Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these dataset with transcriptomic data to create hypotheses concerning specialized metabolism that generates the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.
Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database () has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs.
Fatty acids of more than 18-carbons, generally known as very long chain fatty acids (VLCFAs) are essential for eukaryotic cell viability, and uniquely in terrestrial plants they are the precursors of the cuticular lipids that form the organism’s outer barrier to the environment. VLCFAs are synthesized by fatty acid elongase (FAE), which is an integral membrane enzyme system with multiple components. The genetic complexity of the FAE system, and its membrane association has hampered the biochemical characterization of FAE. In this study we computationally identified Zea mays genetic sequences that encode the enzymatic components of FAE and developed a heterologous expression system to evaluate their functionality. The ability of the maize components to genetically complement Saccharomyces cerevisiae lethal mutants confirmed the functionality of ZmKCS4, ZmELO1, ZmKCR1, ZmKCR2, ZmHCD and ZmECR, and the VLCFA profiles of the resulting strains were used to infer the ability of each enzyme component to determine the product profile of FAE. These characterizations indicate that the product profile of the FAE system is an attribute shared among the KCS, ELO, and KCR components of FAE.
Eukaryotes express a multi-component fatty acid elongase to produce very long chain fatty acids (VLCFAs), which are building blocks of diverse lipids. Elongation is achieved by cyclical iteration of four reactions, the first of which generates a new carbon–carbon bond, elongating the acyl-chain. This reaction is catalyzed by either ELONGATION DEFECTIVE LIKE (ELO) or 3-ketoacyl-CoA synthase (KCS) enzymes. Whereas plants express both ELO and KCS enzymes, other eukaryotes express only ELOs. We explored the Zea mays KCS enzymatic redundancies by expressing each of the 26 isozymes in yeast strains that lacked endogenous ELO isozymes. Expression of the 26 maize KCS isozymes in wild-type, scelo2 or scelo3 single mutants did not affect VLCFA profiles. However, a complementation screen of each of the 26 KCS isozymes revealed five that were capable of complementing the synthetically lethal scelo2; scelo3 double mutant. These rescued strains express novel VLCFA profiles reflecting the different catalytic capabilities of the KCS isozymes. These novel strains offer a platform to explore the relationship between VLCFA profiles and cellular physiology.
Eukaryotes express a multi-component fatty acid elongase to produce very long chain fatty acids (VLCFAs), which are building blocks of diverse lipids. Elongation is achieved by cyclical iteration of four reactions, the first of which generates a new carbon-carbon bond, elongating the acyl-chain. This reaction is catalyzed by either ELONGATION DEFECTIVE LIKE (ELO) or 3-ketoacyl-CoA synthase (KCS) enzymes. Whereas plants express both ELO and KCS enzymes, other eukaryotes express only ELOs. We explored the KCS and ELO enzymatic redundancies by expressing the former in yeast strains that lacked endogenous ELO isozymes. Expression of the 26 maize KCS isozymes in wild-type, scelo2 or scelo3 single mutants did not affect VLCFA profiles. However, five of these KCSs were capable of complementing the lethal scelo2; scelo3 double mutant. These rescued strains express novel VLCFA profiles reflecting the different catalytic capabilities of the KCS isozymes. These novel strains offer a platform to explore the relationship between VLCFA profiles and cellular physiology.
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