Literature DB >> 31685960

A mass spectrometry workflow for measuring protein turnover rates in vivo.

Mihai Alevra1, Sunit Mandad1,2,3, Till Ischebeck4, Henning Urlaub2,3, Silvio O Rizzoli5, Eugenio F Fornasiero6.   

Abstract

Proteins are continually produced and degraded, to avoid the accumulation of old or damaged molecules and to maintain the efficiency of physiological processes. Despite its importance, protein turnover has been difficult to measure in vivo. Previous approaches to evaluating turnover in vivo have required custom labeling approaches, involved complex mass spectrometry (MS) analyses, or used comparative strategies that do not allow direct quantitative measurements. Here, we describe a robust protocol for quantitative proteome turnover analysis in mice that is based on a commercially available diet for stable isotope labeling of amino acids in mammals (SILAM). We start by discussing fundamental concepts of protein turnover, including different methodological approaches. We then cover in detail the practical aspects of metabolic labeling and explain both the experimental and computational steps that must be taken to obtain accurate in vivo results. Finally, we present a simple experimental workflow that enables measurement of precise turnover rates in a time frame of ~4-5 weeks, including the labeling time. We also provide all the scripts needed for the interpretation of the MS results and for comparing turnover across different conditions. Overall, the workflow presented here comprises several improvements in the determination of protein lifetimes with respect to other available methods, including a minimally invasive labeling strategy and a robust interpretation of MS results, thus enhancing reproducibility across laboratories.

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Year:  2019        PMID: 31685960     DOI: 10.1038/s41596-019-0222-y

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  43 in total

1.  Analysis of proteome dynamics in the mouse brain.

Authors:  John C Price; Shenheng Guan; Alma Burlingame; Stanley B Prusiner; Sina Ghaemmaghami
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-10       Impact factor: 11.205

2.  Extremely long-lived nuclear pore proteins in the rat brain.

Authors:  Jeffrey N Savas; Brandon H Toyama; Tao Xu; John R Yates; Martin W Hetzer
Journal:  Science       Date:  2012-02-02       Impact factor: 47.728

3.  Identification of long-lived proteins reveals exceptional stability of essential cellular structures.

Authors:  Brandon H Toyama; Jeffrey N Savas; Sung Kyu Park; Michael S Harris; Nicholas T Ingolia; John R Yates; Martin W Hetzer
Journal:  Cell       Date:  2013-08-29       Impact factor: 41.582

4.  Measurement of protein turnover rates by heavy water labeling of nonessential amino acids.

Authors:  Robert Busch; Yoo-Kyeong Kim; Richard A Neese; Valerie Schade-Serin; Michelle Collins; Mohamad Awada; James L Gardner; Carine Beysen; Michael E Marino; Lisa M Misell; Marc K Hellerstein
Journal:  Biochim Biophys Acta       Date:  2006-01-24

5.  A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC).

Authors:  Shao-En Ong; Matthias Mann
Journal:  Nat Protoc       Date:  2006       Impact factor: 13.491

6.  15N metabolic labeling of mammalian tissue with slow protein turnover.

Authors:  Daniel B McClatchy; Meng-Qiu Dong; Christine C Wu; John D Venable; John R Yates
Journal:  J Proteome Res       Date:  2007-03-22       Impact factor: 4.466

Review 7.  The biology of proteostasis in aging and disease.

Authors:  Johnathan Labbadia; Richard I Morimoto
Journal:  Annu Rev Biochem       Date:  2015-03-12       Impact factor: 23.643

8.  Identification of long-lived synaptic proteins by proteomic analysis of synaptosome protein turnover.

Authors:  Seok Heo; Graham H Diering; Chan Hyun Na; Raja Sekhar Nirujogi; Julia L Bachman; Akhilesh Pandey; Richard L Huganir
Journal:  Proc Natl Acad Sci U S A       Date:  2018-04-02       Impact factor: 11.205

Review 9.  Mitochondrial protein turnover: methods to measure turnover rates on a large scale.

Authors:  X'avia C Y Chan; Caitlin M Black; Amanda J Lin; Peipei Ping; Edward Lau
Journal:  J Mol Cell Cardiol       Date:  2014-11-11       Impact factor: 5.000

10.  Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions.

Authors:  Eugenio F Fornasiero; Sunit Mandad; Hanna Wildhagen; Mihai Alevra; Burkhard Rammner; Sarva Keihani; Felipe Opazo; Inga Urban; Till Ischebeck; M Sadman Sakib; Maryam K Fard; Koray Kirli; Tonatiuh Pena Centeno; Ramon O Vidal; Raza-Ur Rahman; Eva Benito; André Fischer; Sven Dennerlein; Peter Rehling; Ivo Feussner; Stefan Bonn; Mikael Simons; Henning Urlaub; Silvio O Rizzoli
Journal:  Nat Commun       Date:  2018-10-12       Impact factor: 14.919

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  6 in total

1.  Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration.

Authors:  Verena Kluever; Belisa Russo; Sunit Mandad; Nisha Hemandhar Kumar; Mihai Alevra; Alessandro Ori; Silvio O Rizzoli; Henning Urlaub; Anja Schneider; Eugenio F Fornasiero
Journal:  Sci Adv       Date:  2022-05-20       Impact factor: 14.957

2.  JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.

Authors:  Surendhar Reddy Chepyala; Xueyan Liu; Ka Yang; Zhiping Wu; Alex M Breuer; Ji-Hoon Cho; Yuxin Li; Ariana Mancieri; Yun Jiao; Hui Zhang; Junmin Peng
Journal:  Anal Chem       Date:  2021-09-29       Impact factor: 6.986

3.  Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications.

Authors:  Lindsay K Pino; Jacob Rose; Amy O'Broin; Samah Shah; Birgit Schilling
Journal:  Biochem Soc Trans       Date:  2020-10-30       Impact factor: 5.407

4.  NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover.

Authors:  Elisa A Bonnin; Eugenio F Fornasiero; Felix Lange; Christoph W Turck; Silvio O Rizzoli
Journal:  BMC Mol Cell Biol       Date:  2021-01-11

5.  An atlas of protein turnover rates in mouse tissues.

Authors:  Zach Rolfs; Brian L Frey; Xudong Shi; Yoshitaka Kawai; Lloyd M Smith; Nathan V Welham
Journal:  Nat Commun       Date:  2021-11-26       Impact factor: 14.919

Review 6.  Understanding the "SMART" features of hematopoietic stem cells and beyond.

Authors:  Shiru Yuan; Guohuan Sun; Yawen Zhang; Fang Dong; Hui Cheng; Tao Cheng
Journal:  Sci China Life Sci       Date:  2021-07-30       Impact factor: 6.038

  6 in total

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