Genetic and environmental risks for autism

Environmental factors, including gestational exposure to pyrethroid pesticides and valproic acid, are implicated in risk for autism. However, these environmental risks were identified retrospectively, after a large number of people were exposed. We currently lack a way to systematically evaluate which environmental-use chemicals have the greatest potential to harm the developing brain. Our research program is guided by the hypothesis that “candidate” environmental risks for autism and other neurodevelopmental disorders can be identified rationally, by identifying chemicals and mixtures that target molecular pathways implicated in these disorders. Our long term goals are to 1) identify environmental-use chemicals and mixtures that target molecular pathways implicated in neurodevelopmental disorders. These studies will utilize primary human neural progenitor cells (phNPCs), primary neurons, and endpoints that are compatible with high-throughput screening. 2) Assess real world exposure to these chemicals/mixtures. If environmental sampling and biomonitoring data are not available for these chemicals/mixtures, we will work with a network of Environmental Health Science (EHS) researchers to collect these data. 3) Evaluate exposure risk in vivo using wild-type and CRISPR/Cas9-engineered mice that model human de novo autism-linked mutations.

CRISPR/Cas gene therapy for Angelman Syndrome


Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by deletion or mutation of the maternal allele of UBE3AUBE3A is biallelically expressed in nearly all cells of the body except in mature neurons, where the paternal allele is silenced by an extremely long non-coding RNA called UBE3A-ATS.  In light of this biology, the most direct way to treat behavioral dysfunctions associated with AS is to unsilence the intact paternal UBE3A allele. We are evaluating the extent to which CNS-directed delivery of Cas9 and a gRNA that targets Ube3a-ATS can enduringly unsilence paternal UBE3A and treat behavioral phenotypes associated with Angelman syndrome.  We are using adeno-associated virus (AAV) for delivery because it can drive gene expression for years in the brain. This research has the potential to advance a breakthrough first-in-class treatment for a pediatric-onset autism spectrum disorder. 

Automated measurement of spontaneous pain in mice from facial expressions

Opioid analgesics are commonly used to treat pain but have serious side effects, including addiction, dependence, and death from overdose. The Mouse Grimace Scale (MGS) was developed to quantify characteristic facial expressions associated with spontaneous pain. The MGS is reproducible across labs and was used to evaluate the efficacy of analgesics. However, the MGS has not been widely adopted due to its high resource demands and low throughput. To overcome this limitation, we adapted a machine learning model to classify the presence or absence of pain from mouse facial expressions. We called this model the automated Mouse Grimace Scale (aMGS). However, our original “aMGS 1.0” is limited in several respects. We are working to overcome all of these limitations by developing a more sophisticated version of our automated pain classifier (aMGS 2.0). The machine learning algorithm and associated platform will include a cloud-based data repository and analytic tools to facilitate curation of public data, continuous improvement of the model over time, and integration of new analytic tools.