Targeted Metabolite Profiling Research Articles

Physiologic and metabolic responses of wheat seedlings to elevated and super-elevated carbon dioxide

The metabolic consequence of suboptimal (400 μmol mol −1 or ppm), near-optimal (1500 ppm) and supra-optimal (10,000 ppm) atmospheric carbon dioxide concentrations [CO 2] was investigated in an attempt to reveal plausible underlying mechanisms for the differential physiological and developmental responses to increasing [CO 2]. Both non-targeted and targeted metabolite profiling by GC–MS and LC–MS were employed to examine primary and secondary metabolites in wheat ( Triticum aestivum, cv Yocoro rojo) continuously exposed to these [CO 2] levels for 14, 21 and 28 days. Metabolite profile was altered by both [CO 2] and physiological age. In general, plants grown under high [CO 2] exhibited a metabolite profile characteristic of older plants under ambient CO 2. Elevated [CO 2] resulted in higher levels of phosphorylated sugar intermediates, though no clear trend in the content of reducing sugars was observed. Transient starch content was enhanced by increasing [CO 2] to a much greater extent at 10,000 ppm CO 2 than at 1500 ppm CO 2. The percentage increase of starch content resulting from CO 2 enrichment declined as plants develope. In contrast, elevated [CO 2] promoted the accumulation of secondary metabolites (flavonoids) progressively to a greater extent as plants became mature. Elevated [CO 2] to 1500 ppm induced a higher initial growth rate, while super-elevated [CO 2] appeared to negate such initial growth promotion. However, after 4 weeks, there was no difference in vegetative growth between 1500 and 10,000 ppm CO 2-grown plants, both elevated CO 2 levels resulted in an overall 25% increase in biomass over the control plants. More interestingly, elevated atmospheric [CO 2] reduced evapotranspiration rate (ET), but further increase to the supra-optimal level resulted in increased ET (a reversed trend), i.e. ET at 1500 ppm < ET at 10,000 ppm < ET at 400 ppm. The differential effect of elevated and super-elevated CO 2 on plants was further reflected in the nitrogen dynamics. These results provide the potential metabolic basis for the differential productivity and stomatal function of plants grown under elevated and super-elevated CO 2 levels.

Application of Functional Genomics to Pathway Optimization for Increased Isoprenoid Production

Producing complex chemicals using synthetic metabolic pathways in microbial hosts can have many advantages over chemical synthesis but is often complicated by deleterious interactions between pathway intermediates and the host cell metabolism. With the maturation of functional genomic analysis, it is now technically feasible to identify modes of toxicity associated with the accumulation of foreign molecules in the engineered bacterium. Previously, Escherichia coli was engineered to produce large quantities of isoprenoids by creating a mevalonate-based isopentenyl pyrophosphate biosynthetic pathway (V. J. J. Martin et al., Nat. Biotechnol. 21:796-802, 2003). The engineered E. coli strain produced high levels of isoprenoids, but further optimization led to an imbalance in carbon flux and the accumulation of the pathway intermediate 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA), which proved to be cytotoxic to E. coli. Using both DNA microarray analysis and targeted metabolite profiling, we have studied E. coli strains inhibited by the intracellular accumulation of HMG-CoA. Our results indicate that HMG-CoA inhibits fatty acid biosynthesis in the microbial host, leading to generalized membrane stress. The cytotoxic effects of HMG-CoA accumulation can be counteracted by the addition of palmitic acid (16:0) and, to a lesser extent, oleic acid (cis-Delta(9)-18:1) in the growth medium. This work demonstrates the utility of using transcriptomic and metabolomic methods to optimize synthetic biological systems.

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.

Integrative analysis of transcript and metabolite profiling data sets to evaluate the regulation of biochemical pathways during photomorphogenesis

One of the key developmental processes during photomorphogenesis is the differentiation of prolamellar bodies of proplastids into thylakoid membranes containing the photosynthetic pigment–protein complexes of chloroplasts. To study the regulatory events controlling pigment–protein complex assembly, including the biosynthesis of metabolic precursors and pigment end products, etiolated Arabidopsis thaliana seedlings were irradiated with continuous red light (Rc), which led to rapid greening, or continuous far-red light (FRc), which did not result in visible greening, and subjected to analysis by oligonucleotide microarrays and targeted metabolite profiling. An analysis using BioPathAt, a bioinformatic tool that allows the visualization of post-genomic data sets directly on biochemical pathway maps, indicated that in Rc-treated seedlings mRNA expression and metabolite patterns were tightly correlated (e.g., Calvin cycle, biosynthesis of chlorophylls, carotenoids, isoprenoid quinones, thylakoid lipids, sterols, and amino acids). K-means clustering revealed that gene expression patterns across various biochemical pathways were very similar in Rc- and FRc-treated seedlings (despite the visible phenotypic differences), whereas a principal component analysis of metabolite pools allowed a clear distinction between both treatments (in accordance with the visible phenotype). Our results illustrate the general importance of integrative approaches to correlate post-genomic data sets with phenotypic outcomes.

Targeted metabolite profiling provides a functional link among eucalypt taxonomy, physiology and evolution

Adaptation to aridity is considered a major factor in the evolution of the genus Eucalyptus. For the first time, targeted metabolite profiling has uncovered a quantitative yet discrete phytochemical link with eucalypt taxonomy. The distribution of cyclitols among Eucalyptus species, and a range of other Australian tree genera, support their proposed functions in plant tissues and provide putative links with the acclimation of trees to arid environments.

Genomics‐based selection and functional characterization of triterpene glycosyltransferases from the model legume Medicago truncatula

The biosynthesis of triterpene saponins is poorly characterized in spite of the importance of these glycosylated secondary metabolites for plant defense and animal health. The model legume Medicago truncatula synthesizes more than 30 different saponins based on at least five triterpene aglycones; soyasapogenols B and E, medicagenic acid, hederagenin and bayogenin. We have employed an inducible cell culture system, DNA array-based and in silico transcript profiling, and targeted metabolite profiling, to identify triterpene glycosyltransferases (GTs) from among the more than 300 GTs expressed in M. truncatula. Two uridine diphosphate glucosyltransferases were functionally characterized; UGT73K1 with specificity for hederagenin and soyasapogenols B and E, and UGT71G1 with specificity for medicagenic acid. The latter enzyme also glycosylated certain isoflavones and the flavonol quercetin with higher efficiency than triterpenes; however, integrated transcript and metabolite profiling supported a function for UGT71G1 in terpenoid but not (iso)flavonoid biosynthesis in the elicited cell cultures.

Metabolomics--the link between genotypes and phenotypes.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms – the phenotypes – are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.

THE MEDICAL SICKNESS SOCIETY

<h3>Objective</h3> There is a pressing need to develop reliable molecular biomarkers in osteoarthritis. The aim of the study was to identify novel serum biomarkers for osteoarthritis using a metabolomics approach. <h3>Methods</h3> A two-stage study design was utilised. 123 knee osteoarthritis cases and 299 controls were selected from the TwinsUK cohort as a discovery sample. 76 knee osteoarthritis cases and 100 controls from the Chingford Study were used as replication. Knee osteoarthritis was defined as either radiographic, medically diagnosed or total knee joint replacement due to primary osteoarthritis. All the subjects were unrelated white women. Their serum samples were assessed for targeted metabolite profiling by electrospray ionisation tandem mass spectrometry using the AbsoluteIDQ kit. 163 serum metabolites were assessed and their concentrations obtained. The ratios of metabolite concentrations as proxies for enzymatic reaction rates were calculated and tested for the association with knee osteoarthritis. Significance was assessed after adjustment for multiple testing (Bonferroni method) and potential confounders. <h3>Results</h3> In the discovery stage, the authors identified 14 ratios significantly associated with knee osteoarthritis with p≤1.9×10⁻⁶. Two of these 14 ratios were successfully confirmed in the replication stage—the ratios of valine to histidine and xleucine to histidine, with p=0.002. The significance remained after adjustment for age and body mass index. <h3>Conclusion</h3> This is the first serum-based metabolomic study of osteoarthritis in humans. The branched-chain amino acids to histidine ratio has potential clinical use as an osteoarthritis biomarker and shows the clinical potential of metabolomics.