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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".