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Towards identifying intracellular drug targets of Mycobacterium tuberculosis against hit compounds in… Bojang, Adama 2020

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  Towards identifying intracellular drug targets of Mycobacterium tuberculosis against hit compounds in defined growth media  by ADAMA BOJANG  B.Sc., University of Manchester, 2013 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF  MASTER OF SCIENCE in FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  January 2020  ©Adama Bojang, 2020  ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:   Towards identifying intracellular drug targets of Mycobacterium tuberculosis against hit compounds in defined growth media     submitted by Adama Bojang  in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine  Examining Committee: Dr. Yossef Av-Gay Supervisor  Dr. Lindsay Eltis Supervisory Committee Member  Dr. Horacio Bach Supervisory Committee Member       iii  Abstract With the emergence of drug-resistant strains and widespread HIV epidemics, Mycobacterium tuberculosis (Mtb), the causative agent of TB, remains a number one global health problem. Human alveolar macrophages are the natural host and reservoir of Mtb and as an intracellular pathogen, Mtb relies on host lipids as growth substrates. However, little is known about the in vivo availability of carbon and energy sources as substrates to support Mtb growth and how its intracellular environment influences anti-mycobacterial compound activity. In this study, we aimed to use carbon restriction to mimic intracellular environment of Mtb and determined in vitro activities of hit compounds against Mtb. We also aimed to use “chemical genetic” approach to screen for resistant mutants against novel inhibitors and identify intracellular drug targets of Mtb against hit compounds in defined growth substrate media.  Using resazurin and cytotoxicity assays, compound’s activities against Mtb as well as against host cells were determined. Resistant mutants were isolated on a defined growth substrate 7H10 solid media supplemented with hit compounds. Genomic DNA of all resistant mutants were extracted and sequenced to identify mutated genes. Less than 30% of compound were active in albumin dextrose catalase as rich-media. Anti-mycobacterial compounds were all active in single carbon substrate media. Acetate or glycerol in growth media without hit compounds had an inhibitory effect on Mtb. Hit compounds at the highest concentration did not have cytotoxicity effect against host cells. A total of 8 mutants resistant to various novel inhibitors were isolated, with more than a single gene mutation identified in each strain.  We showed albumin dextrose catalase as rich-media is not ideal for compound screening. Media supplemented with glucose alone or cholesterol plus glucose or acetate closely resembles the MIC profile of the intracellular environment. Resistant mutants isolated in vitro showed similar resistant phenotype to intracellularly grown bacteria, suggesting a link between the resistant phenotype and the mutated genes identified.  Identifying intracellular druggable targets will facilitate the development of better approaches in TB treatment. The findings from this project will improve knowledge of the mode of action of particular compounds against Mtb.   iv  Lay Summary Tuberculosis (TB) is a disease caused by a pathogen called Mycobacterium tuberculosis (Mtb). Mtb is difficult to treat because of its ability to remain dormant for decades and its thick waxy cell wall, which reduces the diffusion of antibiotics. During dormancy period, Mtb relies on host nutrients for survival. However, the exact host nutrient utilised by Mtb remains unclear. We aimed to identified which part of Mtb is targeted by anti-mycobacterial compounds inside its host. We mimicked Mtb’s natural environment by growing it in media that has limited nutrients and compounds’ activities against Mtb was determined. All compounds were active against Mtb in media containing a single nutrient source. Mutated genes were identified in Mtb strains that displayed resistance against active compounds, suggesting that they be the actual drug targets. These findings could help predict mode of action of active compounds against Mtb and enhance TB treatment plans.                v  Preface All experiments and data analysis in this study were performed by myself under the supervision of Dr. Yossef Av-Gay (Principal investigator) and Dr. Gagandeep Narula (Postdoc).  Dr. Gagandeep Narula and Dr. Clement Tsui partly helped in DNA extraction which were sent to McGill University, Quebec, for whole genome sequencing.                  vi  Table of Contents Abstract ............................................................................................................................... iii Lay Summary ......................................................................................................................iv Preface ................................................................................................................................... v Table of contents .................................................................................................................vi List of Tables .......................................................................................................................ix List of Figures ....................................................................................................................... x Abbreviations ......................................................................................................................xi Acknowledgements ...........................................................................................................xiv Chapter one .......................................................................................................................... 1  1.1 Introduction .......................................................................................................... 1 1.1.1 Global burden of Tuberculosis .................................................................... 2 1.1.2 Antibiotics and chemotherapeutics against Tuberculosis ......................... 3 1.1.3 Mycobacterium tuberculosis........................................................................ 7 1.1.4 Mtb-Host relationship and pathology ......................................................... 9 1.1.5 Granuloma formation .................................................................................. 9 1.1.6 Macrophage as first line of defence against Mtb ..................................... 11 1.1.7 Mtb evasion of host immunity ................................................................... 14 1.1.8 Mtb response to environmental cues ......................................................... 17  1.1.9 General mechanism of drug resistance in Mtb ........................................ 18 1.2 Passive resistance mechanism ....................................................................... 18 1.2.1 Specialized resistance mechanisms ........................................................... 19 1.2.2 Modification of drug targets ...................................................................... 19 1.2.3 Chemical modification of drugs ................................................................ 20 1.2.4 Enzymatic degradation of drugs ............................................................... 21 1.2.5 Molecular mimicry of drug targets ........................................................... 21 1.2.6 Drug excretion by efflux pumps ................................................................ 21 1.2.7 Mechanism of resistance to first-line anti-TB drugs ............................... 22     1.2.7.1 Isoniazid ........................................................................................... 22   vii      1.2.7.2 Pyrazinamide ................................................................................... 22     1.2.7.3 Rifampicin........................................................................................ 23     1.2.7.4 Ethambutol ...................................................................................... 23 1.2.8 Mechanisms of resistance to second-line anti-TB drugs ......................... 24 1.2.9 Mtb nutritional homeostasis ...................................................................... 25 1.3 Substrates utilised as growth substrate by intracellular Mtb ................... 27   1.3.1 Cholesterol ............................................................................................ 27   1.3.2 Acetate ................................................................................................... 29   1.3.3 Glucose .................................................................................................. 29 1.4 Anti-mycobacterial compounds activities in Mtb growth environment ... 30 1.5 Animal and cellular infection models .......................................................... 32 1.6 Rationale 1: ..................................................................................................... 33 1.6.1 Aim 1: ...................................................................................................... 33 1.7 Rationale 2: ..................................................................................................... 33 1.7.1 Aim 2: ...................................................................................................... 34 Chapter Two: ..................................................................................................................... 34 Materials and methods: ..................................................................................................... 34 2.1 Growth and maintenance of Mtb strain ........................................................... 34 2.2 In-vitro MIC90 determination of anti-mycobacterial compounds ................ 34 2.3 Mtb growth rate in liquid media supplemented with defined growth  substrate but no compounds .................................................................................... 36 2.4 Screening for resistant mutants against hit compounds ................................. 36 2.5 Extraction of genomic DNA from resistant mutants ....................................... 37 2.6 Whole Genome Sequencing of resistant mutants ............................................ 37 2.7 Bioinformatic analysis ........................................................................................ 37 2.8 Intracellular MIC determination of hit compounds against resistant  mutants ...................................................................................................................... 38  2.8.1 Differentiation of THP-1 cells .................................................................... 38 2.8.2 Intracellular Mtb infection of THP-1 cells ............................................... 38   viii  2.8.3 Lysis of THP-1 cells .................................................................................... 39  2.8.4 THP-1 cytotoxicity: using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide (MTT) assay ...................................................... 39 Chapter Three: ................................................................................................................... 40 Results ................................................................................................................................. 40 3.1 Growth substrate-dependent difference in Mtb growth curves ..................... 40 3.2 Analysis of anti-mycobacterial compound activity in growth substrate  media .......................................................................................................................... 44 3.3 Comparison of distribution of compounds with similar activity in  different growth substrate media to intracellular MIC ........................................ 46  3.4 Cytotoxicity of hit compound against THP-1 cells .......................................... 46 3.5 Intracellular MIC values of hit compounds against resistant mutants ......... 48 3.6 Identification of mutated genes in resistant mutants as potential drug  target .......................................................................................................................... 53 Chapter Four:..................................................................................................................... 56 4.1 Discussion ............................................................................................................ 56 4.2 Conclusion ........................................................................................................... 62 4.3 Significance ......................................................................................................... 62 4.4 Limitations .......................................................................................................... 63 4.5 Future directions ................................................................................................ 63 References ........................................................................................................................... 65           ix  List of Tables Table 1: Summary of molecular mechanisms of anti-tuberculosis drugs ............. 5 Table 2: Intracellular MIC values compared to in vitro MIC values ................. 32 Table 3: Composition of 7H9 media supplemented with single vs double  growth substrate ....................................................................................................... 35 Table 4: Growth phases in single growth substrate media ................................... 42 Table 5: Growth phases in double growth substrate media ................................. 42 Table 6: Schematic analysis of growth media-dependent MIC in comparison to intracellular MIC ................................................................................................. 45 Table 7: Isolated resistant Mutants and their intracellular MIC values ............ 49 Table 8: Mutated genes identified in resistant mutants ........................................ 54 Table 9: Description of mutated genes and their activities................................... 55                   x  List of Figures Figure 1: Estimates of global TB prevalence ........................................................... 3 Figure 2:  TB drug development ............................................................................... 4 Figure 3: Mycobacterium tuberculosis cell wall structure ..................................... 8 Figure 4: Transmission and pathology of TB ........................................................ 10 Figure 5: Host immunity and Mtb outcome ...................................................................... 11 Figure 6: Macrophage antimicrobial defence mechanisms .................................. 12 Figure 7: Disruption of host cellular response by Mtb phosphatases.................. 14 Figure 8: Mtb host immune evasion mechanism ................................................... 16 Figure 9: Mode of action of anti-TB drugs and their mechanism of resistance . 20 Figure 10: Mtb metabolic pathways during infection ........................................... 26 Figure 11: Comparison of compound potency in broth (extracellular MIC90) with activity in macrophages (Intracellular MIC90) ............................................ 30  Figure 12: Growth substrate-dependent growth curves of Mtb .......................... 43 Figure 13: THP-1 cells cytotoxicity ......................................................................... 47 Figure 14: Intracellular MIC determination ......................................................... 50 Figure 15: Intracellular MIC determination for resistant mutants .................... 52             xi  Abbreviations:  ADC: Albumin, Dextrose, Catalase BCCDC: British Columbia Center for Disease Control BCG:  Bacillus Calmette-Guerin BWA: Burrows Wheeler Aligner CCM: Central Carbon Metabolism CESTET: Conditional expression-specialised transduction essentiality test CFP-10: Cultured Filtrate Protein 10 kDa CFU: Colony Forming Unit CO2: Carbon Dioxide  CR: Complement Receptor DNA: Deoxyribonucleic Acid EDTA: Ethylenediaminetetraacetic acid EIS: Enhanced Intracellular Survival  ESAT-6:  Early Secretory Antigen Target 6 kDa ESX3: Excretion System 3 FDA: Federal Drug Agency FBS: Fetal Bovine Serum GATK: Genome Analysis Tool Kit GFF: General Feature Format GSK: GlaxoSmithKline  GTE: Glucose Tris-HCl EDTA HCl: Hydrochloric acid HIV: Human immune-deficiency virus   xii  ICL: Isocitrate lyase  IFN-γ: Interferon Gamma IGRA: Interferon Gamma Release Assay IL: Interleukin  kDa: Kilo Dalton MCC: Methyl citrate cycle  MDR-TB:  Multidrug Resistant Tuberculosis  MHC: Major Histocompatibility Complex MIC: Minimum Inhibitory Concentration MOI: Multiplicity of Infection min: minute Mtb: Mycobacterium tuberculosis MSH: Reduced Mycothiol MTBC: Mycobacterium tuberculosis complex MTT: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium     Bromide NADPH: Nicotinamide Adenine Dinucleotide Phosphate NCBI: National Center for Biotechnology Information NEA: Non-essential amino acid OADC: oleic albumin, dextrose, catalase OD: Optical Density PAP: Pulmonary Alveolar Proteinosis  PCR: Polymerase Chain Reaction PDIM: Pthiocerol dimycocerosate PEPCK: Phosphoenolpyruvate Carboxykinase  PHL: Public Health Laboratories   xiii  PI: Pyrimidine-imidazoles PMA: Phorbol-12- Myristate-13-Acetate RNA: Ribonucleic Acid ROS: Reactive Oxygen Species RPMI: Roswell Park Memorial Institute RT: Room Temperature SNPs: Single Nucleotide Polymorphisms SNV: Single Nucleotide Variant  TB: Tuberculosis TCA: Tricarboxcylic acid   TCS: Two-component system TNF-α: Tumor Necrosis Factor Alpha VPS33B: Vacuolar Protein Sorting 33B XDR-TB: Extensively Drug Resistant Tuberculosis w/v: Weight per volume WGS: Whole Genome Sequence WHO: World Health Organisation WT: Wild Type (Mycobacterium tuberculosis) v/v: Volume per volume           xiv  Acknowledgements  First and foremost, I would like to express my sincere and profound gratitude to my supervisor, Professor Yossef Av-Gay for his unflinching support, encouragement and guidance. He has granted me the opportunity and platform to conduct my research, which I would always remain grateful.  This work is a true testament of the tremendous support I had received from all members of the lab, past and present. I appreciate all those who helped me in one way or the other to get to where I am today. It was a great pleasure to have worked and interacted with members of the lab, who I find very intelligent and approachable. I have learnt a lot from all of you in and off the lab and shared good memories together that I will ever cherish. To my family and friends, I thank you all for accommodating and genuinely guiding especially when frustration and impatience would have otherwise taken its toll on me. My special thanks go to my wife for her unconditional love, patience and support. To my committee members, Dr. Lindsay Eltis and Dr. Horacio Bach, I am very thankful for all your valuable inputs and advices during the course of my lab work.  I am grateful to CIHR PJT-148646 grant for their financial support and GSK for kindly providing inhibitors.          1  Chapter One 1.1 Introduction  Tuberculosis (TB) is one of the most ancient and deadly disease of mankind. It was not until 1882 that Robert Koch identified Mycobacterium tuberculosis (Mtb) as the cause of TB (Murray, 2004). Mtb is part of a group of closely related Mycobacterium tuberculosis complex (MTBC) strains that comprises of Mtb, Mycobacterium africanum (M. africanum), Mycobacterium bovis, Mycobacterium mungi, Mycobacterium pinnipedii, Mycobacterium microti, and Mycobacterium caprae (Brites, 2015). Mtb is a contagious and airborne pathogen which is transmitted via bacilli inside aerosolised droplets that are spread into the air via coughing, sneezing or speaking. Mtb primarily infects human lungs (pulmonary TB) but can also spread to other parts of the body (extra-pulmonary TB). Symptoms of TB include chronic cough for at least two weeks, weight loss, fever, night sweats, cavitation and fibrosis  (Philips and Ernst, 2012). MTBC has a remarkable range of hosts and morphologies but yet have ≥99% homology in their genome thus, indicating a recent common evolutionary ancestor (Hershberg et al., 2008). Although they all share totally identical 16S rRNA sequences, they differ in terms of their host tropism, phenotypes and pathogenicity (Hershberg et al., 2008). Even Mycobacterium species that are confined to humans for proliferation and transmission, Mtb and M. africanum have different genomic, phenotypic, clinical and epidemiological features  (BañUls et al., 2015).  Upon infection, innate immune cells are recruited to eliminate Mtb through antimicrobial process of phagocytosis. Majority of the infected people with healthy immune system are asymptomatic and are able to restrain its growth, such that the infection persists for decades. Only about 5-10% of those infected will develop active disease during their lifetime (WHO 2017). Surviving Mtb is believed to enter a stage of metabolic inactivity known as latency and reactivate from latency to active disease in immunocompromised individuals. Delay in mobilisation of adaptive immune response is thought to contribute to disease progression, resulting in a collateral damage to the host. The likelihood determinant of a primary case infecting others could largely depend on some of the following factors; the intimacy and duration of person-person contact, the degree of infectiousness of the case, and the shared environment in which the contact takes place.     2  1.1.1 Global burden of Tuberculosis History suggests that TB might have emerged in humans about 70,000 years ago and remained sporadic up to the 18th Century (Gagneux and Small, 2007). During the industrial revolution, TB became epidemic as result of an increased population density and the unfavourable living conditions. During the 20th century, TB incidence in the developed countries started to decrease rapidly, thanks to improved health infrastructures, nutrition and better housing conditions (WHO, 2017). TB incidence decreased even more rapidly after the introduction of the BCG (Bacillus Calmette-Guerin) vaccine in 1921 and the use of antimicrobial drugs (BañUls et al., 2015).  BCG vaccine has been widely used among neonates and infants particularly in countries where it is part of the national childhood immunization programme. Although its protectiveness against meningitis and disseminated TB in children has been proven, it does not protect primary infection in adults and reactivation of latent infection (Smith et al., 2016). However, despite numerous efforts to eradicate TB, it still poses a significant global public health concern with a high disease burden and mortality rate, primarily in Africa and South East-Asia, low-income countries (Figure 1). In the 1980s, the disease incidence increased due to the human immune-deficiency virus (HIV) pandemic, deterioration of living conditions in large cities and the emergence of drug resistant Mtb (Russell, Barry and Flynn, 2010).    Nearly 2 billion people are estimated to be latently infected with 13.7 million active cases worldwide (WHO, 2017). About 1.5 million people die from the disease annually and only 5-10% of those latently infected individuals may be at risk of reactivation later in life (WHO, 2017).    3   Figure 1: Estimates of global TB prevalence. WHO 2017 report. The global map shows TB prevalence across the different regions, with colour coding dark to light green indicating incidence per 100,000 per year. Pie chart diagram shows the percentage distributions of TB around the globe.     Over half a million new cases of multidrug resistant TB (MDR-TB) each year are estimated to occur (WHO, 2017). Extensively drug resistant TB (XDR-TB) strains had become evident in a large portion of these cases (Dheda and Migliori, 2012). Emergence of MDR-TB and XDR-TB and other factors such as poverty has contributed to the failure of eradicating the disease. Challenges in early diagnosis of TB due to suboptimal diagnostic tools and lack of biomarkers pose huge risk of continuous transmission, leading to the widespread of the disease.  1.1.2 Antibiotics and chemotherapeutics against TB The discovery of antibiotics often referred to as “magic bullet” because of their powerful potency against bacteria without harming the host, revolutionised TB treatment. A series of potent anti-TB drugs were introduced to clinical practice between 1940s–1960s (Figure 2), which was dubbed the “golden age of antibiotics” due to the swift development of a myriad of treatment. Development of new antibiotics became dormant after the early 1960s (Fischbach and Walsh, 2009) with bedaquiline and delamanid being the only anti-TB drug approved by the FDA since 2013 and 2104 respectively. While this thesis was written another drug against Mtb, pretomanid was approved by the FDA against multi resistant form of Mtb.   4    Figure 2:  TB drug development. This shows the period when TB drug development became so active leading to the discovery of the current used drugs.  Before the discovery and approval of bedaquiline and delamanid for MDR-TB treatment, TB drug development has been dormant for more than four decades. Adapted from (Zheng and Av-Gay, 2016).  TB approved treatment for drug sensitive Mtb, consist of a combination of four antibiotics (isoniazid, rifampicin, pyrazinamide and ethambutol) (Table 1) for 6 to 9 months. Full combination is taken for 2 months, followed by only isoniazid and rifampicin for at least 4 months (Kaneko, Cooper and Mdluli, 2011).              The need for disease-relevant high-throughput drug discoveryThe 1940s to 1960s is considered the prime time (see Fig. 1) for TB drugdiscovery, as illustrated by the fact that most of the current TB drugs werediscovered in this period [9]. All of these compounds were likely identified byexamining their ability to inhibit Mtb growth in artificial culturing media; thismethod is known as phenotypic screen. Their mode of actions (MOAs) werelargely mysteries until sophisticated molecular biology techniques becameavailable. RIF, the last TB drug discovered in that period, marked the start of a50-year drought in new TB drug discovery [11].Since completion of the Mtb genome sequencing effort in 1998 [12], TBdrug discovery methods have been divided into two main groups: (1) thebioinformatics/target-based approach and (2) the Bold school^ phenotypicscreening approach. Enthusiasm towards the information contained in theMtb genomedatabasesupported morerational target-based approachesto drugdiscovery. This approach was widely adopted in academic laboratories, whererational design appeals to both researchers and funding agencies. As such,target-based drug discovery is a natural extension of scientists’ curiosity tounderstand biology and to develop compounds that target essential processes.Phenotypic-based approaches rely on screening processes where millions ofcompounds may be tested for their ability to kill Mtb. In this approach, theMOAs, i.e., the target of each hit compound, aredetermined afterward, if at all.Phenotypic-based approaches are typically performed by pharmaceutical com-panies that have large chemical libraries and automation at their disposal. Astudy by Payne et al. evaluated GlaxoSmithKline’s experience on target-baseddrug discovery. Based on a collection of 300 targeted gene studies, the authorsfound the target-based approach to be ineffective and thus unsustainable [13].Their experiencesuggested that it iseasier to find acellular target of acompoundthan it is to chemically engineer an antibiotic from an enzyme inhibitor; thus,phenotypic-based methods are more effective for antibacterial discovery.In vitro screening methods have proven to be very effective for most infec-tious diseases because the target organisms cause topical infections orbacteraemia where the infection is spread to organs through circulating fluids.1940 1950 1960 1970 1980 1990 2000 2010StreptomycinPara-aminosalicylic acidCycloserine RifampicinPyrazinamideEthambutolEthionamideBedaquilineTB drug development droughtFig. 1. Timeline for newTBdrug development. Majority of TB-specific drugsweredeveloped between 1940 and 1963 [9, 10], withan exception of bedaquiline, which was approved at the end of 2012 [8]. From1963 to 2012 represents a near five-decade-longdrought in novel TBdrug discovery.New Era of TBDrug Discovery and Its Impact on Disease Management Zheng and Av-GayAuthor's personal copy2014 Delamanid, 2019 Pretomanid     5  Table 1: Summary of molecular mechanisms of anti-tuberculosis drugs.  Drugs Associated MIC (mg/L) Associated mutation/s Targets Rifampicin 0.05 – 1 rpoB Inhibition of RNA synthesis Isoniazid 0.02– 0.2 katG, inhA, kasA Inhibition of cell wall mycolic acid synthesis Pyrazinamide 16–100 pncA, rpsA, panD Reduction of membrane energy, inhibition of trans-translation, inhibition of pantothenate and coenzyme A synthesis Ethambutol 1–5 embB, ubiA Inhibition of cell wall arabinogalactan biosynthesis Fluoroquinolones 0.5–2.5 gyrA, gyrB Inhibition of DNA synthesis Ethionamide 2.5–25 ethA, ethR mshA, ndh, inhA Inhibition of cell wall mycolic acid synthesis Bedaquiline 0.06–1 Rv0678, atpE, pepQ Inhibition of mycobacterial ATP synthase Streptomycin 2–8 rpsL, rrs, gidB Inhibition of protein synthesis Para-aminosalicylic acid 1–8 thyA, folC, ribD Inhibition of folic acid and thymine nucleotide metabolism  The table highlights some and not a complete list of all TB drugs available. Adapted from (Feng et al., 2015).               6  Despite current antibiotics effectiveness against replicating Mtb, they have shown to be ineffective against dormant Mtb phenotype (Raffetseder et al., 2014). Targeting Mtb at the site of infection such as pulmonary cavities, empysema, pus or solid caseous material may pose some challenges such as low antibiotic penetration. Furthermore, the low pH in these sites may inhibit the activity of most antibiotics. Some deleterious adverse side effects that included gastrointestinal inflammation, pain, liver toxicity and neurological/behavioural manifestations have been noted with the current antibiotics (du Toit, Pillay and Danckwerts, 2006). Prolonged treatment times together with such side effects often lead to poor compliance by patients. Discontinuation of treatment before complete sterilisation was associated with an increase in MDR-TB strains and infection relapse. MDR-TB are strains resistant to both rifampicin and isoniazid, whereas XDR-TB are MDR-TB strains that have become resistant to any of the second-line drugs and at least 1 of the 3 second-line injectable drugs (amikacin, kanamycin and capreomycin) (Gandhi et al., 2006).  Currently, few drugs (bedaquiline, fluroquinoline and ethionamide) (Table 1) are used as second-line treatment for MDR-TB. However, they are only use as last option for treating MDR-TB due to their poor side effects. The re-emergence of high incidence of TB was seen in the 1980s, with outbreaks of MDR-TB strains often associated with the HIV epidemic, which ultimately hampered TB treatment efforts worldwide (Corbett et al., 2018).  Due to an increase in the incidence of MDR/XDR-TB, and other challenges facing TB treatment, the WHO has called for widespread implementation of direct observed therapy (DOT). This is a patient care strategy aimed at ensuring adherence throughout the course of TB treatment. Every TB patient from the point of diagnosis and registration is followed by supervised standardised treatment. Regions that failed to adhere to DOT program policies resulted in an increased TB incidence rate. This was evident is Eastern Europe when the dismantling of the Soviet Union had a huge socioeconomic burden that weakened the public health system (Glaziou et al., 2015).   To combat the burden of TB, development of new drugs that able to shorten treatment duration, avoid significant side effects and be affordable are urgently needed.     7  1.1.3 Mycobacterium tuberculosis  Mtb is a nonmotile, nonsporulating, acid-fast bacilli. It has a high guanine plus cytosine (G + C) content of 62-70% in their genome (Cook et al., 2013). Mtb has a genome size of about 4.4Mbp, encoding about 4000 genes (Cole et al., 1998). Transcriptional annotation of Mtb’s genome revealed approximately 250 genes involved in lipid and fatty acid metabolism (Cole et al., 1998).  Under the microscope, Mtb appear straight or slightly curved rod in shape with 1 to 4 µm in length and 0.3 to 0.6 µm wide. It is a slow-growing, obligate aerobic pathogen that belongs to the Actinomycetales order and Corynebacterineae suborder. The doubling time of Mtb is 12-24h under optimal conditions and a prolong culture period of at least 3 weeks on agar (Sakamoto, 2012). Mtb is a facultative intracellular microorganism, which replicates within phagocytic cells, particularly, macrophages and other mononuclear phagocytes (dendritic cells), key mediators of both innate and adaptive immune response.  Mtb has a unique cell wall that contain mycolic acid, believed to play a critical role in its structure and function. The waxy cell wall serves as an impermeable barrier to toxic agents including resistance to many antibiotics, as well as distinctive immune-stimulatory properties. Peptidoglycan polymer is found within the periplasmic space covalently bound to arabinogalactan and lipoarabinomannan which in turn are bound to mycolic acids (Niederweiss, 2013) (Figure 3). Due to lack of outer cell membrane, Mtb stains weakly to crystal violet and is resistant to decolourisation with acid-alcohol solutions after staining with carbol fuchsin, hence called acid-fast bacilli. Lipids constitute about 40% of the cell envelope’s dry mass and depending on the isolate and growth conditions, this percentage may be altered (Brennan, 1995). The chemical and structural organisation of these lipids (Figure 3) makes the cell wall extremely impermeable, thus rendering it intrinsically resistant to many drugs (Nasiri et al., 2017).     8   Figure 3: Mycobacterium tuberculosis cell wall structure. Schematic diagram showing composition of cell wall components and their distribution. Peptidoglycan within the inner layer is covalently linked to arabinogalactan layer. The cell wall contains mycolic acids, glycolipid like (mannose-capped) lipomannan and mannoglycoproteins. Adapted from (Kleinnijenhuis et al., 2011).  Protein secretion systems are important for Mtb’s physiology and virulence. Mtb encodes the typical bacterial sec and secretion systems and in addition, encodes five type VII Secretion System (T7SS) of which (ESX 1- 5) have been identified in this pathogen (Daleke et al., 2012). Mtb requires ESX1 for full virulence and to secrete antigens, including Early Secretory Antigen Target 6 kDa (ESAT-6) and Culture Filtrate Protein 10 kDa (CFP-10). These two highly immunogenic antigens enable the diagnosis of Mtb infection using interferon-gamma release assays (IGRAs) (Delogu et al., 2013). ESX3 is essential for iron and zinc uptake by Mtb during growth in culture (Delogu et al., 2013). Mycothiol (MSH) is a unique mycobacterial low-molecular-weight thiol, an analogue of glutathione found in Gram-negative bacteria and eukaryotes (Buchmeier et al., 2003). Its distribution is limited to Gram-positive Actinomycetes with Mtb generating the highest level of MSH in the phylum (Buchmeier et al., 2003). MSH functions include protection against anti-mycobacterial agents like toxic oxidant damage and antibiotics as well as the inactivation of electrophilic toxins (Buchmeier et al., 2003). MSH biosynthesis is a multistep process that involves an enzymatic reaction of four genes (MshA, MshB, MshC, and MshD). Mutation in Mtb’s MshB was characterised by an increased sensitivity to the toxic oxidants cumene hydroperoxide and rifampicin, though the mutant still produced approximately 20% of the wild type levels of MSH (Rawat et al., 2002). Lack of MSH in mammalian cells may suggest that, enzymes involved in MSH metabolism or related pathways could be potential drug target for TB treatment.    9  1.1.4 Mtb – Host relationship and pathology Mtb lacks many of the classical virulence factors such as toxins and flagella required by most pathogens to compete with mucosal microflora for colonisation in the host (Stutz et al., 2018).  Mtb resides in sterilised sites like the lungs where aerosolised bacilli are engulfed by alveolar macrophages through receptor-mediated phagocytosis. Surface proteins of Mtb such as glycoprotein, mannose-capped lipoarabinomannan (Man-LAM) (Figure 3) are recognised by the C-type lectins and the macrophage Mannose receptor (MMR) (Geijtenbeek et al., 2003). Other important receptors such as complement receptor 3 (CR3), CR1, CR4 and mannose receptor have been all implicated to involve in the phagocytosis and internalisation of Mtb by alveolar macrophages (Vergne et al., 2004). Toll-like receptors, particularly TLR-2 is shown to be relevant for the attachment of Mtb to macrophages (Mukhopadhyay, Nair and Ghosh, 2012). Soon after Mtb is phagocytosed, the macrophage becomes activated and a robust proinflammatory response is induced. Interleukin 12 (IL-12) secretion, drives T-helper type 1 cell differentiation and interferon ֹgamma (IFN-γ) production is required for macrophage activation (Philips and Ernst, 2012). IFN-γ activated-macrophages produce reactive oxygen and nitrogen intermediates (ROI and RNI) that are important for killing intracellular Mtb. However, after 2-6 weeks of infection, cell-mediated immunity is initiated which leads to a microenvironment of cytokines and chemokines.  This results in expression of integrins, selectins, and adhesins on the surface of lymphocytes and endothelial cells. Secreted proinflammatory cytokines, IFN-γ, tumour necrosis factor-alpha (TNF-α) and interleukin-12 (IL-12) facilitate mass recruitment of innate immune cells (Mortaz et al., 2015), resulting in granuloma formation at the site of infection (Figure 4) (Schaible et al., 2018).    1.1.5 Granuloma formation A well-developed granuloma consists of infected macrophages that are surrounded by epithelioid macrophages, foam cells, and occasionally multinucleated giant cells, with peripheral recruited lymphocytes and fibrous capsule (Figure 4) (Sakamoto, 2012). The inner core of granuloma usually becomes necrotic while the outer surface is covered by fibrous tissue and vasculature is developed (Gengenbacher and Kaufmann, 2012). At nascent stage of granuloma, uninfected macrophages that are recruited to the site of infection may become infected due to apoptosis of the   10  infected macrophages. Granuloma, as a solid structure act as a fine balance between host containment of infection and Mtb protection from IFN-γ producing lymphocytes. The granuloma is maintained by a delayed-type of hypersensitivity response to the persistent present of Mtb’s antigens and immunostimulatory lipids, thus allowing Mtb to persist within macrophages and extracellularly within the granuloma (Kim et al., 2010).  As granuloma progresses over the years, its heterogeneity is accompanied by hypoxic state with weak fibrous sheath vascularity and an increase in foamy macrophages (Schaible et al., 2018).  Ultimately, granuloma’s integrity weakens and ruptures resulting in cellular necrosis and caseation. The caseum is predominantly composed of host-derived lipids and is considered as hypoxic in guinea pigs, rabbits, nonhuman primates and human beings (Via et al., 2008). This environment is an important consideration for Mtb metabolism and antibiotic therapy efficacy.  Immune cells’ recruitment to the lesion and their failure to eradicate Mtb often results in chronic infection which is characterised by slow growing Mtb with progressive immunopathology and an increased bacterial load (Figure 5). Dissemination of Mtb and rapid disease progression is associated with larger necrosis of granulomas. Therefore, crosstalk between host-pathogen interaction, determines disease outcome (Figure 5).   Figure 4: Transmission and pathology of TB. Inhaled bacilli are phagocytosed by alveolar macrophages in the in lungs airways. After initiation of the infection, host induces localised pro-inflammatory immune responses that recruit immune cells to form a granuloma, the hallmark tissue reaction of TB. Granuloma matures and loses solidity of its matrix causing the release of Mtb into the airways and coughed out as contagious aerosol. Adapted from (Ehrt, Schnappinger and Rhee, 2018).   11    Figure 5: Host immunity and Mtb outcome.  Aerosolised bailli are inhaled and phagocytosed by alveolar macrophages. The outcome of TB infection is a balance between the bacilli and the host immune response. When Mtb replication is controlled, about 90-95% of the cases are asymptomatic (latent TB). Once host innate immune system fails, the bacilli become active, multiply and spread causing tissue damage (active TB). Adapted from (Delogu et al., 2013).  1.1.6 Macrophage comprise of the first line of defence against Mtb Mtb is able to infect both phagocytic and nonphagocytic cells, including myeloid dendritic cells, neutrophils, adipocytes and epithelial cells (Pietri-rouxel and Forne, 2006). However, alveolar macrophages undertake the critical homeostatic role such as clearing inhaled materials from the airway and recycling pulmonary surfactant (Gautier et al., 2013) . As part of the immune system homeostasis, alveolar macrophages express a unique transcriptional and epigenetic profile that are highly distinct from other tissue-resident macrophages (Gautier et al., 2013). Phagosome compartment in which Mtb resides is slightly acidic, pH 6.5 (Figure 6) and remains accessible to endosomal network but exhibits minimal acquisition of lysosomal hydrolases (Pieters, 2008).       12   Figure 6: Macrophage antimicrobial defence mechanisms. Macrophages as host first line of defence, eliminate Mtb through mechanisms such as phagosome acidification, phagosome-lysosome fusion, production of ROS and RNS, autophagy and apoptosis. Phagosome becomes acidic as it matures to fuse with the lysosome. Adapted from (Poirier and Av-Gay, 2012).  The classical activation of Mtb-containing macrophages by IFN-γ, allows them to deliver their content to a more acidic, hydrolytic environment of the lysosome. Alveolar macrophages can be activated by Mtb cell surface molecules particularly mannose-capped lipoarabinomannan that recognise and bind to mannose receptors of macrophages to initiate phagocytosis (Kleinnijenhuis et al., 2011). Killing of Mtb by activated macrophages is dependent on most significantly the production of ROI, RNI, nitric oxide (NO), and lower pH (Alonso et al., 2007). Mtb-macrophage interaction facilitated through receptor-mediated mechanism, results in phagosome formation (Figure 6) (Queval, Brosch and Simeone, 2017). Phagosome maturity is indicated by remodelling of its membrane and luminal content through interaction and fusion with endosomal network (Torunn Elisabeth, Torunn and Trond, 2000). Dynamic changes especially in biochemical membrane composition and acquisition of microbicidal features, are part of the characteristics of innate immunity. Phagosome acidification, as part of antimicrobial activity, is achieved by recruiting V-ATPase, a multi-subunit protein-pump complex that actively transports protons across membranes using energy from ATP hydrolysis (Figure 6) (Poirier and Av-Gay, 2012).    13  Recruitment of V-ATPase to the phagosome results in a pH decrease from 6.5 to 4.5 (Figure 6) within minutes of phagosome maturation (Hackam et al., 2002). This generates an acidic lumen required for subsequent fusion with lysosome and the activity of the antimicrobial molecules they deliver (Sun-Wada et al., 2009). Phagosome maturation is characterised by binding and losing of certain effector molecules along the phagocytic pathway. GTP binding protein, Rab5 accumulates transiently on early endosome. This leads to phosphatidylinositol 3-phosphate (PI3P) generation through the action of PI3 kinase VPS34, an effector protein of Rab5. PI3P is a regulatory lipid involved in trafficking of lysosomal constituents to the phagosome and its synthesis is required for phagosome maturation (Vergne et al., 2004). PI3P acts as a ligand for proteins containing FYVE or PX domain such as early endosomal antigen1 (EEA1). As phagosome matures, both Rab5 and EEA1 dissociate and new markers such as Rab7 and lysosome associated membrane protein (LAMP1) are acquired.  Phagosome-lysosome fusion generates toxic oxidative radicals and activates proteases, hydrolases and lipases to kill and digest Mtb (Haas, 2007) (Figure 6). Degraded peptides are loaded and presented to the adaptive immunity through MCH II where T cells mount appropriate immune response. Initiation of the adaptive immunity is characterised by stimulation of macrophages by T-cell derived cytokines such as IFN-γ to produce antibacterial effectors. Production of effector molecules (ROI, RNI and NO) by IFN-γ-activated macrophages via NADPH oxidase complex,  (Miller et al., 2010) can result in the disruption of major cellular components of Mtb such as nucleic acid, proteins and lipids  (Miller et al., 2007).  Host cells restriction of amino acid availability to Mtb also play a role in defence mechanism. This was demonstrated with leucine, proline and lysine auxotroph strains that were attenuated intracellularly and have since been tested as live-attenuated vaccine candidate (Sherman et al., 2006). Apoptosis or cell-programmed death is an important innate defence mechanism to haul the spreading of Mtb. This biological process is regulated by a variety of signalling cascade that involves activation of enzymes called caspases (Danial and Korsmeyer, 2004). Apoptotic bodies exhibit specific morphological features that are recognised by surrounding antigen presenting cells (APCs) (dendritic cells, and macrophages) (Busca, Saxena and Kumar, 2012). Dendritic cells engulf apoptotic vesicles and activates T cells by cross-presenting the antigen through MHC class   14  II. This results in an increased TNF-α synthesis which induces apoptosis alongside IFN-γ which activates infected macrophages to lunch antimicrobial defence activity (Miller et al., 2010).  1.1.7 Mtb evasion of host immunity  Mtb has sophisticated mechanisms to systemically disable, stimulate, or reroute normal host cell signaling pathways to promote its own survival. Intracellular Mtb counters phagosome-acidification and phagosome-lysosome fusion by secreting a repertoire of surface signaling proteins (Hestvik, Hmama and Av-Gay, 2005). Proposed secreted proteins include a lipid secreted acid phosphatase M (SapM), a tyrosine phosphatase (PtpA and PtpB), a serine threonine kinase (PknG), lipoamide dehydrogenase (LpdC), ESX-1-dependent substrate and many others (Figure 7)  (Wong, Chao and Av-Gay, 2013).   Figure 7: Disruption of host cellular response by Mtb phosphatases. Mtb secretes proteins such as, secreted acid phosphatase M (SapM), a tyrosine phosphatase (PtpA and PtpB), a serine threonine kinase (PknG), lipoamide dehydrogenase (LpdC), and ESX-1-dependent substrate to inhibit phagosome-acidification and phagosome-lysosome. PtpA binds to H subunit of the V-ATPase and prevents its recruitment to phagosome, inhibiting phagosome-acidification. PtpA inactivates VPS33B by dephosphorylation and inhibits phagosome-lysosome fusion. Adapted from (Wong, Chao and Av-Gay, 2013).   15  Mtb secrets PtpA into the cytosol where it binds to subunit H of the V-ATPase and disrupts its tethering to the phagosome membrane thereby blocking phagosome-acidification  (Bach et al., 2008). Human vacuolar protein sorting 33B (VSP33B) is a member of the class C VPS complex (VPS-C) (Figure 7)  that is involve in regulating membrane fusion within the endocytic pathway (Peterson, 2001). VPS33B is a cognate substrate of PtpA which inactivates it upon interaction by dephosphorylation. PtpA’s interfering with V-ATPase recruitment and dephosphorylation of VPS33B inhibits phagosome acidification and phagosome-lysosome fusion respectively  (Wong et al., 2011). PtpA is first required to bind to V-ATPase in order to dephosphorylate VPS33B within the macrophages (Wong et al., 2011). Mtb secretes SapM which eliminates PI3P from phagosome by hydrolysing it into PI (Figure 7) leading to inhibit phagosome-lysosome fusion. Dephosphorylation of PI3P by SapM also prevents EEA1 recruitment to the phagosomal membrane, a required process in the endocytic pathway (Vergne et al., 2005). Mtb avoids ROI, RNI and NO damage from activated macrophages by impairing adaptive immune response through blocking MCH class II-mediated antigen presentation by APCs (Figure 8). Failure to present antigens leads to a decrease in IFN-γֹ-specific CD4+ T-cell population (Figure 8) (Sakai et al., 2016). Consequently, less or no IFN-γ required to active macrophages for antimicrobial activities is produced.       16   Figure 8: Mtb host immune evasion mechanism. Phagocytic cells engulf Mtb and become a sanctuary, enabling Mtb to resist killing by toxic radicals such as NO, RNI and ROI. (2) by blocking phagosome-lysosome fusion and (3) by inhibiting lysosome acidification. (4) Virulent Mtb is able to translocate from phagosome to the cytosol whereas BCG strain cannot.  (5) Mtb recovers from latency to active state. (6) MHC II is downregulated thus, disrupting antigen presentation and consequently (7) decreasing the CD4 specific T-cell population. (8) No or insufficient IFN-γ production to activate phagocytic cells. (9) Prevents intercellular interaction through their surface receptors and blocks dendritic cell maturation. (10) Dendritic cell CCR7 induced-migration to lymph nodes is impaired.  Adapted from (Katalinić-Janković, Furci and Cirillo, 2012).  Mtb suppresses apoptosis and favours necrosis as a way of preserving its environment that supports its replication and reinfection of nascent macrophages. Mtb induces the upregulation of anti-apoptotic genes like Mcl-1 and Bcl-2 family in macrophages that inhibit apoptosis (Busca, Saxena and Kumar, 2012). Mtb can also inhibits apoptosis by reducing macrophages’ surface expression of Fas receptors or secreting soluble TNF-α-receptor that blocks apoptotic stimuli from Fas ligand (FasL) or TNF-α (Oddo et al., 1998).       17  1.1.8 Mtb response to environmental cues  Mtb resides in homogenous compartments (Wood, Knabel and Kwan, 2013) which are faced with constantly changing environment such as starvation, hypoxia and low pH as the disease progresses (Barry et al., 2009).  Its physiological adaptation to such compartments is often indicated by an altered replication rate. The growth rate appears to be actively controlled in response to stresses such as hypoxia, low pH, low nutrients and others (Barry et al., 2009). Regulation of gene expression is key in Mtb’s adaptation to the different stages of the infection. Critical regulators of gene expression such as the phoP virulence regulator (Gonzalo-Asensio et al., 2008), the kstR cholesterol regulator (Kendall et al., 2007), the dosR hypoxia regulator (Salina et al., 2009) have been identified through global transcriptome analysis.   WhiB7 is an auto-regulated transcriptional regulator and its expression can be induced by exposure to suboptimal concentration of antibiotics or by stress conditions such as heat shock, iron starvation and entry in stationary phase (Yim et al., 2013). WhiB7 regulates the expression of the eis gene which is important for Mtb’s survival within the macrophage (Wei et al., 2000). Responding to stress, Mtb switch to dormancy phenotype by shutting down its own metabolic activities and depends on host-derived nutrients (Peyron et al., 2008). This growth arrest phenotype is a form of an active response to stress rather than metabolic inefficiency to grow.      Under hypoxia conditions, Mtb adjusts macrophage metabolism to increase accumulation of lipid bodies, giving rise to foamy macrophages, a feature found at the interface of central necrotic regions within granulomas (Peyron et al., 2008). Mtb-containing phagosome interacts with and release Mtb into these host lipid-bodies that serve as source of nutrients (Peyron et al., 2008).  The interaction also provides a safer niche that protects Mtb from bactericidal effects such as respiratory burst. Additionally, Mtb found within these lipid bodies tend to resist first-line antibiotics by acquiring a dormancy phenotype (Daniel et al., 2011).          18  1.1.9 General mechanism of drug resistance in Mtb  All genomes are known to undergo constant mutations due to base changes caused by exogenous agents, DNA polymerase errors, deletions, insertions, and duplications. Bacterial resistance to antibiotics in polymicrobial niches is mostly acquired either through mutation or horizontal gene transfer by mobile genetic elements. Mtb’s unique and restricted intracellular environment limits exchange of genetic materials between organisms. The major cause of drug resistance in Mtb is mainly through spontaneous mutations in genes that code for drug targets or drug-activating enzymes (Gillespie, 2002). These mutations are mainly in the form of single nucleotide polymorphisms (SNPs), insertion or deletions (Gillespie, 2002). Mutations in highly conserved genes such as rpoB and gyrA that encode essential replication functions in Mtb confer resistance (Drlica and Zhao, 1997). Resistance-associated point mutations have been described for all first-line and second-line drugs in Mtb (Palomino and Martin, 2014).  In prokaryotic kingdom, mutation rates vary; RNA viruses have an error rate of 10-4 per nucleotide per infection (Combe and Sanjuán, 2014) whereas, bacteria make one error in every billion nucleotides synthesised. The constant rate of a spontaneous mutation of 0.0033 mutations per DNA replication is uniform for a diverse spectrum of organisms (Dillon et al., 2017). However, individual genes mutations vary significantly between and within genes, with some areas of the genome considered as “hot spots” for mutations. One antibiotic-resistant phenotype can be caused by several mutations, either both in different base pairs or in different genes (Long et al., 2016). In addition to resistance caused by chromosomal mutations, there are arrays of intrinsic resistance mechanisms. The most notable drug resistance mechanisms in Mtb are passive and specialized, whose mechanisms often limits TB treatment.   1.2 Passive resistance mechanism In the cell wall, a family of redundant mycolyltransferase enzymes initially known as “the antigen 85 complex” catalyses the ligation of mycolic acids and sugar moieties (arabinogalactan or trehalose) (Belisle et al., 1997). Mycolyltransferase enzyme is encoded by the fbpA gene, whose deletion resulted in decreased levels of trehalose dimycolates thus, increasing sensitivity to antibiotics widely used for antibacterial chemotherapy (Nguyen and Pieters, 2005).    19  The role of mycobacterial cell wall in intrinsic antibiotic resistance was shown when an M. smegmatis mutant defective in mycolate biosynthesis exhibited increased uptake and sensitivity to erythromycin, chloramphenicol, novobiocin and rifampicin (Liu and Nikaido, 2002). Transposon mutagenesis also confirms the role of cell wall integrity in intrinsic drug resistance (Philalay et al., 2004). Transposon insertions in kasB or the virS-mymA operon (Rv3082 to Rv3089), genes involved in mycolic acid biosynthesis, led to increased chemical penetration and sensitivity to various antibiotics including (rifampicin ciprofloxacin, isoniazid and pyrazinamide) (Gao et al., 2003).   1.2.1 Specialized resistance mechanisms Mtb has other specialised resistance machineries beside cell wall barrier that help slow down antibiotic penetration or detoxify once inside the cytoplasmic space as described below.   1.2.2 Modification of drug targets Modification of structural target by Mtb decreases drug binding affinity (Figure 9), thereby conferring resistance. Ribosomal RNA methylation is an example of enzymatic target modification which mediates resistance to cyclic peptides antibiotics  (Hameed et al., 2018). Macrolides and lincosamides reversibly bind to specific site of the ribosomal RNA in the 50S subunit of bacterial ribosomes and inhibit peptidyl-tRNA translocation (Buriánková et al., 2004). Mtb and other mycobacterial species that express erm37 gene are naturally resistance to macrolides and lincosamides antibiotics (Buria et al., 2004). The erm37 gene encodes a ribosomal RNA methyltransferase which modifies Mtb’s 23S ribosomal RNA via methylation (Buria et al., 2004). Expression of erm37 gene in Mtb is induced by macrolides and lincosamides antibiotics exposure (Buriánková et al., 2004). In vitro macrolide binding assay showed lower inhibitory activity of macrolides on protein synthesis due to their reduced affinity to the ribosomes (Buriánková et al., 2004).      20   Figure 9. Mode of action of anti-TB drugs and their mechanism of resistance. An illustration of anti-TB drugs with their mechanisms of action that Mtb counters through different mechanisms. Adapted from (Nasiri et al., 2017).  1.2.3 Chemical modification of drugs  Mtb is known to inactivate antibiotics through a direct chemical modification process called acetylation. Aminoglycosides are broad-spectrum antibiotics and depending on their concentrations, can act either as bactericidal or bacteriostatic. Acetylation is important to Mtb resistance as studies have shown how an enhanced intracellular survival (EIS) protein can acetylates multiple amine groups of aminoglycosides using acetyl coenzyme A as an acetyl donor, thereby inactivating the antibiotics (Houghton et al., 2013). The protein was first discovered as a determinant of mycobacterial survival in host macrophages (Wei et al., 2000) and therefore, shown to protect Mtb against both mycobactericidal mechanism of the host immunity and antibiotics activity. Aminoglycoside 2'-N-acetyltransferase (aac) confers resistance to gentamicin, dibekacin, tobramycin, and netilmicin in M. smegmatis and M. fortuitum (Sanz-García et al., 2019). A homolog of AAC is found in Mtb (Vetting et al., 2003) but its function in aminoglycoside resistance has not been fully demonstrated. However, the intrinsic resistance of Mtb to aminoglycosides has been recently attributed to a different acetyltransferase (Zaunbrecher et al., 2009).    21  1.2.4 Enzymatic degradation of drugs Penicillin-binding proteins (PBPs) are involved in the assembly of the peptidoglycan network of the prokaryotic cell wall. Antibiotics can bind and inhibit these activities thereby disrupting cell wall biosynthesis and leading to cell death. Hydrolysing effect of β-lactam ring by β-lactamases have proven to be an important factor of β-lactam resistance in Mtb (Shur, Maslov and Mikheecheva, 2017). Mycobacterial β-lactamases are generally considered less active than those of other pathogenic bacteria (Jarlier, Gutmann and Nikaido, 1991). However, the slow penetration of β-lactams across the Mtb cell allows this low β-lactamase activity effective enough to protect Mtb from β-lactam action (Brossier et al., 2011). BlaC, a member to the Ambler class-A β-lactamases, is the most important β-lactamase in Mtb and encodes at least three more β-lactamase genes: blaS, Rv0406c and Rv3677c (Nampoothiri et al., 2008). Expression of BlaC in Mtb is induced by β-lactams (Luthra et al., 2018), indicating a specialized system for β-lactam resistance.   1.2.5 Molecular mimicry of drug targets Fluoroquinolones are bactericidal drugs that kill bacteria through inhibition of DNA replication, transcription, and repair. Their interaction with DNA gyrase or topoisomerase in complexes with DNA result in stabilizing DNA breaks while inhibiting resealing of DNA strands. These events eventually result in DNA degradation and cell death (Andriole, 2005). A protein identified in M. smegmatis called (MfpA) was shown to confer low-level resistance to fluoroquinolones. Overexpression of mfpA from a multicopy plasmid in M. smegmatis and M. bovis resulted in an increased resistance to ciprofloxacin and sparfloxacin (Nasiri et al., 2017). The role of MfpA is believed to mimic DNA structure, thus sequestering fluoroquinolones in the cytoplasm blocking antibiotic targeting DNA (Ferber, 2005).  1.2.6 Drug excretion by efflux pumps Efflux pumps are active secretory systems used by most pathogenic bacteria for protection against antibiotics by expelling toxic reagents through the cell envelope. Membrane-spanning proteins are involved in the transport of nutrients, toxins, wastes, or signalling molecules across the cell wall. There are at least 18 transporters that have been characterized to be involved in antibiotic susceptibility in Mtb (Rodrigues et al., 2012). Two of Mtb’s transporters are encoded by the   22  iniBAC and efpA genes which are negatively controlled by the transcription regulator Lsr2. Expression of multiple efflux pumps including Tap was shown to be induced in Mtb residing within granulomas, supporting the contribution of efflux pumps to drug resistance of Mtb in latent TB infection (Adams et al., 2011).  1.2.7 Mechanism of resistance to first-line anti-TB drugs  1.2.7.1 Isoniazid Isoniazid is a prodrug which requires activation by the catalase-peroxidase enzyme encoded by the katG (Rv 1908c) gene (Table 1). The activated form, targets and inhibits mycolic acid synthesis via NADH-dependent enoyl-acyl carrier protein reductase encoded by the inhA (Rv 1484) gene  (da Silva and Palomino, 2011).  Resistance to isoniazid is associated with mutation in katG, causing a reduced activity of catalase-peroxidase. Another mechanism associated with isoniazid resistance involves mutation in inhA gene or within the promoter region of the gene (Cohen, Bishai and Pym, 2014). More than 80% of isoniazid resistant cases identified had mutations in either katG or inhA (Torres et al., 2015). Although katG mutation is the most common mutation, inhA mutation also decreases the affinity of the isoniazid-NAD product (Dookie et al., 2018). inhA mutation is associated with low-level resistance in isoniazid monoresistance isolates and also implicated in cross resistance to a structural analogue, ethionamide (Ramaswamy et al., 2003). Mutation of katG leads to an inefficient isoniazid-NAD product which inhibits the antimicrobial action of isoniazid (Ramaswamy et al., 2003).   1.2.7.2 Pyrazinamide Pyrazinamide is a prodrug which is converted to its active form pyrazinoic acid by the mycobacterial cytoplasmic pyrazinamidase or nicotinamidase enzyme encoded by the pncA gene (Khan et al., 2019). Pyrazinamide enters the bacterial cell by passive diffusion where it is activated. Once inside, it is usually pumped out of the bacterial cell by a weak efflux pump mechanism. To prevent its removal from the bacterial cell, it is protonated in an acidic environment, allowing its reabsorption and causing cellular damage (Khan et al., 2019). The activated form pyrazinoic acid, disrupts the bacterial membrane energetics thereby inhibiting membrane transport. Pyrazinoic acid   23  and its n-propyl ester have also been shown to inhibit fatty acid synthase I in Mtb (Zimhony et al., 2007). Mtb clinical isolates found to be pyrazinamide resistant were mostly associated with pncA mutations (Scorpio et al., 1997). Loss of enzymatic activity to activate pyrazinamide results in resistance whereas overexpression confers increased susceptibility (Boshoff and Mizrahi, 2000). Other mutations such as rpsA (ribosomal protein I) have been reported but its link to pyrazinamide resistance has not been well demonstrated (Palomino and Martin, 2014). Only a small proportion of resistant isolates were shown to have a mutation in a gene other than pncA mutations, suggesting that another mechanism of pyrazinamide resistance exists (Cheng et al., 2000).   1.2.7.3 Rifampicin Rifampicin is one the most effective first-line anti-TB drugs against drug sensitive TB. It is effective against both active and slow-metabolising bacilli, making it a key component during a treatment regimen (Palomino and Martin, 2014). Rifampicin targets and binds to the β subunit of the RNA polymerase (rpoB), inhibiting the transcription of mRNA. Mutations in the gene coding rpoB mediates resistance and accounts for 96% of rifampicin resistance (Caws et al., 2006). Mutations outside the coding region with no alteration in rpoB gene were identified in a small number of rifampicin-resistant isolates, suggesting a different mechanism of resistance may be present (Caws et al., 2006). Rifampicin monoresistance is rare, as majority of rifampicin resistance phenotypes are also resistant to other drugs, notably isoniazid (Caws et al., 2006). Rifampicin’s ability to inhibit Mtb’s transcriptional machinery, results in downstream inhibition of essential protein synthesis, which may explain its cross resistance with other anti-TB agents. This makes rifampicin resistance as a surrogate marker for MDR-phenotype (Caws et al., 2006).     1.2.7.4 Ethambutol  Ethambutol targets actively replicating bacilli and disrupts the biosynthesis of arabinogalactan in the cell wall. The embCAB (Rv3793-5) operon encodes the Mtb arabinosyl enzyme (Hameed et al., 2018). Mutation in embB is the most common resistance mechanism and it predisposes ethambutol-resistant isolates a cross-resistant phenotype to other drugs (Hameed et al., 2018). Allelic exchange experiments done previously, showed that only certain amino acid substitutions confer resistance to ethambutol (Safi et al., 2008). Studies have shown that simultaneous mutations   24  in the decaprenylphosphoryl-β-D-arabinose biosynthesis and utilisation pathway genes (Rv3806c and Rv3792) together with embB and embC result in variation of minimum inhibitory concentration (MIC) range for ethambutol (Safi et al., 2008). About 30% of the ethambutol resistant-isolates were shown to lack mutation in embB gene, suggesting that a different mechanism of resistance may be present (Palomino and Martin, 2014). Mutations involving embB and ubiA together causes a high-level of ethambutol resistance (Xu et al., 2015). This is because decaprenyl-phosphate 5-phosphoribosyltransferase synthase is an enzyme encoded by ubiA gene and is involved in cell wall synthesis (Safi et al., 2008).  1.2.8 Mechanisms of resistance to second-line anti-TB drugs  The second-line injectable drugs currently used for the treatment of drug-resistant TB (DR-TB) include the aminoglycosides, kanamycin and amikacin and the cyclic polypeptide capreomycin. Despite the three drugs belonging to different classes of antibiotics, they all have a similar target, binding the 16S rRNA and modify its structure, resulting in protein synthesis inhibition.   (Palomino and Martin, 2014). Resistance to these agents are associated with a mutation in the rrs gene with additional mutations of the tlyA gene being associated to capreomycin resistance (Alangaden et al., 1998). Although rrs mutations are the most common cause of resistance, there is evidence of cross-resistance with these drugs (Georghiou et al., 2012).  Fluoroquinolones are second-line drugs used for DR-TB treatment. There are new generation of fluoroquinolones called moxifloxacin and gatifloxacin that are considered for use in DR-TB treatment (Dheda and Migliori, 2012). These antibiotics act on gyrase enzyme encoded by gyrA (Rv0006) and gyrB (Rv0005) genes thereby inhibiting transcription during cell replication. Mutation in gyrB is rare and most fluoroquinolones-resistant Mtb identified had mutations associated with gyrA gene (Takiff et al., 1994). Export of fluoroquinolones via efflux pumps is another mechanism that mediates fluoroquinolone resistance. Ethionamide is a prodrug that has a structural analogue of isoniazid. It is activated by a mono-oxygenase enzyme encoded by the ethA (Rv3854c) gene. The activated form inhibits mycolic acid synthesis by targeting the enoyl-acyl carrier protein reductase (Carette et al., 2012). Regulatory control of ethA gene is mediated by the transcriptional repressor, ethR (Rv3855) (Carette et al., 2012). Resistance to ethionamide is associated with mutations in ethA, ethR and inhA genes. Cross-  25  resistance between ethionamide and isoniazid is mediated by inhA gene mutation (Brossier et al., 2011).  1.2.9 Mtb nutritional homeostasis  Different arrays of organic substrates such as carbohydrates, lipids, amino acids and simple organic acid are available for Mtb to fuel its central carbon metabolism (CCM). The goal of CCM is to transform organic substrates to carbon through glycolysis, gluconeogenesis, pentose phosphate shunt and tricarboxylic acid (TCA) cycle (Figure 10) to meet the stoichiometric requirements needed for Mtb replication. Mtb, while in persistence state, has a reduced metabolism, low levels of ATP, and little or no replication. Fatty acids derived from host vacuolar membrane components serve as source of carbon for Mtb during infection.  However, during chronic infection, metabolism of Mtb depends on β-oxidation of host-derived lipids to fuel the CCM (Figure 10) (Gengenbacher and Kaufmann, 2012).   Transcriptional induction of β-oxidation genes (Schnappinger et al., 2003) by Mtb recovered from human lesions are indications that fatty acid catabolism occurs during infection (Gomez and McKinney, 2004). Isocitrate lyase (ICL1) whose bifunctional effects extends to the glyoxylate shunt, catalyses methyl citrate cycle (MCC) reaction, converting propionyl-CoA into succinate that feeds into the TCA cycle (Figure 10) (Gengenbacher and Kaufmann, 2012).  Most bacteria faced with multiple carbon substrates, will utilise each sequentially in the order in which they support fastest growth. This phenomenon is often characterised by a lag phase growth indicating depletion of preferred substrate as they adapt to next substrate which supports fastest growth (Jõers and Tenson, 2016). This mechanism of diauxic growth is mediated by carbon catabolite repression (CCR), a regulatory trait believed to provide a competitive advantage for a given bacterial species over others residing within a polymicrobial niche (De Carvalho et al., 2010). However, Mtb lacks CCR and therefore, catabolises multiple carbon and energy sources without a diauxic growth phase (De Carvalho et al., 2010).      26      Figure 10: Mtb metabolic pathways during infection. Mtb rely on lipids in-vivo and degrades fatty acids by β-oxidation resulting in acetyl-CoA and propionyl-CoA production. Mtb metabolise C2 units via tricarboxylic acid (TCA) whilst prevent accumulation of toxic propionyl-CoA by two metabolic routes namely: methylcitrate cycle and methylmalonyl pathway. Products of both pathways can enter the TCA either directly through succinate or after conversion to succinyl-CoA. Methylmalonyl-CoA is also a building block of methyl-branched-lipids. Isocitrate lyase is important in the methylcitrate cycle and glyoxylate shunt. Intermediate glyoxylate can be used to produce pyruvate via malate, from which glycolytic substrates can be replenished by gluconeogenesis. These pathways are relevant during Mtb dormancy. Adapted from (Gengenbacher and Kaufmann, 2012).  Mtb’s ability to co-catabolise multiple substrates simultaneously is mainly due to its compartmentalised pathways (De Carvalho et al., 2010). This allows carbon flux through glycolysis and gluconeogenesis simultaneously (De Carvalho et al., 2010). Carbon substrates have been shown to be catabolised into intermediates of the metabolic pathway closest to their point of entry (De Carvalho et al., 2010).  However, hypoxic environment induces a redirection of carbon flux away from the central metabolic pathway into the storage of triacylglycerol (TAG) (Figure 10) (Baek, Li and Sassetti, 2011). Inside hypoxic and lipid-rich macrophages, Mtb derives fatty acids from host TAG and   27  incorporates them into its own TAG supply. Mtb uses LprG, a TAG transporter to regulate intracellular TAG levels (Martinot et al., 2016). Accumulation of intracellular TAG is shown to have strong correlation with a reduced growth rate and tolerance to antibiotics in vitro and in murine infection (Deb et al., 2011).   1.3 Growth substrates utilised by intracellular Mtb  Inside lipid-loaded macrophages, Mtb acquires fatty acids from hydrolysed cellular lipid bodies that form lipid inclusions and act as internal carbon storage (Daniel et al., 2011). Mtb is shown to express a number of lipases and phospholipases capable of catalysing the release of fatty acids from host lipids (Barisch and Soldati, 2017).  Other sources of energy acquired and metabolised by Mtb apart from fatty acids include, cholesterol (Pandey and Sassetti, 2008), carbohydrate (Marrero et al., 2013) and amino acids (Gouzy et al., 2014). Studies have shown  how mutants that lack genes involved in gluconeogenesis (Marrero et al., 2010), cholesterol utilisation (Chang et al., 2007) or MCC failed to establish infection in macrophages.  1.3.1 Cholesterol Cholesterol is a major structural component of cell membranes that helps in maintaining proper membrane permeability and fluidity. It serves as an important anabolic precursor for the biosynthesis of bile acid, vitamin D and steroid hormones (Lamb et al., 1998). Sterol biosynthesis in bacteria is still controversial although equivalent enzymes are found in the proteobacterium, and in the planctomycete (Lamb et al., 2007). In contrast, Mtb lacks enzymes that are essential for sterol biosynthesis. However, Mtb genome encodes a gene called CYP51B1, a cytochrome P450 enzyme that catalyzes the 14α-demethylation of lanosterol to give the 8,14-diene, a key step in cholesterol biosynthesis (Bellamine et al., 2002). Although CYP51B1 is conserved across actinomycetes, its role in Mtb and other bacteria is till unclear (Hargrove et al., 2011).  Uptake and degradation of host cholesterol as growth substrate is thought to play role in development of chronic Mtb infection (Pandey and Sassetti, 2008). Cholesterol-rich diet is shown to significantly enhance bacterial burden in lungs and impairs immunity to Mtb (Martens et al., 2008). An ABC-like transporter system, mce4 is involved in cholesterol import into Mtb (Mohn et   28  al., 2008).  Mutants defective in either uptake or degradation of cholesterol as the primary source showed growth defect in vivo (Griffin et al., 2012).  Oxygen-utilising enzyme, HsaC, is shown to be essential for Mtb growth on cholesterol as well as involved in cholesterol degradation (Van der Geize et al., 2007). Inhibition of HsaC can lead to toxic metabolites including ROS thus, making it a potential therapeutic target for TB treatment. Mutants lacking HsaC gene had impaired survival in immunocompromised SCID mice and guinea pigs (Yam et al., 2009).  Cholesterol-rich microdomains are shown to facilitate Mtb phagocytosis (Muñoz, Rivas-Santiago and Enciso, 2009). Phagocytosed Mtb have been shown to use cholesterol to block host protein, coronin1 (TACO) found on phagosome to inhibit phagosome-lysosome fusion (Nguyen and Pieters, 2005).  Metabolites such as acetyl-CoA and propionyl-CoA produced from cholesterol catabolism contribute to Mtb’s drastic metabolic rearrangement making it vulnerable to chemical interventions (Griffin et al., 2012). Some Mtb’s genes were found to be significantly up-regulated or down-regulated when exposed to inhibitors in media containing cholesterol (VanderVen et al., 2015). About 96 genes have been shown to be important for Mtb growth on cholesterol (Griffin et al., 2012) At least 52 cholesterol-regulated genes have been identified and are found within the 83-gene region known as “Cho-region” of the Mtb genome (Nesbitt et al., 2010). These genes encode primarily homologs of β-oxidation and biphenyl degradation genes from other organisms (Rhodococcus sp. RHA1). Transcription of the Cho-region is controlled by at least two regulators, KstR1 and KstR2 (Kendall et al., 2007). Three selected structurally-diverse inhibitors shown to be active against intracellular Mtb and in cholesterol media were able to induce a common set of 49 genes including those associated with putative efflux pump system (Rv1216 – 19c and Rv0677 -78c) (Balganesh et al., 2010). The common transcriptional profile shared by these inhibitors is their perturbation in cholesterol utilisation (VanderVen et al., 2015). Cholesterol is often required by some conditionally-active compounds to inhibit Mtb growth in liquid culture without targeting its utilisation directly (VanderVen et al., 2015). The down-regulation of the MCC genes involved in propionyl CoA assimilation and genes within KstR1 and KstR2 regulon suggests that cholesterol utilisation was blocked in the presence of these inhibitors (VanderVen et al., 2015).   29  Cholesterol, not only plays a role on Mtb physiology as a mere carbon substrate but also believed to influence carbon flux through central metabolic and biosynthetic pathways (VanderVen et al., 2015). Alteration of carbon flux due to cholesterol, contributes to the structure and abundance of bacterial components that are important for Mtb virulence (Griffin et al., 2012). Integration of cholesterol side chain into pthiocerol-dimycocerosate (PDIM) suggests that the flux of carbon into these lipids is increased when Mtb uses cholesterol (Pandey and Sassetti, 2008). PDIMs are virulence-associated linked lipids that are known to utilise the 3-carbon intermediates such as propionyl-CoA produced from cholesterol degradation.   1.3.2 Acetate Metabolic analysis of the granuloma of guinea pigs has revealed that organic acids such as acetate are present during Mtb infection (Rücker et al., 2015). Although metabolites can be derived from both host and pathogen, acetate formation by Mtb is indicated by the presence of two genes that are homologous to phosphotransacetylase (Pta) and acetate kinase (AckA) in Escherichia coli (E. coli) (Cole et al., 1998). Acetate in E. coli is formed in anaerobic conditions when TCA cycle activity is downregulated and carbon flow is directed through fermentative pathways (Gosset et al., 2012). In contrast, Mtb is a non-fermentative pathogen, which generates acetate through a process called Crabtree. This is a process where excess glycolytic substrates inhibit respiration and TCA cycle activity under aerobic conditions during Mtb growth (Cumming and Steyn, 2015). Therefore, acetate might be produced as a by-product of excessive carbon substrates when flow in the TCA cycle is limited (Thomas et al., 2014). Accumulation of acetate and its subsequent diffusion across the membrane can lead to toxic acidification of the cytoplasm (Thomas et al., 2014). However, Mtb’s ability to reuptake and incorporate excess acetate in the CCM removes its toxicity and provides additional substrate needed for growth.    1.3.3 Glucose Mtb has carbohydrate transporters and enzymes required for glucose metabolism. Whether it utilises the sugar metabolism pathways to support its in vivo growth is not clear (Titgemeyer et al., 2007). What is known is, access to high glucose concentrations by Mtb during infection of macrophages and murine is restricted as expression of sugar catabolism genes are not induced   30  (Timm et al., 2003). However, sugar transport is important in Mtb pathogenesis as mutant with transposon in carbohydrate transport systems were attenuated in murine spleen (Sassetti and Rubin, 2003). Carbohydrate transporter, LpqY-sugA-sugB-sugC, which is specific for the uptake of disaccharide trehalose in murine lungs and spleen is indispensable for Mtb’s normal growth (Kalscheuer et al., 2010). Although trehalose is absent in mammals, it can be release by Mtb from the trehalose-containing cell wall glycolipids and recycled (Kalscheuer et al., 2010). The intracellular fate of recycled trehalose remains to be identified; hence, it is unclear which other sugars are metabolised by Mtb to maintain fitness during infection.    1.4 Anti-mycobacterial compounds activities in Mtb growth environment  Distinct microenvironments present and impose distinct physiological states on the bacterium (Lenaerts and Iii, 2015). This observation is true with phenotypic heterogeneity among bacteria recovered from human sputum, which consist of cell-associated and free bacteria with different antibiotic susceptibility profile (Andries, Gevers and Lounis, 2010). Chemical reactivity of certain enzymes may, in part, contribute to the bactericidal phenotype (Puckett et al., 2017). ICL1 in Mtb is shown to be dispensable for standard in vitro growth but required for survival and maintenance of infection in murine (Ehrt, Schnappinger and Rhee, 2018). Standard culture media developed many years ago for bacilli propagation, were mostly used to identify candidate compounds (Pethe, Patricia C Sequeira, et al., 2010). These media were never optimized for drug screening nor to replicate Mtb’s intracellular lifestyle (Pethe, Patricia C Sequeira, et al., 2010).  These discrepancies noted between in vitro and in vivo antibacterial efficacy were oversighted during earlier screening projects. Studies by Sorrentino et al., 2016 have shown how anti-mycobacterial compounds’ activities can vary from one Mtb growth environment to another (Figure 11). Some compounds were shown to be more active in extracellular growth conditions than in vitro and vice versa (Figure 11). These differences in compounds’ activities were determined by MIC90 value ratios generated from the two growth environments (Figure 11).     31    Figure 11: Comparison of compound potency in broth (extracellular MIC90) with activity in macrophages (intracellular MIC90). The extracellular/intracellular MIC90 ratios of all compounds were plotted. Compounds with ratios above 1 are those that are more active intracellularly than extracellularly. Those with ratios less than 1 have higher extracellular activity. Adapted from (Sorrentino et al., 2016).    Other similar studies also identified compounds that had excellent in vitro activity with desirable pharmacological properties but inactive in a TB murine infection (Pethe, Patricia C. Sequeira, et al., 2010). Pyrimidine-imidazoles (PIs) are examples of inhibitors whose activities were determined in vitro. These inhibitors showed discrepancies in Mtb inhibition between glycerol-containing broth and in vivo results (Pethe, Patricia C. Sequeira, et al., 2010). These compounds were shown to be inactive against Mtb in the absence of glycerol in broth or a functional glycerol dissimilation pathway (Pethe, Patricia C. Sequeira, et al., 2010). The carbon and energy metabolism encountered by intracellular Mtb seems to depend on the β-oxidation of fatty acids (Schnappinger et al., 2003). Composition of culture media, particularly growth substrate is among factors that may influence anti-TB drug activity (Table 2). These differences in compounds’ activity in vitro particularly between (rich-media) highlighted in blue and carbon restricted-media highlighted in green have been shown (Table 2). However, some compounds showed similar MIC values between carbon restricted-media highlighted green and intracellular MIC90 values (Table 2). An in vitro MIC value ≤ 4-fold less or more than intracellular MIC90 value was classified as similar.    32  Table 2: Intracellular MIC values compared to in vitro MIC values from different growth substrate media.  Compound number (Yossi lab ID) Intracellular MIC value (µM) GSK MIC90 in vitro MIC value (µM)    ADC Cholesterol Glucose Acetate GSK1615454A (3) 2 13.03 1.36 N/A 1.35 GSK752705A (5) 0.23 15.14 1.17 3.35 0.6 GW810648X (6) 2.16 12.3 3.55 N/A 3.2 GSK1606781A (21) 2.51 24.83 0.85 0.68 0.65 GSK2426591A (24) 2.51 N/A 1.05 2.69 0.81 GSK1376412A (1) 0.32 2.95 0.97 1.5 2.75 GSK503472A (35) 2 N/A 2.04 3.8 1.8 GSK1074912A (31) 1.58 12.45 5.01 3.09 4.42 GSK1744926A (33) 1 8.04 2.37 2.6 10.12 GSK784739A (36) 2 35.89 1.97 1.66 1.64  Data adapted from Av-Gay lab.  1.5 Animal and cellular infection models  Unfortunately, human observational studies that described TB disease in detail could not probe bacterial physiology at a resolution sufficient enough to inform drug development. For this reason, different experimental approaches including animal models have been extensively studied to mimic natural environment encountered by Mtb during infection.  Different animal models including frog, zebrafish, mouse, guinea pig, rabbit, and nonhuman primate have been investigated as TB models (Berg and Ramakrishnan, 2012). Murine remains the commonly used model mainly due to its low cost and genetic tractability. Although most of TB hallmarks are replicated in murine model,  TB lesions in standard laboratory strain (C57BL/6) do not develop central necrosis (Apt and Kramnik, 2010). TB lesions are not only an indication of histopathology of an organ but also reflects the degree of bacterial colonization as well as adaptive immune response level. Heterogeneity of the TB lesion in animal model is similar to the case in humans (Kramnik and Beamer, 2016).  In mammals, circulating monocytes have the ability to differentiate into tissue macrophages that are ready for phagocytosis. THP-1 cells are immortalised monocyte-like cell lines, derived from peripheral blood of a childhood case of acute monocytic leukemia (Tsuchiya et al., 2007). These   33  cell lines used as ex vivo models are meant to reflect the natural environment encountered by Mtb during infection. THP-1 cells are simple models that are amendable to suit high throughput, genetic screening methodologies and metabolic flux estimation. The use of THP-1 cells as ex vivo model for primary human monocytes demonstrates the concept of translational research. Unfortunately, some of the limitations of these immortalised cells as a proxy for primary cells include their lower response to lipopolysaccharides (LPS) due to low expression of CD14 compared to monocytes (Bosshart and Heinzelmann, 2016). Therefore, a suitable model that reflects Mtb’s intracellular environment will enable screening for compound activities and resistant mutants.   1.6 Rationale 1: Artificial growth conditions used for screening anti-mycobacterial compounds are unlikely to reflect those encountered by Mtb during infection (Table 2).  Intracellular MIC90 values of anti-mycobacterial compounds have been shown to be different from MIC90 values determined in vitro in rich-media (Figure 11 and Table 2) (Sorrentino et al., 2016). The effect of compounds against replicating bacteria in standard broth media compared to infected alveolar macrophages (Marrero et al., 2010) could be due to major difference in carbon metabolism. We hypothesised that in vitro carbon restriction or changes can mimic Mtb intracellular growth conditions hence influencing anti-mycobacterial compound activity.  1.6.1 Aim 1: To highlight the significance of carbon restriction in Mtb metabolic adaption in vitro, we aimed to use carbon restriction to mimic intracellular environment of Mtb and determined in vitro activities of hit compounds against Mtb in defined single or double growth substrates media compared to ADC as in vitro rich-media.  1.7 Rationale 2: Alterations in the availability and metabolism of nutrients during infection can have significant effects on Mtb physiology (Olive and Sassetti, 2016). Mtb growth in artificial conditions that incorporate growth-limiting stressors similar to natural environment is likely to result in dormancy and antibiotic tolerance phenotype (Ehrt, Schnappinger and Rhee, 2018). We hypothesised that intracellular growth and resistance profile of Mtb can be mimicked on defined growth media and use for isolation of mutants. Chemical screening for resistant Mtb in the context of its natural environment can predict intracellular drug targets that would otherwise be missed in rich-media alone. However, isolating resistant Mtb mutants inside macrophages can be challenging as this requires lysing that may result in small or no resistant mutant recovery.   34  1.7.1 Aim 2: To select for Mtb isolates with spontaneous mutations conferring resistance to novel hit compounds active against intracellular Mtb, we aimed to use “chemical genetics” approach to screen for resistant mutants in defined growth substrates media and identify potential intracellular drug targets of Mtb against hit compounds.  Chapter 2: Materials and methods 2.1 Growth and maintenance of Mtb strain Parental H37Rv strain was used to screen for resistant mutants and determined anti-mycobacterial compounds activities. H37Rv strain was cultured in 7H9 Middlebrook (Becton Dickinson, USA) broth supplemented with 10% v/v OADC (oleic acid, albumin, dextrose and catalase) plus Tween80 (0.05% v/v) and incubated at 37oC, 5% CO2. Standing cultures were allowed to grow until mid-logarithmic phase. The growth phase was determined measuring the optical density at a wavelength of 600 nm (OD600) using a spectrophotometer. Culture suspensions were centrifuged at 4000 rpm for 10 mins and diluted in 7H9 media according to the appropriate OD600 required with appropriate supplements for each assay.   2.2 In-vitro MIC90 determination of anti-mycobacterial compounds  To assess Mtb viability in the presence of anti-mycobacterial compounds, resazurin assay was used as described by (Yajko et al., 1995). Resazurin is a redox indicator dye (blue) that is internalised and irreversibly reduced (oxygen consumption by Mtb through metabolism) to highly fluorescent pink compound (resorufin) and freely released from metabolically active cells (John et al., 2000). There is direct correlation between the resorufin and metabolically active cells in the media (Uzarski et al., 2017). The colorimetric change from blue (oxidised form) to pink (reduced form) can be determined visually or measured using spectrophotometer. Despite the correlation between cell viability and fluorescent intensity, resorufin dye is unstable and therefore may not require extended incubation period. To avoid lengthy incubation periods, lesser OD600 (0.01) of Mtb is required. Lesser concentration of resazurin is also required as high concentrations can be toxic to the cells.    35  H37Rv strain was cultured in 7H9 OADC (10% v/v) plus Tween80 (0.05% v/v) to mid-log phase. Culture suspension was centrifuged at 4000 rpm for 10 mins (washing step repeated x 3 to completely remove both OADC and Tween-80) and finally diluted to an OD of 0.02 in 7H9 media.   Diluted culture cells were seeded 100 µl/well into 96-well plate containing equivalent volume of 7H9 growth media (final OD600 0.01) supplemented with appropriate carbon source as growth substrate plus tyloxapol (0.05% v/v). 7H9 media supplemented with one of the following substrates; acetate 33 mM, glucose 11 mM and cholesterol 0.1 mM at a time was referred to as single growth substrate media (Table 3). Double growth substrates media were prepared at half the concentration of a single growth substrate alone mixed together in 7H9 to give an equivalent concentration of the two units (Table 3).  Table 3: Composition of 7H9 media supplemented with single vs double growth substrate  7H9 media Standard media Single growth substrate (conc.) Double growth substrate (conc.) OADC (10% v/v) Acetate (33 mM) Acetate (16.5 mM) + Glucose (5.5 mM) ADC (10% v/v) Glucose (11 mM) Acetate (16.5 mM) + Cholesterol (0.05 mM)  Cholesterol (0.1 mM) Cholesterol (0.05 mM) + Glucose (5.5 mM)  Glycerol (22 mM) Glycerol (11 mM) + Acetate (16.5 mM)  Propionate (0.1 mM) Glycerol (11 mM) + Cholesterol (0.05 mM) Conc.: Concentration Anti-mycobacterial compounds were serially diluted in DMSO (12.5 to 0.05 µM). DMSO (˂ 1%) and rifampicin (20 µM) were included as negative and positive controls respectively. Plates were incubated at 37oC, 5% CO2 for 5 days in humidified incubator. Resazurin reagent solution, 30 µl/well (100 µg/ml) was added and plate re-incubated for 72 hrs. After 72 hrs incubation, Mtb viability was determined through colorimetric readout. Live Mtb was indicated by colour conversion from blue to pink whereas blue indicates no growth over time. The lowest compound concentration, where a well remained blue was considered as the MIC90 value. Assays were performed in duplicate and MIC values between plates were compared for reproducibility. Where there were differences in MIC values between plates, an average value was used.      36  2.3 Mtb growth rate in liquid media supplemented with defined growth substrates but no compounds Mtb at OD600 0.01was grown in multiple standing tubes containing 20 ml 7H9 liquid media each. Each Falcon tube (50ml) with 20 ml liquid culture was supplemented with either single or double growth substrate (Table 3) and tyloxapol (0.05% v/v) but no anti-mycobacterial compound. Liquid culture (standard media, Table 3) with either ADC or OADC (10% v/v) plus tyloxapol (0.05% v/v) was included as rich-media. 7H9 media with no growth substrates supplement was included as a negative control. Concentrations of single or double growth substrates in liquid cultures were maintained the same for all assays. Mtb growth in each media was measured by OD600 reading taken at every 3 – 4 days interval for 47 days.    2.4 Screening for resistant mutants against hit compounds  H37Rv strain was grown until mid-log phase in 7H9 liquid culture supplemented with OADC (10% v/v) plus Tween-80 (0.05% v/v). Culture suspension was centrifuged at 4000 rpm for 10 mins and the pellet was resuspended in 7H9 plus tyloxapol (0.05% v/v). To completely remove OADC and Tween-80 from the culture media, washing step was repeated x 3. Pellet was finally resuspended in 7H9 plus tyloxapol (0.05% v/v) and diluted to OD600 0.33, representing approximately 1 x 108 bacterial cells.  Bacterial cells at OD600 0.33 were plated on 7H10 solid agar supplemented with tyloxapol (0.05% v/v), glycerol (22 mM) and a single carbon substrate (Table 3) per plate. Each plate was supplemented with appropriate concentration of compounds. Plates were incubated at 37oC incubator containing 5% CO2 for at least 4 weeks. Grown colonies were picked and reconfirmed by re-plating on solid and liquid media with lethal dose of the compound.     2.5 Extraction of genomic DNA from resistant mutants Isolated resistant mutants were cultured in 7H9 OADC (10% v/v) plus Tween-80 (0.05% v/v) and allowed to grow for at least 2 weeks to yield enough viable bacterial cells. One day prior to DNA extraction, glycine (1% w/v) was added to the culture and incubated at 37oC overnight. A 10 ml volume of the bacterial cell culture was centrifuged and pellet resuspended in 1ml GTE solution (25mM glucose, 10mM Tris-HCl and 10mM EDTA). The bacterial suspension was transferred in 2 ml microcentrifuge tube and centrifuged. The pellet was resuspended in 450 µl GTE solution   37  and lysosome solution (100 µg/ml) was added and incubated at 37oC overnight to degrade cell wall components. After an overnight incubation, sodium dodecyl sulfate (SDS) (2% w/v) and proteinase K (100 µg/ml) were added, the re-suspension was mixed gently and incubated further at 55oC for 20 to 40 mins. Subsequently, sodium chloride (NaCl) (5 M) and preheated cetyltrimethylammonium bromide (CTAB) (1.6% v/v) were added and incubated at 65oC for 10 mins. An equal volume of chloroform-isoamyl alcohol (24:1) was further added and the resulting resuspension was centrifuged for 5 mins. The aqueous phase (approx. 900 µl) was transferred to a new microcentrifuge tube and an equal volume of chloroform-isoamyl alcohol (24:1) was added again and centrifuged for 5 mins. The aqueous phase (approx. 800 µl) was further transferred into a fresh microcentrifuge tube. Isopropanol (70% of the volume) was added to the aqueous phase, then incubated for 5 mins at room temperature and subsequently centrifuged for 10 mins. The supernatant was discarded and 1ml of 70% ethanol was added and incubated at room temperature (RT) for 5 mins before another centrifugation. Precipitated DNA pellets were air-dried and resuspended in 50 µl of Tris-EDTA (pH 8.0) (10mM Tris-HCl and 10mM EDTA) buffer and stored at 4oC overnight to dissolve.   2.6 Whole Genome Sequencing of resistant mutants DNA libraries were sent to British Columbia Centre for Disease Control Public Health Laboratory (BCCDC PHL) and at Genome Québec Innovation Centre in McGill University. Libraries were normalised, pooled and sequenced using the Illumina MiSeq platform with 250 bp paired-end (PE) reads (MiSeq reagent kit v2). PE DNA libraries were constructed with a Nextera XT DNA kit (illumina, San Diego, USA). Tagmented DNA was amplified using index primers and purified with AMpure XP beads to remove small library fragments.   2.7 Bioinformatic analysis Fastqc was used to assess to the quality of the reads. (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were quality trimmed by Trim Galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Trimmed sequence reads were also aligned to the reference genome sequence of H37Rv (NC_000962.3)   38  using Burrows Wheeler Aligner (BWA-mem), and single nucleotide variants (SNVs) and indels were called using Genome Analysis Tool Kit (GATK v.3). The SNVs generated using GATK were filtered using variant call format tools (vcftools) to ensure high confidence. The parameters for filtering were (i) minimum read depth of 10; (ii) maximum base quality of 30 for every nucleotide in the sample; (iii) minimum mapping quality of 20. SnpEff was used to annotate and to output the SNVs changes in mutants according to the reference genome and General Feature Format (GFF) files of Mtb H37Rv in NCBI. Unique variants in mutants were identified by examining the discordant SNVs between wild type and mutants differed from the H37Rv reference in NCBI.  2.8 Intracellular MIC determination of hit compounds against resistant mutants  2.8.1 Differentiation of THP-1 cells THP-1 cells were maintained in complete media (RPMI 1640 media supplemented with 2% L-glutamine (Invitrogen), 5% fetal bovine serum (FBS), 1% penicillin-streptomycin and 5% Non-essential amino acid (NEA) and kept at 37oC, 5% CO2. Cells required (1 x 105 cells/well) for the assay were determined and differentiated using phorbol-12-myristate-13-acetate (PMA) (Sigma) (40 ng/ml) in incomplete media (RPMI with all supplements present in complete media except penicillin-streptomycin). Cells were seeded (100 µl/well) in a 96-flat bottom well plate and incubated at 37oC, 5% CO2 incubator overnight. Undifferentiated and unattached cells were removed by washing with warm RPMI media prior to infection.   2.8.2 Intracellular Mtb infection of THP-1 cells  Cultured Mtb cells were washed x 3 with 7H9 plus Tween-80 (0.05% v/v) and resuspended with the same media. Bacterial cells density was calculated using the conversion factor of 0.33 OD600 to represent 1 x 108 cells/ml. For efficient phagocytosis of Mtb by macrophages, Mtb cells required for the assay were calculated, 5 x 105 cells/well (96-well plate) in incomplete media with 10% v/v of normal human serum (Gemini Bio-Products, USA) and incubated at 37oC for 30 mins for opsonisation.  After incubation period, differentiated THP-1 cells were mixed with opsonised-Mtb cells in incomplete media at a multiplicity of infection (MOI) of 5 (MTB:THP-1) and 100 µl/well was   39  dispensed. Plates were incubated at 37oC in a humidified incubator containing 5% CO2 for 3 hrs before adding compounds and finally incubated for 72 hrs in the same condition.   2.8.3 Lysis of THP-1 cells  Seventy-two hours of post incubation, plates with infected THP-1 cells were washed once with 100 µl/well of warm RPMI. Infected THP-1 cells were lysed with TritonX-100 (0.2% v/v) at 60 µl/well and incubated at RT for 5 mins. Lysed cells were centrifuged at 4000 rpm for 5 mins and pellet resuspended with 200 µl/well of 7H9 liquid media. Cell lysates (10 µl) from each well were spotted in corresponding well of another 96-well plate containing 7H10 plus OADC (10% v/v) plus Tween-80 (0.05%). Agar plates were incubated at 37oC in a non-humidified incubator containing 5% CO2 for at least 1-2 weeks.     2.8.4 THP-1 cytotoxicity: using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide (MTT) assay THP-1 cells in incomplete media were differentiated in a 96-well plate using PMA (40 ng/ml). Cell density was maintained at 1 x 105/well and incubated at 37oC incubator containing 5% CO2 overnight. Following overnight incubation, cells were washed once with warm RPMI to remove any undifferentiated and unattached cells. Compounds diluted in incomplete media were added to the plate and incubated at 37oC incubator containing 5% CO2 for 72 hrs. MTT solution was prepared (0.5g of MTT in 100 ml of phosphate buffered saline) and 25 µl/well added 2.5 hrs before the end of 72 hrs incubation. After the 72 hrs incubation, 100 µl/well of MTT extraction buffer (50% N, N-dimethyl formamide (DMF), 20% SDS, 2.5% acetic acid, and 2.5% 1 M HCl acid) was added and incubated overnight at 37oC incubator containing 5% CO2. Cell viability was determined by the colorimetric conversion of MTT (yellow) to purple-colored formazan and absorbance read at 570 nm.          40  Chapter 3: Results 3.1 Growth substrate-dependent difference in Mtb growth curves.  To determine the effect of different growth substrates on Mtb’s growth rate, Mtb was grown in 7H9 media supplemented with different carbon substrates and without anti-mycobacterial compound added. Mtb growth rate relative to its cell density was monitored over time by recording OD600 at 2-3 days intervals (Table 4 and 5, Figure 12). Growth rate on equimolar mixture of growth substrates or on a single constituent of each growth substrate alone in media was determined (Table 4 and 5, Figure 12, B-F). Mtb growth rate in liquid media with no growth substrates mixtures or liquid media with propionate was included as negative controls (Figure 12, A). Mtb growth rate in OADC or ADC as rich-media was included as positive controls.  As seen in Table 4 and Figure 12, Mtb grown in OADC or ADC as rich-media had maximal growth at 1.0 OD600, with doubling time of 2.0 and 1.6 days respectively (Table 4). Media containing glucose as the primary growth substrate had the highest maximum growth (0.45 OD600) when compared to rest of the media containing single growth substrate (Table 4 and Figure 12 D). Media containing glycerol or acetate alone had the least growth (0.01 and 0.07 OD600) with a doubling time of 3.2 and 3.8 days respectively. Interestingly, when double growth substrate was used, maximum Mtb growth (0.99 OD600) occurred in media containing glycerol plus cholesterol, similar to rich-media but with a longer doubling time (3.7 days) (Table 5). On the other hand, adding cholesterol or acetate to rich-media, decreased maximal growth from 1.0 to 0.8 OD600 with a shorter doubling time of 1.5 and 1.8 days respectively compared to rich-media alone (Table 5 and Figure 12F). Media containing acetate plus glucose or glycerol as double growth substrates reached maximum growth at 0.47 and 0.58 OD600 with a doubling time of 4.1 and 1.3 days respectively (Table 5 and Figure 12 B and C).  As outlined above (Table 4, Figure 12, B), media with acetate as the primary growth substrate barely supported Mtb growth. Supplementing cholesterol to acetate as double growth substrates media increased Mtb’s maximum growth to about 8-fold (0.53 OD600) compared to media with acetate alone (Table 5 and Figure 12 B). Glycerol as the primary growth substrate did not support Mtb growth (Figure 12, C, Table 4), however, supplementing glycerol with either acetate or cholesterol in culture media increased Mtb’s maximum growth to about 50 and 100-fold   41  respectively (Table 5 and Figure 12 C) compared to media with glycerol alone (Table 4 and Figure 12 C). Mtb growth rate in glucose plus cholesterol-containing media had a lesser growth rate compared to media supplemented with glucose alone or glucose plus acetate whose doubling time were 2.7 and 4.1 days respectively (Table 4 and 5, Figure 12, D). Similar effect was true for the number cells that reached maximum growth (Table 4 and 5). Media with cholesterol as the primary growth substrate could not support Mtb growth compared to cholesterol plus glycerol as double growth substrates (Table 4 and 5, Figure 12, E). Mixing ADC and cholesterol in the media enhanced Mtb growth rate with a doubling time of 1.5 days at a maximum growth of 0.8 OD600 more than cholesterol alone (0.27 OD600) (Table 4). However, cholesterol plus glycerol had a doubling time of 3.7 days with a maximum growth of 0.99 OD600 more than other cholesterol-containing media (Table 4 and 5, Figure 12, E and F).                 42  Table 4: Growth characterisation of single growth substrate media Single Growth Substrate Effective C concentrationa (mM)  Maximum growth (OD600)  Doubling time (day) (SEM±) OADC (rich-media) 2183.0 1.00 2.0 ± 0.1        ADC (rich-media) 2183.0 1.00 1.6 ± 0.01              Cholesterol (0.1 mM) 2.7 0.27 2.6 ± 0.01            Acetate (33 mM) 66.0 0.07 0              Glycerol (22 mM) 66.0 0.01 0               Glucose (11 mM) 66.0 0.45 2.7± 0.01                   Propionate (0.1 mM) 0.3 0.18 3.7± 0.01            7H9 Nilb 0.19 3.4± 0.1                  aNumber of C atoms of growth substrate × concentration of growth substrate  bNo carbon Table 5: Growth characterisation of double growth substrate media Double Growth Substrate Effective C concentrationa (mM) Maximum growth (OD600)  Doubling time (day) (SEM±) ADC + Acetate (16.5 mM) 1124.5 0.80 1.8± 0.002        ADC + Cholesterol (0.05 mM) 1092.85 0.80 1.5± 0.01       Glucose (5.5 mM) + Acetate (16.5 mM) 66.0 0.47 4.1± 0.02 Glucose (5.5 mM) + Cholesterol (0.05 mM) 33.2 0.35 2.2± 0.03       Cholesterol (0.05 mM) + Acetate (16.5 mM) 33.2 0.53 3.9± 0.01       Glycerol (11 mM) + Acetate (16.5 mM) 66.0 0.58 2.5± 0.02       Glycerol (11 mM) + Cholesterol (0.05 mM) 33.2 0.99 3.9± 0.04       aNumber of C atoms of growth substrate × concentration of growth substrate         43   Figure 12: Growth substrate dependent growth curves of Mtb. Mtb growth was determined by measuring the optical density at 600nm every 2 days of liquid cultures containing either: (A) 7H9 media with no carbon mixtures or OADC as rich-media or propionate. (B) either only acetate or acetate and ADC, or only cholesterol or ADC and cholesterol. (C) Glycerol alone, glycerol plus either acetate or cholesterol, then acetate and cholesterol alone. (D) Glucose alone, glucose with acetate or cholesterol, then acetate and cholesterol alone. (E) Cholesterol alone, cholesterol plus either ADC, glucose, glycerol or acetate. (F) ADC as rich-medium alone, ADC with acetate and ADC with cholesterol, then acetate and cholesterol alone. Growth curves are representative of three replicates measurements. Error bars represent the standard deviation of biological replicates (n = 3). Concentrations of single growth substrate were 0.1 mM cholesterol, 11 mM glucose, 33mM glycerol, 33 mM acetate, 0.1 mM propionate and 10% (v/v) for both ADC and OADC. For double growth substrate mixtures, there were equivalent concentration of each growth substrate unit in all cases.  0 10 20 30 40 500,00,20,40,60,81,0OD600time (days) 7H9 OADC propionate Acetate Acetate + ADC Acetate + Cholesterol Cholesterol ADC0 10 20 30 40 500,00,20,40,60,81,0OD600time [days]0 10 20 30 40 500,00,20,40,60,81,0OD600time [days] Glycerol Glycerol + Acetate Glycerol + Cholesterol Acetate Cholesterol0 10 20 30 40 500,00,20,40,60,81,0OD600time [days] Glucose Glucose + Acetate Glucose + Cholesterol Acetate Cholesterol0 10 20 30 40 500,00,20,40,60,81,0OD600time [days] Cholesterol Cholesterol + ADC Cholesterol + Glucose Cholesterol + Glycerol Cholesterol + Acetate ADC ADC Cholesterol ADC + Acetate Acetate Cholesterol0 10 20 30 40 500,00,20,40,60,81,0OD600time [days]A B C D E F   44  3.2 Analysis of selected compounds’ activity in different growth substrate.  Carbon restriction approach was used to mimic intracellular environment of Mtb. The activities of anti-mycobacterial compounds against Mtb strains in defined growth substrate media were assessed using resazurin assay to determine their MIC values. Mtb was grown in 7H9 media supplemented with either single or double growth substrate concentrations (Table 3) as nutritionally challenged environment and ADC as rich-media. The compounds’ activities against Mtb in single or double growth substrates media were determined by MIC values (Table 6). Active compounds were defined as those with MIC values ≤ 12.5 µM in vitro. The cut-off MIC value (12.5 µM) in this study was chosen based on previous in vitro studies by Sorrentino et al., 2016, Zheng and Av-Gay, 2017, who described compound libraries with MIC values ≤ 10 µM as hit compounds. As shown in Table 6, a total of 19 hit compounds with previously described intracellular MIC90 values (Sorrentino et al., 2016, Zheng and Av-Gay, 2017) were tested.                  45  Table 6: Schematic analysis of growth media-dependent MIC of hit compounds NA: Not available, SGS: Single Growth Substrate, DGS: Double Growth Substrate Compound ID Intra MIC90 (µM) In vitro MIC values (µM): SGS In vitro MIC values (µM): DGS ADC Ace Glu Chol Chol + Ace Chol + Glu Ace + Glu ADC + Chol ADC + Ace GSK1376412A (1) 0.32 0.8 3.1 0.05 3.1 3.1 3.1 ≤ 0.05 3.1 3.1 GSK1458296A (2) 1  ≥ 12.5 ≤ 0.05 0.1 0.05 ≤ 0.05 ≤ 0.05 ≤ 0.05  ≥ 12.5 ≤ 0.05 GSK1615454A (3) 2  ≥ 12.5 ≤ 0.05 0.2 3.1 6.3 3.1 NA  ≥ 12.5 NA GSK752705A (5) 1.26 3.1 0.05 0.4 0.8 0.6 0.8 0.4 3.1 1.6 GW810648X (6) 1.58  ≥ 12.5 9.4 9.4 2 2 1.6 6.3  ≥ 12.5  ≥ 12.5 GSK2410966A (7) 2.51  ≥ 12.5 0.3 0.4 ≤ 0.05 0.3 0.4 0.4  ≥ 12.5 0.4 GSK2425587A (8) 1  ≥ 12.5 0.2 0.3 ≤ 0.05 0.4 0.4 0.4  ≥ 12.5 0.2 GSK2443571A (9) 2.51  ≥ 12.5 0.4 1 ≤ 0.05 0.6 0.8 0.4  ≥ 12.5 0.05 GSK2444489A (10) 2  ≥ 12.5 0.6 2.4 ≤ 0.05 0.6 0.8 0.8  ≥ 12.5 0.05 GSK2556052A (11) 2  ≥ 12.5 ≤ 0.05 0.05 0.05 0.8 0.05 ≤ 0.05  ≥ 12.5 ≤ 0.05 GSK2626312A (12) 1.26  ≥ 12.5 ≤ 0.05 0.05 0.05 ≤ 0.05 0.05 ≤ 0.05  ≥ 12.5 ≤ 0.05 GSK2643597A (13) 1.26  ≥ 12.5 0.5 0.3 0.6 0.4 1.6  ≥ 12.5  ≥ 12.5  ≥ 12.5 GSK2733212A (14) 1.26  ≥ 12.5 ≤ 0.05 0.3 1.2 0.2 1.6  ≥ 12.5  ≥ 12.5  ≥ 12.5 GSK3031114A (15) 2.51  ≥ 12.5 0.4 0.8 3.1 0.8 0.4 ≤ 0.05  ≥ 12.5 ≤ 0.05 GSK1606781A (21) 2.51  ≥ 12.5 9.4 0.2 0.2 0.4 0.2 12.5  ≥ 12.5  ≥ 12.5 GSK2426591A (24) 2.51  ≥ 12.5 6.3 4.7 0.4 1.7 0.4 1.6  ≥ 12.5  ≥ 12.5 GSK2426622A (25) 2  ≥ 12.5 ≤ 0.05 0.1 0.1 1.7 0.1  ≥ 12.5  ≥ 12.5 3.1 GSK1074912A (31) 1.58 12.5 0.4 1.2 1.6 0.8 1.6  ≥ 12.5 12.5 3.1 GSK1731114A (60) 2.51 0.4 1.2 0.4 0.6 1.6 0.8  ≥ 12.5 1.6 1.6 No. of hit compounds active at MIC ≤ 12.5   Total = 19 4  19  19  19  19  19  13  4  13  Distribution of hit compounds relative to intracellular MIC ≤ 2.51 Total = 19 2  15  17  16  17  17  11  1  10    46  As illustrated in Table 6, all 19 compounds tested had activity against Mtb in all media containing a single growth substrate as well as media containing cholesterol plus acetate or glucose as a second growth substrate. However, only 13 out of 18 compounds (72%) tested were active in media containing acetate plus glucose (Table 6). Only 4 compounds (˂25%) were active in rich-media alone (ADC) as well as ADC plus cholesterol media (Table 6). In media supplemented with acetate and ADC, only 13 out of 18 compounds (72%) were active.  3.3 Comparison of distribution of hit compounds with similar activity in different growth substrate media to intracellular MIC.  Previous studies by Sorrentino et al., 2016, tested intracellular activities of hit compounds and determined highest intracellular MIC value (2.51µM). Resazurin assay was used to determine in vitro MIC values of all the 19 hit compounds in either single or double growth substrate media (Table 6). Distribution of in vitro MIC values of hit compounds in relation to highest intracellular MIC value (2.51µM) were determined and described activities to be similar to intracellular if in vitro MIC value ≤ 2.51µM.  As shown in Table 6, more than 50% of compounds tested had in vitro MIC values similar to intracellular MIC90 value in all media except media containing ADC or ADC plus cholesterol which had only 2 out of 19 (11%) and 1 out of 19 (6%) compounds with similar intracellular MIC90 value respectively. Media supplemented with glucose as primary growth substrate or cholesterol plus glucose or cholesterol plus acetate had highest number of compounds (17 out 19) (89%) with in vitro MIC values similar to intracellular MIC90 value (Table 6).   3.4 Cytotoxicity of hit compound against THP-1 cells  The intracellular assay relies on the quantitative measurement of bacterial burden which can be affected by viability of host cells. If an inhibitor kills the host, we may get false positive with reduction of mycobacterial load as a result of less host cells. Thus, to determine whether hit compounds target intracellular Mtb and not host cells, MTT cytotoxicity assay was performed to assess host cell viability. Uninfected TPH-1 cells were exposed to compounds at a single concentration (12.5 µM) as the highest concentration and cells survival expressed in percentage (Figure 13). Viable cells were indicated by colorimetric conversation of MTT (yellow) to purple-  47  colored formazan. Compounds cytotoxicity was determined as a reduction of more than 30% in THP-1 cell number (Figure 13). All compounds tested showed no toxicity against THP-1 cells at the highest concentration (12.5 µM) (Figure 13).    Figure 13: No THP-1 cells cytotoxicity. An illustration of THP-1cells cytotoxicity at 12.5 µM concentration of hit compounds (412A (C1), 705 (C5), 114A (C15), 622A (C25), 912A (C31), 472A (C35), 739A (C36) and 486X (C66)) were tested. Toxicity was determined as a reduction of at least more than 30% in THP-1 cell number relative to the negative (DMSO 1.25%). DMSO at 5% was used as positive controls and none of the compounds showed cytotoxicity at 12.5 µM concentration. Error bars represent the standard deviation of three biological replicates (n=3).         48  3.5 Intracellular MIC values of hit compounds against resistant mutants To elucidate the potential mode of action or target of potential inhibitors, we selected for Mtb resistant mutants. A phenotypic screening was performed by plating parental H37Rv strain against hit compounds at various concentrations on 7H10 agar supplemented with a single growth substrate. One to three resistant colonies from each compound were identified and picked (Table 7). Colonies were sub-cultured in liquid media containing lethal dose of the compound to confirm resistance. Between 1-3 colonies were selected from each resistant mutant for intracellular resistant phenotype validation.  THP-1 infection assay was used to determine whether resistant mutants isolated in vitro displayed similar phenotype against hit compounds ex vivo. Differentiated THP-1 cells were infected with resistant mutants or WT strain and exposed to serially diluted active compounds to determine their MIC values. Infected THP-1 cells treated with compounds were lysed and lysates plated on 96-well plate with 7H10 OADC agar for CFU (Figure 14).  Intracellular MIC values of compounds against resistant mutants or WT strain were determined by observing wells with the least concentration that had no Mtb growth (Figure 14). MIC values against resistant mutants were compared to WT strain to confirm resistance.  Resistant mutants isolated against compounds 412A (1), 705A (5), 912A (31), 472A (35) and 739A (36) had intracellular MIC values higher than WT strain MIC value (Table 7). Resistant mutants isolated against compounds 114A (15), 622A (25) and 486X (66) had intracellular MIC values similar to WT strain MIC value (Table 7). All compounds from which our mutants were generated had their intracellular MIC values against WT H37Rv previously determined (Sorrentino et al., 2016) (Table 7).                                                                                                                                                                                                                                                         49  Table 7: Isolated resistant mutants and their intracellular MIC values.    Resistant Mutants Compound name (Lab ID) Media Number of strains  Intra MIC values (µM) Intracellular MIC values (µM) WT strain 1 strain 2 strain 3 GSK1376412A (1)  Acetate 2 0.38 0.8 ≥12.5 ≥12.5 NA  GSK752705A (5) Glucose 1 1.26 1.6 6.3  NA  NA GSK3031114A (15) Glucose 3 2.51 ≥12.5 ≥12.5 ≥12.5 ≥12.5 GSK2426622A (25) Glucose 3 2.0 ≥12.5 ≥12.5 ≥12.5 ≥12.5 GSK1074912A (31) Glucose 3 1.58 3.1 ≥12.5 ≥12.5 ≥12.5 GSK503472A (35) Acetate 1 2.0 3.1 6.3 NA  NA  GSK784739A (36) Acetate 1 2.0 6.3 ≥12.5  NA  NA GR135486X (66) ADC 3 0.16 ≥12.5 ≥12.5 ≥12.5 NA  NA: Not Available              50                                                                                                                           WT-1                                                                                                                          412A-1   Figure 14: Intracellular MIC determination. An illustration of how intracellular MIC values (µM) for all resistant mutants shown in (Table 5) were determined through plating on 7H10 OADC in 96-well plates. Lysates from THP-1-infected cells after anti-mycobacterial compound treatment was plated for CFU. Serially diluted compounds (µM) were in triplicate wells. DMSO and rifampicin were included as positive and negative controls respectively. Lowest concentration where there was no bacterial growth indicates the MIC value. WT strain and resistant mutants’ intracellular sensitivity to compounds were determined by these MIC values.                   DMSO        0.1          0.2           0.4     0.8         1.6            3.1       6.3        12.5        Rif    51                   02468101214WT strain 1 strain 2Intracellular MIC (µM)GSK1376412A (1)02468101214WT strain1strain2strain3Intracellular MIC (µM)GSK3031114A (15)02468101214WT strain1strain2strain3Intracellular MIC (µM)GSK2426622A (25)02468101214WT strain1strain2strain3Intracellular MIC (µM)GSK1074912A (31)02468101214WT strain 1Intracellular MICGSK503472A (35)02468101214WT strain 1Intracellular MICGSK752705A (5)  52   Figure 15: Intracellular MIC determination for resistant mutants. One to three resistant colonies (strain) were isolated from 7H10 agar plates. Mutants were named according to the compounds they were isolated from (1, 5, 15, 25, 31, 35, 36, and 66) (Table 7). Intracellular THP-1 assay was performed to confirm intracellular MIC of compounds against mutants and reference WT strain.  Numbers in parenthesis indicate lab ID. Compound concentration was serially diluted from 12.5 to 0.1µM with each tested in triplicate wells.                    02468101214WT strain 1Intracellular MIC (µM)GSK784739A (36)02468101214WT strain 1 strain 2Intracellular MIC (µM)GR135486X (66)  53  3.6 Identification of mutated genes in resistant mutants  To identify and validate whether resistant phenotype of all mutants isolated in vitro were due to spontaneous mutations, genomic DNA was extracted and subject to whole genome sequencing and bioinformatics analysis to reveal genetic polymorphism among mutants (Table 8 and 9). A total of eight resistant mutants were isolated from 7H10 media containing glucose, acetate and ADC as growth substrates (Table 7). Often, more than one mutated gene was identified in each strain (Table 8). For most strains isolated from the same compound, at least a single common gene mutation (highlighted) was identified within them (Table 8). The different colour coding in Table 8 represents four sets of genes that were identified within strains isolated from the same compound or different compounds. Highlighted in blue and purple are set of genes identified from strains isolated from the same compound (Table 8). Highlighted in yellow and green are set of genes identified from strains isolated from different compounds (Table 8). Set of genes not highlighted indicate those identified in a single strain isolated from a single compound (Table 8).                    54  Table 8: Mutated genes identified in resistant mutants.  Compound name (Lab ID)  Strain Mutated genes Genome position Gene position Quality score  Effect GR135486x (66) 1 phoR, fbiC 853056 1304012 661G>C 1082C>A 1687 1868 Ala221Pro, codon change Thr361Lys, codon change  2 phoR, fbiA 853056 3641408 661G>C 866T>A 1559 645 Ala221Pro, codon change Leu289Gln, codon change  3 phoR, fbiA, Rv3327 853056 3641408 3712046 661G>C 866T>A 296C>G 816 1649 650 Ala221Pro, codon change Leu289Gln, codon change Pro100Ala, codon change GSK1074912A (31) 1 Rv3083, ethA 3449286 4327269 783G>A 205T>C 1782 2581 Trp261*, gained stop Trp69Arg, codon change  2 Rv2542, Rv3083, ethA 2866171 3449309 4327284 1042G>A 806T>C 190T>C 2301 1392 2919 Ala348Thr, codon change Leu269Pro, codon change Phe64Ile, codon change  3 Rv2542, ethA 2866171 4327394 1042G>A 79CA>C 2275 4438 Ala348Thr, codon indels, EFF=frameshift_variant GSK2426622A (25) 2 ftsK, virS, Rv3175 3061315 3447715 3543120 1192T>C 712G>T 261G>A 1462 1859 3801 Ser398Pro, codon change Val238Phe, codon change Lys87Lys, silent GSK3031114A (15) 3 Rv0585c 683071  1202A>C  2693 Asp401Ala, codon change  4 dnaE1 virS  1749908 3447444 2215A>G 983C>A  1695 2484 Met739Val, codon change Pro328His, codon change GSK752705A (5) 1 Rv3083 sugI 3448882 3717105 381 G>A 16C>T 3556 3391 Glu127Thr128 frameshifts Gln6*, stop codon gained GSK1376412A (1) 1 prrB moaC3 1005390 3709857 452T>C 392A>G 2719 2636 Leu151Pro codon change Asp131Gly codon change GSK503472A (35) 1 ethA 4326863 611T>C 1804 Met204Thr codon change GSK784739A (36) 1 ethA 4326863 611T>C 1473 Met204Thr codon change          55  Table 9: Description of mutated genes and their activities Mutated gene Description of gene product Physiological role  Reference phoR (Rv0758) Transmembrane sensor histidine kinase Regulation of phosphate regulon gene expression (Broset, Martín and Gonzalo-Asensio, 2015) phoP (Rv0757) Response regulator  Regulation of phosphate regulon gene expression (Broset, Martín and Gonzalo-Asensio, 2015) fbiA (Rv3261) 2-phospho-L-lactate transferase Involved in the biosynthesis of coenzyme F420 (Fujiwara et al., 2018) fbiC (Rv1173) Intermediary metabolism and respiration Metabolism and respiration (Fujiwara et al., 2018) Rv3327 Transposase fusion protein Catalyzes movement of transposon genes (Roychowdhury, Mandal and Bhattacharya, 2015) ftsK (Rv2748c) Cell division transmembrane protein (A cytosolic ATPase) Recruits ESAT6: CFP10 complex to the plasma membrane (Jiang et al., 2014) Rv0585c Integral membrane protein The promoter of mce2 operon lies in the Rv0585c- Rv0586 gene segment (Rathor et al., 2013) Rv2542 Hypothetical protein unknown https://mycobrowser.epfl.ch/genes/Rv2542   Rv3175 Possible Amidase Involved in cellular metabolism https://mycobrowser.epfl.ch/genes/Rv3175  mymA (Rv3083) FAD-containing monooxygenase  Activate prodrug (Ethionamide) (Grant et al., 2017) ethA (Rv3854C) Baeyer-Villiger monooxygenase  Acts on a variety of ketones, activates prodrug (ethionamide) (Grant et al., 2017) dnaE1(Rv1547) Replicative polymerase  Encodes a novel editing function that proofreads DNA replication  (Rock et al., 2015) moaC3 (Rv3324c) Molybdenum cofactor biosynthesis protein  Catalyze redox reactions in carbon, nitrogen and sulfur metabolism (Williams, Mizrahi and Kana, 2014) prrB (Rv0902c) Sensor histidine kinase Required for early stages of macrophage infection (Ewann, Locht and Supply, 2004) sugI (Rv3331) Sugar-transport integral membrane protein.  Glucose and galactose-specific transporter (Niederweis, 2018) virS (Rv3082c) HTH-type transcriptional regulator Regulates mymA expression (Singh et al., 2003)    56  Chapter 4.0 4.1 Discussion In recent years, chemical genetics, i.e., the isolation of resistant mutants to compounds, alongside whole genome sequencing has dominated approaches to identify potential targets or mode of action of new Mtb inhibitors identified through whole-cell screening. To understand Mtb’s physiology within the context of its host cell as well as to identify drug targets, we conducted chemical genetic screening by mimicking its intracellular environment through carbon restriction. In collaboration with GlaxoSmithKline pharmaceutical company, we isolated and identified resistant mutants against intracellular active compounds in defined growth substrates media. First, we assessed Mtb’s growth rate in vitro in various growth media in the absence of anti-mycobacterial compounds. Many bacteria including Bacillus subtilis, Listeria monocytogenes and Mycoplasma pneumonia were shown to utilise glycerol as sole growth substrate (Joseph et al., 2008). In the case of Mtb, fastest growth was shown to occur when albumin was added in glycerol-containing media (De Carvalho et al., 2010). Our results showed that, omitting albumin from media containing glycerol or acetate as primary source resulted in reduced Mtb growth. However, the growth limitation attributed to each of these independent growth substrates alone in the media was reversed when they were added together as double growth substrates (Table 4 and 5, Figure 12, C). Our results showed that both growth substrates combined as double growth substrate in media had a faster Mtb growth rate. However, the sudden increase in growth after day 45 in acetate-containing media may be due to a possible contamination.  In Mtb growth curve experiment, all carbon-restricted media had no Tween-80 supplement, this was to omit any extra source of growth substrate from its oleic acid composition, and only a nonhydrolyzable detergent (tyloxapol (0.05% v/v)) was supplemented. Therefore, Mtb’s lack of growth in glycerol-containing media (Figure 12, C) could be due to the blocking of its utilisation in the absence of other defined growth substrate. Studies have shown that glycerol utilisation by Mtb as the only growth substrate in media is blocked until when other growth substrates including amino acids are added and are consumed first (Warner, 2015). Our findings with lack of Mtb growth in glycerol-containing media contradicts studies by De Carvalho et al., 2010 who showed that Mtb grows fastest in glycerol-containing media. However, their studies used a complex growth   57  media containing additional growth substrate, Tween-80, a dispersal agent whose de-esterification can provide Mtb with oleic acid, an additional carbon and energy source that can be used to support growth.   The inability of Mtb to grow in media containing acetate as primary growth substrate may be due to its ability to diffuse across the cell wall, causing acidification of the cytoplasm (Thomas et al., 2014). However, adding ADC as rich-media to acetate-containing media enhanced growth (Figure 12, B & F). This suggests that its toxicity effect may have been buffered by the addition of ADC. The enhanced growth rate noted in acetate and glycerol containing-media confirms Mtb’s ability to co-catabolise multiple growth substrates in vitro to augment growth requirement.  We demonstrated that cholesterol, as primary growth substrate, is limited in enhancing bacterial growth (Figure 12, E). However, adding cholesterol to acetate or to glycerol as double growth substrate media, rescued their individual inhibition effect that each had as primary growth substrate (Figure 12, B & C). It is not clear how Mtb segregates metabolism of individual growth substrates from one another during co-catabolism and direct their compartmentalised metabolic fate. We noted that adding cholesterol to ADC-containing media lowered Mtb’s maximum growth (Figure 12, F) but maintained almost a similar doubling time. These results suggest that cholesterol may be utilised first during growth or that it has a dual effect on Mtb’s growth rate. It is shown that enzymes of a given pathway of intermediary metabolism may reversibly form multiprotein complexes in response to nutritionally challenged environment of the cell and its physiological needs (Narayanaswamy et al., 2009). Thus, suggesting that such complexes may facilitate a more efficient channeling of substrates and products through different pathways.  Our findings confirm earlier studies by Av-Gay and Sobouti, 2000, that M. smegmatis grows better in media containing cholesterol as a single growth substrate compared to Mtb which requires the addition of Tween-80, which by itself can be utilised as a growth substrate because of its oleic acid composition (Tang et al., 2009). Our media was supplemented with tyloxapol instead of Tween-80, which is not metabolised yet still can be used to prevent cell clumping (De Carvalho et al., 2010). BCG as a representative of slow-growing mycobacteria pathogen was shown to grow slower in cholesterol-containing media than in minimal media lacking cholesterol (Av-Gay and Sobouti, 2000). Higher concentration of cholesterol in growth media was also shown to inhibit BCG growth (Av-Gay and Sobouti, 2000).    58  Our data showed some similarities in Mtb’s maximum growth among media supplemented with either glucose alone or glucose plus acetate or cholesterol (Table 4 and 5). However, their growth rate was faster in media supplemented with glucose plus cholesterol which had a lesser doubling time (Table 4 and 5).  However, these differences in their growth rate suggest that differences in carbon concentration within substrates in the media may not have played a role (Table 4 and 5). Understanding the relationship between Mtb’s growth niche and how it influences its physiology as well as drug susceptibility is important for effective drug development. Unfortunately, human observational studies that produced a detailed description of TB could not sufficiently probe Mtb physiology in the context of its niche to inform drug development. Therefore, to determine compounds’ activity against Mtb in the context of its natural environment, Mtb was cultured in vitro, an environment likely to been encountered during infection, using carbon-restricted microenvironments model as previously described.  Fewer compounds (≤25%) (Table 6) showed activity against Mtb in rich-media (ADC). Adding ADC to media containing acetate, reduced compound activity compared to when in acetate media alone (Table 6). However, compounds’ activity in media containing ADC plus cholesterol or ADC alone were similar (Table 6). Poor compound activity seen in rich-media also suggests that majority of compounds exclusively target pathways activated during Mtb growth in carbon restricted environment. This phenomenon also suggests a potential compartmentalisation of individual metabolised growth substrate by enzymes into a distinct metabolic fate under certain growth conditions, which may not be interchangeable. We speculate that Mtb’s ability to simultaneously co-catabolise different growth substrates (De Carvalho et al., 2010) may also suggest a compensatory effect in carbon pathway  targeted during growth by compounds. This implies that inhibitors can starve Mtb by limiting entry of a particular growth substrate into the central metabolism pathway. Hence, failure to inhibit Mtb in media containing ADC plus acetate or cholesterol suggests a sequential utilization of carbon/nutrient in a pathway that favours their fastest growth but fails to be targeted by anti-mycobacterial compounds. Acetate reuptake and incorporation into Mtb’s CCM can allow the removal of potentially toxic compounds as well as provide an additional carbon sources for Mtb (Rücker et al., 2015). Our in   59  vitro data showed a decreased in compound activity in media containing acetate and glucose together compared to each individual growth substrate alone (Table 6). We demonstrated by MIC profiles, in which in vitro media provided compound activity similar to their intracellular MIC profile. Our data showed that, compounds in all growth media except media containing ADC alone or ADC plus cholesterol showed more than 50% of in vitro MIC values similarity to intracellular MIC90 values. However, media containing glucose alone and media containing cholesterol plus glucose or acetate all had almost 90% in vitro MIC values similar to intracellular MIC90 values (Table 6), suggesting these media conditions are more closely related to intracellular environment. Mtb’s ability to degrade and derive cholesterol, which serves as a primary source of carbon throughout chronic infection (Pandey and Sassetti, 2008), also suggests that these similarities between these carbon-restricted growth conditions and the intracellular environment may be true. This further strengthens the notion that in the intracellular environment, carbon supply might be limited.  Our data on cytotoxicity assay (Figure 13) showed less than 30% of host cell killing by hit compounds from which resistant mutants were isolated (Table 7). The non-cytotoxicity effect against host cells by these compounds suggests that the expected bacterial cell numbers in any of the intracellular assays would be a true reflection of compounds’ activity against Mtb and not as a result of host cells killing.   We isolated resistant mutants in vitro with the aim to identify relevant spontaneous mutations that may predict potential mode of action or even a potential drug target. Historically, mutated genes encoding essential pathways or enzymes targeted by anti-mycobacterial agents in vitro may often fail to translate into in vivo target (Pethe, Patricia C. Sequeira, et al., 2010) yet can be used to identify activators, efflux pumps or other modes of actions.   To show which resistant mutant isolated in vitro displayed a similar phenotype against intracellular active compounds ex vivo, THP-1 cell lines were used as ex vivo model to determine compounds’ activities against isolated resistant mutants. MIC values of anti-mycobacterial compounds against resistant mutants in THP-1 cells were determined alongside WT strain (Figure 13 and Table 7). We showed that 5 out of 8 isolated resistant mutants in vitro (Table 7, Figure 15) displayed similar resistance phenotype ex vivo with ≥ 2-fold MIC values higher than WT MIC values. Other three   60  mutants from compounds 114A (15), 622A (25) and 486X (66) were resistant ex vivo with similar MIC values to WT MIC values (Table 7, Figure 15). We could not confirm if these three mutants and WT resistance to their respective compounds was due to compounds’ instability. Discordance in drug sensitivity phenotype between these isolated mutants (15, 25, and 66) and WT strain could be a possible acquisition of similar gene mutations in WT strain over time due to the many culture passaging-steps. The five isolated mutants from compounds 412A (1), 705A (5), 912A (31), 472A (35) and 739A (36) showing ≥ 2-fold increase in resistance compared to WT strain (Figure 15), suggests possible mutations occurring that may have a role in their intracellular drug resistance profile. However, we could not make similar speculation for the three mutants isolated from compounds 114A (15), 622A (25) and 486X (66) (Table 8) as WT strain also displayed similar resistance phenotype with similar MIC values (Figure 15).  Whole genome sequencing and bioinformatics analysis have identified several gene mutations among all the resistant mutants isolated (Table 8). Resistant strain from compound 412A (1) had mutations in prrB and moaC3 genes (Table 8 and 9). PrrB, a sensor histidine kinase, is part of a two-component system (TCS) (PrrAB) which is expressed and required by Mtb for intracellular growth (Nowak et al., 2006).  PrrB activates PrrA, a response regulator, through phosphorylation and its loss-of-function may block its phosphorylation activity thus, disrupting the signal transduction circuit inside macrophages. TCSs are major signaling transduction pathways in bacteria that are essential for bacterial adaptation to the environment as well as evasion of host immunity and the development of drug resistance in pathogenic bacteria (Xing et al., 2017). moaC3 encodes an enzyme involved in the early step of molybdenum cofactor biosynthesis, involved in Mtb pathogenesis (Williams, Mizrahi and Kana, 2014). Therefore, targeting MoaC3 may disrupt steps in the pathway thus, reducing its virulence.  Interestingly, resistant strains from compounds 472A (35), 739A (36) and 912A (31) had mutations in ethA common to all. (Table 8 and 9). EthA is a non-essential FAD-dependent monooxygenase which activates second-line anti-TB drugs such as ethionamide (Grant et al., 2017). Mycolic acid synthesis is targeted by ethionamide and mutation in ethA confers resistance (da Silva and Palomino, 2011).     61  Resistant strain from compound 705A (5) had mutations in MymA (Rv3083) and sugI genes (Table 8 and 9). MymA is a cell-wall associated protein which is up-regulated upon infection of macrophages (Saraav et al., 2015). MymA is also shown to play a role in activating ethionamide, with loss-of- function resulting to ethionamide resistance (Grant et al., 2017). EthA and MymA have been both shown to function as activating enzymes within the cell, oxidizing ethionamide as well as newly identified sulfur-containing compounds (Grant et al., 2017). Therefore, we speculate that mutants with mutations in ethA or MymA suggest that these compounds (5, 31, 35, and 36) might all be targeting cell wall synthesis in Mtb.  Three of the resistant strains from compound 486X (66) had each more than one gene mutations (phoR, fbiC, fbiA and Rv3327) identified (Table 8 and 9). However, we found phoR mutated gene to be the one common to all the three strains. PhoR of the PhoPR TCSs in Mtb is a transmembrane sensor histidine kinase, and PhoP is the response regulator that affects the expression of more than 110 genes (Walters et al., 2006). Signals to which TCSs in Mtb response to is still unclear, however, studies have reported that mutants with both phoP and phoR genes mutations were attenuated in macrophages and mice (Nambiar et al., 2012). We speculate that multiple strains isolated from the same compound could be due to different Single Nucleotide Polymorphisms (SNPs), while common SNP in multiple mutants was a common signature. We suggest that, there may be a link between mutated genes identified and their intracellular resistance phenotype. However, we could not establish which among the multiple mutated genes contributed to the resistant phenotype observed. Expressing similar resistance profiles and sharing a single common mutated gene among strains suggests that they may have a similar drug target. Our data could not show any mutant with resistance phenotype in vitro that were sensitive ex vivo.   4.2 Conclusion In this report, we described the suitability of in vitro models using nutrient, specifically carbon starvation for screening activities of hit compounds against Mtb. We showed that carbon restriction by itself has an effect on Mtb’s maximal growth, growth rate and hit compounds’ activity. Our data indicates that rich-media alone is not suitable for determining MIC90 of anti-mycobacterial   62  compounds. Adding ADC to other single growth substrate-containing media lowers compound activity against Mtb.  Individual growth substrates where restricted Mtb growth was observed, do have a growth compensatory effect when added together as double growth substrates. Growth media supplemented with glycerol plus cholesterol as double growth substrates enhanced Mtb’s maximum growth, whilst acetate or glycerol as primary growth substrate had minimum growth. We showed that media containing glucose as primary growth substrate or containing cholesterol plus glucose or acetate as double growth substrate closely resembles intracellular growth environment. None of the compounds from which resistant mutants were isolated from had any cytotoxic effect against THP-1 cells used as ex vivo model.  More than half of the resistant mutants isolated in vitro were found to have similar resistance phenotype ex vivo. Among the mutated genes identified from the resistant mutants were those that encode proteins involved in either activating prodrugs or encode proteins involved in cell wall synthesis or cell signalling transduction. The ex vivo resistant phenotype displayed by mutants and the identification of mutated genes suggest a direct link between the two. Thus, suggesting that proteins involved mainly in cell wall synthesis, cell signalling or prodrug activation as main targets.   4.3 Significance A key success of Mtb as the main causative agent of TB is its ability to survive and grow inside host cells that are supposed to eliminate it. Intracellular nutrient acquisition, more so growth substrate metabolism pathways are required for full Mtb pathogenicity. Mtb’s continuous metabolic alterations associated with its host environment poses challenges both in TB treatment strategies and screening for resistant mutants. Understanding the link between its growth conditions and how they influence anti-mycobacterial agents’ activities would enhance methods designed for drug and resistant mutant screening.  Intracellular target-based drug screening can be challenging, thus recent approaches have been shifted towards in vitro whole-cell screening. Using in vitro methods to generate resistant mutants   63  will help identify potential druggable targets. Therefore, identifying a suitable in vitro growth condition that mimics intracellular environment will be helpful in screening for novel compound libraries against Mtb. We hope future therapeutic interventions that target and disrupt pathways especially carbon metabolism during Mtb infection or disease would be extensively explored to speed up TB treatment. Identifying druggable intracellular targets using our approach will help determine mode of action and enhance specificity of anti-mycobacterial compounds.  4.4 Limitations Due to the tedious nature of lysate CFU determination in 96-well plate, THP-1 intracellular and cytotoxicity assay were not performed using the same passage of THP-1 cells for each experiment. Although they were the same batch of cells but there were at least more than two passages between experiments. Using the same passage of THP-1 cells for both experiments was important to maintain uniformity and minimise discrepancies between results.   To avoid possible contamination during screening for resistant mutants, petri dish plates were only checked between two to four weeks post plating. Therefore, we could not determine the order of growth among resistant colonies selected on 7H10 agar plates for each mutant. We could not make any links how different mutations within strains from the same compound contributed in the order in which they grew on plates.  4.5 Future directions Identification of multiple mutated genes in a single resistant strain through whole genome sequencing and bioinformatics analysis represents a range of resistance mechanisms. Therefore, the first practicable approach would be to confirm if mutated genes identified are responsible for the observable phenotype. One way to do this is by introducing a similar mutation in a WT strain and confirm if the mutation is dominant or recessive. If mutation is recessive, then perform complementation by cloning the normal gene and transforming it into resistant mutant. If mutated gene was the actual target and also is recessive, after complementation, complemented mutant will display a phenotype different from the non-complemented resistant mutant.    64  A genetic tool called conditional expression-specialised transduction essentiality test (CESTET) is used to determine the essentiality of mutated genes in M. smegmatis  (Bhatt et al., 2005). This method incorporates a highly efficient gene knockout technique with the utility of acetamidase promoter. Similar approach can be used to knockout previously identified mutated gene in a WT strain and test its phenotype against hit compounds. Effect of loss of essential gene function in WT similar to that in resistant mutant will result in resistance phenotype similar to resistant mutant. An intracellular dose-dependency assay comparing MICs of WT, over-expressed WT, complemented mutants and resistant mutants for sensitivity against hit compounds they were previously resistant to, would help link associated mutations with resistance phenotypes.  Comparing drug sensitivity of mutants to existing panel of mutants in existing anti-mycobacterial drugs, termed cross-resistance assay could help predict mode of actions of hit compounds against resistant mutants.                 65  References  Adams, K. N. et al. (2011) Drug tolerance in replicating Mycobacteria mediated by a macrophage-induced efflux mechanism, Cell, 145(1), pp. 39–53.  Alangaden, G. J. et al. (1998) Mechanism of resistance to amikacin and kanamycin in Mycobacterium tuberculosis, Antimicrobial Agents and Chemotherapy, 42(5), pp. 1295–1297. Alonso, S. et al. (2007) Lysosomal killing of Mycobacterium mediated by ubiquitin-derived peptides is enhanced by autophagy, PNAS, 104(14), pp. 6031–6036. Andriole, V. T. 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