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Record W2691733691 · doi:10.1016/j.celrep.2017.05.080

PKN1 Directs Polarized RAB21 Vesicle Trafficking via RPH3A and Is Important for Neutrophil Adhesion and Ischemia-Reperfusion Injury

2017· article· en· W2691733691 on OpenAlexaff
Qianying Yuan, Chunguang Ren, Wenwen Xu, Björn Petri, Jiasheng Zhang, Yong Zhang, Paul Kubes, Dianqing Wu, Wenwen Tang

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Calgary
FundersNational Institute of General Medical SciencesNational Institute on Drug AbuseNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsReperfusion injuryIschemiaCell biologyAdhesionVesicleBiologyChemistryMedicineInternal medicineBiochemistryMembrane

Abstract

fetched live from OpenAlex

Polarized vesicle transport plays an important role in cell polarization, but the mechanisms underlying this process and its role in innate immune responses are not well understood. Here, we describe a phosphorylation-regulated polarization mechanism that is important for neutrophil adhesion to endothelial cells during inflammatory responses. We show that the protein kinase PKN1 phosphorylates RPH3A, which enhances binding of RPH3A to guanosine triphosphate (GTP)-bound RAB21. These interactions are important for polarized localization of RAB21 and RPH3A in neutrophils, which leads to PIP5K1C90 polarization. Consistent with the roles of PIP5K1C90 polarization, the lack of PKN1 or RPH3A impairs neutrophil integrin activation, adhesion to endothelial cells, and infiltration in inflammatory models. Furthermore, myeloid-specific loss of PKN1 decreases tissue injury in a renal ischemia-reperfusion model. Thus, this study characterizes a mechanism for protein polarization in neutrophils and identifies a potential protein kinase target for therapeutic intervention in reperfusion-related tissue injury.

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)0.001
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 score0.016
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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