Expression Profiling of Murine Double-Negative Regulatory T Cells Suggest Mechanisms for Prolonged Cardiac Allograft Survival
Bibliographic record
Abstract
Recent studies have demonstrated that both mouse and human alpha beta TCR(+)CD3(+)NK1.1(-)CD4(-)CD8- double-negative regulatory T (DN Treg) cells can suppress Ag-specific immune responses mediated by CD8+ and CD4+ T cells. To identify molecules involved in DN Treg cell function, we generated a panel of murine DN Treg clones, which specifically kill activated syngeneic CD8+ T cells. Through serial cultivation of DN Treg clones, mutant clones arose that lost regulatory capacity in vitro and in vivo. Although all allogeneic cardiac grafts in animals preinfused with tolerant CD4/CD8 negative 12 DN Treg clones survived over 100 days, allograft survival is unchanged following infusion of mutant clones (19.5 +/- 11.1 days) compared with untreated controls (22.8 +/- 10.5 days; p < 0.001). Global gene expression differences between functional DN Treg cells and nonfunctional mutants were compared. We found 1099 differentially expressed genes (q < 0.025%), suggesting increased cell proliferation and survival, immune regulation, and chemotaxis, together with decreased expression of genes for Ag presentation, apoptosis, and protein phosphatases involved in signal transduction. Expression of 33 overexpressed and 24 underexpressed genes were confirmed using quantitative real-time PCR. Protein expression of several genes, including Fc epsilon RI gamma subunit and CXCR5, which are >50-fold higher, was also confirmed using FACS. These findings shed light on the mechanisms by which DN Treg cells down-regulate immune responses and prolong cardiac allograft survival.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".