Lipids are essential signaling molecules that regulate all aspects of cellular function. Unlike proteins, whose location can be tracked in situ with antibodies or expression of tagged fusion proteins, our ability to track signaling lipids in situ i still primitive and constitutes a major impediment to understanding their role in cell function. Ths is especially true for the signaling lipid ceramide 1- phosphate (C1P), which has been implicated in multiple cellular functions including transmitter exocytosis, inflammation, cell proliferation, protein function regulation, and cytoskeletal dynamics. To overcome this technological challenge we will use deep scanning mutagenesis of the C2 lipid-binding motif, combined with deep sequencing and sequence analysis algorithms, to generate proteins with specific, high-affinity binding to C1P. The resulting proteins will serve as the basis for fluorescent biosensors to track C1P in situ. In addition, the novel protein engineering platform we assemble will be a powerful new strategy for developing new sensors for other lipids.

Public Health Relevance

The signaling lipid ceramide 1-phosphate (C1P) plays a role in many cellular processes relevant to disease. To overcome the lack of approaches to track C1P in cells we will employ deep scanning mutagenesis of the C2 lipid-binding motif, combined with deep sequencing and sequence analysis algorithms, to generate proteins with specific, high-affinity binding to C1P. The resulting proteins will serve as the basis fluorescent biosensors to track this signaling lipid in situ.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB020277-02
Application #
9068933
Study Section
Biochemistry and Biophysics of Membranes Study Section (BBM)
Program Officer
Conroy, Richard
Project Start
2015-06-01
Project End
2017-05-31
Budget Start
2016-06-01
Budget End
2017-05-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Washington
Department
Pharmacology
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Rubin, Alan F; Gelman, Hannah; Lucas, Nathan et al. (2017) A statistical framework for analyzing deep mutational scanning data. Genome Biol 18:150
Bluestein, Blake M; Morrish, Fionnuala; Graham, Daniel J et al. (2016) An unsupervised MVA method to compare specific regions in human breast tumor tissue samples using ToF-SIMS. Analyst 141:1947-57