Transformation with <i>TT8</i> and <i>HB12</i> RNAi Constructs in Model Forage (<i>Medicago sativa</i>, Alfalfa) Affects Carbohydrate Structure and Metabolic Characteristics in Ruminant Livestock Systems
Bibliographic record
Abstract
Lignin, a phenylpropanoid polymer present in secondary cell walls, has a negative impact on feed digestibility. TT8 and HB12 genes were shown to have low expression levels in low-lignin tissues of alfalfa, but to date, there has been no study on the effect of down-regulation of these two genes in alfalfa on nutrient chemical profiles and availability in ruminant livestock systems. The objectives of this study were to investigate the effect of transformation of alfalfa with TT8 and HB12 RNAi constructs on carbohydrate (CHO) structure and CHO nutritive value in ruminant livestock systems. The results showed that transformation with TT8 and HB12 RNAi constructs reduced rumen, rapidly degraded CHO fractions (RDCA4, P = 0.06; RDCB1, P < 0.01) and totally degraded CHO fraction (TRDCHO, P = 0.08). Both HB12 and TT8 populations had significantly higher in vitro digestibility of neutral detergent fiber (NDF) at 30 h of incubation (ivNDF30) compared to the control (P < 0.01). The TT8 populations had highest ivDM30 and ivNDF240. Transformation of alfalfa with TT8 and HB12 RNAi constructs induced molecular structure changes. Different CHO functional groups had different sensitivities and different responses to the transformation. The CHO molecular structure changes induced by the transformation were associated with predicted CHO availability. Compared with HB12 RNAi, transformation with TT8 RNAi could improve forage quality by increasing the availability of both NDF and DM. Further study is needed on the relationship between the transformation-induced structure changes at a molecular level and nutrient utilization in ruminant livestock systems when lignification is much higher.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".