The goal of this research proposal is to use patient derived cells with defined disease genotypes to identify disrupted molecular pathways through large-scale proteomics and network analysis. Human induced pluripotent stem cells (hiPSC) have revolutionized the ability to study human diseases from patients. It is now possible to obtain fibroblasts from patients suffering from a disease and to reprogram the cells to pluripotent stem cells and then differentiate them into a cell type associated with the disease state. This reverses a long standing limitation for the proteomic study of human diseases which has been the ability to use cells directly from patients with the appropriate disease phenotype and genotype. We will use Rett Syndrome (RTT) as a prototype for autism spectrum disorders (ASD). Here, we will combine vertical (mutant and control cell lines) and horizontal genetics (different mutations in MeCP2) to measure proteomic changes in affected forebrain neuronal and glial cells derived through fibroblasts and hiPSCs. We will use network analysis techniques developed in the previous grant period to identify molecular phenotypic differences using protein-protein interaction and protein expression patterns. Our hypothesis is that this approach will identify specific molecular processes disrupted in RTT and altered upon rescue of the RTT neuronal phenotype, which will lead to insights into other ASD.
The goal of this research proposal is to use patient derived cells induced pluripotent stem cells with defined disease genotypes to identify disrupted molecular pathways through large-scale proteomics and network analysis. By using a combination of vertical and horizontal genetics to study how protein networks are affected by perturbations to the genetic programs of these cells, we will determine the biochemical implications of the patient genotypes. This research will drive our understanding of the pathways perturbed by the disease, creating a new focus for therapies.
|Thomas, Charles A; Tejwani, Leon; Trujillo, Cleber A et al. (2017) Modeling of TREX1-Dependent Autoimmune Disease using Human Stem Cells Highlights L1 Accumulation as a Source of Neuroinflammation. Cell Stem Cell 21:319-331.e8|
|Ma, Yuanhui; McClatchy, Daniel B; Barkallah, Salim et al. (2017) HILAQ: A Novel Strategy for Newly Synthesized Protein Quantification. J Proteome Res 16:2213-2220|
|Herai, Roberto H; Negraes, Priscilla D; Muotri, Alysson R (2017) Evidence of nuclei-encoded spliceosome mediating splicing of mitochondrial RNA. Hum Mol Genet 26:2472-2479|
|Chatterjee, Sandip; Stupp, Gregory S; Park, Sung Kyu Robin et al. (2016) A comprehensive and scalable database search system for metaproteomics. BMC Genomics 17:642|
|Lavallée-Adam, Mathieu; Yates 3rd, John R (2016) Using PSEA-Quant for Protein Set Enrichment Analysis of Quantitative Mass Spectrometry-Based Proteomics. Curr Protoc Bioinformatics 53:13.28.1-16|
|Chailangkarn, Thanathom; Trujillo, Cleber A; Freitas, Beatriz C et al. (2016) A human neurodevelopmental model for Williams syndrome. Nature 536:338-43|
|Borges, Marcia H; Figueiredo, Suely G; Leprevost, Felipe V et al. (2016) Venomous extract protein profile of Brazilian tarantula Grammostola iheringi: searching for potential biotechnological applications. J Proteomics 136:35-47|
|Buffon, Giseli; Blasi, Édina A R; Adamski, Janete M et al. (2016) Physiological and Molecular Alterations Promoted by Schizotetranychus oryzae Mite Infestation in Rice Leaves. J Proteome Res 15:431-46|
|Carvalho, Paulo C; Lima, Diogo B; Leprevost, Felipe V et al. (2016) Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0. Nat Protoc 11:102-17|
|Savas, Jeffrey N; Park, Sung Kyu; Yates 3rd, John R (2016) Proteomic Analysis of Protein Turnover by Metabolic Whole Rodent Pulse-Chase Isotopic Labeling and Shotgun Mass Spectrometry Analysis. Methods Mol Biol 1410:293-304|
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