The diffusion limited stereospecific enzyme chalcone isomerase represents the adaptive evolution of a catalytically perfected enzyme from non-catalytic, fatty-acid-binding proteins (FAPs) with contemporary roles in plant fatty-acid metabolism as evidenced by altered fatty acid content and marked reproductive defects in Arabidopsis thaliana plants bearing FAP knockouts. In vascular plants, chalcone isomerase converts chalcones to (S)-flavanones in a committed step in flavonoid biosynthesis. This paper reports the crystal structures, ligand-binding properties and in vivo function of several catalytically inactive members of the chalcone isomerase family, which seem to act solely as fatty-acid-binding proteins (FAPs). FAP-knockout Arabidopsis thaliana plants exhibit elevated α-linolenic acid levels and marked reproductive defects, suggesting that chalcone isomerases are a rare example of the emergence and evolution of enzymes from non-enzyme ancestors. Specialized metabolic enzymes biosynthesize chemicals of ecological importance, often sharing a pedigree with primary metabolic enzymes1. However, the lineage of the enzyme chalcone isomerase (CHI) remained unknown. In vascular plants, CHI-catalysed conversion of chalcones to chiral (S)-flavanones is a committed step in the production of plant flavonoids, compounds that contribute to attraction, defence2 and development3. CHI operates near the diffusion limit with stereospecific control4,5. Although associated primarily with plants, the CHI fold occurs in several other eukaryotic lineages and in some bacteria. Here we report crystal structures, ligand-binding properties and in vivo functional characterization of a non-catalytic CHI-fold family from plants. Arabidopsis thaliana contains five actively transcribed genes encoding CHI-fold proteins, three of which additionally encode amino-terminal chloroplast-transit sequences. These three CHI-fold proteins localize to plastids, the site of de novo fatty-acid biosynthesis in plant cells. Furthermore, their expression profiles correlate with those of core fatty-acid biosynthetic enzymes, with maximal expression occurring in seeds and coinciding with increased fatty-acid storage in the developing embryo. In vitro, these proteins are fatty-acid-binding proteins (FAPs). FAP knockout A. thaliana plants show elevated α-linolenic acid levels and marked reproductive defects, including aberrant seed formation. Notably, the FAP discovery defines the adaptive evolution of a stereospecific and catalytically ‘perfected’ enzyme6 from a non-enzymatic ancestor over a defined period of plant evolution.

Significance: Each species contains a subset of genes that are uniquely present in that species; the functions and origins of the vast majority of these “orphan genes” are not well-understood. Expression of theArabidopsis QQS(Qua-Quine Starch;At3g30720) orphan transgene increases the level of protein in soybean lines with high and low protein and acts across flowering plants to increase the protein content of maize and rice. Our results begin to dissect the mechanism of QQS functions by identifying that it binds to the conserved transcription factor nuclear factor Y, subunit C4 (NF-YC4). Increased expression ofNF-YC4inArabidopsismimics the effects of increased expression ofQQS. The ability to optimize protein productivity in plant-based foods would have far-ranging impacts on world health and sustainability.

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.

Summary: The genome of each species contains as high as 8% of genes that are uniquely present in that species. Little is known about the functional significance of these so‐called species specific or orphan genes. TheArabidopsis thalianagene Qua‐Quine Starch (QQS) is species specific. Here, we show that alteringQQSexpression in Arabidopsis affects carbon partitioning to both starch and protein. We hypothesizedQQSmay be conserved in a feature other than primary sequence, and as such could function to impact composition in another species. To test the potential ofQQSin affecting composition in an ectopic species, we introducedQQSinto soybean. Soybean T1 lines expressingQQShave up to 80% decreased leaf starch and up to 60% increased leaf protein; T4 generation seeds from field‐grown plants contain up to 13% less oil, while protein is increased by up to 18%. These data broaden the concept ofQQSas a modulator of carbon and nitrogen allocation, and demonstrate that this species‐specific gene can affect the seed composition of an agronomic species thought to have diverged from Arabidopsis 100 million years ago.