Highly interactive nature of flower‐specific enhancers and promoters, and its potential impact on tissue‐specific expression and engineering of multiple genes or agronomic traits
Bibliographic record
Abstract
Molecular stacking enables multiple traits to be effectively engineered in crops using a single vector. However, the co-existence of distinct plant promoters in the same transgenic unit might, like their mammalian counterparts, interfere with one another. In this study, we devised a novel approach to investigate enhancer-promoter and promoter-promoter interactions in transgenic plants and demonstrated that three of four flower-specific enhancer/promoters were capable of distantly activating a pollen- and stigma-specific Pps promoter (fused to the cytotoxic DT-A gene) in other tissues, as revealed by novel tissue ablation phenotypes in transgenic plants. The NtAGI1 enhancer exclusively activated stamen- and carpel-specific DT-A expression, thus resulting in tissue ablation in an orientation-independent manner; this activation was completely abolished by the insertion of an enhancer-blocking insulator (EXOB) between the NtAGI1 enhancer and Pps promoter. Similarly, AGL8 and AP1Lb1, but not AP1La, promoters also activated distinct tissue-specific DT-A expression and ablation, with the former causing global growth retardation and the latter ablating apical inflorescences. While the tissue specificity of the enhancer/promoters generally defined their activation specificities, the strength of their activity in particular tissues or developmental stages appeared to determine whether activation actually occurred. Our findings provide the first evidence that plant-derived enhancer/promoters can distantly interact/interfere with one another, which could pose potential problems for the tissue-specific engineering of multiple traits using a single-vector stacking approach. Therefore, our work highlights the importance of adopting enhancer-blocking insulators in transformation vectors to minimize promoter-promoter interactions. The practical and fundamental significance of these findings will be discussed.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".