<i>Listeria monocytogenes</i> infection enhances the interaction between rat non-classical MHC-Ib molecule and Ly49 receptors
Bibliographic record
Abstract
Murine NK cell Ly49 receptors, functionally analogous to KIRs in humans recognize MHC class I molecules and play a key role in controlling NK cell function. We have previously shown that the paired activating Ly49s4 and inhibitory Ly49i4 receptors recognize undefined non-classical MHC-Ib ligands from the RT1-CE region in rats. Here, the RT1-CE16 gene of the RT1 d haplotype was stably transfected into the mouse RAW macrophage cell line, termed RAW-CE16 d cells. Combining RAW-CE16 d cells with Ly49 expressing reporter cells demonstrated Ly49i4 and Ly49s4 specificity for CE16 d . The Ly49s4/i4:CE16 d interaction was confirmed by specific MHC-I blocking monoclonal Abs. Further, we used our in vitro model to study the effect of Listeria monocytogenes (LM) on CE16 d after infection. LM infection and IFN-γ stimulation both led to enhanced CE16 d expression on the surface of transfected RAW-CE16 d cells. Interestingly, the reporter cells displayed increased response to LM-infected RAW-CE16 d cells compared with IFN-γ-treated RAW-CE16 d cells, suggesting a fundamental difference between these stimuli in supporting enhanced Ly49 recognition of CE16 d . Collectively, our data show that Ly49s4 and Ly49i4 recognize the non-classical RT1-CE16 d molecule, which in turn is up-regulated during LM infection and thereby may contribute to NK-mediated responses against infected cells.
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 | 0.000 |
| 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".