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Published on February 20, 2008; 10.1104/pp.107.112458

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Received November 4, 2007
Accepted February 18, 2008

Advanced data-mining strategies for the analysis of direct-infusion ion trap mass spectrometry data from the association of Lolium perenne with its endophytic fungus Neotyphodium lolii

Mingshu Cao , Albert Koulman , Linda J. Johnson , Geoffrey A. Lane , and Susanne Rasmussen *

AgResearch Limited, Grasslands Research Centre, P. B. 11008, Palmerston North 4442, New Zealand

* Corresponding author; email: susanne.rasmussen{at}agresearch.co.nz.

Direct infusion mass spectrometry was applied to study the metabolic effects of the symbiosis between the endophytic fungus Neotyphodium lolii and its host Lolium perenne in three different tissues (immature leaf, blade and sheath). Unbiased direct infusion mass spectrometry using a linear ion trap mass spectrometer (DIMSn) allowed metabolic effects to be determined free of any pre-conceptions and in a high-throughput fashion. Not only the full MS1 mass spectra (range 150-1000 m/z) were obtained but also MS2 and MS3 product ion spectra were collected on the most intense MS1 ions as described previously (Koulman et al., 2007b). We developed a novel computational methodology to take advantage of the MS2 product ion spectra collected. Several heterogeneous MS1 bins (different MS2 spectra from the same nominal MS1) were identified with this method. Exploratory data analysis approaches were also developed to investigate how the metabolome differs in Lolium perenne infected with Neotyphodium lolii in comparison to uninfected L. perenne.

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 mass spectrometry (DIMSn) and advanced data-mining strategies for discovering unexpected perturbations of the metabolome, and generating new scientific questions for more detailed investigations in the future.






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