Deficiency of invariant Vα14 natural killer T cells decreases atherosclerosis in LDL receptor null mice
Bibliographic record
Abstract
AIMS: CD1d-restricted natural killer T (NKT) cells function by regulating numerous immune responses during innate and adaptive immunity. Depletion of all populations of CD1d-dependent NKT cells has been shown by several groups to reduce atherosclerosis in two different mouse models of the disease. In this study, we determined if removal of a single (V alpha 14) NKT cell population protects mice from the disease. METHODS AND RESULTS: Targeted deletion of the J alpha 18 gene results in selective depletion of CD1d-dependent V alpha 14 NKT cells in C57BL/6 mice without affecting the population of other NKT, NK, and conventional T cells. Therefore, to study the effect of V alpha 14 NKT cell depletion on the progression of atherosclerosis, we examined the extent of lesion formation using paired littermate LDL receptor null mice that were either +/+ or -/- for the J alpha 18 gene following the feeding of these mice a cholesterol- and fat-enriched diet for 8 weeks. At the end of the study, we found no difference in either serum total- or lipoprotein-cholesterol distributions between groups. However, quantification of atherosclerosis revealed that V alpha 14 NKT cell deficiency significantly decreased lesion size in the aortic root (20-28%) and arch (28-38%) in both genders of mice. By coupling the techniques of laser capture microdissection with quantitative real-time RT-PCR, we found that expression of the proatherogenic cytokine interferon (IFN)-gamma was significantly reduced in lesions from J alpha 18-/- mice. CONCLUSION: This study is the first to identify a specific subpopulation of NKT cells that promotes atherosclerosis via a mechanism appearing to involve IFN-gamma expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".