We propose a Systems Biology approach to map transcriptional regulatory networks and related signaling pathways that are altered in Huntington's disease (HD), a fatal autosomal dominant neurodegenerative disorder. HD is caused by a CAG expansion leading to a polyglutamine extension in the huntingtin protein and is characterized by problems with movement, cognition and behavioral function. Although the genetic basis for the disease is clear, the mechanism by which huntingtin causes the observed symptoms remains enigmatic. Our approach is based on the hypothesis that many of the genes previously linked to HD through proteomic and genetic screens are connected through signaling pathways to many of the transcriptional changes that have been reported in HD studies. Identifying these pathways would provide critical new insights into the molecular changes that underlie the disease, and could lead to novel therapeutic strategies. We have recently developed a technique for identifying such signaling pathways through a combination of computational and experimental methods.
In Specific Aim 1 we will map out changes in recruitment of transcriptional regulatory proteins because these proteins lie at the interface between the signaling and expression changes.
In Specific Aim 2 we will computationally identify signaling changes """"""""upstream"""""""" of these regulators that link the transcriptional changes to the genetic and proteomic data. If successful, this approach will advance knowledge of the etiology of HD and provide a powerful new method for studying many human diseases.

Public Health Relevance

We propose a systems biology approach to understanding the molecular changes that occur in Huntington's disease. This project uses high-throughput experiments and computational modeling to reveal pathways that are altered in the disease and that may lead to new therapeutic approaches.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM089903-03
Application #
8228024
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
Project Start
2010-03-01
Project End
2015-02-28
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
3
Fiscal Year
2012
Total Cost
$473,164
Indirect Cost
$178,012
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
HD iPSC Consortium (2017) Developmental alterations in Huntington's disease neural cells and pharmacological rescue in cells and mice. Nat Neurosci 20:648-660
Pirhaji, Leila; Milani, Pamela; Dalin, Simona et al. (2017) Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements. Nat Commun 8:623
Khurana, Vikram; Peng, Jian; Chung, Chee Yeun et al. (2017) Genome-Scale Networks Link Neurodegenerative Disease Genes to ?-Synuclein through Specific Molecular Pathways. Cell Syst 4:157-170.e14
Ursu, Oana; Gosline, Sara J C; Beeharry, Neil et al. (2017) Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens. PLoS One 12:e0185650
Soltis, Anthony R; Motola, Shmulik; Vernia, Santiago et al. (2017) Hyper- and hypo- nutrition studies of the hepatic transcriptome and epigenome suggest that PPAR? regulates anaerobic glycolysis. Sci Rep 7:174
Gosline, Sara J C; Gurtan, Allan M; JnBaptiste, Courtney K et al. (2016) Elucidating MicroRNA Regulatory Networks Using Transcriptional, Post-transcriptional, and Histone Modification Measurements. Cell Rep 14:310-9
Pirhaji, Leila; Milani, Pamela; Leidl, Mathias et al. (2016) Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13:770-6
Tuncbag, Nurcan; Gosline, Sara J C; Kedaigle, Amanda et al. (2016) Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package. PLoS Comput Biol 12:e1004879
Gosline, Sara J C; Oh, Coyin; Fraenkel, Ernest (2015) SAMNetWeb: identifying condition-specific networks linking signaling and transcription. Bioinformatics 31:1124-6
Lodato, Michael A; Ng, Christopher W; Wamstad, Joseph A et al. (2013) SOX2 co-occupies distal enhancer elements with distinct POU factors in ESCs and NPCs to specify cell state. PLoS Genet 9:e1003288

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