CD4 T cell cooperation is required for the in vivo activation of CD4 T cells
Bibliographic record
Abstract
We address here the role of CD4 T cell cooperation in the activation of CD4 T cells. Administration of aggregated hen egg lysozyme (HEL) without microbial adjuvant to BALB/c mice normally generates cytokine-producing CD4 T cells specific for the HEL major peptide, HEL(105-120), as well as CD4 T cells specific for HEL non-major peptides. The prior administration of HEL(105-120) ablates the generation of cytokine-secreting CD4 T cells specific for HEL(105-120), as well as the CD4 T cells specific for HEL non-major peptides, normally generated upon HEL challenge. Thus, the activation of HEL non-major peptide-specific CD4 T cells appears to depend upon the HEL(105-120)-specific CD4 T cell population. In contrast, when HEL(105-120) and saline-treated mice are challenged with HEL coupled to ovalbumin (OVA), CD4 T cell responses to HEL non-major peptides and to OVA are the same, whereas treated mice still do not generate cytokine-secreting cells specific for HEL(105-120). We infer that the administration of HEL(105-120) does not generate regulatory cells capable of down-regulating CD4 T cell responses to HEL and OVA peptides. OVA-specific CD4 T cells restore the generation of HEL non-major peptide-specific T cells in the absence of HEL major peptide-specific T cells. We conclude that the generation of CD4 T cells producing IL-2, IFN-gamma and IL-4 requires CD4 T cell cooperation and that this cooperation is not mediated simply by CD40-CD40L interactions. We also conclude from these observations that there is no requirement for a microbial or danger signal for CD4 T cell activation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".