Strategy of lipid recognition by invariant natural killer T cells: ‘one for all and all for one’
Bibliographic record
Abstract
Invariant natural killer T (iNKT) cells are evolutionarily conserved lipid-reactive T cells that bridge innate and adaptive immune responses. Despite a relatively restricted T-cell receptor (TCR) diversity, these cells respond to a variety of structurally distinct foreign (i.e. microbial or synthetic) as well as host-derived (self-) lipid antigens presented by the CD1d molecule. These multi-tasking lymphocytes are among the first responders in immunity, and produce an impressive array of cytokines and chemokines that can tailor the ensuing immune response. Accordingly, iNKT cells play important functions in autoimmune diseases, cancer, infection and inflammation. These properties make iNKT cells appealing targets in immune-based therapies. Yet, much has to be learned on the mechanisms that allow iNKT cells to produce polarized responses. Responses of iNKT cells are influenced by the direct signals perceived by the cells through their TCRs, as well as by indirect co-stimulatory (and potentially co-inhibitory) cues that they receive from antigen-presenting cells or the local milieu. A decade ago, biochemists and immunologists have started to describe synthetic lipid agonists with cytokine skewing potential, paving a new research avenue in the iNKT cell field. Yet how iNKT cells translate various antigenic signals into distinct functional responses has remained obscure. Recent findings have revealed a unique and innate mode of lipid recognition by iNKT cells, and suggest that both the lipid antigen presented and the diversity of the TCR modulate the strength of CD1d-iNKT TCR interactions. In this review, we focus on novel discoveries on lipid recognition by iNKT cells, and how these findings may help us to design effective strategies to steer iNKT cell responses for immune intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".