367 Characterization of the Seminal Microbiota in Beef Bulls Subjected to Different Rate of Gains Using 16S Rrna Gene Sequencing and Culturomics
Bibliographic record
Abstract
Abstract Increasing evidence supports the existence of a microbial community in bovine semen, and that this seminal microbiota may influence not only the male reproductive health, but also female and offspring health through microbial transfer. In this study, we evaluated seminal microbiota in beef bulls (BW= 503±7.2 kg) fed a common diet to achieve moderate (1.13 kg/d) or high (1.80 kg/d) rates of weight gain. Semen samples were collected at day 0 and day 112 of dietary intervention (n = 19 per group), and post-breeding (n = 6) using electroejaculation and the microbiota assessed using 16S rRNA gene sequencing, quantification of total bacteria by qPCR, and viable bacteria culturing. A complex and dynamic microbiota was detected in the semen, and the community structure changed significantly over the course of the study (R 2 = 0.126, P < 0.001) but remained unaffected by the dietary treatment (P > 0.05). Microbial richness (number of ASVs) increased from d0 (253 ± 12) to d112 (293 ± 14) while diversity (Shannon index) was reduced (P < 0.05). Twenty-seven bacterial phyla were identified across all samples, with Fusobacteriota (36.3%), Bacteroidetes (30.4%), Firmicutes (17.1%) and Actinobacteriota (14.9%) being the most predominant phyla. Total bacterial load declined over time (P < 0.05). Diet had no effect on alpha diversity metrics, microbial composition, or total bacterial concentration (P > 0.05). A total of 364 bacterial isolates were recovered under aerobic (n = 220) and anaerobic (n = 144) culturing conditions. These isolates represented 48 different genera within the Firmicutes (60%), Proteobacteria (25%), Actinobacteria (9%), Bacteroidetes (4%) and Fusobacteriota (2%) phyla. Bacillus, Staphylococcus, Escherichia, Enterococcus and Arthrobacter were the predominant genera. Overall, our results suggest that bovine semen harbors a rich and complex microbiota which changes over time but appears to be resilient to differential gains achieved via common diet.
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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".