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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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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. |
format | Online Article Text |
id | pubmed-5814408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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