Mingshu Cao

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 MS1 mass spectra (range 150–1,000 mass-to-charge ratio) 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 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.

Semi-quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high-throughput environment. The untargeted DIMSMS method uses top-down data-dependent fragmentation yielding MS2 and MS3 spectra. We have developed software tools to assess the structural homogeneity of the MS2 and MS3 spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS1 bins, 22 ions gave one or more reproducible QTL across the 2 years. Copyright

Identification of Urinary Biomarkers of Colon Inflammation in IL10−/− Mice Using Short-Column LCMS Metabolomics

The interleukin-10-deficient (IL10−/−) mouse develops colon inflammation in response to normal intestinal microflora and has been used as a model of Crohns disease. Short-Column LCMS metabolite profiling of urine from IL10−/− and wild-type (WT) mice was used, in two independent experiments, to identify mass spectral ions differing in intensity between these two genotypes. Three differential metabolites were identified as xanthurenic acid and as the glucuronides of xanthurenic acid and of α-CEHC (2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman). The significance of several differential metabolites as potential biomarkers of colon inflammation was evaluated in an experiment which compared metabolite concentrations in IL10−/− and WT mice housed, either under conventional conditions and dosed with intestinal microflora, or maintained under specific pathogen-free (SPF) conditions. Concentrations of xanthurenic acid, α-CEHC glucuronide, and an unidentified metabolite m/z 495−/497+ were associated with the degree of inflammation in IL10−/− mice and may prove useful as biomarkers of colon inflammation.

Phenotypic characterization of transposon-inserted mutants of Clostridium proteoclasticum B316T using extracellular metabolomics

The development of high-throughput DNA sequencing techniques has enabled the sequencing of several hundred bacterial genomes. However, the major step towards understanding the molecular basis of an organism will be the determination of all gene functions in its genome. Current gene assignments by sequence homology generate numerous hints to putative or unknown functions. Even hits with good homology are often not specific enough to describe the in vivo biochemical functions and the underlying biological roles. In this work we applied metabolic footprinting analysis to characterize Tn916-inserted mutants of a hemicellulose-degrading rumen bacterium grown on complex culture medium. Interestingly, the most distinctive phenotypic difference was observed in a mutant with a transposon insertion in a non-coding region of the genome, while disruption of a gene with high homology to a known alpha-glucosidase/xylosidase showed no distinctive phenotypic effect. Our results demonstrate that extracellular metabolomics data coupled to genome information is a powerful and low-cost approach to rapidly screen and characterize microbial mutants with single gene deletions. However, metabolomics as a stand-alone technique is unlikely to give a complete answer to define gene functions, and, therefore, is an approach to be used to generate hypotheses and direct new experiments to confirm gene function.

Computational Analyses of Spectral Trees from Electrospray Multi-Stage Mass Spectrometry to Aid Metabolite Identification

Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package “iontree” that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.

Relationship between virulence gene profiles of atypical enteropathogenic Escherichia coli and Shiga toxin-producing E. coli isolates from cattle and sheep in New Zealand.

ABSTRACT Virulence gene profiles of atypical enteropathogenic Escherichia coli (aEPEC) and Shiga toxin-producing E. coli (STEC) from cattle, sheep, and humans were examined to determine the relationship between pathotypes. Shared virulence factors (intimin, EHEC hemolysin, serine protease, and a type II secretion system) were identified, suggesting a dynamic evolutionary relationship between aEPEC and STEC.

A novel family of cyclic oligopeptides derived from ribosomal peptide synthesis of an in planta-induced gene, gigA, in Epichloë endophytes of grasses.

Fungal endophytes belonging to the genus Epichloë form associations with temperate grasses belonging to the sub-family Poöideae that range from mutualistic through to pathogenic. We previously identified a novel endophyte gene (designated gigA for grass induced gene) that is one of the most abundantly expressed fungal transcripts in endophyte-infected grasses and which is distributed and highly expressed in a wide range of Epichloë grass associations. Molecular and biochemical analyses indicate that gigA encodes a small secreted protein containing an imperfect 27 amino acid repeat that includes a kexin protease cleavage site. Kexin processing of GigA liberates within the plant multiple related products, named here as epichloëcyclins, which we have demonstrated by MS/MS to be cyclic peptidic in nature. Gene deletion of gigA leads to the elimination of all epichloëcyclins with no conspicuous phenotypic impact on the host grass, suggesting a possible bioactive role. This is a further example of a ribosomal peptide synthetic (RiPS) pathway operating within the Ascomycetes, and is the first description of such a pathway from a mutualistic symbiotic fungus from this Phylum.

Metabolic changes and associated cytokinin signals in response to nitrate assimilation in roots and shoots of Lolium perenne.

The efficiency of inorganic nitrogen (N) assimilation is a critical component of fertilizer use by plants and of forage production in Lolium perenne, an important pasture species worldwide. We present a spatiotemporal description of nitrate use efficiency in terms of metabolic responses and carbohydrate remobilization, together with components of cytokinin signal transduction following nitrate addition to N-impoverished plants. Perennial ryegrass (L. perenne cv. Grasslands Nui) plants were grown for 10 weeks in unfertilized soil and then treated with nitrate (5 mM) hydroponically. Metabolomic analysis by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry revealed a dynamic interaction between N and carbon metabolism over a week-long time course represented by the relative abundance of amino acids, tricarboxylic acid intermediates and stored water-soluble carbohydrates (WSCs). The initial response to N addition was characterized by a rapid remobilization of carbon stores from the low-molecular weight WSC, along with an increase in N content and assimilation into free amino acids. Subsequently, the shoot became the main source of carbon through remobilization of a large pool of high-molecular weight WSC. Associated quantification of cytokinin levels and expression profiling of putative cytokinin response regulator genes by quantitative reverse transcription polymerase chain reaction support a role for cytokinin in the mediation of the response to N addition in perennial ryegrass. The presence of high levels of cis-zeatin-type cytokinins is discussed in the context of hormonal homeostasis under the stress of steady-state N deficiency.

Serum metabolomics using ultra performance liquid chromatography coupled to mass spectrometry in lactating dairy cows following a single dose of sporidesmin

IntroductionPhotosensitization is a common clinical sign in cows suffering from liver damage caused by the mycotoxin sporidesmin. This disease, called facial eczema (FE), is of major importance in New Zealand. Current techniques for diagnosing animals with subclinical sporidesmin-induced liver damage (i.e. without photosensitization) are nonspecific. In addition, little is known of the mechanisms involved in sporidesmin resistance, nor the early effects seen following low-dose sporidesmin intoxication.ObjectiveThe objective of this study was to identify individual metabolites or metabolic profiles that could be used as serum markers for early stage FE in lactating cows.MethodsResults are presented from a 59-day sporidesmin challenge in Friesian-cross dairy cows. Serum metabolite profiles were obtained using reversed phase ultra-performance liquid chromatography (UPLC) electrospray ionization mass spectrometry (MS) and UPLC tandem MS. Multivariate and time series analyses were used to assess the data.ResultsStatistical analysis, both with and without the temporal component, could distinguish the profiles of animals with clinical signs from the others, but not those affected subclinically. An increase in the concentrations of a combination of taurine- and glycine-conjugated secondary bile acids (BAs) was the most likely cause of the separation. This is the first time that MS methods have been applied to FE and that bile acids changes have been detected in cattle exposed to sporidesmin.ConclusionsIt is well known that BA concentrations increase during cholestasis due to damage to bile ducts and leakage of the bile. This is the first study to investigate metabolomic changes in serum following a sporidesmin challenge. Further work to establish the significance of the elevation of individual BAs concentrations in the serum of early-stage sporidesmin-poisoned cows is necessary.

Untargeted Metabotyping Lolium perenne Reveals Population-Level Variation in Plant Flavonoids and Alkaloids

Metabolomics provides a powerful platform to characterize plants at the biochemical level, allowing a search for underlying genes and associations with higher level complex traits such as yield and nutritional value. Efficient and reliable methods to characterize metabolic variation in economically important species are considered of high value to the evaluation and prioritization of germplasm and breeding lines. In this investigation, a large-scale metabolomic survey was performed on a collection of diverse perennial ryegrass (Lolium perenne L.) plants. A total of 2,708 data files, derived from liquid chromatography coupled to high resolution mass spectrometry (LCMS), were selected to assess the effectiveness and efficiency of applying high throughput metabolomics to survey chemical diversity in plant populations. The data set was generated from 23 ryegrass populations, with 3–25 genotypes per population, and five clonal replicates per genotype. We demonstrate an integrated approach to rapidly mine and analyze metabolic variation from this large, multi-batch LCMS data set. After performing quality control, statistical data mining and peak annotation, a wide range of variation for flavonoid glycosides and plant alkaloids was discovered among the populations. Structural variation of flavonoids occurs both in aglycone structures and acetylated/malonylated/feruloylated sugar moieties. The discovery of comprehensive metabolic variation among the plant populations offers opportunities to probe into the genetic basis of the variation, and provides a valuable resource to gain insight into biochemical functions and to relate metabolic variation with higher level traits in the species.