College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China.
Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization (MOA), Guangdong Province Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou 510640, China.
Toxins (Basel). 2020 Apr 14;12(4):254. doi: 10.3390/toxins12040254.
Fusarium wilt caused by f.sp. () is one of the most destructive diseases for banana. For their risk assessment and hazard characterization, it is vital to quickly determine the virulence of isolates. However, this usually takes weeks or months using banana plant assays, which demands a better approach to speed up the process with reliable results. produces various mycotoxins, such as fusaric acid (FSA), beauvericin (BEA), and enniatins (ENs) to facilitate their infection. In this study, we developed a linear regression model to predict virulence using the production levels of the three mycotoxins. We collected data of 40 isolates from 20 vegetative compatibility groups (VCGs), including their mycotoxin profiles (LC-MS) and their plant disease index (PDI) values on Pisang Awak plantlets in greenhouse. A linear regression model was trained from the collected data using FSA, BEA and ENs as predictor variables and PDI values as the response variable. Linearity test statistics showed this model meets all linearity assumptions. We used all data to predict PDI with high fitness of the model (coefficient of determination (R = 0.906) and adjust coefficient (R = 0.898)) indicating a strong predictive power of the model. In summary, we developed a linear regression model useful for the prediction of virulence on banana plants from the quantification of mycotoxins in strains, which will facilitate quick determination of virulence in newly isolated emerging Fusarium wilt of banana epidemics threatening banana plantations worldwide.
尖孢镰刀菌（Fusarium oxysporum）引起的枯萎病是香蕉最具破坏性的病害之一。为了对其进行风险评估和危害特征描述，快速确定分离株的毒力至关重要。然而，使用香蕉植物测定法通常需要数周或数月的时间，这需要一种更好的方法来加快进程并获得可靠的结果。尖孢镰刀菌会产生各种真菌毒素，如镰刀菌酸（FSA）、 beauvericin（BEA）和 enniatins（ENs），以促进其感染。在这项研究中，我们开发了一种线性回归模型，使用三种真菌毒素的产生水平来预测毒力。我们收集了来自 20 个营养体亲和组（VCGs）的 40 个分离株的数据，包括它们的真菌毒素图谱（LC-MS）和在温室中 Pisang Awak 幼苗上的植物病害指数（PDI）值。使用 FSA、BEA 和 ENs 作为预测变量，PDI 值作为响应变量，从收集的数据中训练线性回归模型。线性检验统计数据表明，该模型满足所有线性假设。我们使用所有数据来预测 PDI，模型拟合度高（决定系数（R = 0.906）和调整系数（R = 0.898））表明模型具有很强的预测能力。总之，我们开发了一种线性回归模型，可用于根据菌株中真菌毒素的定量来预测香蕉植株上的毒力，这将有助于快速确定新分离的香蕉枯萎病的毒力，香蕉枯萎病是全球香蕉种植园面临的一种新兴的流行病。
