Rumen Microbiome Composition Determined Using Two Nutritional Models of Subacute Ruminal Acidosis
Bibliographic record
Abstract
Subacute ruminal acidosis (SARA) is a metabolic disease in dairy cattle that occurs during early and mid-lactation and has traditionally been characterized by low rumen pH, but lactic acid does not accumulate as in acute lactic acid acidosis. It is hypothesized that factors such as increased gut permeability, bacterial lipopolysaccharides, and inflammatory responses may have a role in the etiology of SARA. However, little is known about the nature of the rumen microbiome during SARA. In this study, we analyzed the microbiome of 64 rumen samples taken from eight lactating Holstein dairy cattle using terminal restriction fragment length polymorphisms (TRFLP) of 16S rRNA genes and real-time PCR. We used rumen samples from two published experiments in which SARA had been induced with either grain or alfalfa pellets. The results of TRFLP analysis indicated that the most predominant shift during SARA was a decline in gram-negative Bacteroidetes organisms. However, the proportion of Bacteroidetes organisms was greater in alfalfa pellet-induced SARA than in mild or severe grain-induced SARA (35.4% versus 26.0% and 16.6%, respectively). This shift was also evident from the real-time PCR data for Prevotella albensis, Prevotella brevis, and Prevotella ruminicola, which are members of the Bacteroidetes. The real-time PCR data also indicated that severe grain-induced SARA was dominated by Streptococcus bovis and Escherichia coli, whereas mild grain-induced SARA was dominated by Megasphaera elsdenii and alfalfa pellet-induced SARA was dominated by P. albensis. Using discriminant analysis, the severity of SARA and degree of inflammation were highly correlated with the abundance of E. coli and not with lipopolysaccharide in the rumen. We thus suspect that E. coli may be a contributing factor in disease onset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".