CRISPR‐induced knockouts reveal a dual role for the soybean <i>NFR5α</i> gene in symbiotic nitrogen fixation and root hair development
Bibliographic record
Abstract
Nitrogen fixation in soybeans, facilitated by symbiotic interactions with rhizobia, is a cornerstone of sustainable agriculture, reducing reliance on synthetic fertilizers. However, the efficiency of symbiotic nitrogen fixation (SNF) varies due to natural genetic variation in SNF-related genes. Our study underscores the pivotal role of the GmNFR5α gene not only in nodulation but also in root hair development, which is crucial for effective nutrient uptake and plant yield. Through detailed genetic analyses and clustered regularly interspaced short palindromic repeats (CRISPR)-based manipulations, we identified and characterized multiple knockout mutants, notably GmNFR5α-KO and combined GmNFR5α+GmROP6-KO, which exhibited significant reductions in root hair density and nodulation. These phenotypic changes correspond with the downregulation of key root hair development genes such as TTG, RHD1, RHD2, and KJK, establishing a clear link between GmNFR5α function and root hair formation. The potential of leveraging these genetic insights to improve nitrogen fixation in legumes and introduce SNF capabilities into cereal crops could revolutionize crop fertilization strategies, offering a sustainable solution to global agricultural challenges.
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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) | 0.002 | 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".