Modifying the yeast very long chain fatty acid biosynthetic machinery by the expression of plant 3-ketoacyl CoA synthase isozymes.

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.

A single-cell platform for reconstituting and characterizing fatty acid elongase component enzymes.

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.

Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of "Unknown Function".

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 (www.PlantMetabolomics.org) 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.