Integrated biotechnological and AI innovations for crop improvement

• 成果类型: Review
• 署名作者: Li, Guotian; An, Linna; Yang, Wanneng; Yang, Lei; Wei, Tong; Shi, Jiawei; Wang, Jianglin; Doonan, John H.; Xie, Kabin; Fernie, Alisdair R.; Lagudah, Evans S.; Wing, Rod A.; Gao, Caixia
• 署名单位: Hubei Hongshan Laboratory; Huazhong Agricultural University; University of Washington; University of Washington Seattle; Huazhong Agricultural University; Hubei Hongshan Laboratory; Beijing Genomics Institute (BGI); UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Institute of Biological, Environmental, Rural & Sciences (IBERS); Aberystwyth University; Max Planck Society; Commonwealth Scientific & Industrial Research Organisation (CSIRO); CSIRO Agriculture & Food; King Abdullah University of Science & Technology; University of Arizona; Chinese Academy of Sciences; Institute of Genetics & Developmental Biology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2132
• DOI: 10.1038/s41586-025-09122-8
• 发表日期: 2025-07-24
• 页码: 925-937
• 关键词: de-novo design; protein design; maize; FUTURE; gene; root; annotation; DISCOVERY; phenomics; sequence

摘要

Crops provide food, clothing and other important products for the global population. To meet the demands of a growing population, substantial improvements are required in crop yield, quality and production sustainability. However, these goals are constrained by various environmental factors and limited genetic resources. Overcoming these limitations requires a paradigm shift in crop improvement by fully leveraging natural genetic diversity alongside biotechnological approaches such as genome editing and the heterologous expression of designed proteins, coupled with multimodal data integration. In this Review, we provide an in-depth analysis of integrated uses of omics technologies, genome editing, protein design and high-throughput phenotyping, in crop improvement, supported by artificial intelligence-enabled tools. We discuss the emerging applications and current challenges of these technologies in crop improvement. Finally, we present a perspective on how elite alleles generated through these technologies can be incorporated into the genomes of existing and de novo domesticated crops, aided by a proposed artificial intelligence model. We suggest that integrating these technologies with agricultural practices will lead to a new revolution in crop improvement, contributing to global food security in a sustainable manner.