IL-7 shapes a multifaceted CD8 T cell response to airway Influenza/A infection
Bibliographic record
Abstract
Abstract The lungs are a site vulnerable to diseases such as cancer, autoimmunity and infections. With each breath, we risk inhaling infectious agents, and as such, airborne diseases are the leading cause of infectious disease-related deaths in the world. In particular, various strains of Influenza virus can infect airway epithelial cells and activate a network of immune cells leading to clearance, or in cases of pandemic strains, an overzealous response that can be fatal. Adaptive immune cells, particularly cytotoxic CD8 T cell lymphocytes, play a crucial role in controlling viral replication by killing infected cells. Therefore, tight regulation of the cytokines that lead to the proper activation, proliferation and function of these immune cells is necessary to clear infections efficiently while minimizing damage to the host. Interleukin-7 (IL-7) is a cytokine known for its importance in T cell development and survival. While the function of IL-7 in T cell survival is well characterized, how IL-7 shapes T cell effector responses when a pathogen is encountered is less understood. Using IL-7Rα hypomorph mice in chimeric and adoptive transfer experiments we have found that IL-7 is cell-intrinsically important for the priming of antigen specific CD8 T cells in the draining lymph nodes. We found that IL-7 dictates terminal differentiation, cytokine production and degranulation of CD8 T cells locally in the airways. Drugs that manipulate IL-7 signaling are currently under clinical trial for multiple conditions. Our findings on IL-7 and its effects on lower respiratory diseases will be necessary for expanding the utility of these therapeutics.
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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) | 0.000 | 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 | 0.001 | 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".