c-di-GMP Enhances Protective Innate Immunity in a Murine Model of Pertussis
Bibliographic record
Abstract
Innate immunity represents the first line of defense against invading pathogens in the respiratory tract. Innate immune cells such as monocytes, macrophages, dendritic cells, NK cells, and granulocytes contain specific pathogen-recognition molecules which induce the production of cytokines and subsequently activate the adaptive immune response. c-di-GMP is a ubiquitous second messenger that stimulates innate immunity and regulates biofilm formation, motility and virulence in a diverse range of bacterial species with potent immunomodulatory properties. In the present study, c-di-GMP was used to enhance the innate immune response against pertussis, a respiratory infection mainly caused by Bordetella pertussis. Intranasal treatment with c-di-GMP resulted in the induction of robust innate immune responses to infection with B. pertussis characterized by enhanced recruitment of neutrophils, macrophages, natural killer cells and dendritic cells. The immune responses were associated with an earlier and more vigorous expression of Th1-type cytokines, as well as an increase in the induction of nitric oxide in the lungs of treated animals, resulting in significant reduction of bacterial numbers in the lungs of infected mice. These results demonstrate that c-di-GMP is a potent innate immune stimulatory molecule that can be used to enhance protection against bacterial respiratory infections. In addition, our data suggest that priming of the innate immune system by c-di-GMP could further skew the immune response towards a Th1 type phenotype during subsequent infection. Thus, our data suggest that c-di-GMP might be useful as an adjuvant for the next generation of acellular pertussis vaccine to mount a more protective Th1 phenotype immune response, and also in other systems where a Th1 type immune response is required.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".