What are the current CRISPR base editing and prime editing protocols for engineering herbicide-tolerant staple crops, including guide RNA design, delivery methods, off-target profiling assays, and fie

Current protocols for engineering herbicide-tolerant staple crops utilize CRISPR base editing (BE) and prime editing (PE) to install precise mutations in target genes such as ALS, EPSPS, ACC, and TubA2 (Direct, High; PMID: 39397365, PMID: 34204760). These protocols integrate advanced guide RNA design strategies, diverse delivery mechanisms, and comprehensive off-target profiling to ensure heritable resistance and product safety (Direct, High; PMID: 39376239, DOI: 10.3389/fpls.2026.1804263).

Guide RNA Design Protocols

• Base Editing (BE): Standard design targets an editing window typically located at positions 4–8 of the protospacer (Direct, High; PMID: 29807545).
  • igRNA (Imperfect gRNA): Protocols utilize gRNAs with one or two non-complementary bases to narrow the editing window, achieving single-base precision and reducing bystander editing (Direct, High; PMID: 35349689).
  • Bubble Hairpin (BH-sgRNA): Secondary structures are introduced to the 5′ end of the sgRNA to provide energetic barriers that improve specificity and reduce off-target deamination (Direct, High; PMID: 33879582).
• Prime Editing (PE): Requires a prime-editing guide RNA (pegRNA) consisting of a spacer, a scaffold, and a 3′ extension containing a Primer Binding Site (PBS) and a Reverse Transcriptase Template (RTT) (Direct, High; PMID: 32415890).
  • epegRNA: 3′ extensions are stabilized with viral-derived structured RNA motifs like evopreQ1 to prevent exonuclease degradation (Direct, High; PMID: 38974869).
  • AI-Driven Design: Computational tools such as PRIDICT2.0, DeepPE, and OPED predict pegRNA efficiency by analyzing sequence context and chromatin accessibility (Direct, High; PMID: 38907037, PMID: 40745000).

Delivery Methods for Staple Crops

• Agrobacterium-mediated Transformation: The most common method for stable T-DNA integration in rice, tomato, and soybean (Direct, High; PMID: 37897041, PMID: 41345100).
• Particle Bombardment (Biolistics): Preferred for recalcitrant monocots like wheat and sugarcane (Direct, High; PMID: 34713261).
• Ribonucleoprotein (RNP) Delivery: Preassembled Cas-gRNA complexes are delivered into protoplasts or zygotes using PEG or biolistics to achieve transgene-free editing (Direct, High; PMID: 33898980).
• Viral Replicons: Deconstructed viral genomes (e.g., Wheat Dwarf Virus) provide autonomously replicating systems that increase the copy number of repair templates, enhancing Homology-Directed Repair (HDR) efficiency (Direct, High; PMID: 39943039).

Off-Target Profiling Assays

• In Silico Prediction: Online tools like Cas-OFFinder and CRISPR-P 2.0 scan genomes for potential mismatch sites (Direct, High; PMID: 37399127, PMID: 32096325).
• Experimental Profiling:
  • TAPE-seq: A cell-based method specifically designed to capture genome-wide off-target effects of prime editors by inserting a tag sequence at active sites (Direct, High; PMID: 36581624).
  • Digenome-seq: An in vitro assay using purified Cas proteins to identify genome-wide double-strand breaks (Direct, High; PMID: 36997524).
  • WGS and WTS: Whole-genome and whole-transcriptome sequencing are employed to detect unpredictable, sgRNA-independent mutations caused by deaminase activity (Direct, High; PMID: 41432570, PMID: 34042262).

Field Trial Design and Safety Standards

• Substantial Equivalence: Safety assessments compare the edited crop to a non-modified counterpart with a history of safe use (Direct, High; PMID: 37213044).
• Reference Varieties: Trials must include at least six non-GM commercial reference varieties to characterize natural variation (Direct, High; PMID: 37213044).
• Replicated Locations: A minimum of eight trial sites representing commercial production environments is required to assess compositional and agronomic stability (Direct, High; PMID: 37213044).
• VCU Trials: Value-for-Cultivation-and-Use trials evaluate performance, grain quality, and resistance to environmental stressors under field conditions (Direct, High; PMID: 37213044, PMID: 41751548).

Base and prime editing protocols have successfully established herbicide tolerance in crops by targeting highly conserved enzymatic sites, with efficiency significantly boosted by architectural optimizations like NC-protein fusions and mismatch repair (MMR) modulation (Derived, Medium; PMID: 38773623, PMID: 37794706). While Agrobacterium remains the dominant delivery vehicle, the transition toward DNA-free RNP and viral replicon systems is crucial for streamlining regulatory approvals and ensuring the lack of foreign DNA integration (Derived, Medium; PMID: 33898980, PMID: 39943039).

How do secondary structure engineering and 3' motif protection impact the processivity of reverse transcriptase in prime editing protocols?

What specific epigenetic and chromatin features are integrated into AI models like ePRIDICT to improve the prediction of prime editing outcomes?

Compare the regulatory requirements for SDN-1 and SDN-2 gene-edited crops across major rice-growing nations based on the cited literature.

Unverified Citations

To maintain the highest standards of accuracy and transparency, every citation undergoes three independent verification checks to confirm it directly supports the associated claim. The references below did not satisfy all verification stages. While some may still be relevant to the broader topic, we only retain citations that can be confidently validated as direct supporting evidence.

• PMID:33898980 — ** epegRNA: 3′ extensions are stabilized with viral-derived structured RNA motifs like evopreQ1 or mpknot to pr...*
  Failed: entities,conclusion — The paper provides a general review of RNP delivery but does not explicitly mention epegRNA stabilization motifs like evopreQ1 or mpknot.
• PMID:31694550 — ** Particle Bombardment (Biolistics): Preferred for recalcitrant monocots like wheat and sugarcane, often involving...*
  Failed: conclusion — The paper describes an in silico gRNA design tool (WheatCRISPR) but does not discuss experimental results using particle bombardment or HDR donor template co-delivery.
• PMID:39123212 — ** Ribonucleoprotein (RNP) Delivery: Preassembled Cas-gRNA complexes are delivered into protoplasts or zygotes usin...*
  Failed: conclusion — The paper focuses on germline-specific expression using Agrobacterium-mediated floral dip in Arabidopsis, not RNP delivery via PEG or biolistics.

AI models like ePRIDICT and PrimeNet integrate specific epigenetic and chromatin features—including DNA accessibility, methylation patterns, and histone modifications—to quantify how local genomic environments influence the activity of prime editors (Direct, High; PMID: 38907037, PMID: 40745000) «✓ PMID:38907037» «✓ PMID:40745000». By accounting for these non-sequence factors, these models improve the accuracy of prime-editing guide RNA (pegRNA) efficiency predictions across diverse cell types and tissue contexts (Direct, High; PMID: 38907037, DOI: 10.3389/fpls.2026.1804263).

Chromatin Accessibility and Openness

The state of chromatin packaging significantly impacts the ability of the prime editor complex to access target DNA. Features integrated to capture this include:
* ATAC-seq and DNAse-seq Data: ePRIDICT utilizes markers for open chromatin, although initial studies noted that while a weak correlation exists between these markers and editing efficiency, they are not always sufficient to explain low performance in isolation (Direct, High; PMID: 36646933) «✓ PMID:38907037» «✓ PMID:36646933» «✓ DOI:10.3389/fpls.2026.1804263».
* FAIRE-Seq: Related tools like CRISPR-Cereal also incorporate Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE-Seq) to map open chromatin regions specifically for cereal crops like wheat and rice (Direct, High; PMID: 34310056) «✓ PMID:34310056».
* Heterochromatin vs. Active Transcription: ePRIDICT accounts for whether a target site resides within transcriptionally active regions or densely packed heterochromatin, which typically restricts nuclease accessibility (Direct, Medium; PMID: 40745000) «✓ PMID:40745000».

Histone Modifications and Marks

ePRIDICT and its preceding datasets analyzed several key histone modifications to refine predictions of endogenous editing rates:
* Active Promoter and Enhancer Marks: Integration of H3K4me3 (associated with active promoters) and H3K36me3 (associated with actively transcribed gene bodies) (Direct, High; PMID: 36646933) «✓ PMID:36646933».
* Repressive Marks: Inclusion of H3K27me3 and H3K9me3, which are typically indicative of silenced or heterochromatic regions (Direct, High; PMID: 36646933) «✓ PMID:36646933».

DNA Methylation and Other Epigenetic Factors

While ePRIDICT focuses heavily on the chromatin landscape, related advanced models have further expanded the epigenetic feature set:
* DNA Methylation: The PrimeNet model specifically integrates DNA methylation data as an epigenetic factor to enhance generalizability across different genomic contexts (Direct, High; PMID: 40745000, DOI: 10.3389/fpls.2026.1804263).
* Whole-Genome Bisulfite Sequencing (WGBS) «✓ PMID:40745000» «✓ DOI:10.3389/fpls.2026.1804263»: This feature is used in crop-specific design tools to determine the precise methylation level at target sites, which can interfere with Cas-protein binding (Direct, High; PMID: 34310056) «✓ PMID:34310056».

Mismatch Repair (MMR) Environment

Although not a physical chromatin feature, the cellular MMR status is a critical biological context integrated into PRIDICT2.0:
* The model is trained on datasets from both MMR-proficient (e.g., K562, U2OS) and MMR-deficient (e.g., HEK293T) cell lines to predict how different cellular repair environments will handle newly synthesized DNA flaps (Direct, High; PMID: 38907037, PMID: 36646933) «✓ PMID:38907037» «✓ PMID:36646933».

How does the PRIDICT2.0 model differentiate between mismatch repair-deficient and -proficient cell lines in its efficiency predictions?

What is the specific contribution of the 'baseafter_A' feature to prime editing efficiency in mouse hepatocytes compared to human HEK293T cells?

How does DNA methylation impact the predictive performance of the PrimeNet model relative to traditional sequence-only predictors?

The 'baseafter_A' feature (defined as an adenine at the position immediately following the edit) is a critical determinant of prime editing efficiency that exhibits a high positive contribution in mouse hepatocytes but has negligible impact in human HEK293T cells (Direct, High; PMID: 36646933). This discrepancy is primarily attributed to the differences in mismatch repair (MMR) proficiency between the two cell types (Direct, High; PMID: 36646933).

Contribution in Mouse Hepatocytes

In primary cells such as mouse hepatocytes, which possess functional mismatch repair pathways, the 'baseafter_A' feature is one of the most influential sequence context factors:
* Positive Impact: Shapley Additive exPlanations (SHAP) analysis reveals that 'baseafter_A' makes a major contribution to increased editing rates in vivo within primary hepatocytes (Direct, High; PMID: 36646933).
* Consistency with Other MMR-Proficient Cells: This high contribution is mirrored in other MMR-proficient human cell lines, such as K562 and U2OS (Direct, High; PMID: 36646933).
* Biological Correlation: Editing efficiencies in mouse hepatocytes correlate more closely with those in MMR-proficient cell lines (R = 0.80) than with HEK293T cells, largely due to these repair-associated sequence preferences (Direct, High; PMID: 36646933).

Contribution in Human HEK293T Cells

In human HEK293T cells, the influence of this specific nucleotide position is markedly different:
* Minimal Influence: The 'baseafter_A' feature does not provide a significant contribution to the prediction of prime editing efficiency in HEK293T cells (Direct, High; PMID: 36646933).
* MMR Deficiency: Because HEK293T cells are MMR-deficient, they do not utilize the same cellular repair determinants that favor specific nucleotides at the edit-adjacent position (Direct, High; PMID: 36646933).

Role of Mismatch Repair (MMR) Proficiency

The differential weighting of the 'baseafter_A' feature across models (e.g., PRIDICT) highlights it as a biomarker for MMR-dependent editing outcomes:
* Mechanism: In MMR-proficient environments (like hepatocytes), cellular machinery may use specific sequence motifs surrounding the mismatch (the "baseafter" position) to resolve the heteroduplex in favor of the edited strand (Direct, Medium; PMID: 36646933).
* Experimental Validation: When MMR is inhibited in proficient cells (e.g., by expressing a dominant-negative MLH1 variant), the contribution of 'baseafter_A' is significantly reduced, making the editing patterns more similar to those observed in HEK293T cells (Direct, High; PMID: 36646933).
* Model Optimization: Models like PRIDICT2.0 integrate these findings to adjust efficiency scores based on the specific repair status of the target tissue, such as distinguishing between primary cells in vivo and standard laboratory cell lines (Direct, High; PMID: 38907037).

How does the 'edited_GC_content' feature vary in importance between MMR-proficient and MMR-deficient cells?

What specific mutations were used to create the dominant-negative MLH1 variant for MMR inhibition studies?

How do models like PRIDICT2.0 utilize 'baseafter_A' to improve in vivo prime editing predictions in primary cells?

Unverified Citations

To maintain the highest standards of accuracy and transparency, every citation undergoes three independent verification checks to confirm it directly supports the associated claim. The references below did not satisfy all verification stages. While some may still be relevant to the broader topic, we only retain citations that can be confidently validated as direct supporting evidence.

• PMID:40745000 — ** Mechanism: In MMR-proficient environments (like hepatocytes), cellular machinery may use specific sequence motif...*
  Failed: conclusion — This paper is a general review of CRISPR and AI that mentions MMR and hepatocytes broadly but does not contain the specific 'baseafter' sequence motif mechanism.
• PMID:36646933 — 0 integrate these findings to adjust efficiency scores based on the specific repair status of the target tissue, such ...
  Failed: conclusion — Paper 1 describes the original PRIDICT model; the claim refers to PRIDICT2.0 which is the subject of Paper 2.

The research landscape for precise plant genome engineering has evolved from basic CRISPR-Cas9-mediated gene disruption to highly sophisticated, AI-guided systems capable of installing predictable, heritable agronomic traits. This synthesis charts the scientific trajectory of base and prime editing, primarily focusing on the development of herbicide tolerance in staple crops.

1. Phases of Evidence Evolution

The evolution of plant genome editing can be categorized into three distinct phases: Initial Adaptation, Systematic Optimization, and Integrated Precision.

Initial Adaptation (2018–2020)
This early phase focused on transferring base editing (BE) and prime editing (PE) technologies from mammalian systems to crops like rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). Research during this period established the feasibility of adenosine base editors (ABEs) and cytosine base editors (CBEs) for installing point mutations in genes such as ALS and ACC (Tier 1, High; PMID: 29807545, PMID: 32096325). Prime editing was introduced as a "search-and-replace" tool, though initial efficiencies in plants were markedly lower than in animal cells, ranging from 0.26% to 2.0% in early rice trials (Tier 1, High; PMID: 32415890).

Systematic Optimization (2021–2023)
Transitioning from feasibility to efficiency, this phase saw the development of engineered plant prime editors (ePPEs) and optimized architectures like PEmax. Key innovations included the fusion of viral nucleocapsid (NC) proteins to serve as RNA chaperones and the removal of the RNase H domain from reverse transcriptase (RT) to stabilize the RNA-DNA heteroduplex (Tier 1, High; PMID: 37794706, PMID: 36883004). Researchers established modular assembly protocols to enable multiplexing, successfully targeting up to four agronomically important genes simultaneously (Tier 1, High; PMID: 37897041).

Integrated Precision (2024–2026)
The emerging landscape is defined by the integration of artificial intelligence (AI) and complex chromosomal engineering. AI models such as PRIDICT2.0 and ePRIDICT now quantify how local chromatin environments—including DNA accessibility and methylation—impact editing rates (Tier 1, High; PMID: 38907037, PMID: 40745000). This phase also expanded the scope of editing to include large DNA sequence integration and heritable chromosomal rearrangements to break genetic linkages in perennial and semi-perennial crops (Tier 1, High; DOI: 10.3389/fpls.2026.1804263).

2. Network Structure and Relationships

The connectivity of the research corpus reveals a highly integrated network centered on technological efficiency and regulatory safety.

• Network Density and Hubs: The research exhibits high density around the Oryza sativa model, which serves as the primary hub for testing new tools. The ALS and EPSPS genes are critical nodes within this network, as they provide easily selectable phenotypes (herbicide resistance) for validating new editing architectures (Tier 1, High; PMID: 39397365, PMID: 41751548).
• Bridges to Translation: AI-driven design tools (e.g., DeepPE, Easy-Prime) act as essential bridges, translating high-throughput experimental data into predictive design standards that reduce trial-and-error in pegRNA development (Tier 2, High; PMID: 40745000).
• Cross-Domain Integration: Evidence maturity is signaled by the convergence of molecular biology with field trial standards. Protocols have shifted from laboratory-scale protoplast assays to multi-location field trials evaluating "substantial equivalence" and agronomic stability in elite cultivars (Tier 2, High; PMID: 37213044).

3. Mechanisms → Therapies → Outcomes

The progression from biochemical mechanisms to operational outcomes is well-documented across the provided evidence.

• Mechanistic Insights: Base editing relies on deaminases fused to nCas9(D10A) to mediate transition mutations without double-strand breaks (DSBs) (Tier 1, High; PMID: 41096720). Prime editing employs a Cas9 nickase fused to RT, guided by a pegRNA, to synthesize new DNA strands (Tier 1, High; PMID: 37794706).
• Therapeutic Interventions (Genetic "Therapies"): These mechanisms are applied to install resistance-conferring substitutions like T173I/P177S in EPSPS for glyphosate tolerance or W548L in ALS for bispyribac-sodium resistance (Tier 1, High; PMID: 39943039, PMID: 36873055).
• Quantitative Outcomes: Optimization has led to dramatic performance gains. For example, the PE5max system achieved 88.5% editing efficiency in generating disease-resistant xa5 alleles in rice (Tier 2, High; PMID: 41751548). In field trials, prime-edited rice lines exhibited up to 25% higher yields under heat stress through the precise insertion of heat-shock elements into CWIN promoters (Tier 2, High; DOI: 10.3389/fpls.2026.1804263).

4. Biases and Reliability

The reliability of the biological conclusions is subject to several documented patterns and biases.

• Genotype and Recency Effects: A significant "recency bias" exists toward high-efficiency reports in japonica rice, which may not immediately translate to more recalcitrant indica varieties or dicot crops like soybean, where heritable PE remains challenging (Tier 1, High; PMID: 41345100, DOI: 10.3389/fpls.2026.1804263).
• Replication Patterns: Strategies emphasize that nicking the non-editing strand is a highly replicated method for boosting efficiency, yet results remain locus-dependent, necessitating site-specific optimization (Tier 1, High; PMID: 41345100).
• Off-Target Profiling: While in silico tools predict high specificity, whole-genome sequencing (WGS) has revealed that CBEs, but not ABEs, can induce substantial sgRNA-independent off-target mutations (Tier 1, High; PMID: 34042262). This bias in byproduct formation necessitates the use of high-fidelity Cas variants or specialized profiling assays like TAPE-seq to ensure translational readiness (Tier 2, High; PMID: 36581624, PMID: 36997524).

5. Significance Assessment

This landscape matters because it demonstrates the shift from random mutagenesis to "breeding by design." The convergence of AI-optimized design (Tier 1, PMID: 38907037) and DNA-free delivery methods, such as RNP complexes (Tier 1, PMID: 33898980), provides a pathway for developing transgene-free, climate-resilient staple crops that can be commercialized under streamlined regulatory frameworks (Tier 2, Medium; PMID: 38259920, PMID: 37213044).

Unverified Citations

To maintain the highest standards of accuracy and transparency, every citation undergoes three independent verification checks to confirm it directly supports the associated claim. The references below did not satisfy all verification stages. While some may still be relevant to the broader topic, we only retain citations that can be confidently validated as direct supporting evidence.

• PMID:38259920 — , PrimeRoot) and heritable chromosomal rearrangements to break genetic linkages in perennial and semi-perennial crops
  Failed: conclusion — The paper lists PrimeRoot in its citations and references but provides no specific discussion or findings regarding heritable chromosomal rearrangements or genetic linkages in perennial crops.
• PMID:37794706 — ** Replication Patterns: The "double surrogate" and "FLICK-PE" strategies emphasize that nicking the non-editing st...*
  Failed: entities,conclusion — The paper does not contain any mention of 'double surrogate' or 'FLICK-PE' strategies nor the specific mechanism of flanking nicks.