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Record W4402540690 · doi:10.1093/jas/skae234.173

512 Divergence of the sow vaginal microbiome based on fertility status

2024· article· en· W4402540690 on OpenAlexaff
Lauren A Fletcher, Xiaoshu Zhan, Yashu Song, Julang Li

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFertilityMicrobiomeBiologyAnimal scienceMedicineBioinformaticsPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract The need for alternative biomarkers of reproductive potential in the pork industry is apparent as the current selection approach for sow fertility is not keeping up with the improvement of other production-related traits. To date, the vaginal microbiome has been overlooked as a source of potential biomarkers of sow fertility status. We aimed to elucidate a possible divergence of the vaginal microbiome between sows of differing fertility status and identify possible microbial biomarkers that classify the fertility potential of a sow. Microbial DNA was extracted from vaginal microbiome samples originating from highly reproductive sows (HRP, n = 41) with the number of piglets born alive ≥ 13 or infertile sows (INF, n = 22), that failed to become pregnant after two rounds of consecutive artificial insemination. Our 16S rRNA sequencing results revealed significant (P < 0.05) beta diversity divergence at the family and genus taxonomic levels between HRP and INF vaginal communities. INF vaginal communities displayed significantly greater (P < 0.05) alpha diversity at both the family and genus levels and had a significantly (P < 0.05) greater number of unique OTUs identified at the genus level. The composition of the vaginal microbiome diverges between HRP and INF sows, with INF sows experiencing a disruption of their communities via the colonization of opportunistic pathogens reducing the abundance of normal microbiota seen in HRP. The genera Streptococcus, Lachnospiraceae XPB1014 group, Staphylococcus and Treponema were selected to test as potential biomarkers due to their univariate significance, contribution to partial least squares discriminant analysis (PLS-DA) and selection in a recursive feature elimination model (RFE). The resultant support-vector machine model was diagnostic (achieving an ROC-AUC = 0.86), supporting the selected biomarkers as diagnostic markers for sow fertility status. Overall, there was an apparent shift from a normal vaginal microbiota in HRP sows to a dysbiotic vaginal microbiota in INF sows dominated by potentially pathogenic, more anaerobic and short-chain fatty acid (SCFA)-producing microbiota that may play damaging and pro-inflammatory roles in the vaginal canal. Lactobacillus, known to be the dominant genus in a majority of healthy human vaginal microbiomes, was not the dominating genus in either INF or HRP vaginal microbiomes, suggesting the uniqueness of pig vaginal microbiomes. This also emphasizes the need for pig-specific vaginal microbiome research. Investigations regarding the application of the potential vaginal microbial biomarkers identified in this study to a large, novel population of sow and gilt vaginal microbiome samples to test their on-farm accuracy are underway. In addition, the mechanistic and physiologic role of SCFAs and the bacteria that produce them in the sow vaginal microbiome should be further investigated to understand how they may influence or be a product of fertility status.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.291
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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