Sequencing and Culture-based Characterization of the Vaginal and Uterine Microbiota in Beef Cattle that Became Pregnant or Non-pregnant via Artificial Insemination
Bibliographic record
Abstract
Abstract In this study, we evaluated the vaginal and uterine microbiota between beef cattle that became pregnant via artificial insemination (AI) and those that did not to identify microbial signature associated with pregnancy. We also characterized the culturable fraction of these microbiota using extensive culturing and screened some vaginal and uterine bacterial isolates for their antimicrobial resistance. For this, vaginal and uterine swabs from two cohorts of Angus-crossbred cattle: mature cows (vaginal and uterine; 27 open and 31 pregnant) and heifers (vaginal; 26 open and 33 pregnant) that were collected before AI were processed for microbiota assessment using 16S rRNA gene sequencing and culturing. Twenty-nine vaginal and uterine bacterial isolates were screened for resistance against 41 antibiotics. Sequencing results revealed 11 taxa that were more abundant in the vaginal samples from non-pregnant heifers compared to pregnant heifers. No differentially abundant taxa were detected in the vaginal samples from pregnant versus non-pregnant cows. Pregnant cows had a distinct uterine microbiota community structure ( P = 0.008) and interaction network structure compared to non-pregnant cows. Twenty-eight differentially abundant uterine taxa were observed between the two groups. Community structure and diversity were different between the cow vagina and uterus. A total of 733 bacterial isolates were recovered from vaginal (512) and uterine (221) swabs under aerobic (83 different species) and anaerobic (69 species) culturing. Among these isolates were pathogenic species and those mostly susceptible to tested antibiotics. Overall, our results indicate that pregnancy-associated taxonomic signatures are present in the bovine uterine and vaginal microbiota. Importance Emerging evidence suggests that microbiome-targeted approaches may provide a novel opportunity to reduce the incidence of reproductive failures in cattle. To develop such microbiome-based strategies, one of the first logical steps is to identify reproductive microbiome features related to fertility, and isolate the pregnancy associated microbial species for developing a future bacterial consortium that could be administered before breeding to enhance pregnancy outcomes. Here, we characterized the vaginal and uterine microbiota in beef cattle that became pregnant or not via AI and identified some microbiota features associated with pregnancy. We compared similarities between vaginal and uterine microbiota, and between heifers and cows. Using extensive culturing, we provided new insights on the culturable fraction of the vaginal and uterine microbiota, and their antimicrobial resistance. Overall, our findings will serve as an important basis for future research aimed at harnessing the vaginal and uterine microbiome for improved cattle fertility.
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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 | 0.000 | 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 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".