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      Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm

      research-article
      1 , , 1
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept.

          Results

          High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae.

          Conclusion

          More than 1.6 million molecular formulae in the range 0–500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.

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          Most cited references37

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          Is Open Access

          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.
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            Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks.

            A novel extraction protocol is described with which metabolites, proteins and RNA are sequentially extracted from the same sample, thereby providing a convenient procedure for the analysis of replicates as well as exploiting the inherent biological variation of independent samples for multivariate data analysis. A detection of 652 metabolites, 297 proteins and clear RNA bands in a single Arabidopsis thaliana leaf sample was validated by analysis with gas chromatography coupled to a time of flight mass spectrometer for metabolites, two-dimensional liquid chromatography coupled to mass spectrometry for proteins, and Northern blot analysis for RNA. A subset of the most abundant proteins and metabolites from replicate analysis of different Arabidopsis accessions was merged to form an integrative dataset allowing both classification of different genotypes and the unbiased analysis of the hierarchical organization of proteins and metabolites within a real biochemical network.
              • Record: found
              • Abstract: found
              • Article: not found

              Differential metabolic networks unravel the effects of silent plant phenotypes.

              Current efforts aim to functionally characterize each gene in model plants. Frequently, however, no morphological or biochemical phenotype can be ascribed for antisense or knock-out plant genotypes. This is especially the case when gene suppression or knockout is targeted to isoenzymes or gene families. Consequently, pleiotropic effects and gene redundancy are responsible for phenotype resistance. Here, techniques are presented to detect unexpected pleiotropic changes in such instances despite very subtle changes in overall metabolism. The method consists of the relative quantitation of >1,000 compounds by GC/time-of-flight MS, followed by classical statistics and multivariate clustering. Complementary to these tools, metabolic networks are constructed from pair-wise analysis of linear metabolic correlations. The topology of such networks reflects the underlying regulatory pathway structure. A differential analysis of network connectivity was applied for a silent potato plant line suppressed in expression of sucrose synthase isoform II. Metabolic alterations could be assigned to carbohydrate and amino acid metabolism even if no difference in average metabolite levels was found.

                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                2006
                28 April 2006
                : 7
                : 234
                Affiliations
                [1 ]University of California Davis, Genome Center, 451 E. Health Sci Dr., Davis, CA 95616, USA
                Article
                1471-2105-7-234
                10.1186/1471-2105-7-234
                1464138
                16646969
                f25f50b0-7e9f-4ec2-88bb-1a9b71c8f93e
                Copyright © 2006 Kind and Fiehn; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 December 2005
                : 28 April 2006
                Categories
                Research Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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