Does the maternal vaginal microbiota play a role in seeding the microbiota of neonatal gut and nose?
Bibliographic record
Abstract
The acquisition and early maturation of infant microbiota is not well understood despite its likely influence on later health. We investigated the contribution of the maternal microbiota to the microbiota of infant gut and nose in the context of mode of delivery and feeding. Using 16S rRNA sequencing and specific qPCR, we profiled microbiota of 42 mother-infant pairs from the GUSTO birth cohort, at body sites including maternal vagina, rectum and skin; and infant stool and nose. In our study, overlap between maternal vaginal microbiota and infant faecal microbiota was minimal, while the similarity between maternal rectal microbiota and infant microbiota was more pronounced. However, an infant's nasal and gut microbiota were no more similar to that of its own mother, than to that of unrelated mothers. These findings were independent of delivery mode. We conclude that the transfer of maternal vaginal microbes play a minor role in seeding infant stool microbiota. Transfer of maternal rectal microbiota could play a larger role in seeding infant stool microbiota, but approaches other than the generally used analyses of community similarity measures are likely to be needed to quantify bacterial transmission. We confirmed the clear difference between microbiota of infants born by Caesarean section compared to vaginally delivered infants and the impact of feeding mode on infant gut microbiota. Only vaginally delivered, fully breastfed infants had gut microbiota dominated by Bifidobacteria. Our data suggest that reduced transfer of maternal vaginal microbial is not the main mechanism underlying the differential infant microbiota composition associated with Caesarean delivery. The sources of a large proportion of infant microbiota could not be identified in maternal microbiota, and the sources of seeding of infant gut and nasal microbiota remain to be elucidated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".