- Author: JIAO Huijun, RAN Kun, DONG Ran, DONG Xiaochang, GUAN Qiuzhu, WANG Hongwei, WEI Shuwei
- Keywords: Pear; Leaves; Secondary metabolites
- DOI: 10.13925/j.cnki.gsxb.20240071
- Received date: 2024-02-02
- Accepted date: 2024-04-20
- Online date: 2024-06-10
- PDF () Abstract()
Abstract: 【Objective】Plant leaves are rich in secondary metabolites, mainly including phenols, terpenoids, alkaloids and other secondary substances, which have physiological functions in regulating plant growth, biological defense, anti-abiotic stress and so on. Pear is one of the important economic fruits of Rosaceae, and widespread in the world. China is one of the origins of pears, and has a long history of cultivation and rich variety resources. Pyrus pyrifolia, P. ussuriensis, P. sinkiangensis, P. communis and P. bretschnrideri are the main cultivated species of pear in China. Pears contain a wide variety of phenolic compounds, which would be beneficial for human health. Therefore, there have been many researches on the metabolite content of pear fruit. There are also abundant secondary metabolites in pear leaves, which are very important for the growth and development of fruit trees. However, there is a lack of systematic analysis of the metabolites in pear leaves.【Methods】In this study, we collected mature, healthy pear leaves in September, and carried them into lab. The leaves of Fuding Dabai tea rich in secondary metabolites were selected as control, and the differences of metabolites in the mature leaves of five cultivars Kuerle, Chaohong, Nanhong, Yali and Cuiguan were compared and analyzed by metabolomics. PCA analysis, Heatmap analysis, KEGG analysis and K-means analysis were used to analysis differen-tial metabolites.【Results】A total of 9011 metabolites were detected under positive and negative ion mode. 11 747 peaks and 4987 metabolites were identified in the positive ion model, and 11 575 peaks and 4024 metabolites were identified in the negative ion model. The principal component analysis (PCA) showed that the detected intra-species clustering of metabolic species was relatively concentrated, while the interspecific clustering was relatively distant. The metabolites of the leaves of Chaohong were clustered far apart from those of the other four kinds of pear, which indicated the difference of metabolites in the leaves of occidental pear and oriental pear. In the oriental pear varieties, the metabolites of Kuerle fragrant pear were far from those of Yali, Cuiguan and Nanhong, and there were some differences. The number of differential metabolites of Kuerle, Chaohong, Nanhong, Yali, Cuiguan and Fuding Dabai was 1194, 1153, 1176, 1153 and 1164, respectively. There were 456, 468, 469, 481 and 446 kinds of significantly up-regulated differential metabolites in Kuerle, Chaohong, Nanhong, Yali and Cuiguan compared with Fuding Dabai, respectively, and 728, 685, 707, 672 and 718 kinds of significantly downregulated differential metabolites. The number of common differential metabolites was 747, and the specific differential metabolites were 55, 155, 54, 59 and 28, respectively. The heatmap analysis showed that the expression levels of the top 50 differential metabolites were significantly different in the leaves of 5 cultivated pears. Among the top 50 differential metabolites, the number of metabolites in Yali leaves was the largest, with about 24 kinds, mainly including chrysanthemin, genistin, swertiajaponin, quercetin 3- lathyroside, picein, swertiajaponin, 6alpha- Hydroxycastasterone, glycyrrhetinic acid and protobassic acid. The correlation analysis could measure the degree of correlation between different metabolites, and further understand the interrelationship between metabolites in the process of biological state change. Therefore, we used Pearson correlation coefficient to measure the correlation between the top 50 differential metabolites with metabolite expression. It was found that 642 pairs of metabolites were positively correlated, and 633 pairs of metabolites were negatively correlated. We divided all the differential metabolites into 12 subclusters using K-mean analysis, and the results showed that the variation trend of differential metabolites in these 12 subclusters was basically the same. The number of differential metabolites in subclusters 1, 2, 6, 8, 10 and 11 was the most distributed, and the number was 229, 200, 104, 304, 256 and 81, respectively. The variation trend of differential metabolites in cluster 1, 2 and 8 was basically the same. The expression of metabolites in Chaohong leaves was quite different in subgroups 3, 5 and 7, and the contents of metabolites in Nanhong leaves was quite different in subgroup 12. The KEGG analysis were used to analyze the differential enrichment of the metabolic pathways between the leaves of Kuerle, Chaohong, Nanhong, Yali and Cuiguan and the leaves of Fuding Dabai. The 5 comparison groups were enriched in 78 KEGG pathways, there were 76, 71, 73, 70 and 74 KEGG pathways in the 5 cultivars, respectively. The flavone and flavonol biosynthesis (pxb00944), ABC transport (pxb02010) and flavonoid biosynthesis (pxb00941) were the main pathways for the enrichment of differential metabolite, among them, the contents of sorbitol, fructose, mannitol, citric acid, salicylic acid, maleic acid and methylmalonic acid were significantly up-regulated. Additionally, the zeatin synthesis (pxb00908) and monobacterial biosynthesis (pxb00261) were also metabolic pathways in which metabolites were significantly enriched in the leaves of Chaohong.【Conclusion】To sum up, there are obvious differences in kinds and expressions of metabolites in the leaves of different pear cultivars, and this study could provide theoretical basis for the development and utilization of the leaf resources of pear.