Proteomics

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Neuronal proteome dynamics during homeostatic scaling.


ABSTRACT: Homeostatic scaling adjusts synaptic strength in response to persistent changes in neuronal network activity. This compensatory mechanism requires proteome remodeling accomplished via regulation of protein synthesis as well as degradation, but the global patterns of proteome remodeling and the underlying dynamics of individual proteins remain elusive. Here we used dynamic SILAC labeling in cultured hippocampal cells to identify proteins involved in homeostatic up- or down-scaling and to quantify their changes in synthesis and degradation as well as resulting changes in protein abundance or turnover. Our data demonstrate that a large fraction of the neuronal proteome is remodeled during homeostatic scaling. Most proteins were down-regulated by decreased synthesis or up-regulated by decreased degradation. Comparably fewer proteins showed increased synthesis or degradation rates. More than half of the quantified synaptic proteins were regulated, including pre- as well as postsynaptic proteins with diverse molecular functions.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Rattus Norvegicus (rat)

TISSUE(S): Brain, Cell Culture

SUBMITTER: Julian Langer  

LAB HEAD: Julian Langer

PROVIDER: PXD016004 | Pride | 2020-03-31

REPOSITORIES: Pride

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Publications

Proteome dynamics during homeostatic scaling in cultured neurons.

Dörrbaum Aline Ricarda AR   Alvarez-Castelao Beatriz B   Nassim-Assir Belquis B   Langer Julian D JD   Schuman Erin M EM  

eLife 20200402


Protein turnover, the net result of protein synthesis and degradation, enables cells to remodel their proteomes in response to internal and external cues. Previously, we analyzed protein turnover rates in cultured brain cells under basal neuronal activity and found that protein turnover is influenced by subcellular localization, protein function, complex association, cell type of origin, and by the cellular environment (Dörrbaum et al., 2018). Here, we advanced our experimental approach to quant  ...[more]

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