Stacking of Multiple Resistance Genes in Wheat via Transgenic Approaches
Bibliographic record
Abstract
Wheat diseases continue to threaten global food security, leading to yield losses and reduced grain quality. Consequently, effective and sustainable disease resistance strategies are urgently needed. This study explores the stacking of multiple resistance genes in wheat through transgenic approaches as a promising solution to these challenges. We first outline the principles of gene stacking, including the necessity to overcome pathogen evolution, ensure broad and durable resistance, and meet environmental and agricultural needs. We then discuss various transgenic strategies, such as direct genetic transformation, synthetic multigene constructs, and CRISPR/Cas-mediated genome editing, highlighting their potential for assembling and integrating multiple resistance genes. We also detail specific resistance genes commonly used in transgenic wheat, including those targeting rusts (e.g., Lr34, Sr22, Yr36), fungal and viral pathogens, and genes involved in broad-spectrum defense (e.g., pathogenesis-related proteins). Using the case study of transgenic stacking for rust resistance, specifically against Ug99 rust, we illustrate the practical applicability and global impact of this approach. We also explore the technical challenges, biosafety regulations, and genetic complexity that hinder its implementation. Looking ahead, we explore innovations in synthetic biology, precision gene editing, and breeding for climate resilience. Finally, we summarize recent advances in gene stacking, identify key gaps, and highlight the future potential of transgenic technology for enhancing durable disease resistance in wheat.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".