Competition Among Gardnerella Subgroups From the Human Vaginal Microbiome
Bibliographic record
Abstract
Gardnerella spp. are hallmarks of bacterial vaginosis, a clinically significant dysbiosis of the vaginal microbiome. Gardnerella has four subgroups (A, B, C and D) based on cpn60 sequences. Multiple subgroups are often detected in individual women, and interactions between these subgroups are expected to influence their population dynamics and associated signs and symptoms of bacterial vaginosis. In the present study, contact-independent and contact-dependent interactions between the four Gardnerella subgroups were investigated in vitro. The cell free supernatants of mono- and co-cultures had no effect on growth rates of the Gardnerella subgroups suggesting that there are no contact-independent interactions (and no contest competition). For contact-dependent interactions, mixed communities of 2, 3 or 4 subgroups were created and the initial (0 h) and final population sizes (48 h) were quantified using subgroup-specific PCR. Compared to the null hypothesis of additive interactions, most (69.3%) of the mixed communities exhibited competition (p < 0.0001). Competition reduced the growth rates of subgroups A, B and C. In contrast, the growth rate of subgroup D increased in the presence of the other subgroups (p < 0.0001). All subgroups were able to form biofilm alone and in mixed communities. Our study suggests that there is scramble competition among Gardnerella subgroups, which likely contributes to the observed distributions of Gardnerella spp. in vaginal microbiomes and the formation of the multispecies biofilms characteristic of bacterial vaginosis.
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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 | 0.001 | 0.000 |
| 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".