CD11d, an NK cell ally in their fight against tumours
Bibliographic record
Abstract
Abstract NK cells participate in viral clearance and tumour control. They differentiate from bone marrow precursors and undergo a series of guided differentiation steps to acquire various functional properties. We previously showed that pre-mNK cells represent one of these precursors and that a locus on mouse chromosome 7 is linked to pre-mNK abundance. As pre-mNK cells are associated with a heightened anti-tumour activity, we sought to identify genes within this locus that influence their number, and could thus affect NK cell-mediated tumour growth. Through a candidate gene-based approach, we identify Itgad, encoding for CD11d, a member of the ß2 integrin family. We find that the absence of CD11d affects the expression of the other ß2 integrin subunits, particularly on NK cells. This led us to investigate the functional outcome of the loss of CD11d expression in NK cells. Although NK cells from CD11d-KO mice show unabated expression of effector proteins in vitro, their anti-tumour activity is impaired in vivo. Indeed, the growth of NK-sensitive RMA-S lymphoma is significantly accelerated in CD11d-KO mice. This increase in tumour growth was associated with a reduction in the number of NK cells in the tumours, suggesting a role for ß2 integrins in NK cell migration. Altogether, our candidate gene-based approach determined that expression of ß2 integrin subunits are co-regulated in NK cells and revealed an important role for CD11d in NK cell-mediated anti-tumour activity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".