Ruminal-buccal microbiota transmission and their diagnostic roles in subacute rumen acidosis in dairy goats
Bibliographic record
Abstract
Abstract Background Subacute rumen acidosis (SARA) is a common metabolic disorder in ruminants that disrupts the rumen microbiome and animal health, but diagnosis is challenging due to subtle symptoms and invasive testing requirements. This study explores the potential of the buccal (oral) microbiome as a diagnostic indicator for SARA, hypothesizing an interaction with the rumen microbiome. Results The study involved 47 dairy goats, including 11 on a control diet and 36 on high-concentrate diets with increasing rumen-degradable starch. Animals were grouped based on dietary exposure and ruminal pH: Control, Low-RDS Tolerance/SARA (LRDST/LRDSS), and High-RDS Tolerance/SARA (HRDST/HRDSS). Transcriptomics of rumen epithelium showed heightened inflammatory pathway gene expression in SARA-susceptible goats compared to controls and tolerant groups. Alpha diversity of ruminal bacteria showed lower Shannon diversity in HRDSS goats compared to HRDST whereas buccal bacteria displayed significantly lower Chao1 diversity in LRDSS goats compared to HRDST. Beta diversity analyses revealed distinct patterns between SARA-affected goats and healthy controls in both ruminal and buccal microbiomes. Prevotellaceae_UCG-003 emerged as a candidate biomarker, with reduced abundance in SARA-susceptible goats in both rumen and buccal samples. Machine learning classifiers achieved high accuracy in distinguishing SARA-susceptible goats using this genus (rumen AUC = 0.807; buccal AUC = 0.779). Source tracking analysis illustrated diminished cross-population of bacteria from the buccal to rumen (2.86% to 0.25%) and vice versa (8.59% to 1.17%), signifying compromised microbial interchange in SARA-affected goats. A microbiota transplant experiment verified SARA microbiota's ability to induce pH decline, escalate inflammation-related gene expression (MAPK10, IL17B, FOSB, SPP1), disrupt microbial transfer, and reduce Prevotellaceae_UCG-003 in recipients. Conclusion Our findings highlight SARA’s dual impact on ruminal and buccal microbiota, exacerbating epithelial inflammation gene expression. Shifts in the buccal microbiome, specifically reductions in Prevotellaceae_UCG-003, mirror ruminal changes and can be influenced by inter-compartmental bacterial transmission, thereby offering a non-invasive diagnostic approach for SARA.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".