Three Phases of CD8 T Cell Response in the Lung Following H1N1 Influenza Infection and Sphingosine 1 Phosphate Agonist Therapy
Bibliographic record
Abstract
Influenza-induced lung edema and inflammation are exacerbated by a positive feedback loop of cytokine and chemokine production termed a 'cytokine storm', a hallmark of increased influenza-related morbidity and mortality. Upon infection, an immune response is rapidly initiated in the lungs and draining lymph node, leading to expansion of virus-specific effector cells. Using two-photon microscopy, we imaged the dynamics of dendritic cells (DC) and virus-specific eGFP(+)CD8(+) T cells in the lungs and draining mediastinal lymph nodes during the first two weeks following influenza infection. Three distinct phases of T cell and CD11c(+) DC behavior were revealed: 1) Priming, facilitated by the arrival of lung DCs in the lymph node and characterized by antigen recognition and expansion of antigen-specific CD8(+) T cells; asymmetric T cell division in contact with DCs was frequently observed. 2) Clearance, during which DCs re-populate the lung and T cells leave the draining lymph node and re-enter the lung tissue where enlarged, motile T cells come into contact with DCs and form long-lived interactions. 3) Maintenance, characterized by T-cell scanning of the lung tissue and dissociation from local antigen presenting cells; the T cells spend less time in association with DCs and migrate rapidly on collagen. A single dose of a sphingosine-1-phosphate receptor agonist, AAL-R, sufficient to suppress influenza-induced cytokine-storm, altered T cell and DC behavior during influenza clearance, delaying T cell division, cellular infiltration in the lung, and suppressing T-DC interactions in the lung. Our results provide a detailed description of T cell and DC choreography and dynamics in the lymph node and the lung during influenza infection. In addition, we suggest that phase lags in T cell and DC dynamics induced by targeting S1P receptors in vivo may attenuate the intensity of the immune response and can be manipulated for therapeutic benefit.
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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) | 0.001 | 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 | 0.000 |
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".