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Record W4392005706 · doi:10.1016/j.isci.2024.109293

Dysregulated lysosomal exocytosis drives protease-mediated cartilage pathogenesis in multiple lysosomal disorders

2024· article· en· W4392005706 on OpenAlexaff
Jen‐Jie Lee, Tong Wang, Kali Wiggins, Po Nien Lu, Christina Underwood, Katarzyna Ochenkowska, Éric Samarut, Laura Pollard, Heather Flanagan‐Steet, Richard Steet

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational MPS Society
KeywordsExocytosisCathepsin BLysosomeCell biologyCathepsinPathogenesisProteasesCartilageBiologyCathepsin SChemistryImmunologySecretionBiochemistryEnzymeAnatomy

Abstract

fetched live from OpenAlex

The classic view of the lysosome as a static recycling center has been replaced with one of a dynamic and mobile hub of metabolic regulation. This revised view raises new questions about how dysfunction of this organelle causes pathology in inherited lysosomal disorders. Here we provide evidence for increased lysosomal exocytosis in the developing cartilage of three lysosomal disease zebrafish models with distinct etiologies. Dysregulated exocytosis was linked to altered cartilage development, increased activity of multiple cathepsin proteases, and cathepsin- and TGFβ-mediated pathogenesis in these models. Moreover, inhibition of cathepsin activity or direct blockade of exocytosis with small molecule modulators improved the cartilage phenotypes, reinforcing a connection between excessive extracellular protease activity and cartilage pathogenesis. This study highlights the pathogenic consequences in early cartilage development arising from uncontrolled release of lysosomal enzymes via exocytosis, and suggests that pharmacological enhancement of this process could be detrimental during tissue development.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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