TY - JOUR
AU - Yang, Xiaohan
AB - Abstract To meet future food and energy security needs, which are amplified by increasing population growth and reduced natural resource availability, metabolic engineering efforts have moved from manipulating single genes/proteins to introducing multiple genes and novel pathways to improve photosynthetic efficiency in a more comprehensive manner. Biochemical carbon-concentrating mechanisms such as crassulacean acid metabolism (CAM), which improves photosynthetic, water-use, and possibly nutrient-use efficiency, represent a strategic target for synthetic biology to engineer more productive C3 crops for a warmer and drier world. One key challenge for introducing multigene traits like CAM onto a background of C3 photosynthesis is to gain a better understanding of the dynamic spatial and temporal regulatory events that underpin photosynthetic metabolism. With the aid of systems and computational biology, vast amounts of experimental data encompassing transcriptomics, proteomics, and metabolomics can be related in a network to create dynamic models. Such models can undergo simulations to discover key regulatory elements in metabolism and suggest strategic substitution or augmentation by synthetic components to improve photosynthetic performance and water-use efficiency in C3 crops. Another key challenge in the application of synthetic biology to photosynthesis research is to develop efficient systems for multigene assembly and stacking. Here, we review recent progress in computational modelling as applied to plant photosynthesis, with attention to the requirements for CAM, and recent advances in synthetic biology tool development. Lastly, we discuss possible options for multigene pathway construction in plants with an emphasis on CAM-into-C3 engineering. Bioenergy, computational modelling, crassulacean acid metabolism, photosynthesis, synthetic biology, water-use efficiency. Introduction The fulfilment of future global food and energy needs will require improvements in crop productivity and carbon fixation. This realization has driven renewed interest in basic and applied research on photosynthesis. The diverse photosynthetic complexes from microorganisms to land plants all possess the same ability to sequester light, produce adenosine triphosphate (ATP), and reduce nicotinamide adenine dinucleotide phosphate to NADPH, which energizes the process of carbon fixation (Nickelsen and Rengstl, 2013). The conserved nature of light capture and energy conversion across photosynthetic life forms and the deployment of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) for CO2 fixation stands in contrast to the great diversity in pathways, compartmentation and temporal strategies, and CO2-concentrating mechanisms around Rubisco. Different biophysical or biochemical carbon-concentrating mechanisms (CCMs) have evolved in cyanobacteria, algae, and higher plants. In land plants, these mechanisms reduce photorespiration and potentially improve photosynthetic performance and growth in particular environments. The emerging discipline of synthetic biology provides the opportunity to partially or entirely engineer biophysical and biochemical CCMs into C3 plants to enhance carbon gain. Strategies proposed include the introduction of: (1) inorganic CCMs from cyanobacteria (Price et al., 2013); (2) basal CCMs for active recycling of photorespiratory CO2 from mitochondria to chloroplasts (Zabaleta et al., 2012); and (3) single-cell or two-celled C4 photosynthesis (Miyao et al., 2011; Covshoff and Hibberd, 2012). These and other strategies for engineering improved photosynthesis in crop plants are reviewed elsewhere (Maurino and Weber, 2013). More recently, moving crassulacean acid metabolism (CAM) into C3 plants to improve the resource-use efficiency of photosynthesis (www.cambiodesign.org; Borland and Yang, 2013) has received attention. CAM, like C4 photosynthesis, is a biochemical CCM that uses phosphoenolpyruvate carboxylase (PEPC) to fix carbon into organic acids as storage intermediates with subsequent locally concentrated release of CO2 around Rubisco. While the CCM of two-celled C4 photosynthesis relies on spatial separation of PEPC and Rubisco in different cell types, CAM relies on a temporal separation of carboxylases within the same cell type. By shifting all or part of CO2 uptake to the night time when evapotranspiration rates are reduced compared to daytime, CAM plants can use 20–80% less water to produce amounts of biomass comparable to C4 and C3 plants (Borland et al., 2009; von Caemmerer et al., 2012). Having arisen multiple times in a taxonomically diverse range of plants, CAM represents an example of parallel or convergent evolution of a complex trait. This implies that many of the genes and some of the regulatory elements necessary for CAM are already present in C3 species. Thus, CAM represents a strategic target for synthetic biology to introduce improved water-use efficiency into C3 crops for a warmer and drier world (Borland and Yang, 2013). Improving photosynthetic performance by the introduction of CCMs like CAM will require new approaches in bioengineering. Progress demands that research moves beyond the analyses of single genes and builds a broader, more holistic, and interconnected view of photosynthetic metabolism. Advanced computational approaches can utilize experimental data from the analysis of single genes or proteins to group them into dynamic network models, which can subsequently be tested and validated using synthetic biology tools. With the availability of comprehensive genomic, transcriptomic, proteomic, and metabolomic datasets, networks can be assembled and analysed in a temporal manner in order to model particular cellular processes (Li et al., 2010; Abraham et al., 2013). This broad perspective of biological information defines the term ‘systems biology’, and the combination of computational methods with the ability to simulate and analyse the biological information defines the term ‘computational modelling’ (Sauro et al., 2006). Combined, these approaches create a predictive framework for identifying limitations and mechanisms that underlie photosynthetic processes (Zhu et al., 2007). While systems biology and computational modelling are promising approaches, their outputs need to be validated using empirical testing. In this review, we present recent progress in computational modelling of key components involved in CAM CO2 fixation, stomatal movement, and photosystem behaviour, with an emphasis on the challenges and opportunities for CAM biodesign. We then summarize the available cloning technologies for multigene assembly, the elements used to connect the synthetic network, and the methods for in planta gene stacking. Lastly, we outline how synthetic biology provides tools to build multigene pathways in plants with an emphasis on the engineering of CAM photosynthesis. Modelling photosynthesis The modelling of photosynthesis will play a principal role in improving scientific understanding of its regulatory mechanisms, which in turn will be used to accelerate targeted genetic manipulation. For more than 30 years, models have been developed for C3 photosynthesis that encompass physiological and biochemical reactions and transport processes involved in CO2 uptake, photosynthesis, Calvin-Benson cycle activity, and carbohydrate partitioning. In order to inform CAM-into-C3 engineering, the regulation of core C4 acid carboxylation and decarboxylation pathways must be fully understood along with events controlling accompanying metabolic fluxes through glycolysis and gluconeogenesis, the synthesis of storage carbohydrates and their breakdown, and the associated control of stomatal conductance. Application of computational modelling to connect experimental data in a dynamic network will be required to tackle the challenge of understanding photosynthetic metabolism (Langley et al., 2006; Buda, 2009; Safranek et al., 2011; Middleton et al., 2012; Zhu, 2010; Zhu et al., 2013). Such networks can be perturbed and tested, and the simulated outcomes can then be used to identify key elements responsible for the process (Fig. 1). During this progression, emerging tools for synthetic biology can be applied to test, refine, and improve the control of the network. However, to better understand and support global simulations of photosynthetic carbon metabolism, smaller components or modules of the entire process need to be analysed separately. A general view of the C3 and CAM elements that should be considered when developing sub-network and global-network computational modelling and how they interconnect to promote photosynthesis is shown in Fig. 2. Different in silico networks can then be built from diverse modules/components that constitute photosynthetic metabolism. Fig. 1. View largeDownload slide Description of synthetic/computational modelling for identification of key elements within pathways. Once data are generated and connected, manual interpretation coupled with natural gene characterization tools can be used (grey box). This route is very time consuming as elements must be checked individually and the output of each experiment (knockdown/overexpression) evaluated in light of each phenotype. In addition, each transgenic line must be analysed for differences in gene expression profiles (down/upregulated) across different pathways. In contrast, computational modelling is faster, simulation is tuneable, and consequences of specific gene oscillations are evaluated immediately and globally (light orange pathway). Also, synthetic elements can be introduced to connect the network (double arrow). After a few rounds of adjustments (bold looping arrow), key elements can be predicted and a pathway can be generated (using synthetic biology tools). The in planta phenotypes are then analysed and a robust conclusion can be made allowing for technology transfer. Occasionally, a second adjustment is applied after pathway engineering (looping arrow). Fig. 1. View largeDownload slide Description of synthetic/computational modelling for identification of key elements within pathways. Once data are generated and connected, manual interpretation coupled with natural gene characterization tools can be used (grey box). This route is very time consuming as elements must be checked individually and the output of each experiment (knockdown/overexpression) evaluated in light of each phenotype. In addition, each transgenic line must be analysed for differences in gene expression profiles (down/upregulated) across different pathways. In contrast, computational modelling is faster, simulation is tuneable, and consequences of specific gene oscillations are evaluated immediately and globally (light orange pathway). Also, synthetic elements can be introduced to connect the network (double arrow). After a few rounds of adjustments (bold looping arrow), key elements can be predicted and a pathway can be generated (using synthetic biology tools). The in planta phenotypes are then analysed and a robust conclusion can be made allowing for technology transfer. Occasionally, a second adjustment is applied after pathway engineering (looping arrow). Fig. 2. View largeDownload slide Schematic representation of elements to be considered when developing computational modelling applied to CAM. Connections represent interactions within and between different hierarchies. aRepresentative elements that are temporal in CAM and spatial in C4. bCAM has stomata behaviours that differ from C3. Fig. 2. View largeDownload slide Schematic representation of elements to be considered when developing computational modelling applied to CAM. Connections represent interactions within and between different hierarchies. aRepresentative elements that are temporal in CAM and spatial in C4. bCAM has stomata behaviours that differ from C3. CO2 fixation and processing of C-skeletons in C3 plants The dark reactions of C3 photosynthesis are accomplished in three stages: (1) fixation of CO2 into phosphoglycerate (PGA), catalysed by Rubisco; (2) reduction of PGA to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, which is used to synthesize carbohydrate; and (3) regeneration of ribulose 1,5-bisphosphate (RuBP) from glyceraldehyde 3-phosphate, which consumes energy (ATP and NADPH) provided by the light reactions. Computational modelling of photosynthetic metabolism in the chloroplasts of C3 plants has identified cooperative regulation of the velocity of these pathways to minimize fluctuations in metabolite concentration profiles in response to environmental perturbations (Luo et al., 2009). Recent computational analysis has proposed the existence of an oscillatory mechanism between CO2 and O2 for access to the active site of Rubisco (Dubinsky and Ivlev, 2011). Other approaches using structural modelling and interaction predictions for nine proteins of the Calvin-Benson cycle revealed unexpected crosstalk in which phosphoglycerate kinase (3PGK; responsible for reduction step) mediates the interaction between phosphoribulose kinase (responsible for RuBP regeneration) and Rubisco large subunit (Naeem et al., 2013). The predicted formation of this multiprotein complex could be critical for maintaining homeostasis under fluctuating external environmental conditions. Moreover, substrate processing might be accelerated, which could be critical for the efficiency of CO2 fixation in the presence of O2. Taken together, the approaches summarized above are in accordance with a model that proposes co-evolution and positive selection working in combination to shape the properties of Rubisco (Wang et al., 2011a). Consequently, engineering CAM into C3 plants might require the transfer of native CO2 fixation enzymes to permit continual fine-tuning of homeostasis and similarly promote efficient C-fixation. An ‘evolutionary’ algorithm to iteratively search for multiple modes of partitioning between the enzymes of carbon metabolism has been developed (Zhu et al., 2007). The output indicated how the targeted up- and down-regulation of some enzymes could increase carbon gain in C3 plants without any increase in the total protein nitrogen investment in the enzymic machinery of photosynthetic carbon metabolism. Developing such approaches in the future will be critical for predicting the optimal partitioning of protein nitrogen in C3 hosts that will be essential to accommodate the additional enzymic machinery required for functional CAM. Notably, CAM induction in Mesembryanthemum crystallinum is accompanied by a decrease in transcript abundance of genes encoding light harvesting and photosystem complexes and C3 photosynthetic enzymes, including Rubisco large and small subunits (Cushman et al., 2008). Thus, increased investment of resources in the protein machinery required for CAM could possibly be achieved at the expense of proteins required for C3 photosynthesis. Recent kinetic modelling of chloroplastic starch degradation in the C3 system has identified the limiting steps in starch degradation that could be critical for engineering improved (higher-starch-containing) bioenergy feedstocks (Nag et al., 2011). The enzymic pathways and regulation of starch degradation in CAM plants appear to differ from those in C3 plants (Weise et al., 2011), which may be due to dissimilar energy balance over the course of the day among the different photosynthetic pathways. Generation and testing of loss-of-function mutants for various enzymes and transporters implicated in both the hydrolytic and phosphorolytic routes of leaf starch degradation in CAM genetic model species will be necessary to direct optimal engineering of a C3/CAM hybrid. There are two types of CAM storage variants: starch and soluble sugar-type carbohydrate storage (Holtum et al., 2005). In soluble sugar accumulating CAM species, sugar transporters located on the tonoplast membrane represent a potential strategic checkpoint for controlling the supply and demand for carbon over the CAM cycle (Antony and Borland, 2009). The molecular identification of tonoplast sugar transporters in CAM species and studies on mode of regulation will be critical for engineering CAM into C3 hosts where vacuolar sugars are the major form of storage carbohydrate. Carboxylation/decarboxylation in CAM Until recently, efforts to model CAM at a mechanistic level were limited to variables related to the concentrations of CO2 and malate under contrasting regimes of temperature and light (Matsuo et al., 2013). To model the day–night shifts in metabolism that underpin CAM requires a system dynamic approach that considers the external and internal feedbacks that control metabolism over an entire 24-h cycle (Owen and Griffiths, 2013). CAM is commonly described in four temporal phases that encapsulate nocturnal activation (phase 1) and daytime deactivation of PEPC (phases 2–4); nocturnal accumulation (phase 1) and daytime remobilization of malate from the vacuole (phases 2–3); and nocturnal breakdown (phase 1) and daytime assimilation of carbohydrate (phases 2–4). Using this four-phase framework, the system dynamic model for CAM is parameterized with measured environmental, physiological, and biochemical inputs along with temporal control switches that activate regulatory processes for carboxylation and decarboxylation. The range of environmental (abiotic) and physiological data required for model parameterization is summarized in Fig. 2. The fluxes of metabolites calculated from these inputs interact with state-dependent or metabolic feedback (summarized as ‘Metabolism’ in Fig. 2) to generate predicted outputs of net CO2 uptake and malate turnover on a 24-h basis (Owen and Griffiths, 2013). The system dynamic model for CAM provides a framework for moving towards a computational model that would include gene regulatory networks, metabolic network fluxes, and signalling pathways (Borland and Yang, 2013). Such a framework will require the application of experimental approaches to determine the spatial and temporal coordination of proteins, the identification and regulation of intracellular membrane transporters, the kinetics of protein–protein interactions, and also posttranslational modifications of target proteins. To fully exploit such approaches for predicting the compatibility and efficiency of a synthetic C3/CAM hybrid system requires that CAM be broken down into smaller, more manageable sets of components or functional modules that can be analysed separately, for example, by examining the tonoplast phosphoproteome or using nano-injection to express a desired set of proteins in guard cells. Stomatal movement A key challenge for the engineering of CAM into C3 plants will be to elucidate the pathways and mechanisms that generate nocturnal opening and daytime closure of stomata. Stomatal guard cells are regulated by a complex network of molecular crosstalk that affects photosynthetic physiology at various levels (Kwak et al., 2008). Computational analysis of guard-cell homeostasis, transport, and signalling (HoTSig) has been developed for C3 plants and parameterized using biophysical and kinetic data collected for guard cells (Hills et al., 2012; Wang et al., 2012). The software OnGuard (http://www.psrg.org.uk/guard-cell.html) allows simultaneous grouping of HoTSig factors with associated variables assigned by the user to define a reference state. Perturbations in parameters such as ionic concentrations and transporter fluxes can be introduced consecutively to the reference state to predict system response to these variants. For example, perturbations in [H+], [K+], and [Ca2+], and diurnal oscillations in H+, K+, and Ca2+ transport or in malate metabolism, and their influence on stomatal aperture, have been simulated in Arabidopsis. Contrasting effects of changes in [K+] and [Ca2+] were modelled and an increase in cytosolic [K+] was sufficient to increase stomatal aperture (Chen et al., 2012b). The various positive, neutral, or negative flux peaks of H+, K+, and Cl– at the beginning of day or night periods illustrate the coordination of ionic strength across the plasma membrane and tonoplast required to trigger stomata movement (Wang et al., 2012). Taken together, these studies demonstrate a reasonably robust simulation of key elements involved in stomatal movement, but the proposed models still have some limitations, such as the lack of algorithms that self-limit Ca2+, in a manner compatible with guard-cell physiology (Chen et al., 2012b). Successful engineering of CAM into C3 plants will require approaches that can establish whether C3 guard cells per se should be modified to achieve nocturnal opening and daytime closure of stomata. Analysis of the transcriptome and proteome associated with light-controlled mechanisms in guard cells could reveal whether modification of guard-cell physiology or ultrastructure will be necessary. Because mesophyll-derived apoplastic malate can act as a reporter of internal CO2 partial pressure (pCO2) (Hedrich et al., 1994; Araujo et al., 2011), installing the enzymic machinery for day–night turnover of malate in the mesophyll might suffice for engineering stomatal responses during CAM. However, variables such as hormonal stimuli, cytoskeletal changes in guard cells, light responses, and mesophyll signalling may ultimately need to be incorporated in modelling the stomatal responses of C3/CAM synthetic chimeras (Kramer and Boyer, 1995; Kinoshita et al., 2001; Acharya and Assmann, 2009; Gao et al., 2009; Lee, 2010; Chen et al., 2012a; Hubbard et al., 2012; Taylor et al., 2012). These variables interact in a complex network that might need to be aligned in more than a single computational model. One way forward would be a subsequent broader modelling that evaluates the compatibility of various pairwise combinations of single models. Practically speaking, this could be done by creating and phenotyping loss- and gain-of-function of mutants individually in CAM lines and in C3/CAM synthetic hybrids (see Fig. 1). Detailed transcriptomic, proteomic, and metabolomic characterization of the modified mesophyll and guard cells in these lines along with physiological measurements of leaf gas exchange would be essential for a higher-level understanding of the stomatal control network. Modelling of the photosystem Although C3 and CAM each possess different pathways for energy conversion, the means by which their photosystems capture light is conserved and similar to the systems found in microorganisms (Nickelsen and Rengstl, 2013). The first step requires a pigment–protein complex to receive and convey solar energy to the light-harvesting complex/photosynthetic reaction centre (LHC/RC), which then releases excited electrons. These electrons are loaded into a mobile carrier to travel along the electron transfer chain that culminates with NADPH/ATP synthesis (Nelson and Yocum, 2006). As there are different kinds of LHC/RC supercomplexes, optimizing the harvest of solar energy will require a better understanding of their attributes. Only recently has a detailed and comprehensive model for excitation dynamics within the photosystem been elaborated (Strumpfer and Schulten, 2012). Although the study was conducted in Rhodobacter sphaeroides, the model reproduces the RC absorption spectrum observed in the purple photosynthetic bacterial system as well, which leads to improved estimation of excitation dynamics and makes the methodology applicable to understanding the modulation of similar dynamics in other organisms, including plants. Additionally, Strumpfer and Schulten (2012) showed that the excitation transfer rate between the LHC and the RC is strongly affected by the thermal environment of the RC pigments. Other simulations have shown that the membrane environment is crucial for the photosystem to assemble efficiently (Flock and Helms, 2004; Hsin et al., 2010). These data show that the dimensional distribution of the elements in this system is important and studies focused on the development of molecular strategies that better assemble the LHC/RC–pigments–carrier complex in space have been developed (Conlan, 2008; Noy et al., 2006; Larom et al., 2010). These photosynthetic apparati are being explored for improved plant carbohydrate metabolism not only in C3 and CAM species, but also for the production of different sugars, hydrogen, oxygen, ethanol, methane, and lipids in microorganisms and other heterologous systems (Hankamer et al., 2007; Angermayr et al., 2009; Rosenbaum et al., 2010; Patrick et al., 2013). Various strategies have been used to achieve these goals, and although the engineering of the biological energy conversion machinery for different purposes has been difficult, the synthetic biology field can be expected to accelerate these endeavours. Advances in synthetic biology Despite advances in systems biology and computational modelling of photosynthesis, in many cases the in silico output and simulations lack validation. The large number of genes, the expression dynamics of each gene, and the feedback responses necessary for equilibrium suggests that far more knowledge about the control of these processes is needed. The availability of synthetic elements controlling light-mediated responses, transcription factor–DNA binding, and protein domains for subcellular localization and controlled degradation, in addition to rapid and reliable in vitro cloning technologies, will improve the sophistication of future synthetic biology endeavours. Beyond the validation of computational modelling of photosynthesis, synthetic biology also provides an experimental toolbox for realizing computational designs through multigene engineering in plants. In this section, the concept of synthetic biology, advances in cloning technologies and possible applications of synthetic biological approaches to photosynthesis research, including network construction, targeted protein degradation, and transfer of the synthetic network to host organisms are discussed. The era of synthetic biology With the availability of complete genomes, researchers working in the era of synthetic biology (Cheng and Lu, 2012) can begin to raise such fundamental questions as: Can one define the minimal complement of genetic elements required for organismal survival? Or, can each organism’s unique genetic code be linked to the individual phenotype? Or, can a genome be assembled from scratch to create a synthetic organism with defined characteristics? Some of these questions have been answered recently, when the genome of the first synthetic microorganism, Mycoplasma mycoides JCVI-syn1.0, was built (Gibson et al., 2010). However, the engineered prokaryotic Mycoplasma genome is far from expressing the complex network of genetic interactions that are found in eukaryotic cells from multicellular organisms. To develop new tools and increase understanding of simple eukaryotic organisms, the genome of the model Saccharomyces cerevisiae, which has already had parts of its chromosomes synthetically engineered, is being redesigned ab initio as part of an international consortium ‘Synthetic Yeast 2.0’ (Dymond et al., 2011; Enyeart and Ellington, 2011). In terms of strategy, the synthetic organism will initially retain 90% of the original genome and will be engineered in a way that allows evolutionary approaches to be used for functional studies and strain improvement. None of these advances would have been possible without the cloning technologies that permit de novo gene synthesis and rapid and reliable assembly of large DNA fragments (Gibson et al., 2008; Ellis et al., 2011). Cloning technologies for synthetic biology Four hierarchical levels of cloning can be defined that allow for the assembly of parts (defined as promoters, ribosome-binding sites, protein tags, fusion proteins, terminators, and other elements), genes, pathways, or genomes (Table 1). The original method that allowed for the management and combination of parts was Biobricks, which uses a combination of restriction enzymes that leave a compatible end for further assembling of DNA sequences or genes during ligation reactions (Knight, 2003). Because Biobrick construction relies on the presence of these restriction sites, its application to long stretches of DNA was limited. Other methods for combining fragments into pathways and entire genomes have taken advantage of the in vivo recombining ability of yeast (Larionov et al., 1996; Walhout et al., 2000; Gibson et al., 2008). Although these ligation-independent methods rely upon overlapping regions that can be of many different sizes, they produce ‘scar-free’ products, an important consideration if open reading frames are to be maintained. Another noteworthy recent method, Golden Braid 1.0, uses Type IIS restriction enzymes, which release several DNA fragments that can be sequentially combined in one step based on the signatures left at their ends (Engler et al., 2008). Using this method in a subset of plasmids that permit alternating cycles of cloning defined the Golden Braid 2.0 system (Sarrion-Perdigones et al., 2013), which allows binary grouping of parts or genes that accelerates the process of generating the final constructs, especially when a large number of elements are involved. However, these parts or genes need to be carefully engineered, because the system can create scars, among other limitations. The current challenge with cloning is to develop a universal method that allows assembly at all levels, from parts to genomes. To date, a unified method that can be applied to all four hierarchal levels has not been established, leading to approaches that combine different methodologies to reach broader goals (Gibson et al., 2010; Haffke et al., 2013). Table 1. Summary of cloning technologies applicable to synthetic biology‘Fundament’ refers to the main process used by each technique to bring DNA fragments together in a reaction. The circles indicate the application of each technique without (filled) and with (open) some restrictions to build parts, genes, pathways, or genomes. CPEC, circular polymerase extension cloning; MISSA, multiple-round in vivo site-specific assembly; OE-PCR, overlap extension polymerase chain reaction; QC cloning, quick and clean cloning; SLIC, sequence and ligation independent cloning; SRAS, single-selective-marker recombination assembly system; TEFC, ‘T-type’ enzyme-free cloning; TRAS, triple-selective-marker recombination assembly system; USER, uracil-specific excision reagent; Yeast TAR, transformation-associated recombination. Method  Fundament  Parts–genes  Genes–pathways  Pathways–genomes  References  Biobricks  Type IIS RE  ●  ○    (Knight, 2003; Sleight and Sauro, 2013; Smolke, 2009)  BglBricks  Type IIS RE  ●  ●    (Anderson et al., 2010)  Pairwise selection  Type IIS RE  ○  ●  ●  (Blake et al., 2010)  Golden Gate  Type IIS RE  ●  ●    (Engler et al., 2008; Engler and Marillonnet, 2013; Peisajovich et al., 2009)  Golden Braid (2.0)  Type IIS RE  ●  ●    (Sarrion-Perdigones et al., 2013)  MoClo  Type IIS RE  ●  ○    (Weber et al., 2011)  InFusion  Overlap  ●  ●    (Sleight et al., 2010; Sleight and Sauro, 2013)  Isothermal assembly  Overlap  ○  ●  ●  (Gibson et al., 2008; Gibson et al., 2009; Rodrigues and Bayer, 2013)  SLIC  Overlap    ●    (Aslanidis and de Jong, 1990; Li and Elledge, 2007; Wang et al., 2013)  USER  Overlap  ●  ●    (Nour-Eldin et al., 2010; Smith et al., 1993)  OE-PCR (CPEC)  PCR with overlap  ●  ○    (Bryksin and Matsumura, 2010; Zhang et al., 2013)  Bacillus domino  Recombination    ●  ●  (Itaya et al., 2007; Itaya et al., 2005)  Yeast TAR  Recombination    ●  ●  (Benders et al., 2010; Gibson, 2011; Larionov et al., 1996)  QC cloning  Overlap  ●  ○    (Thieme et al., 2011)  MISSA  Recombination  ○  ●    (Chen et al., 2010)  Gateway  Recombination  ○  ●    (Buntru et al., 2013; Walhout et al., 2000)  TEFC  Overlap  ●      (Yang et al., 2013b)  SRAS/TRAS  Recombination  ○  ●    (Shi et al., 2013)  Method  Fundament  Parts–genes  Genes–pathways  Pathways–genomes  References  Biobricks  Type IIS RE  ●  ○    (Knight, 2003; Sleight and Sauro, 2013; Smolke, 2009)  BglBricks  Type IIS RE  ●  ●    (Anderson et al., 2010)  Pairwise selection  Type IIS RE  ○  ●  ●  (Blake et al., 2010)  Golden Gate  Type IIS RE  ●  ●    (Engler et al., 2008; Engler and Marillonnet, 2013; Peisajovich et al., 2009)  Golden Braid (2.0)  Type IIS RE  ●  ●    (Sarrion-Perdigones et al., 2013)  MoClo  Type IIS RE  ●  ○    (Weber et al., 2011)  InFusion  Overlap  ●  ●    (Sleight et al., 2010; Sleight and Sauro, 2013)  Isothermal assembly  Overlap  ○  ●  ●  (Gibson et al., 2008; Gibson et al., 2009; Rodrigues and Bayer, 2013)  SLIC  Overlap    ●    (Aslanidis and de Jong, 1990; Li and Elledge, 2007; Wang et al., 2013)  USER  Overlap  ●  ●    (Nour-Eldin et al., 2010; Smith et al., 1993)  OE-PCR (CPEC)  PCR with overlap  ●  ○    (Bryksin and Matsumura, 2010; Zhang et al., 2013)  Bacillus domino  Recombination    ●  ●  (Itaya et al., 2007; Itaya et al., 2005)  Yeast TAR  Recombination    ●  ●  (Benders et al., 2010; Gibson, 2011; Larionov et al., 1996)  QC cloning  Overlap  ●  ○    (Thieme et al., 2011)  MISSA  Recombination  ○  ●    (Chen et al., 2010)  Gateway  Recombination  ○  ●    (Buntru et al., 2013; Walhout et al., 2000)  TEFC  Overlap  ●      (Yang et al., 2013b)  SRAS/TRAS  Recombination  ○  ●    (Shi et al., 2013)  View Large Construction of the synthetic network The real barriers to creating synthetic pathways and transferring the allied traits into the genome are to identify the parts and genes of interest, to organize them coordinately in a stable genetic network and, in some cases, to transfer the genetic material to the host organism. As discussed above, the elements that control photosynthesis are found in different metabolic routes and sometimes in different cell types and comprise a complex network. Therefore, to identify these elements requires tremendous effort (Borland et al., 2009; Li et al., 2010; von Caemmerer et al., 2012; Sage et al., 2012; Williams et al., 2012; Stitt, 2013). However, synthetic biology offers a promising approach for the identification of the elements needed for construction of various types of pathways. For over two decades now, researchers have engineered pathways such as biosynthetic pathways in microorganisms (Kirby and Keasling, 2009). Again, because complex traits in eukaryotic systems can be elaborate, the redesign of genetic and biochemical networks can sometimes be more effectively accomplished by simply rewriting the networks using synthetic elements. These elements potentially allow for a well-coordinated network that requires fine control of the mechanisms that govern transcription, translation, signalling, and protein turnover. The 5′ upstream regulatory region (URR) dictates the transcription of downstream sequences based on storage and integration of signals (Horst et al., 2013). Further, differences that evolve in the sequence and the distance or position of cis-elements relative to genes lend greater specificity to the function of URRs (Matsumoto et al., 2013; Mehrotra et al., 2013). Various plant URRs that have already been described can be used to control the level and localization of gene expression, along with artificial elements that can fine-tune transcription through positive or negative feedback. These include the shoot-specific phas promoter (Sen et al., 1993), the root-specific promoter RCH1P (Casamitjana-Martinez et al., 2003), a phloem-specific promoter (Dutt et al., 2012), a guard-cell-specific promoter (Plesch et al., 2001), a transcription repression protein element SRDX (Hiratsu et al., 2003), an artificial positive feedback loop (Yang et al., 2013a), and an artificial targeted transcriptional activator (TALE, transcription activator-like effector; VP16, transcription activation domain) (Li et al., 2013a). Tissue-specific transcription is crucial for CAM-into-C3 engineering, and alternation of stomatal movement may require the use of guard-cell-localized transcripts and proteins via such URR elements. Another practical application could be to use TALE- or VP16-fused proteins in negative-feedback loops triggered by downstream signals from starch or sucrose synthesis pathways to keep the synthetic C-fixing metabolism running. Additionally, both transcription and protein functionality can be manipulated through abiotic signals, such as light, temperature, or chemicals. In particular, promoters such as the drought-inducible promoter RD29A (Yamaguchi-Shinozaki and Shinozaki, 1994), the oestrogen-receptor-based transactivator XVE, which activates nuclear transcription through binding of an artificial DNA element upon induction with oestradiol (Zuo et al., 2000), and the auxin-response element DR5, which can upregulate transcription when in the presence of auxin or its analogues (Ulmasov et al., 1997; Pierre-Jerome et al., 2013), could prove useful. Compatible with these transcriptional controllers, localization signals can be coupled to drive proteins to specific subcellular compartments. Many signal sequences are known to target proteins to the endoplasmic reticulum, Golgi apparatus, tonoplast, peroxisomes, plasma membrane, mitochondria, plastids, nucleus, Cajal bodies, nucleolus, nuclear speckles, and mitotic structures (Shaw and Brown, 2004; Nelson et al., 2007; Boruc et al., 2010; DePaoli et al., 2011). Furthermore, light-controlled mechanisms have been identified that function to manage transcription (Martinez-Hernandez et al., 2002; Polstein and Gersbach, 2012; Su et al., 2013), modulate circadian-controlled processes (Lau et al., 2011), control protein stability (Shen et al., 2005), and control shade avoidance responses (Li et al., 2012). These kinds of control mechanisms could form a basis for the engineering of CAM elements that require photoperiod-, circadian-, or light-mediated regulation at the transcriptional level. Unlike C4 metabolism that generally requires enzymes localized in two types of cells, the CAM pathway occurs in a single cell-type environment, which theoretically makes CAM-into-C3 engineering more amenable to the use of synthetic URRs. Targeted protein degradation Along with the coordinated expression of functional protein, the specific degradation of proteins is also important. For example, some key CAM proteins, such as phosphoenolpyruvate carboxylase kinase (PPCK1), are degraded during the daytime to ensure the correct temporal expression of CAM phases (Berry et al., 2013). A number of different pathways control protein degradation in plant cell compartments and are known to be involved in a variety of plant processes (Hellmann and Estelle, 2002; Sakamoto, 2006; Yang et al., 2008). Some of these protein degradation mechanisms from plants are being used to artificially target proteins for degradation in nonplant systems (Nishimura et al., 2009). However, no technology is yet available to specifically control protein turnover in plants, a process that could be useful to selectively degrade proteins at a specific moment or within a specific cell type, or to limit the action of other artificial elements, such as transcription activator-like effector nucleases. Importantly, these kinds of tools are needed to rewire circuits within a metabolic network (for review, see Nandagopal and Elowitz, 2011). One simple approach is to create feedback loops that establish more stable circuits. Endogenous components of metabolic machinery can be swapped for synthetic parts that respond to a particular stimulus, but that do not carry all the response elements of the original endogenous component, and, therefore, will be less likely to respond to secondary signals. A practical example would be the simplification of the CAM decarboxylation system by using only phosphoenolpyruvate carboxykinase (PEPCK) to provide CO2 from malate. This would avoid the use of other enzymes that function in malate metabolism that are already present in regular C3 metabolism (e.g. NADP-malic enzyme (NADP-ME), and could affect photosynthetic rates in the synthetic hybrid via secondary signalling. In this case, some endogenous metabolic machinery in the C3 host might need to be silenced or knocked out. Also, pathway simplification by gene reduction is possible, as demonstrated by reduction of core cell cycle regulators to a single pair of cyclin:cyclin-dependent kinases (CDK) in yeast (Coudreuse and Nurse, 2010), which suggest that gene families could be further simplified. An overview of the potential application of these regulatory mechanisms to develop a CAM into C3 synthetic hybrid is shown in Fig. 3. Fig. 3. View largeDownload slide Representation of pathway engineering for trait transfer. The source organism has unique elements (yellow and green) as well as common elements (black and red) with the target organism. However, only unique elements of interest (green) are engineered into the target (purple arrows) to generate the synthetic hybrid. Occasionally, endogenous elements at the target will need silencing (blue and grey) to allow for optimal functioning of the synthetic construct. Arrows, positive correlation (proportional to arrow size); T-bars, negative correlation; diamond-ended lines, regulatory interaction. Fig. 3. View largeDownload slide Representation of pathway engineering for trait transfer. The source organism has unique elements (yellow and green) as well as common elements (black and red) with the target organism. However, only unique elements of interest (green) are engineered into the target (purple arrows) to generate the synthetic hybrid. Occasionally, endogenous elements at the target will need silencing (blue and grey) to allow for optimal functioning of the synthetic construct. Arrows, positive correlation (proportional to arrow size); T-bars, negative correlation; diamond-ended lines, regulatory interaction. Even with the advances in these tools for the synthetic control of genes and proteins, the operation of both plant and nonplant regulatory systems across species within the plant kingdom has not been validated. Similarly, many signalling and regulatory mechanisms that function at the transcriptional, translational, and posttranslational levels could be useful for artificial control of gene networks, but their applicability within plant cells remains untested (for review, see Cheng and Lu, 2012). Transferring the network to host organism Once the caveats of screening for the key elements involved in a process are overcome and the process of organizing them coordinately into a balanced genetic network is defined, the next step in pathway engineering is to transfer the genetic content to the host organism. Homologous recombination is a common method to accomplish this transfer, and recent advances have made it possible to transfer entire genomes between microorganisms (Karas et al., 2013). In the plant kingdom, homologous recombination has been demonstrated only for Oryza sativa (Terada et al., 2002) and Nicotiana benthamiana (Li et al., 2013b), although for many other species, various transformation methods have been attempted (Rakoczy-Trojanowska, 2002). To date, plant genomes have been modified typically by Agrobacterium-mediated transformation or biolistic delivery of DNA. Although fragments over 70kb in size have been transferred stably using the former method in a variety of plant species (Untergasser et al., 2012), such transformation events are still often limited to transfers at the megabase scale. One way to address this limitation is by in planta gene stacking (Wang et al., 2011b). Briefly, this technique introduces unique recombination sites into the plant genome during a first round of Agrobacterium transformation to create a founder line via insertion of a single copy of the transgene at a genomic location which is not only unaffected by positional effects, but also does not disrupt native genes. Then, a second round of transformation is performed to provide the recombinase and the DNA fragment to be added at the specific site defined in the ‘founder line’. The combined use of positive/negative markers and the excision of the excess elements (i.e. markers, plasmid backbone, and recombinases) then occur in the desired lines carrying the engineered trait. In theory, further rounds of recombinase-mediated integration might occur until the desired fragment is totally rebuilt in the host genome. The crucial advantage of inserting the network into a single locus is to avoid segregation of traits due to independent insertion of consecutive transformation events, especially in genomes with high chromosome numbers (e.g. Populus and Glycine). Pathway engineering in plants can also be accomplished via gene synthesis to create large constructs followed by advanced cloning and transfer technologies for fast, reliable assembly of a wide range of DNA fragments both in vitro and in vivo (Ellis et al., 2011; Wang et al., 2011b; Hamilton and Buell, 2012). In addition, current technologies are being explored to create plant artificial chromosomes (Murata et al., 2013). Perspectives for multigene pathway construction in plants Many types of metabolic pathways, including C4 and CAM, are being investigated and implemented in different species. All of these studies will eventually rely upon the tools of synthetic biology (Maurino and Weber, 2013) engaging knowledge from diverse scientific fields. Early attempts to introduce C4 properties into C3 plants involved classical genetics to generate interspecific hybrids from the genera Flaveria and Atriplex, but these attempts met with limited success (Brown and Bouton, 1993). Advances in molecular biology and plant transformation allowed individual enzymes, which were thought to be compulsory for C4 photosynthesis, to be engineered into C3 plants. Despite achieving substantially increased activity of these C4 enzymes, there was little impact on photosynthetic rates (Matsuoka et al., 2001), indicating the need for multiple gene transfers. Therefore, the C4 rice project was proposed to introduce the entire C4 machinery into O. sativa (C3) (Sheehy et al., 2007). More recently, the US Department of Energy has funded a consortium to explore CAM, its potential for sustainable biofuel feedstocks, and, ultimately, the engineering of this photosynthetic specialization into C3 crops as a means of improving water-use efficiency (Borland and Yang, 2013). The ambitious goals of engineering CAM into C3 crops will require the concerted endeavours of different experimental approaches encompassing individual gene studies, generation of cell-specific transcriptomic data, genetic screens of mutagenized seeds, evaluation of activation-tagged populations and insertion mutant lines, and (re)sequencing of new genomes, as well as some of the promising genome engineering techniques described above (Brutnell et al., 2010; Li et al., 2010, 2013b; Gowik et al., 2011; Langdale, 2011; Bennetzen et al., 2012; von Caemmerer et al., 2012; Christian et al., 2013; Gross et al., 2013). Importantly, CAM efficiency is determined by specific enzyme activity (mainly PEPC, NADP-ME, tonoplast ATPase, and Rubisco), intercellular CO2 level, and stomatal conductance (Nobel, 1991). A recent study showed that CAM expression can be controlled by modulating key parameters such as stomatal or mesophyll conductance, decarboxylation/carboxylation rates, and vacuole capacity (Owen and Griffiths, 2013). Although nocturnal CO2 fixation is not directly affected by photon flux density, light intensity still has an effect on the remaining light-dependent reactions. These limiting factors should be given high priority when engineering CAM into C3 plants. Smaller components of C4 photosynthesis are already being engineered between species (Gowik and Westhoff, 2011; Langdale, 2011; von Caemmerer et al., 2012). Of note is the finding that four key enzymes (PEPC; PPDK, pyruvate orthophosphate dikinase; NADP-MDH, NADP-malate dehydrogenase; and NADP-ME, malic enzyme), responsible for the single-cell C4 metabolism in the aquatic plant Hydrilla verticillata, transferred to rice failed to reproduce C4 metabolism (Miyao et al., 2011). This result suggests that the failure of earlier single-cell C4 engineering was due to inadequate understanding of the genetic control of Kranz anatomy development, particularly in combination with intercellular signalling. Both anatomy and signalling should also be considered when engineering single-cell mechanisms such as CAM. Therefore, a complex network that combines precise multigene expression with leaf structural anatomy or architecture is clearly necessary to introduce this efficient precarboxylation CO2-concentrating mechanism into C3 species. As mentioned above, computational methods will be needed to organize the massive omics information and to generate mechanistic models for CAM regulation. In addition, large-scale transcriptomics data can be combined with sequenced genomes to conduct comparative genomics studies, revealing common elements from taxonomically diverse species that might function as potential drivers of CAM mechanisms (Bennetzen et al., 2012). Recently, this evolutionary approach was applied in the analysis of the expression of the glycine decarboxylation protein-P gene (GLDP) in C3, C3-to-C4, and C4 species of the genus Flaveria. A model for the early evolutionary appearance of C4 metabolism was proposed based on gene duplication with subsequent tissue-specific loss of function, showing that the C4 improvement in photosynthetic efficiency is a combination of both gain- and loss-of-function in different genes (Schulze et al., 2013). This C4-related example helps to shed some light on the kinds of challenges that could be faced in engineering CAM into C3 with a perfect balance between the functions of both exogenous and endogenous genes (Fig. 3, green and blue/grey elements). All of the approaches described above will provide the synthetic biology field with the appropriate ‘substrates’ for multigene pathway engineering. However, it is clear that the work will also face limitations imposed by synthetic biology itself. As long as DNA synthesis is costly and size restricted, flawless cloning technologies for assembling from gene parts to genomes need to be developed. Recent genome engineering technologies and pitfalls still need to be demonstrated in different plant species. The engineering of CAM into C3 plants will require several more years of hard work to start shedding some light onto the next photosynthesis-based green revolution. Conclusions Photosynthesis is potentially the most powerful and sustainable energy conversion machine on earth and as such is a compelling target for improvements in the properties of plants as sources of food, feed, fibre, and fuel. Aligned with the potential of synthetic biology, the design, engineering, and construction of photosynthetic mechanisms is now potentially achievable. Recent progress on the computational modelling of dynamic networks will likely assist in the grand challenge of CAM biodesign. Despite recent progress, the understanding of the elements comprising CAM photosynthetic metabolism is at an early stage and we are just beginning to perceive how these elements are interconnected, coordinately expressed, and ultimately processed. A dynamic model of CAM photosynthesis needs to be developed for synthetic biology research in the coming years. Advanced cloning technologies and synthetic biology tools are yielding novel and experimental approaches to engineer CAM photosynthesis into C3 crops. In the near future, application of synthetic biology approaches based on progress in the engineering of CAM photosynthesis into C3 crops will increase the likelihood of success for future efforts to engineer other multigene traits, reducing agony over the challenges and expanding opportunities. Acknowledgements This material is based upon work supported by the Department of Energy, Office of Science, Genomic Science Program (under award number DE-SC0008834). The authors would like to thank Mary Ann Cushman and Lee E. Gunter for critical review and clarifying comments on the manuscript. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the US Department of Energy (under contract number DE-AC05-00OR22725). References Abraham P Giannone RJ Adams RM Kalluri U Tuskan GA Hettich RL . 2013. 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TI - Synthetic biology as it relates to CAM photosynthesis: challenges and opportunities
JF - Journal of Experimental Botany
DO - 10.1093/jxb/eru038
DA - 2014-02-24
UR - https://www.deepdyve.com/lp/oxford-university-press/synthetic-biology-as-it-relates-to-cam-photosynthesis-challenges-and-tjNC9cWrWG
SP - 3381
EP - 3393
VL - 65
IS - 13
DP - DeepDyve
ER -