The human milk bacteriome and mycobiome and their inter-kingdom interactions viewed across geography
Bibliographic record
Abstract
Background The human milk microbiota is one of the biologically active components of human milk, and factors affecting it and the effect size are not well understood. Assessments of human milk microbiota have mainly been done in small cohorts and/or in single geographical locations, and most have been restricted to the bacteriome. Here we assessed the bacterial, archaeal and fungal composition of human milk and the potential inter-kingdom interactions in milk collected from women living in a wide spectrum of countries, environments, and socio-economical settings. Materials and methods About 518 human milk samples were collected in 16 countries. After DNA extraction, bacterial and fungal metataxonomic analyses were performed via amplification and sequencing of the 16S rDNA and the ITS2 genes, respectively. In parallel, the presence of methanogenic archaea was determined by qPCR. Results Bacterial analysis revealed significant Country variations in human milk microbiota diversity and taxa distribution. Core genera such as Staphylococcus, Streptococcus, and Bifidobacterium were universally prevalent, and their abundance varied geographically. Methanogenic sequences were found in the amplicon sequences, mostly of Methanobrevibacter (11.8% of samples), while qPCR only detected 0.7% (2 out of 268) methanogens. Fungi—mostly Candida—were detected in 7% of samples, with wide country variations. Co-abundance network analysis revealed mostly positive bacterial correlations and negative inter-kingdom interactions. Conclusion This study shows substantial global variation in the human milk microbiome with bacterial-fungal interactions, highlighting the importance of global-scale studies to understand the human microbiome and its role in maternal and infant health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".