Increased Apigenin in <scp>DNA</scp> ‐Edited Hexaploid Wheat Promoted Soil Bacterial Nitrogen Fixation and Improved Grain Yield Under Limiting Nitrogen Fertiliser
Bibliographic record
Abstract
Nitrogen availability remains a principal constraint to crop productivity. Plants cannot directly assimilate the abundant nitrogen available in our atmosphere; instead, they rely on the uptake of inorganic forms of nitrogen, such as ammonium and nitrate from the soil. Nitrogen is a limiting nutrient in wheat production, and wheat yields are very responsive to nitrogen fertilisation. Only diazotrophic bacteria can convert atmospheric nitrogen to ammonia via biological nitrogen fixation (BNF), and although improving BNF in wheat has been a longstanding objective, there have been no descriptions of successful modification of wheat crops showing increased BNF in the literature. Here we describe the use of polycistronic multiplexed CRISPR to modify the flavone biosynthetic pathway of hexaploid wheat (Triticum aestivum) plants, generating DNA-edited plants with increased apigenin content. The apigenin-enriched plants exude apigenin into the soil, inducing the colonisation of the roots and subsequent formation of biofilms in soil by diazotrophic bacteria. The low permeability of the biofilm to oxygen protected the bacterial nitrogenase and stimulated BN. Under nitrogen-limiting conditions, apigenin-enriched wheat lines exhibited increased nitrogen content, improved photosynthetic performance, and higher grain yield relative to wild-type controls. This work demonstrates the feasibility of engineering associative BNF in cereals via metabolic reprogramming of root exudation, offering a sustainable route to reduce dependence on synthetic nitrogen fertilisers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".