INTRODUCTION The color variations of ornamental flowers are often generated by ion-beam and gamma irradiation mutagenesis. However, mutation rates differ significantly even among cultivars of the same species, resulting in high cost and intensive labor for flower color breeding. OBJECTIVES We aimed to establish a metabolome-based strategy to identify biomarkers and select promising parental lines with high mutation rates using Chrysanthemum as the case study. METHODS The mutation rates associated with flower color were measured in 10 chrysanthemum cultivars with pink, yellow, or white flowers after soft X-ray irradiation at the floret-formation stage. The metabolic profiles of the petals of these cultivars were clarified by widely targeted metabolomics and targeted carotenoid analysis using liquid chromatography-tandem quadrupole mass spectrometry. Metabolome and carotenoid data were subjected to an un-supervised principal component analysis (PCA) and a supervised logistic regression with least absolute shrinkage and selection operator (LASSO). RESULTS The PCA of the metabolic profile data separated chrysanthemum cultivars according to flower color rather than mutation rates. By contrast, logistic regression with LASSO generated a discrimination model to separate cultivars into two groups with high or low mutation rates, and selected 11 metabolites associated with mutation rates that can be biomarkers candidates for selecting parental lines for mutagenesis. CONCLUSION This metabolome-based strategy to identify metabolite markers for mutation rates associated with flower color might be applied to other ornamental flowers to accelerate mutation breeding for generating new cultivars with a wider range of flower colors.

中文翻译：

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基于代谢组的菊花品种判别，以在突变育种中有效产生花朵颜色变化。

引言观赏花的颜色变化通常是由离子束和伽马射线辐射诱变产生的。然而，即使在相同物种的栽培品种中，突变率也存在显着差异，从而导致花色育种的高成本和费力的工作。目的我们旨在建立一个基于代谢组学的策略，以鉴定生物标志物，并以菊花为例，选择具有高突变率的有希望的亲本系。方法在小花形成阶段，用软X射线照射10个菊花的粉色，黄色或白色花朵的品种，测定与花色有关的突变率。通过使用液相色谱-串联四极杆质谱仪进行广泛的目标代谢组学和目标类胡萝卜素分析，可以弄清这些品种花瓣的代谢谱。对代谢组和类胡萝卜素数据进行无监督主成分分析（PCA）和无最小收缩和选择算子（LASSO）的有监督逻辑回归。结果代谢谱数据的PCA是根据花的颜色而不是突变率来分离菊花的。相比之下，使用LASSO进行逻辑回归可生成区分模型，将品种分为突变率高或低的两组，并选择与突变率相关的11种代谢物，这些代谢物可以作为选择育种亲本品系的生物标志物候选物。