Metatranscriptomic Profiling Reveals the Effect of Breed on Active Rumen Eukaryotic Composition in Beef Cattle With Varied Feed Efficiency
Bibliographic record
Abstract
Exploring the compositional characteristics of rumen eukaryotic community can expand our understanding of their role in rumen function and feed efficiency. In this study, we applied metatranscriptomics to characterize the active rumen eukaryotic community (protozoa and fungi) in beef cattle (n = 48) of three breeds [Angus (AN), Charolais (CH), Kinsella Composite (KC)] and with divergent residual feed intake (RFI). The composition of active rumen eukaryotic microbiota was evaluated based on enriched 18S rRNAs from the metatranscriptomic datasets. At the phylum level, a total of 4 protozoal taxa (Ciliophora, Parabasalia, unclassified SAR, and unclassified Alveolata), 6 fungal taxa (Neocallimastigomycota, Basidiomycota, unclassified Fungi, Mucoromycota, Ascomycota, and Chytridiomycota), and 1 sister group of kingdom Fungi (unclassified Opisthokonta) were detected with relative abundances higher than 0.01% and in at least 50% of animals within each breed. Among these, Ciliophora, Parabasalia, unclassified Opisthokonta, and Neocallimastigomycota were the top 4 active eukaryotic phyla. At the genus level, a total of 8 ciliated protozoa, 5 flagellated protozoa, 5 anaerobic fungi, and 10 aerobic fungi taxa were detected, with unclassified Trichostomatia, Tetratrichomonas, unclassified Neocallimastigaceae, and Pleurotus being the most predominant taxa of ciliated protozoa, flagellated protozoa, anaerobic fungi, and aerobic fungi, respectively. Differential abundance analysis revealed that breed had a significant effect on the phylogenetic lineages of rumen eukaryotes, and 7 fungal taxa were more abundant (linear discriminant analysis score > 2 with P < 0.05) in the rumen of KC steers than in the rumen of AN and CH steers. Although principal coordinate analysis revealed that the ruminal active eukaryotic profiles were not distinguishable between high- and low-RFI groups, the diversity indices, including Faith’s phylogenetic diversity, observed OTUs, and Shannon index of rumen eukaryotes were higher in low-RFI steers than those in high-RFI steers. Meanwhile, the abundance of genus Entodinium and the kingdom Fungi was higher in low-RFI steers than that in high-RFI steers. This information on active rumen eukaryotic microbiota and identified differential abundance of taxa between high- and low-RFI animals suggests the possibility of improving feed efficiency through altering rumen eukaryotic microbiota.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".