"该系统具有特异性强、功能全面、适应性广和发展潜力大的优势，为基因组精准编辑提供了崭新 的有力工具。"李家洋表示，未来他们将对该系统的供体递送效率和供体制备等技术进行探索和优化， 旨在实现基因组DNA大片段精准插入和编辑，拓展该系统更多应用场景。 中国科学院院士钱前表示，该系统有助于在育种实践中赋能主粮、经济作物的性状改良，加速高 产、抗逆、优质新品种培育，助力破解"抗逆必减产"等瓶颈。该系统的自主知识产权属性，将推动我国 精准编辑领域从"技术引进"向"自主创新"转型，增强在全球种业竞争中的话语权。 北京大学现代农业研究院张华伟研究员团队和中国科学院遗传与发育生物学研究所李家洋院士团队 合作研发出"榫卯"基因编辑系统。相关研究日前发表于国际期刊《分子植物》。 在作物育种领域，实现精准、无瘢痕的DNA片段插入与替换，是突破品种改良瓶颈和保障国家粮 食安全的核心技术。该研究成功开发出一种名为"榫卯连接系统"（MT）的新型基因编辑系统，可实现 靶向DNA高效插入与替换，有望为大片段基因编辑开辟新方法。张华伟表示，该系统的核心设计灵感 源于中国古建筑传统木工中的"榫卯"结构，即通过构建相互匹配的"榫头"与"卯眼"，实 ...

Core Insights - The article discusses the integration of AI technology in agricultural breeding, specifically through the "Fengdeng" project, which aims to enhance crop breeding efficiency and precision using AI models [1][2]. Group 1: AI in Agricultural Breeding - The "Fengdeng" project, launched by a collaborative team including Shanghai Artificial Intelligence Laboratory and other research institutions, introduced the "Fengdeng·Seed Industry Large Model" in April 2024, followed by the "Fengdeng·Gene Scientist" AI tool in July 2024, designed to assist researchers in exploring and validating unknown gene functions [1]. - Traditional breeding methods are time-consuming and heavily reliant on expert experience, often taking years to validate hypotheses with limited success rates [1]. - The AI model is trained on vast datasets to identify relationships between genes and traits, enabling it to predict "gene-trait" associations and design breeding experiments [1][2]. Group 2: Advancements in Breeding Precision - The AI tool allows breeding researchers to combine superior alleles more accurately, addressing both traditional traits like yield and disease resistance, as well as new demands such as nutritional enhancement and flavor improvement [2]. - The "Fengdeng·Gene Scientist" simulates expert reasoning processes, automating the entire research workflow from hypothesis generation to result analysis, thereby enhancing research efficiency [2]. - The project has already identified new gene functions in rice and maize, with predictions aligning closely with field trial results, indicating a high level of accuracy in the AI's capabilities [2]. Group 3: Future Developments - The research team plans to continuously integrate more crop data, environmental data, and breeding knowledge into the system, evolving it into a comprehensive intelligent breeding platform that covers all species and processes [2].

Core Insights - The article emphasizes the importance of ensuring food security and sustainable agricultural development in the face of global population growth, climate change, and decreasing arable land resources [1] Group 1: Technological Innovations in Crop Improvement - A review paper published in Nature discusses the integration of multi-omics, genome editing, protein design, high-throughput phenotyping, and artificial intelligence (AI) in crop genetic improvement [2][3] - Modern omics technologies, such as genomics and metabolomics, provide unprecedented capabilities to analyze crop genetic information, revealing new loci for precise trait improvement [4] - High-throughput phenotyping (HTP) technologies utilize drones and sensors for rapid and accurate assessment of crop traits, effectively linking genotype to phenotype [4] Group 2: Genome Editing and Protein Design - Genome editing technologies, exemplified by CRISPR, enable efficient and precise modifications of crop genomes, significantly shortening breeding cycles and rapidly creating desirable traits [4] - AI-driven protein design technologies are emerging, allowing the creation of novel proteins with specific functions, which can lead to breakthroughs in disease resistance and environmental monitoring [4] Group 3: AI-Assisted Crop Design Framework - The review introduces an "AI-assisted crop design" model that integrates and analyzes multimodal big data from genomics, phenomics, environment, and management practices [19] - Breeders can set specific improvement goals, such as yield enhancement or stress resistance, while AI generates optimized breeding plans through deep learning and knowledge reasoning [19] Group 4: Challenges and Future Directions - The article discusses the challenges and future directions for the application of new technologies, highlighting the need for high-quality, standardized data for training AI models [21] - Regulatory policies for genome-edited crops are evolving towards more scientific and simplified frameworks, creating favorable conditions for the widespread application of new technologies [21]