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Single-cell expression predicts neuron-specific protein homeostasis networks
by
Pechmann, Sebastian
in
Aging
/ Biological Evolution
/ chaperones
/ Humans
/ Neurons
/ Protein Folding
/ protein homeostasis
/ Proteostasis
/ Ubiquitin ligases
2024
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Single-cell expression predicts neuron-specific protein homeostasis networks
by
Pechmann, Sebastian
in
Aging
/ Biological Evolution
/ chaperones
/ Humans
/ Neurons
/ Protein Folding
/ protein homeostasis
/ Proteostasis
/ Ubiquitin ligases
2024
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Single-cell expression predicts neuron-specific protein homeostasis networks
Journal Article
Single-cell expression predicts neuron-specific protein homeostasis networks
2024
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Overview
The protein homeostasis network keeps proteins in their correct shapes and avoids unwanted aggregation. In turn, the accumulation of aberrantly misfolded proteins has been directly associated with the onset of ageing-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. However, a detailed and rational understanding of how protein homeostasis is achieved in health, and how it can be targeted for therapeutic intervention in diseases remains missing. Here, large-scale single-cell expression data from the Allen Brain Map are analysed to investigate the transcription regulation of the core protein homeostasis network across the human brain. Remarkably, distinct expression profiles suggest specialized protein homeostasis networks with systematic adaptations in excitatory neurons, inhibitory neurons and non-neuronal cells. Moreover, several chaperones and Ubiquitin ligases are found transcriptionally coregulated with genes important for synapse formation and maintenance, thus linking protein homeostasis to the regulation of neuronal function. Finally, evolutionary analyses highlight the conservation of an elevated interaction density in the chaperone network, suggesting that one of the most exciting aspects of chaperone action may yet be discovered in their collective action at the systems level. More generally, our work highlights the power of computational analyses for breaking down complexity and gaining complementary insights into fundamental biological problems.
Publisher
The Royal Society
Subject
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