Tolerance-promoting regimens permit extensive T cell activation but alter differentiation in vivo (126.29)
Bibliographic record
Abstract
Abstract Short-term perturbation of the costimulatory molecule CD154 and/or the adhesion molecule LFA-1 can induce allograft tolerance. However, the effect of these agents on antigen-specific T cells is unclear. Therefore, we determined how anti-CD154 or anti-LFA-1 monoclonal antibody (mAb) therapy impacted the reactivity of CD4 and CD8 T cells in vivo. We monitored both antigen-specific responses of ovalbumin (OVA)-specific CD8 (OT-I) or CD4 (OT-II) TCR transgenic T cells and alloreactivity by polyclonal T cells (C57BL/6 anti-BALB/c). CD45.1 OT-I, OT-II or B6 T cells were transferred into CD45.2 B6 hosts locally challenged with mAct-OVA or BALB/c APCs, respectively, in the presence of mAb therapy. Anti-CD154 and anti-LFA-1 each inhibited the magnitude of OT-I and OT-II responses but permitted extensive proliferation and conversion to an antigen-experienced CD44hiCD62Llo phenotype. Both treatments decreased expression of Gzm B in OT-I T cells and IFN-gamma in OT-II T cells. Polyclonal alloreactivity also was markedly inhibited by either anti-LFA-1 or anti-CD154 therapy, though activation and proliferation still occurred in both cases. Importantly, anti-LFA-1 but not anti-CD154 treatment resulted in a striking increase in Treg/ Teff cell ratio in the reactive lymph node. Overall, neither blocking CD154 nor LFA-1 prevented T cell recognition/activation in vivo. However, LFA-1 blockade exhibited a more pronounced ability to skew alloreactivity towards a Treg phenotype in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".