Integrated analysis of nuclear genome-mitochondrial genome interactions
EBPOD 2017: Project 1
This is one of 11 joint postdoctoral fellowships offered by EMBL-EBI, the NIHR Cambridge Biomedical Research Centre and the University of Cambridge’s School of the Biological Sciences in 2017.
- Prof. Patrick Chinnery, Department of Clinical Neurosciences and MRC Mitochondrial Biology Unit, University of Cambridge
- Dr Joanna Howson, Department of Public Health and Primary Care, University of Cambridge
- Dr Daniel Zerbino, European Bioinformatics Institute (EMBL-EBI)
Our aim is to generate a bioinformatic tool that is the first platform for the comprehensive integrated analysis of nuclear genome-mitochondrial genome interactions in health and disease, with the aim of detecting causal mitochondrial variants of cardiometabolic and neurodegenerative diseases. This directly addresses the aims of the EBPOD call by forming a new collaboration between EBI and two Departments of the School of Clinical Medicine to address a new translational research question.
Cell homeostasis is critically dependent upon the production of adenosine triphosphate (ATP) by a group of proteins assembled on the inner mitochondrial membrane. In humans, over 100 proteins are assembled into the five respiratory chain complexes, but the different interlocking peptide components are encoded by two totally distinct genomes. The vast majority of respiratory chain proteins are synthesised from nuclear gene transcripts within the cytosol, but 13 critical components are synthesised within the mitochondrion itself from the small 16.5Kb circular mitochondrial genome (mtDNA). Cell function is therefore critically dependent upon the interaction between mtDNA and nuclear DNA, but the two genomes are inherited in a totally different manner, with mtDNA being inherited exclusively down the maternal line.1
Relevance to health and disease
Candidate gene, exome and sequencing studies in patients with severe biochemical defects of mitochondrial function have identified mutations in either the mtDNA or nuclear DNA as a cause of inherited mitochondrial diseases. These disorders affect tissues and organs that are critically dependent on ATP synthesis, and resemble common complex traits – including diabetes, cerebrovascular disease, dementia and other neurodegenerative diseases.2 Intriguingly, there is also emerging evidence from large genetic association studies that complex common diseases are also influenced by more subtle genetic variation in mtDNA and nuclear DNA, with their combined effect altering disease risk.3
Traditional analytical approaches have studied each genome separately, but mtDNA has a further complexity arising from the presence of multiple copies within the same cell (up to several thousand depending on the cell type).1 Mutations of mtDNA can affect some or all of the molecules, a situation termed heteroplasmy. Recent deep sequencing data indicates that mtDNA heteroplasmy is nearuniversal in humans.4 The proportion of mutated molecule determines the cellular phenotype, but there have been limited attempts to incorporate heteroplasmy into association studies. We are now in a position to tackle these issues through access to large data sets for several common diseases, including the analysis of the entire mtDNA sequence, the amount of mitochondrial DNA in a specific sample, the degree of heteroplasmy, and epistatic interactions with nuclear genes. There are a diversity of statistical methods and models to analyse association,5 epistatic interactions with the nuclear genome,6,7 and heteroplasmy.8 However, there is currently no comprehensive analytical platform or statistical model that accounts simultaneously for what is known about the inheritance pattern of the two genomes, the population genetics of both systems, and heteroplasmy. The postdoctoral researcher will integrate these models and methods together in a single bioinformatic tool. They will test their approach on the UK Biobank data, whole genome sequence data generated through the 100,000 genomes project and the INTERVAL study9 (which includes the mtDNA sequence and a means of determining mtDNA heteroplasmy). To assist them, they will be at the intersection of the Department of Clinical Neurosciences and MRC Mitochondrial Biology Unit, where Pr. Chinnery is specialised in mitochondrial genetics and disease, the Department of Public Health and Primary Care, where Dr Howson is specialised in statistical genomics and EMBL-EBI, where Dr. Zerbino is specialised in high throughput computational genomics.
1. Stewart JB, Chinnery PF. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nature Reviews Genetics 2015.
2. Di Mauro S, Schon EA, Carelli V, Hirano M. The clinical maze of mitochondrial neurology. Nature reviews Neurology 2013; 9(8): 429-44.
3. Hudson G, Nalls M, Evans JR, et al. Two-stage association study and meta-analysis of mitochondrial DNA variants in Parkinson’s Disease. Neurology 2013; 80: 2042-8.
4. Payne BA, Wilson IJ, Yu-Wai-Man P, et al. Universal heteroplasmy of human mitochondrial DNA. Hum Mol Genet 2013; 22(2): 384-90.
5. Anderson CD, Biffi A, Rahman R, et al. Common mitochondrial sequence variants in ischemic stroke. Ann Neurol 2011; 69(3): 471-80.
6. Kurbalija Novicic Z, Immonen E, Jelic M, AnEthelkovic M, Stamenkovic-Radak M, Arnqvist G. Within-population genetic effects of mtDNA on metabolic rate in Drosophila subobscura. J Evol Biol 2015; 28(2): 338-46.
7. Arnqvist G, Dowling DK, Eady P, et al. Genetic architecture of metabolic rate: environment specific epistasis between mitochondrial and nuclear genes in an insect. Evolution 2010; 64(12): 3354-63.
8. Avital G, Buchshtav M, Zhidkov I, et al. Mitochondrial DNA heteroplasmy in diabetes and normal adults: role of acquired and inherited mutational patterns in twins. Hum Mol Genet 2012; 21(19): 4214-24.
9. Moore C, Sambrook J, Walker M et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 2014; 15:363.