Northwestern Events Calendar

Mar
14
2016

Works-in-Progress: Nicole Hawkins and Megan Roy-Puckelwartz

When: Monday, March 14, 2016
4:00 PM - 5:00 PM CT

Where: Ward Building, 5-230, 303 E. Chicago Avenue, Chicago, IL 60611 map it

Audience: Faculty/Staff - Student - Public - Post Docs/Docs - Graduate Students

Contact: Alexa Ann Nash   (312) 503-4893

Group: Department of Pharmacology Seminars

Category: Lectures & Meetings

Description:

Please join the Department of Pharmacology for a Works-in-Progress presentation by Nicole Hawkins and Megan Roy-Puckelwartz. 

Nicole Hawkins, Ph.D. - Research Associate, Kearney Lab

"Fine Mapping of a Dravet Syndrome Modifier Locus on Mouse Chromosome 5"

Several epilepsies can be attributed to mutations identified in voltage-gated sodium channel genes. Mutations in SCN1A result in a spectrum of phenotypes ranging from simple febrile seizures to Dravet syndrome, a severe, infant-onset epileptic encephalopathy. The Scn1aKO/+ Dravet mouse model has a strain-dependent epilepsy and survival phenotype. To identify the genes responsible for the phenotypic differences between strains, we utilized interval-specific congenic lines and performed candidate gene analysis by RNA-Seq. We identified Gabra2 as a high priority candidate gene. Further evaluation of Gabra2 by expression analysis and pharmacological manipulation support Gabra2 as a putative modifier gene that influences survival in the Scn1a+/- Dravet model.

Megan Roy-Puckelwartz, Ph.D. - Research Assistant Professor, Pharmacology

 

"Whole Genome Sequencing as a Diagnostic Tool for Cardiomyopathy"

Cardiomyopathy is a leading cause of heart failure and is highly associated with arrhythmias. Currently, clinical gene panels are sequenced to provide genetic diagnosis for cardiomyopathy. Although these panels contain >80 genes, they have limited sensitivity (<50%). We are using whole genome sequencing to define the genetic landscape of cardiomyopathy and its role in heart failure. Our work focuses on developing strategies for whole genome analysis using high powered computing platforms to improve both speed and accuracy to identify and classify genetic variants. Using a dataset of 120 genomes, we are also developing methods to identify genes that modify phenotype, with a focused interest on arrhythmia.

Add to Calendar

Add Event To My Group:

Please sign-in