Available online at www.sciencedirect.com
Metabolomics-assisted synthetic biology
1 1,2
David I Ellis and Royston Goodacre
As the world progresses from a fossil-fuel based economy to a low-valuable products an increase of only 1% production
more sustainable one, synthetic biology will become yield could be highly rewarding in financial terms (e.g.
increasingly important for the production of high-value fine carbon conversion into biofuel production).
chemicals as well as low-value commodities in bulk. The
integration of metabolomics and fluxomics within synthetic A recent and highly topical study of metabolic pathway
biology projects will be vital at all levels, including the initial engineering based on metabolomics [1] involved the de-
design of the pathways to be generated, through to the velopment of novel bio-ethanol producing yeast strains
optimisation of those pathways so that more efficient with increased tolerance to inhibitors in lignocellulosic
conversion of low-cost starting materials into highly desirable hyrdolysates. Saccharomyces cerevisiae is used to produce
products can be achieved. This review highlights these areas bio-ethanol from lignocellulosic materials from agricul-
and details the most important and exciting advances being ture and industrial wastes, as it is a fast sugar consumer,
made in this area. gives a high ethanol yield from glucose, and is said to have
Addresses a higher resistance to ethanol and other compounds
1
School of Chemistry, Manchester Interdisciplinary Biocentre, University present in lignocellulosic hyrdolysates than bacteria [2].
of Manchester, 131 Princess Street, Manchester M1 7ND, UK These toxic compounds, which negatively affect
2
Manchester Centre for Integrative Systems Biology, Manchester
microbial growth, metabolism, and of course ethanol
Interdisciplinary Biocentre, University of Manchester, 131 Princess
yield, include weak organic acids (such as acetic and
Street, Manchester M1 7DN, UK
formic acids), furan derivatives and phenolics. This study
Corresponding authors: Ellis, David I ([email protected]) and examined the effect of acetic acid on xylose fermentation
Goodacre, Royston ([email protected]) using metabolite profiling to identify and target genes for
improving organic acid tolerance. Results revealed that
several metabolites involved in the non-oxidative pentose
Current Opinion in Biotechnology 2012, 23:22–28
phosphate pathway (PPP) were significantly accumulated
This review comes from a themed issue on by the addition of acetate, and the authors suggested the
Analytical biotechnology
possibility that acetic acid slows down this pathway.
Edited by Wei E. Huang and Jizhong Zhou
Therefore, metabolic engineering was focused on the
non-PPP. A gene encoding a PPP-related enzyme (trans-
Available online 19th November 2011
aldolase or transketolase) was overexpressed in the fer-
0958-1669/$ – see front matter
menting yeast, which successfully conferred ethanol
# 2011 Elsevier Ltd. All rights reserved.
productivity in the presence of both acetic and formic
acid [2].
DOI 10.1016/j.copbio.2011.10.014
Lignocellulosic biomass hyrdolysates are said to be
increasingly used as feedstock for industrial fermenta-
Metabolite and metabolic engineering tions [3]. A recent article studied the performance of six
One of the main goals of synthetic biology is to generate industrial relevant microorganisms (two bacteria, two
the desired material with a good conversion from sub- yeasts, and two fungi) in terms of their ability to utilise
strate(s) to product whilst reducing unwanted (side-) monosaccharides in the biomass, resistance against inhibi-
products. This is not a new goal for biology and has its tors, and their ability to grow on different types of feed-
routes from metabolite/metabolic engineering, whereby stock hydrolysates. The authors concluded that a
genetic and regulatory processes and pathways within substrate-orientated approach, rather than the more com-
cells are optimised with some preconceived idea of which monly used product-oriented approach towards the selec-
pathways are important in order to increase the pro- tion of a microbial production host, would avoid the
duction of a whole range of valuable substances. At the requirement for extensive metabolic engineering [3].
same time efforts are also made to reduce the energy costs
to the cells, reduce the production of any undesirable Whilst many of the studies into biofuels involve
substances (both with respect to yield of the end product approaches utilising terrestrial crops, a recent study
and also metabolite pathway inhibition), and to reduce involved metabolomic analysis of aquatic microbes,
overall production costs (Figure 1 illustrates the meta- specifically, oxygenic photoautotrophs (AMOPs). Some
bolic engineering process). These can all be highly AMOPs, such as cyanobacteria, are said to offer several
important aspects, especially when one considers that advantages over terrestrial crop biofuel production
these may be industrial-scale applications and for methods, a major one being the direct excretion of
Current Opinion in Biotechnology 2012, 23:22–28 www.sciencedirect.com
Synthetic biology Ellis and Goodacre 23
Figure 1
Microbial
cultures
Increased production of high-value products
Tissue/cell
cultures Met abolomics Engineering
Metabolic profiling genome shuffling Redu ced production of
Data integration/ Metabolic flux analysis global transcriptome machinery unwanted produ cts
statistical analysis
Bio- Metabolic fingerprinting directed evolutionary (i.e. toxins, inhibitors)
transformation/ Metabolic footp rinting metabolic pathway produ ction
systems Redu ced energy costs to cells/ reduction in overall production costs In planta systems
Current Opinion in Biotechnology
Flow diagram of the metabolic engineering process highlighting the role metabolomics has to play.
alternative fuel precursors, such as hydrogen [4 ]. This environment in which the organism grows. In the future,
study used metabolic and genetic engineering to con- the optimisation of the genetic makeup may necessitate
struct a mutant of the cyanobacterium Synechococcus sp. careful tailoring of the growth medium so that the host
which lacked the enzyme for the NADH-dependent organisms undergo less perturbation.
reduction of pyruvate to D-lactate. Subsequent metabo-
lomic analysis by nuclear magnetic resonance (NMR) In an excellent review Corynebacterium glutamicum, a
spectroscopy and liquid chromatography-mass spectrom- species of the class Actinobacteria used for the industrial
etry (LC–MS) showed that autofermentation by this scale production of various amino acids of this species.
mutant strain resulted in no D-lactate production and Nesvera and Patek [6] summarized the latest develop-
higher concentrations of excreted acetate, alanine, succi- ments in the field of genetic engineering in C. glutamicum,
nate, and up to a 5-fold increase in hydrogen production as well as the use of ‘omics-based approaches, including
compared to the wild type [4 ]. Importantly, this genetic transcriptomics, proteomics, metabolomics, and fluxo-
elimination of metabolic pathways which improved H2 mics [7]. They showed that large-scale datasets from
levels could be applied to other AMOPs [4 ], illustrating these functional analyses could be combined to produce
that even small changes in a low product forming organ- predictive models, and through integration of the infor-
ism with rational metabolic engineering can result in mation obtained this could result in a complex descrip-
significant product yields. tion of all cell interactions, one of the aims of systems
biology [6]. The review also includes some of the chal-
Other metabolic engineering areas of interest include lenges which need to be surmounted in order to imple-
those involved in the engineering of complex metabolic ment these tools fully, enabling the construction of
pathways in microbes. One recent study involved tar- superior production strains of this important bacterium.
geted metabolic profiling of what these authors termed These include the need for co-ordinated changes in
‘deleterious interactions between pathway intermedi- metabolism which tune higher flux through the engin-
ates and host cell metabolism’ [5]. This was performed eered pathway, and avoiding depletion or overloading
in order to identify modes of toxicity from the accumu- levels of particular metabolites, which can lead to a more
lation of unwanted metabolites. In addition to identify- balanced and stable production strain. This is in addition
ing the pathways affected (including inhibition of fatty to understanding and describing the control mechanisms
acid biosynthesis) and any toxins involved (3-hydroxy-3- within regulatory networks, the ability to control cell
methylglutaryl-coenzyme A (HMG-CoA)), these authors division and optimisation of the cell properties and
also identified ways to counteract this through the downstream processes [6]. Although it is clear to us that
addition of palmitic acid to the growth medium, demon- this is incredibly complex to model at this stage, this
strating the ability to optimise synthetic biological sys- report demonstrates the potential power of coupling
tems with their approach [5]. This also demonstrates the systems biology to metabolic engineering, whereby sys-
ability of metabolic engineering to go beyond genetic tem-level knowledge about the bacterial host could lead
optimisation of the organism and to consider the to the identification of potential bottlenecks within the
www.sciencedirect.com Current Opinion in Biotechnology 2012, 23:22–28
24 Analytical biotechnology
host, which would then be the targets for improved strain quenching and extraction processes for the accurate
design. quantification of intracellular bacterial metabolites [13].
The convoluted relationship between intracellular
Other reviews include those concerning industrial scale metabolism and metabolic footprinting in microbiology,
metabolic engineering [8], highlighting the ‘older’ and and how this method can assist in the interpretation of cell
‘newer’ tools of metabolic engineering and synthetic communication mechanisms, metabolic engineering and
biology respectively. In combination with more recent industrial biotechnological processes is also the subject of
whole cell engineering approaches such as genome shuf- a review by Mapelli et al. [14].
fling, global transcriptome machinery engineering and
directed evolutionary engineering. For a further excellent Tissue and cell culture optimisation
review covering metabolic engineering and synthetic Whilst microbes and yeast are very tractable hosts in terms
biology for the optimisation of both medicinal and aro- of genetics and bioreactors can be easily scaled up, they are
matic plants the reader is directed to Hendrawati et al. [9]. relatively ‘simple’ beasts and the production of recombi-
nant proteins that require correct glycosylation for activity
Many synthetic biology studies have thus far involved need to be produced in more complex eukaryotic systems.
microbes, and given the tractability of the system this is Thus another important area for synthetic biology is pro-
perhaps not surprising. Microbial metabolomics is very duct production in tissue and cell culture systems and these
popular [10 ] and this is not surprising when one considers too need careful optimisation; metabolomics can also play a
how much this field supports and complements a wide valuable role here. One recent study involved two models
range of microbial research areas, ranging from metabolic which were used to evaluate the cellular metabolome and
engineering to drug discovery [11]. Winder et al. used the to optimise the growth media [15]. The first of these was
metabolic fingerprinting (Table 1) approach of Fourier NMR-based metabolic profiling for the quantification of
transform infrared (FT-IR) spectroscopy to monitor metabolites in Chinese hamster ovary (CHO) cell lines
whole-cell dioxygenase-catalysed biotransformation of engineered to express a recombinant protein, followed by
toluene to toluene cis-glycol in fed-batch cultures of E. the second model of metabolomic analysis of superfusion
coli. PLSR could be used to correlate metabolic finger- media used for drug metabolism and toxicology studies in
prints with the level of product concentration, and their in vitro liver slices. Results from the first model highlighted
results demonstrated the potential of this high-through- which culture parameters could be manipulated to opti-
put approach to assess temporal biochemical dynamics in mise growth and protein production. Whilst results from
complex bioprocesses, and also provided rapid infor- the second model showed that two of the medium com-
mation on product yields and quality without the require- ponents were depleted at a faster rate than any other
ment for time-consuming chromatographic product nutrients, and augmentation of the starting medium with
analysis [12]. The same group studied global metabolic these two components (choline and histidine) improved
profiling of E. coli by GC–MS for the evaluation of long-term liver slice viability [15]. A related study involved
Table 1
Glossary of some of the terminology used within metabolomics
Metabolome All low-molecular weight metabolites (i.e. metabolic intermediates, hormones and other signalling
molecules, and secondary metabolites, >1000 kDa) to be found in a biological sample/system,
such as a single organism, which are the end products of gene expression.
Metabolomics The non-biased and non-targeted identification and quantification of all metabolites in a biological system.
13 15
Fluxomics Specific labelled ( C or N) substrates are fed to bacterial, yeast or tissue cultures in order to follow
the destination of carbon or nitrogen within metabolic pathways, using MS- or NMR-based mass
isotopomer analyses.
Metabolic profiling Identification and quantification of a selective number of pre-defined metabolites, which are generally
related to a specific metabolic pathway.
Metabolic fingerprinting Global, high-throughput, rapid analysis to provide sample classification. Can be used as a rapid
high-throughput screening tool to discriminate between samples from different biological status, origin,
and processes. Enables rapid biochemical information regarding production yields at set points within
and/or the end of production lines for example.
Metabolic footprinting Analysis of the metabolites secreted/excreted by an organism, also known as the exometabolome; if the
organism is growing in media/culture this will include its environmental and growth substances. Some of
which can be valuable products whilst others may be unwanted.
Metabolic/metabolite Optimisation of genomic and regulatory processes within cells and tissues to increase production of
engineering valuable substances, and/or reduce production of unwanted substances (i.e. toxins). Can also lead to
more energy efficient biochemical processes and reduce large-scale production costs.
Metabolite target analysis Qualitative and quantitative analysis of one, or several, metabolites related to a specific
metabolic reaction.
Current Opinion in Biotechnology 2012, 23:22–28 www.sciencedirect.com
Synthetic biology Ellis and Goodacre 25
a metabolomics-based approach for the improvement of ‘‘Data does not equal information; information does not
CHO cell growth. This utilised LC–MS to identify equal knowledge; and, most importantly of all, knowl-
extracellular metabolites (the exometabolome, also edge does not equal wisdom. We have oceans of data,
known as metabolic footprinting [16,17]) in the medium rivers of information, small puddles of knowledge, and
of fed-batch CHO reactor cultures, with the aim to the odd drop of wisdom.’’
improve cell numbers through metabolic engineering.
This was achieved after the exometabolome showed that
amongst the metabolites identified malate was the most One method to address this problem is the use of stable
significant, and metabolic engineering to overexpress isotope tracers. Whereby an isotopically labelled substrate
13 15
malate dehydrogenase resulted in a 1.9 fold increase in (e.g. glucose labelled with C, or a N labelled amino acid
integral viable cell numbers [18]. This is a very nice or nitrogen substrate) is fed to the (micro-)organisms being
example of where a data-driven analytical process (meta- studied, the products themselves become labelled and can
bolomics) can lead to ‘hypotheses’ or suggestions for then be measured using a combination of chromatographic,
highly focused rationale metabolic engineering which mass spectrometry, or NMR spectrometry. This form of
resulted in improved synthesis. analysis can then be exploited for metabolic pathway
elucidation and metabolic flux determination, which
Other studies involving nutrient feed, CHO and murine may then provide targets for engineering approaches.
myeloma non-secreting (NS0) cell lines include work by One recent review of this area termed this form of mass
Ma et al. [19]. Here, a chemically defined nutrient feed isotopomer analysis as ‘‘time and relative differences in
(CDF) coupled with basal medium preloading was devel- systems (TARDIS)-based analysis’’, owing to the fact that
oped to replace a hydrolysate-containing feed for a fed- it both measures and quantifies the temporal sequential
batch NS0 process. Results showed that the CDF enabled emergence of these labelled products [28 ].
a completely chemically defined feed with an increased
monoclonal antibody titre of 115%. Tests of the CDF in a A recent and very significant study presented a new
CHO process were also indicative of CDF being able to method, termed non-targeted tracer fate detection
replace hydrolysate-containing feed with a subsequent (NTFD) [27 ]. This combined the use of stable isotope
80% increase in product titre [19]. Sellick et al. have tracers and gas chromatography-mass spectrometry
undertaken a body of work in this area including rapid (GC–MS) with computational analysis, enabling the
monitoring of recombinant antibody production in CHO quantification of all measurable metabolites derived
and NS0 cell lines. This was achieved using the metabolic from a specific labelled compound, without any a priori
fingerprinting [20,21] technique of FT-IR spectroscopy knowledge of a reaction network or compound library.
combined with multivariate statistical analyses (e.g. partial Using a mixture of labelled and non-labelled, tracer the
least squares regression (PLSR)) and was employed to NFTD approach can be applied to bacterial cultures,
predict anti-body levels. It was clearly demonstrated that eukaryotic cell cultures, whole animal systems, and even
a spectroscopic approach could be an appropriate starting non-biological chemical systems. Further, this novel
point for the potential on-line monitoring and measure- method provides information about relative flux magni-
ment of antibody production in industrial-scale bioreactors tudes into each metabolic pool through the determi-
[22]. Other studies by this group include the optimisation nation of mass isotopomer distribution (MID) for all
of technologies for the global metabolomic profile of CHO labelled compounds, and is said to provide a framework
cell lines, including the evaluation of extraction processes for global analysis of metabolic fluxes. The NTFD
[23] and effective quenching processes [24]. In combi- approach truly adds new knowledge to this area and
nation, this has led to enhanced recombinant antibody owing to its non-targeted approach, adds information
production (over double) by specific tailoring of the nutri- about biochemical reactions and metabolites that were
ent feed to CHO cells [25]. We expect this approach to be previously unknown [27 ]. As the authors themselves
widely used to enhance protein yields, as feeding specific rightly state, NTFD adds a new dimension to the meta-
substrates through growth can elongate production times bolic toolbox [27 ]. Reviews of note in this area include
and thus increase product formation. those concerning metabolic flux distributions, genetic
information, computational predictions and experimen-
Stable isotope tracers for engineering tal validation of strain engineering [29], and steady-state
Metabolomics techniques are known to provide very large metabolic flux analysis (MFA) in plants for the measure-
datasets and allow for the high-throughput quantification ment of multiple fluxes in the core network of primary
of metabolites, yet the resultant data-flood [26] from carbon metabolism [30].
these methods has been said to provide minimal infor-
mation on the rates and connectivity of metabolic path- Whilst metabolomics gives only snapshots of metabolite
ways [27 ]. After all, as Henry Nix once said in his 1990 levels these TARDIS-based analyses allow the flux of
Keynote address to AURISA (Australasian Urban and carbon or nitrogen through pathways to be discovered.
Regional Information Systems Association Inc.): The caveat is that cells are cultured in a sole carbon or
www.sciencedirect.com Current Opinion in Biotechnology 2012, 23:22–28
26 Analytical biotechnology
nitrogen source and for complex eukaryotic systems this is from these analyses identified a number of major meta-
not always possible and so isotope calculations can be bolic regulatory configurations that result in strong metab-
more difficult. Though we do believe these flux analyses olite correlations and demonstrated the utility of
will be very valuable for ensuring that the flow of starting biochemical simulation/modelling for the analysis of
materials is directed towards the product of interest rather ‘omics’ data [33].
than off to some side branches of the metabolic pathway.
Finally, a recent and highly cogent review focuses on a
Data integration/analysis processes for critical assessment of mathematical techniques used to
synthetic biology infer metabolic networks from time-resolved metabolo-
As the above studies using flux analyses elegantly demon- mics data [34 ]. This employed the simulation of data and
strate (and as is the case with all ‘omics’ and related analysed four representative methods, as well as in-
disciplines involving large and highly complex datasets), cluding an overview of sampling and methods currently
data integration and analytical methods are absolutely used, compared with reverse engineering methods. Most
vital, the key which can unlock doors to new insights, add significantly, the authors identified large discrepancies
to knowledge, and open up completely new dimensions between the requirements of reverse engineering of
within synthetic biology. A study by Wisselink et al. stated metabolic networks and current practice (if a full infer-
that one of the challenges in strain improvement by ence of a real-world metabolic network is the goal).
engineering is the subsequent determination of the mol- Recommendations were also provided for the improve-
ecular basis of the improved properties which were ment of time-resolved experimental designs [34 ].
enriched from natural genetic variation during selective
conditions [31 ]. The authors demonstrated their Conclusion
approach to this challenge through transcriptome analysis, The marriage of synthetic biology with metabolomics is a
intracellular metabolite measurements and metabolic flux growing area (Figure 2) and recent studies of interest in
analysis, of glucose-limited and arabinose-limited areas not yet mentioned include those involved in plant
anaerobic chemostat cultures of metabolic and evolution- biotechnology, such as the work involving overproduction
ary engineered S. cerevisiae (IMS0002) and its non- of tryptophan in GM rice by Matsuda et al. [35], and of
evolved ancestor IMS0001. Results identified key genetic course the many excellent overviews in this area, in-
changes contributing to efficient arabinose utilisation by cluding the use of plant and algae as cell factories to
the engineered S. cerevisiae strain. Such as confirmation produce numerous high-value compounds such as
that the galactose transporter is essential for growth on carotenoids [36], as well as the engineering of carotenoid
arabinose, and genes which may be involved in flux- formation in tomatoes and the application and potential of
controlling reactions in arabinose fermentation could be both systems biology and synthetic biology approaches
identified. They tested this by deleting these genes [37]. In terms of systems biology, there are of course a
which caused a 21% reduction of the maximum specific number of excellent reviews covering areas such as
growth rate on arabinose [31 ]. This is a very nice industrial systems biology [38], systems biology of indus-
example of how multi ‘omics’ data can be integrated to trial microorganisms [39], and metabolomics, modelling,
generate new knowledge about carbohydrate utilisation and machine learning in systems biology [40].
which can be tested and confirmed to be true. Within
systems biology, this allows the loop within the inductive Figure 2
approach to knowledge discovery to be completed.
1000
Another study also views reverse engineering of high- A
800
throughput ‘omics’ data to infer biological networks as
one of the challenges in biology. Here, the authors 600
B
focused on a systematic analysis of metabolomic network
400
inference from in silico metabolome data from E. coli and
Publications C
yeast and showed that it may be possible to predict the 200
organism’s response and thus underlying metabolic net-
0
work to different intrinsic and environmental conditions
[32]. Another article of interest involved comparative
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
analysis of several mathematical and statistical methods
Year
using synthetic datasets produced by simulation of rea-
Current Opinion in Biotechnology
listic biochemical network models. Using this approach, it
was possible to study how inferences were degraded by
Number of publications per year from Thomas Reuters Web Of Science,
noise, and this allow the study of the extent to which
illustrating the increasing research activity and publication trends from
correlation of metabolomics datasets are capable of reco- the three related areas; (A) Metabolomics, (B) Metabolic Engineering,
vering features from these biochemical systems. Results and (C) Synthetic Biology.
Current Opinion in Biotechnology 2012, 23:22–28 www.sciencedirect.com
Synthetic biology Ellis and Goodacre 27
10. Tang J: Microbial metabolomics. Current Genomics 2011,
The long-term and mutually beneficial relationship be-
12:391-403.
tween synthetic biology and metabolomics incorporating A nice comprehensive recent review on microbial metabolomics.
fluxomics is clearly important for rational metabolic
11. Wang ZW, Ma XH, Chen X, Zhao XM, Chen T: Current methods
engineering. Genome-scale metabolic network recon- and advances in microbial metabolomics. Progress in
Chemistry 2010, 22:163-172.
structions are being generated almost weekly for differ-
ent species (and strains thereof) and systematic 12. Winder CL, Cornmell R, Schuler S, Jarvis RM, Stephens GM,
Goodacre R: Metabolic fingerprinting as a tool to monitor
investigations of these will help pinpoint the so-called
whole-cell biotransformations. Analytical and Bioanalytical
bottlenecks (or what used to be referred to as the Chemistry 2011, 399:387-401.
rate-limiting steps http://pubs.acs.org/doi/pdf/10.1021/
13. Winder CL, Dunn WB, Schuler S, Broadhurst D, Jarvis R,
ed058p32) in metabolic pathway constructions in new Stephens GM, Goodacre R: Global metabolic profiling of
Escherichia coli cultures: an evaluation of methods for
host organisms. Synthetic biology is here to stay and
quenching and extraction of intracellular metabolites.
rational metabolic engineering of bacteria, yeast, fungi Analytical Chemistry 2008, 80:2939-2948.
and mammalian-based systems will become an
14. Mapelli V, Olsson L, Nielsen J: Metabolic footprinting in
important growth area, urgently needed to sustain the microbiology: methods and applications in functional
genomics and biotechnology. Trends in Biotechnology 2008,
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resources. 15. Aranibar N, Borys M, Mackin NA, Ly V, Abu-Absi N, Abu-Absi S,
Niemitz M, Schilling B, Li ZJ, Brock B et al.: NMR-based
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1) for financial support of the MCISB (Manchester Centre for Integrative Metabolic footprinting and systems biology: the medium is the
Systems Biology). message. Nature Reviews Microbiology 2005, 3:557-565.
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