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Updated: Jun 10, 2026

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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling
Published on: May 22, 2021
Analysis of proteome dynamics in the mouse brain
John C Price1, Shenheng Guan, Alma Burlingame
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA 94143, USA.
Summary
This study reveals the first large-scale in vivo measurement of protein turnover rates across mouse tissues. Brain proteins turn over significantly slower than liver and blood proteins, impacting proteome homeostasis.
Area of Science:
- Systems Biology
- Proteomics
- Metabolic Regulation
Background:
- Global mRNA and protein expression analyses are advanced.
- Large-scale in vivo studies of protein dynamics and turnover are lacking.
- Protein turnover is crucial for proteome homeostasis and impacts physiological/pathological processes.
Purpose of the Study:
- To conduct the most comprehensive in vivo analysis of mammalian proteome turnover.
- To measure protein turnover rates across multiple mouse tissues.
- To compare protein turnover dynamics between different tissues and within cellular components.
Main Methods:
- Organism-wide isotopic labeling in mice.
- Quantification of turnover rates for approximately 2,500 proteins.
- Comparative analysis of protein turnover in brain, liver, and blood tissues.
Main Results:
- Protein turnover rates spanned four orders of magnitude.
- Proteins with similar functions within tissues exhibited similar turnover rates.
- Brain proteins showed significantly slower turnover (9.0 d) compared to liver (3.0 d) and blood (3.5 d).
- Mitochondrial proteins displayed synchronized turnover.
- Subunits within well-defined protein complexes turned over coordinately.
Conclusions:
- This study provides the most comprehensive in vivo dataset on mammalian proteome turnover.
- Tissue-specific differences in protein turnover exist, with the brain being notably slower.
- The developed methodology is adaptable for assessing proteome homeostasis in various model organisms.
- This approach may be valuable for studying neurodegenerative diseases.

