204 The vaginal and uterine microbiome of beef cattle that became pregnant or remained open following artificial insemination.
Bibliographic record
Abstract
Abstract The bovine reproductive tract harbors a diverse microbiome that may influence pregnancy outcomes. Recently, we characterized the vaginal and uterine microbiota of virgin yearling heifers and cows at the time of artificial insemination (AI) using both 16S rRNA gene sequencing and culturing approaches. We identified distinct microbial taxa associated with pregnancy success and observed differential abundance between pregnant and non-pregnant groups, however most taxa remained unclassified at the genus level. Therefore, in this study we used shotgun metagenomic sequencing for higher taxonomic resolution and deeper functional insights into the bovine reproductive microbiome. The objective of the present study was to characterize the vaginal and uterine microbiomes of beef cattle that became pregnant compared to those that remained open following AI using shotgun metagenomic sequencing. The vaginal (7 open; 54 pregnant) and uterine (9 open; 41 pregnant) swabs were collected from two different cohorts of Angus-crossbred cattle consisting of mature cows (vaginal and uterine swabs) and heifers (only vaginal swabs) prior to AI. Genomic DNA were extracted from these samples and the microbiomes were profiled using shotgun metagenomic sequencing. We observed that the uterine and vaginal microbiomes had distinct compositions (PERMANOVA: R2 = 0.102 and P < 0.001). The composition (PERMANOVA: R2 = 0.0075, P = 0.7935), as well as the richness and diversity (P > 0.05) of the vaginal microbiome did not differ between open and pregnant cattle. A total of 422 different genera were detected from the vaginal samples, with Negativicutes-UBA1444, Streptococcus, Mycobacterium, and Ureaplasma being the most relatively abundant. Twenty-five of these genera including Aphodosoma, Egerieisoma, Alitiscatomonas, Lentihominibacter, Enterocola, Akkermansia, Ruminococcus, and Faecousia were more abundant (P < 0.05) in the vaginal microbiome of non-pregnant cattle. A significant difference in the composition of the uterine microbiome was observed between pregnant and open cattle (R2 = 0.049 and P = 0.042). Furthermore, microbial richness (P = 0.035) and diversity [(Shannon diversity: P = 0.014), (inverse Simpson diversity: P = 0.011)], as well as evenness (Pielou’s index: P = 0.047) were greater in the uterine microbiome of open than pregnant cattle. Overall, we profiled 329 bacterial genera across uterine samples, with Negativicutes-UBA1444, Cutibacterium, Streptomyces, and Acinetobacter being the most predominant genera. At species level, the vaginal microbiome had 1161 species, including Streptococcus pluranimalium, Ureaplasma diversum, Facklamia hominis, Histophilus somni, and Enterococcus faecalis, whereas the uterine microbiome was dominated by Negativicutes-UBA1444 sp012798135, Cutibacterium acnes, Giesbergeria lacusdiani, Bacillus_J hisashii, Thiopseudomonas sp012518175, and Acinetobacter idrijaensis. While the results of this metagenomic sequencing were negatively impacted by contaminating host DNA and consequently, low microbial sequencing depth, our results suggest that the uterine microbiome may have implications in AI pregnancy success rate.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".