A new method of genetic engineering opens the next frontier of bacterial engineering


From bacteria-made insulin that obviates the use of animal pancreases to a better understanding of infectious diseases and improved treatments, genetic engineering of bacteria has redefined modern medicine.

Yet, serious limitations remain that hamper| progress in numerous other areas.

A decades-old bacterial engineering technique called recombineering (recombination-mediated genetic engineering) allows scientists to scarlessly swap pieces of DNA of their choosing for regions of the bacterial genome.

But this valuable and versatile approach has remained woefully underused because it has been limited mainly to Escherichia coli – the lab rat of the bacterial world – and to a handful of other bacterial species.

Now a new genetic engineering method developed by investigators in the Blavatnik Institute at Harvard Medical School and the Biological Research Center in Szeged, Hungary, promises to super-charge recombineering and open the bacterial world at large to this underutilized approach.

A report detailing the team’s technique is published May 28 in PNAS.

The investigators have developed a high-throughput screening method to look for the most efficient proteins that serve as the engines of recombineering.

Such proteins, known as SSAPs, reside within phages – viruses that infect bacteria.

Applying the new method, which enables the screening of more than two hundred SSAPs, the researchers identified two proteins that appear to be particularly promising.

One of them doubled the efficiency of single-spot edits of the bacterial genome. It also improved tenfold the ability to perform multiplex editing – making multiple edits genome-wide at the same time.

The other one enabled efficient recombineering in the human pathogen Pseudomonas aeruginosa, a frequent cause of life-threatening, hospital-acquired infections, for which there has long been a dearth of good genetic tools.

Recombineering will be a very critical tool that will augment our DNA writing and editing capabilities in the future, and this is an important step in improving the efficiency and reach of the technology,” said study first author Timothy Wannier, research associate in genetics in lab of George Church, the Robert Winthrop Professor of Genetics at HMS.

Previous genetic engineering methods, including CRISPR Cas9-based gene-editing, have been ill-suited to bacteria because these methods involve “cutting and pasting” DNA, the researchers said.

This is because, unlike multicellular organisms, bacteria lack the machinery to repair double-stranded DNA breaks efficiently and precisely, thus DNA cutting can profoundly interfere with the stability of the bacterial genome, Wannier said. The advantage of recombineering is that it works without cutting DNA.

Instead, recombineering involves sneaking edits into the genome during bacterial reproduction. Bacteria reproduce by splitting in two. During that process, one strand of their double-stranded, circular DNA chromosomes goes to each daughter cell, along with a new second strand that grows during the early stages of fission.

The raw materials for recombineering are short, approximately 90 base strands of DNA that are made to order. Each strand is identical to a sequence in the genome, except for edits in the strand’s center. These short strands slip into place as the second strands of the daughter cells grow, efficiently incorporating the edits into their genomes.

Among many possible uses, edits might be designed to interfere with a gene in order to pinpoint its function or, alternatively, to improve production of a valuable bacterial product. SSAPs mediate attachment and proper placement of the short strand within the growing new half of the daughter chromosome.

Recombineering might enable the substitution of a naturally occurring bacterial amino acid – the building blocks of proteins – with an artificial one.

Among other things, doing so could enable the use of bacteria for environmental cleanup of oil spills or other contaminants, that depend on these artificial amino acids to survive, meaning that the modified bacteria could be easily annihilated once the work is done to avoid the risks of releasing engineered microbes into the environment, Wannier said.

“The bacteria would require artificial amino acid supplements to survive, meaning that they are preprogrammed to perish without the artificial feed stock,” Wannier added.

A version of recombineering, called multiplex automated genome engineering (MAGE), could greatly boost the benefits of the technique. The particular advantage of MAGE is its ability to make multiple edits throughout the genome in one fell swoop.

MAGE could lead to progress in projects requiring reengineering of entire metabolic pathways, said John Aach, lecturer in genetics at HMS. Case in point, Aach added, are large-scale attempts to engineer microbes to turn wood waste into liquid fuels.

“Many investigator-years’ effort in that quest have made great progress, even if they have not yet produced market-competitive products,” he said.

Such endeavors require testing many combinations of edits, Aach said.

“We have found that using MAGE with a library of DNA sequences is a very good way of finding the combinations that optimize pathways.”

A more recent descendant of recombineering, named directed evolution with random genomic mutations (DIvERGE), promises benefits in the fight against infectious diseases and could open new avenues for tackling antibiotic resistance.

By introducing random mutations into the genome, DIvERGE can speed up natural bacterial evolution. This helps researchers quickly uncover changes that could arise naturally in harmful bacteria that would make them resistant to antibiotic treatment, explained Akos Nyerges, research fellow in genetics in Church’s lab at HMSs, previously at the Biological Research Center of the Hungarian Academy of Sciences.

“Improvements in recombineering will allow researchers to more quickly test how bacterial populations can gain resistance to new antibacterial drugs, helping researchers to identify less resistance-prone antibiotics,” Nyerges said.

Recombineering will likely usher in a whole new world of applications that would be hard to foresee at this juncture, the researchers said.

“The new method greatly improves our ability to modify bacteria,” Wannier said. “If we could modify a letter here and there in the past, the new approach is akin to editing words all over a book and doing so opens up the scientific imagination in a way that was not previously possible.”

Development of high-throughput ‘omics’ technologies in various fields, including transcriptomics, proteomics, and genomics, has given rise to the era of data-driven biology.

Thus, it is now possible to analyze cellular systems at the genome level and to unveil the complex interconnected path- ways. However, in microbial engineering fields, including metabolic engineering and synthetic biology, it is still difficult to engineer genes or genomes on a large scale; therefore, only a few genes are usually engineered (Muller, 1927; Auer- bach, 1949).

Thus, there is an urgent demand for developing genome-scale engineering tools. The main advantage of genome-scale engineering is to extensively investigate every genetic effect in terms of physiological phenotype or metabolic yields, which provides insights regarding the underlying mechanism as a whole.

Metabolic engineering is the process of manipulating mi- crobial metabolism for converting low-cost carbon resources into valuable substances. To achieve this goal, metabolic engineers have constructed several microbial strains and pro- duced useful bioproducts, including bioenergy (ethanol, butanol, and biodiesel), biorefineries (benzene, phenol, and fatty acid methyl ether), therapeutic proteins (insulin, glucagon, and urate oxidase), and bioactive compounds (penicillin and Taxol) for medical treatments (Hermann, 2003; Lin et al., 2006; Abdullah et al., 2008; Becker and Wittmann, 2015; Xue et al., 2017; Lu et al., 2019).

In brief, metabolic engineers have isolated a microorganism that inherently produces more bioproducts than other bacterial strains and manipulated its gene expression via genetic engineering to drive the metabolic flux toward bioproducts (Abdullah et al., 2008; Makino et al., 2011; Jaffe et al., 2015; Weber et al., 2015).

In metabolic engineering, the identification of appropriate genes, which promotes the production of desired chemical substances in bacterial strains, still remains challenging.

Intracellular metabolism is finely regulated and highly optimized through evolution in order to help the organisms adapt to various environments for survival. Therefore, induction of heterologous pathways or introduction of genetic changes directed toward certain genes could make it challenging for the cells to maintain a balance between the pathways (Matsumoto et al., 2017).

Bacterial systems are often regarded as advanced factories that produce value-added chemical substances and are, therefore, very complex (Lewis et al., 2012). For optimally regulating target gene expression and metabolic pathways, several genome-scale tools have been developed and success- fully applied to microbial metabolic engineering, which can consequently increase bio-productivity, such as the produc- tion of isoprenoid lycopene (Wang et al., 2009), tyrosine (Na et al., 2013; Kim et al., 2015), D-lactate, 2,3-butanol, and 1,3- propanediol (Hwang et al., 2017, 2018).

In conventional metabolic engineering, gene identification is performed via iterated trial and error; such a low-through- put ad hoc strategy is laborious and costly (Clark and Sandler, 1994; Datsenko and Wanner, 2000; Ellis et al., 2001).

To tackle the drawbacks of this low-throughput strategy, several high- throughput methodologies, which are cheaper and more con- venient for editing multiple genes, have been developed (Wang et al., 2012; Lu et al., 2019) as a result of the advances in synthetic biology that have provided tools and relevant information for identifying genes and editing them to fine-tune intracellular metabolism.

Synthetic biology has become a common technique in the field of biotechnology owing to its novel functions and/or regulations (Na et al., 2013; Simon et al., 2019). Early synthetic biology has mainly focused on the implementation of novel pathways rather than on genome-scale engineering (Liu et al., 2015).

To overcome the issue, metabolic engineers and synthetic biologists have been working on developing various methodologies to identify specific genes and manipulate them in a highly wired metabolic network of genes for fine-tuning their expression to avoid the accumulation of toxic intermediates (Matsumoto et al., 2017). Moreover, the following gene engineering methodologies

have been utilized for large-scale engineering of microorganisms: synthetic small regulatory RNA (sRNA) and clustered regularly interspaced short palindromic repeats (CRISPR)- Cas9 systems as well as multiplex automated genome engineering (MAGE) (Wang et al., 2009, 2012, 2016; Jeong et al., 2013; Li et al., 2015; Vervoort et al., 2017).

Furthermore, the expression of targeted genes is directly manipulated via promoter engineering and ribosome-binding site (RBS) engineering at both transcription and translation levels (Wang et al., 2012; Lim et al., 2015; Sterk et al., 2018).

Moreover, the genome hybridization method, which is a combination of protoplast fusion and recursive genome-wide recombina- tion and sequencing method (REGRES), has been developed.

This method could efficiently manipulate protoplast-forming species (Zhang et al., 2002; Quandt et al., 2014). Additionally, trackable multiplex recombineering (TRMR) facilitates large-scale characterization of bacterial genomes using mixtures of barcoded oligonucleotides (Warner et al., 2010).

The biotechnological methods introduced in this re- view are summarized in Table 1.
In this review, we discuss genome-scale genetic engineering methodologies, specifically to identify genes that can be used to construct bacterial cell factories with improved productivity.

At present, it is possible to design genome-scale engineering strategies both precisely and on a large-scale by employing several processes including genome-scale analysis, genome-scale engineering pathways, construction of diverse libraries, and genome-scale screening at high-through- put level (Fig. 1).

Recently, metabolic engineers, with the assistance of synthetic biologists, have developed new technologies to modify genomes globally (Jeong et al., 2013; Liu et al., 2015). Several multiplex genome-editing strategies are introduced in the subsequent sections of this manuscript: MAGE, promoter engineering as a multiplex tool, Crispr- dCas9 (CRISPRi)/Crispr-dCas9–activator (CRISPRa), and sRNA as a multiplex regulation tool in prokaryotes.

Genome-scale editing tools


In vivo systems are finely regulated by complex intracellular pathways to maintain homeostasis. Imbalance in these path- ways could lead to cell death or decrease in the production of desired bioproducts.

To date, metabolic engineers have overexpressed specific genes to promote metabolic flux of the desired pathway (Fernández-Cañón and Peñalva, 1995; Malla et al., 2009, 2010).

Although such a strategy has pre- viously been successful, it has recently been reported that pathways should be fine-tuned to avoid the accumulation of intermediate metabolites that may decrease cell growth and production titer. Such an accumulation may result in the downregulation of both TCA cycle and cell growth and con- sequently decrease L-tryptophan production in Escherichia coli (Nielsen and Keasling, 2016; Du et al., 2019).

Although several models predict protein expression at the transcrip- tional (Beer and Tavazoie, 2004; Budden et al., 2015) and translational levels (Huang et al., 2011; Reeve et al., 2014), it is still difficult to optimize pathways because of the differences between in vitro and in vivo enzyme kinetics or lack of kinetic information.

Fig. 1. Schematic illustration of genome-scale engineering. (A) Genome-scale analysis; (B) Genome-scale engineering pathways; (C) Construction of diverse libraries; (D) Genome-scale screening at high-throughput level.
Fig. 2. Genome-scale editing. (A) Schematic illustration of MAGE, showing introduction of chromosomal mutations via homologous recombination performed using single-stranded oligonucleotides. (B) Promoter modification for optimal gene expression at the transcriptional level.

In 2009, Wang et al. suggested a rapid, automated, and high-throughput multiplex genome engineering tool called MAGE. It could be a good alternative to the traditional genome engineering as it requires only randomized oligomers.

This method uses the homologous recombinase system of phage l, and consequently modifies the promoters or RBS of a few genes by introducing oligomers (90-mer). Because the short oligomers can be chemically synthesized, it is easy to construct a library (Wang et al., 2009).

Mismatched oligonucleotides are inserted into the lagging strand of the replicating DNA. Normally, the sequence is restored as the wild- type by the DNA mismatch repair system composed of MutS, MutL, and MutH.

Therefore, in standard MAGE, the mismatch repair system is eliminated, which allows for the incorporation of mismatched oligomers (Wang et al., 2009). However, permanent disabling of the mismatch repair system also allows for the accumulation of off-target mutations during subsequent replications. The process of MAGE is il- lustrated in Fig. 2A.

For lycopene production in E. coli, first, 24 enzyme genes that are involved in 1-deoxy-D-xylulose-5-phosphate biosynthetic pathway were selected, and their RBS nucleotides were replaced with randomized sequences using MAGE. Consequently, a five-fold increase in the production of industrially important isoprenoid lycopene was achieved (Wang et al.,2009).

Multiple T7 promoters were reportedly optimized using a co-selection MAGE system to facilitate optimal biosynthesis of amino acid derivatives. This method successfully modified T7 promoter nucleotides at 12 genomic loci by employing randomized oligomers (Deng et al., 2012).

Each T7 promoter showed a different transcription level, and the strain with the best production titer was selected. Its modified sequences were subsequently analyzed to infer how mutations caused an increase in the titer.

Additionally, MAGE was simultaneously employed to replace 314 TAG codons with TAA codons across 32 different E. coli strains, and this re- placement turned out to be successful (Isaacs et al., 2011).

Eventually, the effect of codon replacement on viability and physiological phenotype was studied. MAGE is a useful tool to modify multiple loci using oligomer libraries although still not at a genome-scale.

Genome-scale transcription and translation optimization

When heterologous/endogenous pathway genes are intro- duced into the cell, their proteins (enzymes) should be opti- mally produced to balance the overall metabolic flux. Other- wise, certain enzymes may exert a rate limiting effect and lead to the accumulation of intermediate metabolites.

The traditional overexpression strategy, which employs strong promoters, can be used to enhance the metabolic flux of pathways, but it involves the wastage of too many cellular resources during enzyme production.

Even overexpression can not resolve the intermediate accumulation issue. There are two methods for controlling protein expression level: tran- scription and translation.

Promoters can be replaced with stronger or weaker ones (Fig. 2B). RNA polymerase docks on -35 and -10 regions of the promoters to initiate transcription. Nucleotide modifi- cation in these regions can result in different strength levels of the promoter.

Promoter strength is not affected by downstream coding sequences, unlike translation in which RBS may interact with the coding sequence; thus, failure of interaction between 16S rRNA (ribosome) and mRNA could lead to a reduction in protein production (Huang et al., 2007).

Many natural and synthetic promoters with varying strengths and regulatory systems have been studied and developed (Blazeck and Alper, 2013; Bhat et al., 2014; Lim et al., 2015; Engstrom and Pfleger, 2017; Yan and Fong, 2017; Zhang et al., 2017; Zhou et al., 2017). For example, there are several synthetic constitutive promoters with relative strengths rang- ing from 1 to 2547 (iGEM, http://parts.igem.org), which have been derived from part BBa_J23119 (a σ70 consensus pro- moter) (Yan and Fong, 2017); such tunable promoters have been used to increase the flux toward the desired bioproducts as well as elevate their consequent production titers.

For example, in a particular study, several mutant nar pro- moters with diverse strengths were screened via insertion of randomized 15-bp spacer nucleotides between -35 and -10 regions of the wild-type nar promoter for engineering the oxygen-dependent nar promoter to produce D-lactate, 2,3-butanol, and 1,3-propanediol in E. coli (Hwang et al., 2018).

The synthetic nar promoter library was initially constructed via polymerase chain reaction (PCR) using degen- erated primers, and the promoter strength was measured by fluorescence-activated cell sorting (FACS) analysis based on green fluorescent protein (GFP) fluorescence intensity.

Sub- sequently, the selected clones were categorized into three groups: low-, intermediate-, and high strength. The screened mutant nar promoters were applied to D-lactate and 2,3- butanediol production by replacing the natural promoter of lactate dehydrogenase gene with the screened nar promo- ters and 2,3-butanediol pathway gene promoters. Promoter strength optimization causes an increase in the production of D-lactate, 2,3-butanol, and 1,3-propanediol by 34, 72, and 20.5%, respectively (Hwang et al., 2017, 2018).

Similarly, the porin promoter in Halomonas bluephagenesis was engineered to increase poly(3-dydroxybutyrate-co-4- hydroxybutyrate) [p(3HB-co-4HB)] production (Shen et al., 2018).

Various porin promoters were constructed as a library, and the orfZ promoter was replaced with diverse porin promoters in the library; consequently, p(3HB-co-4HB) yield increased by 80%. Furthermore, promoter libraries were used to adjust the phosphoenolpyruvate carboxylase level for balancing cell growth and deoxy-xylulose-P synthase level in order to maximize lycopene production (Alper et al., 2005).

Information on gene expression level can also be obtained via ChIP-seq or RNA-seq, but these methods are time-con- suming and costly. Recently, computational models have been developed to identify promoters and their strengths. In E. coli, the promoters include the -35 region (TTGACA) and -10 region (TATAAT), which are separated and surrounded by variable nucleotides. RNA polymerase recognizes the -35 region, and the -10 region is subsequently bent by RNA poly- merase to loosen the double strands and initiate transcrip- tion.

Based on the sequence patterns and literature data, several models have been developed to predict promoter strength; however, to the best of our knowledge, so far, only one web-based tool (http://github.com/PromoterPredict) is available for use (Weller and Recknagel, 1994; De Mey et al., 2010; Rhodius et al., 2012; Bharanikumar et al., 2018; Xiao et al., 2018).

Even if the transcript level is upregulated, the protein level may be downregulated because of the post-transcriptional regulation caused by mRNA stability, 5′-untranslated region (UTR) structure, and ribosome binding. Unlike promoters, interaction between the ribosome and RBS is affected by the secondary structures present around RBS, including the 5′- UTR and N-terminal coding sequences (de Smit and van Duin, 1990; Omotajo et al., 2015; Sterk et al., 2018).

Because of the complex intramolecular interactions among ribonu- cleotides, it is difficult to intuitively estimate translation efficiency. Several mathematical models have been developed (RBS Designer, RBS Calculator, UTR Designer, EMOPEC, etc.) (Table 2) based on the thermodynamics of mRNA struc- ture and interaction between the Shine-Dalgarno (SD) sequences of RBS and 16S rRNA in the ribosome.

The model for translation initiation, which is a rate-limiting step in trans- lation, accurately predicted protein expression levels from mRNA sequences and enabled the design of mRNA sequences at desired expression levels. ùIn metabolic engineering, control at the translational level has been applied to fine-tune the expression of proteins, instead of the expression of promoters (Vitreschak et al., 2004; Coppins et al., 2007; Kang et al., 2012; Na et al., 2013; Kang et al., 2014; Chae et al., 2015).

For example, L-tyrosine production has been increased to 3.0 g/L by redesigning the 5′-UTR region in E. coli for opti- mizing the 5′-UTR of PEP synthetase (ppsA) (Kim et al., 2015).

This result suggests that the fine-tuning of carbon flux can be accomplished by optimizing the 5′-UTR region. In another study, nudB expression was optimized by modifying RBS nucleotides (George et al., 2015); in that study, the genes involved in mevalonate pathway were introduced into E. coli for producing 3-methyl-3-buten-1-ol.

Among the inserted pathway genes, RBS of the nudB gene (E. coli phos- phatase) was engineered to overexpress nudB to prevent iso- pentenyl diphosphate accumulation. Consequently, nudB ex- pression was increased by nine-fold, which caused an in- crease in the production of 3-methyl-3-buten-1-ol (1.94 g/L) by 60%.

In silico models were used to computationally optimize met- abolic fluxes by designing the expression of pathway genes. However, the models were mostly used to optimize local met- abolic fluxes, instead of genome-scale metabolism, because of the lack of knowledge on enzyme kinetics. However, with an increasing number of researches on this subject, these mo- dels could be used to design a well-balanced global meta- bolism process for better production.

Genome-scale regulation tools

Synthetic sRNAs

Knockout method is one of the procedures for evaluating the genetic effect on metabolite production. By deleting a gene, the effect of gene deletion on the production titer can be investigated. However, even in an E. coli, a single knock-out takes weeks; therefore, it is not possible to study genetic effects at the genome-scale.

Recently, synthetic RNAs have been developed to resolve the limitation imposed by the knockout method. Essentially, they are short antisense RNAs with particular structures and sequences as well as with a considerably higher efficiency than conventional antisense RNAs (Fig. 3A).

Natural sRNAs are used in response to various environmental or intracellular stresses (Vogel, 2009; Felden et al., 2011). In bacterial cells, sRNAs play pleiotropic roles, including regulation of translation initiation and control of mRNA stability (Gottesman, 2004).

The sRNAs effectively bind to their target mRNAs with the assistance of RNA chaperone proteins Hfq or ProQ (Smirnov et al., 2016). Over the last few decades, more than 150 validated and putative sRNAs have been discovered in E. coli (Huang et al., 2009; Raghavan et al., 2011).

Owing to advances in technology in the field of bioinformatics, new sRNAs have been discovered in E. coli and other bacterial species (Lalaouna et al., 2015; Bouloc and Repoila, 2016; De Lay and Garsin, 2016). sRNAs are widely being used as a class of non-protein post-transcriptional re- gulators in bacteria.

Recently, synthetic sRNAs have been designed to efficiently control translation initiation (Sakai et al., 2014; Malecka et al., 2015; Schu et al., 2015). According to a previous study, the rules for sRNA design have been established based on antisense RNA length, thermodynamics, number of mis- matches, Hfq-binding site, and binding location (Hoynes- O’Connor and Moon, 2016).

Because sRNAs can be pro- duced within cells via plasmids and because sRNA genes isolated from plasmids can be easily introduced into various strains, application of sRNA is considerably easier than that of other single-knockout systems.

Synthetic sRNAs can be used to perform a large-scale screening of genes that affect metabolite production. For better metabolic engineering, synthetic sRNAs have recently been utilized instead of other genetic tools to downregulate genes while searching for genetic targets that could be knocked down.

Synthetic sRNAs have been used to screen 130 genes for identifying the ones that improve tyrosine and cadaverine production. Of the 130 genes, four and six genes were effective for tyrosine and cadaverine production, respectively, when downregulated.

On evaluating 14 different E. coli strains, it was found that the engineered E. coli S17-1 strain harboring tyrR and csrA genes, which were downregulated by synthetic sRNA, produced 2 g/L of tyrosine; in another example, when murE gene was suppressed, cadaverine production increased by 55% compared with cadaverine production levels reported in previous studies (Qian et al., 2011; Na et al., 2013).

Synthetic sRNAs have also been used to downregulate sdhCDAB genes to accumulate succinic acid and acetic acid (Kang et al., 2012). When heme biosynthesis pathway was modulated via constitutive expression of RyhB sRNA, 5-amiolevlini acid titer produced by the engineered strain increased by 16% compared with that produced by the parental strain (Kang et al., 2011). Moreover, SgrS sRNA production reduced ace- tate secretion in E. coli K-12 strain (Negrete et al., 2013).

Synthetic sRNAs are used to simultaneously screen several genes and E. coli strains because they can be easily introduced into cells and constructed into a library because of their short length.

CRISPR-Cas9, CRISPRi,  and CRISPRa systems in prokaryotes

CRISPR is a part of the adaptive immune response system of bacteria. CRISPR is a defense system that protects bacteria from mobile genetic elements (Oude Blenke et al., 2016). This system comprises single guide RNA (sgRNA) and Cas9 pro- tein, which together form a complex.

The sgRNA binds to the target DNA sequence, and Cas9 protein subsequently cleaves the target DNA sequence. Class 1 (Type I, III, and IV) and Class 2 (Type II, V, and VI) families of Cas systems consist of a specific endonuclease protein (Cas) and a sgRNA molecule (Oude Blenke et al., 2016). Among these types, Class 2 (Type II) nucleases have a relatively simple architecture and are thereby preferred over large and multi-subunit protein complexes (Class 1) for genetic engineering.

Based on the CRISPR-Cas9 system, modified modulators, CRISPRi (Fig. 3B) and CRISPRa (Fig. 3C), have been developed to regulate gene expression. CRISPRi uses dCas9, which does not have an endonucleolytic function, and the binding of dCas9 to a promoter by sgRNA inhibits the binding of RNA polymerases; in this manner, dCas9 prevents transcrip- tion.

CRISPRa uses the same dCas9, but another domain, which can activate transcription (VP64 or w subunit of RNA polymerase), is attached to dCas9. Therefore, CRISPRi and CRISPRa are useful tools for genome regulation. Because CRISPRi and CRISPRa are dependent on sgRNAs, it may be possible to construct a library of sgRNAs at a genome-scale and perform genome-level screening.

Performing screening experiments at a genome-scale is extremely useful for identifying relevant genes so as to increase metabolic flux toward the desired chemical substances. Recently, CRISPRi and CRISPRa have been used as multiplex genome-regulating tools.

In 2015, CRISPRi technology was applied to p(3HB-co-4HB) production. Five sgRNAs were designed and applied to control the expression of sad gene, encoding E. coli succinate semi-aldehyde dehydrogenase. Consequently, 7.15–11.72 g/L of p(3HB-co-4HB) was ob- tained, suggesting that CRISPRi is a useful method to simul- taneously manipulate gene expression (Lv et al., 2015).

CRISPRa is also a feasible tool for multiplex gene regulation. Transcriptional activators including rpoS, AsiA, TetD, and SoxS were evaluated, and SoxS was found to be more effi- cient than the other transcriptional activators.

The binding site of sgRNA was optimal within approximately 60–90 bases upstream of the transcriptional start site (Dong et al., 2018). Due to the development of CRISPR-based methods, meta- bolic engineers can screen the target genes that can be ma- nipulated to promote bioproduct production in time-saving and site-specific manners.

Recently, CRMAGE, which com- bines CRISPR/Cas9 system and MAGE technology, has been developed to enable efficient and rapid genome engineering. According to a previous study, when CRMAGE was used, recombineering efficiency increased to approximately 90% and the efficiency for protein synthesis modulation increased to 64% (Ronda et al., 2016).

More recently, it has been found that CRISPR-Cas13 has the ability to knock down RNAs using RNA-guided RNA-targeting CRISPR-Cas effector Cas13a (Abudayyeh et al., 2017), which was identified from Leptotrichia wadei (LwaCas13a) but was considered to be more effective in E. coli (Matsoukas, 2018).

This CRISPR-Cas system targeting RNA is more complex than other RNA interaction systems such as sRNAs, but it will open up new strategies for RNA manipulation. Additionally, a high-throughput mapping of genetic variants can be achieved using CRISPR-enabled trackable genome engine- ering (CREATE) that can not only edit loci but also function as barcodes for tracking genotype-phenotype relationships in bacteria (Garst et al., 2017).

For directed evolution, EvolvR system, which can continuously diversify the nucleotides within a tunable window length at user-defined loci in bacteria, has been developed based on the fusion of nCas9 protein with an error-prone DNA polymerase (Halperin et al., 2018).


In traditional metabolic engineering, bacterial genomes are randomly modified using mutagenic chemicals, UV irradi- ation, and transposon random mutagenesis.

These procedures are old, laborious versions of high-throughput engineering. The more advanced version of metabolic engineering involves identification of effective genes based on knowledge, experience, and reconstructed cell models. Since the emergence of synthetic biology, new technologies have been de- veloped that facilitate large-scale metabolic engineering; for example, MAGE, CRISPRi, and CRISPRa have been developed as multiplex genome-editing and -regulating tools. ùThese genome-scale tools are able to manipulate multiple sites (genes) or specific sites on the entire genome. Hence, we believe that the genome-scale engineering technologies described in this review will open new doors in the field of metabolic engineering.


This work was supported by the National Research Found- ation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A5A1025077). This research was also supported by Chung-Ang University Research Scholar- ship Grants in 2018.


Abdullah, M.A., Rahmah, A.U., Sinskey, A.J., and Rha, C.K. 2008. Cell engineering and molecular pharming for biopharmaceuticals. Open Med. Chem. J. 2, 49–61.

Abudayyeh, O.O., Gootenberg, J.S., Essletzbichler, P., Han, S., Joung, J., Belanto, J.J., Verdine, V., Cox, D.B.T., Kellner, M.J., Regev, A., et al. 2017. RNA targeting with CRISPR-Cas13. Nature 550, 280– 284.

Alper, H., Fischer, C., Nevoigt, E., and Stephanopoulos, G. 2005. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA 102, 12678–12683.

Auerbach, C. 1949. Chemical mutagenesis. Biol. Rev. Camb. Philos. Soc. 24, 355–391.

Becker, J. and Wittmann, C. 2015. Advanced biotechnology: meta-bolically engineered cells for the bio-based production of chemi- cals and fuels, materials, and health-care products. Angew. Chem. Int. Ed. Engl. 54, 3328–3350.

Beer, M.A. and Tavazoie, S. 2004. Predicting gene expression from sequence. Cell 117, 185–198.

Bharanikumar, R., Premkumar, K.A.R., and Palaniappan, A. 2018. Promoterpredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between pro- moter strength and sequence. PeerJ 6, e5862.

Bhat, A.P., Shin, M., and Choy, H.E. 2014. Identification of high-spe- cificity H-NS binding site in LEE5 promoter of enteropathogenic Esherichia coli (EPEC). J. Microbiol. 52, 626–629.

Blazeck, J. and Alper, H.S. 2013. Promoter engineering: recent ad- vances in controlling transcription at the most fundamental level. Biotechnol. J. 8, 46–58.

Bonde, M.T., Pedersen, M., Klausen, M.S., Jensen, S.I., Wulff, T., Har- rison, S., Nielsen, A.T., Herrgård, M.J., and Sommer, M.O. 2016. Predictable tuning of protein expression in bacteria. Nat. Methods 13, 233–236.

Bouloc, P. and Repoila, F. 2016. Fresh layers of RNA-mediated re- gulation in Gram-positive bacteria. Curr. Opin. Microbiol. 30, 30–35.

Budden, D.M., Hurley, D.G., and Crampin, E.J. 2015. Predictive mo- delling of gene expression from transcriptional regulatory ele- ments. Brief. Bioinform. 16, 616–628.

Chae, T.U., Kim, W.J., Choi, S., Park, S.J., and Lee, S.Y. 2015. Meta- bolic engineering of Escherichia coli for the production of 1,3- diaminopropane, a three carbon diamine. Sci. Rep. 5, 13040.

Clark, A.J. and Sandler, S.J. 1994. Homologous genetic recombina- tion: the pieces begin to fall into place. Crit. Rev. Microbiol. 20, 125–142.

Coppins, R.L., Hall, K.B., and Groisman, E.A. 2007. The intricate world of riboswitches. Curr. Opin. Microbiol. 10, 176–181.

Datsenko, K.A. and Wanner, B.L. 2000. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640–6645.

De Lay, N.R. and Garsin, D.A. 2016. The unmasking of ‘junk’ RNA reveals novel sRNAs: from processed RNA fragments to marooned riboswitches. Curr. Opin. Microbiol. 30, 16–21.

De Mey, M., Maertens, J., Boogmans, S., Soetaert, W.K., Vandamme, E.J., Cunin, R., and Foulquie-Moreno, M.R. 2010. Promoter knock- in: a novel rational method for the fine tuning of genes. BMC Bio- technol. 10, 26.

de Smit, M.H. and van Duin, J. 1990. Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proc. Natl. Acad. Sci. USA 87, 7668–7672. Deng, Z., Meng, X., Su, S., Liu, Z., Ji, X., Zhang, Y., Zhao, X., Wang, X., Yang, R., and Han, Y. 2012. Two sRNA RyhB homologs from Yersinia pestis biovar microtus expressed in vivo have differential Hfq-dependent stability. Res. Microbiol. 163, 413–418.

Dong, C., Fontana, J., Patel, A., Carothers, J.M., and Zalatan, J.G. 2018. Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria. Nat. Commun. 9, 2489.

Du, L.H., Zhang, Z., Xu, Q.Y., and Chen, N. 2019. Central metabolic pathway modification to improve L-tryptophan production in Escherichia coli. Bioengineered 10, 59–70.

Ellis, H.M., Yu, D., DiTizio, T., and Court, D.L. 2001. High efficiency mutagenesis, repair, and engineering of chromosomal DNA us- ing single-stranded oligonucleotides. Proc. Natl. Acad. Sci. USA 98, 6742–6746.

Engstrom, M.D. and Pfleger, B.F. 2017. Transcription control en- gineering and applications in synthetic biology. Synth. Syst. Bio- technol. 2, 176–191.

Felden, B., Vandenesch, F., Bouloc, P., and Romby, P. 2011. The Sta- phylococcus aureus RNome and its commitment to virulence. PLoS Pathog. 7, e1002006.

Fernández-Cañón, J.M. and Peñalva, M.A. 1995. Overexpression of two penicillin structural genes in Aspergillus nidulans. Mol. Gen. Genet. 246, 110–118.

Garst, A.D., Bassalo, M.C., Pines, G., Lynch, S.A., Halweg-Edwards, A.L., Liu, R.M., Liang, L.Y., Wang, Z.W., Zeitoun, R., Alexander, W.G., et al. 2017. Genome-wide mapping of mutations at single- nucleotide resolution for protein, metabolic and genome engine- ering. Nat. Biotechnol. 35, 48–55.

George, K.W., Thompson, M.G., Kang, A., Baidoo, E., Wang, G.,

Chan, L.J.G., Adams, P.D., Petzold, C.J., Keasling, J.D., and Lee,T.S. 2015. Metabolic engineering for the high-yield production of isoprenoid-based C5 alcohols in E. coli. Sci. Rep. 5, 11128.

Gottesman, S. 2004. The small RNA regulators of Escherichia coli:roles and mechanisms. Annu. Rev. Microbiol. 58, 303–328.

Halperin, S.O., Tou, C.J., Wong, E.B., Modavi, C., Schaffer, D.V., and Dueber, J.E. 2018. CRISPR-guided DNA polymerases enable di- versification of all nucleotides in a tunable window. Nature 560, 248–252.

Hermann, T. 2003. Industrial production of amino acids by cory- neform bacteria. J. Biotechnol. 104, 155–172.

Hoynes-O’Connor, A. and Moon, T.S. 2016. Development of de- sign rules for reliable antisense RNA behavior in E. coli. ACS Synth. Biol. 5, 1441–1454.

Huang, H.Y., Chang, H.Y., Chou, C.H., Tseng, C.P., Ho, S.Y., Yang, C.D., Ju, Y.W., and Huang, H.D. 2009. sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes. Nucleic Acids Res. 37, D150–154.

Huang, W., Nevins, J.R., and Ohler, U. 2007. Phylogenetic simula- tion of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools. Genome Biol. 8, R225.

Huang, T., Wan, S.B., Xu, Z.P., Zheng, Y.F., Feng, K.Y., Li, H.P., Kong, X.Y., and Cai, Y.D. 2011. Analysis and prediction of translation rate based on sequence and functional features of the mRNA. PLoS One 6, e16036.

Hwang, H.J., Kim, J.W., Ju, S.Y., Park, J.H., and Lee, P.C. 2017.Application of an oxygen-inducible nar promoter system in me- tabolic engineering for production of biochemicals in Escherichia coli. Biotechnol. Bioeng. 114, 468–473.

Hwang, H.J., Lee, S.Y., and Lee, P.C. 2018. Engineering and appli- cation of synthetic nar promoter for fine-tuning the expression of metabolic pathway genes in Escherichia coli. Biotechnol. Biofuels 11, 103.

Isaacs, F.J., Carr, P.A., Wang, H.H., Lajoie, M.J., Sterling, B., Kraal,

L., Tolonen, A.C., Gianoulis, T.A., Goodman, D.B., Reppas, N.B., et al. 2011. Precise manipulation of chromosomes in vivo enables genome-wide codon replacement. Science 333, 348–353.

Jaffe, S.R., Strutton, B., Pandhal, J., and Wright, P.C. 2015. Inverse metabolic engineering for enhanced glycoprotein production in Escherichia coli. Methods Mol. Biol. 1321, 17–35.

Jeong, J., Cho, N., Jung, D., and Bang, D. 2013. Genome-scale genetic engineering in Escherichia coli. Biotechnol. Adv. 31, 804–810.

Kang, Z., Wang, Y., Gu, P., Wang, Q., and Qi, Q. 2011. Engineering Escherichia coli for efficient production of 5-aminolevulinic acid from glucose. Metab. Eng. 13, 492–498.

Kang, Z., Wang, X., Li, Y., Wang, Q., and Qi, Q. 2012. Small RNA Ryhb as a potential tool used for metabolic engineering in Esche- richia coli. Biotechnol. Lett. 34, 527–531.

Kang, Z., Zhang, C., Zhang, J., Jin, P., Zhang, J., Du, G., and Chen, J. 2014. Small RNA regulators in bacteria: powerful tools for meta- bolic engineering and synthetic biology. Appl. Microbiol. Biotech- nol. 98, 3413–3424.

Kim, S.C., Min, B.E., Hwang, H.G., Seo, S.W., and Jung, G.Y. 2015.Pathway optimization by re-design of untranslated regions for L-tyrosine production in Escherichia coli. Sci. Rep. 5, 13853.

Lalaouna, D., Carrier, M.C., Semsey, S., Brouard, J.S., Wang, J., Wade, J.T., and Masse, E. 2015. A 3′ external transcribed spacer in a tRNA transcript acts as a sponge for small RNAs to prevent transcriptional noise. Mol. Cell 58, P393–405.

Lewis, N.E., Nagarajan, H., and Palsson, B.O. 2012. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10, 291–305.

Li, Y.F., Lin, Z.Q., Huang, C., Zhang, Y., Wang, Z.W., Tang, Y.J., Chen, T., and Zhao, X.M. 2015. Metabolic engineering of Esche- richia coli using CRISPR-Cas9 meditated genome editing. Metab. Eng. 31, 13–21.

Lim, H.J., Kim, K., Shin, M., Jeong, J.H., Ryu, P.Y., and Choy, H.E. 2015. Effect of promoter-upstream sequence on σ38-dependent stationary phase gene transcription. J. Microbiol. 53, 250–255.

Lin, H., Castro, N.M., Bennett, G.N., and San, K.Y. 2006. Acetyl-coA synthetase overexpression in Escherichia coli demonstrates more efficient acetate assimilation and lower acetate accumulation: a potential tool in metabolic engineering. Appl. Microbiol. Biotech- nol. 71, 870–874.

Liu, R., Bassalo, M.C., Zeitoun, R.I., and Gill, R.T. 2015. Genome scale engineering techniques for metabolic engineering. Metab. Eng. 32, 143–154.

Lu, H., Villada, J.C., and Lee, P.K.H. 2019. Modular metabolic en- gineering for biobased chemical production. Trends Biotechnol. 37, 152–166.

Lv, L., Ren, Y.L., Chen, J.C., Wu, Q., and Chen, G.Q. 2015. Applica- tion of CRISPRi for prokaryotic metabolic engineering involving multiple genes, a case study: controllable P(3HB-co-4HB) biosyn- thesis. Metab. Eng. 29, 160–168.

Makino, T., Skretas, G., and Georgiou, G. 2011. Strain engineering for improved expression of recombinant proteins in bacteria. Microb. Cell Fact. 10, 32.

Małecka, E.M., Stróżecka, J., Sobańska, D., and Olejniczak, M. 2015. Structure of bacterial regulatory RNAs determines their perfor- mance in competition for the chaperone protein Hfq. Biochemistry 54, 1157–1170.

Malla, S., Niraula, N.P., Liou, K., and Sohng, J.K. 2009. Enhancement of doxorubicin production by expression of structural sugar bio- synthesis and glycosyltransferase genes in Streptomyces peucetius.J. Biosci. Bioeng. 108, 92–98.

Malla, S., Niraula, N.P., Liou, K., and Sohng, J.K. 2010. Self-resistance mechanism in Streptomyces peucetius: overexpression of drrA, drrB and drrC for doxorubicin enhancement. Microbiol. Res. 165, 259–267.

Matsoukas, I.G. 2018. Commentary: RNA editing with CRISPR- Cas13. Front. Genet. 9, 134.

Matsumoto, T., Tanaka, T., and Kondo, A. 2017. Engineering meta- bolic pathways in Escherichia coli for constructing a “microbial chassis” for biochemical production. Bioresour. Technol. 245, 1362–1368.

Muller, H.J. 1927. Artificial transmutation of the gene. Science 66, 84–87.

Na, D. and Lee, D. 2010. RBSDesigner: software for designing syn- thetic ribosome binding sites that yields a desired level of pro- tein expression. Bioinformatics 26, 2633–2634.

Na, D., Yoo, S.M., Chung, H., Park, H., Park, J.H., and Lee, S.Y. 2013. Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs. Nat. Biotechnol. 31, 170–174.

Negrete, A., Majdalani, N., Phue, J.N., and Shiloach, J. 2013. Redu- cing acetate excretion from E. coli K-12 by over-expressing the small RNA SgrS. N. Biotechnol. 30, 269–273.

Nielsen, J. and Keasling, J.D. 2016. Engineering cellular metabolism.Cell 164, 1185–1197.

Omotajo, D., Tate, T., Cho, H., and Choudhary, M. 2015. Distribu- tion and diversity of ribosome binding sites in prokaryotic ge- nomes. BMC Genomics 16, 604.

Oude Blenke, E., Evers, M.J., Mastrobattista, E., and van der Oost, J. 2016. CRISPR-Cas9 gene editing: delivery aspects and therapeutic potential. J. Control. Release 244, 139–148.

Qian, Z.G., Xia, X.X., and Lee, S.Y. 2011. Metabolic engineering of Escherichia coli for the production of cadaverine: a five carbon diamine. Biotechnol. Bioeng. 108, 93–103.

Quandt, E.M., Deatherage, D.E., Ellington, A.D., Georgiou, G., and Barrick, J.E. 2014. Recursive genomewide recombination and se- quencing reveals a key refinement step in the evolution of a meta- bolic innovation in Escherichia coli. Proc. Natl. Acad. Sci. USA 111, 2217–2222.

Raghavan, R., Groisman, E.A., and Ochman, H. 2011. Genome-wide detection of novel regulatory RNAs in E. coli. Genome Res. 21, 1487–1497.

Reeve, B., Hargest, T., Gilbert, C., and Ellis, T. 2014. Predicting trans- lation initiation rates for designing synthetic biology. Front. Bio- eng. Biotechnol. 2, 1.

Rhodius, V.A., Mutalik, V.K., and Gross, C.A. 2012. Predicting the strength of up-elements and full-length E. coli σE promoters. Nucleic Acids Res. 40, 2907–2924.

Ronda, C., Pedersen, L.E., Sommer, M.O., and Nielsen, A.T. 2016. CRMAGE: CRISPR optimized mage recombineering. Sci. Rep. 6, 19452.

Sakai, Y., Abe, K., Nakashima, S., Yoshida, W., Ferri, S., Sode, K., and Ikebukuro, K. 2014. Improving the gene-regulation ability of small RNAs by scaffold engineering in Escherichia coli. ACS Synth. Biol. 3, 152–162.

Salis, H.M. 2011. The ribosome binding site calculator. Methods Enzymol. 498, 19–42.

Schu, D.J., Zhang, A., Gottesman, S., and Storz, G. 2015. Alternative Hfq-sRNA interaction modes dictate alternative mRNA recog- nition. EMBO J. 34, 2557–2573.

Seo, S.W., Yang, J.S., Kim, I., Yang, J., Min, B.E., Kim, S., and Jung,G.Y. 2013. Predictive design of mRNA translation initiation re- gion to control prokaryotic translation efficiency. Metab. Eng. 15, 67–74.

Shen, R., Yin, J., Ye, J.W., Xiang, R.J., Ning, Z.Y., Huang, W.Z., and Chen, G.Q. 2018. Promoter engineering for enhanced P(3HB- co-4HB) production by Halomonas bluephagenesis. ACS Synth. Biol. 7, 1897–1906.

Simon, A.J., d’Oelsnitz, S., and Ellington, A.D. 2019. Synthetic evolu- tion. Nat. Biotechnol. 37, 730–743.

Smirnov, A., Forstner, K.U., Holmqvist, E., Otto, A., Gunster, R., Becher, D., Reinhardt, R., and Vogel, J. 2016. Grad-seq guides the discovery of ProQ as a major small RNA-binding protein. Proc. Natl. Acad. Sci. USA 113, 11591–11596.

Sterk, M., Romilly, C., and Wagner, E.G.H. 2018. Unstructured 5′-tails act through ribosome standby to override inhibitory struc- ture at ribosome binding sites. Nucleic Acids Res. 46, 4188–4199. Vervoort, Y., Linares, A.G., Roncoroni, M., Liu, C., Steensels, J., and Verstrepen, K.J. 2017. High-throughput system-wide engineering and screening for microbial biotechnology. Curr. Opin. Bio- technol. 46, 120–125.

Vitreschak, A.G., Rodionov, D.A., Mironov, A.A., and Gelfand, M.S. 2004. Riboswitches: the oldest mechanism for the regulation of gene expression? Trends Genet. 20, 44–50.

Vogel, J. 2009. A rough guide to the non-coding RNA world of Sal- monella. Mol. Microbiol. 71, 1–11.

Wang, H.H., Isaacs, F.J., Carr, P.A., Sun, Z.Z., Xu, G., Forest, C.R., and Church, G.M. 2009. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898.

Wang, H.H., Kim, H., Cong, L., Jeong, J., Bang, D., and Church, G.M. 2012. Genome-scale promoter engineering by coselection MAGE. Nat. Methods 9, 591–593.

Wang, H., La Russa, M., and Qi, L.S. 2016. CRISPR/Cas9 in genome editing and beyond. Annu. Rev. Biochem. 85, 227–264.

Warner, J.R., Reeder, P.J., Karimpour-Fard, A., Woodruff, L.B., and Gill, R.T. 2010. Rapid profiling of a microbial genome using mix- tures of barcoded oligonucleotides. Nat. Biotechnol. 28, 856–862. Weber, T., Charusanti, P., Musiol-Kroll, E.M., Jiang, X., Tong, Y., Kim, H.U., and Lee, S.Y. 2015. Metabolic engineering of anti- biotic factories: new tools for antibiotic production in actinomycetes. Trends Biotechnol. 33, 15–26.

Weller, K. and Recknagel, R.D. 1994. Promoter strength prediction based on occurrence frequencies of consensus patterns. J. Theor. Biol. 171, 355–359.

Xiao, X., Xu, Z.C., Qiu, W.R., Wang, P., Ge, H.T., and Chou, K.C. 2019. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K- tuple nucleotide composition. Genomics 111, 1785–1793.

Xue, C., Zhao, J., Chen, L., Yang, S.T., and Bai, F. 2017. Recent ad- vances and state-of-the-art strategies in strain and process en- gineering for biobutanol production by Clostridium acetobutyli- cum. Biotechnol. Adv. 35, 310–322.

Yan, Q. and Fong, S.S. 2017. Study of in vitro transcriptional bind- ing effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli. J. Biol. Eng. 11, 33.

Zhang, J., Cai, Y., Du, G., Chen, J., Wang, M., and Kang, Z. 2017. Evaluation and application of constitutive promoters for cutinase production by Saccharomyces cerevisiae. J. Microbiol. 55, 538–544. Zhang, Y.X., Perry, K., Vinci, V.A., Powell, K., Stemmer, W.P., and del Cardayre, S.B. 2002. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature 415, 644–646.

Zhou, S., Du, G., Kang, Z., Li, J., Chen, J., Li, H., and Zhou, J. 2017. The application of powerful promoters to enhance gene expre- ssion in industrial microorganisms. World J. Microbiol. Biotechnol. 33, 23.

More information: Timothy M. Wannier et al, Improved bacterial recombineering by parallelized protein discovery, Proceedings of the National Academy of Sciences (2020). DOI: 10.1073/pnas.2001588117


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