http://bura.brunel.ac.uk/handle/2438/166 2026-06-20T22:51:06Z 2026-06-20T22:51:06Z MacCarthy, Gideon http://bura.brunel.ac.uk/handle/2438/33413 2026-06-11T16:31:43Z 2025-01-01T00:00:00Z

Title: Evaluating genetic liability in hypertension and stroke using machine learning and traditional statistical models: Insights from UK biobank studies Authors: MacCarthy, Gideon Abstract: Stroke is one of the leading causes of death and disability worldwide, with hypertension being a major risk factor for stroke. According to the World Stroke Organisation Global Stroke Fact Sheet, hypertension alone is responsible for over half of all stroke-related deaths and disability-adjusted life years (DALYs). While conventional risk factors for both diseases are well established, the added prediction value of genetic liability remains less clear. The traditional risk prediction models for hypertension and stroke, such as the Framingham Hypertension Risk Score (FHRS) and the Framingham Stroke Risk Score (FSRS), typically rely on clinical and demographic factors, often assume linear effects, and typically overlook genetic liability and complex interactions between predictors. In this thesis, three complementary studies were conducted to examine whether genetic liability could enhance the classification of hypertension and the prediction of strokes via both machine learning (ML) and traditional modelling techniques using data from more than 116,000 participants with European ancestry in the UK Biobank. Genetic variants and their effects obtained from genome-wide association studies were used to construct genetic liabilities for selected cardiovascular disease (CVD) risk factors and stroke, respectively. Multiple predictive models, such as Cox proportional hazards, penalized regression models (both logistic and Cox), tree-based algorithms (random forest, gradient boosting, and decision trees), and neural networks, were assessed after participants were randomly divided into training and testing sets. Discrimination (AUC), calibration, and reclassification indices (NRI, IDI, and Brier score) were used to evaluate the models. Incorporating the genetic liabilities resulted in modest but steady improvements across the studies. The genetic liabilities associated with lipids improved the classification of hypertension best with AUC using random forest. Additionally, stroke genetic liability enhanced stroke prediction with the Cox models, outperforming machine learning models. Among hypertensive individuals, the model's predictive performance (AUC) was higher in men and older adults than in women or younger adults. The Cox models outperformed all the machine learning models. Though ML methods allow for the investigation of non-linearities and interactions. Overall, genetic liability slightly enhances classification and risk prediction. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

2025-01-01T00:00:00Z Peros, Stelinda http://bura.brunel.ac.uk/handle/2438/33186 2026-04-22T02:01:10Z 2025-01-01T00:00:00Z

Title: Investigating replication fork blocks, replication-transcription conflicts and replication restart dynamics in Escherichia coli Authors: Peros, Stelinda Abstract: DNA replication is essential for genome stability, but it is constantly jeopardized by various obstacles such as nucleoprotein complexes and transcription–replication conflicts. If not properly resolved, these impediments lead to replication fork collapse, genomic instability, and even cell death. This thesis investigates how Escherichia coli preserves its replication integrity using three experimental systems: site-specific protein– DNA blockades, engineered replication–transcription conflicts, and chemical stress induced by saccharin exposure. These studies were supported by the development of an automated bioimage informatics pipeline, utilizing deep-learning segmentation to enable high-throughput quantitative analysis of cellular morphology and SOS-induced stress phenotypes in live-cell time-lapse microscopy. Using a novel inducible fork-block model, I demonstrate that the PriA–PriB– DnaT pathway is the primary restart mechanism at nucleoprotein obstacles, with PriA helicase activity being essential for efficient replication restart. Surprisingly, when large tandem repeats were placed on the opposite replichore, PriC rather than PriB played the dominant role, raising the possibility that restart pathway usage is influenced by obstacle size or chromosomal context. Replication–transcription conflicts, generated by introducing an ectopic origin of replication (oriZ), similarly required PriA helicase and PriB for efficient fork restart. In their absence, cells displayed severe filamentation, heterogeneous stress phenotypes, and elevated Cas1–Cas2 foci. To further define the nature of these collisions, I utilized an alternative origin (oriX); the comparison between head-on and co-directional orientations confirmed that cellular pathology was specifically conflict- dependent. Genetic suppression with an RNA polymerase–destabilizing mutation confirmed that these defects stem directly from transcriptional collisions rather than indirect effects. Finally, I show that saccharin, a widely used artificial sweetener, induces replication stress in E. coli, with PriB-deficient cells exhibiting pronounced defects and loss of viability. These findings highlight how dietary compounds may disrupt gut microbial physiology. Collectively, this work establishes PriA helicase as a central player in replication restart and a promising antibacterial target. Since stalled fork rescue is also critical in cancer cells, these results also provide conceptual bridges between bacterial DNA replication and oncogene-induced replication stress, opening avenues for both antimicrobial and cancer therapeutic development. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

2025-01-01T00:00:00Z Busharat, Zabreen http://bura.brunel.ac.uk/handle/2438/32769 2026-02-24T13:17:24Z 2025-01-01T00:00:00Z

Title: Novel memory phenotype Tfh cells arise without overt antigen stimulation and are important for adaptive immune responses against viral infection Authors: Busharat, Zabreen Abstract: Pathogen-induced memory Tfh cells exert a Tfh effector response during reinfection, regulating the generation of high-affinity antibodies. Here, we define novel memory-phenotype Tfh cells which are generated from naïve T cells under homeostatic conditions. These MP Tfh cells are phenotypically and functionally similar to pathogen-induced Tfh cells. MP Tfh cells can be defined by Tfh cell specific markers, CXCR5, BCL6, and PD-1, and markers of pathogen-induced long lived Tfh cells, FR4. T-bethigh MP T cells exert an innate-like Th1 response against viral infections. The transcription factor EGR2 is a repressor of T-bet function, and we found that MP Tfh cells are distinct from T-bethigh MP T cells but express EGR2 highly. Previously, we found Egr2 is required for MP T cell homeostasis and inflammation. Here, we observed that, in Egr2/3-/- CD4+ MP T cells, MP Tfh cell development is impaired. FR4+ EGR2 + MP T cells upregulate genes related to homeostatic proliferation, Tfh cell development and metabolic pathways of pathogen-induced memory Tfh cells. MP Tfh cells can exert an adaptive function by regulating B cell-mediated IgG production in vitro whereas MP Tfr cells are involved in suppressing MP Tfh cell function, thereby preventing excessive inflammation. In vivo, MP Tfh cells support germinal centre formation and induce neutralising antibody production after infection with vaccinia virus. Thus, MP Tfh cells with similar characteristics to pathogen-induced memory Tfh cells are developed in absence of environmental antigens and to date are the only CD4+ MP T cell subset associated with an adaptive immune response against viral infection. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

2025-01-01T00:00:00Z Ulukütük Gözlügöl, Zeynep http://bura.brunel.ac.uk/handle/2438/32731 2026-01-27T10:47:26Z 2025-01-01T00:00:00Z

Title: A comprehensive proteomics analysis of Friedreich's ataxia (FRDA) Authors: Ulukütük Gözlügöl, Zeynep Abstract: First and foremost, I would like to express my deepest gratitude to my Principal Supervisor, Dr. Sara Anjomani Virmouni, for her invaluable guidance, constructive feedback, and unwavering support throughout this journey. Her mentorship has been instrumental in shaping both my research and academic growth. I am also sincerely grateful to members of my supervisor team, Supervisor, Prof. Michael Themis. Additionally, I extend my thanks to Dr. Victor Hernandez, my Research Development Advisor, for his guidance especially during the first year of my PhD. I extend my sincere appreciation to Ministry of National Education, TURKEY, for their financial support as well as to Brunel University of London for providing access to the facilities and resources required for this research. My heartfelt appreciation goes to my collaborators, whose contributions have been vital to my research. I appreciate Associate Professor Faraz Mardakheh from the Barts Cancer Institute (Queen Mary University of London) for conducting proteomics analysis in human FRDA cells. I am also thankful to Professor Richard Wade-Martins' team at Oxford University for providing Human iPS-derived cardiomyocytes and to Professor Marek Napierala’s laboratory at the University of Texas Southwestern Medical Centre for supplying Human iPSCs. I would like to acknowledge Dr. Raha Pazoki from Brunel University London for her support in bioinformatics, particularly in creating the transcriptomics analysis heat map. I am also grateful to Dr. Saqlain Suleman, a postdoctoral researcher in our group, for his academical support and help. Additionally, I extend my thanks to Dr. Zenouska Ramchunder, another postdoctoral researcher in our group, for her academical help and endless support. I want to thank my beloved parents for their endless love, trust, and support throughout my journey. Their encouragement and sacrifices have given me the strength to overcome challenges and reach this milestone. I am forever grateful for everything they have done to help me achieve my dreams.I also want to thank my dear soulmate and husband, Haci, for his patience, love, and support. Even when we were far apart, his constant encouragement and understanding made our bond stronger. I am deeply grateful for his steady presence and for our love that has grown even through tough times. I wish to thank the team members of the Brunel technician staff for their assistance, which greatly facilitated the experimental aspects of this study. Their expertise and support have been invaluable in ensuring the smooth execution of laboratory work. I am also grateful to my colleagues in the Ataxia lab for their encouragement, collaboration, and insightful discussions. Their support has made this journey both intellectually enriching and personally rewarding. Finally, I extend my deepest appreciation to everyone who has contributed, directly or indirectly, to the successful completion of this thesis. Your support has been invaluable, and I am truly grateful. This thesis would not have been possible without the support and guidance of many individuals and organizations. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

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