Biomarkers to predict Tuberculosis treatment response Itumeleng Boshielo 1144115 A thesis submitted to the Faculty of Health Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy in medicine. Johannesburg, 2023 ii Declaration I, Itumeleng Boshielo, am a student registered for the degree of Doctor of Philosophy in Medicine at the University of the Witwatersrand in the academic year 2020. I hereby declare the following:  I am aware that the use of someone else’s literature and work without their permission and/or without acknowledging the original source is unacceptable.  I confirm that the work submitted for assessment for the above-mentioned degree is my own unaided work except where explicitly indicated otherwise.  I have not submitted this work before for any other degree or examination at any other university.  I have followed the required conventions in referencing the thoughts and ideas of others.  I understand that the University of the Witwatersrand may take disciplinary action against me if there is a belief that this is not my own unaided work or that I have failed to acknowledge the source of the ideas or words in my writing. Signature of candidate ..... ................ Date...15/06/2023..... iii Dedication This work is dedicated to myself and the most precious gifts that God has given to me, my beautiful children; Onthatile Boshielo and Nala Boshielo. iv Acknowledgements  First of all, I’d like to thank the God of mount Zion for his protection and mercy.  Prof Bavesh Kana, who undertook to act as my supervisor despite his many other academic and professional commitments; His wisdom, knowledge and commitment to the highest standards inspired and motivated me. You have consistently encouraged me despite my circumstances, not just professionally but also as one person to another.  Prof Caroline Tiemessen, my co-supervisor who took her precious time to assist and provide her input throughout my study.  Dr Diana Schramm, for her patience and the time she took to assist me with the luminex bead array assay step by step from the beginning to end.  Dr Julian Peters, for providing the samples used for this study as well as the time she took to assist me with the project setup.  Dr Stanford Kwenda, for providing your expertise in completing this complex statistical analysis.  My grandmother Asnath Moloto for always being the pillar of my strength.  Cathrine and Nelson Boshielo, my parents, who have always supported, encouraged and believed in me, in all my endeavours and who so lovingly and unselfishly cared for me.  My siblings Thabo, Lebogang and Tshepo Boshielo for their constant love and support.  Mokete Mokhothu, for their support and constant encouragement.  The Centre of Excellence for Biomedical TB Research for providing the environment all these years for me to be able to conduct this research.  I would also like to express my appreciation to everyone from the CBTBR and NICD HIV/STI Lab. I am thankful for their aspiring guidance, constructive criticism and friendly advice during the study.  Lastly, my sincere gratitude goes to the Medical Research Council and National Research Foundation and for funding me in the academic year 2018-2021 and 2022, respectively. Psalm 136:1 “O give thanks unto the Lord; for he is good: for his mercy endureth for ever” v Table of Contents DECLARATION .................................................................................................................................. II DEDICATION .................................................................................................................................... III ACKNOWLEDGEMENTS ................................................................................................................. IV TABLE OF CONTENTS ..................................................................................................................... V LIST OF SYMBOLS AND ABBREVIATIONS ................................................................................ X LIST OF TABLES .......................................................................................................................... XVII LIST OF FIGURES ........................................................................................................................ XVII ABSTRACT ...................................................................................................................................... XXI CHAPTER 1 ......................................................................................................................................... 1 1. TUBERCULOSIS ............................................................................................................................ 1 1.1. Introduction .............................................................................................................................................. 1 1.2. Global prevalence of TB ............................................................................................................................ 2 1.3. Diagnosis and treatment Mtb infection .................................................................................................... 3 1.4. Physiology of Mycobacteria ...................................................................................................................... 5 1.5. Pathogenesis of M. tuberculosis ............................................................................................................... 7 1.5.1. Transmission of Mtb ................................................................................................................................ 7 1.5.2. Host immunity against Mtb ..................................................................................................................... 8 1.5.3. Innate immunity..................................................................................................................................... 10 1.5.4. Adaptive immunity................................................................................................................................. 12 1.6. Latent TB infection .................................................................................................................................. 14 1.7. Progression to Active TB ......................................................................................................................... 15 1.8. HIV/TB co-infection ................................................................................................................................. 15 1.9. Rationale for the current study ............................................................................................................... 18 vi CHAPTER 2 ....................................................................................................................................... 19 DIFFERENTIAL CULTURABLE TUBERCLE BACTERIA: IMPLICATIONS FOR IMMUNE RESPONSES DURING TUBERCULOSIS INFECTION .............................................................. 19 2.1. Introduction ............................................................................................................................................ 19 2.2. Viable but non-culturable state............................................................................................................... 19 2.3. Resuscitation of non-culturable bacteria................................................................................................. 21 2.4. Differentially culturable tubercle bacteria .............................................................................................. 22 2.5 DCTB and the immune response .............................................................................................................. 25 2.6. In-vitro modelling of Mtb dormancy ....................................................................................................... 26 2.6.1. Hypoxia .................................................................................................................................................. 26 2.6.2. Nutrient Deprivation .............................................................................................................................. 28 2.6.3. Nitric Oxide ............................................................................................................................................ 30 2.7. Other in vitro models of dormancy ......................................................................................................... 31 2.8. Hypothesis, Aim and Objectives of the study .......................................................................................... 33 2.8.1. Hypothesis ............................................................................................................................................. 33 2.8.2. Aim ......................................................................................................................................................... 33 2.8.3. Objectives .............................................................................................................................................. 33 2.9. Preliminary data supporting our approach .............................................................................................. 33 MATERIALS AND METHODS ....................................................................................................... 37 2.10. Methods for DCTB assay ....................................................................................................................... 37 2.10.1. Bacterial strains cell culture ................................................................................................................. 37 2.10.2. Starvation model assay for DCTB generation ...................................................................................... 37 2.10.3. Culture filtrate preparation ................................................................................................................. 37 2.10.4. MPN assay ............................................................................................................................................ 38 2.10.5. Colony forming units (CFU) Assay ........................................................................................................ 39 2.10.6. Donor recruitment criteria ................................................................................................................... 39 2.10.7. Whole blood assay ............................................................................................................................... 39 2.10.8. Whole blood infection with Mtb .......................................................................................................... 39 2.10.9. Harvesting plasma from whole blood .................................................................................................. 40 2.10.10. Cytokines determination using Luminex multiplex immunoassay .................................................... 40 2.10.11. Data analysis ...................................................................................................................................... 41 2.10.12. Statistical analysis .............................................................................................................................. 42 RESULTS ........................................................................................................................................... 43 2.11. Quantification of DCTB between different bacterial strains .................................................................. 43 2.11.1. Growth kinetics of Mtb laboratory (H37Rv) and clinical strains .......................................................... 43 vii 2.11.2 The propensity of clinical isolates to adopt the DCTB state ................................................................. 44 2.12. Production of Th1/Th2 cytokines in blood samples infected with actively growing and starved DCTB 48 2.12.1. Effect of actively growing Mtb infection on pro-inflammatory cytokine production .......................... 48 2.12.2. Effect of non-replicating DCTB infection on pro-inflammatory cytokine production .......................... 50 2.12.3. Comparison of cytokine production by DCTB (starved cells) compared to replicating bacteria ......... 52 2.13. A summary of the analysis and key observations .................................................................................. 54 DISCUSSION ..................................................................................................................................... 55 CHAPTER 3: ..................................................................................................................................... 60 TUBERCULOSIS TREATMENT BIOMARKERS USING CLEARANCE OF DIFFERENTIALLY CULTURABLE TUBERCLE BACTERIA AS A MEASURE OF THERAPEUTIC SUCCESS .............................................................................................................. 60 3.1. Introduction ............................................................................................................................................ 60 3.2. Pathogen-specific TB biomarkers ............................................................................................................ 62 3.3. Host-derived TB diagnostic biomarkers ................................................................................................... 64 3.3.1. Transcriptomic TB biomarkers ............................................................................................................... 65 3.3.2. Proteomic TB biomarkers ...................................................................................................................... 68 3.3.3. Metabolomic TB biomarkers .................................................................................................................. 72 3.4. TB treatment associated biomarkers ...................................................................................................... 74 3.4.1. Transcriptomic biomarkers in TB treatment .......................................................................................... 75 3.4.2. Proteomic biomarkers in TB treatment ................................................................................................. 76 3.4.3. Metabolomic biomarkers in TB treatment ............................................................................................ 77 3.5 Rationale for this study ............................................................................................................................ 78 3.6. Hypothesis, aims and objectives ............................................................................................................. 79 3.6.1. Hypothesis ............................................................................................................................................. 79 3.6.2. Aim ......................................................................................................................................................... 79 3.6.3. Objectives .............................................................................................................................................. 79 Specific objectives were to: ............................................................................................................................. 79 3.7 Preliminary data to support the approach ............................................................................................... 80 MATERIALS AND METHODS ....................................................................................................... 84 3.7. Study Population .................................................................................................................................... 84 3.8. Sample preparation ................................................................................................................................ 84 3.9. Luminex multiplex immunoassay ............................................................................................................ 84 3.9.1. Data analysis .......................................................................................................................................... 86 viii 3.9.2. Statistical analysis .................................................................................................................................. 86 3.9.3. Heat map, principle component and ROC analysis ................................................................................ 86 RESULTS ........................................................................................................................................... 87 3.10. Stratification of participants and study definitions ............................................................................... 87 3.11. Luminex bead array for cytokine concentrations .................................................................................. 88 3.12. Cytokine/chemokine clustering among healthy controls, HIV+ controls, TB/HIV+ and TB/HIV- at day 3 of TB treatment. ............................................................................................................................................ 93 3.13. Distinguishing cytokine distribution among healthy controls, HIV+ controls, TB/HIV+ and TB/HIV- at day 3 of TB treatment. ................................................................................................................................... 94 3.14. HIV-Associated TB biomarkers. ............................................................................................................. 95 3.15. Biomarkers associated with TB infection. ............................................................................................ 105 3.16. Longitudinal assessment of plasma biomarkers levels over treatment between TB and HIV/TB co- infected individuals. .................................................................................................................................... 111 3.17. Correlation of plasma cytokine/chemokine levels with viral load and CD4 count in HIV/TB co-infected individuals. .................................................................................................................................................. 114 3.18. Heat map analysis of cytokine/chemokine associated DCTB treatment response. .............................. 116 3.19. Analysis of host markers in plasma between healthy controls, Treatment responsive, Delayed- responsive and Non-responsive groups, using PCA analysis. ........................................................................ 117 3.20. Analysis of host markers in plasma between healthy controls, Treatment responsive, Delayed- responsive and Non-responsive groups. ...................................................................................................... 118 3.21. Plasma cytokines that can robustly distinguish treatment-responsive, delayed responsive and non- responsive DCTB response patterns. ............................................................................................................ 126 3.22. Changes in the concentrations of host biomarkers during the course of TB treatment within the Treatment-responsive, Delayed-responsive and Non-responsive groups. .................................................... 130 3.23. The effect of strain type on cytokine profiles during TB treatment. .................................................... 130 3.24. The effect of residual DCTB at the end of treatment. .......................................................................... 131 3.25. A summary of the analysis plan and key observations of the differences of cytokines, chemokines and growth factors observed in each group of patients. ..................................................................................... 132 DISCUSSION .................................................................................................................................. 133 CHAPTER 4: SUMMARY ............................................................................................................. 143 4.1 Conclusion ............................................................................................................................................. 143 ix 4.2. Limitations of the study ........................................................................................................................ 144 4.3. Future studies ....................................................................................................................................... 144 5. REFERENCES ............................................................................................................................ 145 APPENDICES ................................................................................................................................. 176 APPENDIX A ................................................................................................................................................. 176 Supplementary figures ................................................................................................................................. 176 APPENDIX B ................................................................................................................................................. 207 Revised SNT TB ethics certificate ................................................................................................................. 207 Previous TB ethics certificate ...................................................................................................................... 208 Blood collection ethics certificate ................................................................................................................ 209 APPENDIX C ................................................................................................................................................. 210 Turn-it-in report ........................................................................................................................................... 210 x List of Symbols and Abbreviations µg Microgram μL Microlitre μm Micrometre α Alpha β Beta A1AG1 Alpha-1-acid glycoprotein 1 A2GL Alpha-2-glycoprotein AMACR α-methylacyl-CoA racemase AMBP α-1-microglobulin/bikunin precursor APRIL A proliferation-inducing ligand ART Antiretroviral treatment ATP Adenosine triphosphate AUC Area under the Curve BAFF B-cell activating factor BCG Bacillus Calmette-Guérin BK Bradykinin BLC B Lymphocyte Chemoattractant Ca Circa CBTBR Centre of Excellence for Biomedical TB Research CCL-22 C-C Motif Chemokine Ligand 22 CF Culture filtrate CFP-10 Culture filtrate protein-10 kDa xi CFU Colony forming unit CLR C-type Lectin Receptors COVID-19 Coronavirus disease-2019 CRP C-Reactive Protein DABK desArg9-bradykinin DC Differentially culturable DCTB Differentially Culturable Tubercle Bacilli DNA Deoxyribonucleic acid EGF Epidermal Growth Factor ELISA Enzyme-linked immunosorbent assay ELISPOT Enzyme-linked immunospot EMB Ethambutol ENA-78 Epithelial Neutrophil-Activating Peptide-78 FcR Fc Receptor FGF-2 Fibroblast Growth Factor 2 G-CSF Granulocyte colony-stimulating factor GM-CSF Granulocyte-macrophage colony-stimulating factor GROα Growth-regulated oncogene α ESAT-6 Early Secreted Antigenic Target 6 kDa ETH Ethionamide HGF Hepatocyte growth factor HIV Human immunodeficiency virus IFN-α Interferon-alpha xii IFN-γ Interferon-gamma IGKC Immunoglobulin kappa chain C IGCL2 Immunoglobulin lambda-2 chain C IGRAs Interferon-gamma release assays IL Interleukin ILRA Interleukin-1 receptor antagonist INH Isoniazid iNOS inducible nitric oxide synthase IP-10 Interferon γ-induced protein 10 IRIS Immune reconstitution inflammatory syndrome I-TAC Interferon–inducible T Cell Alpha Chemoattractant ISO Isoxyl L Litre Lam Lipoarabinomannan LAM Latin American (LAM) LC–MS/MS Liquid chromatography with tandem mass spectrometry LDHB L-lactate dehydrogenase B chain LIF Leukemia inhibitory factor LJ Lowenstein-Jensen LPS Lipopolysaccharide LTBI Latent tuberculosis infection MCP Monocyte chemoattractant protein MDR Multi drug resistant xiii MGIT Mycobacterial Growth Indicator Tube MET Metronidazole MFI Mean fluorescence intensity MIF Macrophage migration inhibitory factor MHC Major Histocompatibility Complex MIG Monokine induced by gamma interferon MIP Macrophage Inflammatory Protein MiRNAs MicroRNAs mL Millilitre MMP-1 Matrix metalloproteinase-1 MOI Multiplicity of infection MPN Most probable number Mtb Mycobacterium tuberculosis MTBC Mycobacterium tuberculosis complex MVA85A Modified vaccinia virus Ankara expressing antigen 85A NAATs Nucleic acid amplification tests NGFβ Nerve growth factor beta NGS Next-generation sequencing NID1 Nidogen-1 NK Natural killer cells NICD National Institute for Communicable Diseases NLRs (NOD)-like receptors NMR Nuclear magnetic resonance spectroscopy xiv NRP Non-replicating persistence NO Nitric Oxide OADC Oleic acid, Albumin, Dextrose, Catalase OD Optical density OOR Out of range ORM1 α-1-acid glycoprotein 1 PAMP Pathogen-Associated Molecular Patterns PBS Phosphate Buffered Solution PCR Polymerase Chain Reaction PCT Procalcitonin PHRU Perinatal HIV Research Unit PiRNA PIWI-interacting RNAs PRRs Pattern Recognition Receptors PSTK Phosphoseryl-tRNA kinase PTGDS Proteins prostaglandin-H2 D-isomerase PTX-3 Pentraxin-3 PZA Pyrazinamide QFT QuantiFERON® Gold In-Tube assay RAP1B Ras-related protein Rap-1b RIF Rifampicin RNA Ribonucleic acid RNI Reactive nitrogen intermediates ROC Receiver operating curve xv ROI Reactive oxygen intermediates Rpf Resuscitation promoting factor RPMI Roswell Park Memorial Institute Medium RT-MLPA Reverse transcription multiplex ligation dependent probe amplification assay RT-PCR Real-time polymerase chain reaction SAA1 Serum amyloid A1 SCF Stem cell factor SCTM1 Secreted and transmembrane protein 1 SDF-1α Stromal cell-derived factor-1 alpha SELDI-TOF MS Surface enhanced laser desorption/ionization time-of-flight mass spectrometry SEM Standard error of the mean SIV Simian immunodeficiency virus SnRNA Small nuclear RNAs SnoRNA Small nucleolar RNAs SR Scavenger Receptor TAC Thiacetazone Th1 T helper 1 TLR Toll-Like Receptor TNF-α Tumour Necrosis Factor alpha TNF-β Tumour Necrosis Factor beta TNFR2 Tumour necrosis factor receptor 2 xvi TRAIL Tumour necrosis factor (TNF)-Related Apoptosis Inducing Ligand TSLP Thymic stromal lymphopoietin TST Tuberculin Skin Test TWEAK Tumor necrosis factor (TNF)-like weak inducer of apoptosis VBNC Viable But Non-Culturable VEGF-A Vascular Endothelial Growth Factor A WGS Whole genome sequencing WHO World Health Organization XDR Extensively drug-resistant xvii List of Tables Table 1.1: Factors that determine the transmission of Mycobacterium tuberculosis…………8 Table 2.1: Mycobacterium tuberculosis dormancy models frequently used in vitro……...30-31 Table 2.2: Standard curve concentration for each target (lot-specific) in the ProcartaPlex Immune Monitoring Th1/Th2 panel 6-plex………………………………………………….39 Table 3.1: Standard curve assay ranges for each target (lot-specific) in the ProcartaPlex Immune Monitoring 65-plex Panel…………………………………………………………..83 Table 3.2: Patient demographics……………………………………………………………86 Table 3.3: Cytokines changes during TB treatment in TB/HIV co-infected individuals…….94 Table 3.4: Cytokines changes during TB treatment in TB infected individuals……………..104 Table 3.5: Receiver operating characteristic (ROC) analysis of healthy controls vs TB/HIV+ and TB patient treatment groups at different time points……………………………………125 List of Figures Figure 1.1: Estimated annual tuberculosis incidence (per 100,000 population), by region worldwide……………………………………………………………………………………...3 Figure 1.2: Mycobacterial cell wall…………………………………………………………….6 Figure 1.3: A cascade for tuberculosis transmission……………………………………….......7 Figure 1.4: Dynamics of granuloma formation and pathology in tuberculosis………………..10 Figure 1.5: Transmission of tuberculosis and progression from latent infection to reactivated disease………………………………………………………………………………………..15 Figure 2.1: A summary diagram of the in vitro models of non-replicating persistent or dormant Mtb…………………………………………………………………………………………...26 Figure 2.2: The effect of RIF on survival of DCTB in an in vitro model of starvation……...32 Figure 2.3: Generation of DCTB in Mtb using nutrient starvation…………………………..33 Figure 2.4: Schematic diagram for the serial dilution during generation of DCTB………….36 xviii Figure 2.5: Growth kinetics of H37Rv, Beijing and LAM strains……………………………40 Figure 2.6: Schematic diagram for the generation of DCTB…………………………………41 Figure 2.7: The susceptibility of carbon starved clinical strains to assume the DC state……44 Figure 2.8: The susceptibility of starved clinical strains in nutrient-limited acidic conditions to assume the DC state………………………………………………………………………….45 Figure 2.9: Pro-inflammatory cytokine production in whole blood with replicating Mtb infection………………………………………………………………………………………47 Figure 2.10: Pro-inflammatory cytokine production in whole blood with starved Mtb Infection……………………………………………………………………………………...49 Figure 2.11: Pro-inflammatory cytokine production in whole blood with replicating and starved Mtb infection……….………………………………………………………………………...50 Figure 2.12: A Flow diagram summarising the results observed in whole blood assay Infection……………………………………………………………………………………...52 Figure 3.1: Schematic representation of ‘omics’ approach…………………………………..59 Figure 3.2: Study design for monitoring treatment response using DCTB…………………..78 Figure 3.3: A Flow diagram outlining the categorisation of study participants………………79 Figure 3.4: The use DCTB assays to detect bacteria in sputum specimens prior to TB treatment……………………………………………………………………………………...80 Figure 3.5: Bacterial clearance as reported by DCTB assays and routine measure of TB treatment response……………………………………………………………………………87 Figure 3.6: Reproducibility of standard curve relative to manufacturer standard……………88 Figure 3.7: Cytokine profiles in specimens selected to technical assessment………………..89 Figure 3.8: Heat map of cytokine and chemokine concentrations among the controls and different groups of patients…………………………………………………………………...91 xix Figure 3.9: Principal Component Analysis (PCA) of cytokines and chemokines among the controls and different groups of patients……………………………………………………...92 Figure 3.10: Comparison of HIV progressors with TB/HIV co-infected individuals and longitudinal assessment of cytokines to monitor treatment response…………………….95-102 Figure 3.11: Comparison of Comparison of healthy controls with all TB mono-infected individuals and longitudinal assessment of cytokines to monitor treatment response………………105-109 Figure 3.12: Comparison between HIV/TB co-infected and TB infected during treatment……………………………………………………………………………….110-111 Figure 3.13: Correlation between CD4 count, viral load, and plasma cytokines………112-113 Figure 3.14: Heat map of cytokine and chemokine concentrations among the DCTB treatment groups stratified by HIV status ……………………………………………………………..114 Figure 3.15: Principal Component Analysis (PCA) of cytokines and chemokines highlighting the variations between healthy controls, treatment responsive), delayed responsive and non- responsive…………………………………………………………………………………...115 Figure 3.16: Changes in biomarkers between healthy controls and treatment responsive, delayed-responsive as well as non-responsive groups………………………………….117-123 Figure 3:17: ROC analysis to estimate the discriminatory power of plasma cytokines in treatment responsive, delayed responsive and non-responders with/ without HIV infection groups…………………………………………………………………………………..126-127 Figure 3.18: A Flow diagram summarising the changes in cytokines between the different groups of patients……………………………………………………………………………130 Figure A1: The log difference of cells in the MPN wells as compared to CFU in the PBS- starvation, 1% DMSO and 100 µM RIF-treated cultures……………………………………174 Figure A2: Changes in biomarkers between healthy controls, DCTB Treatment responsive, Delayed-responsive and Non-responsive groups………………………………………175-177 Figure A3: Longitudinal assessment of cytokines to monitor treatment response of treatment- responsive, delayed-responsive and non-responsive individuals……………………….178-184 xx Figure A4: The effect of strain type on cytokine profiles during TB treatment………..185-195 Figure A5: The effect of residual DCTB at the end of treatment...................................196-204 xxi Abstract Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (Mtb). Despite the implementation of multifaceted TB prevention and control efforts, a significant number of people still dee from TB. Consistent with this, an uptick in TB-related mortality was recently noted, which has been ascribed to the negative effects of Coronavirus disease-2019 (COVID- 19) on TB programs. The complex life cycle of Mtb is largely due to the use of immune evasion mechanisms to establish initial infection, remain dormant in the host, and reactivate pathogenicity under favourable circumstances. The prolonged TB treatment regimen is necessitated by the slow response of bacterial populations to standard TB chemotherapy, a phenomenon that may be caused by persistent, drug-tolerant bacteria. Scientific literature has provided evidence for these types of bacterial populations in the form of Differentially Culturable Tubercle Bacilli (DCTB). It has been demonstrated that DCTB represent drug tolerant bacteria that appear to be cleared at slower rate than organisms detected by routine culture methods. However, it remains unclear if DCTB populations elicit different immune responses when compared to their conventionally culturable counterparts. Herein, we address this question by optimizing a laboratory model for the generation of DCTB in vitro and test the capacity of clinical isolates of Mtb from Lineage 2 (Beijing) and Lineage 4 (LAM) to adopt the DCTB state. Using the Most probable number (MPN) assay, in the presence of culture filtrate (CF) as a source of growth factors to resuscitate DCTB, and colony forming units, the amount of DCTB in our model was quantified. As demonstrated by the limited growth on agar plates and increased growth in liquid media supplemented with CF from an axenic culture of Mtb, our findings demonstrated that carbon starvation was able to generate DCTB from clinical Mtb strains. After generating these populations, we stimulated whole blood with DCTB and conventionally culturable populations and report on the stimulation of a select set of cytokines (IFN-γ, IL-4, IL-5, IL-6, IL-12p70 and TNF-α) using a Bead Array Multiplex Immunoassay. In comparison to H37Rv-DCTB and LAM-DCTB, Beijing-DCTB induced significantly reduced levels of IL-5 and TNF-α. When comparing cytokine production between culturable and DCTB populations, within a single strain, we noted that LAM-DCTB was delayed in the production of IFN-γ whilst Beijing-DCTB was not able to induce production of this cytokine when compared to conventionally culturable counterparts. These data suggest that shifting to a non-replicating DCTB state does indeed affect the ability of clinical isolates to induce immune responses. xxii Based on these observations, we next set out to determine if DCTB affects immune responses during treatment of Mtb infected individuals. In prior work, using a prospective observational cohort, we demonstrated a substantive heterogeneity in clearance of DCTB in individuals with drug susceptible TB. We were able to classify these response patterns into three broad groups including (I) participants who were able to clear DCTB within the first two weeks of treatment (treatment-responsive); (II) those with delayed ability to clear these organisms (delayed- responsive) and (III) a group of individuals where DCTB did not change substantively during treatment (non-responders). Given these stark differences in treatment response patterns, we hypothesized that the immune responses associated with these patterns would be substantively different. In the second component of this work, we set out identify immune biomarkers that predict an effective response of DCTB to TB treatment. To quantify cytokines, chemokines and growth factors in plasma from these groups, we used a 65-plex Luminex assay, with a broad selection of targets. Statistically significant differences between these groups were analysed using the Kruskal-Wallis test with Dunn’s multiple comparisons, with p<0.05 was considered as statistically significant. When compared to patients who had TB and HIV co- infection, the number of cytokines that may possibly be used to report on the effectiveness of TB treatment was significantly higher in Mtb-only infected patients. This suggests that HIV infection significantly reduces the number of cytokines that can be used to report on TB treatment response. The ROC analysis of I-TAC, G-CSF and VEGF-A showed that these cytokines have a significant discriminatory power to distinguish treatment-responsive and non- responsive patients from HCs using DCTB as the measure of treatment response. No unifying cytokine signature that predicted DCTB response in all groups was identified. Together, our results indicate that some inflammatory markers are elevated in individuals with TB that rapidly clear bacteria during treatment. Given that these responses are based on DCTB, which represent drug tolerant populations, these select cytokines may be useful in evaluating the effectiveness of novel shorter TB treatment regimens. Page | 1 CHAPTER 1 1. TUBERCULOSIS 1.1. Introduction Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is a disease killing millions of people globally (Lourens et al., 2019). TB infection is established when an individual inhales aerosolized particles carrying Mtb. Primary factors in TB transmission include the host's immunological health status, the amount of inhaled bacteria, the proximity of contacts, and the infectiousness of the primary case (Mathema et al., 2008). Dendritic cells and macrophages, as well as non-phagocytic alveolar endothelial cells such as M cells and type 1 and type 2 epithelial cells (pneumocytes), are susceptible to infection by inhaled Mtb. Before the adaptive immune system sets in, Mtb can replicate within macrophages and spread to pulmonary lymph nodes and to a number of extra pulmonary sites (Teitelbaum et al., 1999, Ryndak et al., 2015). A number of scenarios exist that describe the outcome of infection, including: (I) elimination of infection by host immune activation; (II) bacterial growth leading to primary infection, and (III) bacterial dormancy/lack of replication, which renders the host non-contagious and asymptomatic (Trauner et al., 2012). Given these complex outcomes, the clinical presentation of TB can take one of two major forms including, asymptomatic, previously referred to as latent TB infection (LTBI), where the bacteria can persist for many years in the host without replicating and active disease where the bacteria replicate and cause symptoms (Pai et al., 2016). LTBI is a term that has been used to describe a state of long-term immunological control of Mtb infection that develops in most infected people. Approximately 2 to 5% of infected people eventually progress to active TB disease, typically within the first six months to two years of infection (Goletti et al., 2018). With the advent of higher resolution imaging techniques for TB disease in humans, LTBI has been reclassified as a spectrum of TB infection (Drain et al., 2018). Within this spectrum, specific states have been described including subclinical TB infection (asymptomatic with radiological or microbiological evidence of TB) and incipient TB (asymptomatic with radiological or microbiological evidence of TB, together with a biomarker signature that predicts progression to active disease.) As further information becomes available on TB immune responses and bacterial viability/replication competence, it 2 is likely that these definitions will require further revision. In general, here we use the term LTBI to refer to these collective asymptomatic clinical presentations. Active TB often affects the lungs, but it can also affect any organ outside of the lung, resulting the development of extra pulmonary TB (Behr et al., 2018). In this clinical presentation, tubercle bacilli have colonized the lung to form lesions that impair respiratory sufficiency. This results in symptoms associated with TB such as shortness of breath, night sweats, weight loss and persistent coughing with blood. The pathogenesis of active TB will be discussed later. When this condition is diagnosed, treatment is necessary to achieve clinical cure. Historically, streptomycin was the first antibiotic used to treat TB however, resistance to this drug developed quickly rendering it of limited use. Subsequently aminoglycosides and rifamycins were discovered as effective tools to treat TB, leading the first combination regimens to achieving durable cure (Rocha et al., 2021). Currently, TB treatment comprises a combination regimen of isoniazid (INH), rifampicin (RIF), pyrazinamide (PZA), and ethambutol (EMB), the four first-line antibiotics that are routinely used (Somasundaram et al., 2014). Resistance to a select set of these drugs results in multidrug-resistant TB (MDR-TB) or extensively drug-resistant TB (XDR-TB) (Pai et al., 2016). MDR-TB is defined as infection with Mtb strains that are genetically resistant to RIF and INH, with XDR being defined as MDR with additional resistance to second line drugs including aminoglycosides and fluoroquinolones (Yang et al., 2018). In addition to chronic infections brought on by the emergence of M/XDR-TB, a sizable reservoir of people harbouring Mtb in asymptomatic state can progress to active disease, thus contributing to the increase in new TB cases globally (Peddireddy et al., 2017). A good strategy to successfully eradicate TB on a global scale is to provide efficient vaccination, better diagnostics, and new, shortened treatment regimens (Abu- Raddad et al., 2009). Owing in part to the absence of effective methods to accurately identify Mtb or its by-products in host samples from asymptomatic individuals, TB biomarkers that predict disease progression are required. In addition, host biomarkers that aid in TB diagnosis as well as to assess the effectiveness of treatment are urgently needed (Walzl et al., 2011). 1.2. Global prevalence of TB According to World Health Organisation (WHO) global report in 2021, an estimated 10 million people were infected with TB, 5.6 million men, 3.3 million women and 1.1 million children. There was a 18% (7.1 million in 2019 to 5.8 million in 2020) decline in the number of people 3 newly diagnosed with TB and reported. This decline was attributed to the COVID-19 pandemic. The global burden of TB disease differs between countries and overall geographies, with incidence ranging from fewer than five to more than 500 new cases per 100 000 population per year. In 2020, the 30 high TB burden countries accounted for 86% of new TB cases. Eight countries account for two thirds of the total, with India leading the count, followed by China, Indonesia, the Philippines, Pakistan, Nigeria, Bangladesh, and South Africa (see Figure 1.1). WHO reported that in 2020, 1.3 million TB deaths were among human immunodeficiency virus (HIV)-negative people (up from 1.2 million in 2019) and an additional 214 000 among HIV- positive people (up from 209 000 in 2019), with the combined total giving an incidence equivalent to 2017 rates. HIV is one of the leading risk factors for developing active TB followed by diabetes, malnutrition, smoking and excessive alcohol use (Narasimhan et al., 2013). 1.3. Diagnosis and treatment Mtb infection New tools, including enhanced diagnostic tests, are urgently needed to curb the spread of TB. Microbiological, radiographic, and immune-based assays can be used to diagnose TB infections (Gill et al., 2022). Smear microscopy, Mtb culture, and nucleic acid amplification assays such as the GeneXpert® MTB/RIF assay are among the most frequently used tests for the diagnosis of active TB (Cudahy and Shenoi, 2016). In areas with limited resources, smear Figure 1.1. Estimated annual tuberculosis incidence (per 100,000 population), by region worldwide (WHO, 2022). 4 microscopy is the preferred technique, involving detection of acid-fast mycobacteria in sputum clinical samples using the Ziehl-Neelsen stain. Given the ease of use, molecular diagnostics have become widespread for the detection of TB. However, due to its enhanced sensitivity over smear and excellent specificity, Mtb culture is still regarded as the gold standard for the detection of active TB (Halliday et al., 2019). In general, culture compares well with molecular diagnostics, however, with low numbers of bacteria, both of these methods can miss cases. Since the discovery of Mtb, the general approach to growing bacteria has been culturing on Lowenstein-Jensen (LJ) media, which can take up to 4-6 weeks to detect colonies. This method has a relatively high specificity and sensitivity, but it also needs a laboratory with BioSafety Level III facilities. It has been shown that at least 10 viable bacilli per mL of sputum is necessary for Mtb culture using the LJ medium to successfully detect Mtb (Munir et al., 2015). Owing to the slow growth of Mtb, improved methods for culture such the BACTEC mycobacterial growth indicator tubes (MGIT) are being used. This method has a better turn- around time by shortening the time required to detect Mtb by 1-3 weeks (Kolibab et al., 2014). The development of molecular nucleic acid amplification tests (NAATs) tests such as GeneXpert has been considered a major breakthrough in TB diagnosis and most recently, the introduction of an improved GeneXpert Ultra has improved on sensitivity (Nguyen et al., 2019). These tests, use polymerase chain reaction (PCR)-based amplification technique to detect TB and can also be used in drug susceptibility detection for first line drugs such as RIF (MacLean et al., 2020). In developing countries, sputum smear microscopy using Ziehl- Neelsen staining for TB diagnosis is preferred, due to its affordability, high specificity, and reduced requirement for expensive equipment. Results from smear microscopy can be acquired in less than two hours, but this approach is less sensitive as it requires between 5,000 and 10,000 bacilli per milliliter (mL) of sputum to obtain a positive result (Riaz et al., 2016). Alternative methods for diagnosis of active TB such as antibody-based serological tests have been used but have been shown to have poor accuracy (Pai et al., 2008).The Lipoarabinomannan (Lam) urinary detection method is also gaining interest for use in diagnosing active TB (Zijenah et al., 2015). Lam has proven particularly useful for diagnosis of TB in people living with HIV given the extra pulmonary manifestation of disease, which can manifest more than 50% of cases in some instances (Kerkhoff et al., 2020). In addition, Lam has shown promise for detection of TB in children (Iskandar et al., 2017). However, a 5 significant limitation of LAM is that it is only useful with extrapulmonary disease and has low sensitivity. As Mtb infection has a spectrum of clinical outcomes including asymptomatic and active TB disease with chronic symptoms (Lin and Flynn, 2018), there are two primary methods for diagnosis of LTBI. In the health sector, for many years. clinicians have been using tuberculin the skin test (TST) for diagnosis of LTBI, with interferon-gamma release assays (IGRAs) as an alternative (Zellweger et al., 2020, Gooding et al., 2007). This will be discussed later. 1.4. Physiology of Mycobacteria The phylum of Gram-positive bacteria known as Actinobacteria is incredibly complex and contains several species that have developed distinct symbioses (commensal or parasitic) with a variety of hosts, including various mammals. For instance, whereas certain species of the genera Mycobacterium and Nocardia are harmful, others from the genus Bifidobacterium are recognized to be beneficial members of the normal gut microbial flora and have a significant positive impact on human health (Barka et al., 2016, O'Callaghan and Van Sinderen, 2016). In the phylum Actinobacteria, the genus Mycobacterium includes a collection of gram-positive, rod-shaped, acid-fast organisms (Gao and Gupta, 2012). The term "Mycobacterium tuberculosis complex" (MTBC) refers to a group of species that have been found to be genetically similar, including Mycobacterium pinnipedii, Mycobacterium canettii, Mycobacterium africanum, Mycobacterium microti, Mycobacterium bovis, Mycobacterium microti and Mtb. The majority of Mycobacterium species typically live in a variety of environments, including soil and water, and engineered water systems (Falkinham, 2009). Numerous members are recognized human pathogens, most notably Mtb and Mycobacterium leprae, which cause TB and leprosy, respectively (Medjahed et al., 2010). In addition, Mycobacterium ulcerans has been attributed as the causative agent of Buruli ulcer (Stinear et al., 2007). This disease manifests as open sores on the skin, required combination treatment and sometimes amputation of limbs (Yotsu et al., 2018). From the MTBC group, Mtb is one of the most researched species (Pai et al., 2016). One notably distinctive characteristic that sets Mycobacterium species apart from other bacteria is the amazing molecular complexity of the mycobacterial cell wall. Despite being categorized as gram-positive organisms, their envelopes have several characteristics in common with gram- negative cell walls, namely an outer permeability barrier that serves as a pseudo-outer membrane (Ratledge and Stanford, 1982, Brennan and Nikaido, 1995, Alderwick et al., 2015). 6 The extensive structure of the mycobacterial cell envelope is depicted in Figure 1.2. Glycolipids that penetrate the periplasmic region are linked to the inner membrane phospholipid bilayer. A cross-linked peptidoglycan polymer, a highly branched arabinogalactan polysaccharide, and long-chain mycolic acids make up the fundamental core cell wall structure. Solvent-extractable lipids, such as non-covalently coupled glycophospholipids and inert waxes, are intercalated into the mycolate layer to create the outer membrane. The carbohydrate- and lipid-rich layers of the cell wall are crucial for pathogenesis and survival in addition to acting as a permeability barrier that guards against hydrophilic substances (Daffe and Reyrat, 2008, Forrellad et al., 2013, Abrahams and Besra, 2018). Figure 1.2. Mycobacterial cell wall. A schematic representation of the mycobacterial cell wall, depicting the prominent features, including the glycolipids (phosphatidyl-myo-inositol mannosides, mannosylated lipoarabinomannan), peptidoglycan, arabinogalactan and mycolic acids. Intercalated into the mycolate layer are the acyl lipids (including trehalose monomycolate, trehalose dimycolate, diacyltrehalose, polyacyltrehalose, phthiocerol dimycocerosate, sulfoglycolipid). This image was taken from (https://2009.igem.org/wiki/index.php?title=Team:SupBiotech-Paris/Concept1&oldid=159486) Numerous opportunities exist to develop anti-mycobacterial drugs to eradicate Mtb, owing to the complexity of its cell wall. A more effective chemotherapy can be produced by disrupting the structure of the cell wall, which can facilitate the entry of other TB drugs. More significantly, cellular death in mycobacteria can result from suppression of biosynthesis of cell wall components (Vilchèze, 2020). This approach can potentiate the activity of current TB file:///C:/Users/itumeleng.Boshielo/AppData/Roaming/Microsoft/Word/(https:/2009.igem.org/wiki/index.php%3ftitle=Team:SupBiotech-Paris/Concept1&oldid=159486) 7 drugs, increase intracellular concentrations of drugs and also limit the emergence of drug resistance. 1.5. Pathogenesis of M. tuberculosis 1.5.1. Transmission of Mtb Mtb is spread via the air as droplet nuclei when infectious people with pulmonary TB cough, sneeze, talk, laugh or spit. Recent work suggests that tidal breathing also contributes to TB transmission (Dinkele et al., 2022). Mtb in these tiny droplets of small size (1–2 mm or less) can remain viable as airborne droplets, suspended in the air for several hours depending on the environment. A susceptible host needs substantial exposure to multiple droplet nuclei during interaction with a TB diseased individual to become infected as shown in Figure 1.3. The factors that affect transmission are mentioned in Table 1.1 (Mack et al., 2009). TB predominately affects the lungs but can also spread via the systematic and lymphatic circulation and infect other organs and tissues such as the liver, bones, spleen and the brain (Loddenkemper et al., 2016). Figure 1.3. A cascade for tuberculosis transmission is proposed in which (1) a source case of TB (2) generates infectious particles (3) that survive in the air and (4) are inhaled by a susceptible individual (5) who may become infected and (6) who then has the potential to develop TB. This picture was extracted from (Churchyard et al., 2017). 8 1.5.2. Host immunity against Mtb The human immune system comprises the innate and adaptive immune responses, with innate immunity being the first to act during infection. The immune response that results during the interaction between Mtb and the host is complex, multifaceted and depends on the health and nutrition of the host. The encounter may result in numerous potential outcomes including the development of latent infection, subclinical disease, primary disease or elimination of the Table 1.1: Factors that determine the transmission of Mycobacterium tuberculosis. Factors Description Features of the source case Culture or smear positivity-high bacterial load results in increased transmission risk. Drug treatment-prolonged treatment reduces transmission potential. Previous history of TB. Ability to generate aerosols, lung capacity and cough frequency. Health seeking behaviour. Susceptibility of the new host Immune status of the exposed individual (HIV infection). Prior history of lung damage. Any other comorbidities such as diabetes. Environment Environmental factors that affect the concentration of M. tuberculosis such as ventilation, room size and movement of individuals. Other factors include number windows, doors etc. Relative humidity, TB bacilli can survive in humid environments. Recirculation of air in closed settings. Environments that promote congregation such as schools, prisons and places of worship. Exposure Proximity, frequency, and duration of exposure with source cases. tubercle bacilli (de Martino et al., 2019). Once the pathogen successfully reaches the alveoli, it encounters immune cells such as alveolar macrophages, dendritic cells, T-lymphocytes, neutrophils, fibroblast, which aggregate and form a structure called the granuloma via cytokine mediation. This results in further containment of the bacterium following the initial engulfment of the bacilli by recruited macrophages (Marakalala et al., 2018). 9 Due to the lack of oxygen and nutrients within the granulomatous caseous centres, the bacilli can enter a dormant state and stop replicating, thus establishing latent infection (Barry et al., 2009, Lerner et al., 2015). Macrophages and CD4+ T lymphocytes, together with granuloma formation, play an important role in deciding the outcome of the infection which can either favour pathogen survival or kill it (de Martino et al., 2019). Factors such as HIV infection and diabetes cause immune suppression/dysregulation and these individuals have considerably increased risk of Mtb reactivation/disease progression as a result of dissolution of the granuloma leading to replication of the bacilli. This eventually progresses to full-blown disease causing lung cavitation and spread of bacteria into other parts of the lungs, and subsequently into the air during respiratory manoeuvres, which can increase transmission potential (Abrahem et al., 2020). Granulomas are known to assist in controlling TB infection and are therefore a key feature in the host response to Mtb (Basaraba, 2008, Esmail et al., 2014). Paradoxically, whilst mediating this protective role, they facilitate intracellular survival of bacilli. During asymptomatic infection, Mtb bacilli are able to reside and persist within the granulomas for a long time (Kolloli et al., 2018). The granuloma represents the end result of inflammatory mononuclear cell infiltration that limits the growth of Mtb, while also providing a survival niche for the bacteria to disperse (Orme and Basaraba, 2014, Ehlers and Schaible, 2013). Granulomas are formed by aggregation of immune cells at the onset of infection as shown below in Figure 1.4. The granuloma undergoes morphological evolution from early infection to late TB stages, with the bacilli being intracellular at the start of infection and extracellular as the necrotic caseous lesion proceeds (Grosset, 2003). Granulomas can be found in active TB, LTBI, and in cases of recurrent TB. As a result, simply forming a granuloma is insufficient for infection control; rather, the granuloma must operate effectively. In active TB, the host often has several granulomas that are unable to control bacteria, whether extracellular or within macrophages or dendritic cells. The bacteria then migrate throughout the lung or disperse to other organs, resulting in the formation of additional lesions (Flynn et al., 2011). The caseous granuloma is the most advanced granuloma in TB, so named because the centre of the granuloma has a "cheese-like" look. This granuloma is made up of multiple cells, primarily with epithelioid macrophages that surround an acellular necrotic zone, as well as a lymphocytic cuff that includes both B and T cells and fibroblasts (Flynn and Klein, 2011). 10 1.5.3. Innate immunity Innate immune responses play a central role in the pathology of infectious and inflammatory diseases, with phagocytes such as macrophages, dendritic cells and neutrophils playing a critical role in host-pathogen interactions. Together with inflammatory mediators like cytokines, chemokines, and proteases, these innate immune cells are essential contributors to the host defence against Mtb infection (Etna et al., 2014, Muefong and Sutherland, 2020, Newson et al., 2014). 11 Figure 1.4. Dynamics of granuloma formation and pathology in tuberculosis. Mtb triggers a local inflammatory infiltration which may give rise to (i) protective immunity, (ii) balanced inflammation, or (iii) transmission within the bronchi of the lungs in necrotizing granulomas. The varieties of organized granulomas that are pictured are simplified representations of different points along a pathologic continuum. These represent distinct phases of the Mtb life cycle, including either metabolic adaption or slowed development within the granulomatous lesion, or recrudescence and dissemination to the subsequent host after granuloma breakup. Typical cellular and humoral mediators of granuloma differentiation are indicated in italics. This diagram was taken from (Ehlers and Schaible, 2013). 12 Macrophages are the first line of defence against pathogen invasion and play an important role in maintaining overall immune protection. Resident alveolar macrophages are phagocytic cells that encounter bacilli as they reach the pulmonary alveolus but usually fail to eliminate mycobacterial invaders (Naeem et al., 2018). Chemokines produced by alveolar macrophages and pneumocytes attract the first wave of inflammatory cells, including neutrophils, monocyte- derived macrophages, natural killer (NK) cells, Gamma Delta (γδ)-T cells and T cells, which drive inflammation and tissue remodelling (Eum et al., 2010, Feng et al., 2006). This further triggers the release of pro-inflammatory cytokines tumour necrosis factor (TNF), IL (interleukin)-6, IL-1α, and IL-1β (Kroon et al., 2018). This interaction is facilitated by the recognition of Pathogen-Associated Molecular Patterns (PAMPs), present on the bacterial surface, by Pattern Recognition Receptors (PRRs) of the host cell such as Toll-Like Receptors (TLRs), C-type Lectin Receptors (CLRs), Fc Receptors (FcRs), NOD-like receptors (NLRs), Scavenger Receptors (SRs), and cytosolic Deoxyribonucleic acid (DNA) sensors (Ponnusamy and Arumugam, 2022). These immune responses also lead to activation of other cellular processes such as apoptosis, antigen presentation, inflammasome activation, phagosome maturation, and autophagy. Macrophages also play a role in initiating adaptive immunity by presenting antigens to T cells to initiate the cellular response (Mortaz et al., 2014, Queval et al., 2017). Activated macrophages employ multiple strategies to eliminate the phagocytosed bacilli and these include restriction of pathogen growth through phagosome-lysosome fusion, apoptosis, nitric oxide (NO) release, generation of reactive oxygen intermediates (ROI), and the respiratory burst (Khan et al., 2016, Mihret, 2012). 1.5.4. Adaptive immunity The adaptive immune response is initiated early and matures over a period 3-8 weeks post infection. Antibody and cell mediated immunity effector systems, make up the adaptive immune response and their production or effects are initiated by B and T cells, respectively. T cell mediated immunity plays a crucial role in managing TB infection (Bandaru et al., 2020). The presentation of antigens to other cells in host immune response against pathogens is done most efficiently by dendritic cells. This activity, together with antigens presented by macrophages as their secondary role, links the innate and adaptive immune response during TB infection. Dendritic cells exist either in an immature or mature form. Bacterial pathogens trigger immature dendritic cells to develop into mature dendritic cells, which are more efficient in antigen-presenting activity to T cells (Mbongue et al., 2014). DCs transport bacterial 13 antigens from the infection site to the thoracic lymph nodes, where naïve T cells reside. As naïve T cells are not able to recognise antigens, mature dendritic cells are required to breakdown these proteins and present the peptides through Major Histocompatibility Complex (MHC I and II) complexes that can then be recognized by effector CD4+ and CD8 T cells (Mellman and Steinman, 2001). In the lymphatic system, mature dendritic cells can stimulate specific T cell responses by secreting cytokines that induce differentiation of CD4+ T cells into several subtypes which include Th1, Th2, Th17, and regulatory T cells. CD4+ T cells differentiate into Th1 cells when mature dendritic cells secrete IL-12, which in turn causes Th1 cells to secrete interferon-gamma (IFN-γ), causing a positive feedback loop for dendritic cells to further produce IL-12 and produce more Th1 cells. Th2 activation also occurs in a similar fashion, but with IL-6 secretion, allowing these cells to secrete IL-4. IL-6 plays a dual role together with IL-1β and IL-23 in activating Th17 cells (Abrahem et al., 2020, Kim and Kim, 2018). Th17 cells secrete IL-17 that in turn stimulates the production of Granulocyte colony- stimulating factor (G-CSF), Granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein-2 (MIP-2), IL-6, and IL-8 in both CD4+ and CD8 T cells. This pro-inflammatory cytokine is essential in the process of recruiting neutrophils to the infection site (Scordo et al., 2016). Neutrophils also play an important role in inflammatory responses and are some of the first phagocytes recruited from the pulmonary vasculature to the site of infection (Jenne et al., 2013). In the early TB granuloma, during oxidative killing of Mtb engulfed from infected macrophages, neutrophils were observed to exert a protective role (Yang et al., 2012). These cells can create chemotactic signals that attract recruitment of dendritic cells and other immune cells to the infection site. A neutrophil-driven blood transcriptional signature from patients with TB has linked the involvement of these cells in the control of Mtb infection (Berry et al., 2010). The study identified a specific 86-transcript signature that was dominated by a neutrophil-driven IFN- inducible gene profile, consisting of both IFN- γ and type l IFN-αβ signalling and could also differentiate between active TB and other inflammatory infections. Despite the many important functions played by neutrophils, which include chemotaxis, phagocytosis, generation of reactive oxygen metabolites and activation of other immune cells, their presence can also cause dramatic tissue damage (Kruger et al., 2015). There are limited in vitro studies on these cells because they are short-lived and not easy to handle, hence their complete role in the TB immune response requires further investigation. Nevertheless, animal model studies have proven their 14 significance in early immune responses against Mtb. For example, an increased number of neutrophils was observed during early days of infection in Balb/c mice infected with Mtb H37Rv, with a demonstrated role in the elimination of the Mtb and limiting dissemination (Barrios-Payán et al., 2006). Another study showed how neutrophils are recruited to the lungs using Lipopolysaccharides (LPS) during infection in rats (Sugawara et al., 2004). Although such findings illustrate the importance of this subset of cells, another study demonstrated that neutrophils don’t play any role during early control of slow replicating Mtb but are rather crucial in the control of fast-replicating bacteria from other species (Seiler et al., 2000). Neutrophils have higher phagocytosis intensity and oxidative response as compared to macrophages (Nordenfelt and Tapper, 2011). In response to the host and pathogen interaction via PAMPs, neutrophils can express pro- and anti-inflammatory cytokines including IFN-γ, TNF, IL-4, and IL-10 (Lyadova, 2017, Tecchio and Cassatella, 2016). The interaction between neutrophils, macrophages and T cells is tightly regulated and essential in determining the outcome of infection. However, it is still not fully understood as to how neutrophils facilitate cytokine-mediated communication with other cells in the granuloma. In the TB granuloma, activated neutrophils are thought to express cytokines that can dysregulate immune responses and contribute to pathology (Greenlee-Wacker, 2016, Mantovani et al., 2011, Nicolás-Ávila et al., 2017, Tecchio and Cassatella, 2016). Recently, Gideon et al. (2019) showed that in cynomolgus macaques infected with Mtb, neutrophils express pro-and anti-inflammatory cytokines in granulomas which resembled T-cell cytokine expression and thus may play an important immunoregulatory role. 1.6. Latent TB infection LTBI is defined as immunological sensitization to Mtb antigens without the presence of clinical symptoms of TB. During this period of infection, Mtb is thought to survive in a non-replicating (dormant) state, with bacteria mostly limited to hypoxic granulomatous lesions in the lungs (Via et al., 2008). The health status of an individual determines the duration of latency, while most individuals can remain latently infected for a lifetime, there is a 5-10% chance for reactivation to occur, which can happen within the first 2 years post infection (Achkar and Jenny-Avital, 2011). The risk is increased annually to 10% more in immune compromised individuals, such as those infected with HIV (Lin and Flynn, 2010). As mentioned previously, LTBI diagnosis is done by measuring immune response to specific Mtb antigens using the TST or IGRAs diagnostic tests. The TST measures the sensitivity of intradermally injected purified protein derivative prepared from culture filtrate of Mtb, whereas the IGRAs measure the T-cell 15 immune response to Mtb by detecting IFN-γ release in blood (Gooding et al., 2007). The majority of infected people (90-95%) are able to clear the infection or to control the initial infection and enter a latent phase (Denholm et al., 2020). The term "latent" TB or phthisis (as TB was then known) was conceived by French and German physicians in the early 19th century, when the field of anatomopathology in medicine began to develop, to describe the pathological changes of TB in necropsied lungs of people who had not yet displayed any symptoms of the disease before they died (Behr et al., 2021). The state of latency was also observed in cultured tissues of healthy individuals with no pathological evidence of active TB, having died from other causes (Opie, 1927). These and other such observation led to the term LTBI becoming firmly entrenched in the literature and in clinical use. 1.7. Progression to Active TB TB disease is caused by intricate interplay between the immunological state of the infected individual and the bacillary load (Lawn et al., 2011). HIV infection, diabetes, malnutrition, low body weight, smoking, lung illness, drug use, and previous or current use of immunosuppressive drugs have all been recognized as factors that increase the likelihood of developing active TB disease (Horsburgh Jr and Rubin, 2011). Factors related to the bacteria such as strain virulence and inoculum size, also influence the progression of disease (Carranza et al., 2020). Active TB appears to develop within a year of infection, and the rate of progression drops rapidly after the first year and then slowly declines over the following ten years, Figure 1.5 (Brooks-Pollock et al., 2011). 1.8. HIV/TB co-infection The most potent risk factor for the development of TB is HIV infection. According to current knowledge, HIV causes an increased chance of rapidly progressive primary TB following exposure, as well as an increased likelihood of progression to active TB disease from asymptomatic infection (Lawn et al., 2011). TB is the most prevalent disease in immune- suppressed individuals and the primary cause of death in people living with HIV (Swaminathan and Narendran, 2008). WHO reports that there were an estimated 37.7 million people living with HIV at the end of 2020, over two thirds of whom (25.4 million) are in the WHO African region and a total of 214 000 people with HIV and TB died in 2020. According to WHO, HIV- infected people are 18 times more likely than healthy people to get TB (WHO, 2021). 16 In their interactions with the host, HIV and Mtb both seek to achieve the same goal: efficient replication without killing the host for as long as is feasible in order to increase the possibilities of transmission to a new host (Sharan et al., 2020). HIV infection makes the patient more susceptible to TB by weakening the immune system, primarily by killing and changing CD4+ T-cell activity. The CD4+ T cell loss in lymphoid organs and peripheral blood is the most well- known effect of HIV (Ahmed et al., 2016). A study by (Foreman et al., 2016) showed that Mtb/simian immunodeficiency virus (SIV) co-infection in non-human resulted in rapid reactivation of TB disease, although there were a subset of animals that were still able to contain TB infection. Figure 1.5. Transmission of tuberculosis and progression from latent Infection to reactivated Disease. Mtb is transmitted by inhalation of aerosolized microdroplets that are released by the coughing of infected individuals. The initial stages of infection are characterized by innate immune responses that involve the recruitment of inflammatory cells to the lung. Following bacterial dissemination to the draining lymph node, dendritic cell presentation of bacterial antigens leads to T cell priming and triggers an expansion of antigen-specific T cells, which are recruited to the lung. The recruitment of T cells, B cells, activated macrophages and other leukocytes leads to the establishment of granulomas, which can contain Mtb. Most infected individuals will remain in a 'latent' state of infection, in which no clinical symptoms are present. A small percentage of these people will eventually progress and develop active disease, which can lead to the release of Mtb from granulomas that have eroded into the airways. When individuals with active TB cough, they can generate infectious droplets that transmit the infection. This image was taken from the https://scienceandsamosa.com/killer-disease-on-loose-india-grappling- with-tuberculosis/ https://scienceandsamosa.com/killer-disease-on-loose-india-grappling-with-tuberculosis/ https://scienceandsamosa.com/killer-disease-on-loose-india-grappling-with-tuberculosis/ 17 Granuloma formation allows the host to contain tubercle bacilli the lung. As people with HIV/TB co-infection have impaired immunity, particularly in the case of low CD4 T cell counts, the ability to form granulomas is impaired. As a result, bacilli readily traffic out of the lung and take up residence in other parts of the body, leading to the development of extra- pulmonary TB independently (Schutz et al., 2010, Marakalala et al., 2016). HIV co-infection may increase the rate of progression to active TB and subsequent transmission by disrupting any granulomas that have formed (Sharan et al., 2020). This also impairs Mtb-induced systemic pro-inflammatory cytokine/chemokine responses (Kassa et al., 2016, Devalraju et al., 2019). Interestingly, co-infection with Mtb results in enhanced HIV replication, suggesting that the two pathogens work synergistically (Bell and Noursadeghi, 2017, Kwan and Ernst, 2011). The initiation of antiretroviral treatment (ART) in people living with chronic HIV can lead to immunological activation, which can result in a paradoxical worsening of TB in what is known as immune-reconstituted inflammatory syndrome (TB-IRIS) (Silveira-Mattos et al., 2019). IRIS occurs in up to 40% of people who are co-infected with pulmonary TB and HIV, usually after starting ART (Narendran et al., 2013). The mechanism of IRIS pathogenesis is poorly understood, however it appears to be caused by two factors: (i) failure of the immune system to eradicate the pathogen(s), resulting in a prolonged and high burden of infection, as well as (ii) a rapid immunological recovery in response to ART. There are no specific treatments available for IRIS (Barber et al., 2014). The activation of pathogen-specific T-lymphocytes is heightened and dysregulated in IRIS. When compared to people who do not develop IRIS, previous investigations have demonstrated that the frequency of Mtb-specific circulating CD4+ T lymphocytes against Mtb is directly connected with the initiation and prevalence of IRIS (Antonelli et al., 2010, Silveira-Mattos et al., 2019, Vignesh et al., 2017). HIV infection provides a good guide of how biomarkers can be used for both initial diagnosis and monitoring disease development. Viral Ribonucleic acid (RNA) and p24 antigen detection are utilized to establish an early diagnosis after HIV infection and are detectable prior to the formation of HIV antibodies (Pilcher et al., 2005, Kfutwah et al., 2013). Following that, viral load in plasma is detected using viral RNA quantification, and disease progression is assessed using whole blood CD4+ T cell counts (Tucci et al., 2014). Developing similar approaches for TB will enhance clinical management and improve treatment outcomes. 18 1.9. Rationale for the current study The last decade has witnessed a ground swell of research in the discovery and validation of novel biomarkers to monitor risk of disease progression and diagnosis of active TB versus asymptomatic infection (Wykowski et al., 2021). There is however, a huge paucity in the knowledge regarding bacterial biomarkers for reporting on these effects and for reporting on the risk of disease recurrence. In the case of asymptomatic infection, the challenge has been meaningfully sampling bacterial populations from asymptomatic individuals, hence the reliance on host biomarkers. There is a growing body of evidence suggesting that tubercle bacilli can exist in various states of culturability that limit their detection with standard laboratory methods. It is unclear if these phenomena manifest in early TB infection however, the presence of these organisms has also been noted in cases of active TB (Mukamolova et al., 2010, Chengalroyen et al., 2016, McAulay et al., 2018, Peters et al., 2023). It has been demonstrated that these DCTB represent those drug tolerant organisms that resist clearance during early treatment (Zainabadi et al., 2021, Peters et al., 2023). As such, DCTB represent a potential bacterial biomarker to monitor treatment response and also to evaluate the risk of recurrent disease. It also remains unclear if DCTB elicit differential immune responses. The hypothesis in this case being that non-replicating bacteria possibly express a diverse set of antigens compared to their conventionally culturable counterparts, leading to consequent effects of immune responses. This dissertation aimed to address this scientific question through two primary approaches. The first involved the generation of DCTB in vitro, using clinical isolates followed by an assessment of their ability to induce immune responses, when compared to replicating bacteria from the same genetic background. The second approach involved the use of a prospective cohort of individuals with drug susceptible TB who were placed on standard TB chemotherapy. During the early phase of treatment, and upon treatment completion, plasma samples were collected to assess immune responses. Concurrently, sputum specimens were assessed for the presence of DCTB. As such, this cohort provided a unique opportunity to measure immune responses that prevail during TB treatment and to correlate these with bacterial clearance. The experimental components of the dissertation are divided into two broad chapters, the first addressing the question of immune responses to DCTB and the second detailing the results from the cohort study. Individual hypotheses and aims are detailed in these respective chapters. 19 CHAPTER 2 Differential culturable tubercle bacteria: Implications for immune responses during tuberculosis infection 2.1. Introduction Microorganisms are threatened by a range of stresses in the natural habitat and as a result, they use a variety of species-specific tactics to provide tolerance against factors that are hostile for growth and survival. One well described adaptive mechanism is the ability of bacteria to enter into a viable but non-culturable (VBNC) state where bacteria cease to replicate and not recoverable on conventional media (Li et al., 2014). The VBNC state is beneficial for the long- term survival of bacteria as without it, environmental pressures might wipe out the entire population (Pinto et al., 2015). When the stressors are removed, or when the cells get signals indicating favourable, environmental conditions prevail, these seemingly inactive cells emerge again (Oliver, 2016). Similarly, bacterial dormancy, which can or cannot be associated with the VBNC state – depending on the species, is generally referred to as a functional state that is reversible in bacteria and is characterised by decreased metabolic activity such as transcription, translation as well as improved tolerance to harmful factors and a halt to cell division (Chao and Rubin, 2010). The ability to adopt these non-replicative metabolically quiescent states enable entire bacterial populations to control their overall biomass and adapt to antibiotic stress (Stolpovsky et al., 2011). Dormant bacterial states have historically been linked to the development of spores or cysts, which represent substantive morphological changes when compared to vegetative or rapidly replicating states. Nevertheless, the potential for non-spore forming bacteria, such as mycobacteria, to enter a dormant stage has been experimentally proven; it resulted in the development of less distinct, cyst-like forms that are distinct from spores observed in bacillus species (Shleeva et al., 2010). The production of the dormant state by non-spore forming mycobacteria, including Mtb, is of particular interest for this work. 2.2. Viable but non-culturable state The term "VBNC " refers to a dormant state brought on by severe environmental factors, such as nutrient deprivation, extreme temperatures, abrupt changes in pH or salinity, osmotic stress, limited oxygen availability and related conditions. (Fakruddin et al., 2013). The ability to culture bacteria in the laboratory using standard media and growth conditions is an essential feature for the general study of bacteriology (Buck, 1979). However, over a century ago, it emerged that bacteria have different propensities to grow on laboratory media. This is best 20 described by the “Great Plate Count Anomaly”, where the number of organisms that are recoverable on agar plates and are significantly lower than those recovered in liquid media for the same organism. This suggested that bacteria display differential culturability, depending on the growth conditions and prior stress exerted on the bacterial population as a whole. Whilst intimately related to the VBNC state, this differential culturability can also occur in populations that have mixtures of dormant and actively replicating bacteria (Staley and Konopka, 1985). The existence of the VBNC state was first identified in 1982 in Escherichia coli and Vibrio cholerae cells. The use of integrated culture techniques including indirect enumeration by most probable number (MPN) calculation and direct plating with immunofluorescent microscopy, acridine orange direct counting, and direct viable counting demonstrated that E. coli and V. cholerae both go through a "nonrecoverable" state whilst maintaining viability (Xu et al., 1982). Contrary to conventionally culturable cells, which can be cultured on suitable media and form colonies, VBNC cells have lost the capacity to grow on the select media and as they are not replicating, they display tolerance to those antibiotics that target actively replicating processes in bacteria (Oliver, 2000). Mtb can survive after infection and subvert host immune responses to manifest a spectrum of clinical outcomes. Key to this is its ability to reprogram macrophage function to enable survival in the phagosome, induce the careful orchestration of cellular recruitment to the site of infection for the formation of granulomas, and to modulate its own metabolism for the establishment of non-replicating states that display drug tolerance (Gengenbacher and Kaufmann, 2012). In the literature, this non-replicating state has been associated with the term dormancy. For the purpose of this thesis, we will use this term when describing the literature but will use the term Differentially Culturable Tubercle Bacteria (DCTB) in the description of our work, and related literature. In the host, it has been hypothesized that the dormant state in mycobacteria is brought on in response to immune system-imposed stresses during colonization of the lung (Chengalroyen et al., 2016). This state assumed by Mtb is characterized by the absence of growth during stressful conditions, with the ability to spontaneously transition between replicating and non-replicating phenotypes (Caño-Muñiz et al., 2018). Given the important role of environmental conditions for establishment of non-replicating persistence, the ability to adopt this is influenced by both the genetics of the host, which determine the strength of the immune response and specific genetic features of the pathogen (Russell, 2011). An investigation of these genetic features is 21 central to developing a broader understanding of how Mtb adapts to the host and for the development of interventions to combat persisting bacteria. The latter is important for the development of shorter treatment regimens. As an example, in the murine model of TB infection, several host genes such as Nramp1 and loci on chromosomes 1 and 11 affect organ bacillary loads in a strain-specific manner (Di Pietrantonio et al., 2010). With respect to the pathogen, several genetic factors have been implicated in the establishment of heterogeneity or non-replicating persistence. Particularly, a group of 47 genes, known as the DosR regulon, which is tightly regulated by the dormancy survival regulator transcription factor (DosR), regulates bacterial metabolism in response to oxygen deprivation and nitrogen stress (Park et al., 2003, Roberts et al., 2004). This response, collectively serves the purpose of helping Mtb adapt to anaerobic environment and survive in the host granuloma (Bartek et al., 2009). As expression of the DosR regulon is increased during hypoxia, several genes that play an important role in adaptation to these conditions are induced. For example, the DosR regulon comprises genes involved in nitrogen metabolism with the possible use of nitrate as an alternative electron acceptor during energy metabolism (Voskuil et al., 2003, Galagan et al., 2013, Park et al., 2003, Singh et al., 2020). Similarly, there appears to be overlap between the DosR regulon and those genes required for survival in macrophages (Schnappinger et al., 2003) and in mice and guinea pigs (Karakousis et al., 2004, Sharma et al., 2006). 2.3. Resuscitation of non-culturable bacteria It is essential to keep in mind that bacteria that enter the VBNC stage may revert to the culturable state. Roszak et al. (1984) coined the term "resuscitation" to characterize the recovery of Salmonella enteritidis non-culturable cells, following the addition of Heart Infusion broth. Two decades later, Baffone et al. (2006) described resuscitation as the reversal of the physiological and metabolic changes that distinguish VBNC cells from their conventionally culturable counterparts. They demonstrated that cell division, which indicated recovery of Campylobacter jejuni VBNC to culturability, can be achieved via passage in the mouse colon depending on how long the Campylobacter jejuni VBNC cells remained in this state. Mtb was found to be among human pathogens that can enter the VBNC state (Shleeva et al., 2002). Numerous factors, including an increase in temperature, an increase in the concentration of nutrients, and the presence of host cells, led to the resuscitation of these species as summarised by (Li et al., 2014). The first obstacle that scientists came across when conducting resuscitation studies was the inability to distinguish between the normal proliferation of remaining culturable cells in a sample and the resuscitation of VBNC cells. 22 Thus far, there are no simple ways to differentiate culturable cells from those that grow normally as a result of exposure to the stimuli from those that do so after resuscitation. This limitation is important as many laboratory models (including those tested and used in this study) generate VBNC cells, create these in mixtures with conventionally culturable populations. Whilst this limits the study of molecular factors that underpin the DCTB state, it does allow for the best rendition of clinical specimens, which usually harbour these populations in mixtures. The aforementioned environmental pressures lead to the VBNC state and removing them may aid in the return to cultivability. However, this approach is not always successful to revive some species and further growth stimulants are required. Distinct bacterial taxa have different resuscitation processes that can be induced by a variety of triggers (Zhang et al., 2021b). A few resuscitation stimuli have been employed successfully in recent years to rescue bacteria from their native environments. As an example, secreted cell wall hydrolases have been identified in mycobacteria as potent stimulators of growth, this will be discussed in next section. 2.4. Differentially culturable tubercle bacteria Whilst adaption of Mtb during infection in macrophage and animal models of TB has been well described, similar data from human TB is lacking. Consequently, the presence of non- replicating mycobacteria during TB infection in humans has only been inferred through clinical presentation where these inferences have been made primarily based on the presence or absence of symptoms. As mentioned previously, the lack of symptoms has been associated with LTBI whilst symptomatic infection implies the presence actively replicating bacteria. For over a century this dogmatic view has not been challenged. In the last decade several research groups have identified the presence of heterogeneous bacterial populations in various clinical specimens. These bacteria have been characterized by the lack of culturability under routine laboratory conditions. A study by Mukamolova et al. (2010) assessed the number of cells recovered in the sputum of TB patients before and during treatment using the colony-forming unit (CFU) assay and liquid growth in the form of the most probably number (MPN) assay, supplemented with culture filtrate (CF) from an axenic culture of Mtb. They demonstrated that 80-99.99% of the cells recovered by the MPN assay were not detectable on conventional agar plates and these differentially culturable populations increased during chemotherapy (Mukamolova et al., 2010). In this study, the ability to recover these occult organisms was dependent on a group of growth stimulatory proteins termed resuscitation 23 promoting factors (Rpfs). Rpf was initially identified in Micrococcus luteus as a single essential protein that able to stimulate the recovery of dormant bacterial cells (Mukamolova et al., 1998). Subsequently, five homologues (termed RpfA-E) were identified in Mtb. Combinatorial deletion studies demonstrated that these genes were dispensable for growth in vitro (Downing et al., 2004, Downing et al., 2005, Tufariello et al., 2004, Kana et al., 2008) but were required for resuscitation in an in vitro model of mycobacterial dormancy (Downing et al., 2005, Kana et al., 2008). Furthermore, rpf double, triple, quadruple and quintuple deletion mutants displayed attenuation in the murine model of TB, and also displayed defects in reactivation from chronic infection (Kana et al., 2008, Russell-Goldman et al., 2008). Rpfs hydrolyse bacterial peptidoglycan and how this biochemical activity results in growth stimulation has been the subject of much speculation. It has been hypothesized that one or more of several mechanisms prevail, these include: (I) the direct hydrolysis of the cell wall results in metabolic reprogramming within the cytoplasm to restart growth; (II) hydrolysis of the cell wall results in the formation of cell wall breakdown products that serve as signalling molecules to modulate growth; (III) the binding of Rpfs directly to the cell surface triggers initiation of growth and (IV) Rpfs exert their activity through partnering (direct interactions or indirect, combinatorial effects) with other proteins/cofactors/metabolites to either hydrolyse the cell wall and/or directly initiate growth (Kana and Mizrahi, 2010). Prevailing evidence suggests that Rpfs partner with an NplC-P60 endopeptidase RipA (Rpf Interacting Protein A) and this interaction results in synergistic peptidoglycan degradation to yield muropeptides that can stimulate the growth of dormant cells (Hett et al., 2008, Nikitushkin et al., 2015). Given these collective observations, combined with the fact that Rpfs are secreted proteins (Mukamolova et al., 2002), it was tempting to speculate that the growth stimulation observed (Mukamolova et al., 2010) in CF supplemented sputum cultures could be attributed to Rpfs. To further investigate this, a subsequent study assessed growth stimulation of these bacterial populations using CF that was derived from a quintuple Rpf-deletion mutant. In a pretreatment cohort of South African TB patients, bacteria that were unable to grow on agar plates, but were recoverable in MPN assays supplemented with CF were noted in the majority of clinical specimens assessed (Chengalroyen et al., 2016). For the remainder of this dissertation, these differentially culturable bacterial populations will be referred to as Differentially Culturable Tubercle Bacteria (DCTB). In addition to finding CF-dependent DCTB, a substantiative number of sputum specimens haboured Rpf-independent DCTB in addition to CF-dependent DCTB (Chengalroyen et al., 2016). These data provide the first evidence that growth of DCTB 24 could be dependent on other factors. Consistent with this notion, it has been demonstrated, in another distinct cohort of South African TB patients prior to TB treatment, that removal of Rpfs from CF did not significantly reduce recovery of DCTB. In this study the effect of using cyclic-AMP and a combination of structurally diverse fatty acids was also tested, with the observations pointing to the fact that recovery of DCTB is most likely dependent on a combination of factors present in CF (Gordhan et al., 2021). Comparatively greater CF-supplemented MPN counts were seen in the sputum of those without HIV co-infection when compared to HIV infected counterparts leading to the speculation that host immunity was an important driver for the establishment of DCTB populations (Chengalroyen et al., 2016, Peters et al., 2023). In support of this, CD4+ T cell levels also influenced the relative proportions of DCTB (Chengalroyen et al., 2016). These collective data pointed to a complexity in bacterial heterogeneity that prevails in sputum specimens with important implications for TB diagnosis (Dartois et al., 2016). Another study by Júnior et al. (2020) analysed the dynamics of Mtb subpopulations that are CF-dependent in patients throughout a standard 6-months TB treatment. They found that CF supplementation increases the bacillary load by 30% in samples taken before treatment and in patients treated for one month, it increased the bacillary load by 35%. In addition, their findings support the use of CF to maximise the detection of DCTB in liquid media. This aspect was investigated further in another study aimed at enhancing detection of TB in people living with HIV. HIV infection results in reduced granuloma formation, with a consequent reduction in cavities, leading to low numbers of bacteria in sputum that are difficult to detect using conventional culture. To address this, CF was supplemented into standard Mycobacterial Growth Indicator Tubes (MGIT) in a 1:1 ratio and sputum specimens from individuals with HIV-TB coinfection, prior to TB treatment, were cultured in these modified MGIT tubes. It was demonstrated that supplementation of MGIT cultures with CF enabled enhanced detection of TB in people living with HIV who had low bacterial burdens in sputum and low CD4 counts (McIvor et al., 2021). In addition to sputum, DCTB populations have been identified in specimens from various extrapulmonary sites suggesting that this phenomenon is inherent to tubercle bacilli (Rosser et al., 2018). These non-replicating Mtb cells are widely thought to contribute to the protracted treatment period required to achieve durable cure (Dhar and McKinney, 2007). This transient, reversible drug tolerance profile develops when genetically vulnerable bacteria are not eradicated by inhibitory drug levels and can emerge again when antibiotic treatment is stopped (Wallis et al., 25 1999). Even though RIF and PZA have a potent sterilizing capacity, TB treatment is still protracted, suggesting that drug tolerant populations likely prevail thus necessitating 6 months of chemotherapy (Hu et al., 2006). To investigate the role of DCTB in treatment response a prospective cohort study, with individuals presenting with drug susceptible TB was conducted using a combination of methods to quantify bacteria during treatment. It was demonstrated that DCTB levels do not change significantly during early treatment (2 w