A Novel Phenolic Compound, Chloroxynil, Improves Agrobacterium-Mediated Transient Transformation in Lotus japonicus
Bibliographic record
Abstract
Agrobacterium-mediated transformation is a commonly used method for plant genetic engineering. However, the limitations of Agrobacterium host-plant interactions and the complexity of plant tissue culture often make the production of transgenic plants difficult. Transformation efficiency in many legume species, including soybean and the common bean, has been reported to be quite low. To improve the transformation procedure in legumes, we screened for chemicals that increase the transformation efficiency of Lotus japonicus, a model legume species. A Chemical library was screened and chemicals that increase in transient transformation efficiency of L. japonicus accession, Miyakojima MG-20 were identified. The transient transformation efficiency was quantified by reporter activity in which an intron-containing reporter gene produces the GUS protein only when the T-DNA is expressed in the plant nuclei. We identified a phenolic compound, chloroxynil, which increased the genetic transformation of L. japonicus by Agrobacterium tumefaciens strain EHA105. Characterization of the mode of chloroxynil action indicated that it enhanced Agrobacterium-mediated transformation through the activation of the Agrobacterium vir gene expression, similar to acetosyringone, a phenolic compound known to improve Agrobacterium-mediated transformation efficiency. Transient transformation efficiency of L. japonicus with 5 μM chloroxynil was 60- and 6- fold higher than that of the control and acetosyringone treatment, respectively. In addition, transgenic L. japonicus lines were successfully generated by 5 μM chloroxynil treatment.Furthermore, we show that chloroxynil improves L. japonicus transformation by Agrobacterium strain GV3101 and rice transformation. Our results demonstrate that chloroxynil significantly improves Agrobacterium tumefaciens-mediated transformation efficiency of various agriculturally important crops.
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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 | 0.000 |
| 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".