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Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis
Thesis (PhD)--Stellenbosch University, 2025.
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614119341850624 |
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| access_status_str | Open Access |
| author | De la Harpe, Alana |
| author2 | Snoep, Jacky L. |
| author_browse | De la Harpe, Alana Snoep, Jacky L. |
| author_facet | Snoep, Jacky L. De la Harpe, Alana |
| author_sort | De la Harpe, Alana |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132131 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:46:58.390Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132131 Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis De la Harpe, Alana Snoep, Jacky L. Warner, D. Stellenbosch University. Faculty of Science. Dept. of Biochemistry. Mycobacterium tuberculosis -- Pathogenesis Tuberculosis -- Genetic aspects Tuberculosis -- Immunological aspects Cholesterol -- Metabolism -- Mathematical models Genomes -- Data processing Drug targeting Mycobacterium tuberculosis -- Treatment Thesis (PhD)--Stellenbosch University, 2025. de la Harpe, A. 2025. Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/efcb2a03-dfaa-4497-a6f7-a7925423bd77 ENGLISH ABSTRACT: Tuberculosis (TB) remains a global health challenge with significant mortality due to Mycobacterium tuberculosis, a pathogen adept at evading host immune responses and persisting in nutrient-poor environments. Although curable, issues such as latent infection, treatment adherence, and the emergence of multidrug-resistant strains highlight the urgent need for novel therapeutic strategies. Approximately one-quarter of the global population is estimated to have latent TB infection (LTBI). A notable characteristic of M. tuberculosis is its ability to metabolise cholesterol, a key component of host membranes, via a specialised pathway crucial for its survival and persistence. Targeting this pathway presents a promising avenue for new drug development. Genome-scale models integrate computational and experimental data to simulate the entirety of an organism’s metabolic processes, providing insights into metabolic networks and vulnerabilities exploitable for therapeutic interventions. We developed and validated a new genome-scale metabolic model for Mycolicibacterium smegmatis, iADLH1356, consisting of 1356 genes, 1672 reactions, and 1245 metabolites. M. smegmatis is commonly used in laboratory settings for studying M. tuberculosis, as it possesses similar genetic and metabolic characteristics. Additionally, we updated and validated an existing genome-scale model for M. tuberculosis, iEK1011_2.1, with a focus on the cholesterol metabolic pathway to identify potential drug targets. The updated model consists of 1016 genes, 1259 reactions, and 1018 metabolites. Using these two models, we demonstrated various applications of genome-scale models, including analysing growth on cholesterol, identifying potential drug targets, and studying physiological and metabolic characteristics through in silico predictions. We showed that essentiality predictions for growth on cholesterol were consistent across the two models, demonstrating that iADLH1356 can be used to study genes involved in the cholesterol pathway in M. tuberculosis by developing hypotheses that can first be tested experimentally with M. smegmatis. We focused on LTBI for drug target identification, as it is a key area for developing new therapeutic interventions. We identified 19 potential drug targets, with 12 genes involved in the cholesterol degradation pathway and seven genes essential for growth on cholesterol. AFRIKAANSE OPSOMMING: Tuberkulose (TB) bly ‘n wêreldwye gesondheidsuitdaging met hoë sterftesyfers as gevolg van Mycobacterium tuberculosis, ‘n patogeen wat vaardig is in die ontwyking van die gasheerimmuunrespons en wat kan voortbestaan in voedingstof-arme om- gewings. Alhoewel TB behandelbaar is, beklemtoon probleme soos latente infeksie, behandeling-nakoming, en middelweerstandige stamme die dringende behoefte aan nuwe terapeutiese strategieë. Ongeveer ‘n kwart van die wêreldbevolking is beraam om latente TB-infeksie (LTBI) te hê. M. tuberculosis is opvallend vir sy vermoë om cholesterol te metaboliseer, ‘n sleutelkomponent van gasheer membrane, deur ‘n ge- spesialiseerde pad wat noodskaaklik is vir sy oorlewing en voortbestaan. Hierdie pad verteenwoordig ‘n belowende teiken vir die ontwikkeling van nuwe medikasie. Genoom-skaal modelle integreer berekenings- en eksperimentele data om die me- taboliese prosesse van ’n organisme in sy geheel te simuleer. Hierdie modelle bied insigte in metaboliese netwerke en kwesbaarhede wat benut kan word vir terapeutiese intervensies. Ons het ‘n nuwe genoom-skaal model vir Mycolicibacterium smegmatis, iADLH1356, ontwikkel en bekragtig. Die model bestaan uit 1356 gene, 1672 reaksies, en 1245 metaboliete. M. smegmatis, wat geneties en metabolies soortgelyk aan M. tuber- culosis is, word algemeen in laboratoriumstudies gebruik om M. tuberculosis te bestu- deer. Verder het ons ‘n bestaande genoom-skaal model vir M. tuberculosis, iEK1011_2.1, opgedateer en bekragtig, met ‘n fokus op die cholesterol metaboliese pad. Hierdie op- gedateerde model sluit 1016 gene, 1259 reaksies, en 1018 metaboliete in. Met behulp van hierdie modelle het ons verskeie toepassings van genoom-skaal modelle gedemonstreer, insluitend die analise van groei op cholesterol, die identifi- sering van potensiële teikens vir nuwe behandeling, en die studie van fisiologiese en metaboliese eienskappe deur in silico voorspellings. Ons het bevind dat die essenti- aliteitsvoorspellings vir groei op cholesterol konsekwent was oor beide modelle, wat toon dat iADLH1356 gebruik kan word om gene wat betrokke is by die cholesterol metaboliese pad in M. tuberculosis te bestudeer deur hipoteses te ontwikkel wat eers eksperimenteel met M. smegmatis getoets kan word. Deur te fokus op LTBI vir teikeni- dentifikasie vir geneesmiddelontdekking, het ons 19 potensiële teikens geïdentifiseer, insluitend 12 gene wat betrokke is by die cholesterol metaboliese pad en sewe gene wat noodsaaklik is vir groei op cholesterol. Doctoral 2025-05-27T09:23:40Z 2025-05-27T09:23:40Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132131 Stellenbosch University xviii, 211 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mycobacterium tuberculosis -- Pathogenesis Tuberculosis -- Genetic aspects Tuberculosis -- Immunological aspects Cholesterol -- Metabolism -- Mathematical models Genomes -- Data processing Drug targeting Mycobacterium tuberculosis -- Treatment De la Harpe, Alana Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title | Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title_full | Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title_fullStr | Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title_full_unstemmed | Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title_short | Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis |
| title_sort | mathematical modelling of cholesterol metabolism in mycobacterium tuberculosis |
| topic | Mycobacterium tuberculosis -- Pathogenesis Tuberculosis -- Genetic aspects Tuberculosis -- Immunological aspects Cholesterol -- Metabolism -- Mathematical models Genomes -- Data processing Drug targeting Mycobacterium tuberculosis -- Treatment |
| url | https://scholar.sun.ac.za/handle/10019.1/132131 |
| work_keys_str_mv | AT delaharpealana mathematicalmodellingofcholesterolmetabolisminmycobacteriumtuberculosis |