LTβR signaling in Dendritic Cells induces a Type I IFN response that is required for optimal clonal expansion of CD8+ T cells (100.40)
Bibliographic record
Abstract
Abstract During an acute infection, dendritic cells (DC) are triggered by pattern recognition receptors (PRR) to produce inflammatory molecules that are important for the expansion and function of CD8+ T lymphocytes in order to clear the infection. In contrast, tumours or autoimmune responses against tissue antigens produce very little inflammation, yet the immune response can proceed in the absence of obvious PRR activation. In these situations, DC require CD4+ T cell help to initiate CD8+ T cell-mediated immunity. Here we generated mixed chimeric mice lacking Lymphotoxin-β receptor (LTβR) specifically on the DC, and we found that they exhibit reduced CD8+ T cell expansion in a help-dependent response. In contrast, inhibition of the CD40 signaling only impaired CD8+ T cell interferon-γ (IFN-γ) production. Furthermore, we have identified that LTβR signaling in DC triggers IFN-α/β expression in the absence of any PRR ligand and is critical for antigen specific CD8+ T cell expansion. Therefore, this study demonstrates that different TNF family members provide integrative signals that shape the licensing potential of antigen-presenting DC.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".