PDF(Pubmed)
Abstract：
Pectobacterium spp. are important bacterial pathogens that cause soft rot symptoms in various crops. However, their mechanism of pathogenicity requires clarity to help control their infections. Here, genome-wide association studies (GWAS) were conducted by integrating genomic data and measurements of two phenotypes (virulence and cellulase activity) for 120 various Pectobacterium strains in order to identify the genetic basis of their pathogenicity. An artificial intelligence-based software program was developed to automatically measure lesion areas on Chinese cabbage, thereby facilitating accurate and rapid data collection for virulence phenotypes for use in GWAS analysis. The analysis discovered 428 and 158 loci significantly associated with Pectobacterium virulence (lesion area) and cellulase activity, respectively. In addition, 1,229 and 586 epistasis loci pairs were identified for the virulence and cellulase activity phenotypes, respectively. Among them, the AraC transcriptional regulator exerted epistasis effects with another three nutrient transport-related genes in pairs contributing to the virulence phenotype, and their epistatic effects were experimentally confirmed for one pair with knockout mutants of each single gene and double gene. This study consequently provides valuable insights into the genetic mechanism underlying Pectobacterium spp. pathogenicity. IMPORTANCE Plant diseases and pests are responsible for the loss of up to 40% of food crops, and annual economic losses caused by plant diseases reach more than $220 billion. Fighting against plant diseases requires an understanding of the pathogenic mechanisms of pathogens. This study adopted an advanced approach using population genomics integrated with virulence-related phenotype data to investigate the genetic basis of Pectobacterium spp., which causes serious crop losses worldwide. An automated software program based on artificial intelligence was developed to measure the virulence phenotype (lesion area), which greatly facilitated this research. The analysis predicted key genomic loci that were highly associated with virulence phenotypes, exhibited epistasis effects, and were further confirmed as critical for virulence with mutant gene deletion experiments. The present study provides new insights into the genetic determinants associated with Pectobacterium pathogenicity and provides a valuable new software resource that can be adapted to improve plant infection measurements.
摘要：
烟杆菌属。是在各种作物中引起软腐病症状的重要细菌病原体。然而,他们的致病机制需要明确，以帮助控制他们的感染。这里,全基因组关联研究(GWAS)是通过整合基因组数据和两种表型(毒力和纤维素酶活性)的测量来进行的,以确定其致病性的遗传基础。开发了基于人工智能的软件程序来自动测量大白菜上的病变面积，从而促进准确和快速的数据收集毒力表型用于GWAS分析。分析发现428个和158个基因座与弯曲杆菌毒力（病变面积）和纤维素酶活性显着相关，分别。此外,鉴定了1,229个和586个上位位点对的毒力和纤维素酶活性表型，分别。其中,AraC转录调节因子与另外三个与营养转运相关的基因成对产生毒力表型，实验证实了它们的上位效应，其中一对具有每个单基因和双基因的敲除突变体。因此，这项研究为感染杆菌属的遗传机制提供了有价值的见解。致病性。重要性植物病虫害是造成多达40％粮食作物损失的原因，每年因植物病害造成的经济损失达2200多亿美元。与植物病害作斗争需要了解病原体的致病机制。本研究采用了一种先进的方法，将群体基因组学与毒力相关的表型数据结合在一起，以研究鸡杆菌属的遗传基础。,这在全球范围内造成了严重的作物损失。开发了基于人工智能的自动化软件程序来测量毒力表型（病变面积），这极大地促进了这项研究。该分析预测了与毒力表型高度相关的关键基因组基因座，表现出上位效应，并进一步证实突变基因缺失实验对毒力至关重要。本研究提供了与淋球菌致病性相关的遗传决定因素的新见解，并提供了一种有价值的新软件资源，可用于改善植物感染测量。
