Journal of Experimental Botany, Vol. 65, No. 13, pp. 3381–3393, 2014
doi:10.1093/jxb/eru038 Advance Access publication 24 February, 2014
Review paper
Synthetic biology as it relates to CAM photosynthesis:
challenges and opportunities
Henrique C. DePaoli1, Anne M. Borland1,2, Gerald A. Tuskan1, John C. Cushman3 and Xiaohan Yang1,*
1 BioSciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6422, USA
School of Biology, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
3 Department of Biochemistry and Molecular Biology, MS330, University of Nevada, Reno, NV 89557–0330, USA
2 * To whom correspondence should be addressed. E-mail: [email protected]
Received 24 October 2013; Revised 24 December 2013; Accepted 16 January 2014
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.
Key words: 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
© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved.
For permissions, please email: [email protected]
3382 | DePaoli et al.
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 subnetwork 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.
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
Challenges and opportunities in CAM biodesign | 3383
Fig. 2. Schematic representation of elements to be considered when
developing computational modelling applied to CAM. Connections
represent interactions within and between different hierarchies.
a
Representative elements that are temporal in CAM and spatial in C4.
b
CAM has stomata behaviours that differ from C3.
Fig. 1. 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).
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.,
3384 | DePaoli 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-offunction 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
Challenges and opportunities in CAM biodesign | 3385
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 lightmediated 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).
3386 | DePaoli et al.
Cloning technologies for synthetic biology
Four hierarchical levels of cloning can be defined that allow
for the assembly of parts (defined as promoters, ribosomebinding 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 (SarrionPerdigones 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,
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
Biobricks
Type IIS RE
●
○
BglBricks
Pairwise selection
Golden Gate
Type IIS RE
Type IIS RE
Type IIS RE
●
○
●
●
●
●
Golden Braid (2.0)
Type IIS RE
●
●
MoClo
InFusion
Type IIS RE
Overlap
●
●
○
●
Isothermal assembly
Overlap
○
●
SLIC
Overlap
USER
Overlap
●
●
OE-PCR (CPEC)
PCR with overlap
●
○
Bacillus domino
Recombination
●
●
Yeast TAR
Recombination
●
●
QC cloning
MISSA
Gateway
Overlap
Recombination
Recombination
●
○
○
○
●
●
TEFC
SRAS/TRAS
Overlap
Recombination
●
○
●
●
●
●
References
(Knight, 2003; Sleight and
Sauro, 2013; Smolke, 2009)
(Anderson et al., 2010)
(Blake et al., 2010)
(Engler et al., 2008; Engler and
Marillonnet, 2013; Peisajovich
et al., 2009)
(Sarrion-Perdigones et al.,
2013)
(Weber et al., 2011)
(Sleight et al., 2010; Sleight and
Sauro, 2013)
(Gibson et al., 2008; Gibson
et al., 2009; Rodrigues and
Bayer, 2013)
(Aslanidis and de Jong, 1990;
Li and Elledge, 2007; Wang
et al., 2013)
(Nour-Eldin et al., 2010;
Smith et al., 1993)
(Bryksin and Matsumura, 2010;
Zhang et al., 2013)
(Itaya et al., 2007;
Itaya et al., 2005)
(Benders et al., 2010; Gibson,
2011; Larionov et al., 1996)
(Thieme et al., 2011)
(Chen et al., 2010)
(Buntru et al., 2013;
Walhout et al., 2000)
(Yang et al., 2013b)
(Shi et al., 2013)
Challenges and opportunities in CAM biodesign | 3387
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).
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 activatorlike 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
3388 | DePaoli et al.
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:cyclindependent 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.
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).
Fig. 3. 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.
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 Agrobacteriummediated transformation or biolistic delivery of DNA.
Although fragments over 70 kb 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.
Challenges and opportunities in CAM biodesign | 3389
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
3390 | DePaoli et al.
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 DESC0008834). 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).
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