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     

Abstract

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.With the advent of metabolomics, methods for the simultaneous analysis of a large number of small molecules (metabolites) have been developed and improved, providing more details about the metabolism of complex biological systems (Sumner et al., 2003;Dettmer et al., 2007). Metabolite fingerprinting methods provide relatively unbiased and high-throughput information on complex biological systems and have been used for yeast (Saccharomyces cerevisiae) strain classification (Allen et al., 2003) and annotation of gene functions (Raamsdonk et al., 2001). Comprehensive and unbiased metabolite analysis is also an indispensable tool for systems biology together with transcriptomics and proteomics (see commentary by Sauer et al., 2007). Direct-infusion (without prior chromatographic separation) electrospray ionization (ESI) mass spectrometry (MS) was introduced as a tool for the identification of novel fungal metabolites in culture (Smedsgaard and Frisvad, 1996) and is now widely applied in metabolomics (for a recent review, see Dettmer et al., 2007). High resolution MS instrumentation such as time-of-flight MS (Dunn et al., 2005) or Fourier transform ion cyclotron resonance MS (FT-ICR-MS; Aharoni et al., 2002) is often preferred for the specificity provided by resolution of isobaric ions and highly accurate e...

Metabolomics analysis of the Lolium perenne–Neotyphodium lolii symbiosis: more than just alkaloids?

“…Although this type of analysis does not allow the establishment of direct and causal relationships between a certain metabolite and insect population responses, the differential pattern of these responses is intriguing and warrants further metabolomics studies on the effects of non-alkaloid metabolites on insect and other herbivores. A recent study also performed in our lab and using untargeted direct infusion ion trap mass spectrometry revealed the presence of formerly unknown compounds produced in endophyte infected plants (Cao et al 2008). Some of these compounds belong to the class of cyclic oligopeptides, which have been shown to be potentially antimicrobial (Arai et al 1973, Strobel andHess 1997;Seto et al 2007) and it is therefore possible that some of the plant protecting effects seen in endophyte infected plants are due to these or other, still completely unknown, endophyte specific compounds.…”

Can optimality models and an ‘optimality research program’ help us understand some plant–fungal relationships?

“…Since we know that a large range of metabolites varies with endophyte infection, strain and concentration, the overriding conclusion from these results is that alkaloids are certainly not the only factors, and may not even be the most important factor in the endophyte-grass-herbivore story. Furthermore, we also know that in addition to the known alkaloids, there are a range of other endophyte specific metabolites that have just recently been discovered and which may have biological activity as well, 21 but were not part of our analysis. A note of caution should be added here as well, as we have not included possible insect species interactions into our analysis.…”

Metabolite Analysis and Metabolomics in the Study of Biotrophic Interactions between Plants and Microbes

“…It has been suggested that nutrients are taken up from the apoplast (Hinton & Bacon, 1985), or they might be transported to the fungus at the site of attachment to plant cells, but to date, no nutrient transporters have been identified or functionally characterized from fungal endophytes. Targeted (GC-MS, HPLC) and untargeted (direct infusion MS) metabolomics analyses of the L. perenne/ N. lolii association (Rasmussen et al, 2007(Rasmussen et al, , 2008aCao et al, 2008) demonstrated major shifts in primary metabolites in infected compared to endophyte free tissues. A strong reduction in free nitrate, the amino acids asparagine and proline, and proteins, and a concomitant increase in soluble carbohydrates (glucose, fructose, sucrose, fructans), organic acids (quinate, shikimate) and phenylpropanoids (chlorogenic acid) indicate an increase in C/N ratios and defence compounds (Rasmussen et al, 2008a).…”

“…Even though alkaloids are usually perceived as the major factor affecting insect herbivores, a linear regression analysis of metabolic profiles with insect abundances indicated that a particular 'type' of metabolic composition, rather than any one individual compound, might be important for specific insect responses (Rasmussen et al, 2008b). Moreover, untargeted DIMS metabolic profiling also identified the presence of a previously unknown class of cyclic oligopeptides in endophyte infected samples, which might be relevant to ecological effects of these symbiotic associations as well (Cao et al, 2008).…”