CD301 mediates fusion in IL-4-driven multinucleated giant cell formation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".