CRISPR/Cas9 in Planta Hairy Root Transformation: A Powerful Platform for Functional Analysis of Root Traits in Soybean
Bibliographic record
Abstract
Sequence and expression data obtained by next-generation sequencing (NGS)-based forward genetics methods often allow the identification of candidate causal genes. To provide true experimental evidence of a gene’s function, reverse genetics techniques are highly valuable. Site-directed mutagenesis through transfer DNA (T-DNA) delivery is an efficient reverse screen method in plant functional analysis. Precise modification of targeted crop genome sequences is possible through the stable and/or transient delivery of clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (CRISPR/Cas) reagents. Currently, CRISPR/Cas9 is the most powerful reverse genetics approach for fast and precise functional analysis of candidate genes/mutations of interest. Rapid and large-scale analyses of CRISPR/Cas-induced mutagenesis is achievable through Agrobacterium rhizogenes-mediated hairy root transformation. The combination of A. rhizogenes hairy root-CRISPR/Cas provides an extraordinary platform for rapid, precise, easy, and cost-effective “in root” functional analysis of genes of interest in legume plants, including soybean. Both hairy root transformation and CRISPR/Cas9 techniques have their own complexities and considerations. Here, we discuss recent advancements in soybean hairy root transformation and CRISPR/Cas9 techniques. We highlight the critical factors required to enhance mutation induction and hairy root transformation, including the new generation of reporter genes, methods of Agrobacterium infection, accurate gRNA design strategies, Cas9 variants, gene regulatory elements of gRNAs and Cas9 nuclease cassettes and their configuration in the final binary vector to study genes involved in root-related traits in soybean.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".