Ly49G, but not Ly49C/I, is dispensable for diverse antigen-specific memory NK cell responses in H-2d and H-2b mice
Bibliographic record
Abstract
Immunological memory is a hallmark of the adaptive immune system. However, considerable evidence indicates that the natural killer (NK) cell subset of innate lymphoid cells also mediates specific memory responses to diverse antigens, including peptides. Even though the existence of NK cell memory is established, the mechanism behind NK cell adaptive responses is yet to be elucidated. Previously, we observed that the Ly49 family of class-I MHC receptors in mice are critical for the formation of adaptive NK cell memory responses. To define the nature of Ly49 involvement in NK cell memory responses, we investigated the contribution of individual Ly49 receptors and their defined class-I MHC ligands. We determined that the Ly49 requirement for the generation of NK memory responses is not uniform. Specifically, Ly49C and/or Ly49I proteins are indispensable for the adaptive NK cell responses as assessed by contact hypersensitivity recall responses to haptens and peptides, in H-2b and H-2d MHC backgrounds. In contrast, the highly expressed inhibitory receptor, Ly49G, did not appear to play any role in NK cell memory responses as determined using antibody-mediated subset depletion and gene-deficient mouse models, even in strains containing known ligands for Ly49G. These findings point to a unique role for Ly49C/I in adaptive NK cell antigen recognition and provide further insight into the mechanism behind adaptive NK cell responses.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".