Early-life upper airway microbiota are associated with decreased lower respiratory tract infections
Bibliographic record
Abstract
BACKGROUND: Microbial interactions mediating colonization resistance play key roles within the human microbiome, shaping susceptibility to infection from birth. The role of the nasal and oral microbiome in the context of early life respiratory infections and subsequent allergic disease risk remains understudied. OBJECTIVES: Our aim was to gain insight into microbiome-mediated defenses and respiratory pathogen colonization dynamics within the upper respiratory tract during infancy. METHODS: We performed shotgun metagenomic sequencing of nasal (n = 229) and oral (n = 210) microbiomes from our Wisconsin Infant Study Cohort at age 24 months and examined the influence of participant demographics and exposure history on microbiome composition. Detection of viral and bacterial respiratory pathogens by RT-PCR and culture-based studies with antibiotic susceptibility testing, respectively, to assess pathogen carriage was performed. Functional bioassays were used to evaluate pathogen inhibition by respiratory tract commensals. RESULTS: Participants with early-life lower respiratory tract infection were more likely to be formula fed, attend day care, and experience wheezing. Composition of the nasal, but not oral, microbiome associated with prior lower respiratory tract infection, namely lower alpha diversity, depletion of Prevotella, and enrichment of Moraxella catarrhalis including drug-resistant strains. Prevotella originating from healthy microbiomes had higher biosynthetic gene cluster abundance and exhibited contact-independent inhibition of M catarrhalis. CONCLUSIONS: These results suggest interbacterial competition affects nasal pathogen colonization. This work advances understanding of protective host-microbe interactions occurring in airway microbiomes that alter infection susceptibility in early life.
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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 | 0.002 |
| 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 | 0.001 | 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".