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Record W4226137780 · doi:10.1083/jcb.202107174

Detection and quantification of the vacuolar H+ATPase using the <i>Legionella</i> effector protein SidK

2021· article· en· W4226137780 on OpenAlexafffund
Michelle E. Maxson, Y.M. Abbas, Jing Wu, Jonathan Plumb, Sergio Grinstein, John L. Rubinstein

Bibliographic record

VenueThe Journal of Cell Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsEndocytic cycleATPaseCell biologyEffectorLegionella pneumophilaV-ATPaseOrganelleVesicleBiologyEndosomePhagosomeTransport proteinBiophysicsChemistryBiochemistryMembraneEndocytosisReceptorEnzymeBacteria

Abstract

fetched live from OpenAlex

Acidification of secretory and endocytic organelles is required for proper receptor recycling, membrane traffic, protein degradation, and solute transport. Proton-pumping vacuolar H+ ATPases (V-ATPases) are responsible for this luminal acidification, which increases progressively as secretory and endocytic vesicles mature. An increasing density of V-ATPase complexes is thought to account for the gradual decrease in pH, but available reagents have not been sufficiently sensitive or specific to test this hypothesis. We introduce a new probe to localize and quantify V-ATPases. The probe is derived from SidK, a Legionella pneumophila effector protein that binds to the V-ATPase A subunit. We generated plasmids encoding fluorescent chimeras of SidK1-278, and labeled recombinant SidK1-278 with Alexa Fluor 568 to visualize and quantify V-ATPases with high specificity in live and fixed cells, respectively. We show that V-ATPases are acquired progressively during phagosome maturation, that they distribute in discrete membrane subdomains, and that their density in lysosomes depends on their subcellular localization.

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 score0.022
Threshold uncertainty score0.138

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 head0.012
GPT teacher head0.223
Teacher spread0.212 · 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

Citations39
Published2021
Admission routes2
Has abstractyes

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