PSIX-18 Multiomic analysis to identify host and microbiome contributions to digestibility in beef cattle
Bibliographic record
Abstract
Abstract This study evaluated beef heifers selected for high (efficient) or low (inefficient) digestible fiber intake (DFI). Initial analysis showed that high DFI animals had reduced methane production versus low-DFI under a high forage diet. Using the same cohort of animals maintained on a further 4 diets of varying forage:concentrate ratios, we employed multi-kingdom amplicon sequencing and metagenome shotgun sequencing of rumen digesta and feces alongside RNA sequencing of rumen epimural samples to evaluate the compositional and functional interplay between different microbial groups, and their relationship with host gene expression in cattle divergent for DFI. Samples were collected from 16 cattle during 2 metabolism trials, comprising 5 diets. Amplicon sequencing analysis was conducted using QIIME2; 16S rRNA and 18S rRNA reads were analyzed using the SILVA database, while LSU (fungal) sequences were analyzed using a custom D1/D2 database. Additional analysis of archaeal 16S rRNA sequences was conducted using the Rumen and Intestinal Methanogens (RIM) database. Metagenome shotgun reads underwent a two-pass classification with Kraken2 using a database of prokaryotic genomes derived from the GTDB taxonomy, with the unclassified output undergoing classification using a custom database containing all NCBI protozoa, fungi, and phage genomes, enriched with selected rumen-specific ciliate and fungal genomes. Downstream analysis of taxonomic data from all microbiome work was conducted in R, and differentially abundant taxa were identified using ANCOM-BC and Aldex2. Functional analysis of metagenome contigs using the CAZY database implemented in dbCAN3 is ongoing. RNA-seq data were analyzed using the ARS-UCD reference genome, with identification of DE genes conducted using DeSeq2. Preliminary results indicate no major effect of DFI ranking on host gene expression, bacterial 16S rRNA, or metagenome compositional profiles. Several bacterial genera were differentially abundant between digestibility groups (P < 0.05), but these were all minor (< 0.01%) members of the microbiome. Fungal and methanogen communities different significantly (P < 0.05) according to DFI group, with efficient (high DFI) containing and more diverse communities under high-grain diet (P < 0.05). The same difference showed a tendency toward significance for the 18S rRNA protozoa data (P < 0.1). These preliminary data indicate that the microbial factors underpinning divergence in efficiency measured by DFI vary according to diet and may be more prominent in the non-bacterial fraction of the microbiome. Ongoing functional analysis of metagenome data as well as integration of multiomic data will provide deeper insight into these relationships and how they contribute to feed digestibility and efficiency in cattle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".