Periodic Reporting for period 4 - BREEDIT (A NOVEL BREEDING STRATEGY USING MULTIPLEX GENOME EDITING IN MAIZE)
Reporting period: 2024-03-01 to 2025-08-31
Agriculture faces mounting challenges from climate change and population growth, requiring crops with higher yields and stronger resilience against stresses such as drought, heat, flooding, and frost. Traditional forward genetics in plant breeding—where valuable traits are identified in diverse germplasm and gradually introduced into elite material—has proven effective but time-consuming, often requiring decades to bring new varieties to market. Reverse genetics offers a faster alternative by directly targeting known genes regulating traits of interest. Building on this concept, the BREEDIT project set out to revolutionize reverse genetics by developing a scalable CRISPR/Cas9-based multiplex editing platform to knock out multiple genes simultaneously, thereby accelerating the discovery of genetic combinations underpinning yield and drought tolerance in maize, with applications extendable to many other crops.
Project Context and Approach
The project aimed to overcome two key bottlenecks: (1) the long timelines of conventional breeding and single-gene reverse genetics, and (2) functional redundancy within gene families that masks phenotypic outcomes when only a few genes are targeted. BREEDIT introduced an innovative approach combining multiplexed CRISPR/Cas9 gene editing, large-scale genotyping, phenotyping across developmental stages, and advanced computational analyses to identify causative genes and gene combinations affecting yield-related traits under drought. The project focused on maize as proof of concept but was designed to be adaptable to other major crops such as rice, wheat, and sorghum.
Project Context and Approach
The project aimed to overcome two key bottlenecks: (1) the long timelines of conventional breeding and single-gene reverse genetics, and (2) functional redundancy within gene families that masks phenotypic outcomes when only a few genes are targeted. BREEDIT introduced an innovative approach combining multiplexed CRISPR/Cas9 gene editing, large-scale genotyping, phenotyping across developmental stages, and advanced computational analyses to identify causative genes and gene combinations affecting yield-related traits under drought. The project focused on maize as proof of concept but was designed to be adaptable to other major crops such as rice, wheat, and sorghum.
Implementation and Methods
1. EDITOR lines: Stable maize B104 lines expressing Cas9 were created as a basis for super-transformations. New lines with different editing efficiencies (from saturation to low-order editing) were generated to expand experimental flexibility.
2. Target selection: Based on literature and in-house data, 59 genes linked to plant growth and drought tolerance were chosen. Guide RNAs (gRNAs) targeting coding regions were designed using the SMAP design bioinformatic tool, ensuring efficiency and minimal off-target effects.
3. SCRIPT constructs and transformation: Five constructs containing 12 gRNAs each were cloned, targeting all 60 genes in total. These were introduced into EDITOR lines using Agrobacterium-mediated transformation to produce T0 material expressing Cas9 and gRNAs.
4. Genotyping-by-sequencing pipeline: Multiplex amplicon sequencing coupled with the SMAP haplotype pipeline allowed precise detection of mutations. Out-of-frame indels were prioritized as likely gene knockouts. Genetic outcomes were categorized into null, hemi, and full knockouts, better reflecting dosage effects compared to classical heterozygous/homozygous distinctions.
5. Crossing schemes: Intra- and inter-SCRIPT crosses, as well as selfings, were performed to combine edits and increase knockout frequencies. Transgenerational editing was observed, generating new edits in successive generations.
6. Phenotyping: More than 6,000 seedlings were screened for leaf size and other growth traits, followed by mature-plant phenotyping to confirm causative edits. Traits such as plant architecture, stem thickness, and yield potential were associated with specific edits.
7. Data analysis: Machine learning and statistical approaches enabled phenotype–genotype association in highly diverse populations where each plant carried unique combinations of edits. This allowed the identification of both single-gene effects and synergistic gene interactions.
Results and Exploitation
The project generated a wealth of edited maize populations and established a comprehensive genotype–phenotype dataset. Clear growth-related phenotypes, such as larger leaves, modified architecture, and improved biomass and seed yield potential, were directly linked to edits in specific genes and gene combinations. These findings validate the power of multiplex gene editing in unraveling complex regulatory networks.
Greenhouse trials confirmed the robustness of many edits, but validation under field conditions is essential. Starting in 2025, field trials will test promising gene-edit combinations across three consecutive years and in hybrid maize backgrounds to ensure relevance under real-world agricultural settings. This step will be crucial for transferring BREEDIT outcomes into practical breeding pipelines.
1. EDITOR lines: Stable maize B104 lines expressing Cas9 were created as a basis for super-transformations. New lines with different editing efficiencies (from saturation to low-order editing) were generated to expand experimental flexibility.
2. Target selection: Based on literature and in-house data, 59 genes linked to plant growth and drought tolerance were chosen. Guide RNAs (gRNAs) targeting coding regions were designed using the SMAP design bioinformatic tool, ensuring efficiency and minimal off-target effects.
3. SCRIPT constructs and transformation: Five constructs containing 12 gRNAs each were cloned, targeting all 60 genes in total. These were introduced into EDITOR lines using Agrobacterium-mediated transformation to produce T0 material expressing Cas9 and gRNAs.
4. Genotyping-by-sequencing pipeline: Multiplex amplicon sequencing coupled with the SMAP haplotype pipeline allowed precise detection of mutations. Out-of-frame indels were prioritized as likely gene knockouts. Genetic outcomes were categorized into null, hemi, and full knockouts, better reflecting dosage effects compared to classical heterozygous/homozygous distinctions.
5. Crossing schemes: Intra- and inter-SCRIPT crosses, as well as selfings, were performed to combine edits and increase knockout frequencies. Transgenerational editing was observed, generating new edits in successive generations.
6. Phenotyping: More than 6,000 seedlings were screened for leaf size and other growth traits, followed by mature-plant phenotyping to confirm causative edits. Traits such as plant architecture, stem thickness, and yield potential were associated with specific edits.
7. Data analysis: Machine learning and statistical approaches enabled phenotype–genotype association in highly diverse populations where each plant carried unique combinations of edits. This allowed the identification of both single-gene effects and synergistic gene interactions.
Results and Exploitation
The project generated a wealth of edited maize populations and established a comprehensive genotype–phenotype dataset. Clear growth-related phenotypes, such as larger leaves, modified architecture, and improved biomass and seed yield potential, were directly linked to edits in specific genes and gene combinations. These findings validate the power of multiplex gene editing in unraveling complex regulatory networks.
Greenhouse trials confirmed the robustness of many edits, but validation under field conditions is essential. Starting in 2025, field trials will test promising gene-edit combinations across three consecutive years and in hybrid maize backgrounds to ensure relevance under real-world agricultural settings. This step will be crucial for transferring BREEDIT outcomes into practical breeding pipelines.
BREEDIT successfully bridged the gap between molecular biology and breeding by creating an efficient, scalable platform for multiplex CRISPR/Cas9 gene editing in crops. The project demonstrated that complex traits such as growth and drought tolerance can be dissected and engineered by systematically editing multiple members of gene families and regulatory networks.
Key achievements include:
• Development of EDITOR and SCRIPT systems for simultaneous editing of dozens of genes.
• A robust sequencing and genotyping pipeline that quantifies knockout levels across multiple loci.
• Crossing strategies that allow stacking of edits and ongoing transgenerational gene editing.
• Large-scale phenotyping coupled with machine learning–based analyses to uncover gene–trait relationships and synergistic effects.
• Generation of a unique dataset connecting genetic edits to phenotypic outcomes at unprecedented scale.
By narrowing down from numerous putative regulatory genes to validated gene combinations affecting growth and yield, BREEDIT has drastically shortened the path from discovery to breeding application. The project also pioneered the integration of explainable AI with multiplex genome editing, representing a breakthrough in plant biotechnology.
Ultimately, BREEDIT provides a technological and conceptual foundation for crop improvement under climate stress. Its results can accelerate the development of maize and other crops with higher yield and resilience, while also opening opportunities to target other key agronomic traits such as disease resistance, root development, and nutrient-use efficiency. The project therefore not only advances scientific understanding but also has direct potential to support sustainable agriculture and food security in the face of global challenges.
Key achievements include:
• Development of EDITOR and SCRIPT systems for simultaneous editing of dozens of genes.
• A robust sequencing and genotyping pipeline that quantifies knockout levels across multiple loci.
• Crossing strategies that allow stacking of edits and ongoing transgenerational gene editing.
• Large-scale phenotyping coupled with machine learning–based analyses to uncover gene–trait relationships and synergistic effects.
• Generation of a unique dataset connecting genetic edits to phenotypic outcomes at unprecedented scale.
By narrowing down from numerous putative regulatory genes to validated gene combinations affecting growth and yield, BREEDIT has drastically shortened the path from discovery to breeding application. The project also pioneered the integration of explainable AI with multiplex genome editing, representing a breakthrough in plant biotechnology.
Ultimately, BREEDIT provides a technological and conceptual foundation for crop improvement under climate stress. Its results can accelerate the development of maize and other crops with higher yield and resilience, while also opening opportunities to target other key agronomic traits such as disease resistance, root development, and nutrient-use efficiency. The project therefore not only advances scientific understanding but also has direct potential to support sustainable agriculture and food security in the face of global challenges.