Northwestern Events Calendar

Jan
27
2020

Pharmacology Research Works-in-Progress: My Chau Ta, Ph.D. and Layan Nahlawi, Ph.D.

When: Monday, January 27, 2020
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 - Post Docs/Docs - Graduate Students

Contact: Liz Murphy   (312) 503-4892

Group: Department of Pharmacology Seminars

Category: Lectures & Meetings

Description:

Please join the Department of Pharmacology for Works-in-Progress presentations:

My Chau Ta, PhD
Postdoctoral Fellow in the Laboratory of Paul DeCaen, PhD

TRPP3 channels of the primary cilia regulate hippocampal neuronal excitability
Primary/non-motile cilia are antenna-like organelles that are enriched in unique ion channels and other effectors that transduce signaling to the soma. Their function in the brain is commonly overlooked yet their importance in human health is underscored by numerous neuronal ciliopathies (e.g. Joubert’s Syndome)— which are diseases caused by variants that impact cilia localized proteins. Here we determine how a cilia-localized member of the transient receptor potential family of ion channels (TRPP3) contributes to the overall-excitability of hippocampal neurons. I will also discuss our electrophysiology and imaging results establishing the electrical relationship between the cilia and soma.

Layan Nahlawi, PhD
Postdoctoral Fellow in the Laboratory of Minoli Perera, PharmD, PhD

LA-GEM: Local Ancestry based Gene Expression Model
Our previous work has shown that incorporating local-ancestry (LA) improves eQTL mapping in ad- mixed populations [5]. Thus we propose a LA-based model to predict GE in African Americans (AA), an admixed minority population. We extend PrediXcan's framework to incorporate loci-specific inferred LA into the prediction model. For training, we use genotype and GE data from cultured primary hepatocytes isolated from 60 AA donor livers. We analyzed 7 million SNPs in 14000 genes. We train a linear model per gene to map genotype to GE levels. For each model, we generate 3 sets of predictors: dosage data for cis-SNPs, LA data for the respective loci, and interaction terms consisting of the product of dosage and LA data for each locus. Our models led to 1323 well predicted genes in comparison to 1027 genes predicted using GTEx models. We were able to identify 11 genes, related to xenobiotic metabolism, a key determinant of drug response, including CYP1A1, unlike previously published models. Our LA-based models pave the way for a population-specific GE prediction, facilitating the elucidation of the genetic impact on complex traits in admixed populations.

 

 

 

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