Lung CD103+ Dendritic Cells Efficiently Transport Influenza Virus to the Lymph Node and Load Viral Antigen onto MHC Class I for Presentation to CD8 T Cells
Bibliographic record
Abstract
The uptake, transport, and presentation of Ags by lung dendritic cells (DCs) are central to the initiation of CD8 T cell responses against respiratory viruses. Although several studies have demonstrated a critical role of CD11b(low/neg)CD103(+) DCs for the initiation of cytotoxic T cell responses against the influenza virus, the underlying mechanisms for its potent ability to prime CD8 T cells remain poorly understood. Using a novel approach of fluorescent lipophilic dye-labeled influenza virus, we demonstrate that CD11b(low/neg)CD103(+) DCs are the dominant lung DC population transporting influenza virus to the posterior mediastinal lymph node as early as 20 h postinfection. By contrast, CD11b(high)CD103(neg) DCs, although more efficient for taking up the virus within the lung, migrate poorly to the lymph node and remain in the lung to produce proinflammatory cytokines instead. CD11b(low/neg)CD103(+) DCs efficiently load viral peptide onto MHC class I complexes and therefore uniquely possess the capacity to potently induce proliferation of naive CD8 T cells. In addition, the peptide transporters TAP1 and TAP2 are constitutively expressed at higher levels in CD11b(low/neg)CD103(+) DCs, providing, to our knowledge, the first evidence of a distinct regulation of the Ag-processing pathway in these cells. Collectively, these results show that CD11b(low/neg)CD103(+) DCs are functionally specialized for the transport of Ag from the lung to the lymph node and also for efficient processing and presentation of viral Ags to CD8 T cells.
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) | 0.002 | 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".