1H NMR analysis of metabolites from leaf tissue of resistant and susceptible oil palm breeding materials against Ganoderma boninense.

IF 3.5 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM Metabolomics Pub Date : 2024-08-02 DOI:10.1007/s11306-024-02160-9
Hernawan Yuli Rahmadi, Muhamad Syukur, Widodo, Willy Bayuardi Suwarno, Sri Wening, Arfan Nazhri Simamora, Syarul Nugroho
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Abstract

Introduction: Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using 1H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations.

Objectives: The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using 1H NMR analysis.

Methods: Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy.

Results: Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma.

Conclusion: A metabolomic study with 1H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR.

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抗性和易感性油棕育种材料叶组织代谢物的 1H NMR 分析。
简介:培育油棕抗骨干灵芝引起的基部茎腐病的品种既具有挑战性,又耗费时间。先进的油棕基因库非常有限,因此假定亲本棕榈经历了基因漂移,失去了抗灵芝的基因。应制定高通量筛选标准。利用 1H 核磁共振(NMR)光谱进行代谢组学分析非常容易,由此产生的代谢物可用作诊断工具,用于检测各种宿主-病原体组合中的疾病:本研究的目的是利用 1H NMR 分析法确定对灵芝和中度抗性 DxP 具有不同抗性水平的 Dura(D)和 Pisifera(P)亲本棕榈的代谢物变化:方法:对七种不同油棕榈树(包括抗性、中度和易感的 Dura(D);中度和易感的 Pisifera(P);抗性 Tenera/Pisifera (T/P)亲本棕榈树;中度抗性 DxP 品种后代)的叶组织进行取样,并使用核磁共振光谱测定其代谢物:结果:共鉴定出 29 种代谢物,大部分代谢物属于单糖、氨基酸和脂肪酸化合物类。PCA、PLS-DA和热图多元分析表明,根据代谢物的不同,抗药性可分为两组。第一组包括抗性 T/P、中度抗性 P、抗性 D 和中度抗性 DxP。路径分析表明,甘氨酸、丝氨酸和苏氨酸代谢以及牛磺酸和低牛磺酸代谢参与了油棕对灵芝的防御机制:利用 1H NMR 进行的代谢组学研究能够描述代谢物的组成,从而区分油棕对骨灵芝引起的基部茎腐病(BSR)的抗性特征。本研究揭示的这些代谢物具有巨大的潜力,可成为培育具有更强抗性的油棕新品种的辅助工具。
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来源期刊
Metabolomics
Metabolomics 医学-内分泌学与代谢
CiteScore
6.60
自引率
2.80%
发文量
84
审稿时长
2 months
期刊介绍: Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.
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