MétaCan
Menu
Back to cohort
Record W2996720413 · doi:10.1038/s41598-019-56137-z

JNK-mediated Slit-Robo signaling facilitates epithelial wound repair by extruding dying cells

2019· article· en· W2996720413 on OpenAlexfundno aff
Chiaki Iida, Shizue Ohsawa, Kiichiro Taniguchi, Masatoshi Yamamoto, Tatsushi Igaki

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyTakeda Science FoundationSenri Life Science FoundationToray IndustriesInamori FoundationToray Science FoundationJapan Agency for Medical Research and DevelopmentInstitute of GeneticsNaito Foundation
KeywordsCell biologySlitDownregulation and upregulationWound healingDecapentaplegicSignal transductionBiologyEpitheliumKinaseImaginal discImmunologyGeneGeneticsDrosophila melanogaster

Abstract

fetched live from OpenAlex

Multicellular organisms repair injured epithelium by evolutionarily conserved biological processes including activation of c-Jun N-terminal kinase (JNK) signaling. Here, we show in Drosophila imaginal epithelium that physical injury leads to the emergence of dying cells, which are extruded from the wounded tissue by JNK-induced Slit-Roundabout2 (Robo2) repulsive signaling. Reducing Slit-Robo2 signaling in the wounded tissue suppresses extrusion of dying cells and generates aberrant cells with highly upregulated growth factors Wingless (Wg) and Decapentaplegic (Dpp). The inappropriately elevated Wg and Dpp impairs wound repair, as halving one of these growth factor genes cancelled wound healing defects caused by Slit-Robo2 downregulation. Our data suggest that JNK-mediated Slit-Robo2 signaling contributes to epithelial wound repair by promoting extrusion of dying cells from the wounded tissue, which facilitates transient and appropriate induction of growth factors for proper wound healing.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.250
Teacher spread0.223 · 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.

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

Citations23
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicAxon Guidance and Neuronal SignalingFrench-language works237,207