Coping with complexity in metabolic engineering

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Coping with complexity in metabolic engineering Joerg Mampel, Joerg Martin Buescher, Guido Meurer, and Juergen Eck B.R.A.I.N. AG (Biotechnology Research and Information Network), Darmstaedter Strasse 34–36, D-64673 Zwingenberg, Germany

In the past decade, systems biology has revealed great metabolic and regulatory complexity even in seemingly simple microbial systems. Metabolic engineering aims to control this complexity in order to establish sustainable and economically viable production routes for valuable chemicals. Recent advances in systems-level data generation and modeling of cellular metabolism and regulation together with tremendous progress in synthetic biology will provide the tools to put biotechnologists on the fast track for implementing novel production processes. Great potential lies in the reduction of cellular complexity by orthogonalization of metabolic modules. Here, we review recent advances that will eventually enable metabolic engineers to predict, design, and build streamlined microbial cell factories with reduced time and effort. Confronting complexity After decades of mostly reductionistic biological research that focused on single genes or proteins, systems biology is on its way to assemble these pieces of information into one holistic picture. These efforts build heavily on two types of technological advances: (i) analytical techniques to quantify and characterize DNA, RNA, proteins, or metabolites that are now sensitive and comprehensive enough to produce meaningful snapshots of microbial cells; (ii) mathematical representations of biological knowledge and statistical data analysis that enable the analysis of everincreasing amount of data. Recent milestones in systems-level data generation include the sequencing of an Escherichia coli isolate in just 62 h [1], the in-depth characterization of transcriptomes by RNA sequencing [2], and the coverage of the full dynamic range of complete proteomes [3]. Because phenotypes are generated by the interplay of all cellular components, the parallel analysis of these components offers additional insight. For instance, the combination of transcriptomics, proteomics, and metabolomics identified a general mechanism for metabolic homeostasis [4]. The next level of data complexity was reached by generating multiomics data in a time-resolved manner, thus enabling the identification of the pivotal regulatory events during dynamic adaptation processes [5]. Constraint-based modeling greatly eases the quantitative understanding of metabolism on a genome scale and facilitates linking genotypes to phenotypes [6]. Recently, a single whole-cell in silico model described the dynamic interplay of all cellular components of the near Corresponding author: Mampel, J. ([email protected]).

minimal-genome model bacterium Mycoplasma genitalium [7]. Modularization of cellular functions and iterative modeling of the interactions among modules establishes non-obvious links across cellular systems such as the control of cell cycle by metabolite concentrations. We expect that further refinement of this approach and its extension to more complex organisms will enable improved predictions of phenotypes from genotypes in the future. The very first production strains that were derived from natural isolates by random mutagenesis were treated as ‘black boxes’ with little knowledge of their intracellular processes. Metabolic engineering has come a long way, but for the time being, metabolic engineers must still accept much unaccounted complexity inherent to their standard microbial workhorses when developing biotechnological processes. Because of socioeconomic implications, future biotechnological production processes will face the additional challenge of using more complex feedstocks such as lignin or waste streams instead of defined carbon sources to produce even more advanced bio-based monomers or polymers [8]. In this article, we review concepts confronting this complexity challenge. We focus on efforts to gain control over the simplest biotechnological production facilities: the prokaryotes. Managing complexity by systems level engineering A fundamental lesson that can be learned from systemwide analyses of even simple microbial cells is the large diversity in prokaryotic lifestyles. Dead-end roads in classical (i.e. not model-based) targeted metabolic engineering are often caused by the multigenic nature of the desired phenotype combined with our still rudimentary understanding of the cell’s complexity. Here, we spotlight recent approaches to gain control over a microorganism either by reducing the complexity or by coping with only a partial understanding of the biological system at hand. In the latter case, targeted systems level engineering builds upon a genome scale model prediction and implements the required molecular modifications in a host strain of choice. The alternative approach of nontargeted systems level engineering randomly introduces genome-wide modifications to the transcriptome and selects the most favorable mutant. Targeted systems level engineering Targeted systems level engineering is highly rational and generates fully characterized strains via precise genetic modifications. Prototypes of this design principle were established for L-threonine and L-valine biosynthesis in

0167-7799/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tibtech.2012.10.010 Trends in Biotechnology xx (2012) 1–9

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Review E. coli [9]. Overexpression of enzymes required for product synthesis, deletion of genes encoding enzymes of reactions branching off the production pathway, and removal of known feedback inhibition by enzyme engineering yielded a strain that produced L-threonine at roughly 25% of the theoretical maximum yield. Additional targets for metabolic engineering were identified by comparative transcriptomics. A final yield of about 50% of the theoretical maximum was reported using a fed-batch cultivation. Most likely, this value could be further increased by subsequent optimization of the production process. An alternative approach is to optimize pathways based on comparisons of in vivo metabolic fluxes of a wild type strain and a simulated ideal producer strain. Lysine production with Corynebacterium glutamicum was analyzed, and subsequently 12 defined mutations were introduced to render the strain more similar to the simulated ideal producer. The resulting strain yielded up to 55% of the theoretical maximum, comparing well to industrial producer strains [10]. These studies used a limited set of system-level analyses to identify targets for metabolic engineering. The current cost of system-level data in terms of time, manpower, and expertise might have impeded comprehensive systems biology approaches in applied biotechnology so far. However, the above examples demonstrate the power of systems metabolic engineering based on less-than-comprehensive data sets. With a better understanding of regulatory networks, regulatory modifications could be used in a targeted way to enter the next level of engineering production strains. Hybrid models that include both metabolic reactions and their regulation are already available for small systems such as the osmotic shock response in yeast and in a simplified form at genome scale [11]. A first application is the production of reduced products in arcA deletion mutants of E. coli [12]. Functional units in prokaryotes are already organized in coregulated modules such as operons and regulons that affect all involved pathways in a synchronized and balanced manner. Eventually, engineering these modules can complement or even replace engineering single genes to further increase the yield of engineered productions strains. Nontargeted systems level engineering Nontargeted systems level engineering requires less prior knowledge than targeted engineering and is thus more widely applicable. Global changes in gene expression generate phenotypes that are subsequently screened for the desired properties. Random mutagenesis based on chemical mutagens or ultraviolet light routinely generates multiple DNA modifications at once [13]. Although deep sequencing allows for identification of all resulting mutations, this is often uninformative concerning the genotype– phenotype relationship. Thus, the exact basis for the respective phenotype often remains elusive. In contrast, mutations introduced by genome-scale engineering are widely traceable. Genome-scale engineering includes technologies such as global transcription machinery engineering, which introduces mutations in defined components of the transcription machinery [14], or genome editing, which 2

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addresses multiple genomic targets in parallel by transforming cells with a library of transcription–regulation oligonucleotides [15,16]. Multiplex Automated Genome Engineering (MAGE) enables simultaneous modification of several targets, thereby producing millions of separate clones with defined combinatorial genetic diversity. For example, expression of 24 selected genes was altered simultaneously to increase lycopene production 5-fold in E. coli within just 3 days [15]. Trackable multiplex recombineering (TRMR) has generated a mixture of barcoded mutants that were successfully screened for toxin resistance [16]. Further elaborating on the MAGE engineering principle yielded genome editing technologies that allowed for dramatic increase in the number of addressed targets in a genome, currently culminating in more than 80 genome edits within a single E. coli cell [17,18]. TRMR and MAGE offer synergistic potential that was explored recently to optimize the growth of E. coli in complex model environments [19]. Several other examples for multiplex genome engineering exist (reviewed elsewhere [20]). The most important prerequisite for the successful application of nontargeted systems level engineering is the availability of a reliable screening assay for the selection of a desired clone. In cases for which this prerequisite is fulfilled, we expect this approach to become more widely used as the cost for oligonucleotide synthesis decreases. Reduced complexity by minimal genomes Current knowledge for even the best-studied free-living prokaryotes such as E. coli and Bacillus subtilis, does not provide a truly predictive understanding of cellular responses to genetic and environmental perturbations. As a consequence, synthetic biologists strive to build biological systems based solely on the essential parts that constitute a living system [21]. Reducing the complexity facilitates comprehensive and predictive modeling [6], thereby improving speed and precision of the strain engineering process. Two alternative approaches currently compete for the creation of the smallest selfreplicating cell: (i) de novo synthesis of a minimal genome [21] or (ii) the stepwise defined reduction of a complex genome (Table 1). The minimal genome by reduction The genome size of typical biotechnological workhorses such as E. coli or B. subtilis is in the range of 4 Mbp encoding some 4000 genes, whereas the common amino acid producer C. glutamicum only possesses 3.3 Mbp. The smallest genomes of natural self-replicating prokaryotic cells encode as few as 500 genes. Global transposon mutagenesis has revealed the putatively minimal set of 387 genes indispensible for life of Mycoplasma genitalium [22]; however, the minimal number might be smaller (Table 2), implying that the cell’s vital functions can be encoded by a surprisingly small set of genes. Molecular biologists try to take advantage of these calculations by stepwise reduction of genome sizes. A rich toolbox of ‘molecular-surgery’ technologies enables sophisticated strategies for genome reduction. Among these technologies are engineered zinc-finger or meganucleases [20,23,24], Transcription Activator-Like Effector Nucleases

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Table 1. Genome sizes of wild type and engineered prokaryotic microbes Organism

Lifestyle

Wild type strains Heterotrophic free living Corynebacterium glutamicum Autotrophic free living Prochlorococcus marinus Heterotrophic free living Pelagibacter ubique Autotrophic symbiont Vesicomyosocius okutanii Heterotrophic symbiont Mycoplasma mycoides Capri GM12 Heterotrophic symbiont Mycoplasma pneumoniae Heterotrophic symbiont Mycoplasma genitalium G37 Heterotrophic symbiont Nanoarchaeum equitans a Heterotrophic symbiont Buchnera aphidicola sp. Cinara cedri a Heterotrophic symbiont Candidatus Hodgkinia cicadicola a Wild type strains and corresponding genome reduced derivatives Heterotrophic free living Streptomyces avermitilis Heterotrophic free living Streptomyces avermitilis SUKA17 Heterotrophic free living Pseudomonas putida KT2440 Heterotrophic free living Pseudomonas putida TEC1 Heterotrophic free living Escherichia coli K12 MG1655 Heterotrophic free living Escherichia coli MG1655 D16 Heterotrophic free living Escherichia coli MG1655 MGF-01 Heterotrophic free living Bacillus subtilis 168 Heterotrophic free living Bacilllus subtilis 168 MGB874

Metabolism

Genome size (Mbp)

No. of predicted proteins

Genome reduction (%)

Refs

Prototroph Prototroph Prototroph Auxotroph Auxotroph Auxotroph Auxotroph Auxotroph Auxotroph Auxotroph

3.31 1.75 1.31 1.02 1.08 0.82 0.58 0.49 0.43 0.14

2993 1884 1354 975 830 688 475 540 357 169

NA NA. NA. NA. NA NA NA NA NA NA

[63] [64] [64] [64] [63] [64] [65] [63] [66] [67]

Prototroph Prototroph Prototroph Prototroph Prototroph Prototroph Prototroph Prototroph Prototroph

9.12

7676

6.18

5350

4.64

4146

4.21

4280

NA 18.5 NA 7.4 NA 29.7 21.7 NA 20.7

[63] [30] [63] [31] [63] [68] [28] [63] [29]

NA, not applicable; ND, not determined. a

Obligate symbiont.

(TALENs) with tailored specificities [25,26], or programmable DNA scissors based on a bacterial type II-CRISPR endonuclease [27]. E. coli remains viable after removal of nearly one-third of its genome, but its growth is impaired (Table 1). In contrast, less extensive genome reductions improve uptake and maintenance of heterologous DNA or increase growth rates [28]. Deletion of one quarter of B. subtilis genome also yields phenotypes with unstable growth rates, cell morphology, and protein production. A slightly less reduced genome variant, however, has improved production of cellulases and proteases by a factor of 1.7 and 2.5, respectively. The positive effect is partially due to inactivation of arginine degradation and to the possible synergistic effects of multiple mutations [29]. Streptomyces hosts have also been improved for heterologous production of antibiotics [30], and Pseudomonas has undergone stepwise shrinkage to improve growth rate and biomass formation [31]. A proper choice of the genome segments to be deleted – guided by smart models and algorithms – appears to be the key to extend genome reductions over present levels. The minimal genome by de novo synthesis The first genome assembly independent of a biological template was demonstrated by the chemical synthesis of

a 7.5-kb poliovirus genome [32]. After 15 years of work, the chemically synthesized genome of Mycoplasma mycoides (1.08 Mbp) was successfully introduced into M. capricolum recipient shell to give M. mycoides JCVI, a self-replicating organism with a de novo synthesized, yet copied genome [21]. Lessons learned from these pioneering works will facilitate and accelerate future de novo genome syntheses. However, genome transplantation protocols will most likely have to be adapted for each organism individually, probably restricting this approach to a few chassis organisms. Nevertheless, the door to the construction of truly engineered, streamlined, and thus less complex genomes and organisms is now open. Orthogonalization to reduce complexity The term orthogonal is routinely used in mathematics and engineering sciences (Figure 1). It describes independence of coexisting subsystems that together build an entity. The term is closely linked to the concept of modularity (Figure 1b). In a simplified view, a naturally evolved microbial cell is a fully integrated entity without subsystems. The sum of all interdependent molecular reactions builds the interactome. Basically, fully orthogonal subsystems are absent from the interactome; however, few natural orthogonal systems exist. They come as mobile genetic

Table 2. Estimating the number of essential genes in prokaryotic microorganisms Model organism M. genitalium G37 E. coli K12 BW25113 B. subtilis 168 NA/comparative genomics NA/extrapolation

Genome size (Mbp) 0.58 ND 4.21 NA 0.090

No. of essential genes a 382 303 271 206 167

Evidence In vivo In vivo In vivo In silico In silico

Refs. [22] [69] [70] [71] [72]

NA, not applicable; ND, not determined. a

Essentiality was assigned if the knockout of a single gene could not be rescued under precisely defined environmental conditions.

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Figure 1. Orthogonality. (a) In mathematics, change of the magnitude of vector a has no influence on the magnitude of vector b if both vectors are orthogonal. (b) In engineering sciences, orthogonality refers to building complex products from smaller subsystems that can be designed independently but function together as a whole. Typically, orthogonal subsystems are organized as modules, such as a personal computer is composed of major subsystems (e.g. keyboard, monitor, computer), which by themselves are composed of modules, e.g. the hard disk drive, graphics card, multiprocessor units, and RAM units. (c) From an engineering perspective, a naturally evolved microbial cell is a fully integrated entity and orthogonal subsystems are absent. The different layers of cellular activity are connected by various nodes building a network, the interactome. In a heterotrophic organism, a single carbohydrate substrate can serve as sole source for carbon and energy and thus has to be channeled in several metabolic pipelines in order to sustain life. Catabolism (black arrows) alters levels of cofactors and generates small molecule intermediates that interact with various targets, e.g. by introducing feedback loops (red lines). The native or heterologous pathway toward the desired product is subject to those intrinsic interactions expanding the interactome (and thus complexity). (d) True orthogonal metabolism of product-specific ‘‘substrate 2’’ (blue arrows) does not interfere with the host cell’s native interactome. Hence, it can be designed following simple forward engineering principles with high accuracy of prediction. (e) Spatial orthogonalization is achieved by localization of the enzymes involved in the productive pathway to increase and optimize flux through the enzymatic pipeline so that diffusion of intermediates is absent. Optimal scaffold design might be guided by the kinetic parameters of the enzymes or explored randomly. The scaffold on which pathways enzymes (E1, E2, E3) are lined up via ligands (gray, red, blue) is provided by intracellular polymers such as DNA (or XNA), RNA (50 –30 ) or proteins (N0 –C0 ); in vitro synthesis of DNA allows to program the polymer composition following virtually any design. (f) Reprogramming of bacterial microcompartments (BMC) is currently restricted to alter the composition of the enzymes (red balls) encapsulated by the proteinogenous shell (blue and gray balls); 1,2-PDO, 1,2-propanediol. (g) Oxidoreductases were successfully engineered to use orthogonal (NFCD or NCD) rather than natural cofactors (NAD) for catalysis [49]. NFCD, nicotinamide flucytosine dinucleotide (R = F); NCD, nicotinamide cytosine dinuncleotide (R = H).

elements or bacteriophages, and have found broad biotechnological applications in T7-protein expression systems [33], various Tn5-transposon-based molecular tools [34] or group II introns [35]. Ideally, orthogonal production pathways (e.g. for valuable chemicals) rely on the host strain only for enzyme synthesis and operate independently of the host’s metabolism. Consequently, the unit that needs to be engineered (and thus to be understood) is significantly less complex than a whole self-replicating cell. Synthetic biology, especially synthetic xenobiology, offers a variety of potentially useful orthogonal modules (Box 1). Precise and predictable regulation is key to control engineered parts or pathways in natural hosts but is notoriously prone to crosstalk. Molecular biologists succeeded in the isolation of synthetic control elements from the cell’s regulatory network. For example, natural riboswitches can be reengineered to interact with virtually any ligand of choice [36], such as theophylline [37], azacytosine, or ammeline. 4

Specifically, the adenine-sensing add A-riboswitch has been engineered to interact with ammeline, a nonnatural analogue of adenine, so that orthogonally selective expression of eGFP is dose dependent [38]. As alternatives to riboswitches, classical regulator–promoter pairs might be either rationally engineered or recruited from operons involved in biodegradation of xenobiotic compounds. Recent examples include synthetic TAL effectors engineered to insulate gene circuits [39], orthogonal antisense RNA-mediated translation systems [40], and RNA polymerase with reprogrammed specificity toward foreign promoters [41,42]. Orthogonal metabolism The holy grail in metabolic engineering is the introduction of heterologous metabolism decoupled from the host-cell’s metabolic network. This avoids unproductive crosstalk of life-sustaining and product-forming biochemical reactions. In the case of one single product-forming reaction, this is

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Review Box 1. A spotlight on xenobiology Xenobiology is one extreme discipline within the broad field of synthetic biology. It aims to build living systems guided by biological blueprints but ideally independent of standard biological building blocks and constraints [73]. Hence, xenobiology is energized by two of the most fundamental questions in life sciences: what makes up a living system, and can it be created de novo? Concepts within xenobiology are fueled by rather independent research on the origin of life (protocells), exobiology (astrobiology), and systems chemistry. The most distinguished projects within the field comprise research to modify or replace DNA for information storage. Examples include size expanded DNAs (xDNA/yDNA) obtained by a process called benzo-homologation or benzo-fusion, engineering polymerases to accept XNA nucleotides to synthesize and amplify XNA (Xeno Nucleic Acids; desoxyribose replaced by alternative carbohydrates) or vice versa [74], or extending the genetic alphabet to a six-base code (see [75] for review). One extreme theoretical embodiment of xenobiology was put forward by George Church introducing the mirror-life concept [76], in which proteins that would normally be made of left-handed amino acids would instead contain right-handed amino acids. Extending on this line of speculation, these proteins would give rise to a novel, thus fully orthogonal biochemistry. Less distinguished but already real are efforts to expand the standard genetic code by either recoding amber codons [77,78] or even altering the code by the introduction of a quadruplet codon [79]. Decoding of the altered code was also addressed and demonstrated by successful reengineering of orthogonal mRNA–ribosome pairs (ribo-X, ribo-Q1 [80]). The Greek prefix ‘‘xenos’’ means foreign, so the concept of xenobiology is also seen as an opportunity to install a genetic firewall to prevent exchange with existing ‘‘natural’’ biological systems as long as building blocks are restricted [73]. The modules constituting xenobiology are still in their infancy, not yet integrated in operative (sub-)systems and far from demonstrating paradigm-shifting case studies. However, individual modules appear ideally suited to fill the orthogonality toolbox.

called whole-cell biotransformation and separation from cellular metabolism occurs by using nonnatural substrates and products. Biodiversity provides a rich toolbox of biocatalysts [43] assembled in operons or regulons that enable bacteria to degrade or synthesize a broad variety of even highly toxic and/or xenobiotic small molecules such as dioxins or nitroaromatic compounds [44]. Intermediates of those pathways are mostly xenobiotic and thus do not interfere with the host cell’s core metabolic or regulatory network [45]. Consequently, even multistep whole-cell biotransformations can operate orthogonally. Orthogonal design of pathways consuming or producing common carbon sources or central metabolites is particularly challenging as intermediates can often be exchanged with the host cell’s metabolism. In some cases, the natural diversity offers alternative parallel pathways without common intermediates. For example, the Embden–Meyerhof– Parnas and Entner–Doudoroff (ED) pathways both use glucose 6-phosphate to generate pyruvate. Embden– Meyerhof–Parnas is used exclusively in E. coli and ED can be used in E. coli for orthogonal synthesis of a desired product from glucose. Orthogonal metabolic modules might also be recruited from microbes living in extreme habitats. Examples include non-phosphorylative variants of the ED pathway or the glyceraldehyde-3-phosphate dehydrogenase in some Archaea that are thought to be adaptations to life at high temperatures [46]. Dimethylallyl pyrophosphate (DMAPP) and isopentenyl pyrophosphate (IPP) are the universal precursors for

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terpenoid biosynthesis in pro- and eukaryotes. Two major pathways are known to provide IPP and DMAPP: the wellestablished mevalonate pathway and the more recently discovered 5-methyl erythritol phosphate (MEP) pathway. Both pathways do not share common intermediates and are thus orthogonal (Figure 2). In natural systems, they seem to be mutually exclusive as only plants were reported to posses both pathways, however, in segregated compartments. Nevertheless, E. coli was successfully engineered to produce high titers of isoprene via a recombinant mevalonate pathway, demonstrating that two orthogonal pathways can operate simultaneously without interference [47]. Combining metabolic modules from diverse organisms can often achieve only partial orthogonality of biosynthetic pathways because energy and redox cofactors such as ATP and NADH need to be regenerated by the host cell’s metabolism. Truly orthogonal metabolism therefore requires orthogonal alternatives to these common metabolites [48]. A bioorthogonal in vitro redox cycle has been constructed by altering the cofactor specificity of malic enzyme and lactate dehydrogenase to preferentially accept the abiotic redox cofactors nicotinamide flucytosine dinucleotide (NFCD) or nicotinamide cytosine dinucleotide (NCD) (Figure 1g), without altering the stereospecificity or kinetics of the reaction. Unlike NFCD, NCD is composed of two natural building blocks allowing biosynthesis based on established pathways. Interestingly, the exchange of only one conserved residue close to the Rossman-fold motif changes the preference of the enzymes to accept NCD instead of NAD [49]. This finding raises hope that ATPdependent proteins might be engineered accordingly. Spatial orthogonality by engineering bacterial microcompartments Eukaryotic metabolism is a prime example for partial orthogonalization by spatial separation of major cellular processes such as protein degradation (lysosomes), RNA synthesis (nucleus), or energy production (mitochondria). The prototype textbook prokaryotic cell has no subcellular compartments. However, bacteria of the ubiquitous phylum Planctomycetes challenge this concept [50]; additional evidence comes from observations of numerous intracellular structures in bacteria and archaea [51]. Among those structures, bacterial microcompartments (BMC) are distinguished as protein polyhedral inclusions [52] (Figure 1f). The prototype was shown to contain the CO2-fixing enzyme RUBISCO together with carbonic anhydrase in order to facilitate CO2 fixation under conditions of low concentrations of inorganic carbon. Further examples include BMCs for the degradation of ethanolamine and 1,2-propanediol in Salmonella and Citrobacter. BMCs are widespread in bacteria and are involved in different metabolic pathways to protect cells from cytotoxic intermediates or to prevent loss of volatile hydrocarbons. Thus, the protein shells insulate catalysis and represent simple metabolic organelles (metabolosomes). The 1,2-propanediol metabolizing BMC from Citrobacter freundii is fully functional in E. coli, correctly loaded and assembled. Short N-terminal signal sequences that allow interaction of shell proteins with BMC-associated enzymes load the 1,2-propanediol BMC with GFP [53]. 5

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Figure 2. Orthogonal biosynthetic pathways to produce isoprenoids in bacteria. (a) Glycolysis provides the building blocks to fuel the two known major pathways that generate the universal precursors of terpenoid biosynthesis, IPP (isopentenyl pyrophosphate) and DMAPP (dimethylallyl pyrophosphate) (e) that are essential for life. (b) The mevalonate (MEV) pathway exclusively relies on acetyl-CoA, whereas (c) the MEP (5-methyl erythritol phosphate) pathway (DXP pathway) is fueled by glyceraldehyde 3-phosphate (GAP) and pyruvate (Pyr). (d) The Mev pathway shows variations in Archaea, where mevalonate-phosphate is decarboxylated prior to another phosphorylation step to give IPP. E. coli uses the MEP pathway to provide IPP and DMAPP, and the DXP intermediate is the precursor for essential vitamins and cofactors. The Mev pathway was introduced to E. coli to provide an orthogonal module for terpenoid biosynthesis. The production of isoprene, lycopene, and arteminisic acid demonstrated the success of this design principle. MEP pathway: DXS, 1-deoxy-D-xylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductase; IspD, 4-diphosphocytidyl-2-Cmethyl-D-erythritol synthase; IspE, 4-diphosphocytidyl-2-C-methyl-derythritolkinase; IspF, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; IspG, (E)-4-hydroxy-3methyl-but-2-enyl pyrophosphate synthase; IspH, (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate reductase; Mev pathway: AACT, acetyl-CoA transferase; HMGS, 3hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthase; HMGR, HMG-CoA reductase; MK, mevalonate kinase; PMK, phosphomevalonate kinase; DPMD, diphosphomevalonate decarboxylase; PMD, phosphomevalonate decarboxylase; IPK, isopentenyl phosphate kinase; IDI, isopentenyl pyrophosphate isomerase.

Assembly of microcompartments is independent of the internalized proteins, and even the outer surface of MCPs might be decorated with heterologous proteins [54]. BMCs might be rationally engineered to function as insulated intracellular bioreactors thus conferring orthogonality to otherwise interconnected metabolic pathways. To utilize their full potential, transport mechanisms of metabolites across the BMC’s proteinogenous barrier need to be understood in more detail. Spatial orthogonality by synthetic scaffolding of pathways Inspired by natural blueprints such as tryptophan synthase, polyketide synthases, or purinosomes, the 6

concept of metabolic channeling was adopted by engineers to optimize metabolic fluxes, e.g. for mevalonate or glucaric acid biosynthesis [55,56]. Generally, a scaffold is designed that determines the spatial organization of sequential enzymatic reactions (Figure 1e). By means of short peptide ligands, the enzymes constituting the (artificial) pathway dock to scaffolds, thereby forming metabolic pipelines. All major intracellular polymers have been successfully recruited as scaffolds: proteins [55], RNA [57], and DNA [58]. The bacterial cytoskeleton offers further options for spatial organization of proteins [59]; however, because of advances in DNA-synthesis technologies and prediction of 3D structures of RNA, these two polymers promise ease in designing and reprogramming scaffolds.

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Review The initial goal of metabolic channeling was to increase flux through pathways. In poorly optimized systems, the yield could be improved 4- to 5-fold, e.g. for recombinant production of resveratrol, propanediol, or mevalonate [58]. Optimized flux through metabolic pipelines also prevents accumulation of otherwise toxic intermediates such as methylglyoxal or aldehydes during propanediol metabolism or synthesis. Following the same rationale, synthetic enzyme scaffolds have broad potential to sufficiently isolate metabolic modules or pathways from the host’s intrinsic metabolic and regulatory network, thus introducing partially orthogonal metabolism. Perspective: next generation’s metabolic engineering In this review, we present alternative approaches that a metabolic engineer might use to gain control over bacterial metabolism. Systems level engineers accept the complexity of naturally evolved host strains. Based on the available observations and knowledge, they simulate a biological unit of interest to obtain predictions for the modifications required to reach a desired outcome. After the implementation of these modifications, deviations from the desired outcome are generally attributed to insufficiently detailed models and insufficient knowledge. This can lead to iterative cycles of molecular modifications, analyses of the resulting phenotype, and refinement of the model. Multiplex engineering technologies such as MAGE or TRMR might accelerate this somewhat laborious process. An increasing understanding of the regulatory modules present in cells, and engineering of these modules – rather than of single enzymes – will facilitate the engineering process further. In order to become predictive at high levels of accuracy, scientists need to understand the molecular principles of living systems – a challenging task. However, meaningful predictions even with incomplete data can be obtained if the design principles that are implemented in naturally evolved cells are known. For instance, flux balance analysis can predict biomass yield, which results from complex interactions of many cellular components, based solely on the known stoichiometry of the metabolic network and simple objective functions such as maximization of growth. Recently, more generally applicable objective functions have been identified [60,61] that still await predictive applications. Shortcomings in understanding the molecular principles of life currently restrict a truly successful de novo design of complex microbial cell factories. To date, scientists succeeded in chemically copying natural blueprints and modeling of a near-minimal genome organism – milestones of synthetic and systems biology. However, a microbial cell designed and synthesized independent of blueprints will be the ultimate test for our insight into those principles. Joint efforts by systems and synthetic biologists might decipher those principles in the future. Given the enormous efforts necessary to model a minimal genome organism, the introduction of orthogonal metabolic modules within an existing cell is presently the most efficient way to obtain control over engineered microbial production processes. Not interfering with the yet unresolved interconnectivity of the different endogenous cellular

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networks, orthogonal metabolism enables the production of valuable products at high yields. Recent success in introducing or reprogramming spatial segregation of pathways will extend orthogonalization to endogenous pathways of the host [53,58]. However, truly orthogonal metabolism will have to operate independently of cofactor regeneration by the host cell. We have already seen the first studies addressing this issue [49]. As rather limited protein engineering efforts are required to alter cofactor specificity of enzymes and genome-editing technologies are available [18,62], we expect to see examples of multistep pathways physically decoupled from the host cell’s cofactor pool in the near future. From the viewpoint of an industrial metabolic engineer, time-to-market aspects are paramount. Therefore, generation of orthogonal systems based on standard production organisms might be the direction in the short term because introduction of heterologous pathways is state of the art and not too far off from true orthogonality. In the near future, however, fast-track development of orthogonal systems will be assisted by minimal chassis organisms, as modeling of these systems is more simple, and interaction with their metabolism is minimized, allowing precise ‘firsttime-right’ predictions. Disclosure statement The authors declare no conflict of interest. Acknowledgments We thank John T. Sauls for carefully reading the manuscript.

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