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Metabolomics

, Volume 9, Issue 5, pp 949–959 | Cite as

Lipidomic profiling and discovery of lipid biomarkers in Stephanodiscus sp. under cold stress

  • Deying Chen
  • Xiaojun Yan
  • Jilin Xu
  • Xiaolin Su
  • Lanjuan Li
Original Article

Abstract

Changes in membrane lipid composition play multiple roles in plant adaptation and survival in the face of chilling and freezing damage. The ultra-performance liquid chromatography/quadrupole-TOF-MS (UPLC/Q-TOF-MS)-based approach was developed for investigating the lipid changes during cold exposure in Stephanodiscus sp. followed by multivariate statistical analysis including principal components analysis, partial least squares discriminant analysis and orthogonal projection on latent structure discriminant analysis for data classification and potential biomarkers selection. The analysis demonstrated dramatic lipid alterations take place in both extraplastidic and plastidic membranes. Thirty-eight lipid molecules were selected and identified as putative biomarkers, including chlorophyll a degradation products, triacylglycerol, phosphatidylcholine, phosphatidylglycerol, sulfo-quinovosyldiacylglycerol, monogalactosyldiacylglyceroll, lyso-phosphatidylglycerol, lyso-phosphatidylcholine, lyso-monogalactosyldiacylglycerol and lyso-sulfoquinovosyldiacylglycerol. These metabolites have been shown previously to function in energy storage, membrane stability and photosynthesis efficiency. This study is the first one using UPLC/Q-TOF-MS-based lipidomic profiling with multivariate statistical analysis to explore the lipidomic changes of microalgae in response to stress conditions, which promotes better understanding of their physiology and ecology.

Keywords

Stephanodiscus sp. Cold stress Lipidomics UPLC-Q-TOF-MS 

Abbreviations

UPLC

Ultra-performance liquid chromatography

Q-TOF-MS

Quadrupole time-of-flight mass spectrometry

MS/MS

Tandem mass spectrometry

ESI

Electrospray ionization

PCA

Principal component analysis

PLS-DA

Projections to latent structures discriminant analysis

OPLS-DA

Orthogonal projections to latent structures discriminant analysis

PG

Phosphatidylglycerol

Lyso-PG

Lyso-phosphatidylglycerol

PC

Phosphatidylcholine

Lyso-PC

Lyso-phosphatidylcholine

SQDG

Sulfoquinovosyldiacylglycerol

Lyso-SQDG

Lyso-sulfoquinovosyldiacylglycerol

MGDG

Monogalactosyldiacylglycerol

Lyso-MGDG

Lyso-monogalactosyldiacylglycerol

DGDG

Digalactosyldiacylglycerol

TAG

Triacylglycerol

ER

Endoplasmic reticulum

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (31172448), Zhejiang Natural Science Foundation, China (Y3100534 and Z3100565), Ningbo Science and Technology Research Projects, China (2011C11003), Zhejiang marine biotechnology innovation team, China (2012R10029), Ningbo Marine Algae Biotechnology Team, China (2011B81007), and partly sponsored by K. C. Wong Magna Fund in Ningbo University.

Supplementary material

11306_2013_515_MOESM1_ESM.tif (148 kb)
S-Fig. 1 PCA scores plot in negative ion scan mode for the first two components of Stephanodiscus sp. samples were harvested on time point 0, 4, 12 and 24 h, successively (TIFF 147 kb)
11306_2013_515_MOESM2_ESM.tif (158 kb)
S-Fig. 2 PLS-DA scores plot in negative ion scan mode for the first two components of Stephanodiscus sp. samples were harvested on time point 0, 4, 12 and 24 h, successively. (TIFF 157 kb)
11306_2013_515_MOESM3_ESM.tif (217 kb)
S-Fig. 3 validation plot of PLS-DA analysis on Stephanodiscus sp., (A) in positive ion scan mode; (B) in negative ion scan mode (TIFF 216 kb)
11306_2013_515_MOESM4_ESM.tif (194 kb)
S-Fig. 4 Scores scatter plot of OPLS-DA (A) and validation plot (B) of OPLS-DA analysis on Stephanodiscus sp. in positive ion scan mode, which compared group 2 versus group 3 (TIFF 194 kb)
11306_2013_515_MOESM5_ESM.tif (173 kb)
S-Fig. 5 Scores scatter plot of OPLS-DA (A) and validation plot (B) of OPLS-DA analysis on Stephanodiscus sp. in negative ion scan mode, which compared group 2 versus group 3 (TIFF 173 kb)
11306_2013_515_MOESM6_ESM.tif (188 kb)
S-Fig. 6 S-plot used in our biomarkers selection compared with group 2 versus group 1. The variables marked (red) are the metabolites selected as potential biomarkers. (A) in the positive ion mode; (B) in the negative ion mode (TIFF 188 kb)
11306_2013_515_MOESM7_ESM.tif (191 kb)
S-Fig. 7 S-plot used in our biomarkers selection compared with group 3 versus group 2. The variables marked (red) are the metabolites selected as potential biomarkers. (A) in the positive ion mode; (B) in the negative ion mode (TIFF 190 kb)
11306_2013_515_MOESM8_ESM.tif (34 kb)
S-Fig. 8 A: Growth of the culture on different days under normal condition; B: Comparison of growth of Stephanodiscus. sp. in normal condition (upper line, ∆) and cold stress condition (lower line,□) in 24 h (TIFF 34 kb)
11306_2013_515_MOESM9_ESM.pptx (157 kb)
Supplementary-PPT The variation trend plot of some representative lipid markers during the course of the cold treatment in the whole four sampling groups, which were harvested on time points 0, 4, 12 and 24 h (PPTX 157 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Deying Chen
    • 1
    • 2
  • Xiaojun Yan
    • 2
  • Jilin Xu
    • 2
  • Xiaolin Su
    • 1
    • 2
  • Lanjuan Li
    • 1
  1. 1.State Key Laboratory for Diagnosis and Treatment of Infectious Diseases1st Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
  2. 2.Key Laboratory of Applied Marine BiotechnologyNingbo University, Ministry of EducationNingboChina

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