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Record W4297238535 · doi:10.1038/s42003-022-03988-4

Kinetics of phagosome maturation is coupled to their intracellular motility

2022· article· en· W4297238535 on OpenAlexafffund
Yanqi Yu, Zihan Zhang, Glenn F. W. Walpole, Yan Yu

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchUniversity of TorontoNational Science FoundationNational Institutes of HealthU.S. Department of Health and Human ServicesDivision of Chemical, Bioengineering, Environmental, and Transport SystemsDirectorate for Engineering
KeywordsPhagosomeCell biologyMotilityIntracellularKineticsDegradation (telecommunications)ChemistryBiophysicsBiologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Immune cells degrade internalized pathogens in phagosomes through sequential biochemical changes. The degradation must be fast enough for effective infection control. The presumption is that each phagosome degrades cargos autonomously with a distinct but stochastic kinetic rate. However, here we show that the degradation kinetics of individual phagosomes is not stochastic but coupled to their intracellular motility. By engineering RotSensors that are optically anisotropic, magnetic responsive, and fluorogenic in response to degradation activities in phagosomes, we monitored cargo degradation kinetics in single phagosomes simultaneously with their translational and rotational dynamics. We show that phagosomes that move faster centripetally are more likely to encounter and fuse with lysosomes, thereby acidifying faster and degrading cargos more efficiently. The degradation rates increase nearly linearly with the translational and rotational velocities of phagosomes. Our results indicate that the centripetal motion of phagosomes functions as a clock for controlling the progression of cargo degradation.

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 score0.387
Threshold uncertainty score0.387

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 science0.0010.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

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