Characterization of Variation in Rumen Methanogenic Communities under Different Dietary and Host Feed Efficiency Conditions, as Determined by PCR-Denaturing Gradient Gel Electrophoresis Analysis
Bibliographic record
Abstract
Understanding ruminal methanogens is essential for greenhouse gas mitigation, as well as for improving animal performance in the livestock industry. It has been speculated that ruminal methanogenic diversity affects host feed efficiency and results in differences in methane production. This study examined methanogenic profiles in the rumen using culture-independent PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis for 56 beef cattle which differed in feed efficiency, as well as diet (the cattle were fed a low-energy diet or a high-energy diet). The methanogenic PCR-DGGE profiles detected were greatly affected by diet, and the major pattern changed from a community containing predominantly Methanobrevibacter ruminantium NT7 with the low-energy diet to a community containing predominantly Methanobrevibacter smithii, Methanobrevibacter sp. AbM4, and/or M. ruminantium NT7 with the high-energy diet. For each diet, the methanogenic PCR-DGGE pattern was strongly associated with the feed efficiency of the host. Diet-associated bands for Methanobrevibacter sp. AbM4 and M. smithii SM9 and a feed efficiency-related band for M. smithii PS were identified. The abundance of total methanogens was estimated by determining the numbers of copies of the 16S rRNA genes of methanogens. However, the size of the methanogen population did not correlate with differences in feed efficiency, diet, or metabolic measurements. Thus, the structure of the methanogenic community at the species or strain level may be more important for determining host feed efficiency under different dietary conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".