Early life exposure to Streptococcus pneumonia has sex-specific long-term impact on inflammatory responses
Bibliographic record
Abstract
Abstract INTRODUCTION Lung microbiota plays a central role in health and disease development. Recently, recurrent viral infections in preschool age children were associated with lung microbiota carriage of Streptococcus pneumonia (SP). In addition, immune development and inflammatory responses have sexual dimorphism, but it is still controversial whether there is sex-specific microbiota composition. OBJECTVE Given that immune development occurs early in life, the objective of this project is to evaluate the consequences of early life lung microbiota alteration by SP exposure on inflammatory response later in life. METHODS Neonatal Brown Norway rats were exposed to SP via intranasal administration (sub inflammatory dose). Lung microbiota was assessed until weaning age (21 days old), using v3–v4 16S rRNA sequencing. At 8 weeks old (post-puberty), rats were infected with LPS and their inflammatory responses were assessed 24h post using bronchoalveolar lavage (BAL). RESULTS Early life exposure to SP reduced lung microbiota diversity and impacted its constitution until weaning age. Although control females exposed to LPS had higher inflammatory response than control males, early life exposure to SP significantly reduced BAL inflammatory cell recruitment in females but not in SP-males. Opposingly, in SP-males, the inflammatory response shifted from a neutrophilic response to a more eosinophilic inflammation. CONCLUSION Early life exposure to SP modulated both lung microbiota and immune responses later in life. The sex-specific immune modulation by lung microbiota composition in early life could have large implication for many inflammatory diseases presenting sexual bias. Supported by grants from Quebec Health Research Network and Fondation IUCPQ (Quebec, Canada).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".