Literature DB >> 32997975

Photoaffinity Labeling and Quantitative Chemical Proteomics Identify LXRβ as the Functional Target of Enhancers of Astrocytic apoE.

Uthpala Seneviratne1, Zhen Huang2, Christopher W Am Ende3, Todd W Butler3, Leah Cleary3, Erica Dresselhaus2, Edelweiss Evrard2, Ethan L Fisher3, Michael E Green2, Christopher J Helal3, John M Humphrey3, Lorraine F Lanyon3, Michael Marconi2, Paramita Mukherjee3, Simone Sciabola2, Claire M Steppan3, Emily K Sylvain2, Jamison B Tuttle2, Patrick R Verhoest2, Travis T Wager2, Longfei Xie3, Gayathri Ramaswamy2, Douglas S Johnson2, Martin Pettersson4.   

Abstract

Utilizing a phenotypic screen, we identified chemical matter that increased astrocytic apoE secretion in vitro. We designed a clickable photoaffinity probe based on a pyrrolidine lead compound and carried out probe-based quantitative chemical proteomics in human astrocytoma CCF-STTG1 cells to identify liver x receptor β (LXRβ) as the target. Binding of the small molecule ligand stabilized LXRβ, as shown by cellular thermal shift assay (CETSA). In addition, we identified a probe-modified peptide by mass spectrometry and proposed a model where the photoaffinity probe is bound in the ligand-binding pocket of LXRβ. Taken together, our findings demonstrated that the lead chemical matter bound directly to LXRβ, and our results highlight the power of chemical proteomic approaches to identify the target of a phenotypic screening hit. Additionally, the LXR photoaffinity probe and lead compound described herein may serve as valuable tools to further evaluate the LXR pathway.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  CETSA; LXRβ; MOA; apoE; chemical proteomics; phenotypic screening; target identification

Year:  2020        PMID: 32997975     DOI: 10.1016/j.chembiol.2020.09.002

Source DB:  PubMed          Journal:  Cell Chem Biol        ISSN: 2451-9448            Impact factor:   8.116


  7 in total

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7.  Protocol for clickable photoaffinity labeling and quantitative chemical proteomics.

Authors:  Wankyu Lee; Zhen Huang; Christopher W Am Ende; Uthpala Seneviratne
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  7 in total

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