Postbiotics from Saccharomyces cerevisiae fermentation stabilize rumen solids microbiota and promote microbial network interactions and diversity of hub taxa during grain-based subacute ruminal acidosis (SARA) challenges in lactating dairy cows
Bibliographic record
Abstract
Background: High-yielding dairy cows are commonly fed high-grain rations. However, this can cause subacute ruminal acidosis (SARA), a metabolic disorder in dairy cows that is usually accompanied by dysbiosis of the rumen microbiome. Postbiotics that contain functional metabolites provide a competitive niche for influential members of the rumen microbiome, may stabilize and promote their populations, and, therefore, may attenuate the adverse effects of SARA. Methods: This study used a total of 32 rumen-cannulated lactating dairy cows, which were randomly assigned into four treatments: no SCFP (control), 14 g/d Original XPC (SCFPa), 19 g/d NutriTek (SCFPb-1X), and 38 g/d NutriTek (SCFPb-2X) (Diamond V, Cedar Rapids, IA) from 4 weeks before until 12 weeks after parturition. Grain-based SARA challenges were conducted during week 5 (SARA1) and week 8 (SARA2) after parturition by replacing 20% dry matter of the base total mixed ration (TMR) with pellets containing 50% ground barley and 50% ground wheat. The DNA of rumen solids digesta was extracted and subjected to V3-V4 16S rRNA gene sequencing. The characteristics of rumen solids microbiota were compared between non-SARA (Pre-SARA1, week 4; Post-SARA1, week 7; and Post-SARA2, weeks 10 and 12) and SARA stages (SARA1/1, SARA1/2, SARA2/1, SARA2/2), as well as among treatments. Results: , unclassified Lachnospiraceae, and unclassified Ruminococcaceae during the SARA challenges. These challenges also reduced the positive interactions and the numbers of hub taxa in the microbiota. The SCFPb treatment increased positive interactions among microbial members of the solids digesta and the number of hub taxa during the SARA and non-SARA stages. The SCFPb-2X treatment prevented changes in the network characteristics, including the number of components, clustering coefficient, modularity, positive edge percentage, and edge density of the microbiota during SARA challenges. These challenges reduced predicted carbohydrate and nitrogen metabolism in microbiota, whereas SCFP supplementation attenuated those reductions. Conclusions: Supplementation with SCFP, especially the SCFPb-2X attenuated the adverse effects of grain-based SARA on the diversity and predicted functionality of rumen solids 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 | 0.000 | 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".