MARCO Is Required for TLR2- and Nod2-Mediated Responses to <i>Streptococcus pneumoniae</i> and Clearance of Pneumococcal Colonization in the Murine Nasopharynx
Bibliographic record
Abstract
Streptococcus pneumoniae is a common human pathogen that accounts for >1 million deaths every year. Colonization of the nasopharynx by S. pneumoniae precedes pulmonary and other invasive diseases and, therefore, is a promising target for intervention. Because the receptors scavenger receptor A (SRA), macrophage receptor with collagenous structure (MARCO), and mannose receptor (MR) have been identified as nonopsonic receptors for S. pneumoniae in the lung, we used scavenger receptor knockout mice to study the roles of these receptors in the clearance of S. pneumoniae from the nasopharynx. MARCO(-/-), but not SRA(-/-) or MR(-/-), mice had significantly impaired clearance of S. pneumoniae from the nasopharynx. In addition to impairment in bacterial clearance, MARCO(-/-) mice had abrogated cytokine production and cellular recruitment to the nasopharynx following colonization. Furthermore, macrophages from MARCO(-/-) mice were deficient in cytokine and chemokine production, including type I IFNs, in response to S. pneumoniae. MARCO was required for maximal TLR2- and nucleotide-binding oligomerization domain-containing (Nod)2-dependent NF-κB activation and signaling that ultimately resulted in clearance. Thus, MARCO is an important component of anti-S. pneumoniae responses in the murine nasopharynx during colonization.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".