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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

The global demand is rising for greener manufacturing processes that are cost-competitive and available in a timely manner. This has led to the development of a series of new tools and integrative platforms enabling rapid engineering of complex phenotypes in industrial microbes. By blending “old classical methods” of strain isolation with “newer approaches” of cell engineering, researchers are demonstrating the ability to stack multiple complex phenotypes in industrial hosts with some level of certainty. Newer tools for dissecting the genotype-phenotype correlation include association analysis (Precision Engineering), multiSCale Analysis of Library Enrichment (SCALE) in competition experiments, whole-genome transcriptional analysis, and proteomics and metabolomics technology. These newer and older tools of metabolic engineering and synthetic biology when combined with recent whole cell engineering approaches like whole genome shuffling, global transciptome machinery engineering, and directed evolutionary engineering, provide a powerful platform for engineering complex phenotypes in industrial strains. This review attempts to highlight and compare these newer tools and approaches with traditional strain isolation procedures as it applies to genome engineering with examples taken from literature.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

Exploiting the metabolic capabilities of prokaryotic and microbial eukaryotic cells such as yeast and other fungi for the commercial production of valuable chemicals has been the hallmark of the biotechnology industry for decades. These products are diverse and find their application in industries such as chemical, pharmaceutical, food, health care, oenology, and biofuel. Production methods utilizing biological systems, either fermentation- or enzyme-based, were traditionally preferred over chemical routes for their inherent ability to convert simple substrates to complex molecules at mild reaction conditions with desired specificity and chemistry. However, recent successful commercial applications of bioprocesses are feasible as a result of the availability of engineered biocatalysts that not only do novel chemistry but also are optimized for economical performance at large scale. The capability for manipulating the biocatalyst to express desired complex traits (phenotypes) in industrial settings is possible as a result of the integration of the tools of “classical” and “modern” strain engineering approaches (18) and the remarkable resilience of microbial cells to such non-natural interventions. Examples of common industrially relevant traits include higher rates of reaction, increase in yields on substrate, tolerance to products and inhibitors, and adaptability to process environments that differ significantly from natural habitats.

Classical Strain Engineering Methodology

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

The “classical” strain development approach relies on and begins with iterative cycles of genetic diversity creation through mutagenesis and/or genetic recombination followed by selection or screening of desired phenotypes (9, 10). A typical mutagenesis program starts by exposing the genetic material of a population of strains to known mutagens either in vitro or in vivo, which results in random damage to the DNA through strand breakage, transversion, addition, deletion, or substitution of bases. The next step in the process involves rescuing the survivors from the mutagenized population by plating on rich medium followed by selection or screening of the survivors for desired phenotype. The process is repeated on improved strains until desired phenotypes are obtained or the program is terminated due to availability of resources and time. Although many classes of mutagens (radiation, UV rays, chemicals, intercalating agents, biological agents) with different modes of action are available, the most favored and widely used is the alkylating agent N-methyl-N' '-nitro-N-nitrosoguanidine (NTG). Once an improved strain is isolated from a mutagenesis program, it is subjected to further iterative cycles of fermentation development and assay to optimize and stabilize the phenotype under scale-up conditions.

Although this approach of strain improvement is quite old and laborious, its long history of success continues to fascinate industrial fermentation researchers in the post-“omics” era. Recent developments in high throughput screening and analytical technologies such as liquid chromatography−mass spectrometry, fluorescence activated cell sorting (FACS), and robotic miniaturization of assays has renewed interest in this classical approach because they enable screening and evaluating large mutant libraries under many different process-like conditions relatively quickly. Furthermore, the long-standing history of successful application of this methodology for introducing new fermentation products has led to its increased acceptability from regulatory perspective compared to newer recombinant DNA-based technologies.

One notable application of this approach is in the field of secondary metabolite production such as antibiotics. Strains producing metabolites in excess of 50 g/L (penicillin) (11) and other secondary metabolites in titers as high as 80 g/L is reported in the literature (9, 12). Zymomonas mobilis strains overproducing fructose polymer, levan, at titers as high as 20 g/L in 24 h have been isolated by NTG mutagenesis followed by selections (13). Classical random mutagenesis followed by selection of desired phenotypes is not limited to the bacterial hosts but is equally prevalent in engineering yeasts and mammalian cells. Historically, using this approach, wine flavors were engineered into yeast by isolating specific amino acid auxotrophic mutants devoid of making the corresponding alcohol (14). Similar strategies to control aroma in wine through selecting mutants devoid of esterase activity, and isolation of astaxanthin hyperproducing mutants of Phaffia yeast by flow cytometry and cell sorting has been reported (15, 16). Engineering of other non-typical phenotypes, for example, the ability to produce flocculating agents in yeast and reduction of byproduct formation such as ethanol during fermentative production of optically pure l-lactic acid by Rhizopus oryzae, using the approach of classical random mutagenesis followed by relevant screenings is documented in the literature (17, 18).

Although classical approaches for strain engineering have their niche, it has the drawback of being inherently a slow laborious process especially for engineering complex phenotypes that are polygenic, i.e., phenotypes that are dependent on multiple coordinated changes at the genetic level. Furthermore, engineering of multiple polygenic phenotypes using classical approaches requires the screening of large combinatorial libraries for successful isolation of desired phenotypes, which is practically impossible to implement. In some cases, the desired traits could be antagonistic to the fitness of the organism and each other, which further compounds the screening or selection process. For example, engineering a simple phenotype that is dependent on three mutations using classical approaches calls for the accumulation of these beneficial mutations in three sequential rounds of mutagenesis and screening. Each round of screening could involve assaying library sizes as high as 105. Some of the above-mentioned limitations of the classical approach are alleviated by the development of recombinant DNA (rDNA) technology, directed evolutionary engineering, metabolic engineering, and systems biology in the past 15 years (8, 19, 2023). These newer approaches enable targeted mutagenesis or manipulation of cellular metabolism to amplify or create the desired phenotype. Identification of genetic targets for rDNA manipulation is achieved by systematic analysis of the physiology of the organism by using a number of experimental and mathematical tools that essentially provide insight into the genotype-phenotype landscape of the host strain. Extensive reviews on this topic are available in the literature, and therefore for brevity this review will only refer to new concepts in some of these areas.

Metabolic Engineering Tools and Approaches

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

Most strain improvement programs are initiated with objectives centered around increasing rates of product formation in a host of choice, maximizing yields on substrate, and maximizing volumetric productivity through high titers of the product. Engineering these types of phenotypes individually into a host is already complex even in the simplest of microbes such as Escherichia coli because of the intricate interaction of central metabolic pathways with the peripheral biosynthetic and other housekeeping pathways. Despite this complexity, the modern tools of metabolic engineering (2427), rDNA technology, and synthetic biology have remarkably enabled targeting of necessary genetic changes to express a desired phenotype. The successful integration of these methods has addressed the even more complex problem of engineering all three (rate, titer, and yield) phenotypes into a single organism.

The tools of rDNA technology, synthetic biology, and directed evolution of enzymes and pathways have advanced the field of constructing phenotypes at a much faster pace as compared with tools for identifying the underlying genetic basis of the phenotypes. Nonetheless, for simple traits involving one or two specific genes, genome sequences and DNA microarrays are closing the gap. Briefly, for well-defined systems where the genetic basis for a given phenotype is adequately mapped, the traditional tools of metabolic engineering combined with the tools of synthetic biology generally deliver the results needed.

Literature is exhaustive with examples demonstrating the efficacy and efficiency of these approaches in producing both natural and unnatural products in a wide variety of fermentative hosts. To highlight a few, amino acids such l-phenylalanine, glutamate, and lysine in E. coli and Corynebacterium glutamicum have been successfully produced in excess of 50−80 g/L (2832). Food additives, such as vitamins and glucosamine, in Bacillus subtilis and E. coli have been produced in excess of 30−100 g/L, and similar titers for organic acids such as citric, lactic, and succinic acid are observed (3339). The success of these metabolic engineering targets was predominantly due to the availability of detailed knowledge about the metabolism of the hosts and the ability to draw phenotype to genotype correlations. Other recent pioneering examples where the modern tools of metabolic engineering resulted in engineering complex phenotypes are the production of 1,3-propanediol in E. coli at commercially relevant titers, synthesis of 3-hydroxypropionic acid (3-HP) or lycopene in E. coli, and fermentation producing artemisimic acid in Saccharomycescerevisiae (4044). 1,3-Propanediol is used as one of the monomers in production polymers such as Sorona, 3-HP has the potential to be a platform chemical building block, and artemisimic acid is a precursor for antimalaria drug artemisin.

The choice of a host strain for commercial production of native or non-native products is a critical one that is influenced by the amenability of the host to the tools of rDNA, synthetic biology, and metabolic engineering approaches. Other factors that influence such decisions are the suitability of the host for commercial fermentation operation and history with regulatory requirements. Metabolic engineering strategies for increasing rate, titer, and yield of a product is context-dependent upon the host metabolism; it is for this reason that many novel pathways are metabolically engineered into well-characterized hosts such E. coli, B.subtilis, and yeasts. Complex molecules such as hydrocortisone, a 27-carbon molecule, are synthesized in S. cerevisiae from ethanol (2-carbon molecule) by the cloning of nine cDNAs and the deletion of four host genes (45, 46). Similar attempts to make taxol intermediates, taxadien-5d-aceoxy-10β-ol, and long-chain polyunsaturated fatty acids in yeast by assembling heterologous pathways have been reported, although with moderate success in improving rates and yields of the desired product (titers are in mg/L) (4751). Production of plant-specific polyphenols such as naringenin and resveratrol in E. coli and yeast has been successful through engineering of heterologous pathways (5257). Although the titers reported are in milligrams per liter, it is conceivable that once the biosynthetic pathway is assembled in a process and genetically friendly host such as E. coli or yeast, tools of metabolic engineering and evolutionary engineering can be applied to improve titers in a targeted fashion.

Another class of products that are traditionally improved in their natural hosts using classical techniques is secondary metabolites and other small molecules in Streptomyces and fungi (9, 58). Due to the complex nature of the biosynthetic machinery used for the synthesis of these classes of molecules, the tools of metabolic engineering had very little impact on them until very recently. For example, the macrocyclic core of the antibiotic erythromycin, 6-deoxyerthronolide B (6dEB), is a complex product that has been produced in E. coli via multiple changes to the genome and introduction of heterologous genes from a variety of sources (59, 60). Recently, metabolic engineering of the glycolytic pathway in Streptomyces calvuligerus by inactivation of two genes (gap1 and gap2) encoding for glyceradehyde-3-phosphate dehydrogenases resulted in increased clavulanic acid production by channeling more precursor flux (61). To overcome the limitation of classical strain improvement approaches for producing novel secondary metabolites, the demand for which has been rising exponentially as a result of the speed at which new genes are cloned, researchers have reported engineering of industrially optimized host platforms such as Saccharopolyspora erythraea and Streptomyces fradiae to produce these new compounds relatively rapidly (62). These secondary metabolite production platforms by analogy mimic E. coli or the yeast platforms used for primary metabolite production because of the enormous data available about their metabolism and amenability to metabolic engineering analysis.1

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Figure Figure 1.. Typical roadmap for engineering complex phenotypes in industrial strains.

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Analysis at Genomic and Population Scale

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

In the absence of detailed knowledge about the biosynthetic pathways and/or metabolism of the host strain, the use of metabolic engineering tools is limited. Although the theoretical and analytical aspect of new metabolic engineering tools such as 13C tracer studies, etc. have the appropriate framework to probe and understand the metabolism of ill-characterized strains, it is time-consuming and not guaranteed to develop a strong phenotype−genotype correlation to identify genetic targets for manipulation (6365). Furthermore, the available classical strain improvement approaches of random mutagenesis and screening/selection are laborious and time-consuming to achieve commercially relevant product titers. The introduction of new tools to analyze strains at the genomic and population scale such as association analysis (Precision Engineering), and parallel monitoring of mutant population libraries in enrichment experiments (SCALE) using microarrays have enabled identifying multiple targets simultaneously at a faster pace. Typically, these targets are further manipulated using synthetic biology, metabolic engineering, or evolutionary approaches to engineer a desired complex phenotype.

Whole Genome Microarrays and Transcriptome Profiling. The availability of complete genome sequence, DNA microarray technology (6670), and associated data analysis tools enables faster mapping of the phenotype−genotype correlation for a given host. The generation of this mapping provides new insights to identify both local and global targets of metabolic engineering. In one example integration of transcriptional and metabolic profiles through association analysis enabled engineering a fungal strain with 2-fold improvement in lovastatin titers (1). In this approach, a genomic fragment microarray for Aspergillus terreus consisting of 21,000 elements was used to generate the transcriptional profiles for a set of 21 strains that were producing either more or less of the desired metabolite, lovastatin. Association analysis of both sets of data, transcriptional and metabolite profiles, led to the identification of targets for further metabolic engineering. Microarray-based transcriptome analysis in engineering solvent (butanol) tolerance (71, 72) and identification of genetic determinants contributing to a variety of phenotypes is reported in the literature (68, 7382).

Identification of genes contributing to a desired phenotype by genome-wide transposon mutagenesis is classically implemented by generating an insertional library, followed by screen or selection, and then identifying the site of insertion. Finding the site of transposon insertion can be laborious, as each selected clone has to be analyzed individually using gene-specific PCR. Recent methodologies for analyzing insertional libraries using whole genomic microarrays enables analysis and quantitation of an entire library population in parallel by monitoring dynamically the enrichment of the population under various selective growth conditions (3, 83). In one case, an E. coli K-12 Tn5 insertion library was subjected to various growth conditions (LB vs M9 minimal medium) and mutants enriched were analyzed for site of insertion using whole-genome oligonucleotide microarrays. As a result of the parallel mode of monitoring a population using this method, in a single enrichment experiment the effect of different gene knockouts on the desired fitness could be evaluated simultaneously, saving significant time of analysis. In the future, as more genome sequences are available, these high-resolution whole-genome microarrays will play a critical role in identifying and quantifying in parallel the contribution of multiple mutant loci toward the expression of desired polygenic phenotype(s). Output targets from these types of experiments can be utilized by rDNA or synthetic biology for further engineering such that the overall fitness of the host is optimized.

SCALE (MultiSCale Analysis of Library Enrichment). In addition to screening and analysis of insertional knockout libraries for identifying targets for genetic manipulation, oftentimes analysis of genomic libraries in surrogate hosts results in getting a handle on a phenotype-enhancing genetic target. Traditional approaches such as screening of genomic libraries under selective conditions to isolate phenotype-specific genes for further targeted metabolic engineering has proven successful, although such an approach is strictly limited to phenotypes that respond to overexpression of a single gene or an operon. Once the phenotype-conferring gene fragment is isolated, further subcloning is required to identify the protein products conferring the desired trait. This process is time-consuming as each isolated genomic fragment is analyzed by subcloning individually. Therefore, isolating all genomic determinants and evaluating combinations contributing to the phenotype using traditional methods is going to be resource-intensive if not impossible. Newer genome-wide methods in which populations expressing genomic libraries of different size DNA fragments (or SCALEs) in plasmids are grown competitively under selective conditions and analyzed for plasmid DNA enrichment using hybridization to genomic microarrays, and subsequent mathematical analysis of microarray signals enables parallel analysis of the entire library population (2). In this approach, in a single experiment, the impact of overexpression of different loci of the genome on the phenotypic fitness of the strain could be quantified. A practical advantage of this methodology is the ability to isolate multiple trait-conferring loci for further analysis in a single experiment.

This approach has been applied to identify genes enriched and conferring growth advantage to E. coli in the presence of the antimicrobial agent Pine-Sol (70). Recently, a similar approach was used to identify solvent-tolerant genes in Clostridium acetobutylicum. Mechanisms for solvent tolerance include alteration of membrane-lipid headgroups, overexpression of stress proteins such as groESL, and active pumping out of the solvent molecules (8487). In the case of butanol toxicity in C. acetobutylicum, overexpression of groESL and knocking out of butyrate kinase resulted in increased butanol production, suggesting the complexity of the phenotype (88, 89). Often times engineering solvent tolerance leads to compromise in other desirable traits such as decreased growth rate and/or lower glucose uptake rates. In this study, a modified version of SCALE was used in which a genomic library covering 73% of the genome (3 Kb fragments) was monitored for specific gene enrichments in growth assays challenged with different levels of butanol (0−1.5% v/v) (90). In a single experiment 16 genes were isolated that were significantly enriched in response to butanol challenge. Further characterization of two such genes demonstrated a 13−81% increase in relative butanol tolerance over control strains carrying the parental plasmid.

Metagenomics. Engineering of complex phenotypes, especially stacking of multiple phenotypes, in an industrial host sometimes requires introduction of new genetic material from sources that are either harnessed from nature and/or artificially engineered in the laboratory. Access to genomic diversity in nature has been predominantly through culturable microorganisms. Although it is well-known that a vast majority of microbes in nature are unculturable, access to their genomic potential is being made possible through new innovations in metagenomics (91). Metagenomics is the field of research that enables application of genomics-based tools to uncultivated microorganisms. Briefly, the metagenomics approach involves isolation of total genomic DNA from environmental communities, purification of isolated DNA, and generation of large insert libraries using cosmids and bacterial artificial chromosomes, followed by either function-based or sequence-based screening technologies to isolate or identify genetic loci contributing to a desired phenotype. Refinement and continuous improvement in each of the above steps of the metagenomic methodology is enabling researchers to access not only genes but also operons encoding biosynthesis of complex molecules from under represented uncultured microbes in an environmental sample. Because metagenomic libraries are large, novel methods have been developed to focus or enrich the library first for a desired phenotype before screening (92). Common methodologies for enrichment involves physical separation of the symbiont microbe before extraction of DNA, differential centrifugation to segregate genomes based on GC content, enrichment of metabolically active cells by incorporation of bromodeoxyuridine (BrdU) into DNA followed by imunocapture, or enrichment of genomic DNA from a subset of an environmental population that has been preselected for a given phenotype. One of the common problems of screening metagenomic libraries is the accessibility frequency of the clone and the ability of the clones to express in surrogate hosts such as E. coli. Because different host strains have unique regulatory circuits and physiology, the outcome of an activity screen could be different depending on the choice of surrogate host. E. coli, Streptomyces lividans, Pseudomonas putida, and Rhizobium leguminosarium have been used as surrogate hosts for screening metagenomic libraries (93). Recently, high-density DNA microarrays made from genomes of bacterial isolates from the environment, reference strains, and environmental DNA have been used for metagenomic profiling that enables rapid characterization of DNA from metagenomic libraries and DNA from strains that have not been isolated in pure cultures (94).

Most of the biotechnological applications of metagenomics have been in the area of isolating useful enzymes, antibiotics, and pharamaceutical molecules from environmental libraries. Lipolytic enzymes, polysaccharide enzymes, enzymes involved in the vitamin biosynthesis pathway, nitrilases, nitrile hydratases, amidases and glycerol hydratases have been isolated from metagenomic libraries through activity screens (91). Although metagenomics research to date has concentrated on the characterization of individual clones, it is conceivable that future applications will derive from our ability to enrich, characterize, isolate, and assemble genetic targets from consortia of strains that express complex phenotypes as a whole system but not as individual members.

In contrast to the above tools used for identification of multiple targets for further metabolic or synthetic engineering, newer whole genome scale engineering approaches such global transcriptional machinery engineering (gTME), directed evolution of whole genomes, and whole genome shuffling are enabling direct engineering of complex phenotypes by modulating and manipulating multiple phenotype conferring loci's in the host of choice.

Whole Genome Engineering Approaches

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

Industrial fermentations often use substrate feedstocks and nutrients that are complex and not well characterized. The economic viability of the process is dependent on the ability of the host to tolerate not only product and substrate inhibition but also the presence of other inhibitors that are byproducts or impurities in the medium components. Rational metabolic engineering of these tolerance phenotypes into strains in addition to meeting the desired criteria of rate, titer, and yield is a daunting task since generating the phenotype−genotype correlation for each desired phenotype and its interaction with each other is laborious and data-intensive. Some of the product inhibition phenotypes are monogenic like the classical feedback inhibition at an enzyme target. These types of phenotypes are successfully engineered by isolating mutants resistant to an analog of the product or overexpression of certain genes or loci of the genome (95). Availability of high-throughput screens and microarrays for analyzing mutants generated in directed evolution experiments or in classical mutagenesis libraries have resulted in isolation of strains with marginal improvements of the desired phenotype, especially monogenic phenotypes. However, many of the other tolerance phenotypes such as tolerance to ethanol or butanol, tolerance to inhibitors such as acetate in saccharified cellulosic biomass, or low pH and high temperature are polygenic that involve distributed genes in the genome. Engineering of such polygenic distributed phenotypes has been demonstrated by the newer whole genome wide engineering strategies.

Evolutionary Engineering and ScreeningTechnologies. Advances in screening technologies and automation in handling small amounts of liquids under aseptic conditions have increased the throughput of the classical tools of directed evolution such as serial dilution enrichments and chemostat selections. Using a combination of targeted knock-outs followed by directed evolution under growth-selective conditions, E. coli strains producing 1.2 M d-lactate from 12% (w/v) glucose have been isolated (96, 97). A similar strategy was used for converting S.cerevisiae to utilize xylose by first assembling the xylose pathway followed by prolonged evolution under selective conditions (98). Advances in application of FACS in high-throughput format has enabled much more efficient searching of the genetic diversity created in random or combinatorial mutagenesis libraries, enabling isolation of strains with phenotypes for which there is no good selection. These strategies were successfully developed and applied in screening libraries of Synechosystis sp. PCC6803 and E. coli overproducing poly-3-hydroxybutyrate (99). Application of multi-parameter flow cytometry for studying formation of inclusion bodies (IB) in high-density E. coli fermentations enables quick optimization of IB formation (100, 101). This method can be adapted to screen mutant host libraries generated by either classical random mutagenesis or directed evolution for increased IB formation in high-throughput format. Another application of FACS demonstrated the screening of a genomic library of Acinetobacter calcoacetius in E.coli for expression of a surface antigen (102).

gTME. gTME uses a mutant transcriptional factor that perturbs the whole transcriptome in subtle ways that enables expression of polygenic phenotypes (103). Polygenic phenotypes are typically dependent on loci that are distributed in the entire genome. This approach is used in overproduction of lycopene in E. coli and the engineering of ethanol tolerance in yeast (103, 104). Similarly, engineering of global regulatory genes could potentially lead to subtle balancing of pathway networks to maximize product titer or yield without significant genetic intervention at the local enzyme or pathway level. For example, deleting the global carbon storage regulator increases the flux into phenylalanine biosynthesis in E. coli by affecting 25 different enzymes and/or genes of carbon metabolism (105). An extension of this approach would be to use a library of mutant global regulators in the desired host and screen/select for desired phenotypes.

The genetic basis for ethanol tolerance, a phenotype of critical importance for the cellulosic ethanol technology, is complex and potentially involves 40−60 genes. gTME has successfully isolated ethanol-tolerant E. coli and yeast strains by selecting a population library harboring mutant transcriptional factors in the presence of increasing levels of ethanol. E. coli strains tolerant to 60 g/L of ethanol have been isolated, and yeast strains with a 70% improvement in volumetric productivity have been reported (103, 104, 106). Although not impossible, accessing these types of complex phenotypes using random mutagenesis and selection would be time-consuming. gTME has also been successfully applied to stack multiple phenotypes in a single host such as tolerance to ethanol and SDS in E. coli (103).

WGS (Whole Genome Shuffling). Genome shuffling is a relatively new tool, which has enabled improving titers of products synthesized using complex pathways in Streptomyces, Lactobacillus, Sphingobium, and E. coli. In all these cases, genetic diversity was generated using classical mutagenesis techniques such as NTG, UV, and/or chemostat enrichments, followed by recursive protoplast fusion of mutant populations and screening/or selection of the desired phenotypes. Recursive protoplast fusion enables multiparental recombination in each generation in contrast to classical breeding approaches that allow for two parents mating, thus presenting a combinatorial library for selection or screening in each generation (7). Once a beneficial mutation is accumulated in a host background, recombination due to protoplast fusion between multiple parents evaluates the synergistic effect of that mutation in the background of all other beneficial traits already present in the population without having to generate all possible combinations experimentally. Similarly, non-beneficial mutations are weeded out, rendering greater fitness to the host, in the same manner as backcrossing in a classical breeding program.

Tolerance to low pH and acid stress, like solvent tolerance, are complex phenotypes that involve at least 18 different loci in the case of Lactococcus lactis. In E. coli, the acid response results in induction of several loci, suggesting that a targeted approach of metabolic engineering will be laborious. Genome shuffling via recursive protoplast fusion was used to isolate Lactobacillus strains with increased tolerance to low pH (3.8), which has not been reported to date using random mutagenesis/selection or targeted metabolic engineering approaches (7, 107). In these studies, isolated pH-tolerant mutants were not analyzed to uncover the underlying genotypic makeup presumably because of the laborious nature of the tools available. Lack of phenotype−genotype analysis of these low-pH-tolerant mutants rules out the potential for targeted local optimization using the tools of metabolic engineering. However, recent genome-wide approaches to characterize mutant populations in parallel using whole-genome microarrays and/or superimposing a gTME-based approach could complement above genome-shuffling strategies to semi rationally improve desirable traits on a faster time scale.

Volumetric productivity of lactic acid was increased 3.1-fold in Lactobacillusrhamnosus in just three rounds of genome shuffling via recursive protoplast fusion (107). Improvement of titers of antibiotic tylosin in Streptomyces fradiae and organic acid hydroxycitric acid in Streptomyces sp. U121 via genome shuffling has been recently demonstrated (6, 108). The degradation of pentachlorophenol (PCP) has also been successfully improved in Sphingobium chlorophenolicum ATCC 39723 using genome shuffling approaches (109).

Above highlighted applications of genome shuffling via recursive protoplast fusion reiterates the power of this technology in engineering complex phenotypes in ill-characterized hosts, which are not mature for metabolic and/or synthetic biology approaches. Although the above format of genome engineering via genome shuffling is advantageous for global optimization of phenotypes, it has several drawbacks. Genome shuffling via recursive protoplasting generates huge combinatorial libraries that are difficult to evaluate in screens in the absence of selections. In the case of hydroxycitric acid production, selective screening strategies by inclusion of analog trans-epoxyaconitic acid (EAA) in the regeneration medium enabled weeding out of non-fused protoplast from the library, thus allowing screening that is more effective. Protoplast fusion of two parental yeast strains to generate new traits for the wine yeast is an old technology, whose major limitation is the stability of the new progeny as the genotype of the parental strains diverge (110113). Genome shuffling of yeast via recursive protoplast fusion of multiple parents is yet to be demonstrated.

Another format of genome breeding where beneficial mutations are reconstituted into a single host to express dual complex phenotypes is the development of a 40 °C fermentation process for lysine overproduction (31, 114). Since fermentations operating at lower temperatures require more cooling capacity, especially during the summer months, there is incentive to develop thermotolerant hosts for production of chemicals such as lysine. However, readily available thermotolerant hosts such as Bacillus licheniformis, Bacillus methanolicus, and Corynebacteriumthermoaminogenes have limited efficiency in improving lysine production. Commercial l-lysine producers such as C. glutamicum, which are typically isolated via multiple rounds of mutagenesis and selection, produce lysine in very high titers (100 g/L) (114). However, often these strains are compromised on other traits such as growth rate, robustness, or sugar consumption rates as a result of the accumulation of undesirable mutations. This scenario is often the case in many industrial hosts where random mutagenesis and selection over the years improves the selected phenotype but at the cost of other relevant traits compared to the wild type. Comparative genomics based on sequence and biochemical information between the mutant lysine hyperproducer and the wild type enabled identifying the beneficial mutations that were engineered into the wild type by allele exchange. Sequentially accumulating the relevant mutations in the wild-type background resulted in a strain that could produce lysine in high titers at 40 °C, a temperature range that was not conducive to the original mutant. An alternative approach to regain phenotypes lost as a result of deleterious mutations is backcrossing the mutant with the wild-type strain via recursive protoplast fusion or isolating all the loci of the wild-type genome that rescues the lost phenotype(s). A practical approach for isolating all the genetic determinants contributing to the desired fitness in a single experiment is implementing a whole-genomic DNA microarray-based enrichment experiment that characterizes genomic libraries in parallel, as in SCALE.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes

Demand for rapid engineering of multiple complex phenotypes into a single production host background will increase in the next several years as fermentation-based products compete with chemicals derived from oil, especially in the area of biofuels. In addition to meeting the economic targets of desired rate, titer, and yield, these biocatalysts will also have to stack other process relevant phenotypes such as tolerance to products, impurities, sensitivity to feedstock variability, resistance to phages and robustness to process upsets. In complementation with classical approaches of strain development via random mutagenesis and selection, the newer approaches and tools of genome-wide engineering such as genome shuffling, gTME, and high-throughput characterization of genomic libraries in parallel through whole-genome microarrays are providing powerful platforms for identifying and engineering multiple complex phenotypes. The age-old question of whether to engineer a desired phenotype-A to a host that already has another desired phenotype-B or vice versa continues to be a critical decision in any strain development program. As more and more industrial hosts are sequenced and method for engineering complex traits in them is broadened, the application of fermentation-based processes is going to expand rapidly.

Future innovations in mimicking the principles of natural whole genome evolution in a laboratory setting by similar analogy to the evolution of enzymes and proteins will undoubtedly fast-track strain improvement programs. In recent years laboratory methods for evolving genomes has relied predominantly on mutations (NTG mutagenesis) and homologous recombination (genome shuffling), whereas the feasibility for exploiting horizontal gene transfer (HGT) mechanisms such as transformation, conjugation, transduction, and non-homologous recombination-based events such as transposition for engineering complex phenotypes needs to be evaluated in much more depth and ingenuity (115, 116). Recent reports on the development of a method for creation of a novel xenobiotic gene (activity for lindane degradation) by metagenomic (environmental DNA) DNA shuffling and access to metagenomic gene cassettes that are a diverse source of material for bacterial evolution reiterate the potential of using HGT for genome evolution (117119). Examples where complex phenotypes such as phage resistance and production of a metabolite (nisin) could be transferred between different species of Lactococci using conjugation highlights their utility in the dairy industry as a non-rDNA whole genome engineering tool (120, 121). Recent interests in the area of transportation biofuels, particularly ethanol and butanol, from diverse sources of cellulosic biomass will accelerate development of some of these newer genome-scale engineering approaches in order to engineer multiple complex phenotypes into the biocatalyst in a realistic time frame.

References and Notes

  1. Top of page
  2. Abstract
  3. Introduction
  4. Classical Strain Engineering Methodology
  5. Metabolic Engineering Tools and Approaches
  6. Analysis at Genomic and Population Scale
  7. Whole Genome Engineering Approaches
  8. Discussion
  9. Acknowledgements
  10. References and Notes
  • 1
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