Cooperation between 4-1BB and ICOS in the Immune Response to Influenza Virus Revealed by Studies of CD28/ICOS-Deficient Mice
Bibliographic record
Abstract
CD28, ICOS, and 4-1BB each play distinct roles in the CD8 T cell response to influenza virus. CD28-/- mice are severely impaired in primary CD8 T cell expansion and fail to mount a secondary response to influenza. Influenza-specific CD8 T cells expand normally in ICOS-/- mice, with only a small and transient defect late in the primary response and an unimpaired secondary response. Conversely, 4-1BB/4-1BBL interaction is dispensable for the primary CD8 T cell response to influenza, but maintains CD8 T cell survival and controls the size of the secondary response. Previous results showed that a single dose of agonistic anti-4-1BB Ab at priming allowed partial restoration of primary CD8 T cell expansion and full recovery of the secondary CD8 T cell responses to influenza in CD28-/- mice. In this study we show that anti-4-1BB fails to correct the CD8 T cell defect in CD28-/-ICOS-/- mice, suggesting that ICOS partially compensates for CD28 in this model. In support of this hypothesis, we found that anti-4-1BB enhances ICOS expression on both T cell subsets and that anti-4-1BB and anti-ICOS can synergistically activate CD4 and CD8 T cells. Furthermore, ICOS and 4-1BB can cooperate to directly stimulate isolated CD28-/- CD8 T cells. These results reveal a novel interaction between the ICOS and 4-1BB costimulatory pathways as well as unexpected redundancy between CD28 and ICOS in primary CD8 T cell expansion. These findings have implications for costimulation of human T cell responses in diseases such as AIDS or rheumatoid arthritis, in which CD28- T cells accumulate.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".