Culturomics as a tool to better understand the human milk microbiota and host–microbiota interactions
Bibliographic record
Abstract
For almost a century, it has been accepted that human milk contains viable microbial cells. However, for a considerable amount of this period, it was believed that they were the result of exogenous contamination, primarily from the skin or non-sterile handling. Early work using culture-dependent methods, supported by molecular profiling, however, identified the presence of lactic acid bacteria from an endogenous origin. This provided evidence that the human milk microbiota consisted of microorganisms that were not found solely on the skin surface and therefore could not result from contamination. Through the advent of next-generation sequencing, the field of microbiota research has caused a paradigm shift away from a typical focus on the presence of pathogenic microorganisms in human milk. This had led to a broad appreciation that the human milk microbiota consists of several hundred species of non-pathogenic commensal microbes – with many anaerobic microbial taxons being found only in the gastrointestinal tract outside of human milk. Nevertheless, as our appreciation of the complexity and diversity of the human milk microbiota has improved, many questions relating to the functional basis of host–microbiota interactions in the newborn infant’s gastrointestinal tract remain outstanding. To address these, mechanistic studies will be required in which the utilisation of isolated microorganisms will be essential. As such, a return to culture-dependent methods in the new paradigm of culturomics will be required. In this review, we bring together the current understanding of the human milk microbiota and how culturomics could play a fundamental role in furthering our understanding.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".