A high-throughput Agrobacterium tumefaciens-mediated transformation system for molecular breeding and functional genomics of rice (Oryza sativa L.)
Bibliographic record
Abstract
Agrobacterium tumefaciens-mediated transformation is the preferred method for genetic transformation of rice. Here we provide a comprehensive and high-through-put protocol for Agrobacterium-mediated transformation of rice for generating the large numbers of transgenic plants that are required for functional analysis and for evaluating traits of agronomic importance. In a project designed to produce nitrogen use efficient rice plants, we refined/improved several factors including optimization of conditions for inducing vir genes prior to transformation, infection and co-cultivation medium for better interaction of target tissues with Agrobacterium. These optimizations supported not only the survival of co-cultured calli in high frequency, but also the production of multiple resistant cell lines per co-cultured embryogenic and nodular callus. In addition, improvements in plant tissue culture, selection and regeneration media has enabled us to produce large numbers of transgenic rice plants containing genes of agronomical importance. Partial desiccation and ABA treatment to hygromycin-resistant callus tissue significantly (P<0.05) enhanced both regeneration frequency and regeneration of transgenic plantlets per callus tissue. Regeneration frequency was further improved by optimizing the concentration of copper sulphate in the regeneration medium. The majority of the transgenic plants obtained using this improved protocol displayed a normal phenotype and the gene of interest was inherited by the progeny in a Mendelian fashion. Homozygous plants of selected lines showed stable phenotypes both in soil and hydroponic conditions even after five generations. Availability of such a high-throughput Agrobacterium-mediated transformation system will improve future opportunities for rice genetics and functional genomics study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".