SummaryThe integration of metabolomics and transcriptomics can provide precise information on gene-to-metabolite networks for identifying the function of unknown genes unless there has been a post-transcriptional modification. Here, we report a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over-expressing the PAP1 gene encoding an MYB transcription factor, for the identification of novel gene functions involved in flavonoid biosynthesis. For metabolome analysis, we performed flavonoid-targeted analysis by high-performance liquid chromatography-mass spectrometry and non-targeted analysis by Fourier-transform ion-cyclotron mass spectrometry with an ultrahigh-resolution capacity. This combined analysis revealed the specific accumulation of cyanidin and quercetin derivatives, and identified eight novel anthocyanins from an array of putative 1800 metabolites in PAP1 over-expressing plants. The transcriptome analysis of 22 810 genes on a DNA microarray revealed the induction of 38 genes by ectopic PAP1 overexpression. In addition to well-known genes involved in anthocyanin production, several genes with unidentified functions or annotated with putative functions, encoding putative glycosyltransferase, acyltransferase, glutathione S-transferase, sugar transporters and transcription factors, were induced by PAP1. Two putative glycosyltransferase genes (At5g17050 and At4g14090) induced by PAP1 expression were confirmed to encode flavonoid 3-O-glucosyltransferase and anthocyanin 5-O-glucosyltransferase, respectively, from the enzymatic activity of their recombinant proteins in vitro and results of the analysis of anthocyanins in the respective T-DNA-inserted mutants. The functional genomics approach through the integration of metabolomics and transcriptomics presented here provides an innovative means of identifying novel gene functions involved in plant metabolism.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all ''omics'' sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such geneto-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants. P lants produce a huge array of compounds used for foods, medicines, flavors, and industrial materials. These plant metabolites are synthesized and accumulated by the networks of proteins encoded in the genome of each plant. However, even after the completion of the genome sequencing of Arabidopsis (1) and rice (2, 3), function of those genes and networks of gene-to-metabolite are largely unknown. To reveal the function of genes involved in metabolic processes and gene-to-metabolite networks, the metabolomics-based approach is regarded as a direct way (4-7). In particular, integration of comprehensive gene expression profile with targeted metabolite analysis is shown to be an innovative way for identification of gene function for specific product accumulation in plant (8) and microorganisms (9). However, to depict a whole-cellular process of metabolism, integration of comprehensive gene expression analysis (transcriptomics), and nontargeted metabolite profiling (metabolomics) is needed. Bioinformatics designed suitably for data mining helps the integration efficiently.The gene expression profiling can be achieved by DNA array analysis. For metabolomics, a nontargeted, high-throughput analytical system is required. Traditionally, GC-MS has been used to detect Ͼ300 metabolites in plant tissues (5, 6). Fourier transform-ion cyclotron MS (FT-MS) is a system for metabolome analysis in which crude plant extract is introduced by means of direct injection without prior separation of metabolites by chromatography (10). The mass resolution (Ͼ100,000) and accuracy (Ͻ1 ppm) of FT-MS is extremely high; hence, comple...

Since the completion of genome sequences of model organisms, functional identification of unknown genes has become a principal challenge in biology. Postgenomics sciences such as transcriptomics, proteomics, and metabolomics are expected to discover gene functions. This report outlines the elucidation of gene-togene and metabolite-to-gene networks via integration of metabolomics with transcriptomics and presents a strategy for the identification of novel gene functions. Metabolomics and transcriptomics data of Arabidopsis grown under sulfur deficiency were combined and analyzed by batch-learning self-organizing mapping. A group of metabolites/genes regulated by the same mechanism clustered together. The metabolism of glucosinolates was shown to be coordinately regulated. Three uncharacterized putative sulfotransferase genes clustering together with known glucosinolate biosynthesis genes were candidates for involvement in biosynthesis. In vitro enzymatic assays of the recombinant gene products confirmed their functions as desulfoglucosinolate sulfotransferases. Several genes involved in sulfur assimilation clustered with O-acetylserine, which is considered a positive regulator of these genes. The genes involved in anthocyanin biosynthesis clustered with the gene encoding a transcriptional factor that up-regulates specifically anthocyanin biosynthesis genes. These results suggested that regulatory metabolites and transcriptional factor genes can be identified by this approach, based on the assumption that they cluster with the downstream genes they regulate. This strategy is applicable not only to plant but also to other organisms for functional elucidation of unknown genes.In the era of post-genomics, a systematic and comprehensive understanding of the complex events of life is a great concern in biology. The first step in this process is to identify all gene functions and gene-to-gene networks as the components of the system, the whole events of life. The metabolome is the final product of a series of gene actions. Hence, metabolomics has a potential to elucidate gene functions and networks, especially when integrated with transcriptomics. A promising approach is pair-wise transcript-metabolite correlation analysis, which can reveal unexpected correlations and bring to light candidate genes for modifying the metabolite content (1). Gene functions involved in the specific pathway of interest have been identified by the integration of transcript and targeted metabolic profiling in experimental systems where the pathway was activated (2-6). However, up to now, no gene function has been identified by non-targeted analyses of the transcriptome and metabolome. In this report, we analyzed the non-targeted metabolome and transcriptome of a model plant Arabidopsis under sulfur (S) 1 deficiency whose genome sequencing has been completed. Our strategy for integrated analyses using batch-learning-selforganizing mapping (BL-SOM) (7-9) enabled the identification of gene-to-gene and metabolite-to-gene networks and new gene fun...

Advanced functional genomic tools now allow the parallel and high-throughput analyses of gene and protein expression. Although this information is crucial to our understanding of gene function, it offers insufficient insight into phenotypic changes associated with metabolism. Here we introduce a high-capacity Fourier Transform Ion Cyclotron Mass Spectrometry (FTMS)-based method, capable of nontargeted metabolic analysis and suitable for rapid screening of similarities and dissimilarities in large collections of biological samples (e.g., plant mutant populations). Separation of the metabolites was achieved solely by ultra-high mass resolution; Identification of the putative metabolite or class of metabolites to which it belongs was achieved by determining the elemental composition of the metabolite based upon the accurate mass determination; and relative quantitation was achieved by comparing the absolute intensities of each mass using internal calibration. Crude plant extracts were introduced via direct (continuous flow) injection and ionized by either electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI) in both positive or negative ionization modes. We first analyzed four consecutive stages of strawberry fruit development and identified changes in the levels of a large range of masses corresponding to known fruit metabolites. The data also revealed novel information on the metabolic transition from immature to ripe fruit. In another set of experiments, the method was used to track changes in metabolic profiles of tobacco flowers overexpressing a strawberry MYB transcription factor and altered in petal color. Only nine masses appeared different between transgenic and control plants, among which was the mass corresponding to cyanidin-3-rhamnoglucoside, the main flower pigment. The results demonstrate the feasibility and utility of the FTMS approach for a nontargeted and rapid metabolic "fingerprinting," which will greatly speed up current efforts to study the metabolome and derive gene function in any biological system.

Although dementia of the Alzheimer's type (DAT) is the most common form of dementia, the severity of dementia is only weakly correlated with DAT pathology. In contrast, postmortem measurements of cholinergic function and membrane ethanolamine plasmalogen (PlsEtn) content in the cortex and hippocampus correlate with the severity of dementia in DAT. Currently, the largest risk factor for DAT is age. Because the synthesis of PlsEtn occurs via a single nonredundant peroxisomal pathway that has been shown to decrease with age and PlsEtn is decreased in the DAT brain, we investigated potential relationships between serum PlsEtn levels, dementia severity, and DAT pathology. In total, serum PlsEtn levels were measured in five independent population collections comprising .400 clinically demented and .350 nondemented subjects. Circulating PlsEtn levels were observed to be significantly decreased in serum from clinically and pathologically diagnosed DAT subjects at all stages of dementia, and the severity of this decrease correlated with the severity of dementia. Furthermore, a linear regression model predicted that serum PlsEtn levels decrease years before clinical symptoms. The putative roles that PlsEtn biochemistry play in the etiology of cholinergic degeneration, amyloid accumulation, and dementia are discussed. The most severe consequence of the aging brain is dementia. The number of elderly people is increasing rapidly within our society, and as a consequence, dementia is growing into a major health problem. It has been estimated that 25% of the population older than 65 years has some form of dementia (1) and that the cumulative incidence of dementia in individuals living to the age of 95 years is .80% (2, 3).The clinical manifestation of dementia can result from neurodegeneration [e.g., dementia of the Alzheimer's type (DAT), dementia with Lewy bodies, and frontotemporal lobe dementia], a vascular event (e.g., multi-infarct dementia) or anoxic event (e.g., cardiac arrest), brain trauma [e.g., dementia pugilistica (boxer's dementia)], or exposure to an infectious agent (e.g., Creutzfeldt-Jakob disease) or a toxic agent (e.g., alcohol-induced dementia) (4). Given that dementia can result from diverse neurological insults, the biochemical mechanism of dementia is likely to be separate and distinct from these precipitating events.The differential diagnosis of the types and causes of dementia is not straightforward. A prospective study of the prevalence of DAT in people older than 85 years indicated that more than half of the individuals with neuropathological criteria for DAT were either nondemented or incorrectly diagnosed with vascular dementia. As well, 35% of the clinically diagnosed DAT subjects did not exhibit neuropathological features sufficient to support the diagnosis (5). Clearly, dementia can arise from multiple pathological states that are often clinically indistinguishable. Because DAT is the most common type of dementia and

BackgroundThere are currently no accurate serum markers for detecting early risk of colorectal cancer (CRC). We therefore developed a non-targeted metabolomics technology to analyse the serum of pre-treatment CRC patients in order to discover putative metabolic markers associated with CRC. Using tandem-mass spectrometry (MS/MS) high throughput MS technology we evaluated the utility of selected markers and this technology for discriminating between CRC and healthy subjects.MethodsBiomarker discovery was performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Comprehensive metabolic profiles of CRC patients and controls from three independent populations from different continents (USA and Japan; total n = 222) were obtained and the best inter-study biomarkers determined. The structural characterization of these and related markers was performed using liquid chromatography (LC) MS/MS and nuclear magnetic resonance technologies. Clinical utility evaluations were performed using a targeted high-throughput triple-quadrupole multiple reaction monitoring (TQ-MRM) method for three biomarkers in two further independent populations from the USA and Japan (total n = 220).ResultsComprehensive metabolomic analyses revealed significantly reduced levels of 28-36 carbon-containing hydroxylated polyunsaturated ultra long-chain fatty-acids in all three independent cohorts of CRC patient samples relative to controls. Structure elucidation studies on the C28 molecules revealed two families harbouring specifically two or three hydroxyl substitutions and varying degrees of unsaturation. The TQ-MRM method successfully validated the FTICR-MS results in two further independent studies. In total, biomarkers in five independent populations across two continental regions were evaluated (three populations by FTICR-MS and two by TQ-MRM). The resultant receiver-operator characteristic curve AUCs ranged from 0.85 to 0.98 (average = 0.91 ± 0.04).ConclusionsA novel comprehensive metabolomics technology was used to identify a systemic metabolic dysregulation comprising previously unknown hydroxylated polyunsaturated ultra-long chain fatty acid metabolites in CRC patients. These metabolites are easily measurable in serum and a decrease in their concentration appears to be highly sensitive and specific for the presence of CRC, regardless of ethnic or geographic background. The measurement of these metabolites may represent an additional tool for the early detection and screening of CRC.

Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networks ab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly information-rich and clearly non-random. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systems-wide analysis of cellular metabolism.

59Background: Plasmalogens, which are key structural phospholipids in brain membranes, are decreased in the brain and serum of patients with Alzheimer disease (AD). We performed this pilot study to evaluate the relation between the levels of circulating plasmalogens and Alzheimer Disease Assessment Scale-Cognitive (ADAS-Cog) scores in patients with AD. Methods: We evaluated participants' ADAS-Cog scores and serum plasmalogen levels. For the 40 included AD patients with an ADAS-Cog score between 20 and 46, we retested their ADAS-Cog score 1 year later. The levels of docosahexaenoic acid plasmalogen were measured by use of liquid chromatography-tandem mass spectrometry. Results: We found that the ADAS-Cog score increased significantly in AD patients with circulating plasmalogen levels that were ≤ 75% of that of age-matched controls at entry into the study. There was no change in score among participants with normal serum plasmalogen levels at baseline (> 75%). Limitations: This was a pilot study with 40 patients, and the results require validation in a larger population. Conclusion: Our study demonstrates that decreased levels of plasmalogen precursors in the central nervous system correlate with functional decline (as measured by ADAS-Cog scores) in AD patients. The use of both ADAS-Cog and serum plasmalogen data may be a more accurate way of predicting cognitive decline in AD patients, and may be used to decrease the risk of including patients with no cognitive decline in the placebo arm of a drug trial.