Population density affects the outcome of competition in co-cultures of <i>Gardnerella</i> species isolated from the human vaginal microbiome
Bibliographic record
Abstract
Abstract Negative frequency-dependent selection is one possible mechanism for maintenance of rare species in communities, but the selective advantage of rare species may be checked at lower overall population densities where resources are abundant. Gardnerella spp. belonging to cpn60 subgroup D, are detected at low levels in vaginal microbiomes and are nutritional generalists relative to other more abundant Gardnerella spp., making them good candidates for negative frequency-dependent selection. The vaginal microbiome is a dynamic environment and the resulting changes in density of the microbiota may explain why subgroup D never gains dominance. To test this, we co-cultured subgroup D isolates with isolates from the more common and abundant subgroup C. Deep amplicon sequencing of rpoB was used to determine proportional abundance of each isolate at 0 h and 72 h in 152 co-cultures, and to calculate change in proportion. D isolates had a positive change in proportional abundance in most co-cultures regardless of initial proportion. Initial density affected the change in proportion of subgroup D isolates either positively or negatively depending on the particular isolates combined, suggesting that growth rate, population density and other intrinsic features of the isolates influenced the outcome. Our results demonstrate that population density is an important factor influencing the outcome of competition between Gardnerella spp. isolated from the human vaginal microbiome.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".