Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (6): 1149-1158.doi: 10.3864/j.issn.0578-1752.2022.06.008

• PLANT PROTECTION • Previous Articles     Next Articles

Widely Targeted Metabolomics Analysis of the Effects of Myzus persicae Feeding on Prunus persica Secondary Metabolites

YAN LeLe(),BU LuLu,NIU Liang,ZENG WenFang,LU ZhenHua,CUI GuoChao,MIAO YuLe,PAN Lei(),WANG ZhiQiang()   

  1. Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009
  • Received:2021-08-13 Accepted:2021-09-17 Online:2022-03-16 Published:2022-03-25
  • Contact: Lei PAN,ZhiQiang WANG E-mail:275101462@qq.com;panlei@caas.cn;wangzhiqiang@caas.cn

RichHTML

37

PDF

215

Abstract:

【Objective】The objective of this study is to clarify the underlying biochemical mechanisms related to the resistance and susceptibility of peach towards Myzus persicae, and to identify the key secondary metabolites of peach responding to the M. persicae infection.【Method】New shoots of resistant (‘96-5-1’ (9651), ZaoYouTao (ZYT)) and susceptible (Zhong You 13 (CN13), Zhong Nong Jin Hui (ZNJH)) peach trees were inoculated with M. persicae for 3 days and used for secondary metabolite extraction and UPLC-MS/MS analysis. Differentially altered metabolites (DAMs) were screened with |log2 fold change|≥1, P-value≤0.01 as threshold. The VIP value of the OPLS-DA model was used to perform differences between resistant and susceptible peach.【Result】To illustrate the biochemical mechanisms of M. persicae resistance in peach, aphid-resistant/aphid-susceptible peach varieties infested with M. persicae for 3 days. A total of 528 metabolites were identified in the treated samples through widely targeted metabolomics analysis. Using principal component analysis (PCA), hierarchical cluster analysis (HCA) and Venn diagram analysis, it was found that 7 DAMs were identified from the susceptible variety CN13, and 2 of them were significantly decreased, 5 of them were significantly increased. 7 DAMs were identified from the susceptible variety ZNJH, and 3 of them were significantly decreased, 4 of them were significantly increased. 33 DAMs were identified from the resistant variety ‘96-5-1’, and 1 of them was significantly decreased, 32 of them were significantly increased. 55 DAMs were identified from the resistant variety ZYT, and 12 of them were significantly decreased, 43 of them were significantly increased. The majority of the DAMs were identified from the two resistant varieties, and the overall magnitude of change was greater in the resistant varieties than that in the susceptible varieties. Finally, 15 secondary metabolites (6 amino acids and their derivatives, 5 phenolic acids, 3 nucleotides and their derivatives, and 1 organic acid) were considered to be involved in M. persicae resistance of ‘Shou xing tao’.【Conclusion】The significantly up-regulated secondary metabolites obtained in M. persicae-resistant peach varieties were mainly involved in the response to M. persicae feeding. The regulation of these secondary metabolites (amino acids and their derivatives, phenolic acids, nucleotides and their derivatives, organic acid) is the important mechanism of defense reaction to M. persicae.

Key words: peach, green peach aphid (Myzus persicae), resistance, metabolomics, secondary metabolite, UPLC-MS/MS

Table 1

Classification of the 528 metabolites detected in peach shoots before and after M. persicae feeding"

序号
Code		 代谢物种类
Species of metabolites		 数目
Number
1 黄酮类Flavonoid 129
2 酚酸类Phenolic acids 88
3 脂质Lipid 65
4 氨基酸及其衍生物Amino acids and their derivatives 57
5 有机酸Organic acid 49
6 核苷酸及其衍生物Nucleotides and their derivatives 38
7 生物碱Alkaloid 20
8 木脂素和香豆素Lignin and coumarin 14
9 萜类Terpene 12
10 鞣质Tannin 3
11 其他类Others 53

Fig. 1

Principal component analysis (PCA) of metabolite profiles before and after M. persicae treatment of different varieties Each point in the figure represents one sample and the sample in the same treatment using the same color. There were four biological repeats for each M. persicae treatment. For samples with poor repeatability, one sample point can be removed"

Fig. 2

Hierarchical cluster analysis of metabolite profiles before and after M. persicae treatment of different varieties Heat map of secondary metabolites detected in the total sample. Red indicates high abundance and green indicates low abundance"

Fig. 3

The numbers of differential altered metabolites before and after M. persicae treatment of different varieties"

Fig. 4

Volcano plot of significantly changed metabolites before and after M. persicae treatment of susceptible varieties ZNJH and CN13 One point in the figure represents one metabolite, the red and green dots correspond to up-regulated and down-regulated metabolites, respectively. The gray part is the metabolite that can be detected but the content change is not significant. The same as Fig. 5"

Fig. 5

Volcano plot of significantly changed metabolites before and after M. persicae treatment of resistant varieties ‘96-5-1’ and ZYT"

Fig. 6

Differentially altered metabolites of four varieties exposed to M. persicae feeding for 3 days"

[1] 牛良. 寿星桃抗蚜性鉴定及分子机制解析[D]. 武汉: 华中农业大学, 2019.
NIU L. Identification of resistance to green peach aphids of Shouxing peach and its molecular mechanism[D]. Wuhan: Huazhong Agricultural University, 2019. (in Chinese)
[2] CUTLER G C, RAMANAIDU K, ASTATKIE T, ISMAN M B. Green peach aphid, Myzus persicae (Hemiptera: Aphididae), reproduction during exposure to sublethal concentrations of imidacloprid and azadirachtin. Pest Management Science, 2009,65(2):205-209.
doi: 10.1002/ps.v65:2
[3] MASSONIÉ G, MAISON P, MONET R, GRASSELLY C. Résistance au puceron vert du pêcher, Myzus persicae Sulzer (Homoptera Aphididae) chez Prunus persica (L.) Batsch et d’autres espèces de Prunus. Agronomie, 1982,2(1):63-70.
doi: 10.1051/agro:19820109
[4] MONET R, MASSONIÉ G. Déterminisme génétique de la résistance au puceron vert (Myzus persicae) chez le pêcher. Résultats complémentaires. Agronomie, 1994,14(3):177-182.
doi: 10.1051/agro:19940304
[5] SAUGE M H, KERVELLA J, PASCAL T. Settling behaviour and reproductive potential of the green peach aphid Myzus persicae on peach varieties and a related wild Prunus. Entomologia Experimentalis et Applicata, 1998,89(3):233-242.
doi: 10.1046/j.1570-7458.1998.00404.x
[6] SAUGE M H, LACROZE J P, POëSSEL J L, PASCAL T, KERVELLA J. Induced resistance by Myzus persicae in the peach cultivar ‘Rubira’. Entomologia Experimentalis et Applicata, 2002,102(1):29-37.
doi: 10.1046/j.1570-7458.2002.00922.x
[7] SAUGE M H, MUS F, LACROZE J P, PASCAL T, KERVELLA J, POËSSEL J L. Genotypic variation in induced resistance and induced susceptibility in the peach - Myzus persicae aphid system. Oikos, 2006,113(2):305-313.
doi: 10.1111/j.2006.0030-1299.14250.x
[8] 王力荣, 朱更瑞, 方伟超, 左覃元, 韩立新. 桃种质资源对桃蚜的抗性评价. 果树学报, 2001,18(3):145-147.
WANG L R, ZHU G R, FANG W C, ZUO Q Y, HAN L X. Study on the resistance to peach aphid of peach germplasm. Journal of Fruit Science, 2001,18(3):145-147. (in Chinese)
[9] 牛良, 鲁振华, 曾文芳, 崔国朝, 潘磊, 徐强, 李国怀, 王志强. ‘粉寿星’对桃绿蚜抗性的遗传分析. 果树学报, 2016,33(5):578-584.
NIU L, LU Z H, ZENG W F, CUI G C, PAN L, XU Q, LI G H, WANG Z Q. Inheritance analysis of resistance to green peach aphids (Myzus persicae Sülzer) for peach cultivar ‘Fen Shouxing’ (Prunus persica var. densa). Journal of Fruit Science, 2016,33(5):578-584. (in Chinese)
[10] NIU L, PAN L, ZENG W F, LU Z H, CUI G C, FAN M L, XU Q, WANG Z Q, LI G H. Dynamic transcriptomes of resistant and susceptible peach lines after infestation by green peach aphids (Myzus persicae Sülzer) reveal defence responses controlled by the Rm3 locus. BMC Genomics, 2018,19:846.
doi: 10.1186/s12864-018-5215-7
[11] 张南南, 鲁振华, 崔国朝, 潘磊, 曾文芳, 牛良, 王志强. 基于SNP标记桃抗蚜性状的基因定位. 中国农业科学, 2017,50(23):4613-4621.
ZHANG N N, LU Z H, CUI G C, PAN L, ZENG W F, NIU L, WANG Z Q. Gene mapping of aphid-resistant for peach using SNP markers. Scientia Agricultura Sinica, 2017,50(23):4613-4621. (in Chinese)
[12] POËSSEL J L, SAUGE M H, STAUDT M, DUFOUR C, DEBORDE C, RAHBÉ Y, JACKSON B, RENAUD C, MAUCOURT M, CORRE M N, EL-AOUNI H, LACROZE J P, MOING A. PR-Proteins and Induced Resistance Against Pathogens and Insects. Neuchâtel, Switzerland, 2011.
[13] NAWROT J, HARMATHA J. Phytochemical feeding deterrents for stored product insect pests. Phytochemistry Reviews, 2012,11(4):543-566.
doi: 10.1007/s11101-013-9273-9
[14] KANDA D, KAUR S, KOUL O. A comparative study of monoterpenoids and phenylpropanoids from essential oils against stored grain insects: Acute toxins or feeding deterrents. Journal of Pest Science, 2017,90:531-545.
doi: 10.1007/s10340-016-0800-5
[15] APPLEBAUM S W, MARCO S, BIRK Y. Saponins as possible factors of resistance of legume seeds to the attack of insects. Journal of Agricultural and Food Chemistry, 1969,17(3):618-622.
doi: 10.1021/jf60163a020
[16] HUSSAIN M, DEBNATH B, QASIM M, BAMISILE B S, ISLAM W, HAMEED M S, WANG L, QIU D. Role of saponins in plant defense against specialist herbivores. Molecules, 2019,24(11):2067.
doi: 10.3390/molecules24112067
[17] DÍAZ A, HERFINDAL L, RATHE B A, SLETTA K Y, VEDELER A, HAAVIK S, FOSSEN T. Cytotoxic saponins and other natural products from flowering tops of Narthecium ossifragum L. Phytochemistry, 2019,164:67-77.
doi: 10.1016/j.phytochem.2019.04.014
[18] CHEN W, GONG L, GUO Z, WANG W S, ZHANG H Y, LIU X Q, YU S B, XIONG L Z, LUO J. A novel integrated method for large-scale detection, identification, and quantification of widely targeted metabolites: Application in the study of rice metabolomics. Molecular Plant, 2013,6(6):1769-1780.
doi: 10.1093/mp/sst080
[19] FRAGA C G, CLOWERS B H, MOORE R J, ZINK E M. Signature-discovery approach for sample matching of a nerve-agent precursor using liquid chromatography-mass spectrometry, XCMS, and chemometrics. Analytical Chemistry, 2010,82(10):4165-4173.
doi: 10.1021/ac1003568
[20] CHEN Y H, ZHANG R P, SONG Y M, HE J M, SUN J H, BAI J F, AN Z L, DONG L J, ZHAN Q M, ABLIZ Z. RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: Finding potential biomarkers for breast cancer. Analyst, 2009,134(10):2003-2011.
doi: 10.1039/b907243h
[21] CONRATH U, BECKERS G, FLORS V, GARCÍA-AGUSTÍN P, JAKAB G, MAUCH F, NEWMAN M A, PIETERSE C M J, POINSSOT B, POZO M J, et al. Priming: Getting ready for battle. Molecular Plant-Microbe Interactions, 2006,19(10):1062-1071.
doi: 10.1094/MPMI-19-1062
[22] BRUCE T J, PICKETT J A. Plant defence signalling induced by biotic attacks. Current Opinion in Plant Biology, 2007,10(4):387-392.
doi: 10.1016/j.pbi.2007.05.002
[23] TON J, D’ALESSANDRO M, JOURDIE V, JAKAB G, KARLEN D, HELD M, MAUCH-MANI B, TURLINGS T. Priming by airborne signals boosts direct and indirect resistance in maize. The Plant Journal, 2007,49(1):16-26.
doi: 10.1111/tpj.2007.49.issue-1
[24] 陈晓亚, 薛红卫. 植物生理与分子生物学. 4版. 北京: 高等教育出版社, 2012: 716-734.
CHEN X Y, XUE H W. Plant Physiology and Molecular Biology. 4th ed. Beijing: Higher Education Press, 2012: 716-734. (in Chinese)
[25] PEDERSEN M W, BARNES D K, SORENSEN E L, GRIFFIN G D, NIELSON M W, HILL R R, FROSHEISER F I, SONODA R M, HANSON C H, HUNT O J, et al. Effects of low and high saponin selection in alfalfa on agronomic and pest resistance traits and the interrelationship of these traits. Crop Science, 1976,16(2):193-199.
doi: 10.2135/cropsci1976.0011183X001600020007x
[26] AHUJA I, KISSEN R, BONES A M. Phytoalexins in defense against pathogens. Trends in Plant Science, 2012,17(2):73-90.
doi: 10.1016/j.tplants.2011.11.002
[27] MOVVA V, PATHIPATI U R. Feeding-induced phenol production in Capsicum annuum L. influences Spodoptera litura F. larval growth and physiology. Archives of Insect Biochemistry and Physiology, 2017,95(1):e21387.
doi: 10.1002/arch.v95.1
[28] BARTH C, JANDER G. Arabidopsis myrosinases TGG1 and TGG2 have redundant function in glucosinolate breakdown and insect defense. The Plant Journal, 2006,46:549-562.
doi: 10.1111/tpj.2006.46.issue-4
[29] BEDNAREK P, PISLEWSKA-BEDNAREK M, SVATOS A, SCHNEIDER B, DOUBSKY J, MANSUROVA M, HUMPHRY M, CONSONNI C, PANSTRUGA R, SANCHEZ-VALLET A, MOLINA A, SCHULZE-LEFERT P. A glucosinolate metabolism pathway in living plant cells mediates broad-spectrum antifungal defense. Science, 2009,323(5910):101-106.
doi: 10.1126/science.1163732
[30] SØNDERBY I E, GEU-FLORES F, HALKIER B A. Biosynthesis of glucosinolates - gene discovery and beyond. Trends in Plant Science, 2010,15(5):283-290.
doi: 10.1016/j.tplants.2010.02.005
[31] ASHIHARA H, CROZIER A. Caffeine: A well known but little mentioned compound in plant science. Trends in Plant Science, 2001,6(9):407-413.
doi: 10.1016/S1360-1385(01)02055-6
[32] SANO H, KIM Y S, CHOI Y E. Like cures like: Caffeine immunizes plants against biotic stresses. Advances in Botanical Research, 2013,68:273-300.
[33] VAN DAMME M, ZEILMAKER T, ELBERSE J, ANDEL A, DE SAIN-VAN DER VELDEN M, VAN DEN ACKERVEKEN G. Downy mildew resistance in Arabidopsis by mutation of HOMOSERINE KINASE. The Plant Cell, 2009,21(7):2179-2189.
doi: 10.1105/tpc.109.066811
[1] ZHANG XiaoLi, TAO Wei, GAO GuoQing, CHEN Lei, GUO Hui, ZHANG Hua, TANG MaoYan, LIANG TianFeng. Effects of Direct Seeding Cultivation Method on Growth Stage, Lodging Resistance and Yield Benefit of Double-Cropping Early Rice [J]. Scientia Agricultura Sinica, 2023, 56(2): 249-263.
[2] TANG YuLin, ZHANG Bo, REN Man, ZHANG RuiXue, QIN JunJie, ZHU Hao, GUO YanSheng. Evaluation of Regulatory Effect of Guiqi Yimu Oral Liquid on Rumen of Postpartum Dairy Cows Based on UPLC-MS/MS Metabolomics Technology [J]. Scientia Agricultura Sinica, 2023, 56(2): 368-378.
[3] LIN XinYing,WANG PengJie,YANG RuXing,ZHENG YuCheng,CHEN XiaoMin,ZHANG Lei,SHAO ShuXian,YE NaiXing. The Albino Mechanism of a New High Theanine Tea Cultivar Fuhuang 1 [J]. Scientia Agricultura Sinica, 2022, 55(9): 1831-1845.
[4] LI QingLin,ZHANG WenTao,XU Hui,SUN JingJing. Metabolites Changes of Cucumber Xylem and Phloem Sap Under Low Phosphorus Stress [J]. Scientia Agricultura Sinica, 2022, 55(8): 1617-1629.
[5] LÜ XinNing,WANG Yue,JIA RunPu,WANG ShengNan,YAO YuXin. Effects of Melatonin Treatment on Quality of Stored Shine Muscat Grapes Under Different Storage Temperatures [J]. Scientia Agricultura Sinica, 2022, 55(7): 1411-1422.
[6] LIU Jiao,LIU Chang,CHEN Jin,WANG MianZhi,XIONG WenGuang,ZENG ZhenLing. Distribution Characteristics of Prophage in Multidrug Resistant Escherichia coli as well as Its Induction and Isolation [J]. Scientia Agricultura Sinica, 2022, 55(7): 1469-1478.
[7] GUO ZeXi,SUN DaYun,QU JunJie,PAN FengYing,LIU LuLu,YIN Ling. The Role of Chalcone Synthase Gene in Grape Resistance to Gray Mold and Downy Mildew [J]. Scientia Agricultura Sinica, 2022, 55(6): 1139-1148.
[8] WANG Kai,ZHANG HaiLiang,DONG YiXin,CHEN ShaoKan,GUO Gang,LIU Lin,WANG YaChun. Definition and Genetic Parameters Estimation for Health Traits by Using on-Farm Management Data in Dairy Cattle [J]. Scientia Agricultura Sinica, 2022, 55(6): 1227-1240.
[9] ZHANG YaLing, GAO Qing, ZHAO Yuhan, LIU Rui, FU Zhongju, LI Xue, SUN Yujia, JIN XueHui. Evaluation of Rice Blast Resistance and Genetic Structure Analysis of Rice Germplasm in Heilongjiang Province [J]. Scientia Agricultura Sinica, 2022, 55(4): 625-640.
[10] WANG MengRui, LIU ShuMei, HOU LiXia, WANG ShiHui, LÜ HongJun, SU XiaoMei. Development of Artificial Inoculation Methodology for Evaluation of Resistance to Fusarium Crown and Root Rot and Screening of Resistance Sources in Tomato [J]. Scientia Agricultura Sinica, 2022, 55(4): 707-718.
[11] PENG JiaKun, DAI WeiDong, YAN YongQuan, ZHANG Yue, CHEN Dan, DONG MingHua, LÜ MeiLing, LIN Zhi. Study on the Chemical Constituents of Yongchun Foshou Oolong Tea Based on Metabolomics [J]. Scientia Agricultura Sinica, 2022, 55(4): 769-784.
[12] XIANG MiaoLian, WU Fan, LI ShuCheng, WANG YinBao, XIAO LiuHua, PENG WenWen, CHEN JinYin, CHEN Ming. Effects of Melatonin Treatment on Resistance to Black Spot and Postharvest Storage Quality of Pear Fruit [J]. Scientia Agricultura Sinica, 2022, 55(4): 785-795.
[13] HU ChaoYue, WANG FengTao, LANG XiaoWei, FENG Jing, LI JunKai, LIN RuiMing, YAO XiaoBo. Resistance Analyses on Wheat Stripe Rust Resistance Genes to the Predominant Races of Puccinia striiformis f. sp. tritici in China [J]. Scientia Agricultura Sinica, 2022, 55(3): 491-502.
[14] TANG ZiYun,HU JianXin,CHEN Jin,LU YiXing,KONG LingLi,DIAO Lu,ZHANG FaFu,XIONG WenGuang,ZENG ZhenLing. Relationship Between Biofilm Formation and Molecular Typing of Staphylococcus aureus from Animal Origin [J]. Scientia Agricultura Sinica, 2022, 55(3): 602-612.
[15] LI ZhiLing,LI XiangJu,CUI HaiLan,YU HaiYan,CHEN JingChao. Development and Application of ELISA Kit for Detection of EPSPS in Eleusine indica [J]. Scientia Agricultura Sinica, 2022, 55(24): 4851-4862.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd