Mass spectrometry-based metabolomics to elucidate functions in marine organisms and ecosystems

Abstract

Marine systems are very diverse and recognized as being sources of a wide range of biomolecules. This review provides an overview of metabolite profiling based on mass spectrometry (MS) approaches in marine organisms and their environments, focusing on recent advances in the field. We also point out some of the technical challenges that need to be overcome in order to increase applications of metabolomics in marine systems, including extraction of chemical compounds from different matrices and data management. Metabolites being important links between genotype and phenotype, we describe added value provided by integration of data from metabolite profiling with other layers of omics, as well as their importance for the development of systems biology approaches in marine systems to study several biological processes, and to analyze interactions between organisms within communities. The growing importance of MS-based metabolomics in chemical ecology studies in marine ecosystems is also illustrated.

Keywords: GC-MS; LC-MS; chemical ecology; databases; mass spectrometry; metabolomics; systems biology; targeted and untargeted profiling.

Figure 1

Metabolomic data obtained for two strains of Ectocarpus (brown alga) by Gas chromatography-mass spectrometry (GC-MS) analysis. Both strains corresponded to the reference genome-sequenced marine (SWS, CCAP 1310/4; [114]) grown in undiluted seawater (32 ppt) and a freshwater strain isolated from river falls in Australia (FWS, CCAP 1310/196; [115,116]) grown in undiluted (32 ppt) and highly diluted seawater (1.6 ppt), respectively. Algae were rinsed with distillated water before freezing and grinding in liquid nitrogen. Extractions were carried out with ethyl-acetate, and 12-hydroxy-lauric acid was added as internal standard. Metabolites were converted as Me-TMS-derivatives, and analyzed with an Agilent 7890 gas chromatograph equipped with a HP-5ms column (30 m, 0.25 mm internal diameter, 0.25 mm film thickness) coupled with a 5975 N mass spectrometer. Feature detection was done with AMDIS (version 2.1; Automated Mass Spectral Deconvolution and Identification System; National Insitute of Standards and Technology: Gaithersburg, MD, USA, 2006) [112], and comparative analysis with the online available SpectConnect [113]. All metabolites were considered for (A) Hierarchical tree clustering (p-value < 0.01) performed with TigrMeV 4.8; and (B) PLS-DA (p-value < 0.05) generated by SIMCA-P + 12.0 (Umetrics); (C) Relative abundance of arachidonic acid (ARA, C20:4n-6), lactic acid, and of an unknown compound with the more intense ion m/z 146. Spectra obtained for the latter compound were compared in Golm metabolome database (GMD), Massbank, Human Metabolome Database (HMDB), and did not give any match allowing identification. * and # indicate that a mean differed significantly from the mean obtained for the samples FWS 1.6 ppt at p < 0.01 and p < 0.05 respectively.

Figure 2

MetaboMER workflow for a metabolomic approach of marine ecology (adapted from [138]). We propose to create a marine biotope in the laboratory. On the left side of the net, a macroalga is submitted to herbivory through incubation in presence of marine grazers (e.g., snails, helcions). Other organisms are placed in the right compartment to test the existence and the impact of chemical cues. Phenomena such as priming, attraction of marine grazers predators, and metabolome modifications of macroalgae of the same species or of other species could be investigated. Kinetic studies may also be relevant to determine primary and secondary chemical cues. Symbols used for organisms are courtesy of the Integration and Application Network (http://ian.umces.edu/symbols/, accessed on 10 February 2012), University of Maryland Center for Environmental Science.