Identification of a Potent Microbial Lipid Antigen for Diverse NKT Cells
Bibliographic record
Abstract
Semi-invariant/type I NKT cells are a well-characterized CD1d-restricted T cell subset. The availability of potent Ags and tetramers for semi-invariant/type I NKT cells allowed this population to be extensively studied and revealed their central roles in infection, autoimmunity, and tumor immunity. In contrast, diverse/type II NKT (dNKT) cells are poorly understood because the lipid Ags that they recognize are largely unknown. We sought to identify dNKT cell lipid Ag(s) by interrogating a panel of dNKT mouse cell hybridomas with lipid extracts from the pathogen Listeria monocytogenes. We identified Listeria phosphatidylglycerol as a microbial Ag that was significantly more potent than a previously characterized dNKT cell Ag, mammalian phosphatidylglycerol. Further, although mammalian phosphatidylglycerol-loaded CD1d tetramers did not stain dNKT cells, the Listeria-derived phosphatidylglycerol-loaded tetramers did. The structure of Listeria phosphatidylglycerol was distinct from mammalian phosphatidylglycerol because it contained shorter, fully-saturated anteiso fatty acid lipid tails. CD1d-binding lipid-displacement studies revealed that the microbial phosphatidylglycerol Ag binds significantly better to CD1d than do counterparts with the same headgroup. These data reveal a highly potent microbial lipid Ag for a subset of dNKT cells and provide an explanation for its increased Ag potency compared with the mammalian counterpart.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".