Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity

Exact mass GC-MS analysis: Protocol, database, advantages and application to plant metabolic profiling

Plant metabolomics has been used widely in plant physiology, in particular to analyse metabolic responses to environmental parameters. Derivatization (via trimethylsilylation and methoximation) followed by GC-MS metabolic profiling is a major technique to quantify low molecular weight, common metabolites of primary carbon, sulphur and nitrogen metabolism. There are now excellent opportunities for new generation analyses, using high resolution, exact mass GC-MS spectrometers that are progressively becoming relatively cheap. However, exact mass GC-MS analyses for routine metabolic profiling are not common, since there is no dedicated available database. Also, exact mass GC-MS is usually dedicated to structural resolution of targeted secondary metabolites. Here, we present a curated database for exact mass metabolic profiling (made of 336 analytes, 1064 characteristic exact mass fragments) focused on molecules of primary metabolism. We show advantages of exact mass analyses, in particular to resolve isotopic patterns, localise S-containing metabolites, and avoid identification errors when analytes have common nominal mass peaks in their spectrum. We provide a practical example using leaves of different Arabidopsis ecotypes and show how exact mass GC-MS analysis can be applied to plant samples and identify metabolic profiles.

Chemical and Genotypic Variations in Aniba rosiodora from the Brazilian Amazon Forest

Aniba rosiodora has been exploited since the end of the nineteenth century for its essential oil, a valuable ingredient in the perfumery industry. This species occurs mainly in Northern South America, and the morphological similarity among different Aniba species often leads to misidentification, which impacts the consistency of products obtained from these plants. Hence, we compared the profiles of volatile organic compounds (essential oils) and non-volatile organic compounds (methanolic extracts) of two populations of A. rosiodora from the RESEX and FLONA conservation units, which are separated by the Tapajós River in Western Pará State. The phytochemical profile indicated a substantial difference between the two populations: samples from RESEX present α-phellandrene (22.8%) and linalool (39.6%) in their essential oil composition, while samples from FLONA contain mainly linalool (83.7%). The comparison between phytochemical profiles and phylogenetic data indicates a clear difference, implying genetic distinction between these populations.

A Complete Pipeline for Generating a High-Resolution LC-MS-Based Reference Mass Spectra Library

Databases containing mass spectrometry (MS) spectral data (i.e., MS reference libraries) are currently the most reliable and widely accepted approach to annotate unknown features in MS-based metabolomics. While for gas chromatography (GC)-MS data, a strategy for collecting, storing, and comparing to raw data has been established, this is not the case for liquid chromatography (LC)-MS data. Here, we present our approach for high-throughput data collection and automated MS reference library generation, as applied recently in the WEIZMASS library of plant metabolites. Methodologies to experimentally generate pools of chemical standards and computationally convert them into a unique source of reference data are detailed.

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

The WEIZMASS spectral library for high-confidence metabolite identification

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Unambiguous metabolite annotation is a critical, yet problematic step, in mass spectrometry based metabolomics. Here, Shahaf et al. present WEIZMASS, a platform consisting of a diverse spectral library of more than 3500 plant metabolites and software to aid their identification in biological samples.

Modern plant metabolomics: advanced natural product gene discoveries, improved technologies, and future prospects

Plant metabolomics has matured and modern plant metabolomics has accelerated gene discoveries and the elucidation of a variety of plant natural product biosynthetic pathways. This review covers the approximate period of 2000 to 2014, and highlights specific examples of the discovery and characterization of novel genes and enzymes associated with the biosynthesis of natural products such as flavonoids, glucosinolates, terpenoids, and alkaloids. Additional examples of the integration of metabolomics with genome-based functional characterizations of plant natural products that are important to modern pharmaceutical technology are also reviewed. This article also provides a substantial review of recent technical advances in mass spectrometry imaging, nuclear magnetic resonance imaging, integrated LC-MS-SPE-NMR for metabolite identifications, and X-ray crystallography of microgram quantities for structural determinations. The review closes with a discussion on the future prospects of metabolomics related to crop species and herbal medicine.

Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism

Plants produce structurally diverse secondary (specialized) metabolites to increase their fitness for survival under adverse environments. Several bioactive compounds for new drugs have been identified through screening of plant extracts. In this study, genome-wide association studies (GWAS) were conducted to investigate the genetic architecture behind the natural variation of rice secondary metabolites. GWAS using the metabolome data of 175 rice accessions successfully identified 323 associations among 143 single nucleotide polymorphisms (SNPs) and 89 metabolites. The data analysis highlighted that levels of many metabolites are tightly associated with a small number of strong quantitative trait loci (QTLs). The tight association may be a mechanism generating strains with distinct metabolic composition through the crossing of two different strains. The results indicate that one plant species produces more diverse phytochemicals than previously expected, and plants still contain many useful compounds for human applications.

Metabolic fingerprinting of Arabidopsis thaliana accessions

In the post-genomic era much effort has been put on the discovery of gene function using functional genomics. Despite the advances achieved by these technologies in the understanding of gene function at the genomic and proteomic level, there is still a big genotype-phenotype gap. Metabolic profiling has been used to analyze organisms that have already been characterized genetically. However, there is a small number of studies comparing the metabolic profile of different tissues of distinct accessions. Here, we report the detection of over 14,000 and 17,000 features in inflorescences and leaves, respectively, in two widely used Arabidopsis thaliana accessions. A predictive Random Forest Model was developed, which was able to reliably classify tissue type and accession of samples based on LC-MS profile. Thereby we demonstrate that the morphological differences among A. thaliana accessions are reflected also as distinct metabolic phenotypes within leaves and inflorescences.

Rethinking Mass Spectrometry-Based Small Molecule Identification Strategies in Metabolomics

The CASMI 2013 (Critical Assessment of Small Molecule Identification 2013, http://casmi-contest.org/) contest was held to systematically evaluate strategies used for mass spectrometry-based identification of small molecules. The results of the contest highlight that, because of the extensive efforts made towards the construction of databases and search tools, database-assisted small molecule identification can now automatically annotate some metabolite signals found in the metabolome data. In this commentary, the current state of metabolite annotation is compared with that of transcriptomics and proteomics. The comparison suggested that certain limitations in the metabolite annotation process need to be addressed, such as (i) the completeness of the database, (ii) the conversion between raw data and structure, (iii) the one-to-one correspondence between measured data and correct search results, and (iv) the false discovery rate in database search results.

The food metabolome: a window over dietary exposure

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.

The flavonoid biosynthetic pathway in Arabidopsis: structural and genetic diversity

Flavonoids are representative plant secondary products. In the model plant Arabidopsis thaliana, at least 54 flavonoid molecules (35 flavonols, 11 anthocyanins and 8 proanthocyanidins) are found. Scaffold structures of flavonoids in Arabidopsis are relatively simple. These include kaempferol, quercetin and isorhamnetin for flavonols, cyanidin for anthocyanins and epicatechin for proanthocyanidins. The chemical diversity of flavonoids increases enormously by tailoring reactions which modify these scaffolds, including glycosylation, methylation and acylation. Genes responsible for the formation of flavonoid aglycone structures and their subsequent modification reactions have been extensively characterized by functional genomic efforts - mostly the integration of transcriptomics and metabolic profiling followed by reverse genetic experimentation. This review describes the state-of-art of flavonoid biosynthetic pathway in Arabidopsis regarding both structural and genetic diversity, focusing on the genes encoding enzymes for the biosynthetic reactions and vacuole translocation.

Phytochemical genomics--a new trend

Phytochemical genomics is a recently emerging field, which investigates the genomic basis of the synthesis and function of phytochemicals (plant metabolites), particularly based on advanced metabolomics. The chemical diversity of the model plant Arabidopsis thaliana is larger than previously expected, and the gene-to-metabolite correlations have been elucidated mostly by an integrated analysis of transcriptomes and metabolomes. For example, most genes involved in the biosynthesis of flavonoids in Arabidopsis have been characterized by this method. A similar approach has been applied to the functional genomics for production of phytochemicals in crops and medicinal plants. Great promise is seen in metabolic quantitative loci analysis in major crops such as rice and tomato, and identification of novel genes involved in the biosynthesis of bioactive specialized metabolites in medicinal plants.

Integrated LC-MS/MS system for plant metabolomics

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is highly sensitive, selective, and enables extensive detection of metabolites within a sample. The result allows us to characterize comprehensive metabolite accumulation patterns without dependence on authentic standard compounds and isolation of the individual metabolites. A reference database search is essential for the structural assignment process of un-targeted MS and MS/MS data. Moreover, the characterization of unknown metabolites is challenging, since these cannot be assigned a candidate structure by using a reference database. In this case study, integrated LC-MS/MS based plant metabolomics allows us to detect several hundred metabolites in a sample; and integrated omics analyses, e.g., large-scale reverse genetics, linkage mapping, and association mapping, provides a powerful tool for candidate structure selection or rejection. We also examine emerging technology and applications for LC-MS/MS-based un-targeted plant metabolomics. These activities promote the characterization of massive extended detectable metabolites.

PRIMe Update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation

PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a website that was designed and implemented to support research and analyses ranging from metabolomics to transcriptomics. To achieve functional genomics and annotation of unknown metabolites, we established the following PRIMe contents: MS2T, a library comprising >1 million entries of untargeted tandem mass spectrometry (MS/MS) data of plant metabolites; AtMetExpress LC-MS, a database of transcriptomics and metabolomics approaches in Arabidopsis developmental stages (AtMetExpress Development LC-MS) and a data set of the composition of secondary metabolites among 20 Arabidopsis ecotypes (AtMetExpress 20 ecotypes LC-MS); and ReSpect, hybrid reference MS/MS data resources (acquisitions and literature). PRIMeLink is a new web application that allows access to the innovative data resources of PRIMe. The MS2T library was generated from a set of MS/MS spectra acquired using the automatic data acquisition function of mass spectrometry. To increase the understanding of mechanisms driving variations in metabolic profiles among plant tissues, we further provided the AtMetExpress Development LC-MS database in PRIMe, facilitating the investigation of relationships between gene expression and metabolite accumulation. This information platform therefore provides an integrative analysis resource by linking Arabidopsis transcriptome and metabolome data. Moreover, we developed the ReSpect database, a plant-specific MS/MS data resource, which allows users to identify candidate structures from the suite of complex phytochemical structures. Finally, we integrated the three databases into PRIMeLink and established a walk-through link between transcriptome and metabolome information. PRIMeLink offers a bi-directional searchable function, from the gene and the metabolite perspective, to search for targets seamlessly and effectively.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware

A new, multi-threaded version of the GC-MS and LC-MS data processing software, metAlign, has been developed which is able to utilize multiple cores on one PC. This new version was tested using three different multi-core PCs with different operating systems. The performance of noise reduction, baseline correction and peak-picking was 8-19 fold faster compared to the previous version on a single core machine from 2008. The alignment was 5-10 fold faster. Factors influencing the performance enhancement are discussed. Our observations show that performance scales with the increase in processor core numbers we currently see in consumer PC hardware development.

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.

Plantmetabolomics.org: mass spectrometry-based Arabidopsis metabolomics--database and tools update

The PlantMetabolomics (PM) database (http://www.plantmetabolomics.org) contains comprehensive targeted and untargeted mass spectrum metabolomics data for Arabidopsis mutants across a variety of metabolomics platforms. The database allows users to generate hypotheses about the changes in metabolism for mutants with genes of unknown function. Version 2.0 of PlantMetabolomics.org currently contains data for 140 mutant lines along with the morphological data. A web-based data analysis wizard allows researchers to select preprocessing and data-mining procedures to discover differences between mutants. This community resource enables researchers to formulate models of the metabolic network of Arabidopsis and enhances the research community's ability to formulate testable hypotheses concerning gene functions. PM features new web-based tools for data-mining analysis, visualization tools and enhanced cross links to other databases. The database is publicly available. PM aims to provide a hypothesis building platform for the researchers interested in any of the mutant lines or metabolites.