MicroRNA-155 Is Required for Clearance of Streptococcus pneumoniae from the Nasopharynx
Bibliographic record
Abstract
Pneumonia caused by Streptococcus pneumoniae is a major cause of death and an economic burden worldwide. S. pneumoniae is an intermittent colonizer of the human upper respiratory tract, and the ability to control asymptomatic colonization determines the likelihood of developing invasive disease. Recognition of S. pneumoniae by resident macrophages via Toll-like receptor 2 (TLR-2) and the macrophage receptor with collagenous structure (MARCO) and the presence of interleukin-17 (IL-17)-secreting CD4(+) T cells are required for macrophage recruitment and bacterial clearance. Despite the fact that the primary cellular effectors needed for bacterial clearance have been identified, much of the underlying regulatory mechanisms are unknown. Herein, we demonstrate that the small, noncoding RNA microRNA-155 (mir-155) is critical for the effective clearance of S. pneumoniae. Our studies show that mir-155-deficient mice maintain the ability to prevent acute invasive pneumococcal infection but have significantly higher bacterial burdens following colonization, independently of macrophage recognition by TLR-2, MARCO expression, or bactericidal capacity. The observed defects in bacterial clearance parallel reduced IL-17A and gamma interferon CD4(+) T-cell responses in vivo, lower IL-17A mRNA levels in the nasopharynx, and a reduced capacity to induce Th17 cell polarization. Given that knockout mice are also limited in the capacity to generate high-titer S. pneumoniae-specific antibodies, we conclude that mir-155 is a critical mediator of the cellular effectors needed to clear primary and secondary S. pneumoniae colonizations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".