Diversity and dynamics of bacteriocins from human microbiome
Bibliographic record
Abstract
Human commensal microbiota are an important determinant of health and disease of the host. Different human body sites harbour different bacterial microbiota, bacterial communities that maintain a stable balance. However, many of the factors influencing the stabilities of bacterial communities associated with humans remain unknown. In this study, we identified putative bacteriocins produced by human commensal microbiota. Bacteriocins are peptides or proteins with antimicrobial activity that contribute to the stability and dynamics of microbial communities. We employed bioinformatic analyses to identify putative bacteriocin sequences in metagenomic sequences obtained from different human body sites. Prevailing bacterial taxa of the putative bacteriocins producers matched the most abundant organisms in each human body site. Remarkably, we found that samples from different body sites contain different density of putative bacteriocin genes, with the highest in samples from the vagina, the airway, and the oral cavity and the lowest in those from gut. Inherent differences of different body sites thus influence the density and types of bacteriocins produced by commensal bacteria. Our results suggest that bacteriocins play important roles to allow different bacteria to occupy several human body sites, and to establish a long-term commensal relationship with human hosts.
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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 | 0.000 |
| 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".