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Supervisors: Professor Shamima Rahman, Professor Hannah Mitchison, Dr Damian Smedley
Background:
Primary mitochondrial diseases have been defined as disorders caused by mutations that lead primarily or secondarily to oxidative phosphorylation (OXPHOS) dysfunction or other disturbances of mitochondrial structure and function including perturbed mitochondrial ultrastructure, aberrant synthesis of cofactors and vitamins, or other impaired metabolic processes within the mitochondrion [1]. These disorders are characterized by enormous clinical, biochemical and genetic heterogeneity, leading to considerable diagnostic challenges [2].
Using exome and more recently genome next generation sequencing in the 100,000 Genomes Project [3] we have been able to establish a precise genetic diagnosis for 50% of our large cohort of >200 patients in the specialist Mitochondrial Disease Clinic at Great Ormond Street Hospital [4].
Initial genome analysis focussed on a panel of validated genes in the Genomics England PanelApp. We hypothesis that a further 1000 genes may cause mitochondrial disease and that analysis of these genes in our undiagnosed cases will identify novel gene defects leading to paediatric mitochondrial disease.
The interdisciplinary supervisory team have extensive experience of bioinformatics, novel gene discovery and mitochondrial and cell biology.
Aims/Objectives:
1. Bioinformatics analysis of genomic data from unsolved cases recruited with suspected mitochondrial disease into the Genomics England 100,000 genomes project.
2. Functional validation of novel variants and disease genes identified by bioinformatics analysis.
Methods:
1. Bioinformatics analysis will be performed within the Genomics England research environment and will focus on utilising the Exomiser software framework [5] developed by the secondary supervisor (Smedley) with initial analysis of genes encoding the entire mitochondrial proteome, and widening to the entire genome. Novel machine learning methods to identify pathogenic variants in mitochondrial disease genes will be developed and incorporated into Exomiser.
2. Functional analysis of prioritised gene variants will involve a range of biochemical techniques already embedded in the primary supervisor’s laboratory, including analysis of OXPHOS enzyme activities by spectrophotometric assays, OXPHOS assembly by Blue native gel electrophoresis, mitochondrial copy number using droplet digital PCR, electron microscopy to image the mitochondrial ultrastructure, live cell microscopy to visualise mitochondrial dynamics and functional complementation in studies in yeast models and/or lentiviral rescue with the wild type gene in patient cells.
Timeline:
Year 1: Bioinformatics analysis
Years 2-3: Functional validation of prioritised genetic variants
Year 3: Thesis writing, papers
References:
1. Rahman and Rahman (2018) Mitochondrial medicine in the omics era. Lancet. 391(10139):2560-2574. PMID: 29903433.
2. Rahman (2020) Mitochondrial diseases in children. J Intern Med 287(6):609-633. PMID: 32176382.
3. Wei et al (2019) Germline selection shapes human mitochondrial DNA diversity. Science. 364(6442). pii: eaau6520. doi: 10.1126/science.aau6520. PMID: 31123110.
4. Wortmann et al (2020) Bi-allelic Variants in TKFC Encoding Triokinase/FMN Cyclase Are Associated with Cataracts and Multisystem Disease. Am J Hum Genet 106(2):256-263. PMID: 32004446.
5. Cipriani et al (2020) An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data. Genes 11(4):460. doi: 10.3390/genes11040460. PMID: 32340307.
