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Systematic analysis of protein turnover in primary cells

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC...

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Autores principales: Mathieson, Toby, Franken, Holger, Kosinski, Jan, Kurzawa, Nils, Zinn, Nico, Sweetman, Gavain, Poeckel, Daniel, Ratnu, Vikram S., Schramm, Maike, Becher, Isabelle, Steidel, Michael, Noh, Kyung-Min, Bergamini, Giovanna, Beck, Martin, Bantscheff, Marcus, Savitski, Mikhail M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814408/
https://www.ncbi.nlm.nih.gov/pubmed/29449567
http://dx.doi.org/10.1038/s41467-018-03106-1
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author Mathieson, Toby
Franken, Holger
Kosinski, Jan
Kurzawa, Nils
Zinn, Nico
Sweetman, Gavain
Poeckel, Daniel
Ratnu, Vikram S.
Schramm, Maike
Becher, Isabelle
Steidel, Michael
Noh, Kyung-Min
Bergamini, Giovanna
Beck, Martin
Bantscheff, Marcus
Savitski, Mikhail M.
author_facet Mathieson, Toby
Franken, Holger
Kosinski, Jan
Kurzawa, Nils
Zinn, Nico
Sweetman, Gavain
Poeckel, Daniel
Ratnu, Vikram S.
Schramm, Maike
Becher, Isabelle
Steidel, Michael
Noh, Kyung-Min
Bergamini, Giovanna
Beck, Martin
Bantscheff, Marcus
Savitski, Mikhail M.
author_sort Mathieson, Toby
collection PubMed
description A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000–6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types.
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spelling pubmed-58144082018-02-20 Systematic analysis of protein turnover in primary cells Mathieson, Toby Franken, Holger Kosinski, Jan Kurzawa, Nils Zinn, Nico Sweetman, Gavain Poeckel, Daniel Ratnu, Vikram S. Schramm, Maike Becher, Isabelle Steidel, Michael Noh, Kyung-Min Bergamini, Giovanna Beck, Martin Bantscheff, Marcus Savitski, Mikhail M. Nat Commun Article A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000–6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types. Nature Publishing Group UK 2018-02-15 /pmc/articles/PMC5814408/ /pubmed/29449567 http://dx.doi.org/10.1038/s41467-018-03106-1 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Mathieson, Toby
Franken, Holger
Kosinski, Jan
Kurzawa, Nils
Zinn, Nico
Sweetman, Gavain
Poeckel, Daniel
Ratnu, Vikram S.
Schramm, Maike
Becher, Isabelle
Steidel, Michael
Noh, Kyung-Min
Bergamini, Giovanna
Beck, Martin
Bantscheff, Marcus
Savitski, Mikhail M.
Systematic analysis of protein turnover in primary cells
title Systematic analysis of protein turnover in primary cells
title_full Systematic analysis of protein turnover in primary cells
title_fullStr Systematic analysis of protein turnover in primary cells
title_full_unstemmed Systematic analysis of protein turnover in primary cells
title_short Systematic analysis of protein turnover in primary cells
title_sort systematic analysis of protein turnover in primary cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814408/
https://www.ncbi.nlm.nih.gov/pubmed/29449567
http://dx.doi.org/10.1038/s41467-018-03106-1
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