Background: Acyl-acyl carrier protein thioesterases (acyl-ACP TEs) catalyze the hydrolysis of the thioester bond that links the acyl chain to the sulfhydryl group of the phosphopantetheine prosthetic group of ACP. This reaction terminates acyl chain elongation of fatty acid biosynthesis, and in plant seeds it is the biochemical determinant of the fatty acid compositions of storage lipids. Results: To explore acyl-ACP TE diversity and to identify novel acyl ACP-TEs, 31 acyl-ACP TEs from wide-ranging phylogenetic sources were characterized to ascertain their in vivo activities and substrate specificities. These acyl-ACP TEs were chosen by two different approaches: 1) 24 TEs were selected from public databases on the basis of phylogenetic analysis and fatty acid profile knowledge of their source organisms; and 2) seven TEs were molecularly cloned from oil palm (Elaeis guineensis), coconut (Cocos nucifera) and Cuphea viscosissima, organisms that produce medium-chain and short-chain fatty acids in their seeds. The in vivo substrate specificities of the acyl-ACP TEs were determined in E. coli. Based on their specificities, these enzymes were clustered into three classes: 1) Class I acyl-ACP TEs act primarily on 14- and 16-carbon acyl-ACP substrates; 2) Class II acyl-ACP TEs have broad substrate specificities, with major activities toward 8- and 14-carbon acyl-ACP substrates; and 3) Class III acyl-ACP TEs act predominantly on 8-carbon acyl-ACPs. Several novel acyl-ACP TEs act on short-chain and unsaturated acyl-ACP or 3-ketoacyl-ACP substrates, indicating the diversity of enzymatic specificity in this enzyme family. Conclusion: These acyl-ACP TEs can potentially be used to diversify the fatty acid biosynthesis pathway to produce novel fatty acids.

Summary: The chemical industry is currently reliant on a historically inexpensive, petroleum‐based carbon feedstock that generates a small collection of platform chemicals from which highly efficient chemical conversions lead to the manufacture of a large variety of chemical products. Recently, a number of factors have coalesced to provide the impetus to explore alternative renewable sources of carbon. Here we discuss the potential impact on the chemical industry of shifting from non‐renewable carbon sources to renewable carbon sources. This change to the manufacture of chemicals from biological carbon sources will provide an opportunity for the biological research community to contribute fundamental knowledge concerning carbon metabolism and its regulation. We discuss whether fundamental biological research into metabolic processes at a holistic level, made possible by completed genome sequences and integrated with detailed structural understanding of biocatalysts, can change the chemical industry from being dependent on fossil‐carbon feedstocks to using biorenewable feedstocks. We illustrate this potential by discussing the prospect of building a platform technology based upon a concept of combinatorial biosynthesis, which would explore the enzymological flexibilities of polyketide biosynthesis.

Covering: up to 2013 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 publicly 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 datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate 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.