Rice endosperm iron biofortification by targeted and synergistic action of nicotianamine synthase and ferritin
Bibliographic record
Abstract
Nearly one-third of the world's population, mostly women and children, suffer from iron malnutrition and its consequences, such as anaemia or impaired mental development. Iron fortification of food is difficult because soluble iron is either unstable or unpalatable, and non-soluble iron is not bioavailable. Genetic engineering of crop plants to increase iron content has therefore emerged as an alternative for iron biofortification. To date, strategies to increase iron content have relied on single genes, with limited success. Our work focuses on rice as a model plant, because it feeds one-half of the world's population, including the majority of the iron-malnourished population. Using the targeted expression of two transgenes, nicotianamine synthase and ferritin, we increased the iron content of rice endosperm by more than six-fold. Analysis of transgenic rice lines confirmed that, in combination, they provide a synergistic effect on iron uptake and storage. Laser ablation-inductively coupled plasma-mass spectrometry showed that the iron in the endosperm of the transgenic rice lines accumulated in spots, most probably as a consequence of spatially restricted ferritin accumulation. Agronomic evaluation of the high-iron rice lines did not reveal a yield penalty or significant changes in trait characters, except for a tendency to earlier flowering. Overall, we have demonstrated that rice can be engineered with a small number of genes to achieve iron biofortification at a dietary significant level.
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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 | 0.000 |
| 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 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".