Opposing effects of actin signaling and LFA‐1 on establishing the affinity threshold for inducing effector T‐cell responses in mice
Bibliographic record
Abstract
Mature CD8(+) T cells use a narrow antigen affinity threshold to generate tissue-infiltrating cytotoxic effector T cells and induce autoimmune pathology, but the mechanisms that establish this antigen affinity threshold are poorly understood. Only antigens with affinities above the threshold induce stable contacts with APCs, polarization of a T cell, and asymmetric T-cell division. Previously published data indicate that LFA-1 inside-out signaling might be involved in establishing the antigen affinity threshold. Here, we show that subthreshold antigens weakly activate all major distal TCR signaling pathways. Low-affinity antigens are more dependent on LFA-1 than suprathreshold antigens. Moreover, augmenting the inside-out signaling by hyperactive Rap1 does not increase responses to the subthreshold antigens. Thus, LFA-1 signaling does not contribute to the affinity-based antigen discrimination. However, we found that subthreshold antigens do not induce actin rearrangement toward an APC, mediated by Rho-family GTPases, Cdc42, and Rac. Our data suggest that Rac and Cdc42 contribute to the establishment of the antigen affinity threshold in CD8(+) T cells by enhancing responses to high-affinity antigens, or by reducing the responses to low-affinity antigens.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".