TALE protein mediated overexpression of embryogenesis related marker genes in wheat microspores
Bibliographic record
Abstract
Stress treatment of microspore embryogenesis leads to the reprogramming of male gametophyte development pathways, which generates haploid green plants. However, low green plant production efficiency is a major hindrance in the adoption of double haploid (DH) production platform for wheat breeding. The aim of the current study was to induce the expression of microspore embryogenesis associated genes by over expressing the RKD transcription factor using Transcription activator-like effector (TALE) proteins. The TALE protein can modulate the gene expression by recognizing and binding host plant promoter sequences. Taking into consideration, we have cloned and analysed RKD promoter from wheat and triticale and used for the custom synthesis of TaRKD-TALE protein. The isolated and purified protein was conjugated with R9 (Cys (Npys)-(D-Arg)9) cargo peptides and successfully transduced into wheat microspores. The expression of early-culture abundant (ECA1), RWP-RK domain-containing proteins (RKD1) and Tapetum determinant 1 (TPD1) marker genes in wheat linked to microspore embryogenesis was studied. The expression of all three (ECA1, TPD1 and RKD1) embryogenesis related marker genes in the TALE transduced microspore was significantly higher than the control. Microspore culture transduced by R9-TaRKD-TALE protein yielded a significantly higher number of embryo-like structures (ELS) and the total green plants in wheat cultivars AC Fielder. Conversely, the microspore cell death has not been significantly affected by the transduction of R9-TaRKD-TALE, while showing a higher recovery at 120 h of microspore culture. This work will be helpful to exploit the TALE protein to improve our understanding of DH production and other pathways related genes.
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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) | 0.001 | 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".