An In Vitro Co-culture Mouse Model Demonstrates Efficient Vaccine-Mediated Control of Francisella tularensis SCHU S4 and Identifies Nitric Oxide as a Predictor of Efficacy
Bibliographic record
Abstract
Francisella tularensis is a highly virulent intracellular bacterium and cell-mediated immunity is critical for protection, but mechanisms of protection against highly virulent variants, such as the prototypic strain F. tularensis strain SCHU S4, are poorly understood. To this end, we established a co-culture system, based on splenocytes from naïve or immunized mice and in vitro infected bone marrow-derived macrophages, that allowed assessment of mechanisms controlling infection with F. tularensis. We utilized the system to understand why the clpB gene deletion mutant, ΔclpB, of SCHU S4 shows superior efficacy as a vaccine in the mouse model as compared to the existing human vaccine, the live vaccine strain (LVS). Compared to naïve splenocytes, ΔclpB- or LVS-immune splenocytes conferred very significant control of a SCHU S4 infection and the ΔclpB-immune splenocytes were superior to the other splenocytes. Cultures with the latter splenocytes also contained higher levels of IFN-gamma and nitric oxide, and T cells expressing combinations of IFN-gamma, TNF-alpha, and IL-17 than did cultures with LVS-immune splenocytes. There was strong inverse correlation between bacterial replication and levels of nitrite, an end product of nitric oxide, and essentially no control was observed when BMDM from iNOS-/- mice were infected. Collectively, the mouse co-culture model identified a critical role of nitric oxide for protection against a highly virulent strain of F. tularensis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".