Mitochondrial dysfunction has been recognized in the last decade to play a primary role in several common diseases, including diabetes, aging, cancer, and neurodegeneration. While 158 of the 714 known human mitochondrial proteins have been implicated in a hereditary diseases, many other mitochondria-related disorders remain to be identified, as about a third of the mitochondrial proteome is still uncharacterized. To this end, improved tools to systematically study mitochondrial disease genes need to be developed. Genomic and proteomic approaches in model organisms such as yeast and mouse, in conjunction with the evolutionary conservation of mitochondria, have been used to develop an integrative analysis of systematic approaches to better define the mitochondrial parts list. This integrative approach, which also includes data for a proteins involvement in a mitochondrial disease, achieves significantly higher specificity and sensitivity than any single approach in assigning mitochondrial probability scores. This proposal will apply this strategy to a starting set of 714 human mitochondrial reference proteins, to define a subset of 600 proteins that will be assessed for cSNP variants that contribute to mitochondrial disease. Mismatch Repair Detection (MRD) will first be used to discover DNA variants in these 600 genes in 400 mitochondrial disease patients, including low-frequency alleles that occur at 1 % or higher in the disease population. Second, direct sequencing will be used to identify variations in 13 proteins encoded by the human mitochondrial DNA in 200 affected individuals. These two approaches will together define approximately 1800 cSNPs, which will then be used to genotype patients and controls using Molecular Inversion Probes (MIP), which enables high-throughput cost-effective analysis. Stratification analysis will be applied to these data to ensure matching of patients and controls; and patients haplotypes will ultimately be correlated with clinical and biochemical data. As an initial proof-of-concept, these complementary approaches will be applied to study Leber's Hereditary Optic Neuropathy (LHON), which is characterized by the selective death of retinal ganglion cells leading to optic nerve atrophy and blindness. While mtDNA mutations are associated with most LHON cases, homoplasmy and variable penetrance demonstrate that other unidentified genes are also required. Our proposal will provide the foundation to identify these variants, as well as genes involved in other mitochondrial diseases. ? ? ?
| Schlecht, Ulrich; Suresh, Sundari; Xu, Weihong et al. (2014) A functional screen for copper homeostasis genes identifies a pharmacologically tractable cellular system. BMC Genomics 15:263 |
| Peng, Gang; Fan, Yu; Palculict, Timothy B et al. (2013) Rare variant detection using family-based sequencing analysis. Proc Natl Acad Sci U S A 110:3985-90 |
| St Onge, Robert; Schlecht, Ulrich; Scharfe, Curt et al. (2012) Forward chemical genetics in yeast for discovery of chemical probes targeting metabolism. Molecules 17:13098-115 |
| Zhang, Nianxiang; Xu, Yan; O'Hely, Martin et al. (2012) SRMA: an R package for resequencing array data analysis. Bioinformatics 28:1928-30 |
| Wang, Wenyi; Shen, Peidong; Thiyagarajan, Sreedevi et al. (2011) Identification of rare DNA variants in mitochondrial disorders with improved array-based sequencing. Nucleic Acids Res 39:44-58 |
| Shen, Peidong; Wang, Wenyi; Krishnakumar, Sujatha et al. (2011) High-quality DNA sequence capture of 524 disease candidate genes. Proc Natl Acad Sci U S A 108:6549-54 |
| Lin, Shengrong; Wang, Wenyi; Palm, Curtis et al. (2010) A molecular inversion probe assay for detecting alternative splicing. BMC Genomics 11:712 |
| Farias-Hesson, Eveline; Erikson, Jonathan; Atkins, Alexander et al. (2010) Semi-automated library preparation for high-throughput DNA sequencing platforms. J Biomed Biotechnol 2010:617469 |
| Scharfe, Curt; Lu, Henry Horng-Shing; Neuenburg, Jutta K et al. (2009) Mapping gene associations in human mitochondria using clinical disease phenotypes. PLoS Comput Biol 5:e1000374 |
