Dataset DZNE-2026-00508

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Dataset: Systematically defining selective autophagy receptor-specific cargo using autophagosome content profiling

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2021
Mendeley

Mendeley () [10.17632/mwsncvr8rv.1]

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Abstract: Autophagy deficiency in fed conditions leads to the formation of protein inclusions highlighting the contribution of this lysosomal delivery route to cellular proteostasis. Selective autophagy pathways exist that clear accumulated and aggregated ubiquitinated proteins. Receptors for this type of selective autophagy (aggrephagy) include p62, NBR1, TOLLIP and OPTN which all possess LC3-interacting regions and ubiquitin-binding domains (UBDs), thus working as a bridge between LC3/GABARAP proteins and ubiquitinated substrates. However, the identity of aggrephagy substrates and the redundancy of aggrephagy and related UBD-containing receptors remains elusive. Here, we combined proximity labeling and organelle enrichment with quantitative proteomics to systematically map the autophagic degradome targeted by UBD-containing receptors under basal and proteostasis challenging conditions. We therewith identified various new autophagy substrates, some of which were differentially engulfed by autophagosomal and endosomal membranes via p62 and TOLLIP, respectively. Overall, this resource will allow to dissect autophagy’s proteostasis contribution to numerous individual proteins.

Keyword(s): Confocal Microscopy ; Electron Microscopy ; Western Blot


Contributing Institute(s):
  1. Neuronal Cell Biology (AG Misgeld)
Research Program(s):
  1. 351 - Brain Function (POF4-351) (POF4-351)

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Document types > Other Resources > Datasets
Institute Collections > M DZNE > M DZNE-AG Misgeld
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Publications Database


Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article  ;  ;
Systematically defining selective autophagy receptor-specific cargo using autophagosome content profiling.
Molecular cell 81(6), 1337 - 1354.e8 () [10.1016/j.molcel.2021.01.009]  Download fulltext Files BibTeX | EndNote: XML, Text | RIS


 Record created 2026-05-13, last modified 2026-05-14



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