Role of Chemokines and Formyl Peptides in Pneumococcal Pneumonia-Induced Monocyte/Macrophage Recruitment
Bibliographic record
Abstract
Host-derived chemoattractant factors are suggested to play crucial roles in leukocyte recruitment elicited by inflammatory stimuli in vitro and in vivo. However, in the case of acute bacterial infections, pathogen-derived chemoattractant factors are also present, and it has not yet been clarified how cross-talk between chemoattractant receptors orchestrates diapedesis of leukocytes in this context of complex chemoattractant arrays. To investigate the role of chemokine (host-derived) and formyl peptide (pathogen-derived) chemoattractants in leukocyte extravasation in life-threatening infectious diseases, we used a mouse model of pneumococcal pneumonia. We found an increase in mRNA expression of eight chemokines (RANTES, macrophage-inflammatory protein (MIP)-1alpha, MIP-1beta, MIP-2, IP-10, monocyte chemoattractant protein (MCP)-1, T cell activation 3, and KC) within the lungs during the course of infection. KC and MIP-2 protein expression closely preceded pulmonary neutrophil recruitment, whereas MCP-1 protein production coincided more closely than MIP-1alpha with the kinetics of macrophage infiltration. In situ hybridization of MCP-1 mRNA suggested that MCP-1 expression started at peribronchovascular regions and expanded to alveoli-facing epithelial cells and infiltrated macrophages. Interestingly, administration of a neutralizing Ab against MCP-1, RANTES, or MIP-1alpha alone did not prevent macrophage infiltration into infected alveoli, whereas combination of the three Abs significantly reduced macrophage infiltration without affecting neutrophil recruitment. The use of an antagonist to N-formyl peptides, N-t-Boc-Phe-D-Leu-Phe-D-Leu-Phe, reduced both macrophages and neutrophils significantly. These data demonstrate that a complex chemokine network is activated in response to pulmonary pneumococcal infection, and also suggest an important role for fMLP receptor in monocyte/macrophage recruitment in that model.
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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) | 0.000 | 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".