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Salt stress in rice: multivariate analysis separates four components of beneficial silicon action

  • Chakkree Lekklar
  • Supachitra Chadchawan
  • Preeda Boon-Long
  • Wolfgang Pfeiffer
  • Anchalee Chaidee
Original Article

Abstract

How many subcellular targets of the beneficial silicon effect do exist in salt-stressed rice? Here, we investigate the effects of silicon on the different components of salt stress, i.e., osmotic stress, sodium, and chloride toxicity. These components are separated by multivariate analysis of 18 variables measured in rice seedlings (Oryza sativa L.). Multivariate analysis can dissect vectors and extract targets as principal components, given the regressions between all variables are known. Consequently, the exploration of 153 correlations and 306 regression models between all variables is essential, and regression parameters for variables of shoot (silicon, sodium, chloride, carotenoids, chlorophylls a and b, and relative growth rate) and variables of shoot and root (hydrogen peroxide, ascorbate peroxidase (APX), catalase (CAT), fresh weight, dry weight, root-to-shoot ratio) are determined. The regression models [log (y) = y0 + a × log (x)] are confirmed by variance analysis of global goodness of fits (p < 0.0001). Thereby, logarithmic transformation yields linearization for multivariate analysis by Pearson’s correlation. Four principal components are extracted: two targets of osmotic stress, one target of sodium toxicity, and one target of chloride toxicity. Thereby, silicon improves salt tolerance by increasing APX and CAT activities and decreasing hydrogen peroxide, salt ion accumulation, photosynthetic pigment losses, and growth inhibition. Salt stress increases silicon uptake pointing to a physiological regulation of plant salt stress in the presence of silicon. This mechanism and its four components are promising targets for further agricultural application.

Keywords

Chloride toxicity Multivariate analysis Osmotic stress Rice (Oryza sativa L.) Silicon targets Sodium toxicity 

Abbreviations

ANOVA

Analysis of variance

APX (10)

Ascorbate peroxidase

C-1, C-2, C-3, C-4

Principal components determined by multivariate statistics

CAR (8)

Carotenoids

CAT (11)

Catalase

CHLA (6), CHLB (7)

Chlorophyll a, chlorophyll b

CL (13)

Shoot chloride

DW (16)

Dry weight

FW (15)

Fresh weight

Na (12)

Shoot sodium

PER (9)

Hydrogen peroxide

RAPX (4), RCAT (5), RDW (2), RFW (1), RPER (3)

Parameters as before measured in roots instead of shoots

RGR (18)

Relative growth rate of shoot

RS (17)

Root-to-shoot ratio

Si (14)

Shoot silicon

u

Unit of enzymatic activity of APX and CAT measured as described in the section “Materials and methods

Notes

Acknowledgements

We would like to thank Dr. Aniela Sommer for the comments on the manuscript.

Funding

This work was supported by the Grant for New Scholar (co-funded by TRF and CHE, MRG5280200) and the Higher Education Research Promotion and National Research University Project of Thailand (FW656B).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

709_2018_1293_MOESM1_ESM.docx (29 kb)
Supplementary Table 1 (DOCX 29 kb)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of ScienceChulalongkorn UniversityBangkokThailand
  2. 2.Fachbereich Biowissenschaften, Abteilung PflanzenphysiologieUniversität SalzburgSalzburgAustria

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