Project ID NS-MH2026_52

ThemeNS-MH

Co Supervisor 1A Dr Alan Hodgkinson Faculty of Life Sciences & Medicine, School of Basic & Medical Biosciences, Department of Medical & Molecular GeneticsEmail

Co Supervisor 1B Dr Alfredo Iacoangeli Institute of Psychiatry, Psychology & Neuroscience, School of Mental Health & Psychological Sciences, Department of Biostatistics & Health InformaticsEmail

Genetic modulation of mitochondria in the causal pathways of neurodegenerative disorders

Mitochondria, the energy generators of our cells, are essential for healthy brain function. Growing evidence links mitochondrial dysfunction to neurodegenerative disorders such as Parkinson’s Disease, Alzheimer’s Disease and ALS. However, it remains unclear whether this dysfunction is a driver of disease or merely a response to it, and in which brain regions mitochondrial pathology might be most important. This PhD project aims to resolve these questions by combining advanced computational and genetic approaches to determine whether inherited variation in mitochondrial gene activity (especially in specific brain regions and cell types) contributes directly to disease risk. The student will analyse large-scale brain transcriptomic and genetic datasets to uncover how genetic variation influences mitochondrial processes. These insights will be used to build predictive models using machine learning, which will then be applied to large population cohorts to test whether mitochondrial variation predicts disease onset or severity. This project also has strong translational potential. Recent studies have shown that small-molecule inhibitors of mitochondrial activity can improve cognitive function in Alzheimer’s mouse models. This work will provide the first large-scale genetic evidence to assess whether such therapies could be effective in humans.

The student will develop expertise in genomics, transcriptomics, machine learning, mitochondrial biology and neurodegenerative disease research. They will also gain practical skills in bioinformatics, statistical genetics and disease association modelling, alongside transferable skills in data visualisation, scientific communication and project leadership.

Year-by-year objectives:

Year 1: Learn core analytical skills; process brain RNA-seq data; identify genetic variants that regulate mitochondrial transcription.

Year 2: Build and validate machine learning models of mitochondrial expression; integrate protein and metabolite data to assess downstream impacts.

Year 3: Apply models to large-scale population datasets; identify links between mitochondrial pathways and disease onset or progression.

Year 4: Finalise analyses and publications; write and submit thesis; present findings at national and international meetings.

Rotation project: During the rotation, the student will investigate the genetic regulation of mitochondrial genes in a selected brain region. They will quantify gene expression, perform QTL mapping and develop an initial prediction model of mitochondrial transcript abundance, laying the foundation for their PhD research.

Representative Publications

1) Ali, A.T., Boehme, L., Antona, G-C, Seitan, V.C., Small, K.S., Hodgkinson, A. (2019) Nuclear Genetic Regulation of the Human Mitochondrial Transcriptome. eLife 8: e41927; 2) Saukkonen, A., Kilpinen, H., & Hodgkinson, A. 2022. Highly accurate quantification of allelic gene expression for population and disease genetics. Genome Research. gr.276296.121; 3) Ali, A.T., Idaghdour, Y, Hodgkinson, A. (2020) Analysis of mitochondrial m1A/G RNA modification reveals links to nuclear genetic variants and associated disease processes. Communications Biology 3: 147.

1) Spargo, Thomas P., et al. “Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics.” Elife 12 (2024): RP88768; 2) Marriott, Heather, et al. “DNAscan2: A versatile, scalable, and user-friendly analysis pipeline for human next-generation sequencing data.” Bioinformatics 39.4 (2023): btad152; 3) Hu, Jiajing, et al. “DGLinker: flexible knowledge-graph prediction of disease–gene associations.” Nucleic acids research 49.W1 (2021): W153-W161.