Advanced data-mining strategies for the analysis of direct-infusion ion trap mass spectrometry data from the association of perennial ryegrass with its endophytic fungus, Neotyphodium lolii.

Direct-infusion mass spectrometry (MS) was applied to study the metabolic effects of the symbiosis between the endophytic fungus Neotyphodium lolii and its host perennial ryegrass (Lolium perenne) in three different tissues (immature leaf, blade, and sheath). Unbiased direct-infusion MS using a linear ion trap mass spectrometer allowed metabolic effects to be determined free of any preconceptions and in a high-throughput fashion. Not only the full MS(1) mass spectra (range 150-1,000 mass-to-charge ratio) were obtained but also MS(2) and MS(3) product ion spectra were collected on the most intense MS(1) ions as described previously (Koulman et al., 2007b). We developed a novel computational methodology to take advantage of the MS(2) product ion spectra collected. Several heterogeneous MS(1) bins (different MS(2) spectra from the same nominal MS(1)) were identified with this method. Exploratory data analysis approaches were also developed to investigate how the metabolome differs in perennial ryegrass infected with N. lolii in comparison to uninfected perennial ryegrass. As well as some known fungal metabolites like peramine and mannitol, several novel metabolites involved in the symbiosis, including putative cyclic oligopeptides, were identified. Correlation network analysis revealed a group of structurally related oligosaccharides, which differed significantly in concentration in perennial ryegrass sheaths due to endophyte infection. This study demonstrates the potential of the combination of unbiased metabolite profiling using ion trap MS and advanced data-mining strategies for discovering unexpected perturbations of the metabolome, and generating new scientific questions for more detailed investigations in the future.