Phytochemical Genomics Research Articles

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos.

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Integrated Metabolomics and Phytochemical Genomics Approaches for Studies on St. John’s Wort

Metabolomics is widely applied for investigation of the correlation between metabolites and genes responsible for the synthesis of the particular sets of metabolites. In this review, we discuss metabolomics research on Hypericum perforatum (St. John’s Wort) to elucidate the overall regulation of the metabolism related to the mechanisms of natural variations and environmental stresses such as fungal infections, light stresses, and chemical elicitors. We also focus on phytochemical genomics and genomic information. St. John’s Wort is a medicinal plant with high potential of producing hypericin used for mild depression remedy, so knowledge on the biosynthetic pathway of unique metabolites is fundamental for their biotechnological commercial production. These metabolites have often complex biosynthetic pathway and it is challenging to identify all of the catalyzing enzymes. The development of metabolic systems biology could open new channels for high-speed construction and evaluation of hypotheses for cellular regulatory systems.

Development of Plant Metabolomics and Medicinal Plant Genomics

A variety of chemicals produced by plants, often referred to as 'phytochemicals', have been used as medicines, food, fuels and industrial raw materials. Recent advances in the study of genomics and metabolomics in plant science have accelerated our understanding of the mechanisms, regulation and evolution of the biosynthesis of specialized plant products. We can now address such questions as how the metabolomic diversity of plants is originated at the levels of genome, and how we should apply this knowledge to drug discovery, industry and agriculture. Our research group has focused on metabolomics-based functional genomics over the last 15 years and we have developed a new research area called 'Phytochemical Genomics'. In this review, the development of a research platform for plant metabolomics is discussed first, to provide a better understanding of the chemical diversity of plants. Then, representative applications of metabolomics to functional genomics in a model plant, Arabidopsis thaliana, are described. The extension of integrated multi-omics analyses to non-model specialized plants, e.g., medicinal plants, is presented, including the identification of novel genes, metabolites and networks for the biosynthesis of flavonoids, alkaloids, sulfur-containing metabolites and terpenoids. Further, functional genomics studies on a variety of medicinal plants is presented. I also discuss future trends in pharmacognosy and related sciences.

Phytochemical genomics – From a model plant to medicinal plants

Phytochemical genomics – From a model plant to medicinal plants

Integrated metabolomics and phytochemical genomics approaches for studies on rice.

Metabolomics is widely employed to monitor the cellular metabolic state and assess the quality of plant-derived foodstuffs because it can be used to manage datasets that include a wide range of metabolites in their analytical samples. In this review, we discuss metabolomics research on rice in order to elucidate the overall regulation of the metabolism as it is related to the growth and mechanisms of adaptation to genetic modifications and environmental stresses such as fungal infections, submergence, and oxidative stress. We also focus on phytochemical genomics studies based on a combination of metabolomics and quantitative trait locus (QTL) mapping techniques. In addition to starch, rice produces many metabolites that also serve as nutrients for human consumers. The outcomes of recent phytochemical genomics studies of diverse natural rice resources suggest there is potential for using further effective breeding strategies to improve the quality of ingredients in rice grains.

Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses.

Metabolomics plays an important role in phytochemical genomics and crop breeding; however, metabolite annotation is a significant bottleneck in metabolomic studies. In particular, in liquid chromatography–mass spectrometry (MS)-based metabolomics, which has become a routine technology for the profiling of plant-specialized metabolites, a substantial number of metabolites detected as MS peaks are still not assigned properly to a single metabolite. Oryza sativa (rice) is one of the most important staple crops in the world. In the present study, we isolated and elucidated the structures of specialized metabolites from rice by using MS/MS and NMR. Thirty-six compounds, including five new flavonoids and eight rare flavonolignan isomers, were isolated from the rice leaves. The MS/MS spectral data of the isolated compounds, with a detailed interpretation of MS fragmentation data, will facilitate metabolite annotation of the related phytochemicals by enriching the public mass spectral data depositories, including the plant-specific MS/MS-based database, ReSpect.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-013-0619-5) contains supplementary material, which is available to authorized users.

Phytochemical Genomics on the Way

This is a really exciting time for phytochemistry research. Plant scientists studying specialized plant metabolism (previously often known as ‘secondary metabolism’) can foresee tremendous advances in their understanding of the mechanisms underlying the production and function of an enormous variety of plant metabolites, and of their regulation and evolution. Such remarkable progress would never have been expected 10 years ago and has taken place in just the last few years (Yonekura-Sakakibara and Saito 2009). Two technological breakthroughs seem to have been the key triggers for this unforeseen progress: metabolomics and mass DNA sequencing. Metabolomics is <15 years old, yet the concept was immediately applied to the plant field as scientists realized its tremendous potential for understanding the capacity of plants for making a huge array of compounds (Bino et al. 2004). Metabolomics now makes possible comprehensive profiling of nearly all the metabolites that accumulate in plant cells (Saito and Matsuda 2010). The second technological breakthrough was ultra-high-throughput DNA sequencing, with so-called ‘next-generation DNA sequencers’ (Wang et al. 2009, Ozsolak and Milos 2011). Until the realization of this technology, no one would have anticipated genomics-based studies of hundreds of medicinal or exotic plant species— genomics was only for model plants or major crops, whose genome sequences could only be revealed by large international research consortia (or by huge well-funded undertakings). However, newly developed mass DNA sequencers have made comprehensive genome and transcriptome sequencing possible for ordinary phytochemical researchers who are investigating the synthesis of particular plant metabolites out of their own curiosity. By combining data sets from metabolomics and genomics/transcriptomics, one can understand relationships between metabolites and genes that allow the generation of testable hypotheses about the functions of genes and metabolites (Higashi and Saito 2013, Hur et al. 2013, Yonekura-Sakakibara et al. 2013). During this integration and hypothesis-generation phase, databases and bioinformatics play indispensable roles in reducing this ‘systems biology’ workload. Such studies will provide us with an understanding of the evolution of genes and pathways and hence in-depth fundamental insights into plant life. This special focus issue (SFI) on ‘Phytochemical Genomics’ is intended to serve as a synthesis of the most up to date information in this field, indicating not only current trends but also its future prospects. For this SFI of Plant and Cell Physiology, we invited one review article and six original articles reporting on the exciting and growing field of phytochemical genomics. The huge numbers of specialized metabolites produced in plants are mainly classified into three basic groups: alkaloids, terpenoids and phenolic compounds. Among alkaloids, benzylisoquinoline alkaloids (BIAs), including the narcotic analgesics codeine and morphine, are one of the groups of compounds most exploited for their medicinal properties. In this SFI, Hagel and Facchini (2013; see pp. 647–672) review recent advances in biosynthetic studies of BIAs including transcriptomics, proteomics and metabolomics; they also discuss the application of synthetic biology to the development of production by microbes as an alternative to plants as a potential commercial source of valuable BIAs. Another three deep transcritome analyses are reported in this SFI. Catharanthus roseus synthesizes numerous terpenoid indole alkaloids, such as the anticancer drugs vinblastine and vincristine. So far, pathway databases and metabolic networks reconstructed from omics sequence data have been mainly built for model plant species such as Arabidopis and rice. Van Moerkercke et al. (2013; see pp. 673–685) conducted a deep transcriptome analysis in the medicinal plant, C. roseus, and constructed a detailed metabolic pathway database ‘CathaCyc’ that contains 390 pathways with more than a thousand assigned enzymes. Yamazaki et al. (2013; see pp. 686–696) coupled the deep transcriptome analysis with untargeted metabolic profiling in Ophiorrhiza pumila, which produces the anti-cancer alkaloids camptothecin and anthraquinones. Yamazaki and colleagues compared both transcriptome and metabolome data sets in the alkaloid-producing hairy root with a cell suspension that does not produce alkaloids. Glycyrrhiza plants produce various phytochemicals, among them many different terpenoids and flavonoids. Among the phytochemicals, glycyrrhizin exhibits several sorts of pharmacological activity and is also used as a natural sweetener. Two cytochrome P450 monooxygenase (P450s) genes for glycyrrhizin biosynthesis have been recently identified (Seki et al. 2008, Seki et al. 2011). Ramilowski et al. (2013; see pp. 697–710) conducted deep transcriptome analysis in G. uralensis, and

Metabolomics for unknown plant metabolites

In this article we discuss current trends in the techniques available for plant metabolomics. Chemical assignment of unknown metabolites leads to understanding of biosynthetic mechanisms at the gene level for genome-sequenced plants. Metabolomics using mass spectrometry has achieved innovative results in phytochemical genomics for primary and secondary metabolism in the model plant Arabidopsis thaliana by using publicly and commercially available information and standard compounds. However, finding a consolidated analytical technique for elucidation of structural information (e.g., elemental composition and structure) remains challenging. Recently, hyphenated analytical techniques and computer-assisted structural analysis with high-throughput and high-accuracy have been developing. Metabolite-driven approaches using such technology will be of central importance in phytochemical genomics.

Combination of Liquid Chromatography–Fourier Transform Ion Cyclotron Resonance-Mass Spectrometry with 13C-Labeling for Chemical Assignment of Sulfur-Containing Metabolites in Onion Bulbs

Phytochemicals containing heteroatoms (N, O, S, and halogens) often have biological activities that are beneficial to humans. Although targeted profiling methods for such phytochemicals are expected to contribute to rapid chemical assignments, thus making phytochemical genomics and crop breeding much more efficient, there are few profiling methods for the metabolites. Here, as an ultrahigh performance approach, we propose a practical profiling method for S-containing metabolites (S-omics) using onions (Allium cepa) as a representative species and (12)C- and (13)C-based mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses by liquid chromatography-Fourier transform ion cyclotron resonance-mass spectrometry (LC-FTICR-MS). Use of the ultrahigh quality data from FTICR-MS enabled simplifying the previous methods to determine specific elemental compositions. MS analysis with a resolution of >250,000 full width at half-maximum and a mass accuracy of <1 ppm can distinguish S-containing monoisotopic ions from other ions on the basis of the natural abundance of (32)S and (34)S and the mass differences among the S isotopes. Comprehensive peak picking using the theoretical mass difference (1.99579 Da) between (32)S-containing monoisotopic ions and their (34)S-substituted counterparts led to the assignment of 67 S-containing monoisotopic ions from the (12)C-based MS spectra, which contained 4693 chromatographic ions. The unambiguous elemental composition of 22 ions was identified through comparative analysis of the (12)C- and (13)C-based MS spectra. Finally, of these, six ions were found to be derived from S-alk(en)ylcysteine sulfoxides and glutathione derivatives. This S-atom-driven approach afforded an efficient chemical assignment of S-containing metabolites, suggesting its potential application for screening not only S but also other heteroatom-containing metabolites in MS-based metabolomics.

Phytochemical genomics in Arabidopsis thaliana: A case study for functional identification of flavonoid biosynthesis genes

Abstract The completion of the whole genome sequence of Arabidopsis thaliana has made it possible to explore the phytochemical genomics in this species by determining gene-to-metabolite correlation through the comprehensive analysis of metabolite accumulation and gene expression. In this study, flavonoid profiling of wild-type plants and T-DNA insertion mutants was analyzed using ultra-performance liquid chromatography (UPLC)/photodiode array detection (PDA)/electrospray ionization (ESI)/multiple-stage mass spectrometry (MSn). Detailed analysis of the metabolite changes in the mutants suggested the functions of genes that have been mutated. In silico coexpression analysis of genes involved in flavonoid metabolism in Arabidopsis was performed using a publicly available transcriptome database of DNA microarrays. We inferred a coexpression framework model of the genes involved in the pathways of flavonol, anthocyanin, and proanthocyanidin synthesis, suggesting specific functions and coregulation of the genes of pathway enzymes and transcription factors. The metabolic profiling of the omt1 mutant lacking a methyltransferase gene narrowed down by the coexpression analysis showed that AtOMT1 (At5g54160) is involved not only in the production of lignins and sinapoyl esters but also in the methylation of flavonols forming isorhamnetin. These results suggest that the functional genomics approach by detailed target-metabolite profiling with transcriptome coexpression analysis provides an efficient way of identifying novel gene functions involved in plant metabolism.