Downregulation of key genes involved in carbon metabolism in <i>Medicago truncatula</i> results in increased lipid accumulation in vegetative tissue
Bibliographic record
Abstract
Abstract Alfalfa (Medicago sativa L.) is the most widely grown perennial forage crop and is a close relative of the model diploid legume Medicago truncatula Gaertn. However, use of alfalfa leads to substantial greenhouse gas emissions and economic losses related to inefficiencies in rumen fermentation. The provision of supplemental lipids has been used as a strategy to mitigate these issues, but it is a costly approach. The ability to enhance lipid content within the vegetative tissues of alfalfa would therefore be very advantageous. As such, our aim was to assess and select gene candidates to increase total shoot lipid content in M. truncatula using a virus‐induced gene silencing (VIGS) approach. We targeted gene homologs of the SUGAR‐DEPENDANT 1 (SDP1), ADP‐GLUCOSE‐PYROPHOSPHORYLASE SMALL SUBUNIT 1 (APS1), TRIGALACTOSYLDIACYLGLYCEROL 5 (TGD5), and PEROXISOMAL ABC TRANSPORTER 1 (PXA1) in M. truncatula for silencing. Reduced target transcript levels were confirmed and changes of shoot lipid content and fatty acid composition were measured. Silencing of SDP1, APS1, and PXA1 each resulted in significant increases in shoot total lipid content. Significantly increased proportions of α‐linolenic acid (18:3Δ9cis,12cis,15cis) were observed, and stearic acid (18:0) levels significantly decreased in the total acyl lipids extracted from vegetative tissues of each of the M. truncatula silenced plants. In contrast, palmitic acid (16:0) levels were significantly decreased in only SDP1‐ and PXA1‐silenced plants. Genes of PXA1 and SDP1 would be ideal targets for mutation as a means of improving the quality of alfalfa to increase feed efficiency and minimize greenhouse gas emissions from livestock production in the future.
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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) | 0.001 | 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 | 0.000 |
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".