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Earthworm species of the genus Eisenia can be phenotypically differentiated by metabolic profiling
Author(s) -
Bundy Jacob G,
Spurgeon David J,
Svendsen Claus,
Hankard Peter K,
Osborn Daniel,
Lindon John C,
Nicholson Jeremy K
Publication year - 2002
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(02)02854-5
Subject(s) - earthworm , biology , profiling (computer programming) , eisenia andrei , phenotype , computational biology , genetics , ecology , gene , computer science , operating system
The universality of low molecular weight metabolites allows rapid and straightforward investigation of the biochemistry of genetically uncharacterised species. Thus ex vivo metabolic profiling in combination with multivariate data analysis (metabonomics) offers great potential in comparative biology. Here we present the first use of high resolution nuclear magnetic resonance (NMR) spectroscopy to distinguish closely related animal species via their metabolic phenotype (metabotype). We have profiled the three Eisenia (Oligochaeta, Lumbricidae) species Eisenia fetida , Eisenia andrei and Eisenia veneta using tissue extracts and coelomic fluid analysis. The low molecular weight biochemical profiles of tissue extracts were highly conserved for all three species, with E. fetida and E. andrei being more similar to each other than to E. veneta . However the metabolic profiles of the coelomic fluid of the different species were highly distinctive – the NMR spectra allowed unequivocal identification of species. Multivariate statistics were also used to quantify these spectral differences and to enable simplified graphical visualisation of species similarity. These results show that two morphologically undistinguishable species ( E. fetida and E. andrei ) differ markedly in their biochemical profiles despite apparently occupying the same ecological niche, and indicate that metabolic phenotype profiling can be used as a powerful functional genomics tool.