The simple genetic cause of Huntington?s disease contrasts starkly with the vast number of pathways that are affected by the mutation. Some of these pathway-level changes may persist even if the mutated allele of the disease-causing gene (HTT) can be corrected through gene therapy or related methods. During the first granting period, our analysis of HD models identified several potential therapeutic directions, including ones closely tied to epigenetics (the transcriptional regulators NEUROD1, WNTand ELK-1), as well as pathways that interact with epigenomic changes (energy metabolism and lipid biochemistry). Some of these effects were restricted to particular cell types in the brain. We also found evidence that mutant HTT (mHTT) expression causes neurodevelopmental impairments, changing the distribution of cell types in the brain. We and others have also identified a significant number of genetic variants in the human population for which there is significant support for an impact of that variant on HD age of onset (AOO). In the current proposal, we examine the therapeutic potential of interventions based on these findings. We will target these pathways in mice, measuring how interventions alter transcription, the epigenome, signaling and metabolomics. A critical innovation is our use of single-cell and spatially resolved methods to examine how responses to mHTT and therapeutics vary among different types of cells. Equally important, we will differentiate specific cell types from induced-pluripotent stem cells (iPSC) in vitro to examine cell-type specific effects in human cells. Using an approach based in systems biology we will look for common pathways that are affected by the genetic AOO modifiers, the candidates from our prior grant period and leads from the literature. Our approach is highly innovative, as it uses cutting edge experimental methods with single-cell and spatial resolution to reveal aspects of HD that cannot be detected in homogenates. We also computationally integrate multi-omic data (genomics, epigenomics, transcripts, proteins and metabolites) from the individual cells and brain regions to uncover therapeutic pathways. The research is highly significant, as it seeks to guide therapeutic discovery for an invariably fatal neurodegenerative disease. We expect that the impact of our work will extend beyond HD, by providing a model for how to measure and model cell-type specific neurodegeneration to identify therapeutic approaches.

Public Health Relevance

Huntington?s Disease is an inherited, fatal disease with no cure or effective treatment. We will explore how the mutation causing the disease affects different types of cells in the brain. We will combine these data with information about genes that either slow or accelerate the age when patients develop symptoms to uncover new approaches to treating the disease.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
Project #
Application #
Study Section
Cellular and Molecular Biology of Neurodegeneration Study Section (CMND)
Program Officer
Miller, Daniel L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Massachusetts Institute of Technology
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Kedaigle, Amanda J; Fraenkel, Ernest (2018) Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data. Methods Mol Biol 1711:13-26
Kedaigle, Amanda; Fraenkel, Ernest (2018) Turning omics data into therapeutic insights. Curr Opin Pharmacol 42:95-101
HD iPSC Consortium (2017) Developmental alterations in Huntington's disease neural cells and pharmacological rescue in cells and mice. Nat Neurosci 20:648-660
Lim, Ryan G; Quan, Chris; Reyes-Ortiz, Andrea M et al. (2017) Huntington's Disease iPSC-Derived Brain Microvascular Endothelial Cells Reveal WNT-Mediated Angiogenic and Blood-Brain Barrier Deficits. Cell Rep 19:1365-1377
Soltis, Anthony R; Kennedy, Norman J; Xin, Xiaofeng et al. (2017) Hepatic Dysfunction Caused by Consumption of a High-Fat Diet. Cell Rep 21:3317-3328
Pirhaji, Leila; Milani, Pamela; Dalin, Simona et al. (2017) Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements. Nat Commun 8:623
Pirhaji, Leila; Milani, Pamela; Leidl, Mathias et al. (2016) Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13:770-6
Wong, Alan S L; Choi, Gigi C G; Cui, Cheryl H et al. (2016) Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc Natl Acad Sci U S A 113:2544-9