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Population dynamics of mitochondrial heteroplasmy in Drosophila Project Summary: Mitochondrial sequences can now be readily characterized by mining existing genome sequence data 1 3. Given the continually increasing number of genome sequences available, it is possible to identify mitochondrial heteroplasmy and the frequency of alternate mitochondrial genomes at both the individual and population levels. I propose to reconstruct mitochondrial genomes from publicly and privately available Drosophila genome sequences to analyze the population level genetics of heteroplasmy both within and between species. Quantifying heteroplasmy on both population and species levels is an important step towards understanding evolutionary persistence of heteroplasmy and mitochondrial disorders. Public Summary: Evolutionary medicine strives to understand why human disorders occur. My research will use existing fruit fly genomic data to analyze mitochondrial DNA in Drosophila fruit flies. Mitochondria are small organelles that exist within the cells of our bodies, and they produce the energy we use for life: everything from running a marathon to digesting lunch. Mitochondria are different from other types of organelles because each mitochondria contains its own DNA genome. Therefore, different parts of your body could have mitochondria with different DNA: a phenomenon called heteroplasmy. This is important to understand because if two patients come to the doctor with mitochondrial disorder symptoms in different organs, they may have the same mitochondrial DNA mutation but simply have broken mitochondria in different cells. The goal of my research is to understand how heteroplasmy has evolved and how it varies within and between populations of Drosophila. COPYRIGHTED Introduction and Goals Mitochondrial dysfunction is the most common cause of metabolic disorder in humans 4. Approximately 15% of mitochondrial disorders are caused by mutations in mitochondrial DNA 5. Mitochondrial disorders are quite difficult to diagnose 6. One of the difficulties with diagnosis is that two people with the same mitochondrial DNA mutation can show very different symptoms 5. Heteroplasmy is the presence of more than one mitochondrial haplotype within an individual. The phenotypic heterogeneity of mitochondrial disorders is likely due to accumulations of dysfunctional mitochondria in particular cell lineages 7,8. This provides a simple explanation for why mitochondrial dysfunction can differ between tissue types and individuals 5,9,10. While the field of medical genetics has made significant strides to understand the molecular underpinnings of how mitochondrial heteroplasmy can lead to disease, there is little known about the dynamics of heteroplasmy of the entire mitochondrial genome within an individual. Drosophila genomes provide an excellent opportunity to study heteroplasmy at a population level because we have data for species with varying degrees of genetic and ecological divergence 11,12. The synthesis of these different ecological strategies coupled with high-quality mitochondrial haplotypes allows us to address how heteroplasmy has evolved within different evolutionary lineages. Proposed Activities These are the genomic data collections I will synthesize to reconstruct individual mitochondrial genomes. Number of individual genomes available are given in parentheses. : D. melanogaster (20), D. yakuba (40), D. santomea (20), D. simulans (40), D. sechellia (50). Drosophila Population Genomics Project 13 : D. melanogaster (197). Drosophila Genome Nexus 14 : D. melanogaster (426). Rogers et al. 15 : D. yakuba (20), D. simulans (20) MATERIAL
I will assemble mitochondrial genomes for each individual by mapping off-target genomic sequences to the published mitochondrial reference sequence 16 for each according to the pipeline described by Picardi and Pesole (2012) 3. After mapping reads to the mitochondrial genome, I will use the pipeline described by Diroma et al. (2015) 2 to call heteroplasmic sites within an individual. I will (1) Remove PCR duplicates with the Picard tools MarkDuplicates. (2) Assign a quality score to ensure that variants are variable haplotypes and not sequencing error. (3) Calculate heteroplasmic fractions (HF) for each site: HF will be calculated as the fraction of the variant read depth over total read depth at each position for SNPs and deletions or 5 flanking position for insertions. (4) Count sites with HF>.75 as variable sites in the genomes. (5) Calculate heteroplasmic sites (HS) for each individual describing the number of variants present. (6) Analyze patterns of heteroplasmy within D. melanogaster and between Drosophila species. These analysis represent a significant synthesis of existing genomic data and mine an underutilized component of these data: the mitochondrial genome. This project aligns closely with the computational nature of the lab group, who will provide teaching and support as I learn these methods. Rationale for Support Evolutionary medicine strives to understand why human disorders occur based on evolutionary theory. By synthesizing mitochondrial genome data from existing Drosophila nuclear genome sequences, I propose to describe the evolutionary patterns of mitochondrial heteroplasmy in Drosophila. Understanding how heteroplasmy segregates within populations represents an important step towards understanding human mitochondrial disorders. Drosophila DNA sequences have a fundamental advantage for studying mitochondrial heteroplasmy: these sequences have been generated from DNA extractions performed on individual whole flies. The fact that we have raw sequences from entire bodies will allow me to assess the proportion of different mitochondrial genomes within an individual, and compare between individuals, thus avoiding the problematic bias of sampling only particular cell types. Anticipated Results This work will construct for the first time a database of mitochondrial genetic diversity across Drosophila species and within a single extensively sampled population of D. melanogaster. The first paper from this project will describe whether or not there is heteroplasmy in Drosophila and the patterns within and between populations. By taking a phylogenetic and population genetic perspective, a second paper will assess to what extent mitochondrial introgression has occurred among Drosophila species. My results will constitute an improvement over the current knowledge because I will incorporate a phylogenetic framework to mitochondrial heteroplasmy. All results generated from this work will be deposited into a publicly available database of Drosophila mitochondrial genomes that will be an important resource for all researchers. In this database I will maintain all genomic products (bam, san, and vcf files). Proposed Timetable
References 1 Samuels, D.C. et al. (2013) Finding the lost treasures in exome sequencing data. Trends Genet. 29, 593 599 2 Diroma, M.A. et al. (2014) Extraction and annotation of human mitochondrial genomes from 1000 Genomes Whole Exome Sequencing data. BMC Genomics 15(Suppl. 3), S2 3 Picardi, E. and Pesole, G. (2012) Mitochondrial genomes gleaned from human wholeexome sequencing. Nat. Methods 9, 523 524 4 Elliott, H.R. et al. (2008) Pathogenic mitochondrial DNA mutations are common in the general population. Am. J. Hum. Genet. 83, 254 260 5 Dimauro, S. and Davidzon, G. (2005) Mitochondrial DNA and disease. Ann. Med. 37, 222 232 6 Dinwiddie, D.L. et al. (2013) Diagnosis of mitochondrial disorders by concomitant nextgeneration sequencing of the exome and mitochondrial genome. Genomics 102, 148 156 7 Mancuso, M. et al. (2013) Phenotypic heterogeneity of the 8344A>G mtdna MERRF mutation. Neurology 80, 2049 2054 8 Chan, D.C. (2006) Mitochondria: dynamic organelles in disease, aging, and development. Cell 125, 1241 1252 9 Lightowlers, R.N. et al. (1997) Mammalian mitochondrial genetics: Heredity, heteroplasmy and disease. Trends Genet. 13, 450 455 10 Chinnery, P.F. et al. (2000) The inheritance of mitochondrial DNA heteroplasmy: random drift, selection, or both? Trends Genet. 16, 247 251 11 Lachaise, D. et al. (1988) Historical biogeography of the Drosophila melanogaster species subgroup. Evol. Biol. 22, 159 225 12 Lindsley, D. (2007) The historical discovery of the nine species in the Drosophila melanogaster species subgroup. Genetics 177, 1969 1973 13 Drosophila Population Genomics Project: DPGP3: Siavonga.. [Online]. Available: http://www.dpgp.org/dpgp3 14 Lack, J.B. et al. (2015) The Drosophila Genome Nexus: a population genomic resource of 623 Drosophila melanogaster genomes, including 197 from a single ancestral range population. Genetics 199, 1229 1241
15 Rogers, R.L. et al. (2014) Landscape of standing variation for tandem duplications in Drosophila yakuba and Drosophila simulans. Mol. Biol. Evol. 31, 1750 1766 16 Complete mitochondrial genome sequences.. [Online]. Available: http://megasun.bch.umontreal.ca/ogmp/projects/other/mt_list.html