Characterization of the longissimus lumborum transcriptome response to adding propionate to the diet of growing Angus beef steers
Bibliographic record
Abstract
Development of management paradigms that enhance the rate of gain and qualitative characteristics of beef carcass development has the potential to impact production and nutrient use efficiency but also mitigate losses to the environment. We used eight Black Angus beef steers (272.5 ± 17.6 kg initial body wt) fed a forage-based pelleted diet alone (n = 4) or supplemented with sodium propionate included (n = 4) for 42 days. High-quality RNA was extracted from the longissimus lumborum and subjected to transcriptome sequencing using RNA-seq technology. Trimmed reads were aligned to the bovine reference genome (Btau4.0, release 63) and uniquely mapped reads from control and propionate treatment groups were subject to further analysis using edgeR. Candidates were filtered to account for multiple testing and differentially expressed genes (153 at a false discovery rate of <5%) were analyzed using Gene Ontology (GO) analysis (GOseq) to select terms where enrichment had occurred. Significant GO terms included regulation of cholesterol transport, regulation of sterol transport, and cellular modified amino acid metabolic process. Furthermore, the top four identified gene networks included lipid metabolism, small molecule biochemistry, carbohydrate metabolism, and molecular transport-related categories. Notably, changes in lipid metabolism specific genes reflect both increased oxidative and lipid synthetic capacities. Metabolism-related gene changes are reflective of expected enhancements in lean tissue accretion patterns exhibited in steers where high ruminal propionate relative to other short chain fatty acids is observed. Propionate feeding induced increased N retention in rapidly growing Angus cattle, and the observed alterations in LL tissue lipid metabolism-related gene networks are consistent with enhanced cell formation and function (protein synthesis, and lipogenic vs. lipolytic activities).
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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) | 0.001 | 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".