Abstract

The pool of quality control proteins (QC) that maintains protein-folding homeostasis (proteostasis) is dynamic but can become depleted in human disease. A challenge has been in quantitatively defining the depth of the QC pool. With a new biosensor, flow cytometry-based methods and mathematical modeling we measure the QC capacity to act as holdases and suppress biosensor aggregation. The biosensor system comprises a series of barnase kernels with differing folding stability that engage primarily with HSP70 and HSP90 family proteins. Conditions of proteostasis stimulation and stress alter QC holdase activity and aggregation rates. The method reveals the HSP70 chaperone cycle to be rate limited by HSP70 holdase activity under normal conditions, but this is overcome by increasing levels of the BAG1 nucleotide exchange factor to HSPA1A or activation of the heat shock gene cluster by HSF1 overexpression. This scheme opens new paths for biosensors of disease and proteostasis systems.

Details

Title
A biosensor-based framework to measure latent proteostasis capacity
Author
Wood, Rebecca J 1 ; Ormsby, Angelique R 1 ; Radwan, Mona 1 ; Cox, Dezerae 1 ; Sharma, Abhishek 2 ; Vöpel, Tobias 2   VIAFID ORCID Logo  ; Ebbinghaus, Simon 2 ; Oliveberg, Mikael 3 ; Reid, Gavin E 4 ; Dickson, Alex 5 ; Hatters, Danny M 1   VIAFID ORCID Logo 

 Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia 
 Department of Physical Chemistry II, Ruhr-University Bochum, Bochum, Germany 
 Department of Biochemistry and Biophysics, Arrhenius Laboratories of Natural Sciences, Stockholm University, Stockholm, Sweden 
 Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, Australia; School of Chemistry, University of Melbourne, Parkville, VIC, Australia 
 Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, USA; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA 
Pages
1-10
Publication year
2018
Publication date
Jan 2018
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1988934992
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.