Browsing by Author "Ehlers, Ashley"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Leveraging shotgun proteomics for optimised interpretation of data-independent acquisition data: identification of diagnostic biomarkers for paediatric tuberculosis
    (Stellenbosch : Stellenbosch University, 2020-12) Ehlers, Ashley; Tabb, David; Steen, Hanno; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences: Molecular Biology and Human Genetics.
    ENGLISH ABSTRACT: Althoughdiagnostic tests for paediatric tuberculosis (TB)are available,no specific test has been tailored to fit the diagnostic challenges children present as well as cater to limited resource settings. The high mortality rates recorded annually are associated with late diagnosis as well as insufficient household contact management (HCM). Further, urine has been identified as an attractive biofluid for urine protein biomarker discovery. Urine is non-invasive, easily attainablein large quantities and is associated with a low cost of collection. Improved data analysis approaches for protein and peptide identification and quantification has paved the way for the development of novel urine protein biomarkers for paediatric TB.Data-dependent acquisition (DDA) is a powerful approach in discovery of possible urine protein markers. By leveraging the shotgun proteome capabilities of protein and peptide identification using database search algorithms, an optimized data-independent acquisition (DIA) analysis method was developed. In this study, prior to data analysis, the quality of the DDA and DIA approach was evaluated by identifying batch effects and assessing the dissimilarity to allow abnormal runs to be identified and subsequently excluded. It is hypothesized that the quantity of specific host proteins in urine is different for children with TB compared to symptomatic control children who do not have TB. Using an optimised DIA data analysis method leveraging DDA data will allow a statistical identification of differentially abundant proteins in comparative proteomics. In this study,the MSstatsR-package for protein-level abundance testing was employed to generate comparisons between two groups, TB cases and controls,for a South African human-immunodeficiency virus (HIV) negative cohort.Three human proteins, leucine-rich alpha-2-glycoprotein (A2GL), aggrecan core protein (PGCA) and cartilage intermediate layer protein 2 (CILP2) were identified as significantly different. The findings of this study support the hypothesis that using an optimised DIA data analysis method leveraging DDA data will identify the differential proteins, potentiallyleading to validation for useas discovery phase urine protein markersin the clinical settings.