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pmid:32290654

Revealing Dynamic Protein Acetylation across Subcellular Compartments.

Josue Baeza · Alexis J Lawton · Jing Fan · Michael J Smallegan · Ian Lienert · Tejas Gandhi · Oliver M Bernhardt · Lukas Reiter · John M Denu

Journal of proteome research · 2020 doi:10.1021/acs.jproteome.0c00088

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

absent

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title
Revealing Dynamic Protein Acetylation across Subcellular Compartments.
doi
10.1021/acs.jproteome.0c00088
pmid
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Josue Baeza
[1]
Alexis J Lawton
[2]
Jing Fan
[3]
Michael J Smallegan
[4]
Ian Lienert
[5]
Tejas Gandhi
[6]
Oliver M Bernhardt
[7]
Lukas Reiter
[8]
John M Denu
journal
Journal of proteome research
year
2020
abstract
Protein acetylation is a widespread post-translational modification implicated in many cellular processes. Recent advances in mass spectrometry have enabled the cataloging of thousands of sites throughout the cell; however, identifying regulatory acetylation marks have proven to be a daunting task. Knowledge of the kinetics and stoichiometry of site-specific acetylation is an important factor to uncover function. Here, an improved method of quantifying acetylation stoichiometry was developed and validated, providing a detailed landscape of dynamic acetylation stoichiometry within cellular compartments. The dynamic nature of site-specific acetylation in response to serum stimulation was revealed. In two distinct human cell lines, growth factor stimulation led to site-specific, temporal acetylation changes, revealing diverse kinetic profiles that clustered into several groups. Overlap of dynamic acetylation sites among two different human cell lines suggested similar regulatory control points across major cellular pathways that include splicing, translation, and protein homeostasis. Rapid increases in acetylation on protein translational machinery suggest a positive regulatory role under progrowth conditions. Finally, higher median stoichiometry was observed in cellular compartments where active acetyltransferases are well described. Data sets can be accessed through ProteomExchange via the MassIVE repository (ProteomExchange: PXD014453; MassIVE: MSV000084029).
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