Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain.
Phytopathology. 2021 Jan;111(1):32-39. doi: 10.1094/PHYTO-08-20-0355-FI. Epub 2020 Dec 11.
The genomics era has revolutionized studies of adaptive evolution by monitoring large numbers of loci throughout the genomes of many individuals. Ideally, the investigation of emergence in plant viruses requires examining the population dynamics of both virus and host, their interactions with each other, with other organisms and the abiotic environment. Genetic mechanisms that affect demographic processes are now being studied with high-throughput technologies, traditional genetics methods, and new computational tools for big-data. In this review, we discuss the utility of these approaches to monitor and detect changes in virus populations within cells and individuals, and over wider areas across species and communities of ecosystems. The advent of genomics in virology has fostered a multidisciplinary approach to tackling disease risk. The ability to make sense of the information now generated in this integrated setting is by far the most substantial obstacle to the ultimate goal of plant virology to minimize the threats to food security posed by disease. To achieve this goal, it is imperative to understand and forecast how populations respond to future changes in complex natural systems.
基因组学时代通过监测许多个体基因组中的大量基因座，彻底改变了适应性进化的研究。理想情况下，对植物病毒出现的研究需要检查病毒和宿主的种群动态，它们之间的相互作用，以及它们与其他生物和非生物环境的相互作用。现在正在使用高通量技术、传统遗传学方法以及用于大数据的新计算工具来研究影响人口动态的遗传机制。在这篇综述中，我们讨论了这些方法在监测和检测细胞和个体内部以及跨越物种和生态系统群落的更大区域内病毒种群变化方面的效用。病毒学中的基因组学的出现促进了一种多学科方法来解决疾病风险。在这种综合环境中生成的信息，目前能够理解是实现植物病毒学的最终目标（即最大程度地降低疾病对食品安全构成的威胁）的最主要障碍。要实现这一目标，就必须了解和预测种群如何应对复杂自然系统中未来的变化。
