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Record W2911686738 · doi:10.1083/jcb.201804172

Deubiquitinating enzyme USP30 maintains basal peroxisome abundance by regulating pexophagy

2019· article· en· W2911686738 on OpenAlexafffund
Victoria Riccio, Nicholas D. Demers, Rong Hua, Miluska Vissa, D. Cheng, Amy W. Strilchuk, Yuqing Wang, G. Angus McQuibban, Peter K. Kim

Bibliographic record

VenueThe Journal of Cell Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPeroxisomeDeubiquitinating enzymeMitophagyUbiquitin ligaseCell biologyUbiquitinOrganelleAutophagyRegulatorBiologyBiochemistryReceptorGene

Abstract

fetched live from OpenAlex

The regulation of organelle abundance is critical for cell function and survival; however, the mechanisms responsible are not fully understood. In this study, we characterize a role of the deubiquitinating enzyme USP30 in peroxisome maintenance. Peroxisomes are highly dynamic, changing in abundance in response to metabolic stress. In our recent study identifying the role of USP30 in mitophagy, we observed USP30 to be localized to punctate structures resembling peroxisomes. We report here that USP30, best known as a mitophagy regulator, is also necessary for regulating pexophagy, the selective autophagic degradation of peroxisomes. We find that overexpressing USP30 prevents pexophagy during amino acid starvation, and its depletion results in pexophagy induction under basal conditions. We demonstrate that USP30 prevents pexophagy by counteracting the action of the peroxisomal E3 ubiquitin ligase PEX2. Finally, we show that USP30 can rescue the peroxisome loss observed in some disease-causing peroxisome mutations, pointing to a potential therapeutic target.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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 teacher head, 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

Citations75
Published2019
Admission routes2
Has abstractyes

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