Yeast synthetic biology toolbox and applications for biofuel production.

MINIREVIEW

Yeast synthetic biology toolbox and applications for biofuel production Ching-Sung Tsai1, Suryang Kwak1,2, Timothy L. Turner1,2 & Yong-Su Jin1,2 1

Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA; and 2Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Correspondence: Yong-Su Jin, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. Tel.: +1 217 333 7981; fax: +1 217 333 0508; e-mail: [email protected] Received 16 June 2014; revised 13 August 2014; accepted 31 August 2014. DOI: 10.1111/1567-1364.12206 Editor: Hyun Ah Kang Keywords yeast; synthetic biology; biofuel.

Abstract Yeasts are efficient biofuel producers with numerous advantages outcompeting bacterial counterparts. While most synthetic biology tools have been developed and customized for bacteria especially for Escherichia coli, yeast synthetic biological tools have been exploited for improving yeast to produce fuels and chemicals from renewable biomass. Here we review the current status of synthetic biological tools and their applications for biofuel production, focusing on the model strain Saccharomyces cerevisiae. We describe assembly techniques that have been developed for constructing genes, pathways, and genomes in yeast. Moreover, we discuss synthetic parts for allowing precise control of gene expression at both transcriptional and translational levels. Applications of these synthetic biological approaches have led to identification of effective gene targets that are responsible for desirable traits, such as cellulosic sugar utilization, advanced biofuel production, and enhanced tolerance against toxic products for biofuel production from renewable biomass. Although an array of synthetic biology tools and devices are available, we observed some gaps existing in tool development to achieve industrial utilization. Looking forward, future tool development should focus on industrial cultivation conditions utilizing industrial strains.

YEAST RESEARCH

Introduction The design and construction of synthetic biological devices capable of executing precise functions has become feasible owing to rapid advances in DNA sequencing and synthesizing technologies (Keasling, 2009; Weeks & Chang, 2011; Unkles et al., 2014). Among the numerous applications of synthetic biology, production of biofuels and chemicals from renewable biomass is a promising field. Within this area of research, well-characterized microorganisms such as Escherichia coli and Saccharomyces cerevisiae serve as hosts to test the functionality of exhaustive combinations of biological components and metabolic pathways. Saccharomyces cerevisiae has been widely used in the food and biotechnology industry and is generally regarded as safe (GRAS) for large-scale operation. Molecular and cell biology of S. cerevisiae has been studied in-depth for decades as a model eukaryotic system. Therefore, yeast is a good platform microorganism for practicing synthetic biology. There are many advantages in using S. cerevisiae

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as a synthetic biological chassis. Saccharomyces cerevisiae is not only famous for its unmatched homologous recombination ability allowing for assembly of long-length DNA fragments in vivo, but also famous for its genetic manipulability, with many genetic tools for overexpression and knockout of target genes have been developed. Unlike E. coli, S. cerevisiae has multiple organelles providing different environments to perform compartmentalized biosynthesis. Lastly, S. cerevisiae exhibits high tolerance against products and toxic inhibitors present in most cellulosic hydrolysates. As such, S. cerevisiae is the most popular engineering host for producing biofuels and chemicals via synthetic biology. We will first review basic synthetic biology techniques including DNA assembly and genomic engineering. In the second part, synthetic genetic devices developed through assembly and mutagenesis will be discussed. Finally, applications of these genetic devices to solve issues related to substrate utilization, improved product formation such as advanced biofuels, and increasing tolerance to inhibitors and toxic products will be discussed.

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Synthetic biology approaches for S. cerevisiae DNA assembly techniques

Whether through intuitive selection or model prediction, the first task in synthetic biology is to assemble DNA sequences of biological parts into an expression cassette. To achieve this, rapid and efficient methods capable of connecting pathway genes in long length are required. As the candidate pathways may be varied in nature, methods offering higher capacity and easier combinatorial assembly are required for obtaining concatenated genes with diverse promoters and terminators. The assembly techniques can be classified into three groups. The first group

involves techniques based on constrained sequences, such as restriction enzyme sites or integrase and clonase sites. The second group contains techniques relying on homologous recombination (HR). The unique de novo DNA synthesis is the third group, but it is beyond our discussion here. Table 1 listed the mechanisms of these methods. Among the HR-dependent methods, DNA assembler has advantages in yeast biofuel production (Shao & Zhao, 2008, 2012, 2013; Shao et al., 2009). Saccharomyces cerevisiae exhibits a high homologous recombination activity; thus, the direct assembly of linearized DNA fragments inside yeast cells bypasses the cumbersome work of E. coli cloning, plasmid extraction, and retransformation into yeast. Regularly, DNA assembler can be used to recon-

Table 1. DNA assembly techniques and their usage in pathway construction of yeast biofuel production Assembly techniques

Description

References

BioBrick/BglBrick

Using the compatible cohesive overhangs of BamHI and BglII cutting sites to repetitively clone genes Using type IIS restriction enzymes which cut arbitrary sequences downstream of the enzyme recognition site to clone; the restriction enzyme sites are removed during cloning; hence, the connection is seamless Specifically using type IIS RE MspJI to do Golden Gate cloning, which recognizes only methylated sites to perform cutting; this method avoids the problem of regular type IIS sites inside inserts or vectors Adding Streptomyces phage ΦBT1 integrase sequences onto fragments and using the enzyme to combine units Creating homologous sequences between fragments and use DNA polymerase to extend and anneal each fragment by repetitive cycles Creating homologous sequences between fragments and use yeast as a natural assembler to anneal Utilizing homologous sequences, add T5 exonucleases to chew back the DNA fragments, then use polymerase and ligase to link the fragments together PCR amplification with homologous ends and use T4 DNA polymerase to create single-stranded DNA, denature, and anneal the fragments with PCRs and transform into E. coli to complete the linking PCR amplification with homologous ends to the vector, also PCR amplification of the vector and mixing the two fragments in the presence of JM109 or DH10 cell extracts to obtain final clones Incorporating deoxyuridine into PCR primers which create homologous ends of PCR fragments to vector ends, use a combination of E. coli uracil DNA glycosylase and endonuclease VIII to create single-strand overhang and anneal the fragments Employs the fact that most of PCR products were not fully double-stranded especially at the 50 end; the singlestranded region offers sites for hybridization, and hence, DNA fragments can be annealed in vivo after transformation

Rokke et al. (2014) and Lee et al. (2011a, b) Engler et al. (2009) and Sarrion-Perdigones et al. (2011)

Golden Gate/Golden Braid

Master (Methylation-assisted tailorable ends rational)

SSRTA (Site-specific recombination-based tandem assembly) CPEC (Circular polymerase extension cloning) DNA assembler Gibson assembly

SLIC (Sequence- and ligation-independent cloning)

SLiCE (Seamless ligation cloning extract)

USER (Uracil-specific excision reagent)

PIPE (Polymerase incomplete primer extension)


Chen et al. (2013a, b)

Zhang et al. (2011) Quan & Tian (2009)

Shao & Zhao (2008, 2012, 2013) Gibson et al. (2008, 2009)

Li & Elledge (2012)

Zhang et al. (2012)

Bitinaite et al. (2007)

Klock et al. (2008) and Klock & Lesley (2009)


Yeast synthetic biology toolbox for biofuel production

struct pathway genes on a multi- or single-copy plasmid. In vivo assembled constructs can be integrated into the target site in the yeast genome by designing the 50 and 30 ends of the contigs to be homologous to flanking chromosome sequences of the target insertion coupled with addition of a selection marker. Applications of DNA assembler to improve strain performance have been reported; for example, compacter (Du et al., 2012), with the assembly of promoter libraries included in the DNA assembler technology, can produce a variety of assembled genes under the control of promoters with different strengths. Further library optimization by developing a good selection system is possible. DNA assembler’s best known in vitro homologous recombination counterpart is Gibson assembly. Developed in 2009 (Gibson et al., 2009), the method exploits a three-enzyme complex which contains the T5 exonuclease to create single-strand overhangs, DNA polymerase to fill in the gaps after hybridization, and DNA ligase to nick the ends. In a survey published in 2013, Gibson assembly was the most widely used gene assembly method (Kahl & Endy, 2013). Despite high success rates of gene assembly, homologous recombination-based methods may falter when sequences of high similarity exist among the fragments that will be linked. Under such conditions, methods based on constrained sequences could offer solutions. Golden Gate cloning (Engler et al., 2009), and its derivatives, such as Golden Braid (Sarrion-Perdigones et al., 2011) and master (Chen et al., 2013a, b), is efficient and seamless cloning techniques that utilize type IIS restriction enzymes. Type IIS restriction enzymes recognize specific asymmetric sequences and cleave the DNA downstream of its recognition site, creating a four-base overhang. The cutting sites are not restricted to any sequences; therefore, flexibility is offered in designing the overhangs to link consecutive fragments by ligase. Genome editing techniques

Pathway assembly is the foundation of synthetic biology for biosynthesizing value-added products. However, the host genomic environment often plays important roles in determining the efficacy of the introduced pathways. For example, the function of the xylose utilization pathway consisting of XYL1, XYL2, and XYL3 in the S. cerevisiae strain D452-2 was inefficient until the PHO13 and ALD6 genes on the chromosome were mutated by evolutionary engineering. A highly efficient xylose-fermenting strain was developed by combination of the optimal expression of XYL1, XYL2, and XYL3 with deletions of PHO13 and ALD6 (Kim et al., 2013). Therefore, mutations of target genes on chromosomes are necessary for constructing FEMS Yeast Res && (2014) 1–18

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optimized strains. Traditional knockout strategies such as plasmid integration are based on homologous recombination and solely dependent on auxotrophic markers or the carried antibiotic genes for transformant selection. The marker requirement largely confines the number of deletions or necessitates burdensome marker recycling maneuvers. Moreover, recombination rates of traditional gene disruption techniques are low, appromixately 106, when the homologous arm is 40–50 bp. For longer homology sequences, the rate is only around 103–104 (Wach et al., 1994). Storici et al. (2003) modified the traditional gene deletion methods by first using homologous recombination to integrate a PCR fragment containing a GAL1 promoter-controlled I-SceI endonuclease and its cutting site. After integration, the I-SceI was induced by galactose and a double-strand break (DSB) was created. Thus, to rescue the break, simultaneously introduced oligonucleotides or PCR products with modifying sequences in the middle with flanking homologous arms on both ends were used. By creating a DSB, this technique, named delitto perfetto, increases the rate of modifying the genome by simple transformation of desired DNA (Storici et al., 2003). However, this technique still requires the first homologous recombination step to transform the endonuclease to the targeting site. With the high recombination rates of delitto perfetto, methods focusing on creating a sequence-specific DSB first and then rescuing the cleavage with a replacing DNA fragment were developed. Zinc-finger nucleases (ZFNs) linking the zinc-finger DNA recognition domains to FokI endonucleases were created and introduced to different animals or plants for genome editing (Kim et al., 1996; Collin & Lako, 2011; Li et al., 2014). The FokI nuclease works as a dimer; thus, two molecules of ZFNs must be coexpressed to cleave DNA. However, the stacking of zinc finger motifs was reported to be difficult (Ramirez et al., 2008). Moreover, the assembly of ZFNs is costly (Esvelt & Wang, 2013). These two factors decrease the use of ZFNs. Instead, attention has turned to transcription activator-like effector (TALE) nucleases (TALENs). Originally from a plant pathogen Xanthomonas, TALE proteins are peptides with 33–34 amino acids in length with their 12th and 13th amino acids serving as DNA recognition fingers. The two amino acids were thus named as repeat variable di-residues (RVD). Each RVD recognizes a base pair; NI recognizes A, NN recognizes G, NG recognizes T, and HD recognizes C. The modularity of TALEs is high, with stacking TALEs having high specificities in recognizing a long stretch of DNA fragments. Linked with FokI, the peptides are named TALE nucleases (TALENs), and dimers of this fusion protein can target specific sites inside a genome to produce site-specific DSBs (Christian et al., 2010; Li et al., 2011). Recently, a ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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modified, single-chain TALEN (scTALEN) designed to conquer the requirement of dimerization to create cutting by linking two FokI domains tandemly inside the peptide has been proposed (Sun & Zhao, 2014). Although TALENs seem very applicable to genome editing, combining the TALEs to detect a specific sequence is a daunting task as each DNA fragment encoding different TALEs needs to be assembled. Recently, the discovery of bacterial immune system clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins facilitated the development of a site-specific DSB-creating method. Parts of the Cas proteins, originally found in Streptococcus pyogenes, are capable of picking heterologous DNA sequences (protospacers) and integrating the DNA fragments into the CRISPR pool. The CRISPR region is constitutively expressed into a large pre-crRNA molecule, which contains heterologous spacer sequences and conserved repeat sequences. Three groups of CRISPR/Cas systems exist in nature. While in the type I and III groups, the pre-crRNAs are usually processed by Cas protein complexes and perform recognition and cleavage of target dsDNA directly, the type II system has another participant called the trans-acting crRNA (tracrRNA) (Jinek et al., 2012). TracrRNA binds to the repeat sequences of the pre-crRNAs and stimulates the maturation of the pre-crRNA molecules, which are further cleaved by RNase III into units of spacers and repeats in the presence of Cas9, an RNA-guided nuclease found in the Cas operon. The RNA–protein complex, which contains Cas9 protein and the hybrid of matured crRNA and tracrRNA, targets the complimentary regions of crRNA on the chromosome. Cas9 also recognizes the nearby protospacer adjacent motif (PAM) sequences on the target DNA and performs cutting on both strands several bases downstream of the PAM within protospacer sequences (Jinek et al., 2012). The type II CRISPR/Cas system has been widely used to edit genomes from bacteria to humans by combining tracrRNA and crRNA into a single-guide RNA (sgRNA) (Jinek et al., 2012, 2013) and replacing the protospacer sequences with the desired sequences on the chromosome. Specifically, DiCarlo et al. (2013a) developed a CRISPR/Cas system in S. cerevisiae. With the electroporation of a PCR fragment rescuing the DSB created by Cas9/sgRNA, the gene replacing frequency approached 100%. Compared with TALENs, Cas9/sgRNA-guided genome editing is simpler and convenient because only a fragment of a 20 bp protospacer needs to be synthesized for each target site, and no concern for gene assembly or protein expression levels is needed. Oligonucleotide-mediated in vivo gene manipulation offers a new dimension for genomic engineering. In 2001, Ellis et al. (2001) found that the b-protein of lambda ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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phage is able to enhance recombination by annealing single-stranded oligonucleotides onto genome target sites. Wang et al. (2009) exploited this fact and created a multiplex automated genome engineering (mage) platform to repeatedly introduce designed mutations into targeted sites of E. coli genome through the overexpression of the b-protein and electroporation of 90 bp oligonucleotides with intended sequence changes. In yeast, oligonucleotide-mediated genome editing existed for decades but with extremely low efficiencies (Moerschell et al., 1988). Many strategies were attempted including synchronizing the cells into S-phase by hydroxyurea or adding trichostatin A (van Brabant et al., 2004). However, the correction efficiency (CE) remained low, at most around 0.1%. The George Church’s group tried to apply their success in E. coli MAGE to yeast, creating a protocol named yeast oligo-mediated genome engineering (YOGE) (DiCarlo et al., 2013b). However, many genes needed to be modified to elevate the correction efficiency, including the deletion of MLH1 related to mismatch repair: the overexpression of DNA recombinase RAD54 and a mutant of RAD51 (K342E). Nevertheless, the correction efficiency was raised to just 1.94% for the ADE2 mutation, while the CEs of modifying other genes were lower. In summary, although some techniques are still under development, genome editing and modification techniques in yeast are becoming more powerful and the second goal of general synthetic biology, to modify existing biological parts to fulfill future applications, is easily achieved.

Yeast synthetic devices The combinations of basic DNA assembly and mutagenesis techniques could provide numerous devices with biological purposes. In the consensus model for gene expression, many steps could be regulated to control gene expression; thus, synthetic devices were created according to the mechanisms of different steps. Here, we review the six major classes of devices created in yeast including the promoter engineering for transcriptional control, translational control, protein engineering, genetic circuits, metabolic pathway redesigning, and genome synthesis and integration (Fig. 1). Promoter engineering for transcriptional control

Being the first step of gene expression, successful transcription is beneficial to achieve biosynthesis. In E. coli, the structure of the promoter is simple, with the activator/repressor binding sites, the 10 and 35 boxes and the transcriptional start site well defined and studied. The FEMS Yeast Res && (2014) 1–18

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2. Translational Control • 5’ end interfering protein • Trans-acting RNAs • Cis-acting elements

4. Genetic circuits

Promoter Promoter Promoter 1. Promoter engineering for transcriptional control

GOI GOI GOI

3. Protein engineering • Protein-ligand interactions • Protein-DNA/RNA interaction • Protein targeting • Clustering proteins of the same pathway

Terminator Terminator Terminator 5. Redesign of metabolic pathways

6. Genome Synthesis and Integration

Fig. 1. Yeast synthetic devices.

transcriptional machinery of E. coli is also relatively simple compared with its eukaryotic counterpart, mainly composed of the activator that attracts the RNA polymerase (usually composed of a2bb’r units) to the promoter region or the repressor that occludes the RNA polymerase. Inside the RNA polymerase, there exists another level of gene regulation. Expression of certain genes can be regulated through the recruitment of alternative sigma factors rather than the regular r 70 sigma factor. However, yeast promoters possess complicated structures due to the abundance of protein participators involved. Yeast promoters can be roughly defined as sequences with the upstream activation sequence (UAS) or upstream repression sequence (URS) regions upstream or downstream of the core promoter sequences. All signals generated through the upstream protein–DNA binding and protein– protein interacting events converge on the core promoter. The core promoter is the assembly of sequences of TATA binding protein (TBP) recognizing site (usually with a sequence of 50 -TATAAA-30 , TATA box) and the binding sites of TBP-associated proteins. Except for the TATA box, the TBP-associated binding sites are usually not conserved, making it difficult to define and apply those sequences orthogonally in systems biology (Hahn & Young, 2011). Thus, the engineering of yeast promoters was based on known constitutive promoters with different strengths such as PCYC1, PADH, PTEF, and PGPD (Mumberg et al., 1995) and heavily relied on shuffling of known UAS regions or the core promoters (Blazeck et al., 2012). Random mutagenesis such as error-prone PCR focusing on the whole promoter region or the TBP binding sites was also employed (Nevoigt et al., 2006; Blazeck et al., 2012, 2013), creating an array of promoters with different strengths. Similar methods have been applied to Pichia pastoris, promoters with activities


between 5% and 160% of the wild-type were constructed by deleting or duplicating the elements inside PAOX1. (Hartner et al., 2008). Moreover, at least 12 cisacting elements were recognized to be involved in activity control (Hartner et al., 2008). Transcriptional control could play an important role for fine-tuning the expression of genes in different cassettes for creating optimizing and balancing product titers. To achieve this, DNA assembly techniques were used in creating so-called promoter libraries, and the effect of the balanced promoters could be screened with fluorescence intensity (Alper et al., 2005) or simple colony sizes on an agar plate (Du et al., 2012; Yuan & Zhao, 2013). However, when the fitness of the screened pathways could not be easily represented with simple colony sizes, a linear regression model was proposed to predict the behavior of a pathway with a limited number of wet experiments serving as the training set of the model (Lee et al., 2013). Combining these tools, pathway optimization is feasible through promoter libraries. Besides the promoter DNA sequences, trans-acting elements such as transcription factors (TFs) can also be engineered to regulate promoters. TetR protein, which is extensively used in bacterial genetics, retains its DNA binding functions in yeast and still responds to artificial inducers such as anhydrotetracycline or doxycycline. Promoters including the TetR binding sites have been used to control genes in different yeast species (Blount et al., 2012b). TetR and Gal4p had been fused with activators or repressors to control gene expression (Sadowski et al., 1988; Deuschle et al., 1995). Recently, CRISPR-mediated modular RNA-guided regulation of transcription using nuclease-deficient Cas9 (dCas9) was proposed to control gene expression inside yeast with designed single-guide RNAs (sgRNAs) targeting the repression sites on a chromosome. The dCas9 was


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further fused to Mxi1, a protein that attracts histone deacetylase Sin3p homolog in yeast. The dCas9 alone with sgRNAs repressed the TEF promoter about 10-fold, while the fusion protein of Mxi1 could even downregulate the promoter by 53-fold (Gilbert et al., 2013). Except transcriptional regulator proteins, RNA was also proven to be able to activate or repress a promoter in yeast. Buskirk et al. (2003) used the yeast three-hybrid system to evolve an RNA activator that has a 50% activity of the well-known potent VP16 activation domain; the same strategy was further used to create an RNA molecule that silenced nearby genes with a Sirdependent manner (Kehayova & Liu, 2007). Recently, the role of nucleosome disfavoring sequences, or the poly (dA : dT) tracts were investigated (RavehSadka et al., 2012). By adjusting the sequences, the length of poly (dA : dT) and their distance to a transcriptional activator binding site, the promoter strength could be modified. In the foreseeable future, more elements of the promoter region will be discovered and our choices to modify promoter strength and regulate transcription will be increased. Translational control

Messenger RNA is the most labile part in consensus gene expression model. mRNAs are now known as good targets for synthetic biology manipulation due to the inherent structural vulnerability and recently found flexibility in ligand binding as shown in diverse aptamers or riboswitches. Eukaryotic mRNA is a molecule that combines gene expression-controlling elements such as the cap-dependent translation initiation element, internal ribosome entry sequences (IRES) that control cap-independent translation initiation, as well as structures such as pseudoknots and hairpins impeding translation and sequences specific to RNA-binding proteins to attenuate stability (Gebauer & Hentze, 2004). Specific RNA-binding proteins, trans-acting RNA molecules, or cis-acting RNA tracts could be used for major translation control. In the case of protein–RNA interference, for example, phage MS2 coat protein and the human UlA protein were introduced into yeast, and both could repress a luciferase gene by about fivefold (Stripecke et al., 1994). Recently, TetR protein was found to be able to bind to RNA aptamers, and the sequences of the TetR binding hairpin were introduced into the 50 UTR of yeast genes to perform repression. The regulation could be derepressed with tetracycline analogs such as aTc and Dox (Goldfless et al., 2012). Besides 50 -UTR binding proteins, RNA-degrading enzymes such as Rnt1p from S. cerevisiae could be used to induce RNA degradation (Babiskin & Smolke, 2011b). With the varª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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ied sequences incorporated to the 30 -UTR region of a gene, expression levels could be controlled (Babiskin & Smolke, 2011a). Antisense RNAs are widely used in gene regulations (Good, 2003a, b). A trans-acting ligand-binding antiswitch was proposed to regulate translation in yeast. The antiswitch is composed of two parts, with an ‘attacking’ part folded with its complementary sequences in the absence of the ligand and a ‘sensing’ part responding to the ligand with different concentration ranges. By adding the theophylline ligand to the cell culture, the conformation induced by ligand binding with the sensing part releases the attacking part to hybridize with the mRNA target in the transcriptome, hence exerting a repression effect. The repression caused by this RNA device could reach an impressive 15-fold (Bayer & Smolke, 2005). Cis-acting RNA structures were well known to inhibit the translation machinery or to cause alternative splicing in mRNA processing (Weigand et al., 2012). In the past decade, many cis-acting modules were invented to finetune the translation. Aptamers sensing different antibiotic drugs such as tetracycline and neomycin are the most straightforward modules (Suess et al., 2003; Weigand & Suess, 2007; Weigand et al., 2008). An aptazyme structure was developed by connecting ligand-inducible or ligand-repressible aptamers with a ribozyme (Win & Smolke, 2007). Thus, apatazymes performing self-cleavage can be regulated by a ligand. This structure was installed in the 30 -UTR of yeast mRNA and controlled RNA stability (Win & Smolke, 2007). In addition, the aptazyme module was expanded to form different genetic devices such as the AND gate, which functions only when two inputs were both seen, the NOR gate, which can be functional only when both signal inputs were silent, and also the NAND gate, where either or both signals could activate the gene expression (Win & Smolke, 2008). Protein engineering

Proteins are the major players of synthetic biology. Through the protein engineering, metabolic flux bottlenecks can be overcome, flux direction of bifurcated pathways can be adjusted, ligand affinities of enzyme cofactors can be modified, and nucleic acid binding properties of DNA- or RNA-binding regulators can be changed. Through the creation of scaffolds and different tags, we can localize proteins spatially or target them into specific organelles. As more protein structures are solved and the increasing data of conformations accumulate in the Protein Data Bank (PDB), knowledge-based structure prediction has become possible. Rossetta, designed for de novo protein FEMS Yeast Res && (2014) 1–18

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structure prediction and combines knowledge-guided Metropolis Monte Carlo sampling protocols with knowledge-based energy functions, can predict protein structures with an impressive backbone root-mean-square deviation of 5.0 A (Kaufmann et al., 2010). The programme was used to design scaffolds inhibiting hen egg lysozymes with the unsurpassed docking simulation ability (Procko et al., 2013). Rossetta was also used to understand the TALE–DNA interactions, guiding future TALEN designs (Christian et al., 2012; Doyle et al., 2013). To improve biosynthesis system productivity, especially related to cofactor imbalance created by initial pathway steps, ligand-binding specificity can be adjusted. For cellulosic biofuel production, the most famous example occurs in the xylose utilization pathway of Scheffersomyces stipitis, where xylose reductase (XR) utilizes NAD(P)H to convert xylose into xylitol and produce NAD(P)+, and then xylitol dehydrogenase (XDH) consumes NAD+ to produce L-xylulose and excess NADH. The lack of NADH recycling into NAD+ locks the reactions and accumulates xylitol creating a metabolic bottleneck (Kotter & Ciriacy, 1993). As XR usually uses NADPH, adjusting the XR specificity to allow usage of NADH could reduce this bottleneck. Site-specific mutants of the K270 of XR achieved balanced use of NADPH and NADH and increased the ethanol production (Jeppsson et al., 2006). A mutant K270R also altered the XR cofactor preference to NADH (Bengtsson et al., 2009). The place where a protein is secreted to may decide the function of this protein as different organelles have shown to have distinct protein distributions (Wiederhold et al., 2010). Proteins have been shown to function better inside specific organelles such as the vacuole (Bayer et al., 2009). To produce methyl halides, a methyl halide transferase enzyme which uses S-adenosylmethionine (SAM) was tagged with N-terminal vacuole targeting sequences to transfer into the vacuole, as most SAM molecules are transferred into the vacuole in yeast (Bayer et al., 2009). Another example is the transfer of amorpha-4,11-diene synthase (ADS) and heterologous farnesyl diphosphate synthase (FDPS) into mitochondria to produce amorpha4,11-diene, with a possible viable farnesyl diphosphate pool inside mitochondria to provide substrates (Farhi et al., 2011). The relocation of the enzymes successfully increased the productivity by 20-fold. The closeness of proteins functioning side-by-side can largely increase the local concentrations of enzymes and metabolites. To link proteins, several macromolecules including DNA, RNA, and protein scaffolds can be used, and proteins of interest need to be fused with scaffold binding domains. Using plasmid DNA sequences as scafFEMS Yeast Res && (2014) 1–18

folding, Conrado et al. (2012) showed that zinc-fingerfused enzymes assembled by the DNA scaffold have improvements of product titers two- to five-fold, depending on the combinations of different enzymes. Delebecque et al. (2011) used RNA hairpin structures and RNA–RNA hybridization to form 1-D or 2-D scaffolds to aggregate [FeFe]-hydrogenase and ferredoxin. The 2-D scaffold increased hydrogen production 48-fold. Dueber et al. created protein scaffolds using protein–protein interactions and recruited three mevalonate biosynthetic enzymes in different stoichiometric ratios. By measuring the product concentrations, the best stoichiometric ratio of enzymes can be determined. This synthetic biology tool created a 77-fold improvement of the original titer (Dueber et al., 2009). Genetic circuits

It has been proposed that timely expression of genes inside a pathway could minimize the energy or nutrient resources and maximize productivity. This concept stimulates the developments of genetic circuits, which comprise multiple regulatory elements arranged to create different kinds of logical gates, such as the AND, the OR, the NOR, or the NAND gates. By combining the basic modules, complicated mechanisms can be formed, including genetic oscillators (Elowitz & Leibler, 2000) and genetic toggle switches (Gardner et al., 2000). Blount et al. reviewed the genetic circuits using yeast as a genetic background (Blount et al., 2012a). Since 2000, there are about a dozen genetic circuits developed in yeast and this amount is far behind the amount of the counterparts developed in E. coli (Blount et al., 2012a). They also proposed three principles to create circuits inside yeast: (1) the elements should be independent of the host physiological parts to achieve orthogonality; (2) the elements should be able to be turned on or off to achieve inducibility, and (3) self-contained groups should be built to obtain modularity. A good example is creating a circuit regulated by the bacterial fatty acid-sensing protein FadR expressed heterologously in yeast. Teo et al. (2013) inserted FadR binding sites into the GAL1 promoter after the TATA binding site but before the + 1 transcription start site. Binding of FadR here represses the activity of the promoter. However, in the presence of ligand fatty acids such as myristic acid (C14:0), FadR changes its conformation, leaving its DNA binding site and deregulate the expression of downstream genes (Teo et al., 2013). Moreover, Teo & Chang (2014) added different upstream enhancer elements which sense copper and phosphate starvation; thus, the downstream gene will only be activated by the depletion of both fatty acid and copper or fatty acid and phosphate. This device is useful ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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for sensing fatty acid and can be integrated into pathways with the production or depletion of fatty acids. Redesign of metabolic pathway

With high-throughput genome sequencing, transcriptomic and proteomic profiling, and advancements of metabolomics and flux measurements, de novo assembling of genomes and reconstruction of metabolic pathways is becoming feasible (Thiele & Palsson, 2010). To design a synthetic metabolic pathway, the ‘Push and Pull’ principle is a promising guide. The principle, proposed by Gregory Stephanopoulos, emphasizes increasing the upstream and downstream fluxes of the main pathway (Tai & Stephanopoulos, 2013). This includes the overexpression of genes which enlarge the fluxes and removal of the enzymes that divert the fluxes. To identify gene targets, a predictive model could smooth the process. Currently, several algorithms and websites provide functionality to support the model constructions in silico, and a review of these tools has been published recently (Hamilton & Reed, 2014). Several studies have used genome-scale metabolic network modeling to choose strategies to modify yeast strains. Based on OptKnock (Burgard et al., 2003), an algorithm that searches for the combinations of gene deletions to optimize the productivity as well as cell growth, Patil et al. (2005) integrated another algorithm called genetic algorithm (GA) which simulates the Darwinian evolution process to find ideal solutions and thus created a new algorithm named OptGene. Starting from an artificial set of deletion knockouts, for example, a set of genomes which each has single gene deleted in silico from the 6607 yeast genes, GA first calculates the fitness of each set using the Flux Balance Analysis functions inside OptKnock. From the population, GA selects the sets with the best fitness scores. The gene lists of the selected sets are then ‘crossed over’ and produce another set of individuals. Another run of FBA analysis is performed, and new sets are generated (Patil et al., 2005). Through these evolution-like calculations, the OptGene algorithm was reported to be able to choose the best knockout strategies. To produce succinate, Otero et al. (2013) used OptGene to identify genes to be deleted in yeast metabolic pathways. Unsurprisingly, the calculation resulted in removing SDH3, which is the only enzyme that drains succinate into fumarate, although previous studies showed this deletion did not lead to improvements of succinate production. Interestingly, OptGene suggested removing SER3 and SER33, both converting 3P-glycerate in the lower part of the glycolytic pathway to produce glycine. With the knockout of these two genes, glycine production can only come from glyoxylate, the ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

C.-S. Tsai et al.

byproduct of the decomposition of isocitrate into succinate. Therefore, the synthesis of glycine was coupled with the synthesis of succinate. As glycine is essential, succinate production was largely enhanced. The unintuitive gene deletions of SER3 and SER33 increased the titer of succinate from 30 to 400 mg L1 (Otero et al., 2013). Asadollahi et al. (2009) also used OptGene to select targets of deletion in S. cerevisiae for producing sesquiterpene (Asadollahi et al., 2009); however, they used objective functions which focused on minimization of metabolic adjustments (MOMA) between a wild-type and a deletion mutant. With this final goal, OptGene selected to delete GDH1, which is an NADPH-dependent glutamate dehydrogenase. The GDH1 mutant accumulated abundant NADPH, which can be used by other NADPHdependent enzymes, such as HMG-CoA reductase, the third enzyme in the mevalonate pathway which processes the production of sesquiterpene. Deletion of GDH1 enhanced the titer by about 1.8-fold. Genome synthesis and integration

The ultimate goal of synthetic biology has been achieved recently: to synthesize a eukaryotic genome in yeast (Annaluru et al., 2014). Inside the yeast genome, 83% of genes are nonessential; hence, the working space for de novo synthesis of genomes fitting biosynthesis purposes is huge. Through rational reductions of the nonuseful elements, such as transposons, pseudogenes, and introns, a more efficient genome for growth or specific production of add-value products is possible. Annaluru et al. (2014) redesigned the yeast chromosome III with multiple changes including stop codon swapping, removal of nonessential elements such as transposons, introns, and tRNAs. They also integrated loxPsym sites for future genome shuffling and deleted the subtelomeric fragments. The synthetic steps include 750-bp building block (BB) PCR amplification and stepwise assembly with a technique such as DNA assembler. A novel chromosome III (SynIII) of 272 871 bp was created compared to the wildtype of 316 617 bp. Interestingly, the native strain and SynIII mutant were undifferentiated in growth and gene expression, although one heat shock protein gene (HSP30) and one cyclin (PCL1) were expressed differently from the wild-type. The integrity and stability of the newly synthesized genome was also indistinguishable from the wild-type. Recently, a method integrating a whole bacteria genome into yeast has been developed. This may also speed up the metabolic engineering development as genetic operation systems for many bacteria are not available (Karas et al., 2014). The integration of a whole bacterial genome into yeast enables us to focus on not only desired FEMS Yeast Res && (2014) 1–18

9


pathways but also some unknown native host factors important for the success of the pathway. Combining these new synthetic biology tools, a more extensive redesign and synthesis of yeast genome is expected and beneficial effects from these new organisms on biofuel production are very possible.

Application of synthetic devices in yeast biofuel production In the past decades, engineering yeast for producing fuels and chemicals from cellulosic sugars has been a driving force of yeast synthetic biology research. The application of engineered yeast for producing biofuels and chemicals encountered three challenges: to broaden the substrate ranges from just sugars to biomass, to develop new synthesis pathways for producing advanced biofuels, and to minimize product inhibition by toxic products. Widening the substrate spectrum and increasing the productivity

Extensive efforts have been made to expand substrate ranges through metabolic engineering. Efficient and rapid fermentation of nonglucose carbon sources, such as xylose, arabinose, galactose, and cellobiose, has been achieved through discovery of necessary metabolic enzymes and transporters for construction of heterologous pathways in yeast (Tables 2–5). Then, the expression levels of each piece have been optimized using various promoters and terminators with different strengths. In addition, protein engineering approaches to alter cofactor preferences and enhance catalytic activity were undertaken to improve product yield and productivity. Tables 2–5 summarized the impacts of various genetic perturbations for improving the use of different substrates

by S. cerevisiae. From the tables, we can conclude that the basic themes in extending the substrate utilization range and improving the utilization rates were to optimize the expression levels of the heterologous pathways and to modify a rate-limiting enzyme to achieve complete and rapid consumption of nonglucose sugars. The pathway layouts are also shown in Fig. 2. Advanced biofuel production

While ethanol is a biofuel which can currently be made at large scale, there are drawbacks in using ethanol as a liquid fuel. Therefore, the production of ‘drop-in’ biofuels which not only provide similar performances to petroleum-based fuels, but also can be compatible with existing liquid fuel infrastructures has been attempted using engineered yeast. Metabolic pathways to produce 1-butanol, isobutanol, fatty acid ethyl esters (FAEEs), and isoprenoids have been reconstituted in yeast (Fig. 2 and Table 6). While the titers of long-chain alcohol and isoprenoids could be significantly increased by deletion of flow-divergent pathways and overexpression of heterologous or endogenous synthesis pathways, FAEEs could hardly be enhanced to a large-scale level. To produce FAEEs at an industrial level, alternative pathways or other significant modifications are likely required. Improvement of tolerance against inhibitors and biofuel products

While metabolic pathways for producing advanced biofuels have been introduced into yeast, engineered yeast could not produce advanced biofuels at high titers because of product toxicity. The toxicity of advanced biofuels comes from the hydrophobic natures of these fuels (Dunlop, 2011). The hydrophobic nature induces the

Table 2. Metabolic engineering strategies to increase D-xylose utilization and productivity Strategies

Capacity or improvements

Reference

Protein engineering for altering cofactor preference of Candida tenuis XR Protein engineering for altering cofactor preference of Scheffersomyces stipitis XR Optimized expression levels of Candida shehatae XR, Candida tropicalis XDH, and Pichia pastoris XK by combinatorial transcriptional engineering Overexpressed Clostridium phytofermentans XI (codon optimized)

42% increased in ethanol yield 51% decreased in xylitol yield 20% increased in ethanol titer 52% decreased in xylitol titer 12-fold higher xylose consumption rate than wild-type promoter conditions

Petschacher & Nidetzky (2008)

0.07 g g1 h1 sp. xylose uptake 0.03 g g1 h1 sp. ethanol productivity 0.43 g g1 ethanol yield 1.87 g g1 h1 sp. xylose uptake 0.41 g g1 ethanol yield (after evolutionary engineering)

Brat et al. (2009)

Overexpressed Piromyces XI, S. stipitis XK, and pentose phosphate pathway

Watanabe et al. (2007) Du et al. (2012)

Zhou et al. (2012)

XR, xylose reductase; XDH, xylitol dehydrogenase; XK, xylulokinase; XI, xylose isomerase.



10

C.-S. Tsai et al.

Table 3. Metabolic engineering strategies to increase L-arabinose utilization and productivity Strategies


Reference

Overexpressed heterologous fungal L-arabinose assimilating pathway from Trichoderma reesei and S. stipitis, and endogenous XK Overexpressed heterologous bacterial L-arabinose assimilating pathway from Bacillus subtilis and E. coli, and endogenous Gal2 Overexpressed heterologous bacterial L-arabinose assimilating pathway from Lactobacillus plantarum

Ethanol titer increased from 0 to 0.1 g L1

Richard et al. (2003)

0.08 g g1 h1 sp. ethanol productivity

Becker & Boles (2003)

0.70 g g1 h1 sp. L-arabinose uptake 0.29 g g1 h1 sp. ethanol productivity 0.43 g g1 ethanol yield 38% increased in sp. L-arabinose uptake 157% increased in sp. ethanol productivity Improved L-arabinose uptake in low concentrations

Wisselink et al. (2007)

Codon optimization of genes in bacterial L-arabinose pathways Introduction of heterologous L-arabinose transporters from Arabidopsis thaliana and S. stipitis

Wiedemann & Boles (2008) Subtil & Boles (2011)

XK, xylulokinase; Gal2, galactose permease.

Table 4. Metabolic engineering strategies to increase D-galactose utilization and productivity Strategies


Reference

Deletion of negative regulators of GAL genes Increase expression level of a positive regulator of GAL genes Overexpressed endogenous phosphoglucomutase

41% improveds in max sp. galactose uptake 26% improved in max sp. galactose uptake 74% improved in max sp. galactose uptake 172% improved in max sp. ethanol productivity 96% improved in ethanol productivity Much shorter transition period from glucose to galactose

Ostergaard et al. (2000)

Truncation of negative regulators of GAL genes

Bro et al. (2005) Lee et al. (2011a, b)

Table 5. Metabolic engineering strategies to increase cellobiose utilization and capability of cofermentation with other sugars Cellobiose

Cellobiose + Xylose Cellobiose + Galactose

Introduction of Neurospora crassa Cdt-1 and Gh1-1 Introduction of mutant N. crassa Cdt-1 (F213L) and Saccharophagus degradans CBP Introduction of N. crassa Cdt-1 and Gh1-1 Introduction of N. crassa Cdt-1 and Gh1-1

0.44 g g1 ethanol yield 1.72 g L1 h1 cellobiose uptake 0.74 g L1 h1 ethanol productivity 0.44 g g1 ethanol yield 0.39 g g1 ethanol yield 0.65 g L1 h1 ethanol productivity 0.36 g g1 ethanol yield 0.75 g L1 h1 ethanol productivity 0.33 g g1 h1 sp. ethanol productivity

Galazka et al. (2010) Ha et al. (2013)

Ha et al. (2011b) Ha et al. (2011a)

Cdt-1, cellodextrin transporter 1; Gh1-1, b-glucosidase; CBP, cellobiose phosphorylase.

Fig. 2. Metabolic engineering of yeast biofuel production through synthetic biology. Endogenous pathways: Gal1, galactokinase; Gal2, galactose permease; Gal7, Galactose-1-phosphate uridyl transferase; Gal10, UDP-glucose-4-epimerase; Pgm, phosphoglucomutase; Pdc, pyruvate decarboxylase; Adh, alcohol dehydrogenase; Ald, aldehyde dehydrogenase; Acs, acetyl-CoA synthetase, Acc1, acetyl-CoA carboxylase; Fas, fatty acid synthase; Erg10, acetyl-CoA C-acetyltransferase; Erg13, HMG-CoA synthase; HMGR, HGM-CoA reductase; Isopentenyl pyrophosphate : dimethylallyl pyrophosphate isomerase; Erg20, farnesyl pyrophosphate synthetase; MTSs, monoterpene synthases; STSs, sesquiterpene synthases. Ilv2, acetolactate synthase; Ilv5, acetohydroxyacid reductoisomerase; Ilv3, dihydroxyacid dehydratase; Bat1, branchedchain amino acid transaminase; Bat2, cytosolic branched-chain amino acid transaminase; Ilv1, threonine deaminase; Cha1, L-serine (L-threonine) deaminase; Leu4, 2-isopropylmalate synthase; Leu1, isopropylmalate isomerase; Leu2, b-isopropylmalate dehydrogenase; Aro10, phenylpyruvate decarboxylase. Heterogenous pathways. Xylose and arabinose assimilating pathway: XI, xylose isomerase; XR, xylose reductase; XDH, xylitol dehydrogenase; XK, xylulokinase; LAD, L-arabitol-4-dehydrogenase; LXR, L-xylulose reductase; AraA, L-arabinose isomerase; AraB, L-ribulokinase; AraD, L-ribulose-5-phosphate 4-epimerase. Cellobiose assimilating pathway: Cdt, cellodextrin transporter; Gh1-1, b-glucosidase; CBP, cellobiose phosphorylase. FAEE biosynthesis pathway: WS/DGAT, wax ester synthase/acyl-CoA : diacylglycerol acyltransferase. Isobutanol biosynthesis pathway: KivD, a-ketoisovalerate decarboxylase. n-Butanol and isoprenoid biosynthesis pathway: AtoB and PhaA, acetyl-CoA acetyltransferase (thiolase); Hbd and PhaB, 3-hydroxybutyryl-CoA dehydrogenase; Crt, crotonase; Ccr, butyryl-CoA dehydrogenase; AdhE2, butanol dehydrogenase.



11


D-Xylose

D-Glucose L-Arabinose

Cellobiose

D-Galactose

Cdt D-Xylose

Cellobiose

L-Arabinose NADPH

XR

Gal2

CBP

Pi

AraA

Gh1-1

D-Glucose

L-Arabinitol LAD

XI

LXR

Xylitol

Glucose-1-P

L-Ribulose

NAD+

AraB

L- Xylulose

Pgm

Pgm

Glucose-6-P

Gal10

ATP

UDP-Galactose

ATP ATP

D-Xylulose

Galactose-1-P

XK

Fructose-1,6-bisP

D-Xylulose-5-P

XK

NADH

Glyceraldehyde-3-P

Repression Endogenous pathways Heterogenous pathways (eukaryote) Heterogenous pathways (bacteria) Cytosol targeted Mitochondria targeted FAEEs

Adh

Ethanol

Pyruvate

Pdc

Ilv2

Ald

Malonyl-CoA

Acc1

HMGR

Mevalonate

2NADPH

3ATP

Erg20

HMG-CoA

Erg13

GPP

Erg20 IPP

MTSs Monoterpene

FPP

CO2 ctIlv5

CoA ATP

NADPH

Ilv3 Bat2

2-Ketoisovalerate Bat1

KivD

mtKivD

Valine Isobutyraldehyde

Isobutyraldehyde

mtAdh

Adh

Acetoacetyl-CoA

NADPH

2,3-Dihydroxyisovalerate

α-Ketoisovalerate

Isobutanol

Isobutanol

3-Hydroxybutylryl-CoA Crt

Sesquiterpene

L-Aspartate

Homoserine

Crotonyl-CoA Ccr

Butyraldehyde

AdhE2

L-Threonine

Butyryl-CoA

Adh

Acetyl-CoA

Aro10 2-Ketovalerate

Leu2

3-Ethylmalate

accumulation of the products inside the membranes, which raises the penetrability of membranes and disrupts membrane integrity. Meanwhile, the ATP-generating systems on the membrane are thus inhibited or destroyed, FEMS Yeast Res && (2014) 1–18

Ilv5

ctIlv3

AtoB PhaA

CO2

2-Acetolactate

2,3-Dihydroxyisovalerate

Hbd PhaB

STSs

AdhE2 n-Butanol

2-Acetolactate

NAD(P)+

Acetyl-CoA Erg10

Acetyl-CoA CO2

ctIlv2

CO2

Acetate Acs

Fas

Glycerol

Pyruvate

Acetaldehyde

WS/DGAT

β-oxidation Fatty acyl-CoA

NAD+

Dihydroxyacetone-P

NADH, 2ATP

NADH

DMAPP

Gal7

UDP-Glucose

ATP

AraD

Idi

ATP

PPP

L-Ribulose-5-P

XDH

IPP

Gal1

Glucose-1-P

Fructose-6-P

NADPH NAD+

ct mt

α-D-Galactose

ATP

NAD(P)H

XR

β-D-Galactose

H2O

Leu1

2-Ethylmalate

Leu4

Ilv1 Cha1

2-Ketovalerate

the fluidity of lipid molecules of the membrane is increased, and the conformations of functional proteins are demolished (Jeffries & Jin, 2000; Hong et al., 2010; Dunlop, 2011). Numerous genetic perturbations eliciting ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

12

C.-S. Tsai et al.

Table 6. Metabolic engineering strategies to enhance advanced biofuel production

Compound

Strategies

Longer-chain alcohol 1-Butanol Overexpressed combinations of bacterial 1-butanol pathway Overexpressed deamination and leucine pathways, deleted competing and 1-butanol oxidation pathways Isobutanol Promoter engineering for mitochondrial valine pathway expression Overexpressed mitochondrial valine pathway and the Ehrlich pathway, deleted competing pathways Protein engineering to relocate valine pathway into cytosol, overexpressed Ehrlich pathway Protein engineering to relocate valine pathway into cytosol, overexpressed Ehrlich pathway Protein engineering to relocate the Ehrlich pathway into mitochondria Sesquiterpenes Farnesol Overexpressed mevalonate pathway, reduced squalene pathway activity Bisabolene Overexpressed mevalonate pathway, transcriptional regulator of sterol biosynthesis, and Abies grandis bisabolene synthase, downregulated squalene pathway Farnesene Overexpressed mevalonate pathway and Artemisia annua farnesene synthase, downregulated squalene pathway Fatty acid-derived biofuels Fatty acid ethyl Overexpressed glycerol metabolism and esters (FAEE) Acinetobacter baylyi wax ester synthase, deleted glycerol synthetic and releasing pathway Overexpressed fatty acid biosynthesis pathway and Marinobacter hydrocarbonoclasticus wax ester synthase Overexpressed fatty acid biosynthesis pathway and Acinetobacter calcoaceticus wax ester synthase (codon optimized), deleted b-oxidation-related pathways

improved tolerance against advanced biofuels have been identified. However, improvements of tolerance against advanced biofuels have been marginal (Table 7). There remains a critical need to improve yeast tolerance against fermentation inhibitors present in cellulosic hydrolysates as well as advanced biofuels. As tolerance phenotypes are mostly determined by complex interactions of numerous gene targets, large-scale assembly of genes and optimization of their expression levels will be necessary to implement tolerance phenotypes in yeast.

Future perspectives In this review, we surveyed the varieties and usage of synthetic biology tools for engineering yeast for producing ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

Titer/yield after Engineering

Titer/yield before Engineering

Reference

2.5 mg L1

0.25 mg L1

Steen et al. (2008)

242.8 mg L1

–

3.86 mg g1

0.28 mg g1

143 mg L1

11 mg L1

Kondo et al. (2012)

151 mg L1

20 mg L1

Lee et al. (2012)

630 mg L1

40 mg L1

Brat et al. (2012)

486 mg L1

25 mg L1

Avalos et al. (2013)

4.63 g L1

3.25 g L1

994 mg L1

0 mg L1

Millis & Maurina-Brunker (2004) Peralta-Yahya et al. (2011)

762 mg L1

9.8 mg L1

0.52 g L1

–

Yu et al. (2012)

8.2 mg L1

0

Shi et al. (2012)

5.44 mg L1

0.06 mg L1

Si et al. (2014)

Chen et al. (2011)

Renninger & McPhee (2008)

Runguphan & Keasling (2014)

biofuels. Both DNA assembly and genome editing/engineering tools are available for yeast synthetic biology. As S. cerevisiae exhibits strong homologous recombination activity, in vivo assembly of small DNA fragments and replacement of a target locus after making a DSB using Cas9 nuclease can happen with high frequencies. Numerous synthetic biological devices such as promoter libraries, RNA attenuators, and engineered proteins have been constructed owing to these efficient DNA writing capabilities. In addition, the construction of complicated devices such as genetic circuits is also feasible. There are numerous applications of these synthetic devices to engineer metabolic pathways for improving biofuel production. However, several gaps in applying synthetic biological tools into yeast for biofuel production were identified. FEMS Yeast Res && (2014) 1–18

13


Table 7. Improving biofuel product tolerance by synthetic biological approaches Biofuel Product

Description

Effect

References

Ethanol

Mutated TATA-binding protein (SPT15) and selected the mutants that survived high concentration of ethanol

Alper et al. (2006)

n-Butanol

Overexpressed and mutated TFs related to proteasome gene expression and interorganelle communication Increased the oleic acid (C18:1n-9) content to enhance short-chain alcohol tolerance Overexpressed endogenous alkane induced genes SNQ2 and PDR5

A mutant of Spt15p (spt15-300 mutant) was found to enable the cells to survive even 20% of ethanol Marginal in butanol tolerance enhancement Ethanol tolerance was enhanced to 8%, n-Butanol was marginally increased to 1.3% Snq2p and Pdr5p decreased intracellular C10 to 70–80%, C11 5 and 10-fold, respectively ABC2 enhanced the growth under decane 80-fold, ABC3 also significantly enhanced the growth

Yazawa et al. (2011)

Ethanol and nButanol Alkane

Alkane

Overexpressed heterologous pumps ABC2 and ABC3 from Yarrowia lipolytica

The first gap is that yeast has fewer usable genetic circuits than E. coli to timely and precisely regulate the expression of specific genes. One reason might be the complexity of participants in the process of gene regulation. The transcription machinery in yeast has more elements than their bacterial counterpart, and the functions of the transcription machinery units are still unknown, hampering modularization. The second reason may be because of differences in protein translation process. In yeast, the translation of proteins is decoupled from transcription, and the translation process does not occur inside the nucleus. Therefore, the transportation of the de novo synthesized protein back to the nucleus defers the timely requirements of regulations. The third reason might be that until now the transfer of useful and reliable bacterial biological parts into yeast research has not been optimized. For example, in bacterial research, quorum sensing regulators are widely used, but are simply not seen inside yeast research. More fundamental research should be carried out to identify potential orthogonal biological parts from bacterial studies. The second gap is also related to the synthetic devices: very few studies use genetic circuits which require inducer molecules in controlling production of biofuels (Tables 2–6). Genetic circuits have several Achilles’ heels for industrial fermentation. To guarantee orthogonality, inducer molecules cannot be produced endogenously and need to be supplemented into medium. However, the costs of supplementing inducers are often beyond feasibility at an industrial scale. The second weakness of genetic circuits is that the regulatory proteins must be activated or inactivated with a sharp rise and decline to achieve rapid regulation, resulting in wasting energy for the cells. Without meticulous investigation of the installed FEMS Yeast Res && (2014) 1–18

Gonzalez-Ramos, et al. (2013)

Ling et al. (2013)

Chen et al. (2013a)

regulatory circuits, there is significant risk for employing genetic circuits with inducer molecules. The most likely reason that genetic devices are not widely used might be due to their inherent design. Most genetic circuits were designed and implemented for creating intended behaviors under balanced growth states, that is, in the exponential phase of cell growth. However, most industrial fermentation processes involve stationary phase for enhanced production of a target product. The lack of knowledge of the behaviors of the genetic circuits under stationary phase renders the beauty of these circuits useless. Still another gap exists between design and build processes during the strain improvement based on synthetic biology. Most yeast biofuel synthesis studies were governed by intuition, not by systematic modeling. The power of systematic modeling has been reported for yeast succinate production (Otero et al., 2013); however, in some cases, the modeling did not, or only very slightly, improve the productivity of a target product (Asadollahi et al., 2009). This can be ascribed to the incompleteness of genome annotations and many phantom pathways which may actually control the production of specific compounds. We anticipate that with the further advances in gene discovery and annotation, systematic pathway modeling will become a superior tool to save efforts which have been made for trial and error approaches. Interestingly, from our review of previous studies for biofuel production in yeast (Tables 2–5), we found that S. cerevisiae was more often used in producing ethanol, long-chain alcohols, and isoprenoids. It seems that there is still a space for developing fatty acid-derived fuels in S. cerevisiae, although literature suggests researchers prefer to use oleaginous yeast strains in this field. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

14

With the combinations of using different promoter libraries, RNA structures for translational controls, mutant proteins, and metabolic pathways engineered, a screening or selection system is essential to sort through the numerous combinations. However, the development of highthroughput detection of metabolites does not match the explosion of genetic manipulations. Therefore, using synthetic biology tools to detect metabolite levels inside cells is gaining more attention. For in vivo detection of metabolites, RNA or protein libraries could be used (Michener et al., 2012). Michener et al. (Michener & Smolke, 2012) created a sensing RNA switch with an input domain sensing theophylline and a ribozyme output domain which could cut the mRNA. This RNA switch was installed in the end of a reporter GFP gene. When the ligand concentration was high, the RNA structure was arranged to repress the ribozyme; thus, the mRNA of GFP was intact and the GFP signal was generated. When the ligand concentration was low, the ribozyme was derepressed and performed GFP mRNA cutting, thus inhibiting the GFP signal. With a library created by error-prone PCR and DNA shuffling mutating the CDM1 gene which produces theophylline and the theophylline-sensing RNA switch, a high-throughput screening was conducted and a high theophylline-producing mutant was isolated. Several types of protein-based sensors have been developed, and these protein sensors could also be used for high-throughput screening (Michener et al., 2012). To produce biofuel economically by engineered yeast through synthetic biology, an integrated synthetic biology pipeline needs to be developed. Specifically, the pipeline can be used for predicting optimal pathways, gene assembly, and high-throughput target product screening. With the recent developments of systems biology, more and more new functional modules may appear, and these modules can serve as our future synthetic biology tools.

Acknowledgements This work was supported by funding from the Energy Biosciences Institute.

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