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Record W3118211175 · doi:10.1242/jcs.248864

CD301 mediates fusion in IL-4-driven multinucleated giant cell formation

2020· article· en· W3118211175 on OpenAlexafffund
Patricia J. Brooks, Yongqiang Wang, Marco Magalhaes, Michael Glogauer, Christopher A. McCulloch

Bibliographic record

VenueJournal of Cell Science · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchAlpha Omega Foundation
KeywordsBiologyGiant cellMultinucleateCell fusionCell biologyFusionCellGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Multinucleated giant cells (MGCs) are prominent in foreign body granulomas, infectious and inflammatory processes, and auto-immune, neoplastic and genetic disorders, but the molecular determinants that specify the formation and function of these cells are not defined. Here, using tandem mass tag-mass spectrometry, we identified a differentially upregulated protein, C-type lectin domain family 10 member (herein denoted CD301, also known as CLEC10A), that was strongly upregulated in mouse RAW264.7 macrophages and primary murine macrophages undergoing interleukin (IL-4)-induced MGC formation. CD301+ MGCs were identified in biopsy specimens of human inflammatory lesions. Function-inhibiting CD301 antibodies or CRISPR/Cas9 deletion of the two mouse CD301 genes (Mgl1 and Mgl2) inhibited IL-4-induced binding of N-acetylgalactosamine-coated beads by 4-fold and reduced MGC formation by 2.3-fold (P<0.05). IL-4-driven fusion and MGC formation were restored by re-expression of CD301 in the knockout cells. We conclude that in monocytes, IL-4 increases CD301 expression, which mediates intercellular adhesion and fusion processes that are required for the formation of MGCs. This article has an associated First Person interview with the first author of the paper.

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 score0.021
Threshold uncertainty score0.196

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 science0.0000.000
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 head0.014
GPT teacher head0.260
Teacher spread0.245 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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