Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification

Untargeted Multimodal Metabolomics Investigation of the Haemonchus contortus Exsheathment Secretome

In nematodes that invade the gastro-intestinal tract of the ruminant, the process of larval exsheathment marks the transition from the free-living to the parasitic stages of these parasites. To investigate the secretome associated with larval exsheathment, a closed in vitro system that effectively reproduces the two basic components of an anaerobic rumen environment (CO2 and 39 °C) was developed to trigger exsheathment in one of the most pathogenic and model gastrointestinal parasitic nematodes, Haemonchus contortus (barber‘s pole worm). This study reports the use of multimodal untargeted metabolomics and lipidomics methodologies to identify the metabolic signatures and compounds secreted during in vitro larval exsheathment in the H. contortus infective third-stage larva (iL3). A combination of statistical and chemoinformatic analyses using three analytical platforms revealed a panel of metabolites detected post exsheathment and associated with amino acids, purines, as well as select organic compounds. The major lipid classes identified by the non-targeted lipidomics method applied were lysophosphatidylglycerols, diglycerides, fatty acyls, glycerophospholipids, and a triglyceride. The identified metabolites may serve as metabolic signatures to improve tractability of parasitic nematodes for characterizing small molecule host–parasite interactions related to pathogenesis, vaccine and drug design, as well as the discovery of metabolic biomarkers.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

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

INTRODUCTION

Photosensitization 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.

OBJECTIVE

The 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.

METHODS

Results 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.

RESULTS

Statistical 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.

CONCLUSIONS

It 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.

Chemical analysis of the Alphaproteobacterium strain MOLA1416 associated with the marine lichen Lichina pygmaea

Alphaproteobacterium strain MOLA1416, related to Mycoplana ramosa DSM 7292 and Chelativorans intermedius CC-MHSW-5 (93.6% 16S rRNA sequence identity) was isolated from the marine lichen, Lichina pygmaea and its chemical composition was characterized by a metabolomic network analysis using LC-MS/MS data. Twenty-five putative different compounds were revealed using a dereplication workflow based on MS/MS signatures available through GNPS (https://gnps.ucsd.edu/). In total, ten chemical families were highlighted including isocoumarins, macrolactones, erythrinan alkaloids, prodiginines, isoflavones, cyclohexane-diones, sterols, diketopiperazines, amino-acids and most likely glucocorticoids. Among those compounds, two known metabolites (13 and 26) were isolated and structurally identified and metabolite 26 showed a high cytotoxic activity against B16 melanoma cell lines with an IC₅₀ 0.6 ± 0.07 μg/mL.

Differentiating signals to make biological sense - A guide through databases for MS-based non-targeted metabolomics

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their online ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as identifier assignment, structural assignment and interpretation of results.

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.

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.

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics

Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

A new ion mobility-linear ion trap instrument for complex mixture analysis

A new instrument that couples a low-pressure drift tube with a linear ion trap mass spectrometer is demonstrated for complex mixture analysis. The combination of the low-pressure separation with the ion trapping capabilities provides several benefits for complex mixture analysis. These include high sensitivity, unique ion fragmentation capabilities, and high reproducibility. Even though the gas-phase separation and the mass measurement steps are each conducted in an ion filtering mode, detection limits for mobility-selected peptide ions are in the tens of attomole range. In addition to ion separation, the low-pressure drift tube can be used as an ion fragmentation cell yielding mobility-resolved fragment ions that can be subsequently analyzed by multistage tandem mass spectrometry (MS(n)) methods in the ion trap. Because of the ion trap configuration, these methods can be comprised of any number (limited by ion signal) of collision-induced dissociation (CID) and electron transfer dissociation (ETD) processes. The high reproducibility of the gas-phase separation allows for comparison of two-dimensional ion mobility spectrometry (IMS)-MS data sets in a pixel-by-pixel fashion without the need for data set alignment. These advantages are presented in model analyses representing mixtures encountered in proteomics and metabolomics experiments.