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Record W2096494569 · doi:10.1242/jcs.114819

NBR1 acts as an autophagy receptor for peroxisomes

2012· article· en· W2096494569 on OpenAlexafffund
Elizabeth Deosaran, Kenneth Bowitz Larsen, Rong Hua, Graeme Sargent, Yuqing Wang, Sarah Kim, Trond Lamark, Miluska Jauregui, Kelsey B. Law, Jennifer Lippincott-Schwartz, Andreas Brech, Terje Johansen, Peter K. Kim

Bibliographic record

VenueJournal of Cell Science · 2012
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsAutophagyBiologyCell biologyPeroxisomeUbiquitinReceptorBiochemistryGeneApoptosis

Abstract

fetched live from OpenAlex

Selective macro-autophagy is an intracellular process by which large cytoplasmic materials are selectively sequestered and degraded in the lysosomes. Substrate selection is mediated by ubiquitylation and recruitment of ubiquitin-binding autophagic receptors such as p62, NBR1, NDP52 and Optineurin. Although it has been shown that these receptors act cooperatively to target some types of substrates to nascent autophagosomes, their precise roles are not well understood. We examined selective autophagic degradation of peroxisomes (pexophagy), and found that NBR1 is necessary and sufficient for pexophagy. Mutagenesis studies of NBR1 showed that the amphipathic α-helical J domain, the ubiquitin-associated (UBA) domain, the LC3-interacting region and the coiled-coil domain are necessary to mediate pexophagy. Strikingly, substrate selectivity is partly achieved by NBR1 itself by coincident binding of the J and UBA domains to peroxisomes. Although p62 is not required when NBR1 is in excess, its binding to NBR1 increases the efficiency of NBR1-mediated pexophagy. Together, these results suggest that NBR1 is the specific autophagy receptor for pexophagy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.343
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations354
Published2012
Admission routes2
Has abstractyes

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Same venueJournal of Cell ScienceSame topicAutophagy in Disease and TherapyFrench-language works237,207