Characterization of 35 Masson pine (Pinus massoniana) half-sib families from two provinces based on metabolite properties

Abstract

Plant metabolism is an important functional trait, and its metabolites have physiological and ecological functions to adapt to the growth environment. However, the physiological and ecological functions of metabolites from different provinces of the same plant species are still unclear. Therefore, this study aimed to determine whether metabolites from different provinces of Masson pine (Pinus massoniana Lamb.) have the corresponding metabolic traits. The gas chromatography–mass spectrometry technique and metabonomic analysis methods were used to characterize 35 Masson pine half-sib families from two provinces. A total of 116 metabolites were putatively identified in 35 families of Masson pine, among which the average content of organic acids was the highest, followed by saccharides and alcohols, and phosphoric acids. Comparative analysis of metabolite groups showed that organic acids, amines, and others were significantly different between the Masson pine families from Guangxi and Guizhou provinces. Six differential metabolites were found between the provinces from Guizhou and Guangxi, namely caffeic acid, L-ascorbic acid, gentiobiose, xylitol, d-pinitol, and β-sitosterol. The most significantly enriched pathways among differentially expressed metabolites between the two provinces were steroid biosynthesis, phenylpropanoid biosynthesis, glutathione metabolism, pentose and glucuronate interconversions. Overall, the results showed that Masson pine half-sib families from different geographical provinces have different metabolite profiles and their metabolites are affected by geographical provenance and growth environment adaptability. This study revealed that the breeding of Masson pine families from different provinces changed the metabolite profiles, providing a reference for the multipurpose breeding of Masson pine.

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“…Unsupervised principal component analysis (PCA) was performed using prcomp in R version 3.5.1. Data were scaled unit variance before unsupervised PCA ( Ren et al., 2016 ; Quan et al., 2023 ). The hierarchical cluster analysis (HCA) results of the samples and metabolites are presented as heatmaps with dendrograms.…”

“…The hierarchical cluster analysis (HCA) results of the samples and metabolites are presented as heatmaps with dendrograms. The HCA of the accumulation pattern of metabolites among different samples was carried out using the R package ComplexHeatmap version 2.8.0 ( Quan et al., 2023 ).…”