Research
Translational genome editing
Gene-based therapies hold great promise for treatment of genetic diseases. We aim to accelerate the translation of such therapeutics for patients with genetic neurological diseases, including ongoing projects for treating spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS), and neurodegenerative movement disorders and dementias that are caused by tandem repeat sequences. We develop therapeutic genome editing strategies for these genetic disorders in cells and animal models of disease. We use high-throughput molecular techniques to engineer and improve the safety and efficacy of our vectors, and work towards the preclinical validation of these novel proof-of-concept drugs for future application in the clinic.
Studying genetic neurodegenerative diseases
Many neurodegenerative diseases are linked to known, often highly or completely penetrant genetic abnormalities. The cellular pathology that follows from these genetic abnormalities to cause disease is often poorly understood, which has hindered the development of effective therapeutic interventions. Repeat sequence expansions in somatic cells of the central nervous system are linked to several dozen distinct neuropsychiatric disorders, including Huntington’s disease (HD), ataxia’s, ALS, and frontotemporal dementia (FTD). Currently there are no treatments that can alter the course of these severe and often fatal diseases. The genes associated with these disorders vary, however, shared sequence features suggest a potentially common mechanism may underlie the disease pathology of many. Using genome editing technologies, we modify nucleotide sequences implicated in these diseases and use genomic, transcriptomic and proteomic assays to gain insight into the molecular changes that underlie neurodegeneration.
Improving precise and predictable genome editing using machine learning
With the wide variety of genome editing tools available, it is difficult to predict what strategy will work best for a target of interest, even by an expert user. To ensure the effective and safe implementation of future genome editing therapeutics, a deep understanding of both the intended and unintended outcomes of genome editing in cells is imperative. Empirically evaluating the genotypes and resulting phenotypes of every possible strategy is cumbersome and costly. We study genome editing tools to understand and predict the outcomes of genome editing to streamline the strategy optimization process, enable non-canonical editing of alleles, and improve the precision and safety of genome editing at desired targets.