Synthetic biology of metabolism: using natural variation to reverse engineer systems

Abstract

A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models to be generated. One avenue to reverse engineer the necessary information is to study the genetic control of natural variation in plant primary and secondary metabolism. These studies are showing that any engineering model will have to incorporate information about 1000s of genes in both the nuclear and organellar genome to optimize the function of the introduced pathway. Further, these genes may interact in an unpredictable fashion complicating any engineering approach as it moves from the one or two gene manipulation to higher order stacking efforts. Finally, metabolic engineering may be influenced by a previously unrecognized potential for a plant to measure the metabolites within it. In combination, these observations from natural variation provide a beginning to help improve current efforts at metabolic engineering. Introduction A significant goal of synthetic biology or biological engineering is to take an existant organism and alter it in a directed and predictive fashion to introduce a new phenotype that had not previously existed in that organism. Some of these efforts start with introducing a new metabolic pathway into an organism to provide a better source of a commercially important chemical or to make new chemicals entirely [1][2][3]. Other efforts are focused at taking agronomically important traits such as defensive chemicals, nitrogen fixation or perennial growth from one species and transferring these traits into other species that do not contain them [4 ,5,6]. However these efforts while exciting frequently run into great difficulty and for this review article, I will focus on one the potential of using information from natural variation studies to facilitate these efforts for metabolic engineering.To predictively engineer a new metabolic pathway into a plant with optimal efficiency requires a base level of knowledge that likely does not exist to any full level. To generate full predictive ability we would need much deeper understanding of the following questions. First, how many genes within the plant need to be manipulated to facilitate the integration of the new pathway into the organisms metabolic and regulatory network. How do these genes interact, are they solely additive or is there evidence of more complex epistasis that complicates predictive capacity? Finally, are these genes solely in the nuclear genome or is there causal variation also in the organellar genomes? Most efforts to answer these questions focus largely on forward or single-gene reverse genetics approaches which are highly time consuming and difficult to scale up to the necessary level.An alternative approach to understanding a system is to use the concept of reverse engineering [7,8]. The goal of reverse engineering is to take a functioning system, say an existing metabolic pathway, ...

Natural variation of plant metabolism: genetic mechanisms, interpretive caveats, evolutionary and mechanistic insights.

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Isolate Dependency of Brassica rapa Resistance QTLs to Botrytis cinerea

“…Defense metabolites including glucosinolates (GSLs), have frequently been linked to resistance to B. cinerea and other generalist necrotrophic pathogens within the Brassicas ( Stotz et al, 2011 ; Buxdorf et al, 2013 ; Cargnel et al, 2014 ; Calmes et al, 2015 ). GSLs are sulfur containing secondary metabolites unique to the order Capparales whose genetics and chemistry have been extensively studied ( Chan et al, 2010 ; Sønderby et al, 2010 ; Kliebenstein, 2014 ). In addition to necrotrophic resistance, these compounds also alter resistance to biotrophic pathogens, insects and aphids indicating that they are likely key players in numerous biotic interactions of Brassica plants ( Kroymann and Mitchell-Olds, 2005 ; Pfalz et al, 2007 , 2009 ; Fan et al, 2011 ; Weis et al, 2014 ; Kerwin et al, 2015 ).…”

Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways

“…This is particularly relevant given the promiscuity of enzymes in secondary plant metabolism, which has led to exceptional chemodiversity in plants (Weng et al, 2012). This challenge can be tackled by modern developments in plant systems biology that integrate genomics, transcriptomics, proteomics, and metabolomics data (Kliebenstein, 2014;Tohge et al, 2015). Revealing novel promiscuous enzyme functions in plants and other organisms will provide the possibility for engineering enzymes through directed evolution (Tracewell and Arnold, 2009;Chakraborty et al, 2013), which can then be used for the execution of pathway designs.…”