生物技术通报 ›› 2025, Vol. 41 ›› Issue (10): 6-19.doi: 10.13560/j.cnki.biotech.bull.1985.2025-0548

• 作物高光效专题 • 上一篇    下一篇

作物高遗传力光合性状分析与高光效基因挖掘

罗春梅1(), 李艳君1, 陈根云2, 曲明南1,2()   

  1. 1.扬州大学农学院 教育部植物功能基因组学重点实验室 江苏省作物基因组学和分子育种重点实验室,扬州 225009
    2.中国科学院分子植物科学卓越创新中心,上海 200032
  • 收稿日期:2025-05-30 出版日期:2025-10-26 发布日期:2025-10-28
  • 通讯作者: 曲明南,男,博士,教授,研究方向 :植物光合效率调控基因挖掘、系统生物学及水稻碳中和生物育种;E-mail: qmn@yzu.edu.cn
  • 作者简介:罗春梅,女,博士研究生,研究方向 :作物遗传育种;E-mail: 18605861790@163.com
  • 基金资助:
    国家自然科学基金项目(32170245);国家自然科学基金项目(32260447)

Analysis of Photosynthetic Traits of High Heritability in Crops and Mining of High Light-efficiency Regulatory Genes

LUO Chun-mei1(), LI Yan-jun1, CHEN Gen-yun2, QU Ming-nan1,2()   

  1. 1.Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Agricultural College of Yangzhou University, Yangzhou 225009
    2.Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032
  • Received:2025-05-30 Published:2025-10-26 Online:2025-10-28

摘要:

随着全球气候变化和人口增长,提高作物光能利用效率成为保障粮食安全的关键。光合作用是作物产量和生物量积累的核心驱动力,光能转化为化学能的速率受遗传和环境因素共同调控。然而,高光效这一关键农艺性状的遗传解析非常复杂,由于涉及到多基因的精细调控、显著的表型可塑性和传统光合表型精准测量技术的通量低、侵入性强等局限,导致相关基因挖掘进展缓慢。近年来,多组学技术(基因组、转录组、蛋白组、代谢组等)、高通量表型平台(如基于无人机、高光谱成像、激光雷达的非侵入式动态检测)和人工智能(AI)算法(机器学习和深度学习)的融合,为系统解析作物光合作用的复杂调控网络提供了新契机。本文聚焦于作物高光效形成生理与分子机制,阐述了相关优化途径(包括改造光合机构、增强碳同化、削弱光呼吸及优化环境响应等),并结合高通量光合表型组学和数据算法驱动的光合表型遗传力解析,深入探讨了作物高光效基因挖掘的前沿策略、技术突破及未来挑战,旨在为作物光合效率的遗传改良提供理论参考。

关键词: 作物, 高光效基因, 光能利用效率, 高遗传力光合性状

Abstract:

With global climate change and population growth, improving the efficiency of crop light energy utilization has become crucial to ensuring food security. Photosynthesis serves as the core driving force for crop yield and biomass accumulation, with the conversion of light energy into chemical energy regulated by both genetic and environmental factors. However, the genetic dissection of high photosynthetic efficiency, a crucial agronomic trait, is highly complex, involving factors such as fine regulation of multiple genes and significant phenotypic plasticity, as well as the limitations of traditional photosynthetic phenotype measurement techniques, such as low throughput and strong invasiveness, which have led to slow progress in the discovery of related genes. In recent years, the integration of multi-omics technologies (genomics, transcriptomics, proteomics, metabolomics, etc.), high-throughput phenotyping platforms (such as non-invasive dynamic detection based on drones, hyperspectral imaging, and LiDAR), and artificial intelligence (AI) algorithms (machine learning and deep learning) has provided new opportunities for systematically dissecting the complex regulatory network of crop photosynthesis. This article focuses on summarizing the physiological and molecular mechanisms underlying high light- efficiency in crops, elaborating on related optimization approaches (including modification of photosynthetic apparatus, enhancement of carbon assimilation, reduction of photorespiration, and optimization of environmental responses), and, in combination with high-throughput photosynthetic phenomics and data algorithm-driven genetic dissection of photosynthetic phenotypic heritability, delves into the latest frontier strategies, technological breakthroughs, and future challenges in the mining of high-light efficiency genes in crops, aiming to provide theoretical references for the genetic improvement of crop photosynthetic efficiency.

Key words: crops, high light-efficiency genes, light utilization efficiency, high heritability photosynthetic traits