Browsing by Author "Nkonyane, Tshepiso Ruth"

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    Chemokine and cytokine measurements using Luminex and Selected Reaction Monitoring (SRM): comparing technologies
    (Stellenbosch : Stellenbosch University, 2020-12) Nkonyane, Tshepiso Ruth; Chegou, Novel N.; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences.
    ENGLISH ABSTRACT: Background: There is an urgent need for tools for the rapid and accurate diagnosis of tuberculosis (TB) disease in resource-constrained settings. Tests based on host immunological biomarkers may be useful, especially if based on easily available samples. Objectives: To evaluate the usefulness of plasma proteins as biomarkers for diagnosing TB, to validate previously identified biosignatures [4-marker biosignature (CCL1+CRP+IP10+NCAM), 2-marker biosignature (CCL1+CRP) and 3-marker biosignatures; [(CCL1+NCAM+SAA), (CRP+IP-10+SAA), (CCL1+CRP+NCAM), (CCL1+CRP+SAP), (CRP+IP-10+MIG) and (CRP+SAP+MIG)] in a new cohort of adults suspected of having TB, and to develop a Selected Reaction Monitoring (SRM) for detecting TB protein biomarkers. Methods and Materials: We collected plasma samples from 151 individuals that presented with symptoms requiring investigation for TB disease at a primary health care clinic in the outskirts of Cape Town, South Africa, prior to assessment for TB disease. We evaluated the concentrations of 18 host markers in stored plasma samples using a multiplex platform. Using a combination of clinical, radiological and laboratory diagnosis, patients were later classified as having TB disease or other respiratory diseases (ORD). The diagnostic potential of individual analytes was analyzed using the receiver operating characteristic curve while the predictive abilities of a combination of analytes for TB disease were analyzed using the general discriminant analysis. Selected Reaction Monitoring (SRM) was used to develop an assay for the diagnosis of TB. Skyline software was used for the development of the SRM assay and analysis of the generated data. Results: Out of the 151 individuals that enrolled, 56 were pulmonary TB cases. Out of the 18 host markers that were evaluated in Chapter 3, there were significant differences in the levels of 9 host markers between patients with TB disease and those with ORDs. The concentrations of VEGF-A, IP-10, MMP-3, ferritin, SAA, CFH and CCL 1 were significantly higher in the TB cases than individuals with ORD, whereas NCAM and Apo A1 levels were significantly higher in the ORD group. When the diagnostic accuracies of individual host markers were investigated by ROC curve analysis, two markers (IP-10 and CCL 1) showed strong potential with AUC ≥0.85. When the data obtained from all study participants were fitted into GDA models regardless of HIV status, combinations between up to seven different host markers showed potential in the diagnosis of TB disease. However, an optimal diagnostic biosignature comprising of five proteins (transthyretin+MMP-3+IP-10+CFH+CCL1) diagnosed TB with an AUC of 0.90 (0.83- 0.97). Eight (8) previously identified biosignatures were assessed in study participants regardless of HIV status. A 4-marker biosignature (CCL1+CRP+IP-10+NCAM), 2-marker biosignature (CCL1+CRP) and 3-marker biosignatures [(CCL1+NCAM+SAA), (CRP+IP10+SAA), (CCL1+CRP+NCAM), (CCL1+CRP+SAP) and (CRP+IP-10+MIG)] all diagnosed TB with AUC ≥0.85, while one 3-marker signature (CRP+SAP+MIG) only diagnosed TB with AUC of 0.62. The most optimal diagnostic biosignature in all participants regardless of HIV status was the 4-marker (CCL1+CRP+IP-10+NCAM) signature which diagnosed TB with a sensitivity of 0.76(0.50-0.92) and specificity of 0.85(0.65-0.95). We further evaluated these biosignatures in HIV negative participants only and these biosignatures showed promise with areas under the ROC curves (AUC) ≥0.85 for all, except one 3-marker (CRP+SAP+MIG) biosignature. The most optimal diagnostic biosignature in HIV negative participants only was the 3-marker biosignature (CCL1+NCAM+SAA) that diagnosed TB with a sensitivity of 0.79(0.49-0.94) and specificity of 0.84(0.63-0.95). An SRM assay consisting of 83 proteins was developed, based on cytokines and chemokines that were previously investigated in diagnosing TB and reported in chapter 4. Conclusion We have shown that different candidate plasma biosignatures have potential in the diagnosis of TB disease. The observed results require further validation in a larger study. The previously identified biosignatures showed promise when validated in the current study. The biosignatures both identified and validated in this thesis hold promise as good candidate biomarkers/ biosignatures to be considered for the future development of point of-care TB tests. The developed SRM- based assay may be a useful alternative tool to diagnose TB. The assay requires further validation and optimization.