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Metabolomics

, 15:151 | Cite as

Global metabolite profiles of rice brown planthopper-resistant traits reveal potential secondary metabolites for both constitutive and inducible defenses

  • Umaporn UawisetwathanaEmail author
  • Olivier P. Chevallier
  • Yun Xu
  • Wintai Kamolsukyeunyong
  • Intawat Nookaew
  • Thapakorn Somboon
  • Theerayut Toojinda
  • Apichart Vanavichit
  • Royston Goodacre
  • Christopher T. Elliott
  • Nitsara Karoonuthaisiri
Original Article

Abstract

Introduction

Brown planthopper (BPH) is a phloem feeding insect that causes annual disease outbreaks, called hopper burn in many countries throughout Asia, resulting in severe damage to rice production. Currently, mechanistic understanding of BPH resistance in rice plant is limited, which has caused slow progression on developing effective rice varieties as well as effective farming practices against BPH infestation.

Objective

To reveal rice metabolic responses during 8 days of BPH attack, this study examined polar metabolome extracts of BPH-susceptible (KD) and its BPH-resistant isogenic line (IL308) rice leaves.

Methods

Ultra high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QToF-MS) was combined with multi-block PCA to analyze potential metabolites in response to BPH attack.

Results

This multivariate statistical model revealed different metabolic response patterns between the BPH-susceptible and BPH-resistant varieties during BPH infestation. The metabolite responses of the resistant IL308 variety occurred on Day 1, which was significantly earlier than those of the susceptible KD variety which showed an induced response by Days 4 and 8. BPH infestation caused metabolic perturbations in purine, phenylpropanoid, flavonoid, and terpenoid pathways. While found in both susceptible and resistant rice varieties, schaftoside (1.8 fold), iso-schaftoside (1.7 fold), rhoifolin (3.4 fold) and apigenin 6-C-α-l-arabinoside-8-C-β-l-arabinoside levels (1.6 fold) were significantly increased in the resistant variety by Day 1 post-infestation. 20-hydroxyecdysone acetate (2.5 fold) and dicaffeoylquinic acid (4.7 fold) levels were considerably higher in the resistant rice variety than those in the susceptible variety, both before and after infestation, suggesting that these secondary metabolites play important roles in inducible and constitutive defenses against the BPH infestation.

Conclusions

These potential secondary metabolites will be useful as metabolite markers and/or bioactive compounds for effective and durable approaches to address the BPH problem.

Keywords

LC-HRMS Multi-block principal component analysis Metabolite profiling Brown planthopper resistance Thai Jasmine rice Oryza sativa 

Notes

Acknowledgements

This work was financially supported by Platform Technology Program (P-12-01893, P-16-50339 and P-18-50973), National Center for Genetic Engineering and Biotechnology (BIOTEC, Thailand). The PhD scholarship to Umaporn Uawisetwathana was awarded from Graduate and Professional Development Division, National Science and Technology Development Agency (NSTDA, Thailand).

Author contributions

NK, TT, AV, CE and RG conceived and designed research. WK and UU conducted experiments. UU, OPC contributed analytical tools. UU and YX analyzed data. UU, NK, IN, RG, YX, OPC, CE and TP wrote the manuscript. All authors read and approved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This article does not contain any studies with human subjects.

Supplementary material

11306_2019_1616_MOESM1_ESM.tif (2.3 mb)
Supplementary Fig. 1. Experimental design in this study. Paddy soil was dug from the field and put in the pots. Rice seeds were germinated in the pots for one week before water was added into the pot to cover the soil. Ten gram of nitrogen fertilizer (46-0-0) was added to each pot at the 3-week-old seedling stage. The water was always maintained to cover the soil during the experiment. Supplementary material 1 (TIFF 2387 kb)
11306_2019_1616_MOESM2_ESM.tif (3 mb)
Supplementary Fig. 2. Base peak intensity (BPI) chromatogram of leaf extracts from BPH-susceptible KD (A) and BPH-resistant IL308 (B) at Day 1 by UPLC-QToF-MS analysis in ESI + and ESI−. −BPH = control group and +BPH = treatment group. Supplementary material 2 (TIFF 3117 kb)
11306_2019_1616_MOESM3_ESM.tif (498 kb)
Supplementary Fig. 3. Principal component analysis (PCA) scores plots of rice BPH-resistant traits including with quality control samples to show reproducibility. A) PCA scores plot obtained from ESI + and B) PCA scores plot obtained from ESI-. Supplementary material 3 (TIFF 497 kb)
11306_2019_1616_MOESM4_ESM.tif (1.3 mb)
Supplementary Fig. 4. MBPCA Super scores of rice BPH-resistant traits. Diamond symbol represents BPH-susceptible KD variety and circle symbol represents BPH-resistant IL308 variety. Clear symbol represents the samples in control group and solid symbol represents the samples in treatment group. Blue, green and red colors represent days 1, 4 and 8, respectively. Supplementary material 4 (TIFF 1340 kb)
11306_2019_1616_MOESM5_ESM.xlsx (117 kb)
Supplementary Table 1. Significant metabolite features selected by three-way ANOVA (p-value < 0.05) based on genotype factor and its interactions (time and treatment). A sheet “Supplementary Table 1_Pos” represents the significant metabolite features obtained from ESI + (801 features) and a sheet “Supplementary Table 1_Neg” represents the significant metabolite features obtained from ESI- (444 features) modes. Significant metabolite features labelled in red color with bold letter showed obvious alteration patterns in cluster analysis. Supplementary material 5 (XLSX 117 kb)
11306_2019_1616_MOESM6_ESM.xlsx (61 kb)
Supplementary Table 2. Mean ± SD (n = 3X3/group) of metabolite features based on their alteration pattern with level changes > 1.5 folds were analyzed by Mann–Whitney U test to select significant differences between control and treatment. A sheet “Supplementary Table 2_Pos” represents 40 metabolite features and a sheet “Supplementary Table 2_Neg” represents 56 metabolite features. Supplementary material 6 (XLSX 61 kb)
11306_2019_1616_MOESM7_ESM.xlsx (19 kb)
Supplementary Table 3. Summary of the metabolite annotation and identification of the significant metabolite features. Supplementary material 7 (XLSX 18 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Umaporn Uawisetwathana
    • 1
    Email author
  • Olivier P. Chevallier
    • 2
  • Yun Xu
    • 3
  • Wintai Kamolsukyeunyong
    • 4
  • Intawat Nookaew
    • 5
  • Thapakorn Somboon
    • 1
  • Theerayut Toojinda
    • 4
    • 6
  • Apichart Vanavichit
    • 7
  • Royston Goodacre
    • 3
  • Christopher T. Elliott
    • 2
  • Nitsara Karoonuthaisiri
    • 1
  1. 1.Microarray LaboratoryNational Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA)PathumthaniThailand
  2. 2.Institute for Global Food Security, School of Biological SciencesQueen’s University BelfastBelfastNorthern Ireland, UK
  3. 3.Department of Biochemistry, Institute of Integrative BiologyUniversity of LiverpoolLiverpoolUK
  4. 4.Rice Gene Discovery and Utilization LaboratoryInnovative Plant Biotechnology and Precision Agriculture Research Team, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA)Pathum ThaniThailand
  5. 5.College of Medicine, Department Biomedical InformaticsUniversity of Arkansas for Medical SciencesLittle RockUSA
  6. 6.Integrative Crop Biotechnology and Management Research GroupNational Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA)Pathum ThaniThailand
  7. 7.Agronomy Department, Faculty of Agriculture at Kamphaeng SaenKasetsart UniversityNakhon PathomThailand

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