Longitudinal Variability of <i>Bifidobacterium</i> Species in the Infant Gut is Independent of Maternal Milk HMO Composition
Bibliographic record
Abstract
The development of the infant gut microbiome is primarily influenced by the infant feeding type, with breast milk serving as the optimal source of nutrition. Breast milk contains human milk oligosaccharides (HMOs) that act as nourishment for the developing gut microbiome, potentially conferring advantages to specific bacterial species. Previous studies have demonstrated the ability of certain Bifidobacterium species to utilize individual HMOs, however a longitudinal study examining the evolving microbial community at a high resolution in the context of mothers’ milk HMO composition is lacking. Here, we explored the relationship between the HMO composition in mothers’ milk and the abundance of Bifidobacterium species in the infant gut throughout the course of early life. To enable subspecies taxonomic classification, we developed a high-throughput method for quantifying the abundance of Bifidobacterium longum subsp. infantis ( BL. infantis ; the best known HMO-utilizer) from metagenomic sequencing. We applied this method to a longitudinal cohort consisting of 21 mother-infant dyads, from whom we collected matched breast milk and infant stool samples at multiple time points during the first year of life. We observed substantial changes in the infant gut microbiome over the course of several months, while the HMO composition in mothers’ milk remained relatively stable. Bifidobacterium species were a prominent factor contributing to the variation observed among samples; however, no significant associations were found between specific HMOs in mothers’ milk and the abundance of Bifidobacterium species. Finally, the longitudinal nature of our cohort enabled us to characterize the dynamic colonization of BL. infantis in the infant gut, which surprisingly began late in the breastfeeding period. Applying our BL. infantis quantification method to additional datasets from various geographical locations, we found similar, late-colonization by BL. infantis , highlighting the importance of quantifying BL. infantis in the infant gut.
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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 | 0.001 |
| 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 | 0.000 |
| 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".