Bibliographic record
Abstract
The CRISPR/Cas9 system has emerged as a revolutionary tool for genome editing, offering unprecedented precision and efficiency in modifying genetic material. This systematic review focuses on the application of CRISPR/Cas9 technology in the biosynthesis of lignin in poplar species, highlighting recent advances and future prospects. Lignin, a complex polymer in the cell walls of plants, plays a crucial role in providing structural integrity and resistance to pathogens. However, its recalcitrance poses challenges for industrial processes such as pulping and biofuel production. Recent studies have demonstrated the potential of CRISPR/Cas9 to target and modify genes involved in lignin biosynthesis, thereby reducing lignin content and altering its composition to enhance industrial utility. Several research efforts have successfully employed CRISPR/Cas9 to edit lignin biosynthesis genes in poplar. For instance, the efficient knockout of the phytoene desaturase gene in Populus alba × Populus glandulosa using a single guide RNA (sgRNA) has shown promising results in generating targeted mutations with high efficiency. Similarly, the application of CRISPR/Cas9 in Populus tomentosa Carr. has demonstrated the system's capability to create precise genomic edits, resulting in significant phenotypic changes. Moreover, studies have evaluated the efficiency of various guide RNAs (gRNAs) in poplars, identifying key factors that influence gene editing success, such as GC content and the accessibility of the seed region. The review also discusses the broader implications of CRISPR/Cas9 technology in plant research, including its potential to enhance disease resistance, improve nutritional content, and develop drought-tolerant varieties Despite these advancements, challenges such as off-target effects and the need for efficient delivery methods remain. Future research directions include the development of high-fidelity Cas9 variants and the optimization of delivery systems to minimize off-target modifications and enhance editing efficiency.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".