School of Pathology (ETDs)
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Item Characterisation of the genetic variation in pharmacogenes involved in anti-tuberculosis drug metabolism across African populations(University of the Witwatersrand, Johannesburg, 2024) Malinga, Thandeka Vuyiswa Bongiwe; Twesigomwe, David; Othman, HoucemeddineTuberculosis (TB) is a major health burden in Africa. Although TB is treatable, anti-TB drugs are associated with adverse drug reactions (ADRs) which are partly attributed to pharmacogenetic variation. The distribution of star alleles (haplotypes) influencing anti-TB drug metabolism, is unknown in many African populations. This presents challenges in implementing genotype-guided therapy in Africa to decrease the occurrence of ADRs and enhance the efficacy of anti-TB drugs. Therefore, this study aimed to characterise the distribution of star alleles in genes that are involved in anti-TB drug metabolism (mainly isoniazid), namely CYP2E1, NAT1, NAT2, GSTM1 and GSTT1, across diverse African populations. We used 794 high-depth whole genome sequence datasets representative of eight Sub-Saharan African (SSA) population groups. Data sources included the 1000 Genomes Project and H3Africa AWi-Gen. CYP2E1, NAT1, NAT2, GSTM1 and GSTT1 star alleles were called from the WGS data using StellarPGx. Subsequently, novel star alleles were analysed, and their allele defining variants were annotated using the Ensembl Variant Effect Predictor. We present the distribution of both common and rare star alleles influencing anti-TB drug metabolism across various SSA populations, in comparison to other global populations. Various key star alleles were identified in the SSA study populations at relatively high frequencies including NAT1*10, GSTT1*0 (>50%), GSTM1*0 (49%), and NAT2*5B (21%). Additionally, we predicted varying phenotypic proportions for NAT1 and NAT2 (acetylation) and the GST enzymes (detoxification activity) between SSA and other global populations. Fifty potentially novel haplotypes were identified computationally across the five genes. This study provides insight into the distribution of star alleles in genes relevant to isoniazid metabolism across various African populations. The high number of potentially novel star alleles exemplifies the need for pharmacogenomics studies in the African context. Overall, our analysis provides a foundation for implementing pharmacogenetic testing in Africa to reduce the risk of ADRs related to TB treatment.Item Characterising the combined eects of cytochrome P450 missense variants within the star allele nomenclature(University of the Witwatersrand, Johannesburg, 2024) Khoza, Nhlamulo; Othman, Houcemeddine; Hazelhurst, ScottGenetic variations in Cytochrome P450 (CYP) enzymes shape drug responses. However, many CYP haplotypes (star alleles) lack functional annotations, posing a barrier to under- standing drug metabolism comprehensively. To address this, our study investigates combined missense variant eects on CYP enzyme structures, analyzing 261 variants across 91 CYP haplotypes. We utilized Normal Mode Analysis (NMA; FoldX and ENCoM) to explore variant impact on protein stability. Subsequently, we conducted Molecular Dynamics (MD) simulations on key alleles, CYP2D6*2 and CYP2D6*17, to reveal star allele impact on protein dynamics. Integrating NMA and MD, we uncover the interactions that collectively shape the conformation and attributes of CYP enzymes. Notably, our investigation highlights significant deviations between wild-type and CYP2D6*17 -encoded proteins in the F/G loop region, pivotal for CYP functionality. Additionally, we compare NMA results with CYP2C9 and CYP2C19 Deep Mutational Scanning (DMS) results, identifying 65% concordance. Furthermore, our NMA predictions show 80% concordance with commonly used Variant Eect Predictor tools. This alignment underscores our approach’s reliability in predicting variant eects. Our study illuminates missense variants’ nuanced impact on CYP protein structures, significant for personalized medicine and drug response prediction, as accurate drug response predictions hinge on a comprehensive understanding of CYP missense variants. Moreover, our study highlights multi-scalemodelling potential for interpreting CYP missense variants, especially in star alleles. The synergy of NMA, MD simulation, and assays like DMS is invaluable, each with distinct strengths and limitations. This research deepens our understanding of the complexity of CYP metabolism profiles, providing insights into the functional assessment of CYP star alleles and missense variants with unknown functional classifications.