Singh Nikhil Kumar, Dutta Anik, Puccetti Guido, Croll Daniel
Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland.
Plant Pathology, Institute of Integrative Biology, ETH Zurich, CH-8092 Zurich, Switzerland.
Comput Struct Biotechnol J. 2020 Dec 29;19:372-383. doi: 10.1016/j.csbj.2020.12.018. eCollection 2021.
Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.
病原体和害虫是全球农业生产力面临的主要威胁之一。几十年来，针对性的抗性育种被用于培育能够抵抗病原体和环境压力同时保持产量的作物品种。然而，病原体抗性的迅速增强对通常长达十年的杂交、选择和田间试验以培育新品种的过程构成了挑战。同样，抗菌化合物也可能因抗性进化而迅速失去效力。在此，我们综述了计算、成像和实验方法正在彻底改变作物病原体损害管理的三个主要领域。通过适用于温室控制条件下以及直接在田间的高通量成像技术，识别和评估植物病害的能力有了显著提高。然而，复杂病害表型的计算机视觉仍需大幅改进。与此同时，类似于高通量药物发现筛选的实验设置使得能够筛选数千种病原体菌株以寻找抗性和其他相关表型特征的变异。共聚焦显微镜和荧光技术能够捕捉病原体基因型的丰富表型信息。通过全基因组关联图谱方法，表型数据有助于揭示与胁迫和毒力相关性状的遗传结构，从而加速抗性育种。最后，对作物和病原体性状变异进行联合大规模筛选，可以深入了解病原体在适应抗性作物品种时如何面临权衡。我们讨论了此类创新方法在育种和病原体筛选中的未来应用如何能够实现更持久的病害控制。
