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Record W4379769379 · doi:10.1083/jcb.202205128

Arf1-PI4KIIIβ positive vesicles regulate PI(3)P signaling to facilitate lysosomal tubule fission

2023· article· en· W4379769379 on OpenAlexafffund
Maxime Boutry, Laura F. DiGiovanni, Nicholas D. Demers, Aaron Fountain, Sami Mamand, Roberto J. Botelho, Peter K. Kim

Bibliographic record

VenueThe Journal of Cell Biology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenCanada Research Chairs
KeywordsCell biologyVesiclePhosphatidylinositolLysosomeBiologyTubuleOrganellePiChemistrySignal transductionBiochemistryEndocrinologyKidneyEnzyme

Abstract

fetched live from OpenAlex

Formation and fission of tubules from autolysosomes, endolysosomes, or phagolysosomes are required for lysosome reformation. However, the mechanisms governing these processes in these different lysosomal organelles are poorly understood. Thus, the role of phosphatidylinositol-4-phosphate (PI(4)P) is unclear as it was shown to promote the formation of tubules from phagolysosomes but was proposed to inhibit tubule formation on autolysosomes because the loss of PI4KIIIβ causes extensive lysosomal tubulation. Using super-resolution live-cell imaging, we show that Arf1-PI4KIIIβ positive vesicles are recruited to tubule fission sites from autolysosomes, endolysosomes, and phagolysosomes. Moreover, we show that PI(4)P is required to form autolysosomal tubules and that increased lysosomal tubulation caused by loss of PI4KIIIβ represents impaired tubule fission. At the site of fission, we propose that Arf1-PI4KIIIβ positive vesicles mediate a PI(3)P signal on lysosomes in a process requiring the lipid transfer protein SEC14L2. Our findings indicate that Arf1-PI4KIIIβ positive vesicles and their regulation of PI(3)P are critical components of the lysosomal tubule fission machinery.

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 score0.018
Threshold uncertainty score0.436

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)0.0000.000

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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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
Published2023
Admission routes2
Has abstractyes

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