The Need for Integrated Approaches in Metabolic Engineeringcshperspectives.cshlp.org/content/early/2016/08/13/... · 13.08.2016 · The Need for Integrated Approaches in Metabolic

The Need for Integrated Approachesin Metabolic Engineering

Anna Lechner,1,2 Elizabeth Brunk,2,3 and Jay D. Keasling1,2,4

1Joint Bioenergy Institute (JBEI), Emeryville, California 946082Department of Chemical & Biomolecular Engineering, Department of Bioengineering, Universityof California, Berkeley, California 94720

3Department of Bioengineering, University of California, San Diego, California 920934Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720

Correspondence: [email protected]

This review highlights state-of-the-art procedures for heterologous small-molecule biosyn-thesis, the associated bottlenecks, and new strategies that have the potential to acceleratefuture accomplishments in metabolic engineering. We emphasize that a combination ofdifferent approaches over multiple time and size scales must be considered for successfulpathway engineering in a heterologous host. We have classified these optimization proce-dures based on the “system” that is being manipulated: transcriptome, translatome, prote-ome, or reactome. By bridging multiple disciplines, including molecular biology, biochem-istry, biophysics, and computational sciences, we can create an integral framework for thediscovery and implementation of novel biosynthetic production routes.

CURRENT APPROACHES IN METABOLICENGINEERING AND THEIR LIMITATIONS

Microbial organisms are able to package a setof biocatalysts into “molecular factories”

for the energy-efficient generation of value-add-ed compounds derived from simple sugars(Keasling 2010). Making use of these molecularfactories is an attractive alternative to organicsyntheses that rely on petrochemical feedstocks,finite resources, or environmentally unfriendlyproduction processes. However, microbial or-ganisms have not evolved to meet the demandsof a scaled-up production process at the indus-trial scale and tend to have a poor ratio ofachieved versus theoretical yield. Thus, one of

the main goals of metabolic engineering is totransform organisms into efficient systems forthe production of active pharmaceutical ingre-dients, commodity chemicals, and energy. Met-abolic engineering has already provided sustain-able access to a number of chemical classes. Arecent milestone of bio-based industrial pro-duction is the engineered microbial biosynthesisof artemisinic acid, a plant-derived precursor tothe antimalarial drug artemisinin (Paddon et al.2013). Additional high-value compounds pro-duced via sustainable processes include antibi-otics, such as erythromycin A, daptomycin, andpenicillin, as well as precursors to pharmaceuti-cals like taxadien-5a-acetoxy-10b-ol, which arereviewed elsewhere (Lee et al. 2009). Industrial

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fermentation processes have also been estab-lished for production of commodity chemicalslike 1,3-propanediol, 1,4-butanediol, farnesene,and other terpenoids (for a review on this topic,see Klein-Marcuschamer et al. 2007). Furtherprogress in metabolic engineering could facili-tate industrial biosynthesis of the biofuels iso-butanol, isopentenol, bisabolene, and pinene, aswell as other hydrocarbons (for a review on thistopic, see Lennen and Pfleger 2013). However,some important classes of small molecules, suchas alkanes, alkenes, alkaloids, polyketides, andpeptides, remain accessible mostly from nativesources or through organic synthesis.

Why do we succeed in biomanufacturingcertain compound classes while others remainrecalcitrant? One limitation is the lack of inte-grated approaches toward pathway optimiza-tion. Most strategies deal with bottlenecks ononly one of multiple levels in pathway engineer-ing, often addressing either gene expressionor enzyme engineering. This semirational ap-proach is popular because quantitative behaviorremains difficult to predict for complex biolog-ical systems. Inaccurate predictions in turn arepartly a result of unknown or unpredictablecellular behavior most likely caused by addi-tional undefined layers of control. Sometimesthis is compounded by limitations in geneticaccessibility of the host. Of the many micro-bial species that have potential as efficient fac-tories, only a small number are currently genet-ically tractable. The recent discovery of theCRISPR/Cas system has the potential to readilyengineer previously intractable species, but thistechnique has yet to be implemented widelyamong species (Jakociunas et al. 2015). In allcases, metabolic engineering is limited by ana-lytical methods, which require specific methodoptimization for each compound class beingproduced. Despite these shortcomings, thereare clear advancements in the development ofnew -omics data acquisition and analysis tech-niques that enable metabolic engineering onvarious levels. These challenges and perspec-tives highlight the need to implement a multi-layer optimization framework to perfect the“design–build–test– learn” engineering cycleof synthetic biology.

THE NEED FOR INTEGRATED APPROACHESIN METABOLIC ENGINEERING

The ideal design for maximal metabolite pro-duction would generate active enzymes withsufficient catalytic turnover in considerationof timing and cellular location. This is a hardgoal to achieve because of the complexity of thecell. Overlaid systems interact at different timescales and a single manipulation may have apositive impact at one metabolic or regulatorylayer and a negative or neutral impact at anoth-er. Modifications at the transcriptome level ad-just the timing and strength of gene expression,whereas changes to the translatome (measuredby ribosome occupancy) influence protein syn-thesis rates, local translational speed, as wellas protein solubility, activity, and specificity.Changes to the sequence and structure of pro-teins affect the catalytic efficiency of pathwayenzymes and allosteric regulation at the prote-ome level. At the reactome level, balancedenzyme activity, sufficient transfer of interme-diates between enzymes, and cofactor balanceare all required for high flux through the engi-neered pathway.

A multilevel engineering approach can besubdivided into manageable processes (Fig. 1).At the transcriptome level, mRNA amounts arecontrolled by promoter strength, gene copynumber, and mRNA stability. At the transla-tome level, translational efficiency and proteinsolubility are tuned by ribosome-binding site(RBS) strength, mRNA secondary structure,and codon usage. At the protein/proteome lev-el, efficient catalysis is engineered by site-spe-cific enzyme modifications and release of feed-back inhibition. At the reactome level, enzymeratio balancing and protein colocalization af-fect the efficient turnover of pathway interme-diates. Robust and optimal calibration of allof these nested and interlocked mechanismsis necessary to achieve maximum flux throughan engineered pathway. We propose a multilay-er framework for metabolic engineering by in-tegrating design and control elements on mul-tiple levels to accelerate the implementationand optimization of novel biosynthetic pro-duction routes.

A. Lechner et al.

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ENGINEERING AT THE TRANSCRIPTOMEAND TRANSLATOME LEVEL

The most straightforward way to achieve a de-sired gene expression profile is to regulate thetranscription and translation processes, whichspan a wide dynamic range (102–105-fold) formRNA and protein amounts (Salis et al. 2009;Blazeck et al. 2012). Metabolic engineers wouldlike to temporally regulate the expressionstrength of nonnative genes to minimize themetabolic burden of protein synthesis becausenonoptimal expression may draw cellular re-sources away from essential functions and lowerthe overall fitness of the host (Poelwijk et al.2011). This, in turn, can lead to decreased path-way productivity, reducing the design space atthe reactome and proteome levels. Synthetic bi-ology efforts have provided characterized nativeand synthetic promoters to control mRNA ex-

pression levels (Leavitt and Alper 2015). How-ever, our ability to forward engineer heterolo-gous gene expression with a precise outcomeregarding protein amounts and activity is cur-rently lacking (Figs. 2 and 3). To avoid unnec-essary rounds of expression optimization, it isadvisable to identify common bottlenecks thatcan be bypassed in advance. We will discuss afew studies that have addressed these issues andsuggest an improved workflow.

Predictable and Reliable Gene Expression

It is well known that mRNA and protein levelsdo not perfectly correlate in native or engi-neered systems (Jayapal et al. 2008; Vogel andMarcotte 2012; Payne 2015). This variation isespecially prevalent in prokaryotic expression,but remained unaccounted for in genetic design

Reactome

Protein expression levelsProtein–protein interactions

Flux measurementsGenome scale modeling

Biofuels and commoditychemicals

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Protein posttranslational modificationAllosteric regulation

mRNA structureCodon usage

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Factors involved and effectsMetabolic engineering level

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Figure 1. Integrated approaches in metabolic engineering. To achieve high metabolite flux through a biosyn-thetic pathway, it is essential to consider bottlenecks on the transcriptome, translatome, protein/proteome, andreactome level. Integrated metabolic engineering approaches need to occur both on the molecular-scale andsystem-scale level.

Integrated Approaches in Metabolic Engineering

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principles until recently. Synthetic biology isstriving to establish a knowledge base for mod-ular expression design based on a comprehen-sive understanding of the relationship betweenmRNA sequence and protein abundance. To-ward this goal, Kosuri et al. (2013) analyzed alibrary of .12,500 promoter and RBS combi-nations in Escherichia coli. As a result of thisstudy, a short list of part combinations was cre-ated to guide future expression constructs withdesired mRNA and protein level profiles. How-ever, when predictions and measurements werecompared in this study, the correlation wasmuch lower for protein abundance (R2 ¼

0.82) than for mRNA abundance (R2 ¼ 0.96).

A small fraction of constructs displayed aneven greater discrepancy, which indicates thepresence of unknown variables in this com-binatorial design. One significant contributorto the context-dependent variation is se-quence-dependent mRNA secondary structureformation around the translation start site,which greatly influences synthesized proteinamounts and complicates accurate predictions(Fig. 2).

To close the gap between designed and test-ed gene-expression patterns, Mutalik et al. cre-ated a new bicistronic expression cassette incombination with a previously reported insu-lated promoter design (Davis et al. 2011; Muta-

Measured

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edFigure 2. Predictable and reliable gene expression. (A) To precisely predict translation initiation rates, the idealtranscript would be depleted in secondary structures close to the ribosome-binding site (RBS). However, mRNAsecondary structures are crucial to overall transcript stability and translation speed control. (B) Secondarystructures present in most nascent mRNA molecules complicate accurate computational predictions of expres-sion strength. Depending on the location of the hairpin, secondary structure formation can slow down trans-lation initiation rates or completely prevent translation initiation in case of an occluded RBS. (C) The bicistronicgene design developed by Mutalik and coworkers (Davis et al. 2011; Mutalik et al. 2013) allows for higherpredictability, because a constant short open reading frame is placed upstream of the target expression cassettethat allows for read-through despite the presence of downstream hairpins.

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lik et al. 2013). In this design, the RBS initiatingtranslation of the reporter gene was embeddedwithin an upstream open reading frame for ashort leader peptide (Fig. 2). The investigatorssuggest that the intrinsic helicase activity of thetranslation machinery is able to unwind anysecondary structures present in the target RBSwhen reading through the upstream gene. Test-ing a library of promoter and RBS combinationarranged as part of the bicistronic design, theinvestigators were able to significantly reducethe error rate in forward engineering. The bicis-

tronic design had a correlation rate of 93%within a twofold window of expected expressionlevels compared with 84% for the monocis-tronic design. This allowed the characterizationof .500 composable parts over a wide dynamictranscription and translation range to enablepredictable engineering in E. coli.

The existence of context-dependency hasalso been described for eukaryotic gene expres-sion. Dvir et al. (2013) measured a sevenfoldchange in protein abundance when manipu-lating a very small nucleotide sequence space

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Figure 3. Soluble and active protein biosynthesis. (A) Proteins expressed in their native host naturally assume asoluble and catalytically active tertiary structure, whereas protein synthesis in nonnative hosts leads to misfoldedor aggregated proteins. (B) Relative synonymous codon usage plots (RSCUs) for a representative protein innative and nonnative hosts correlated with structural motifs within the protein.





surrounding the RBS of a reporter gene inSaccharomyces cerevisiae. Based on these mea-surements, a quantitative model was developedto correctly predict 74% of the protein levelsconsidering a list of factors (i.e., mRNA second-ary structure, in-frame and out-of-frame startcodons, predicted nucleosome occupancy, se-quence composition statistics, and arbitrary k-mer sequences). Translation efficiency has alsobeen correlated with mRNA secondary struc-ture formation in the 50 coding region (Canna-rozzi et al. 2010; Tuller et al. 2010; Goodmanet al. 2013). Importantly, these studies implythat protein synthesis rates are affected by non-coding and coding sequences in a context-de-pendent manner, which may be disentangledusing computational approaches. Finally, totest protein-specific influences on translationefficiency and to drive these predictions towardgeneral design guidelines for expression of alltypes of protein classes, additional studieswith proteins other than fluorescence reportersare required. Additional and unknown layers oftranscriptional, translational, and posttransla-tional control remain to be identified.

Soluble and Active Protein Biosynthesis

The successful implementation of a heterolo-gous pathway also requires the production offunctional enzymes, whereas suboptimal trans-lation of mRNA into misfolded proteins canlead to low catalytic turnover (reviewed in Li2015). The use of enzymes from hosts that aredistantly related to the expression host is onewell-known stumbling block for expression ofcatalytically active proteins (Fig. 3). Certain bot-tlenecks in heterologous expression can be at-tributed to intrinsic host factors, such as trans-lation speed, tRNA abundance, and chaperones.For example, tRNA abundance and codon usagebias is known to vary between and even withinorganisms depending on the growth stage (Ike-mura 1985; Dong et al. 1996; Kanaya et al. 1999).Different temporal factors in recombinant pro-tein expression are important, from the growthstage of the host organism to the initiation andelongation rate of nascent peptide chains (Niss-ley and O’Brien 2014). Changing expression

timing and translation rates can affect not onlytranslational efficiency but also important pro-tein properties, such as solubility, activity, andspecificity (Plotkin and Kudla 2011; Hunt et al.2014). Other bottlenecks arise from the genecoding sequence itself, such as mRNA folding,transcript length, type of encoded amino acids,codon context, and codon usage.

The most influential factor affecting trans-lational efficiency in bacterial expression is be-lieved to be codon usage bias (Lithwick andMargalit 2003). This notion is an essential mo-tivation for the common use of codon-adjustedtransgenes (Gustafsson et al. 2004; Gould et al.2014). However, synonymous codon changespose the potential risk of disrupting importantcharacteristics of a nucleotide sequence, such asmRNA stability and translation rates. It comesas no surprise that idiosyncratic reports reveala poor correlation between codon “optimiza-tion” and functional protein levels. There are amyriad of variables affecting translation, suchas mRNA stability, translation speed and accu-racy, cotranslational folding, and cotransla-tional localization. However, the influence ofthese factors in distinct organisms or even pro-tein families might vary. For instance, transgen-ic expression in eukaryotic systems is burdenedby gene-silencing issues or low expression levels(Jackson et al. 2014). On the other hand, bacte-rial host systems are known to commonly pro-duce misfolded proteins (Sander et al. 2014).There is a great need for synthetic codonadaptation strategies that consider multiple pre-vailing design parameters in a host- and time-dependent manner.

Recently, Lanza et al. (2014) created a con-dition-specific codon optimization approach,which considers the dynamic character of pro-tein translation. The investigators generated acodon usage table based on a subset of highlyexpressed genes at the target growth stage andwere able to improve heterologous protein levelsof a bacterial gene in yeast. Other studies haveused combinatorial libraries with synonymouscodon variants in the coding region to experi-mentally disentangle the factors involved intranslational efficiency and protein integrity(Goodman et al. 2013; Cheong et al. 2015).

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Such studies have established that synonymouscodons are translated at different rates affectingcotranslational folding (Ciryam et al. 2013;Chevance et al. 2014) and localization (Fig. 3)(Fluman et al. 2014; Pechmann et al. 2014). Insummary, we emphasize that the interrelationbetween codon usage, cotranslational folding,and protein integrity can greatly affect the cat-alytic activity of heterologous proteins (Kim-chi-Sarfaty et al. 2007; Agashe et al. 2013;Zhou et al. 2013).

ENGINEERING AT THE PROTEIN LEVEL

Increasing yields of desired compounds can fur-ther be influenced by the capacity of the pro-teins themselves to catalyze a desired reaction.Native and nonnative enzymes commonly re-quire further optimization to meet the demandsof biomanufacturing (Fig. 4). Recent advancesin enzyme engineering bring about significantexpansions in the catalytic repertoire of en-zymes with respect to increased efficiencies, im-proved selectivities, and novel substrate and co-factor specificities. In addition, the de novoengineering of enzymes has enabled the designof novel catalysts in which natural enzymes arenot available (Bolon et al. 2002; Kaplan andDeGrado 2004; Jiang et al. 2008; Rothlisbergeret al. 2008). Numerous engineering strategieshave been developed to tailor enzyme activitiesfor specific industrial purposes, which includedirected evolution and mutagenesis (Mooreand Arnold 1996; Arnold 1998; Brustad andArnold 2011; Illanes et al. 2012; Wang et al.2012b), modular enzyme components (Dueberet al. 2009; Good et al. 2011), and computer-aided design techniques (Bornscheuer and Pohl2001; Mandell and Kortemme 2009; Vermaet al. 2012; Hilvert 2013; Damborsky and Bre-zovsky 2014) (for a recent review of the impactof directed evolution on synthetic biology, seeCurrin et al. 2015). The enzymatic characteris-tics that are most often targeted by these tech-niques include activity, stability, selectivity, sol-ubility, and optimal pH (Fig. 4). For example,engineered fungal endoglucanases operate atoptimal temperatures of 17˚C higher than thewild-type enzyme and hydrolyze 1.5 times

as much cellulose over 60 h (Trudeau et al.2014). Engineering stable enzymes such as thesehas benefited the production of isobutanol atelevated temperatures in thermophilic Geoba-cillus thermoglucosidasius (Lin et al. 2014).However, in contrast to the numerous success-ful accounts of directly evolved single enzymecatalysts, limited examples exist for directlyevolved enzymes that exist as catalysts within abiosynthetic pathway. In general, this can beexplained by the fact that a coordinated im-provement of performance of an entire pathwayof enzymes has typically not been discoveredthrough the optimization of a single gene.

The complexity of pathway design often-times requires overcoming metabolic bottle-necks, such as accumulation of toxic intermedi-ates, cofactor imbalance, and inefficient enzymeactivities, which remains a significant challengefor metabolic engineering and the focus of nu-merous research studies (Fig. 4). Metabolic en-gineering has traditionally integrated existingenzymes from different organisms into the pro-duction host to modify an existing pathway orcreate a nonnatural pathway (e.g., a set of en-zymes that are not normally found within thehost metabolism). This approach has generatedsignificant improvements in product yields. Forexample, a 120% yield improvement was re-ported for the isoprenoid-derived sesquiter-pene, amorphadiene, which was produced byan engineered strain of E. coli using the seven-gene mevalonate pathway from S. cerevisiae (Maet al. 2011). However, the catalytic efficienciesand selectivities of many naturally occurringenzymes may require further optimization tobecome highly optimized biocatalysts withcapacities to achieve higher rates, yields, andproduct purities. This section reviews the ef-ficiency of the individual enzyme componentsthemselves within existing or nonnaturalbiosynthetic pathways and poses the follow-ing question: “Can engineering individual en-zymes transform and improve the performanceof biosynthetic routes?” Here, we focus on sev-eral successful examples, in which engineeredcharacteristics of individual enzymes did im-prove the efficiency of entire biosyntheticpathways. The engineered characteristic enzyme





properties that are highlighted in this sec-tion include enzyme regulation and enzymespecificity.

Reduced Feedback Inhibition

Heterologous proteins expressed in host organ-isms can suffer from a number of design issues,

which include being tightly regulated via post-translational modifications and/or allostericinhibition (Fig. 4). In cases such as these, di-rected evolution of key enzymes within meta-bolic pathways has shown to benefit productyields (Lee et al. 2012; Brinkmann-Chen etal. 2013) (for reviews on these topics, seeJohannes and Zhao 2006; Cobb et al. 2013;

Computer-aided design

Phosphorylation Allosteric regulation Complexation

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KLLKVVKFGKKLLKVVKFGKKLLKVVKFGKKLLKVVKFGKKLLKVVKFGK

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GGAMRGNPVPGGAMRGNPVPXTALRGNPVPXTALRGNPVPXTALRGNPVP

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Figure 4. Proteome-level optimization. (A) Optimal versus nonoptimal enzyme-level activities in engineereddesigns. Nonoptimal factors that influence activities may include allosteric regulation, protein posttranslationalmodifications, incorrect protein–protein interactions, and many others. (B) Graphical representation of di-rected evolution of enzyme that directly influences its stability and reactivity. (Adapted from Mollwitz et al.2012.) (C) An in silico workflow to establish rational engineering through novel complementary computationalmethods, such as machine learning, genetic algorithms, and molecular modeling.

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Renata et al. 2015). For example, an improvedvariant of glucosamine synthase (GlmS) wasgenerated via an error-prone polymerase chainreaction (PCR) and its overexpression in anengineered strain of E. coli led production ofglucosamine at 17 GL21 (Deng et al. 2005).The directed evolution of GlmS enabled in-creased production, in part, by generatingvariants that showed significantly reduced sen-sitivity to product inhibition (Deng et al.2005). Another example of enzyme engineeringthat reduced feedback-inhibition mechanismsis that of L-phenylalanine production. By per-forming DNA shuffling on pheA, a chorismatemutase-prephenate dehydratase enzyme, mu-tations led to activation of enzymatic activitiesat low phenylalanine concentrations and nearlycomplete resistance to feedback inhibition ofprephenate dehydratase by phenylalanine con-centrations up to 200 mM in certain variants(Nelms et al. 1992). These variants were incor-porated into an E. coli K12 strain to constructa pathway producing S-mandelic acid via thismodified L-phenylalanine pathway (Sun et al.2011). In addition to directed evolution tech-niques, computational design strategies pro-vide further possibilities for configuring novelallosteric modulators (for a review of this sub-ject, see Lu et al. 2014).

Increased Substrate Specificity

Enzyme engineering can also play a key role inshaping enzyme specificity and, as a result, lim-iting erroneous and potentially deleterious sidereactions and products. By using existing pro-miscuous enzyme activities as a starting pointfor directed evolution, the creation of new en-zymatic functions, such as catalyzing reactionson nonnative substrates, has been shown(Broadwater et al. 2002). The knowledge ofthe promiscuous activities catalyzed by an en-zyme not only provides a starting point for en-gineering novel functionalities, but it has shownto guide the engineering of native activities withincreased specificity and higher activity (Yoshi-kuni et al. 2006). For example, g-humulene syn-thase, a sesquiterpene synthase from Abies gran-dis (Steele et al. 1998; Little and Croteau 2002),

produces 52 different sesquiterpenes from a solesubstrate, farnesyl diphosphate, through a widevariety of cyclization mechanisms (Lesburget al. 1997; Starks et al. 1997; Caruthers et al.2000; Rynkiewicz et al. 2001). Yoshikuni et al.(2006) showed that site-directed mutagenesis ofa set of previously identified plasticity residuesshifted the relative selectivity of one productfor another by 100- to 1000-fold. An additionalexample of how directed evolution improvedenzyme specificity has been reported in xyloseisomerase-based pathways in S. cerevisiae,which typically suffer from poor ethanol pro-ductivity and low xylose consumption rates.Improving the specific activity of a heterologousenzyme, Piromyces sp. xylose isomerase, viarounds of random mutagenesis and growth-based screening has led to a variant that showeda 77% increase in activity. When expressed in aminimally engineered yeast host, the strain im-proved its aerobic growth rate by 61-fold andethanol and xylose consumption rates by nearlyeightfold (Lee et al. 2012). Identifying poten-tially promiscuous enzyme activities is a majorchallenge and has been one of the main rolesof certain computational pathway predictionalgorithms, such as the Biochemical NetworkIntegrated Computational Explorer (BNICE)framework (Hatzimanikatis et al. 2005). BNICEuses existing enzyme chemistry as a ground-work for novel enzyme mechanisms that canbe fine-tuned and tailored via minimal enzymereengineering. Several examples of in silicostrategies for the reengineering of naturally oc-curring enzymes into novel biocatalysts areshown by the work of (1) Cho et al. (2010),who predicted novel enzyme activities on thebasis of physicochemical properties of sub-strate/product pairs, (2) Brunk et al. (2012),who predicted novel enzyme activity within abiosynthetic pathway (Henry et al. 2010) on thebasis of 3D protein structural characteristicsand molecular dynamics simulations (Fig. 4),and (3) Campodonico et al. (2014), who pre-dicted enzyme similarity on the basis of enzymecommission classification. In certain cases, acomprehensive assessment was performed for20 predicted heterologous pathways in E. coli(Campodonico et al. 2014).





ENGINEERING AT THE REACTOME LEVEL

In the previous sections, we have shown theimportance of engineering correctly foldedand catalytically active pathway enzymes. Here,we address the significance of balanced metab-olite fluxes. Achieving maximum protein abun-dance does not necessarily give rise to highestproduct titers (Jones et al. 2000). Simultane-ously, an increase in Vmax (¼kcat � [E]) of aheterologous enzyme can create a metabolicsink causing nutrient limitation and growth in-hibition (Fig. 5). Excessive catalytic turnovercan also lead to intermediate buildup causingfeedback inhibition or even toxicity (Ajikumaret al. 2010). Therefore, it is important to balanceprotein synthesis and activity levels to engineera pathway with stable steady-state behavior and

minimal metabolic burden (Fig. 5). In this sec-tion, we review methods to assess the optimaldesign space of metabolic pathways. Studies arebeginning to use multivariate statistical ap-proaches to guide predictable forward engineer-ing efforts. We will also present a putative work-flow to guide the integration of a heterologouspathway within the host metabolic and regula-tory network.

Balanced Pathway Fluxes

Unlike other areas of engineering, biologicalsystems are extremely nonlinear, which makespredicting how pathway networks function invivo a challenge. Often, engineered pathwayshave been optimized through sampling of largedesign spaces (Pfleger et al. 2006; Lutke-Ever-

Increase in kcat or enzyme amountB

A Equal enzyme levels,different catalytic activities

E2 E3E1

E2 E3E1

Substrate Intermediates Product

Figure 5. Reactome-level optimization. (A) Nonoptimal metabolite flux limited by low turnover of one pathwayenzyme. (B) Optimal flux through the pathway can be achieved by engineering the bottleneck enzyme leading toeither higher enzyme levels or increased catalytic activity.

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sloh and Stephanopoulos 2008; Du et al. 2012;Wang et al. 2012a; Xu et al. 2013; Nowroozi et al.2014). Such combinatorial approaches are spe-cifically valuable for organisms with less-de-fined genetic and metabolic networks (Oliver etal. 2014). However, combinatorial libraries arelimited by analytical capabilities and requirehigh-throughput screens or selection assays.For a review on high-throughput library selec-tion and screening strategies, the reader is re-ferred to Dietrich et al. (2010).

The main difficulty remains in understand-ing how interactions among components of abiochemical network within an organism giverise to the function and behavior of the entiresystem. In lieu of probing large design spacesthrough traditional metabolite analysis, a fewstudies have used smaller data sets with multi-variate statistical analysis to predict behavior.The resulting models can be used to forward-engineer required enzyme levels for optimalpathway flux. Lee et al. (2013), for example, cre-ated a regression model to analyze a subset of acombinatorial promoter library. To assess thecapacity of the generated model, they chose toexpress the highly branched violacein pathwayand trained the model on only a small fractionof the measured data (�100 random clones, 3%of total library). The model was able to correctlypredict production levels of the violacein con-geners under different expression conditionswith a correlation coefficient between 0.77 and0.92, which was sufficient to guide further op-timization efforts.

Alonso-Gutierrez et al. (2014) used princi-pal component analysis of quantitative proteo-mics data (PCAP) to correlate product titers inE. coli. Gaining insight into optimal pathwaystoichiometry of limonene biosynthesis, the in-vestigators were able to further improve yieldsby �40%. Expression timing was also addressedin this study, and there is an indication that dif-ferent induction times lead to largely varied pro-duction levels because of toxic intermediate ac-cumulation. Altogether, this study revealed thedelicate balance between enzyme levels that isrequired for optimal pathway performance. Atthe same time, the holistic approach of PCAPemphasized that multiple optima exist in the

combinatorial expression space of a mevalo-nate-dependent pathway. These examples showpathway-centric approaches by correlating pro-tein synthesis and product flux. Such statisticalapproaches can be further complemented withconstraint-based flux balance analysis (FBA) tocreate an integrative approach, which also con-siders the host metabolism (Brunk et al. 2016).

FUTURE DIRECTIONS TOWARDINTEGRATED APPROACHES IN METABOLICENGINEERING

To tackle the existing challenges in metabolicengineering, strategies need to account for allaspects of product synthesis in which controlcan be exerted: the transcriptome, the transla-tome, the proteome, and the reactome (Fig. 1).First, engineering the transcriptome involvesmanipulation of mRNA stability and expressionlevels, which is commonly mastered throughpromoter and terminator strength engineering.Second, translatome engineering requires anunderstanding of the factors involved in trans-lational efficiency and cotranslational processessuch as folding and translocation. Third, thedesign of chemical transformations and proteinengineering is controlled at the proteome level.And last, reactome manipulations allow us toaddress aspects of enzymes ratios and, therefore,metabolite flux (Fig. 1). Inaccuracies at any ofthese levels can prohibit successful implemen-tation of an engineered pathway. Here, we em-phasize the current potentials at the differentcontrol levels.

Translatome Manipulations Toward ReliableEnzyme Expression

Despite having made great strides in metabolicengineering and synthetic biology (i.e., orthog-onal design, parts characterization), there re-mains a gap between designed and measuredenzyme activity. For example, protein solubilityand, therefore, activity is currently optimizedthrough a rather random approach (Batardet al. 2000; Burgess-Brown et al. 2008; Cheonget al. 2015). General optimization strategies forheterologous enzyme synthesis that do not suf-





fer losses of catalytic activity are currently lack-ing. To narrow this gap between design and bi-ology, a deeper characterization of the factorsinvolved in gene expression is desired (Latifet al. 2014). Currently, computational modelsonly exist for the analysis and prediction oftranslation initiation rates (for a review on thistopic, see Reeve et al. 2014). Beyond controllingtranslational efficiency, there is a prevailingneed to establish design guidelines for robustand specific codon optimization approaches.For this goal, emerging methods to experimen-tally assess ribosomal speed for different hostshave the potential to provide a reliable founda-tion for computational codon optimizationstrategies (Ingolia et al. 2009; Chevance et al.2014; Hockenberry et al. 2014; Latif et al. 2014).

Protein-Level Manipulations Toward EfficientEnzyme Catalysis

Rational design will not move beyond its cur-rent limitations until our understanding of theunderlying mechanisms of enzyme catalysis hasimproved. A great deal of advancement has beenmade in understanding structure–function–activity relationships in enzyme chemistry(Thornton et al. 2000). This is, in part, a resultof the ever-increasing number of high-resolu-tion structures available through X-ray crystal-lography (Berman et al. 2003) as well as theinsights on detailed enzyme catalytic mecha-nisms from numerous theoretical-based studies(Colombo et al. 2002; Brunk and Rothlisberger2015). Understanding protein promiscuity andits great potential for the design of novel cata-lysts has been the subject of many recent reviews(Nobeli et al. 2009; Humble and Berglund2011). Synergistic efforts to couple computa-tional enzyme design and metabolic pathwayengineering with directed evolution bringspromise of reengineering biosynthetic pathwaysand, in particular, in secondary metabolism,which offers a wider range of metabolic andbiosynthetic routes, an increased scope of sub-strates and products, and has been shown to beless shaped by natural selection pressures thanthat of primary metabolism (Bar-Even andTawfik 2013).

Reactome Manipulations Toward OptimalProduction Fluxes

Typically, designing a new technical system re-quires starting from scratch and testing variouspotential prototypes, usually by means of trialand error. Thus, designing a procedure of inter-pretation or translation from biology to tech-nology is a necessary goal to overcome the engi-neering bottlenecks. Although databases, suchas KEGG (Kanehisa 2002) and BRENDA(Schomburg et al. 2004; Chang et al. 2009), pro-vide information mainly focused on primarypathways in metabolism, categorization of novelbiosynthetic pathways, the enzymes involved inbiosynthesis, and the small molecules tailored todesired tasks will also be needed. Computationaltools that can have access to resources such asthese will undoubtedly have the potential to pre-dict pathways, identify variations in enzymes,and model these changes in genome-scale met-abolic networks of candidate host systems. Beingable to model, understand, and predict complexmetabolic networks has been made possible byrecent developments of genome-scale metabolicmodels (GEMs), which have been reconstructedfor .60 organisms (Reed and Palsson 2003;Monk et al. 2013; Bordbar et al. 2014). Thesereconstructions are converted into constraint-based models (Becker et al. 2007), allowinguseful calculations like FBA (Orth et al. 2010)to be performed. Methods such as these arecomplementary and amenable to pathway de-sign and offer great promise for advancing thefield of synthetic pathway engineering and thedesign of microbial biofactories (Monk et al.2014; Brunk et al. 2016).

CONCLUSIONS

Recent developments in the field of metabolicengineering bring promise to the design of bio-synthetic pathways, in which entire metabolicpathways can be (re)designed and expressed inhost organisms. We attempt to give the reader acomprehensive view on the aspects pertinent tosuccessful pathway engineering. Additionally,we emphasize that there remains a gap betweenoptimization efforts at the molecular level andthose at the systems level. One important con-

A. Lechner et al.




sideration is to address the different complica-tions or bottlenecks that may occur on vastlydifferent chemical and biological scales. Feasi-bility of engineered components can be assessedusing molecular-scale analyses, which typicallyevaluate feasibility in terms of the ability of en-zymes to catalyze a desired transformation on asubstrate, and systems-level analyses, whichgenerally assess feasibility on the basis ofchanges in the range of intracellular metabolitesand enzyme concentrations (e.g., proteome al-locations, pathway ratios). At the same time,these remaining challenges reveal many oppor-tunities to fill knowledge gaps and develop moresophisticated design tools in metabolic engi-neering. First, we emphasize that computationalpredictions of pathway performance will only beas accurate as our models that heavily rely onexisting omics data sets. Within the context ofthe design–build–test– learn cycle, we are suc-cessfully designing and building productionstrains; however, there is a delay in the learningprocess because “testing” is highly limited by thecapabilities of existing high-throughput analyt-ical methods. Additional omics data sets of ra-tionally engineered microbes are required tobuild accurate prediction models and to facili-tate the de novo design of constructs. Second,there remains a need to create an open accessknowledge database to organize and analyzelarge data sets. And last, “learning” is informedby our basic understanding of cellular processes.Yet, there remain multiple control levels that arenot well understood on a cellular scale such asallosteric regulation exerted through interactionbetween proteins and metabolites. Posttransla-tional regulation is an additional control layerthat has recently attracted much attention be-cause of novel methods assessing posttransla-tional modifications on an organism scale. Fu-ture milestones in “test and learn” willtransform the field of metabolic engineeringfrom a semirational to a fully rational forwardengineering discipline.

ACKNOWLEDGMENTS

We are grateful to Dr. Daniel Liu, Dr. SarahRichardson, Dr. Nathan Hillson, and Dr. Chris-

topher Petzold for valuable comments and dis-cussion on this review. The authors also thankProf. Vassily Hatzimankatis and Prof. UrsulaRothlisberger for the many useful discussionson computational design strategies over theyears. This work is funded by the Departmentof Energy Joint BioEnergy Institute ( jbei.org)supported by the U.S. Department of Energy,Office of Science, Office of Biological and Envi-ronmental Research, through contract DE-AC02-05CH11231 between Lawrence BerkeleyNational Laboratory and the U.S. Departmentof Energy and by the Synthetic Biology Engi-neering Research Center (SynBERC) throughNational Science Foundation Grant NSF EEC0540879. The authors acknowledge supportfrom the Swiss National Science Foundation(Grant p2elp2_148961 to E.B.).

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published online August 15, 2016Cold Spring Harb Perspect Biol Anna Lechner, Elizabeth Brunk and Jay D. Keasling The Need for Integrated Approaches in Metabolic Engineering

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The Need for Integrated Approaches in Metabolic Engineeringcshperspectives.cshlp.org/content/early/2016/08/13/... · 13.08.2016 · The Need for Integrated Approaches in Metabolic