Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis

Abstract

When whole-cell extracts are analyzed, proton nuclear magnetic resonance (1H NMR) spectroscopy provides biochemical profiles that contain overlapping signals of the majority of the compounds. To determine whether cyanobacteria could be taxonomically discriminated on the basis of metabolic fingerprinting, we subjected whole-cell extracts of the cyanobacteria to1H NMR. The1H NMR spectra revealed a predominance of signals in the aliphatic region. Principal component analysis (PCA) of the data then enabled discrimination of the cyanobacteria. The hierarchical dendrogram, based on PCA of the aliphatic region data, showed that six cyanobacterial taxa were discriminated from two eukaryotic microalgal species, and that the six taxa could be subsequently divided into three groups. This agrees with the current taxonomy of cyanobacteria. Therefore, our overall results indicate that metabolic fingerprinting using1H NMR spectra and multivariate statistical analysis provide a simple, rapid method for the taxonomical discrimination of cyanobacteria.

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Correspondence to Suk Weon Kim or Sung Hee Ban or Chi Yong Ahn or Hee Mock Oh or Hoeil Chung or Soo Hwa Cho or Young Mok Park or Jang Ryol Liu.

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Kim, S.W., Ban, S.H., Ahn, C.Y. et al. Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis. J. Plant Biol. 49, 271–275 (2006). https://doi.org/10.1007/BF03031154

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Keywords

  • cyanobacteria
  • dendrogram
  • pattern recognition
  • principal component analysis
  • taxonomy