Control of Francisella tularensis Intracellular Growth by Pulmonary Epithelial Cells
Bibliographic record
Abstract
The virulence of F. tularensis is often associated with its ability to grow in macrophages, although recent studies show that Francisella proliferates in multiple host cell types, including pulmonary epithelial cells. Thus far little is known about the requirements for killing of F. tularensis in the non-macrophage host cell types that support replication of this organism. Here we sought to address this question through the use of a murine lung epithelial cell line (TC-1 cells). Our data show that combinations of the cytokines IFN-γ, TNF, and IL-17A activated murine pulmonary epithelial cells to inhibit the intracellular growth of the F. tularensis Live Vaccine Strain (LVS) and the highly virulent F. tularensis Schu S4 strain. Although paired combinations of IFN-γ, TNF, and IL-17A all significantly controlled LVS growth, simultaneous treatment with all three cytokines had the greatest effect on LVS growth inhibition. In contrast, Schu S4 was more resistant to cytokine-induced growth effects, exhibiting significant growth inhibition only in response to all three cytokines. Since one of the main antimicrobial mechanisms of activated macrophages is the release of reactive nitrogen intermediates (RNI) via the activity of iNOS, we investigated the role of RNI and iNOS in Francisella growth control by pulmonary epithelial cells. NOS2 gene expression was significantly up-regulated in infected, cytokine-treated pulmonary epithelial cells in a manner that correlated with LVS and Schu S4 growth control. Treatment of LVS-infected cells with an iNOS inhibitor significantly reversed LVS killing in cytokine-treated cultures. Further, we found that mouse pulmonary epithelial cells produced iNOS during in vivo respiratory LVS infection. Overall, these data demonstrate that lung epithelial cells produce iNOS both in vitro and in vivo, and can inhibit Francisella intracellular growth via reactive nitrogen intermediates.
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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 | 0.000 | 0.000 |
| 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".