Harnessing the Versatility of Invariant NKT Cells in a Stepwise Approach to Sepsis Immunotherapy
Bibliographic record
Abstract
Abstract Sepsis results from a heavy-handed response to infection that may culminate in organ failure and death. Many patients who survive acute sepsis become immunosuppressed and succumb to opportunistic infections. Therefore, to be successful, sepsis immunotherapies must target both the initial and the protracted phase of the syndrome to relieve early immunopathology and late immunosuppression, respectively. Invariant NKT (iNKT) cells are attractive therapeutic targets in sepsis. However, repeated treatments with α-galactosylceramide, the prototypic glycolipid ligand of iNKT cells, result in anergy. We designed a double-hit treatment that allows iNKT cells to escape anergy and exert beneficial effects in biphasic sepsis. We tested the efficacy of this approach in the sublethal cecal ligation and puncture mouse model, which mirrors polymicrobial sepsis with progression to an immunosuppressed state. Septic mice were treated with [(C2S, 3S, 4R)-1-O-(α-d-galactopyranosyl)-N-tetracosanoyl-2-amino-1,3,4-nonanetriol] (OCH), a TH2-polarizing iNKT cell agonist, before they received α-galactosylceramide. This regimen reduced the morbidity and mortality of cecal ligation and puncture, induced a transient but robust IFN-γ burst within a proinflammatory cytokine/chemokine landscape, transactivated NK cells, increased MHC class II expression on macrophages, and restored delayed-type hypersensitivity to a model hapten, consistent with recovery of immunocompetence in protracted sepsis. Structurally distinct TH2-polarizing agonists varied in their ability to replace OCH as the initial hit, with their lipid chain length being a determinant of efficacy. The proposed approach effectively exploits iNKT cells’ versatility in biphasic sepsis and may have translational potentials in the development of new therapies.
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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) | 0.001 | 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 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".