Divergence in the sow vaginal microbiota is associated with fertility
Bibliographic record
Abstract
In brief: Vaginal microbiota composition influences female fertility, however it has not been studied for measuring fertility level in female pigs. This study reveals significant vaginal microbiota composition differences between high reproductive performance and infertile sows, and demonstrates that the vaginal microbiota has promise for improving female pig selection using machine learning modeling. Abstract: There is a need for reliable and effective biomarkers of female fertility and reproductive potential in the pork industry, as current selection protocols are not keeping up with the rate of improvement for other production-related traits. This study aimed to investigate the vaginal microbiota composition between sows of differing fertility status and identify candidate vaginal microbiota biomarkers of sow fertility. The vaginal microbiota of high reproductive performance sows (HRP, n = 52) with number of piglets born alive ≥13 and infertile sows (INF, n = 23), that remained nonpregnant after two consecutive rounds of artificial insemination, were investigated. Sequencing results revealed significantly different (P < 0.05) beta diversity at the genus level between HRP and INF vaginal microbiota communities. Accordingly, the composition of the vaginal microbiota diverged between HRP and INF sows, with INF sows having increased (P < 0.05) relative abundance of Lachnospiraceae XPB1014 group and HRP sows having increased (P < 0.05) relative abundance of Aerococcus and Staphylococcus at the genus level. Forty-two genera were selected as candidate biomarkers of sow fertility via partial least squares discriminant analysis (PLS-DA) and recursive feature elimination. The support-vector machine model classified sow fertility with 93.3% accuracy, supporting potential industry application to improve upon current methods for selection and recruitment in the breeding herd. Future investigations should validate the candidate vaginal microbiota biomarkers in a large, independent population of sows and gilts to evaluate their application for predicting future reproductive performance and assess their true industry applicability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".