The granulocyte colony-stimulating factor produced during Streptococcus suis infection controls neutrophil recruitment in the blood without affecting bacterial clearance
Bibliographic record
Abstract
Streptococcus suis causes diseases in pigs and has emerged as a zoonotic agent. When infected, the host develops an exacerbated inflammation that can lead to septic shock and meningitis. Although neutrophils greatly infiltrate the lesions, their dynamics during S. suis infection remain poorly described. Moreover, very few studies reported on the production and role of a key factor in the regulation of neutrophils: the colony-stimulating granulocyte factor (G-CSF). In this study, we characterized the G-CSF-neutrophil axis in the pathogenesis of S. suis induced disease. Using a mouse model of S. suis infection, we first evaluated the recruitment of neutrophils and their activation profile by flow cytometry. We found that infection provokes a massive neutrophil recruitment from the bone marrow to the blood and spleen. In both compartments, neutrophils displayed multiple activation markers. In parallel, we observed high systemic levels of G-CSF, with a peak of production coinciding with that of neutrophil recruitment. We then neutralized the effects of G-CSF and highlighted its role in the release of neutrophils from the bone marrow to the blood. However, it did not affect bacteremia nor the cytokine storm induced by S. suis. In conclusion, systemic G-CSF induces the release of neutrophils from the bone marrow to the blood, but its role in inflammation or bacterial clearance seems to be compensated by unknown factors. A better understanding of the role of neutrophils and inflammatory mediators could lead to better strategies for controlling the infection caused by S. suis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".