Lactobacilli Are Prominent Members of the Microbiota Involved in the Ruminal Digestion of Barley and Corn
Bibliographic record
Abstract
The chemical composition of barley grain can vary among barley varieties and result in different digestion efficiencies in the rumen. It is not known if compositional differences in barley can affect the microbiome involved the ruminal digestion of barley. The objective of this study was to characterize the in situ rumen digestion and microbiome of four barley grain varieties. Three ruminally-cannulated heifers were fed a low (60% barley silage, 37 % barley grain and 3% supplement) or high grain (37% barley silage, 60% barley grain and 3% supplement) diets. Four different barley varieties (Fibar, Xena, McGwire and Hilose) and corn as a comparison were included in the experiment. One set of bags was used to estimate dry matter (DM), starch and crude protein (CP) disappearance. A second set was used to extract DNA from the adherent microbiome and a third set was used to visualize grain after incubation using scanning electron microscopy (SEM). DNA was subjected to amplicon 16S rRNA gene sequencing followed by analysis using QIIME. In the low grain diet, McGwire had the highest effective degradability (ED) of DM (P<0.01). The ED of starch was highest (P<0.01) for Fibar, McGwire and Xena, but the ED of CP was not affected by variety. For the high grain diet, Xena and McGwire had the highest ED of DM (P<0.01). The ED of starch was highest (P<0.01) for Xena and Fibar. The ED of protein was highest (P<0.01) for Xena and McGwire. Although the microbiome did not differ between barley varieties, they did differ from corn and with incubation time. Lactobacilli were dominant members of the mature biofilms associated with corn and barley and were accompanied by a notable increase in the lactic acid utilizing genera, Megasphaera. As none of the cattle exhibited subclinical or clinical acidosis during the study, our results suggest that lactobacilli play a more prominent role in routine starch digestion than presently surmised.
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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 | 0.001 | 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".