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Record W3150928009 · doi:10.3389/fcell.2021.661298

Autophagy Inhibitors Do Not Restore Peroxisomal Functions in Cells With the Most Common Peroxisome Biogenesis Defect

2021· article· en· W3150928009 on OpenAlexfundno aff
Femke C. C. Klouwer, Kim D. Falkenberg, Rob Ofman, Janet Koster, Démi van Gent, Sacha Ferdinandusse, Ronald J. A. Wanders, Hans R. Waterham

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsnot available
FundersFP7 People: Marie-Curie ActionsZonMwE-RareHersenstichtingMcGill University Health CentreMcGill University
KeywordsPeroxisomeAutophagyBiogenesisCell biologyPeroxisome proliferatorBiologyChemistryBiochemistryReceptorGene

Abstract

fetched live from OpenAlex

gene (PEX1-G843D), which results in impaired import of peroxisomal matrix proteins and, consequently, defective peroxisomal functions. A recent study suggested that treatment with autophagy inhibitors, in particular hydroxychloroquine, would be a potential therapeutic option for PBD-ZSD patients carrying the PEX1-G843D mutation. Here, we studied whether autophagy inhibition by chloroquine, hydroxychloroquine and 3-methyladenine indeed can improve peroxisomal functions in four different cell types with the PEX1-G843D mutation, including primary patient cells. Furthermore, we studied whether autophagy inhibition may be the mechanism underlying the previously reported improvement of peroxisomal functions by L-arginine in PEX1-G843D cells. In contrast to L-arginine, we observed no improvement but a worsening of peroxisomal metabolic functions and peroxisomal matrix protein import by the autophagy inhibitors, while genetic knock-down of ATG5 and NBR1 in primary patient cells resulted in only a minimal improvement. Our results do not support the use of autophagy inhibitors as potential treatment for PBD-ZSD patients, whereas L-arginine remains a therapeutically promising compound.

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)0.000
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 score0.282
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.199
Teacher spread0.194 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

Explore more

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