From whole genomes to probiotic candidates: A study of potential lactobacilli strains selection for vaginitis treatment
Bibliographic record
Abstract
Vaginitis, characterized by pathogenic invasion and a deficiency in beneficial lactobacilli, has recognized lactobacilli supplementation as a novel therapeutic strategy. However, due to individual differences in vaginal microbiota, identifying universally effective Lactobacillus strains is challenging. Traditional methodologies for probiotic selection, which heavily depend on extensive in vitro experiments, are both time-intensive and laborious. The aim of this study was to pinpoint possible vaginal probiotic candidates based on whole-genome screening. We sequenced the genomes of 98 previously isolated Lactobacillus strains, annotating their genes involved in probiotic metabolite biosynthesis, adherence, acid/bile tolerance, and antibiotic resistance. A scoring system was used to assess the strains based on their genomic profiles. The highest-scoring strains underwent further in vitro evaluation. Consequently, two strains, Lactobacillus crispatus LG55-27 and Lactobacillus gasseri TM13-16, displayed an outstanding ability to produce D-lactate and adhere to human vaginal epithelial cells. They also showed higher antimicrobial activity against Gardnerella vaginalis, Escherichia coli , Candida albicans , Staphylococcus aureus , and Pseudomonas aeruginosa compared to reference Lactobacillus strains. Their resilience to acid and bile environments highlights the potential for oral supplementation. Oral and vaginal administration of these two strains were tested in a bacterial vaginosis (BV) rat model at various doses. Results indicated that combined vaginal administration of these strains at 1 × 10 6 CFU/day significantly mitigated BV in rats. This research offers a probiotic dosage guideline for vaginitis therapy, underscoring an efficient screening process for probiotics using genome sequencing, in vitro testing, and in vivo BV model experimentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".