Isolation of high-quality total RNA from rumen anaerobic bacteria and fungi, and subsequent detection of glycoside hydrolases
Bibliographic record
Abstract
The rumen is one of the most powerful fibrolytic fermentation systems known. Gene expression analyses, such as reverse transcription PCR (RT-PCR), microarrays, and metatranscriptomics, are techniques that could significantly expand our understanding of this ecosystem. The ability to isolate and stabilize representative RNA samples is critical to obtaining reliable results with these procedures. In this study, we successfully isolated high-quality total RNA from the solid phase of ruminal contents by using an improved RNA extraction method. This method is based on liquid nitrogen grinding of whole ruminal solids without microbial detachment and acid guanidinium - phenol - chloroform extraction combined with column purification. Yields of total RNA were as high as 150 µg per g of fresh ruminal content. The typical large subunit/small subunit rRNA ratio ranged from 1.8 to 2.0 with an RNA integrity number (Agilent Technologies) greater than 8.5. By eliminating the detachment step, the resulting RNA was more representative of the complete ecosystem. Our improved method removed a major barrier limiting analysis of rumen microbial function from a gene expression perspective. The polyA-tailed eukaryotic mRNAs obtained have successfully been applied to next-generation sequencing, and metatranscriptomic analysis of the solid fraction of rumen contents revealed abundant sequences related to rumen fungi.
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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".