Browsing by Author "Kotze, Leigh Ann"
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- ItemCharacterization of the innate and adaptive immune systems during active TB disease and during treatment(Stellenbosch : Stellenbosch University, 2019-04) Kotze, Leigh Ann; Walzl, Gerhard; Du Plessis, Nelita; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences: Molecular Biology and Human Genetics.ENGLISH ABSTRACT: Individuals presenting with symptoms of active tuberculosis (TB) disease currently undergo a lengthy diagnostic procedure, followed by an intense six-month treatment regimen. Delays in accurate diagnoses and severe treatment side effects contribute to low treatment adherence and drive the emergence of drug resistant Mycobacterium tuberculosis (M.tb) strains. Improvements in diagnostic technologies have allowed for same day diagnoses, however the roll-out of such devices are limited to settings with stable infrastructure. Additionally, many patients do not require the full treatment regimen when diagnosed early or when presenting with mild disease, however few reliable methods are available for identifying fast-responders. Immune biomarkers show great promise in addressing the need for improved diagnostic and treatment response prediction techniques. This study aimed to investigate multiple promising biomarkers, including (1) promising host diagnostic biomarkers for accurate discrimination of active TB disease from other diseases at point-of-care level; (2) promising host cell surface biomarkers for identifying treatment response shortly after treatment initiation; and also (3) functional host biomarkers for elucidating host-pathogen interaction responses for translation into diagnostic or treatment response biomarkers. Host diagnostic biomarkers were investigated (Chapter 2) in individuals presenting with symptoms suggestive of active TB disease, stratified according to an algorithm based on a combination of clinical, radiological and laboratory findings. Nine acute-phase proteins were investigated in participants' serum, and a biosignature comprising the biomarkers C-reactive protein (CRP) and serum amyloid A (SAA), accurately discriminated between participants with and without active TB disease with a sensitivity of 87.4% and specificity of 75.7%, irrespective of human immunodeficiency virus (HIV) co-infection. The validated biosignature performance in Ascaris lumbricoides sensitized participants remained relatively stable (Chapter 3; Sensitivity of 78%; Specificity of 75%). Validation of this biosignature in scenarios where both HIV- and Ascaris co-infection were considered, identified a robust and reliable biosignature suitable for use in high-burden settings. Individuals with confirmed active TB disease and healthy controls were recruited to investigate cell surface biomarkers of treatment response (Chapter 4). Markers of interest (CD126, CD120b, CD62L, CD197, and CD58) were investigated via flow cytometry on various immune cell subsets across three time points (diagnosis, month 1, end of treatment). No markers were differentially expressed at month 1, whereas CD120b and CD58 were upregulated the end of treatment on CD4+ and CD8+ T-cells, limiting their use as early treatment response biomarkers. Innate and adaptive response cytokines were then investigated from isolated cell subsets (neutrophils, monocytes, T-cells, combination) and compared to whole blood under unstimulated and antigen-stimulated conditions (Chapter 5). The downregulation of protective innate cytokines in the whole blood compared to the culture of monocytes with T-cells, suggested an active suppressive mechanism. A potential innate suppressive immune cell, myeloid-derived suppressor cells (MDSC), was then investigated in TB patients and healthy controls. MDSC were significantly upregulated in peripheral blood mononuclear cells (PBMC) from active TB patients, and cytokine production from MDSC co-cultured with T-cells displayed T-cell-specific downregulation of IFN- (Chapter 6). MDSC should be considered as a potential TB diagnostic biomarker, and future studies investigating frequency changes during treatment will inform on their value as treatment response biomarker.