The advent of next generation sequencing (NGS) technologies has made it possible to identify the full spectrum of genetic variations in single individuals. This has made it possible to identify rare variants that contribute to human genetic diseases. However, this has quickly created a need for mechanisms to prove or demonstrate that the candidate genes are causal. One mechanism to begin to understand specific genetic variations in the context of the pathophysiology of PD is to identify the impact that changes in the expression of the candidate genes have on key cellular pathways, molecular functions and regulatory networks, so called pathway analysis. By examining the effect that the knockout or knockdown of specific candidate genes have on the transcriptome, we can begin to identify the key pathways responsible for the disease phenotype. To that end, an integrated, multi-organism disease modeling core has been established to bring together expertise in yeast, zebrafish and induced pluripotent stem cells (IPSCs) modeling. Each model organism in the core represents a different level of complexity and can be used to better understand different aspects of the pathology of PD. This core will facilitate the rapid identification of the most appropriate models for examining the impact that loss of function of specific genes (derived from Project 1 and 3) and non-coding RNAs (Project 2) have on the transcriptome (analyzed in Core C). Knockdown or knockout models of the specific candidate genes will be developed in the appropriate organisms to provide the biological material (total RNA) for transcriptome analysis. In addition to providing total RNA from the knockout/knockdown cells and tissues (and control cells and tissues), this core will generate important reagents (morpholinos, shRNAs, yeast knock strains and iPSCs) for future functional studies into the pathology of PD for members of the UM Udall Center, as well as, other Udall Centers and PD investigators.
The pace at which genetic variations can be identified has rapidly accelerated due to the advent of high content DNA sequencing technologies. By examining changes in candidate gene expression across several organisms simultaneously, we hope to gain a better understanding of PD pathology.
|Alcalay, Roy N; Caccappolo, Elise; Mejia-Santana, Helen et al. (2014) Cognitive and motor function in long-duration PARKIN-associated Parkinson disease. JAMA Neurol 71:62-7|
|Nuytemans, Karen; Inchausti, Vanessa; Beecham, Gary W et al. (2014) Absence of C9ORF72 expanded or intermediate repeats in autopsy-confirmed Parkinson's disease. Mov Disord 29:827-30|
|Nalls, Mike A; Pankratz, Nathan; Lill, Christina M et al. (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46:989-93|
|Huang, Anhui; Martin, Eden R; Vance, Jeffery M et al. (2014) Detecting genetic interactions in pathway-based genome-wide association studies. Genet Epidemiol 38:300-9|
|Wang, Liyong; Nuytemans, Karen; Bademci, Guney et al. (2013) High-resolution survey in familial Parkinson disease genes reveals multiple independent copy number variation events in PARK2. Hum Mutat 34:1071-4|
|Nuytemans, Karen; Bademci, Guney; Inchausti, Vanessa et al. (2013) Whole exome sequencing of rare variants in EIF4G1 and VPS35 in Parkinson disease. Neurology 80:982-9|
|Hedges, Dale J; Guettouche, Toumy; Yang, Shan et al. (2011) Comparison of three targeted enrichment strategies on the SOLiD sequencing platform. PLoS One 6:e18595|
|Williams, Sion L; Huang, Jia; Edwards, Yvonne J K et al. (2010) The mtDNA mutation spectrum of the progeroid Polg mutator mouse includes abundant control region multimers. Cell Metab 12:675-82|