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

被引:7
作者
Kim, Suk Weon
Ban, Sung Hee
Ahn, Chi Yong
Oh, Hee Mock
Chung, Hoeil
Cho, Soo Hwa
Park, Young Mok
Liu, Jang Ryol [5 ]
机构
[1] Hanyang Univ, Biol Resources Ctr, Seoul 133791, South Korea
[2] Hanyang Univ, Environm Biol Ctr, Seoul 133791, South Korea
[3] Hanyang Univ, Dept Chem, Seoul 133791, South Korea
[4] Proteome Anal Team, Korea Basic Sci Inst, Taejon 305333, South Korea
[5] KRIBB, Plant Genom Res Ctr, Taejon 305333, South Korea
关键词
cyanobacteria; dendrogram; pattern recognition; principal component analysis; taxonomy;
D O I
10.1007/BF03031154
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
When whole-cell extracts are analyzed, proton nuclear magnetic resonance (H-1 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 to H-1 NMR. The H-1 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 using 1H NMR spectra and multivariate statistical analysis provide a simple, rapid method for the taxonomical discrimination of cyanobacteria.
引用
收藏
页码:271 / 275
页数:5
相关论文
共 25 条
[1]  
[Anonymous], 1966, Multivariate Analysis
[2]   Effects of UV-B radiation on inorganic carbon acquisition by the marine microalga Dunaliella tertiolecta (Chlorophyceae) [J].
Beardall, J ;
Heraud, P ;
Roberts, S ;
Shelly, K ;
Stojkovic, S .
PHYCOLOGIA, 2002, 41 (03) :268-272
[3]  
Brock TD, 1994, BIOL MICROORGANISMS
[4]  
CURK MC, 1994, FEMS MICROBIOL LETT, V123, P241, DOI [10.1111/j.1574-6968.1994.tb07231.x, 10.1016/0378-1097(94)90204-6]
[5]   Phylogenetic classification and the universal tree [J].
Doolittle, WF .
SCIENCE, 1999, 284 (5423) :2124-2128
[6]   RAPID IDENTIFICATION OF SPECIES WITHIN THE MYCOBACTERIUM-TUBERCULOSIS COMPLEX BY ARTIFICIAL NEURAL-NETWORK ANALYSIS OF PYROLYSIS MASS-SPECTRA [J].
FREEMAN, R ;
GOODACRE, R ;
SISSON, PR ;
MAGEE, JG ;
WARD, AC ;
LIGHTFOOT, NF .
JOURNAL OF MEDICAL MICROBIOLOGY, 1994, 40 (03) :170-173
[7]   An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences:: application to the C57BL10J and Alpk:ApfCD mouse [J].
Gavaghan, CL ;
Holmes, E ;
Lenz, E ;
Wilson, ID ;
Nicholson, JK .
FEBS LETTERS, 2000, 484 (03) :169-174
[8]   Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks [J].
Goodacre, R ;
Timmins, ÉM ;
Burton, R ;
Kaderbhai, N ;
Woodward, AM ;
Kell, DB ;
Rooney, PJ .
MICROBIOLOGY-SGM, 1998, 144 :1157-1170
[9]   Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks [J].
Goodacre, R ;
Timmins, EM ;
Rooney, PJ ;
Rowland, JJ ;
Kell, DB .
FEMS MICROBIOLOGY LETTERS, 1996, 140 (2-3) :233-239
[10]   CLASSIFICATION AND IDENTIFICATION OF BACTERIA BY FOURIER-TRANSFORM INFRARED-SPECTROSCOPY [J].
HELM, D ;
LABISCHINSKI, H ;
SCHALLEHN, G ;
NAUMANN, D .
JOURNAL OF GENERAL MICROBIOLOGY, 1991, 137 :69-79