204 The vaginal and uterine microbiome of beef cattle that became pregnant or remained open following artificial insemination.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».