45 46 142 injected standards, and are listed in Table 1. 47 48 49 143 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 7 65 144 Table 1: The 50 urinary metabolite markers identified at time of diagnosis that best explain the 1 2 145 variation detected between the successful and unsuccessful treatment outcome groups. 3 4 5 Successful Unsuccessful treatment outcome: treatment outcome: 6 Mann- Average Average 7 PLS–DA Fold Whitney Metabolite name concentration concentration 8 (VIP) change test (mg/g creatinine) (mg/g creatinine) 9 (P-value) 10 (Standard error of (Standard error of 11 the mean) the mean) 12 Amino acid metabolites: 13 14 2-Ketovaleric acid 2.093 (0.428) 3.799 (1.302) 1.30 1.82 0.1318 15 2-Methyl-3-hydroxybutyric acid 3.777 (0.489) 5.487 (0.708) 2.30 1.45 0.0273 16 2-Piperidinecarboxylic acid (Pipecolic acid) 0.893 (0.230) 0.428 (0.104) 1.24 2.09 0.0197 17 18 3-Hydroxy-3-methylglutaric acid 9.437 (0.456) 12.179 (0.945) 1.08 1.29 0.0077 19 3-Hydroxyvaleric acid 7.691 (1.341) 8.896 (1.861) 1.05 1.16 0.3301 20 21 4-Methylcatechol 6.916 (1.150) 10.299 (1.854) 1.36 1.49 0.1022 22 Glyceric acid 0.268 (0.098) 0.356 (0.091) 1.14 1.33 0.0062 23 Methylsuccinic acid 3.912 (0.448) 6.581 (1.020) 2.60 1.68 0.0030 24 25 N-Tiglylglycine 0.834 (0.247) 1.567 (0.371) 1.91 1.88 0.0094 26 o-Hydroxyphenylacetic acid 3.093 (0.384) 5.293 (0.602) 2.50 1.71 0.0030 27 Oxalic acid 14.250 (1.308) 19.933 (2.116) 2.29 1.40 0.0191 28 29 Phenylacetic acid 0.305 (0.157) 0.903 (0.273) 2.14 2.97 0.0306 30 Quinolinic acid 5.311 (0.854) 9.448 (1.403) 5.50 1.78 0.0040 31 32 Vanillylmandelic acid 17.088 (0.878) 22.269 (1.830) 1.27 1.30 0.0040 33 M. tuberculosis cell wall components: 34 Ribitol 1.910 (0.627) 2.455 (0.523) 1.84 1.29 0.0708 35 36 Gut microbiota imbalance metabolites: 37 2,3-Butanediol 17.831 (4.351) 16.301 (5.137) 3.65 1.09 0.4450 38 39 3-(3-Hydroxyphenyl)-3-hydroxypropionic acid (HPHPA) 20.030 (3.029) 28.087 (4.093) 5.40 1.40 0.0553 40 3-Hydroxy-3-(4-hydroxy-3-methoxyphenyl)propionic acid 2.912 (0.575) 8.366 (2.431) 6.45 2.87 0.0474 41 3-Hydroxyhippuric acid 17.580 (1.716) 23.662 (2.525) 2.37 1.35 0.0289 42 43 4-Hydroxybenzoic acid 4.642 (0.663) 9.966 (1.754) 4.92 2.15 0.0027 44 4-Hydroxyphenyllactic acid 5.695 (1.526) 6.724 (1.720) 1.21 1.18 0.1485 45 46 5-Hydroxymethyl-2-furoic acid 17.563 (4.279) 21.994 (6.053) 4.46 1.25 0.1795 47 Benzoic acid 0.936 (0.275) 1.367 (0.251) 1.56 1.46 0.0229 48 cis-4-Hydroxycyclohexanecarboxylic acid 1.428 (0.610) 0.666 (0.190) 0.87 2.15 0.0306 49 50 Citramalic acid 8.700 (1.608) 10.742 (3.124) 1.66 1.23 0.4550 51 Furan-2,5-dicarboxylic acid 8.755 (2.540) 10.156 (3.446) 1.14 1.16 0.3032 52 53 Furoylglycine 7.466 (1.443) 10.421 (2.241) 3.39 1.40 0.1115 54 Vanillic acid 7.731 (0.876) 11.249 (1.230) 2.23 1.46 0.0082 55 DNA damage and oxidative stress markers: 56 57 2-Deoxyribonic acid 0.477 (0.113) 1.080 (0.245) 1.85 2.27 0.0169 58 Parabanic acid 2.984 (0.799) 3.584 (0.865) 2.68 1.20 0.0474 59 60 TCA and GABA metabolites: 61 62 63 64 8 65 3,4-Dihydroxybutyric acid 9.160 (0.825) 11.730 (0.89) 1.03 1.28 0.0108 1 4-Hydroxybutyric acid 0.305 (0.097) 0.544 (0.158) 1.68 1.79 0.0088 2 3 Citric acid 2.730 (0.838) 3.526 (1.619) 1.34 1.29 0.2691 4 Malic acid 4.579 (2.712) 5.085 (2.668) 3.13 1.11 0.4351 5 Dicarboxylic acids and 3-hydroxy fatty acids: 6 7 2,3-Dihydroxypentonic acid 4.632 (1.255) 6.118 (2.265) 2.56 1.32 0.3485 8 3,5-Dihydroxypentonic acid 3.956 (0.469) 5.698 (0.790) 1.29 1.44 0.0743 9 10 3-Hydroxydodecanedioic acid 4.946 (1.659) 2.313 (1.075) 2.27 2.14 0.2000 11 3-Hydroxysebacic acid 1.499 (0.386) 3.716 (1.140) 2.36 2.48 0.0582 12 3-Ketosebacic acid 2.419 (0.535) 6.451 (1.560) 6.96 2.67 0.0324 13 14 3-Methylhexanoic acid (3-Methyladipic acid) 7.723 (0.800) 12.631 (1.637) 3.30 1.64 0.0058 15 cis,cis-4,7-Decadiene-1,10-dioic acid 9.523 (1.259) 12.113 (1.359) 1.19 1.27 0.0743 16 cis-4-Decene-1,10-dioic acid 1.403 (0.316) 2.561 (0.649) 1.74 1.83 0.0499 17 18 Heptanedioic acid (Pimelic acid) 2.895 (0.434) 4.967 (0.684) 2.32 1.72 0.0024 19 Hexanedioic acid (Adipic acid) 0.713 (0.192) 1.369 (0.443) 1.38 1.92 0.0816 20 21 Nonanedioic acid (Azelaic acid) 4.596 (0.918) 6.465 (1.271) 2.01 1.41 0.0894 22 Octanedioic acid (Suberic acid) 3.452 (0.595) 4.577 (0.394) 1.68 1.33 0.0169 23 Other: 24 25 1,2,3-Trihydroxybutane 1.962 (0.540) 2.842 (1.415) 1.22 1.45 0.4950 26 2-Methyl-1,2-dihydroxypropane 5.360 (1.768) 6.456 (2.985) 2.78 1.20 0.3672 27 28 4-Pentenoic acid 5.860 (0.898) 8.201 (1.289) 1.86 1.40 0.0611 29 Glucuronic acid 5.923 (0.442) 8.307 (0.757) 1.22 1.40 0.0062 30 146 *Abbreviations: mg/g, milligram per gram; PLS–DA, partial least squares–discriminant analysis; VIP, variable influence on the projection. 31 32 33 34 35 147 When considering the metabolite markers best differentiating the successful and unsuccessful 36 37 148 treatment outcome patient groups, the first important observation is the elevated amounts of 38 39 149 metabolites associated with tryptophan (quinolinic acid (Heyes et al. 1992)), phenylalanine 40 41 42 150 (phenylacetic acid (Puri 2006), o-hydroxyphenylacetic acid (Taniguchi and Armstrong 1963)), and 43 44 151 tyrosine (4-methylcatechol (Li et al. 2007), vanillylmandelic acid (Eisenhofer et al. 2004)) metabolism 45 46 152 (see Figure 2). The same metabolic flux and the resulting accumulation of these amino acids, were 47 48 153 also previously reported by Luies and Loots (2016) in TB-positive patient urine, and explained by 49 50 154 elevations in interferon gamma (IFN-γ) due to the host’s immune response to M. tuberculosis and a 51 52 53 155 compromised insulin secretion by the host (Luies and Loots 2016). Furthermore, markers directly 54 55 156 associated with the presence of M. tuberculosis (ribitol (Silhavy et al. 2010)), a gut microbiota 56 57 157 imbalance (2,3-butanediol (Hong 2011), 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (Shaw 2013), 58 59 158 3-hydroxy-3-(4-hydroxy-3-methoxyphenyl)propionic acid, 3-hydroxyhippuric acid (Gonthier et al. 60 61 62 63 64 9 65 159 2003), 4-hydroxybenzoic acid (Russell et al. 2013; Tomás‐Barberán and Clifford 2000), 1 2 160 4-hydroxyphenyllactic acid (Russell et al. 2013), 5-hydroxymethyl-2-furoic acid (Jellum et al. 1973), 3 4 161 benzoic acid (Russell et al. 2013), cis-4-hydroxycyclohexanecarboxylic acid (Kronick et al. 1983), 5 6 162 citramalic acid (Du Preez and Loots 2013), furan-2,5-dicarboxylic acid (Pettersen and Jellum 1972; 7 8 9 163 Jellum et al. 1973), furoylglycine (Pettersen and Jellum 1972), vanillic acid (Tomás‐Barberán and 10 11 164 Clifford 2000)), and DNA damage (2-deoxyribonic acid (Zhou and Greenberg 2012)) due to oxidative 12 13 165 stress (parabanic acid (Marklund et al. 2000)), were also detected and correlate with those markers 14 15 166 previously characterizing TB, as described by Luies and Loots (2016). Considering the fact that these 16 17 18 167 markers are not only indicative of a general TB disease state (Luies and Loots 2016), but also more 19 20 168 pronounced in the treatment failure group in this study, may indicate that these individuals who are 21 22 169 unsuccessfully treated have an increased disease severity due to a microbiota imbalance and/or 23 24 170 underlying host abnormality, which will be discussed in greater detail below. 25 26 27 171 The highly significant evidence from the almost three times elevated 3-hydroxy-3-(4-hydroxy- 28 29 30 172 3methoxyphenyl)propionic acid and more than two times lower cis-4-hydroxycyclohexanecarboxylic 31 32 173 acid in the treatment failure group, strongly testifies towards an imbalance in gut microbiota in the 33 34 174 unsuccessfully treated patients, which is associated with a weaker immune system and inability to 35 36 175 fight disease (Clemente et al. 2012). Both the innate and adaptive immune response systems have 37 38 39 176 evolved to rely on microbiota interactions, which not only promote immune cell maturation but also 40 41 177 influence the normal development of immune functions (Clemente et al. 2012). Additionally, gut 42 43 178 microbiota is also well known to interact with various drugs, influencing many factors relating to their 44 45 179 absorption and subsequent plasma concentrations, and hence is considered an important contributor to 46 47 48 180 xenobiotic/drug bioavailability and toxicity (Gonzalez et al. 2011). 49 50 51 181 Furthermore, altered levels of various dicarboxylic acids, acylcarnitines and 3-hydroxy fatty acids 52 53 182 were detected in the urine of the treatment failure group. Lipid molecules are increasingly recognized 54 55 183 as having the potential to shape the immune response to infectious pathogens (Kaushal 2012) since 56 57 184 these are important bioactive mediators of cellular activity during pathophysiological processes, and 58 59 60 185 affects various activities including cell apoptosis, monocyte adhesion, platelet aggregation, and 61 62 63 64 10 65 186 regulation of immune responses (Hasanally et al. 2014). Interestingly, however, the presence of these 1 2 187 metabolites in urine are also used to distinguish between individuals with inherited defects of the 3 4 188 following long-chain fatty acid (LCFA) transport-associated enzymes: (a) carnitine 5 6 189 palmitoyltransferase 1 (CPT-1), (b) carnitine palmitoyltransferase 2 (CPT-2), (c) translocase (TL), 7 8 9 190 (d) very-long-chain acyl-coenzyme A (CoA) dehydrogenase (VLCAD) or (e) any of the three enzymes 10 11 191 making up the mitochondrial trifunctional protein (MTP); long chain 2-enoyl-Co A hydratase, long- 12 13 192 chain 3-hydroxyacyl-CoA dehydrogenase and 3-ketoacyl-CoA thiolase (Sim et al. 2002; Garg and 14 15 193 Dasouki 2006). A CPT-1 deficiency results in reduced C16–C18 acylcarnitines and increased free 16 17 18 194 carnitine, a CPT-2, TL or VLCAD deficiency results in both elevated long chain acylcarnitines and 19 20 195 dicarboxylic acids with reduced free carnitine, and a MTP deficiency (on any of its three associated 21 22 196 enzymes) results in elevated long chain acylcarnitines, dicarboxylic acids and 3-hydroxy fatty acids 23 24 197 with reduced free carnitine (Garg and Dasouki 2006). Since the organic acid extraction method used 25 26 198 in this study was not suited for the extraction or detection of acylcarnitines, hence their absence in 27 28 29 199 Table 1, we did a further ultra-performance liquid chromatography (UPLC) (Van Aardt et al. 2016) 30 31 200 urinary acylcarnitine analysis (Venter et al. 2015), and found significantly elevated palmitoylcarnitine 32

33 201 (C16) in the urine of the treatment failure group comparatively (136.19 vs. 58.22 mmol/L; P=0.0095). 34 35 202 Additionally, although not statistically significant, the total short chain acylcarnitines were 36 37 38 203 comparatively decreased in the treatment failure group (8.77 vs. 2.75 mmol/L; P=0.2298). Free 39 40 204 carnitine (C0) was also decreased (0.64 vs. 2.58 mmol/L; P=0.4329), as expected in such deficiencies, 41 42 205 because these remain bound to the LCFAs and are unable to cross the mitochondrial membrane. 43 44 206 Considering this diagnostic metabolite profile, a MTP deficiency or abnormality is suggested to occur 45 46 47 207 in the treatment failure group (see Figure 3). MTP mutations are estimated at a prevalence of 48 49 208 approximately 1:75 000 (Garg and Dasouki 2006), and according to unpublished results of the 50 51 209 Potchefstroom Laboratory for Inborn Errors of Metabolism (PLIEM), may be as high as 1 in 100 for a 52 53 210 mutation in any of the seven enzyme/protein systems mentioned above. Additional evidence for this, 54 55 56 211 is the serum carnitine deficiencies previously reported in 47.7% of all TB-positive patients 57 58 212 (Hatamkhani et al. 2014), which are also associated with these carnitine transporters (Flanagan et al. 59 60 213 2010). Furthermore, L-carnitine has been shown to play a significant role in T-cell-dependent 61 62 63 64 11 65 214 antibacterial activity in the host, and enhance the immune response via reduced macrophage and 1 2 215 lymphocyte malfunction in TB patients (Emilio Jirillo et al. 1993; E Jirillo et al. 1991), hence a 3 4 216 deficiency in L-carnitine, due to an underlying enzyme/protein system deficiency, may explain why 5 6 217 these individuals did not respond to treatment. Confirmation of a reduced capacity for mitochondrial 7 8 9 218 β-oxidation due to such a deficiency, is the elevated concentrations of fatty acids with odd numbered 10 11 219 carbons, such as azelaic acid and heptanedioic (pimelic) acid in the treatment failure group, due to the 12 13 220 above mentioned accumulating fatty acids undergoing peroxisomal α-oxidation prior to peroxisomal 14 15 221 β-oxidation (Figure 3) (Van Veldhoven 2010). 16 17 18 222 Accumulation of fatty acyl-CoA derivatives, due to the defective LCFA transport, can also contribute 19 20 21 223 to a compromised insulin secretion (Luies and Loots 2016). Long-chain fatty acyl-CoA’s are strong 22 23 224 inhibitors of glutamate dehydrogenase (GDH) (Lai et al. 1994), and since this enzyme is responsible 24 25 225 for the conversion of glutamate to α-ketoglutarate, and an elevated flux of the tricarboxylic acid 26 27 226 (TCA) cycle, eventually resulting in insulin secretion (see Figure 3) (Newsholme et al. 2006), suggests 28 29 30 227 an additional means by which insulin secretion may be compromised in the treatment failure group. 31 32 228 Further confirmation of this is the elevated levels of 3,4-dihydroxybutyric acid and 4-hydroxybutyric 33 34 229 acid, indicating an increased metabolic flux in the gamma-aminobutyric acid (GABA) shunt pathway 35 36 230 (Shinka et al. 2002), in the opposite direction of the glutamate to α-ketoglutarate reaction previously 37 38 39 231 mentioned (see Figure 2). Compromised insulin secretion has also been shown to result in the 40 41 232 accumulation of various branched chain amino acids (BCAAs) (Lu et al. 2013), which explains the 42 43 233 elevated leucine (3-hydroxyvaleric acid, 3-hydroxy-3-methylglutaric acid) and isoleucine (2-methyl-3- 44 45 234 hydroxybutyric acid, methylsuccinic acid, N-tiglylglycine) metabolite intermediates observed in the 46 47 235 treatment failure group comparatively. 48 49 50 51 236 Whatever the proposed mechanism, the patients in the treatment failure group show a reduced capacity 52 53 237 to transport LCFA into the mitochondria for metabolism into short-chain fatty acids (SCFAs), which 54 55 238 are well known for their antimicrobial effects (i.e. increase the fluidity of the bacterial cell wall and 56 57 239 subsequently negatively influence cell wall integrity (Royce et al. 2013), and induce host defense 58 59 60 240 peptide LL-37 (Mily et al. 2015)). Considering this, apart from the reduced L-carnitine detected in the 61 62 63 64 12 65 241 treatment failure group, the reduced SCFAs may be an additional explanation as to why these 1 2 242 individuals may not be responding to the treatment and why the metabolite markers suggest elevated 3 4 243 disease severity. 5 6 7 8 244 4. CONCLUSION 9 10 11 12 This study highlights the capacity of metabolomics to identify markers which predict a poor response 13 245 14 15 246 to treatment, and also better characterize or propose previously unknown mechanisms resulting in TB 16 17 247 treatment failure. The most significant observations in this metabolomics study were the elevated 18 19 248 levels of those metabolites associated with an imbalance in the gut microbiome. 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C., Du Preez, I., & Loots, D. T. (2012). A comparison of four sputum pre-extraction preparation 14 15 342 methods for identifying and characterising M. tuberculosis using GCxGC-TOFMS metabolomics. Journal of 16 343 microbiological methods, 91, 301-311. 17 18 344 Shaw, W. (2013). Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an 19 345 abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from 20 21 346 patients with autism and schizophrenia. Nutritional neuroscience. 22 347 Shinka, T., Inoue, Y., Ohse, M., Ito, A., Ohfu, M., Hirose, S., et al. (2002). Rapid and sensitive detection of 23 24 348 urinary 4-hydroxybutyric acid and its related compounds by gas chromatography–mass spectrometry in a 25 349 patient with succinic semialdehyde dehydrogenase deficiency. Journal of Chromatography B, 776(1), 57-63. 26 27 350 Silhavy, T. J., Kahne, D., & Walker, S. (2010). The bacterial cell envelope. 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The effect of temperature on 45 362 the respiration and metabolism of the African burrowing scorpion (Opistophthalmus latimanus). 46 47 363 Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, 20, 50-56. 48 364 Van Veldhoven, P. P. (2010). Biochemistry and genetics of inherited disorders of peroxisomal fatty acid 49 50 365 metabolism. Journal of lipid research, 51(10), 2863-2895. 51 366 Venter, L., Lindeque, Z., Van Rensburg, P. J., Van Der Westhuizen, F., Smuts, I., & Louw, R. (2015). 52 53 367 Untargeted urine metabolomics reveals a biosignature for muscle respiratory chain deficiencies. 54 368 Metabolomics, 11(1), 111-121. 55 56 369 Walzl, G., Ronacher, K., Siawaya, J. F. D., & Dockrell, H. M. (2008). Biomarkers for TB treatment response: 57 Challenges and future strategies. Journal of Infection, 57(2), 103-109. 58 370 59 60 61 62 63 64 16 65 371 Wang, S.-Y., Kuo, C.-H., & Tseng, Y. J. (2012). Batch normalizer: A fast total abundance regression calibration 1 372 method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight 2 3 373 mass spectrometry-based metabolomics data and comparison with current calibration methods. Analytical 4 374 Chemistry, 85(2), 1037-1046. 5 6 375 Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J. J., et al. (2008). 7 376 Assessment of PLSDA cross validation. Metabolomics, 4(1), 81-89. 8 9 377 Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent 10 378 Laboratory Systems, 2(1), 37-52. 11 12 379 World Health Organization (2014). Definitions and reporting framework for tuberculosis – 2014 revision. (pp. 13 380 47). Geneva, Switzerland (WHO Press). 14 15 381 World Health Organization (2016). Global tuberculosis report 2016. (pp. 214). Geneva, Switzerland (WHO 16 382 Press): World Health Organization. 17 18 383 Zhou, C., & Greenberg, M. M. (2012). Histone-catalyzed cleavage of nucleosomal DNA containing 2- 19 384 deoxyribonolactone. Journal of the American Chemical Society, 134(19), 8090-8093. 20 21 22 385 23 24 386 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 17 65 1 387 FOOTNOTE PAGE 2 3 4 5 388 6 AUTHOR CONTRIBUTION 6 7 8 9 389 DTL conceptualized the study design; KR and GW provided all urine samples. LL performed the GC 10 11 390 data acquisition/analysis and CM the UPLC data acquisition/analysis. LL, JM and DTL interpreted 12 13 14 391 the data and drafted the article. All authors approved the final version to be submitted. 15 16 17 18 392 7 COMPLIANCE WITH ETHICAL REQUIREMENTS 19 20 21 22 393 7.1 Conflict of interest 23 24 25 26 394 The authors declare that there are no conflicts of interest, and that this manuscript, and the work 27 28 395 described therein, is unpublished and has not been submitted for publication elsewhere. 29 30 31 32 33 396 7.2 Funding statement 34 35 36 37 397 The authors have no specific funding to report. 38 39 40 41 398 7.3 Ethical approval 42 43 44 45 399 Ethical approval for this investigation, conducted according to the Declaration of Helsinki and 46 47 400 International Conference on Harmonization Guidelines, was obtained from the Ethics Committee of 48 49 the North-West University, South Africa (reference number NWU-00127-11-A1), as well as from 50 401 51 52 402 Stellenbosch University Health Research Ethics Committee (reference number 99/039) and Cape 53 54 403 Town City Health. All participants gave written informed consent for study participation and HIV 55 56 404 testing. 57 58 405 59 60 61 62 63 64 18 65 1 2 406 7. TABLE AND FIGURE LEGENDS 3 4 5 6 407 Table 1: The 50 urinary metabolite markers identified at time of diagnosis that best explain the 7 8 408 variation detected between the successful and unsuccessful treatment outcome groups. 9 10 11 12 409 Figure 1: Principal components analysis (PCA) scores plots of principal component 1 versus 13 14 410 principal component 2 of the successful and unsuccessful treatment outcome groups, showing clear 15 16 differentiation of these two groups at (a) time of diagnosis and (b) week 26, due to variation in their 17 411 18 19 412 underlying metabolite profiles. The variances accounted for are indicated in parenthesis. 20 21 22 23 413 Figure 2: Amino acid metabolism of the identified urinary metabolite markers, which are either 24 25 414 increased (↑) or decreased (↓) in the treatment failure group comparetively. 26 27 28 29 415 Figure 3: Fatty acid oxidation of the identified urinary metabolite markers, which are either increased 30 31 416 (↑) or decreased (↓) in the treatment failure group comparetively. Abbreviations: CPT I, carnitine 32 33 417 palmitoyltranferase 1; CPT II, carnitine palmitoyltransferase 2; CACT, carnitine-acylcarnitine 34 35 36 418 translocase; VLCAD, very long chain acyl-CoA dehydrogenase; MCAD, medium chain acyl-CoA 37 38 419 dehydrogenase; SCAD, short chain acyl-CoA dehydrogenase; M/SCHAD, medium/short chain 39 40 420 hydroxyacyl-CoA dehydrogenase; MTP, mitochondrial trifunctional protein; SCYD, short chain 41 42 421 enoyl-CoA hydratase; MCT, medium chain 3-ketoacyl-CoA thiolase; SCT, medium chain 3-ketoacyl- 43 44 45 422 CoA thiolase; ACOX 1, acyl-CoA oxidase 1; ACOX 2, acyl-CoA oxidase 2; D-PBE, peroxisomal 46 47 423 bifunctional enzyme; ACAA1, 3-ketoacyl-CoA thiolase; PHYH, Phytanoyl-CoA hydroxylase; 48 49 424 HPLC2, Unknown; pistanalDH, pristanaldehyde dehydrogenase. 50 51 52 53 425 54 55 56 57 58 59 60 61 62 63 64 19 65 426 Supplementary figure: Principal components analysis (PCA) scores plots of principal component 1 1 2 427 versus principal component 2 of the successful and unsuccessful treatment outcome groups, at (a) time 3 4 428 of diagnosis, (b) week 1, (c) week 2, (d) week 4 of treatment and (e) two weeks after treatment 5 6 429 completion (week 26), subsequent to an organic acid extraction and GCxGC-TOFMS analyses. The 7 8 9 430 explained variances are indicated in parenthesis. 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 20 65 Figure1 Click here to download Figure Figure_1.jpg Figure2 Click here to download Figure Figure_2.png Figure3 Click here to download Figure Figure_3.png Biomarkers in Medicine

PREDICTING TUBERCULOSIS TREATMENT OUTCOME USING METABOLOMICS For Review Only

Journal: Biomarkers in Medicine

Manuscript ID BMM-2017-0133.R1

Manuscript Type: Preliminary Communication

Keywords: tuberculosis, Metabolomics, predicting treatment outcome

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1 2 3 ABSTRACT 4 5 6 7 Background: Predicting a poor treatment outcome would offer significant benefits for patient care and for 8 9 new drug development. 10 11 12 13 Methods/Results: Urine samples from TBpositive patients with a successful and unsuccessful treatment 14 15 outcome were collected at baseline and analyzed. The identified metabolites were used in a forward logistic 16 17 regression model, which achieved a ROC AUC of 0.94 (95% CI 0.84–1) and crossvalidated well in a leave 18 For Review Only 19 oneout context, with an AUC of 0.89 (95% CI 0.7–1). Two possible predictors were identified, which are 20 21 associated with a gut microbiota imbalance. 22 23 24 25 Discussion/Conclusion: Our findings show the capacity of metabolomics to predict treatment failure at time 26 27 of diagnosis, which potentially offers significant benefits for use in new drug development clinical trials and 28 29 individualized patient care. 30 31 32 33 34 35 36 37 Key words: M. tuberculosis ; tuberculosis; metabolomics; predicting treatment outcome; treatment failure 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 2 of 25

1 2 INTRODUCTION 3 4 5 6 The latest reports indicate 10.4 million individuals are infected with M. tuberculosis globally, resulting in an 7 8 estimated 1.8 million deaths from tuberculosis (TB), annually. Clearly, current strategies to control this 9 10 pandemic are inadequate, especially in highburden countries with limited resources, where the treatment 11 12 target goals, as recommended by the World Health Organization (WHO), have not yet been met [1] . 13 14 15 Currently, the most effective antiTB treatment strategy, Directly Observed Treatment Shortcourse (DOTS), 16 17 consists of an initial two month (intensive) phase using a combination of isoniazid (INH), rifampicin (RIF), 18 For Review Only 19 pyrazinamide (PZA) and ethambutol (EMB), followed by a four month (maintenance) phase, consisting of 20 21 only INH and RIF [2] . The DOTS strategy, when fully adhered to, has a relatively high success rate in 22 23 patients with drugsusceptible TB [1] . TB treatment failure using this approach, however, still occurs with 24 25 drugsensitive TB, and is defined as the occurrence of persistently positive sputum smears or cultures at the 26 27 end of treatment [3] . An additional concern is that these individuals, who are not treated successfully, 28 29 remain infectious for prolonged periods of time, and continue to transmit the disease to others. Improved 30 31 treatment outcomes and timely intervention is a priority for TB control programs and requires the 32 33 identification of those factors associated with treatment failure [4] . Previous studies suggest the following 34 35 risk factors for drugsusceptible TB treatment failure and relapse: (a) a less than 5% gain in bodyweight [5] ; 36 37 38 (b) age; (c) human immunodeficiency virus (HIV) coinfection; (d) diabetes type II, (e) illiteracy; (f) 39 40 alcoholism and (g) prior TB treatment [6] . The most prominent contributor to this occurrence, however, is a 41 42 slow patient response to treatment. It is also important to note that a number of novel immunological 43 44 biomarkers (i.e cytokines and chemokines) have been proposed for monitoring TB treatment response and 45 46 predicting treatment outcome [7] , including those directly associated with M. tuberculosis , such as 47 48 lipoarabinomannan [8, 9] or those associated with the overall inflammatory response, such as induced 49 50 protein (IP)10 [1012] . 51 52 53 Two diagnostic approaches are frequently used to detect active TB and monitor treatment outcome, namely 54 55 sputum smear microscopy and bacteriological cultures, the latter of which is considered the gold standard. 56 57 Response to treatment is usually determined based on sputum culture conversion after two months of 58 59 treatment [13] . However, a period of two months or more before the first indications of treatment efficacy 60 2 https://mc04.manuscriptcentral.com/fm-bmm Page 3 of 25 Biomarkers in Medicine

1 2 can be determined, is considered too long, since the bacteria have had the opportunity to adapt and create 3 4 drugresistant strains [14, 15] , and also considering that these patients may still transfer the disease to others 5 6 during this time. Additionally, these treatment failure predictive methods require frequent laboratory 7 8 monitoring, which is not only time consuming, but also costly. Considering this, there is an urgent need to 9 10 search for alternative approaches or biomarkers which can be used for the early detection of treatment failure 11 12 [16] . 13 14 15 Over the last 10 years, metabolomics has proved to be a very effective tool to search for and identify new 16 17 diagnostic and prognostic biomarkers and/or disease risk factors [17] . Metabolomics is defined as the 18 For Review Only 19 unbiased identification and quantification of all intermediates of metabolism in a biological system, using 20 21 specialized analytical techniques, in conjunction with biostatistical and mathematical analyses [18] . In 2012, 22 23 Olivier and Loots applied a gas chromatography mass spectrometry (GCMS) metabolomics approach to 24 25 characterize and differentiate various infectious Mycobacterium species (i.e. M. tuberculosis, M. bovis, 26 27 M. kansasii and M. avium ) and Pseudomonas aeruginosa based on their characteristic lipid profiles. Twelve 28 29 lipid biomarkers were identified and used to build a multivariate discriminant model, which could correctly 30 31 assign unknown samples to their respective species groups with probabilities ranging from 72 to 100% [19] . 32 33 Che et al. (2013) used a similar approach to compare serum samples collected from healthy controls and TB 34 35 patients, before and after TB therapy, to find diagnostic markers for active TB, not influenced by antiTB 36 37 therapy. They demonstrated nine potential diagnostic TB biomarkers, one of which (i.e. 5oxoproline) 38 39 remained unaffected by firstline TB therapy [20] . These studies indicate the capacity of metabolomics to 40 41 not only differentiate M. tuberculosis from other Mycobacterium species, but also to identify useful 42 43 biomarkers that can be used for the early prediction of treatment outcome, and for possible use in new anti 44 45 TB drug development. Considering this, we used an untargeted twodimensional gas chromatography time 46 47 offlight mass spectrometry (GCxGCTOFMS) urinary metabolomics approach to differentiate TBpositive 48 49 patients with a successful (n=21) and unsuccessful (n=10) treatment outcome at time of diagnosis, and 50 51 identified those metabolite markers which best differentiate the groups, in order to build a logistic regression 52 53 model to possibly predict treatment failure before firstline antiTB drug administration commences. 54 55 56 57 MATERIALS AND METHODS 58 59 60 3 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 4 of 25

1 2 Clinical samples 3 4 5 Anonymized archived urine samples were procured from the Faculty of Medicine and Health Sciences, 6 7 NRF/DST Centre of Excellence for Biomedical Research, at the Stellenbosch University/MRC Centre for 8 9 TB Research, from where they were transported (frozen at 80ºC) to the NorthWest University (NWU), 10 11 Human Metabolomics: Infectious Disease Laboratory, for metabolomics analysis. These samples were part 12 13 of a prospective observational cohort study of individuals with active pulmonary TB, diagnosed with 14 15 16 M. tuberculosis infection using smear microscopy, bacteriological cultures and strain typing [21] . 17 18 Representation of differentFor M. tuberculosis Review strains was not significantly Only different between cured and failed 19 20 participants. The samples were not collected at a specific time of day in order to ensure robustness of any 21 22 identified metabolites. From the original samples, all treatment failure cases were included and matched by 23 24 age, gender and extent of disease on chest Xrays to cured cases. Thus, the samples included were from 31 25 26 cultureconfirmed active TBpositive South African patients (16 males and 15 females, aged between 17 and 27 28 58) at baseline (i.e. time of diagnosis, thus prior to initiation of treatment). These patients had drug 29 30 susceptible TB, were HIVseronegative, not pregnant, and had no other diseases (i.e. diabetes, malignancy, 31 32 lung cancer, chronic bronchitis and sarcoidosis), nor were they receiving any other medication, including 33 34 antibiotics. The samples were divided into successful (n=21) and unsuccessful (n=10) treatment outcome 35 36 groups. 37 38 39 40 Sample analysis 41 42 43 A creatinine value was determined for each urine sample collected using a creatinine enzyme kit (Thermo 44 45 Scientific; reference number 981845) and analyzed using an Indiko Clinical Analyzer, Type 863 (Thermo 46 47 Scientific). This value is used to determine the amount of urine, internal standard, bis(trimethylsilyl) 48 49 trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS) and pyridine needed for each extraction and 50 51 derivatization. An organic acid extraction was performed on all patientcollected urine samples, after which 52 53 they were analyzed by randomly injecting each sample, along with the necessary quality control (QC) 54 55 samples, into a Pegasus 4D GCxGCTOFMS (LECO Africa (Pty) Ltd, Johannesburg, South Africa) and 56 57 58 59 60 4 https://mc04.manuscriptcentral.com/fm-bmm Page 5 of 25 Biomarkers in Medicine

1 2 processed in order to identify compounds by comparison of their mass spectra to libraries generated from 3 4 previously injected standards, as described by Luies and Loots (2016) [22] . 5 6 7 Statistical data analysis 8 9 10 11 It is standard practice in metabolomics investigations to perform various data cleanup procedures, prior to 12 13 statistical data analysis [23] . The concentrations of all the identified metabolites were calculated relative to 14 15 the internal standard and expressed in relation to the creatinine value. A 50% zero filter was applied to each 16 17 variable [24] to eliminate compounds with more than 50% zero values in both groups. Any batch effects 18 For Review Only 19 were corrected for using quantile equating [25] , followed by a 50% quality control–coefficient of variation 20 21 (QC–CV) filter. Finally, in order to select the most significant metabolite markers, various univariate 22 23 statistical analyses (i.e. fold change, MannWhitney test and effect sizes) were applied to the untransformed 24 25 data using MATLAB with Statistics and PLS Toolbox Release (2012; The MathWorks Inc., Natick, MA, 26 27 USA) and SAS (2015; The SAS System for Windows Release 9.3 TS Level 1M0 Copyright© by SAS 28 29 Institute Inc., Cary, NC, USA). Fold change compares the absolute value changes of specific variables 30 31 across two groups, and fold changes greater than two are considered significant [26] . The MannWhitney 32 33 test is considered the nonparametric alternative to the ttest, when assessing independent samples. This test 34 35 determines significance by comparing the medians of variables across two groups, where a pvalue below 36 37 38 0.05 is considered significant [27] . Effect sizes indicate practical significance by comparing variables 39 40 individually between groups. For this nonparametric dataset, effect sizes were based on the associated 41 42 MannWhitney zvalues and an effect size above 0.3 (which indicates a moderate effect) were deemed 43 44 practically relevant [28] . 45 46 47 RESULTS 48 49 50 51 As indicated in Figure 1, compounds with an absolute fold change exceeding two, a MannWhitney pvalue 52 53 of 0.05 or lower and an effect size larger than 0.3 were selected as the metabolite markers most capable of 54 55 predicting treatment failure, and are listed in Table 1 (n=18). This multiselection approach is based on the 56 57 assumption that these different statistical methods compensate for each other’s limitations and should reduce 58 59 the rate of false discovery of unimportant compounds [22] . A volcano plot was used to compare the size of 60 5 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 6 of 25

1 2 the fold change to the statistical significance level by plotting the log 10 scaled MannWhitney pvalues 3 4 against the log 2 scaled fold change values (see Figure 2). 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Review Only 19 20 Figure 1: Venn-diagram indicating the multi-selection approach used to select the most significant 21 22 metabolite markers for predicting treatment failure. 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Figure 2: Volcano plot of the log 10 scaled Mann-Whitney p-values against the log 2 scaled fold change 44 45 values. 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 6 https://mc04.manuscriptcentral.com/fm-bmm Page 7 of 25 Biomarkers in Medicine

1 2 3 4 Table 1: The 18 univariate urine metabolite markers considered for potentially predicting treatment outcome at time of diagnosis. 5 6 Successful Unsuccessful 7 treatment treatment Mann 8 outcome: outcome: Fold Mann Retention Samples Samples Whitney 9 change Whitney Metabolite name time (RT) Average detected Average detected Effect (absolute test 10 concentration concentration Size 11 (s) (n=21) (n=10) value) (mg/g creatinine) (mg/g creatinine) (pvalue) (ω value) 12 (Standard (Standard 13 For Reviewdeviation) Onlydeviation) 14 2,3,4,5Tetrahydroxypentanoic acid1,4lactone 1396,1.59 17.509 (12.067) 95.2% 35.154 (35.4) 100% 2.008 0.008 0.429 15 2Hydroxy1,2,3propanetricarboxylic acid 1573,1.52 13.388 (6.72) 100% 31.249 (36.752) 100% 2.334 0.020 0.368 16 17 2Methoxy5methylphenol 1018,2.12 0.489 (0.35) 95.2% 5.458 (13.715) 100% 11.171 0.016 0.383 18 2Octenoic acid 1021,1.69 0.576 (0.6) 95.2% 1.364 (1.018) 100% 2.367 0.011 0.414 19 20 3,5Dihydroxybenzoic acid 1573,1.64 1.054 (0.49) 100% 2.374 (1.655) 100% 2.251 0.001 0.573 21 3(4Hydroxy3methoxyphenyl)propionic acid 1720,1.78 7.285 (4.019) 100% 22.472 (21.333) 100% 3.085 0.003 0.497 22 3Hydroxyhexanedioic acid 1444,1.48 1.737 (0.779) 100% 3.554 (3.813) 100% 2.046 0.050 0.296 23 24 3Methoxy4hydroxybenzenepropanoic acid 1642,1.97 1.077 (1.799) 100% 2.649 (2.383) 100% 2.460 0.015 0.391 25 cis 4Decene1,10dioic acid 1612,1.71 0.679 (0.706) 100% 1.759 (1.714) 100% 2.591 0.036 0.323 26 Phthalic acid mono2ethylhexyl ester 2128,2.53 11.231 (39.786) 100% 1.926 (1.077) 100% 5.832 0.030 0.338 27 28 tert Butylhydroquinone 1363,1.58 0.45 (0.725) 90.5% 2.234 (2.357) 100% 4.969 0.008 0.429 29 Unknown 1 1489,1.7 0.43 (0.364) 100% 0.967 (0.858) 100% 2.251 0.040 0.315 30 Unknown 2 1648,1.77 0.158 (0.179) 95.2% 0.347 (0.232) 100% 2.201 0.013 0.402 31 32 Unknown 3 1753,1.59 0.087 (0.096) 100% 0.248 (0.21) 100% 2.838 0.001 0.535 33 Unknown 4 2050,1.84 98.212 (168.565) 100% 289.043 (416.387) 100% 2.943 0.036 0.323 34 35 Unknown 5 2101,1.85 8.294 (7.618) 100% 16.9 (13.627) 100% 2.038 0.036 0.323 36 Unknown 6 2116,2.05 0.129 (0.112) 95.2% 12.211 (36.959) 100% 94.334 0.011 0.410 37 Unknown 7 520,1.65 1.324 (0.511) 95.2% 2.874 (2.568) 100% 2.172 0.016 0.387 38 39 40 41 42 43 7 44 45 46 https://mc04.manuscriptcentral.com/fm-bmm 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Biomarkers in Medicine Page 8 of 25

1 2 DISCUSSION 3 4 5 The predictive ability of these compounds (Table 1) were compared and combined by performing forward, 6 7 backward and stepwise selection logistic regression. Therefore, all metabolites in Table 1 were given an 8 9 equal opportunity to form part of the final model since all were provided to the logistic regression procedure. 10 11 The procedure then considers various models, but penalizes models with higher numbers of predictors. 12 13 Metabolites are added (forward), removed (backward) or both (stepwise) until a model is found which 14 15 16 performs the best in terms of possible prediction and number of predictors. The final model was based on 17 18 forward selection and wasFor chosen as Reviewit showed the most predictive Only potential (i.e. had the highest area under 19 20 the receiver operating characteristic) — it should be noted however that not all compounds with a high fold 21 22 change or low MannWhitney pvalue are necessarily good predictors (see Figure 3). The model contained 23 24 two metabolite markers as potential predictors of treatment outcome, namely 3,5dihydroxybenzoic acid and 25 26 3(4hydroxy3methoxyphenyl)propionic acid. The model showed acceptable fit (goodnessoffit) with a 27 28 nonsignificant HosmerLemeshow statistic (p=0.444), as well as a reasonable relationship between 29 30 treatment outcome and the above predictors with a maximum rescaled Rsquared statistics of 0.75. Both 31 32 predictors made significant contributions to the prediction, as indicated by the Wald criterion reported in 33 34 Table 2. 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Figure 3: Box-like plots of the top four fold change metabolites, demonstrating that not all compounds are 58 59 necessarily good predictors when judged on their fold change value only. 60 8 https://mc04.manuscriptcentral.com/fm-bmm Page 9 of 25 Biomarkers in Medicine

1 2 Table 2: The maximum likelihood parameter estimates and odds ratios. 3 4 Wald Chi 95% CI for Odds Ratio 95% CI for Parameter Estimate pvalue 5 square Estimates (unit=1) Odds Ratio 6 7 3,5Dihydroxybenzoic acid 3.24 5.71 0.017 1.2 – 6.8 25.6 3.2 – 886.1 8 3(4Hydroxy3methoxyphenyl) 9 0.29 3.53 0.06 0.1 – 0.7 1.3 1.1 – 2.0 10 propionic acid 11 12 The odds ratio, reported for each of the possible predictors in Table 2, is a measure of the association 13 14 between a specific feature (i.e. metabolite) and a treatment outcome (i.e. failure to respond to treatment) [29] . 15 16 Thus, the odds ratio represents the increased or decreased probability that a patient will not be cured 17 18 successfully (treatment For failure) after Review antiTB drug treatment, Only for every unit increase or decrease in the 19 20 metabolite measured. An odds ratio of greater than 20 is considered significant for a diagnostic biomarker 21 22 [17] . An increase of one unit in the levels of 3,5dihydroxybenzoic acid and 3(4hydroxy3 23 24 methoxyphenyl)propionic acid are associated with an increased risk of treatment failure, i.e. the odds of 25 26 treatment failure increase by 25.6 and 1.3, respectively, for each one unit increase. Although the odds ratio 27 28 associated with 3(4hydroxy3methoxyphenyl)propionic acid are not indicative of a biomarker, the 29 30 combination of multiple “weak” metabolite markers into a single model may also provide a high level of 31 32 discrimination [17] . The large confidence intervals (CIs) are due, as least in part, to the small groups used 33 34 here and the relatively large variation within these groups. As with all newly developed models, further 35 36 validation of these possible predictors is an absolute requirement prior to application. 37 38 39 The predictive ability of the model was assessed based on the receiver operating characteristic (ROC) curve, 40 41 as shown in Figure 4. The model achieved an area under the ROC (AUC) value of 0.94 (95% CI 0.84 – 1) 42 43 and crossvalidated well in a leaveoneout context, with an AUC equal to 0.89 (95% CI 0.7 – 1) . The 44 45 predictive ability of a model is established if it achieves an AUC of 0.9 or higher [17] , however, to truly 46 47 validate the model will require a larger, independent sample cohort. Therefore, we focused our attention on 48 49 interpreting the two possible predictors in the model. 50 51 52 53 54 55 56 57 58 59 60 9 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 10 of 25

1 2 1 3 4 0.9 5 6 0.8 7 8 0.7 9 10 0.6 11 12 0.5 13

14 Sensitivity 0.4 15 16 17 0.3 18 For Review OnlyROC (AUC=0.94) 19 0.2 20 CV LOO ROC (AUC=0.89) 21 0.1 22 23 0 24 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 25 1Specificity 26 27 28 29 Figure 4: Receiver operating characteristic (ROC) curve of the final logistic regression model. The black 30 31 line represents the ROC when all samples are used to access sensitivity and specificity of the model, while 32 33 the red line represents the ROC when cross-validated using a leave-one-out approach. 34 35 36 37 The boxlike plots of these two possible predictors (and their chemical structures), as illustrated in Figure 5, 38 39 indicate the individual variation in the data. These plots were generated using notBoxPlot.m, a MATLAB 40 41 function developed by Rob Campbell ( http://www.mathworks.com/matlabcentral/fileexchange/26508 42 43 raacampbell13notboxplot ). The raw data is scattered over a 95% confidence interval (red box) and one 44 45 standard deviation (blue box) of the mean (red line). The individual observations (indicated by the grey 46 47 dots) show a much larger variation in the unsuccessful treatment outcome group comparatively, as well as 48 49 some overlap. It is therefore evident from these graphs that neither predictor will perform well in isolation. 50 51 We also include a bivariate scatter plot (see Figure 6) to graphically illustrate the combined predictive ability 52 53 of 3,5dihydroxybenzoic acid and 3(4hydroxy3methoxyphenyl)propionic acid to discriminate between 54 55 individuals with successful and unsuccessful treatment outcome. 56 57 58 59 60 10 https://mc04.manuscriptcentral.com/fm-bmm Page 11 of 25 Biomarkers in Medicine

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Review Only 19 20 21 22 23 24 25 26 27 28 29 Figure 5: Box-like plots of the two identified possible predictors, with the accompanying chemical 30 31 structures. 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 Figure 6: Bivariate scatter plot of the raw data generated for 3,5-dihydroxybenzoic acid versus 3-(4- 56 57 hydroxy-3-methoxyphenyl)propionic acid. The line indicates clear separation between the two comparative 58 59 groups based on the two metabolites selected as predictors. 60 11 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 12 of 25

1 2 When considering these two possible predictors from a biological mechanistic perspective, both 3,5 3 4 dihydroxybenzoic acid (αresorcylic acid) [30] and 3(4hydroxy3methoxyphenyl)propionic acid 5 6 (dihydroferulic acid) [31] are compounds with aromatic rings associated with a microbialderived microbial 7 8 imbalance [30, 31] , and also with a TB disease state [22, 32] . Hence, an altered microbiome in the treatment 9 10 failure group may be contributing to the lacking efficacy of the antiTB drugs [33] . These results also 11 12 confirm previous findings by Luies et al., (2017), who identified a number of metabolites related to a 13 14 microbial imbalance, using a combination of multivariate and univariate statistical approaches, which 15 16 characterized treatment failure to firstline drugs [34] . Since the innate and adaptive immune response 17 18 systems rely on various Formicrobiota interactionsReview to promote immune Only cell maturation and function, a microbial 19 20 imbalance results in a weakened immune system and an inability to fight disease [35] . Additionally, the gut 21 22 microbiota is considered an important contributor to xenobiotic/drug bioavailability and toxicity since it 23 24 interacts with various drugs, influencing factors relating to their absorption, distribution and secretion, and 25 26 hence plasma concentrations [36] . 27 28 29 Compounds with aromatic rings [33, 37, 38] , including the majority of the antiTB drugs used in this 30 31 investigation (INH, RIF and PZA), and their phase II metabolites, are transported across cell membranes and 32 33 eliminated via the ATPbinding cassette (ABC)type multidrug transporters, which are highly expressed in 34 35 human enterocytes of the liver, intestine, bloodbrain barrier, bloodtestis barrier, placenta and kidneys [38, 36 37 39] as well as on the cell surface of various intersinal microbiota [40] . If for whatever reason (such as a 38 39 microbial imbalance for instance), there is an overexpression/activity of these proteins on the host’s cell 40 41 surfaces, it would lead to an elevated excretion of these aromatics, explaining the elevated concentrations of 42 43 the 3,5dihydroxybenzoic acid and 3(4hydroxy3methoxyphenyl)propionic acid in the urine of the 44 45 treatment failure patient group, suggesting that these aromatic antiTB drugs would have suffered the same 46 47 fate, possibly explaining treatment failure. 48 49 50 The genome of M. tuberculosis also contains 2.5% ABC transporters, which may be classified as either 51 52 importers or exporters, determined by the direction of translocation of substrates [39] . The known 53 54 mechanisms of drugresistance to firstline antiTB drugs, can be summarized as follows: INHresistance is 55 56 due to mutations in the katG and inhA genes; RIFresistance results from alterations or point mutations in the 57 58 rpoB gene of RNA polymerase; PZAresistance may result from various mutations in the pncA , rpsA and 59 60 12 https://mc04.manuscriptcentral.com/fm-bmm Page 13 of 25 Biomarkers in Medicine

1 2 panD genes; and EMBresistance is mainly due to mutations on the embB gene [41] . However, these 3 4 mutations do not explain the mechanism of drugresistance in all clinical isolates, suggesting that additional, 5 6 unknown mechanisms may also be at play, such as the production of enzymes/proteins which 7 8 modify/degrade drugs resulting in inactivation, cell wall impermeability and efflux mechanisms. Although 9 10 such individual mechanisms may not result in clinical resistance, the combinations of these may confer high 11 12 level resistance, which may help explain the variation in resistance seen in M. tuberculosis isolates [39] . 13 14 15 Although, the scope of this investigation was only to identify metabolite markers and test their capacity for 16 17 predicting treatment failure, using patientcollected urine samples, and not to elucidate the possible 18 For Review Only 19 mechanisms for treatment failure, the above mentioned hypotheses are interesting topics for future 20 21 investigation, and also serve as further validation for their possible use for predicting a failed treatment 22 23 outcome to firstline antiTB drugs. Furthermore, it may be interesting to compare these patients to healthy 24 25 humans with a normal intestinal biosis in order to firmly allocate the microbiota dysbiosis to the treatment 26 27 failure cohort. Additionally, the issue of whether the metabolites analyzed in the urine of these patients are 28 29 due to human host metabolism or whether they originate from the commensal intestinal bacteria can be 30 31 addressed in future by experiments with germfree and conventionalized M. tuberculosis infected laboratory 32 33 animals. 34 35 36 37 CONCLUSION 38 39 40 41 We were able to identify two urinary metabolite markers which possibly predict treatment failure of TB 42 43 patients to firstline antiTB drugs, before treatment onset. When these possible predictors were combined 44 45 into a logistic regression model, they performed well with regards to sensitivity and specificity using leave 46 47 oneout crossvalidation. It is important to note that the purpose of this model was to identify a combination 48 49 of variables with high discriminatory ability containing as few variables as needed to achieve this, for 50 51 potential diagnostic purposes. We propose that these two possible predictors warrant further investigation 52 53 and validation using a bigger sample cohort. Once validated, the development of a prediction rule or 54 55 predictive model can commence. Nonetheless, our findings show the capacity of metabolomics to identify 56 57 patients at greater risk of a poor treatment outcome, at time of diagnosis, which potentially offers significant 58 59 benefits for use in new drug development clinical trials and an individualized approach to patient care. 60 13 https://mc04.manuscriptcentral.com/fm-bmm Biomarkers in Medicine Page 14 of 25

1 2 SUMMARY POINTS 3 4 5 6 • Several independent studies have suggested that an unsuccessful sputum culture conversion after two 7 8 months of treatment is a risk factor for treatment failure and can possibly be used for predicting this 9 10 outcome. However, predicting a poor treatment outcome well before two months would offer 11 12 significant benefits for patient care and also in the development of new antiTB agents. 13 14 • We identified metabolite markers in urine samples collected from TBpositive patients at time of 15 16 diagnosis (before treatment commences) using GCxGCTOFMS metabolomics, which when used in a 17 18 logistic regression model,For serve asReview possible predictors for TBOnly treatment failure. 19 20 • The predictive ability of the model was assessed based on the ROC curve, and achieved an AUC of 0.94 21 22 (95% CI 0.84 – 1) and crossvalidated well in a leaveoneout context, with an AUC of 0.89 (95% CI 23 24 0.7 – 1) . 25 26 • These two identified possible predictors are also associated with a gut microbiota imbalance, confirming 27 28 previously proposed mechanisms related to treatment failure in these individuals. 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 14 https://mc04.manuscriptcentral.com/fm-bmm Page 15 of 25 Biomarkers in Medicine

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1 2 39. Louw GE, Warren RM, Gey Van Pittius NC, Mcevoy CR, Van Helden PD, Victor TC. A balancing act: 3 efflux/influx in mycobacterial drug resistance. Antimicrob. Agents Chemother. 53(8), 31813189 (2009). 4 40. MercadoLubo R, Mccormick BA. The interaction of gut microbes with host ABC transporters. Gut Microbes 5 6 1(5), 301306 (2010). 7 41. Zhang Y, Yew WW. Mechanisms of drug resistance in Mycobacterium tuberculosis : update 2015. Int. J. Tuberc. 8 9 Lung Dis. 19(11), 12761289 (2015). 10 11 12 13 Reference annotations: 14 15 16 * Lawn et al . 2017 17 18 For Review Only 19 This study determined the diagnostic yield, accuracy and prognostic value of urinelipoarabinomannan 20 21 (LAM) testing. They reported that the diagnostic yield of urineLAM was unrelated to respiratory 22 23 symptoms, and that a positive urineLAM status was strongly associated with a poor prognosis. 24 25 26 * Dunn et al . 2012 27 28 This review provides an understanding/importance of ROC (AUC) and odds ratios, as well as how to 29 30 interpret these, which forms an important part of this manuscript. 31 32 33 * Louw et al . 2009 34 35 36 This minireview describes ATPbinding cassette (ABC)type multidrug transporters in the context of 37 38 M. tuberculosis and drug delivery. 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 17 https://mc04.manuscriptcentral.com/fm-bmm