Streptococcus pneumoniae Colonization Disrupts the Microbial Community within the Upper Respiratory Tract of Aging Mice
Bibliographic record
Abstract
Nasopharyngeal colonization by the Gram-positive bacterium Streptococcus pneumonia is a prerequisite for pneumonia and invasive pneumococcal diseases. Colonization is asymptomatic, involving dynamic and complex interplay between commensals, the host immune system, and environmental factors. The elderly are at an increased risk of developing pneumonia, which might be due to changes in the respiratory microbiota that would impact bacterial colonization and persistence within this niche. We hypothesized that the composition of the upper respiratory tract (URT) microbiota changes with age and subsequently can contribute to sustained colonization and inefficient clearance of S. pneumoniae To test this, we used a mouse model of pneumococcal colonization to compare the composition of the URT microbiota in young, middle-aged, and old mice in the naive state and during the course of colonization using nasal pharyngeal washes. Sequencing of variable region 3 (V3) of the 16S rRNA gene was used to identify changes occurring with age and throughout the course of S. pneumonia colonization. We discovered that age affects the composition of the URT microbiota and that colonization with S. pneumoniae is more disruptive of preexisting communities in older mice. We have further shown that host-pathogen interactions followingS. pneumonia colonization can impact the populations of resident microbes, including Staphylococcus and Haemophilus. Together, our findings indicate alterations to the URT microbiota could be detrimental to the elderly, resulting in increased colonization of S. pneumonia and decreased efficiency in its clearance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".