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Expanding the metabolic engineering toolbox: more options to engineer cells
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TRENDS in Biotechnology
Vol.25 No.3
Expanding the metabolic engineering toolbox: more options to engineer cells Keith E. Tyo, Hal S. Alper and Gregory N. Stephanopoulos Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge, MA 02139, USA
Metabolic engineering exploits an integrated, systems-level approach for optimizing a desired cellular property or phenotype; and great strides have been made within this scope and context during the past fifteen years. However, due to limitations in the concepts and techniques, these have relied on a focused, pathway-oriented view. Recent advances in ‘omics’ technologies and computational systems biology have brought the foundational systems approach of metabolic engineering into focus. At the same time, protein engineering and synthetic biology have expanded the breadth and precision of the methods available to metabolic engineers to improve strain properties. Examples are presented that illustrate this broader perspective of tools and concepts, including a recent approach for global transcriptional machinery engineering (gTME), which has demonstrated the ability to elicit multigenic transcriptional changes that have improved phenotypes compared with single-gene perturbations. Introduction Some people believe that cells contain the answer to many of the problems of the world. A healthy body, a cure for disease, the answer to our energy problems, production of pharmaceuticals and much more are promised in this new ‘century of biology’ [1]. However, the path to turn these visions into reality is not immediately obvious. Charged with such a mission, both engineers and scientists are faced with finding ways to remodel highly interconnected cellular networks to add properties that are often orthogonal to its design for survival. Established nearly fifteen years ago, metabolic engineering (ME) is a systematic approach to the problem of remodeling and reconfiguring the many pieces of the cell through careful understanding and measurement of how small changes affect the network as a whole. ME is a systems approach to engineering a cellular phenotype. Biological molecules are highly interconnected in robust networks, which are optimized for survival in nature. Unlike molecule-centric approaches, which focus on only the final product-forming reaction, ME emphasizes the metabolic pathway in its entirety. In this regard, a key contribution of ME is the advancement of the concept that the production of a molecule (such as ethanol) is a property of the ethanol biosynthetic pathway, just as more complex phenotypes, such as ethanol tolerance, are a property of the cell in its entirety. As such, improvements Corresponding author: Stephanopoulos, G.N. ([email protected]). Available online 24 January 2007. www.sciencedirect.com
to these properties are possible through the modification of multiple genes, often simultaneously. Genomic sequencing efforts have expanded the initial focus from local metabolic pathways to global gene targets, such as competing pathways, redox balancing and regulatory elements that might not be obvious from considering just the product-forming biochemical pathway [2,3]. By making changes at these higher regulatory levels, the cellular network can be reprogrammed to give the desired phenotype. Many of the tools required to analyze and reprogram cells have been developed as part of the core expertise of ME, particularly for microorganisms. However, advances in other complementary biological disciplines, specifically ‘omics’ technologies, computational systems biology, protein engineering and synthetic biology, stand to contribute greatly to our ability to engineer the cell by improving the means to identify and make perturbations at will. In this review, we will discuss the advances in the engineering of organisms for the production of a desired molecule, as well as how the complementary disciplines mentioned above could potentially contribute useful concepts and technology to ME. Recent advances in metabolic engineering In general, ME has been best implemented for producing molecules that are difficult to produce by other methods (e.g. organic synthesis) owing to chirality or the complexity of their organic structure. However, as petroleum supply issues become more problematic and social awareness of the environmental impact of petroleum use increases, the application of metabolic engineering to the bio-production of commodity chemicals is coming to the forefront. Biocatalyst processes typically rely on renewable feedstocks that do not add additional carbon dioxide to the atmosphere, or require toxic solvents, and operate at near ambient conditions. Although these processes can suffer from low yields or titers, ME can often substantially improve the processes. Recent advances have been spurred on by more-comprehensive genetic tools and increased cellular knowledge of microorganisms. For example, the non-mevalonate pathway of Escherichia coli has been engineered to produce lycopene by identifying distal gene targets using flux balance analysis and screening transposon libraries [3,4]. The mevalonate pathway has been incorporated and engineered in E. coli and Saccharomyces cerevisiae to produce amorphadiene and artemisimic acid, respectively, which are precursors of the anti-malaria drug artemisin [5,6]. The need to produce ethanol from cellulosic feed
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stocks has led to extensive engineering of xylose usage in S. cerevisiae [7,8]. Additionally, several groups have modified pathways from higher plants for the production of natural and non-natural flavonoids in S. cerevisiae and E. coli [9,10]. Finally, organisms have been engineered for high yields of the industrial chemical-intermediates succinic acid [11], 1,3-propanediol [12], and 3-hydroxypropionic acid [13]. It is important to note that the success of these projects hinged on elucidating the metabolic network, modifying the proteins, controlling the expression of genes, and implementing high-throughput screening. Hence, the power of the systems approach to ME has thus far been limited by the basic knowledge and technologies affecting these four key areas. As such, the engineering of higher systems, including mammalian systems, is limited by both the tools and systems available for their identification. Despite this fact, recent advances support the premise that ME will certainly affect and enhance our capacity to engineer these systems as well. For example, Chinese hamster ovary (CHO) cell culture viability has been improved by altering the levels of proto-oncogenes, cell cycle control genes, growth factor genes and anti-apoptotic genes [14–16]. Insect cell lines, yeast and fungi are alternative candidates for producing glycosylated therapeutic proteins that were previously only made in mammalian hosts, based on advantages in productivity and consistency in protein glycosylation structures [17–19]. These ‘humanized’ hosts are created by the introduction of mammalian glycosylation enzymes, and the modifications made by the enzymes enable them to work properly in the new host. Important contributions have been made in identifying altered mammalian enzymes that are active in the new recombinant host by protein screening [20]. Most of these remarkable accomplishments in higher organisms have used approaches that are molecule-based or achieved through local pathway engineering. Although global approaches have been developed further in some well-studied model microbial systems, these are still limited by the need for improved measurement and understanding of cellular networks. We foresee that approaches such as the introduction of new pathways into organisms with high metabolic activity, such as E. coli, or increasing the specific activity of rate-limiting enzymes will continue unabated in the future and will enable many industrial precursors and therapeutic molecules to be made at reduced production costs. At the same time, endeavors in engineering cellular systems will greatly benefit from the expanded systems predicted for ME, along with advances in related fields of study. Specifically, the latter could provide engineers and scientists with an unprecedented capacity to modulate the various components of cellular networks. We are referring, in particular, to the advances in omics technologies, computational systems biology, protein engineering, and synthetic biology that raise exciting prospects for helping to realize the long-standing goals of ME of strain improvement and cell engineering (Figure 1). Omics technologies The increased capacity to measure and catalogue both cellular components and their interactions through www.sciencedirect.com
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advances in omics technologies have provided an expanded view of the cell. The genomics era ushered in high-throughput genetic analysis of both well-studied and newly isolated cellular systems. This information provided us with a list of parts in the cell, and comparing the components between two organisms (through a comparative genomics approach) has been successful in identifying targets in the pathways [11]. Ultimately, this information can be useful for both the identification of novel catalytic activities as well as using metagenomic discoveries for identifying bottlenecks in metabolic networks [21]. Beyond genomics, omics technologies, such as protein measurement using MS [22], high-throughput metabolomics [23], ChIP-chip and yeast two-hybrid systems, enable more accurate and sensitive measurements of cellular components, even at the single cell level, which accurately define the state of the cell and further refine and expand our understanding. Cellwide metabolic fluxes are often missing in the list of measured cellular parameters. Although their estimation is not as straightforward as other measurements – they require sophisticated isotopic experiments and computational algorithms [24–29] – fluxes embody the most direct measurement of how the substrate is being used by the organism and perhaps the most relevant information from a chemical production standpoint. As such, they form an indispensable element of the collection of omics variables. This large collection of measurements or ‘cellular biometrics’ provides a holistic characterization of the cell. The challenge of interpreting all this data is still a formidable task and has become the impetus for systems biology research. In simple analyses, these biometrics can either be mined to identify biomarkers or used in a comparative manner to infer the metabolic capacities of a newly identified organism [30]. Beyond data generation, new advances in metabolomic analytical methods are still required to expand our capacity to quantify, in an exhaustive manner, all the metabolites in a cell. ME has had success in using techniques such as the investigation of flux through isotopomer analysis and metabolite profiling; however, these are often limited to central carbon-metabolism pathways, owing to the difficulty in measuring other metabolites. Comprehensive cellular biometrics can complement more traditional flux measurements, to provide an unmatched amount of information of the cellular state. Omics approaches can, in all, provide ME with a more complete understanding of the cell in various environments. This new level of information can be exploited for strain improvement. Computational systems biology The integration and analysis of omics datasets in the context of a network is a central tenet of systems biology research. Ultimately, this area of research most closely resembles the integrated, systemic approach that has been applied by metabolic engineers for many years in the analysis and optimization of cellular systems [31]. The creation of a computational framework for the true automated integration of large datasets into cellular interaction networks [32] should lead to automated methods for the rapid identification of genetic targets from these
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Figure 1. Metabolic engineering stands to gain significantly from advances in complementary biological fields. Omics technologies and computational systems biology can provide large amounts of data about a cellular state and the means by which to analyze it, whereas protein engineering and synthetic biology can provide toolsets for new ways to manipulate a cell to improve the cellular properties. These four fields have a unique set of expertise that could be applied to further metabolic engineering analysis and implementation.
cellular biometric datasets. Currently, this snapshot of a cell is visualized and analyzed only in part; it should be expanded by making use of not only transcriptional data but also protein, metabolite and flux data, which has always been at the heart of ME analysis. Finally, computational systems biology research focuses on the reconstruction of these cellular networks with the ultimate goal of advancing cellular models in silico. Although these models are being developed for simpler organisms, such as bacteria and yeast [33,34], they can be expanded to higher cellular systems [35]. An E. coli metabolic model has recently been successful in predicting a triple gene knockout for improving lycopene production that would have been intractable to identify experimentally [4]. A great contribution to ME can be made by integrating new genomic data (e.g. from a newly sequenced organism) in an automated manner to reconstruct pathways [36]. Knowing the pathways and metabolic capabilities of a newly sequenced organism will expedite the process of selecting the optimal host strain for a given biocatalysis www.sciencedirect.com
goal. The collective advances in omics technologies and computational systems biology enhance the global systems focus of metabolic and cellular engineering. The most valuable contribution of computational systems biology to ME would be cellular models with increasing kinetic and regulatory content, to facilitate the rational selection of gene targets. Protein engineering After global analysis of the cell, local targets might need fine-tuning of their cellular function. This usually involves the modulation of smaller components such as protein functionality and gene expression. Methods from protein engineering are important to achieve this. To date, much of the effort of protein engineering has focused on developing computational or combinatorial methods that efficiently probe the protein sequence, to identify potential mutants with superior properties [37,38]. In this area, adapting and improving proteins for alternative cellular hosts brings the prospect of unique ME opportunities through
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optimizing heterologous pathways, such as flavonoids and amorphadiene in E. coli [9,39]. Because proteins are functioning cellular components, it becomes obvious that engineering their performance is important when constructing functioning pathways with desirable properties in various host cells. Frequently, rate-limiting steps along a pathway are relieved by increasing the amount of enzyme through gene overexpression; however, the concept of rate-limiting steps is transient, and control is ultimately distributed across a pathway: as one bottleneck is relieved, another step becomes limiting. Particularly in large pathways, cellular resources as well as physical space restrict the overexpression of all the enzymes in a pathway. Hence, it would be useful to perform protein-level pathway engineering, in which specific catalytic rates are altered to enable lower concentrations of the more potent proteins to perform as well, enzymatically, as strong overexpression of the slower enzymes would. Furthermore, protein engineering can expand the range of products through the design and discovery of novel catalytic functions by de novo design and selection, as has been illustrated in the cytochrome P450 family [40]. Beyond catalytic functions, the field of protein engineering has been successful in engineering non-catalytic functions, such as localization, allosteric activation and binding affinity, in proteins [5,41,42]. In general, the engineering of proteins for optimal function in a cellular context can be of paramount importance to ME, particularly in the transfer of pathways across species and drastically amplifying the capacity of pathways by engineering more active or differentially regulated individual enzymes. Synthetic biology Recent developments have drastically expanded the capacity to synthesize, chemically, large fragments of DNA virtually error-free [43]. This technology has fostered a variety of activities, all programmed at the DNA level, aimed at: (i) the design and synthesis of novel biomolecules; (ii) the construction of individual elements (e.g. sensors, toggle switches) of integrated genetic networks (or circuits) with a variety of dynamic responses; and (iii) combining these components into metabolic networks for the synthesis of products. Although the concept of designing genetic circuits and their dynamic responses is not new [44], the enhanced capability to synthesize DNA fragments has important implications in the construction of novel response systems at both the transcriptional and metabolic levels in many types of cells [45–49]. Essentially, the engineering efforts of synthetic biology mirror the goals of protein engineering, but at the DNA level. As such, advances from synthetic biology can advance the field of ME by designing new methods for engineering genetic control. Libraries of functional promoters can be used to control the expression of a single gene [50]. Beyond a single gene, it can be envisioned how a phenotype can be controlled through the use of designed genetic circuits, which can impart toggle switch, oscillatory or other responses. Finally, DNA synthesis technologies can be used in the construction of synthetic pathways, which can be optimized for codon usage in the host and the arrangement www.sciencedirect.com
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of genes into operons. Together, protein engineering and synthetic biology provide tools for the precise manipulation of discreet pieces of cellular machinery in the quest for a desired phenotype. These would enable ME to design product pathways and fermentation approaches from the genetic level. Global transcription machinery engineering Collectively, advances in the above fields will provide support and expand the strengths and multiple facets of metabolic and cellular engineering. Although omics technologies and computational systems biology help us understand the cell on a large scale, and protein engineering and synthetic biology help us manipulate the cell on a local scale, it is known that multiple, distal genetic changes might be important to elicit a desired phenotype. However, random mutagenesis approaches in finding these genetic changes have either been single-function, where perturbations are made in one area of the genome at a time and easily identified (i.e. transposon mutagenesis or library overexpression), or spontaneous mutagenesis that elicits many point mutations throughout the genome, which cannot be easily identified or transferred to other strains. Recently, a tool termed global transcription machinery engineering (gTME) was created that enables broad perturbations of the whole transcriptome through the modification of the proteins responsible for orchestrating transcription [50]. This methodology permits changes in the expression of many distal genes, and it can easily be linked back to a single transcriptional protein. Specifically, it is possible to engineer the sigma factors of E. coli and TFIID components in S. cerevisiae to elicit a multigenic reprogramming of the transcriptome. In doing so, several proof-of-concept studies have been obtained, including increasing cellular tolerances (including increased ethanol tolerance) [51], metabolic overproduction (including lycopene production) and multiple phenotypes (including multiple tolerance phenotypes) [52]. This approach is purely generic, because in all types of cellular system a subset of proteins is responsible for coordinating the global transcriptomes. The development of such tools can enable the translation of ME goals into other organisms and systems. Library screening Combinatorial library approaches, whether from tools such as gTME or libraries of enzymes, can be powerful because they can quickly improve phenotypes as well as elucidate the pathways and loci that should be targeted for further improvement. However, these library approaches are often limited by the ability to screen for the desired phenotype. Although tolerance can be found using growth and selection, many desirable phenotypes – such as increased flux in a particular metabolic pathway, mutants with high yield, or the identification of a new product – cannot be readily screened. This limitation has long been recognized as a limiting step to the ‘inverse ME’ approach [53]. Chemical labeling combined with high-throughput automated measurements are primary approaches to screening. However, techniques for transporting labeling molecules into the cell [54] or the development of constitutive molecular
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detectors that can be genetically connected to a reporter gene are required for intracellular product screening [55]. New automated microfluidics-based devices that miniaturize and speed up the assay of cellular properties will also have to be developed. By increasing the ability to screen large libraries, many more problems could be solved. Future directions Metabolic engineering emerged as a field aimed at improving cellular properties for the production of useful chemicals and for broader biomedical applications. As such, ME defined and developed the tools to address the most challenging questions to accomplish these goals, most notably the identification of gene targets where modulation was crucial in achieving a desirable phenotype. In this endeavor, ME assumed a strategic role in examining the various biosynthetic pathways in their entirety and considering the global cellular effects of single-gene modulations, a central tenet of modern-day systems biology research. This approach was successful in engineering cells with remarkable properties in terms of their ability to synthesize useful chemical and pharmaceutical products; it also helped to define fundamental relationships between gene expression and cell function, which is of central interest to the field of functional genomics. It has become increasingly clear that further cellular improvements and the modulation of generic multigenic cellular properties will require the simultaneous modulation of multiple genes. The general problem that ME initially defined, thus, becomes more difficult as the vastness of genome-wide modifications of multigene combinations amply demonstrate. New concepts and tools will be required to accomplish this goal, and ideas from systems biology and omics technologies will certainly help in this endeavor. At the same time, methods from protein engineering and synthetic biology will enhance the toolkit through which the control of gene expression, enzyme activity and the regulatory control of key enzymes and other proteins are accomplished. ME will pursue these goals through a combination of rational and combinatorial approaches. In the former, system-wide rational pathway analysis will define the targets for gene modulation, aided by cell-wide models that will be developed by the computational arm of systems biology. In parallel, combinatorial methods, such as gTME in combination with efficient screening, will enable the efficient probing of the gene– protein space in the quest for phenotypes that have been impossible to elicit using traditional methods. These approaches hold great promise for the potential of ME to meet some pressing industrial, pharmaceutical and biomedical challenges. Acknowledgements This work was supported by the Department of Energy grant DEFG02– 94ER14487 and the National Science Foundation grant 0331364.
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Elsevier celebrates two anniversaries with a gift to university libraries in the developing world In 1580, the Elzevir family began their printing and bookselling business in the Netherlands, publishing works by scholars such as John Locke, Galileo Galilei and Hugo Grotius. On 4 March 1880, Jacobus George Robbers founded the modern Elsevier company intending, just like the original Elzevir family, to reproduce fine editions of literary classics for the edification of others who shared his passion, other ‘Elzevirians’. Robbers co-opted the Elzevir family printer’s mark, stamping the new Elsevier products with a classic symbol of the symbiotic relationship between publisher and scholar. Elsevier has since become a leader in the dissemination of scientific, technical and medical (STM) information, building a reputation for excellence in publishing, new product innovation and commitment to its STM communities. In celebration of the House of Elzevir’s 425th anniversary and the 125th anniversary of the modern Elsevier company, Elsevier donated books to ten university libraries in the developing world. Entitled ‘A Book in Your Name’, each of the 6700 Elsevier employees worldwide was invited to select one of the chosen libraries to receive a book donated by Elsevier. The core gift collection contains the company’s most important and widely used STM publications, including Gray’s Anatomy, Dorland’s Illustrated Medical Dictionary, Essential Medical Physiology, Cecil Essentials of Medicine, Mosby’s Medical, Nursing and Allied Health Dictionary, The Vaccine Book, Fundamentals of Neuroscience, and Myles Textbook for Midwives. The ten beneficiary libraries are located in Africa, South America and Asia. They include the Library of the Sciences of the University of Sierra Leone; the library of the Muhimbili University College of Health Sciences of the University of Dar es Salaam, Tanzania; the library of the College of Medicine of the University of Malawi; and the University of Zambia; Universite du Mali; Universidade Eduardo Mondlane, Mozambique; Makerere University, Uganda; Universidad San Francisco de Quito, Ecuador; Universidad Francisco Marroquin, Guatemala; and the National Centre for Scientific and Technological Information (NACESTI), Vietnam. Through ‘A Book in Your Name’, these libraries received books with a total retail value of approximately one million US dollars.
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Expanding the metabolic engineering toolbox: more options to engineer cells Keith E. Tyo, Hal S. Alper and Gregory N. Stephanopoulos Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge, MA 02139, USA
Metabolic engineering exploits an integrated, systems-level approach for optimizing a desired cellular property or phenotype; and great strides have been made within this scope and context during the past fifteen years. However, due to limitations in the concepts and techniques, these have relied on a focused, pathway-oriented view. Recent advances in ‘omics’ technologies and computational systems biology have brought the foundational systems approach of metabolic engineering into focus. At the same time, protein engineering and synthetic biology have expanded the breadth and precision of the methods available to metabolic engineers to improve strain properties. Examples are presented that illustrate this broader perspective of tools and concepts, including a recent approach for global transcriptional machinery engineering (gTME), which has demonstrated the ability to elicit multigenic transcriptional changes that have improved phenotypes compared with single-gene perturbations. Introduction Some people believe that cells contain the answer to many of the problems of the world. A healthy body, a cure for disease, the answer to our energy problems, production of pharmaceuticals and much more are promised in this new ‘century of biology’ [1]. However, the path to turn these visions into reality is not immediately obvious. Charged with such a mission, both engineers and scientists are faced with finding ways to remodel highly interconnected cellular networks to add properties that are often orthogonal to its design for survival. Established nearly fifteen years ago, metabolic engineering (ME) is a systematic approach to the problem of remodeling and reconfiguring the many pieces of the cell through careful understanding and measurement of how small changes affect the network as a whole. ME is a systems approach to engineering a cellular phenotype. Biological molecules are highly interconnected in robust networks, which are optimized for survival in nature. Unlike molecule-centric approaches, which focus on only the final product-forming reaction, ME emphasizes the metabolic pathway in its entirety. In this regard, a key contribution of ME is the advancement of the concept that the production of a molecule (such as ethanol) is a property of the ethanol biosynthetic pathway, just as more complex phenotypes, such as ethanol tolerance, are a property of the cell in its entirety. As such, improvements Corresponding author: Stephanopoulos, G.N. ([email protected]). Available online 24 January 2007. www.sciencedirect.com
to these properties are possible through the modification of multiple genes, often simultaneously. Genomic sequencing efforts have expanded the initial focus from local metabolic pathways to global gene targets, such as competing pathways, redox balancing and regulatory elements that might not be obvious from considering just the product-forming biochemical pathway [2,3]. By making changes at these higher regulatory levels, the cellular network can be reprogrammed to give the desired phenotype. Many of the tools required to analyze and reprogram cells have been developed as part of the core expertise of ME, particularly for microorganisms. However, advances in other complementary biological disciplines, specifically ‘omics’ technologies, computational systems biology, protein engineering and synthetic biology, stand to contribute greatly to our ability to engineer the cell by improving the means to identify and make perturbations at will. In this review, we will discuss the advances in the engineering of organisms for the production of a desired molecule, as well as how the complementary disciplines mentioned above could potentially contribute useful concepts and technology to ME. Recent advances in metabolic engineering In general, ME has been best implemented for producing molecules that are difficult to produce by other methods (e.g. organic synthesis) owing to chirality or the complexity of their organic structure. However, as petroleum supply issues become more problematic and social awareness of the environmental impact of petroleum use increases, the application of metabolic engineering to the bio-production of commodity chemicals is coming to the forefront. Biocatalyst processes typically rely on renewable feedstocks that do not add additional carbon dioxide to the atmosphere, or require toxic solvents, and operate at near ambient conditions. Although these processes can suffer from low yields or titers, ME can often substantially improve the processes. Recent advances have been spurred on by more-comprehensive genetic tools and increased cellular knowledge of microorganisms. For example, the non-mevalonate pathway of Escherichia coli has been engineered to produce lycopene by identifying distal gene targets using flux balance analysis and screening transposon libraries [3,4]. The mevalonate pathway has been incorporated and engineered in E. coli and Saccharomyces cerevisiae to produce amorphadiene and artemisimic acid, respectively, which are precursors of the anti-malaria drug artemisin [5,6]. The need to produce ethanol from cellulosic feed
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stocks has led to extensive engineering of xylose usage in S. cerevisiae [7,8]. Additionally, several groups have modified pathways from higher plants for the production of natural and non-natural flavonoids in S. cerevisiae and E. coli [9,10]. Finally, organisms have been engineered for high yields of the industrial chemical-intermediates succinic acid [11], 1,3-propanediol [12], and 3-hydroxypropionic acid [13]. It is important to note that the success of these projects hinged on elucidating the metabolic network, modifying the proteins, controlling the expression of genes, and implementing high-throughput screening. Hence, the power of the systems approach to ME has thus far been limited by the basic knowledge and technologies affecting these four key areas. As such, the engineering of higher systems, including mammalian systems, is limited by both the tools and systems available for their identification. Despite this fact, recent advances support the premise that ME will certainly affect and enhance our capacity to engineer these systems as well. For example, Chinese hamster ovary (CHO) cell culture viability has been improved by altering the levels of proto-oncogenes, cell cycle control genes, growth factor genes and anti-apoptotic genes [14–16]. Insect cell lines, yeast and fungi are alternative candidates for producing glycosylated therapeutic proteins that were previously only made in mammalian hosts, based on advantages in productivity and consistency in protein glycosylation structures [17–19]. These ‘humanized’ hosts are created by the introduction of mammalian glycosylation enzymes, and the modifications made by the enzymes enable them to work properly in the new host. Important contributions have been made in identifying altered mammalian enzymes that are active in the new recombinant host by protein screening [20]. Most of these remarkable accomplishments in higher organisms have used approaches that are molecule-based or achieved through local pathway engineering. Although global approaches have been developed further in some well-studied model microbial systems, these are still limited by the need for improved measurement and understanding of cellular networks. We foresee that approaches such as the introduction of new pathways into organisms with high metabolic activity, such as E. coli, or increasing the specific activity of rate-limiting enzymes will continue unabated in the future and will enable many industrial precursors and therapeutic molecules to be made at reduced production costs. At the same time, endeavors in engineering cellular systems will greatly benefit from the expanded systems predicted for ME, along with advances in related fields of study. Specifically, the latter could provide engineers and scientists with an unprecedented capacity to modulate the various components of cellular networks. We are referring, in particular, to the advances in omics technologies, computational systems biology, protein engineering, and synthetic biology that raise exciting prospects for helping to realize the long-standing goals of ME of strain improvement and cell engineering (Figure 1). Omics technologies The increased capacity to measure and catalogue both cellular components and their interactions through www.sciencedirect.com
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advances in omics technologies have provided an expanded view of the cell. The genomics era ushered in high-throughput genetic analysis of both well-studied and newly isolated cellular systems. This information provided us with a list of parts in the cell, and comparing the components between two organisms (through a comparative genomics approach) has been successful in identifying targets in the pathways [11]. Ultimately, this information can be useful for both the identification of novel catalytic activities as well as using metagenomic discoveries for identifying bottlenecks in metabolic networks [21]. Beyond genomics, omics technologies, such as protein measurement using MS [22], high-throughput metabolomics [23], ChIP-chip and yeast two-hybrid systems, enable more accurate and sensitive measurements of cellular components, even at the single cell level, which accurately define the state of the cell and further refine and expand our understanding. Cellwide metabolic fluxes are often missing in the list of measured cellular parameters. Although their estimation is not as straightforward as other measurements – they require sophisticated isotopic experiments and computational algorithms [24–29] – fluxes embody the most direct measurement of how the substrate is being used by the organism and perhaps the most relevant information from a chemical production standpoint. As such, they form an indispensable element of the collection of omics variables. This large collection of measurements or ‘cellular biometrics’ provides a holistic characterization of the cell. The challenge of interpreting all this data is still a formidable task and has become the impetus for systems biology research. In simple analyses, these biometrics can either be mined to identify biomarkers or used in a comparative manner to infer the metabolic capacities of a newly identified organism [30]. Beyond data generation, new advances in metabolomic analytical methods are still required to expand our capacity to quantify, in an exhaustive manner, all the metabolites in a cell. ME has had success in using techniques such as the investigation of flux through isotopomer analysis and metabolite profiling; however, these are often limited to central carbon-metabolism pathways, owing to the difficulty in measuring other metabolites. Comprehensive cellular biometrics can complement more traditional flux measurements, to provide an unmatched amount of information of the cellular state. Omics approaches can, in all, provide ME with a more complete understanding of the cell in various environments. This new level of information can be exploited for strain improvement. Computational systems biology The integration and analysis of omics datasets in the context of a network is a central tenet of systems biology research. Ultimately, this area of research most closely resembles the integrated, systemic approach that has been applied by metabolic engineers for many years in the analysis and optimization of cellular systems [31]. The creation of a computational framework for the true automated integration of large datasets into cellular interaction networks [32] should lead to automated methods for the rapid identification of genetic targets from these
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Figure 1. Metabolic engineering stands to gain significantly from advances in complementary biological fields. Omics technologies and computational systems biology can provide large amounts of data about a cellular state and the means by which to analyze it, whereas protein engineering and synthetic biology can provide toolsets for new ways to manipulate a cell to improve the cellular properties. These four fields have a unique set of expertise that could be applied to further metabolic engineering analysis and implementation.
cellular biometric datasets. Currently, this snapshot of a cell is visualized and analyzed only in part; it should be expanded by making use of not only transcriptional data but also protein, metabolite and flux data, which has always been at the heart of ME analysis. Finally, computational systems biology research focuses on the reconstruction of these cellular networks with the ultimate goal of advancing cellular models in silico. Although these models are being developed for simpler organisms, such as bacteria and yeast [33,34], they can be expanded to higher cellular systems [35]. An E. coli metabolic model has recently been successful in predicting a triple gene knockout for improving lycopene production that would have been intractable to identify experimentally [4]. A great contribution to ME can be made by integrating new genomic data (e.g. from a newly sequenced organism) in an automated manner to reconstruct pathways [36]. Knowing the pathways and metabolic capabilities of a newly sequenced organism will expedite the process of selecting the optimal host strain for a given biocatalysis www.sciencedirect.com
goal. The collective advances in omics technologies and computational systems biology enhance the global systems focus of metabolic and cellular engineering. The most valuable contribution of computational systems biology to ME would be cellular models with increasing kinetic and regulatory content, to facilitate the rational selection of gene targets. Protein engineering After global analysis of the cell, local targets might need fine-tuning of their cellular function. This usually involves the modulation of smaller components such as protein functionality and gene expression. Methods from protein engineering are important to achieve this. To date, much of the effort of protein engineering has focused on developing computational or combinatorial methods that efficiently probe the protein sequence, to identify potential mutants with superior properties [37,38]. In this area, adapting and improving proteins for alternative cellular hosts brings the prospect of unique ME opportunities through
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optimizing heterologous pathways, such as flavonoids and amorphadiene in E. coli [9,39]. Because proteins are functioning cellular components, it becomes obvious that engineering their performance is important when constructing functioning pathways with desirable properties in various host cells. Frequently, rate-limiting steps along a pathway are relieved by increasing the amount of enzyme through gene overexpression; however, the concept of rate-limiting steps is transient, and control is ultimately distributed across a pathway: as one bottleneck is relieved, another step becomes limiting. Particularly in large pathways, cellular resources as well as physical space restrict the overexpression of all the enzymes in a pathway. Hence, it would be useful to perform protein-level pathway engineering, in which specific catalytic rates are altered to enable lower concentrations of the more potent proteins to perform as well, enzymatically, as strong overexpression of the slower enzymes would. Furthermore, protein engineering can expand the range of products through the design and discovery of novel catalytic functions by de novo design and selection, as has been illustrated in the cytochrome P450 family [40]. Beyond catalytic functions, the field of protein engineering has been successful in engineering non-catalytic functions, such as localization, allosteric activation and binding affinity, in proteins [5,41,42]. In general, the engineering of proteins for optimal function in a cellular context can be of paramount importance to ME, particularly in the transfer of pathways across species and drastically amplifying the capacity of pathways by engineering more active or differentially regulated individual enzymes. Synthetic biology Recent developments have drastically expanded the capacity to synthesize, chemically, large fragments of DNA virtually error-free [43]. This technology has fostered a variety of activities, all programmed at the DNA level, aimed at: (i) the design and synthesis of novel biomolecules; (ii) the construction of individual elements (e.g. sensors, toggle switches) of integrated genetic networks (or circuits) with a variety of dynamic responses; and (iii) combining these components into metabolic networks for the synthesis of products. Although the concept of designing genetic circuits and their dynamic responses is not new [44], the enhanced capability to synthesize DNA fragments has important implications in the construction of novel response systems at both the transcriptional and metabolic levels in many types of cells [45–49]. Essentially, the engineering efforts of synthetic biology mirror the goals of protein engineering, but at the DNA level. As such, advances from synthetic biology can advance the field of ME by designing new methods for engineering genetic control. Libraries of functional promoters can be used to control the expression of a single gene [50]. Beyond a single gene, it can be envisioned how a phenotype can be controlled through the use of designed genetic circuits, which can impart toggle switch, oscillatory or other responses. Finally, DNA synthesis technologies can be used in the construction of synthetic pathways, which can be optimized for codon usage in the host and the arrangement www.sciencedirect.com
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of genes into operons. Together, protein engineering and synthetic biology provide tools for the precise manipulation of discreet pieces of cellular machinery in the quest for a desired phenotype. These would enable ME to design product pathways and fermentation approaches from the genetic level. Global transcription machinery engineering Collectively, advances in the above fields will provide support and expand the strengths and multiple facets of metabolic and cellular engineering. Although omics technologies and computational systems biology help us understand the cell on a large scale, and protein engineering and synthetic biology help us manipulate the cell on a local scale, it is known that multiple, distal genetic changes might be important to elicit a desired phenotype. However, random mutagenesis approaches in finding these genetic changes have either been single-function, where perturbations are made in one area of the genome at a time and easily identified (i.e. transposon mutagenesis or library overexpression), or spontaneous mutagenesis that elicits many point mutations throughout the genome, which cannot be easily identified or transferred to other strains. Recently, a tool termed global transcription machinery engineering (gTME) was created that enables broad perturbations of the whole transcriptome through the modification of the proteins responsible for orchestrating transcription [50]. This methodology permits changes in the expression of many distal genes, and it can easily be linked back to a single transcriptional protein. Specifically, it is possible to engineer the sigma factors of E. coli and TFIID components in S. cerevisiae to elicit a multigenic reprogramming of the transcriptome. In doing so, several proof-of-concept studies have been obtained, including increasing cellular tolerances (including increased ethanol tolerance) [51], metabolic overproduction (including lycopene production) and multiple phenotypes (including multiple tolerance phenotypes) [52]. This approach is purely generic, because in all types of cellular system a subset of proteins is responsible for coordinating the global transcriptomes. The development of such tools can enable the translation of ME goals into other organisms and systems. Library screening Combinatorial library approaches, whether from tools such as gTME or libraries of enzymes, can be powerful because they can quickly improve phenotypes as well as elucidate the pathways and loci that should be targeted for further improvement. However, these library approaches are often limited by the ability to screen for the desired phenotype. Although tolerance can be found using growth and selection, many desirable phenotypes – such as increased flux in a particular metabolic pathway, mutants with high yield, or the identification of a new product – cannot be readily screened. This limitation has long been recognized as a limiting step to the ‘inverse ME’ approach [53]. Chemical labeling combined with high-throughput automated measurements are primary approaches to screening. However, techniques for transporting labeling molecules into the cell [54] or the development of constitutive molecular
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detectors that can be genetically connected to a reporter gene are required for intracellular product screening [55]. New automated microfluidics-based devices that miniaturize and speed up the assay of cellular properties will also have to be developed. By increasing the ability to screen large libraries, many more problems could be solved. Future directions Metabolic engineering emerged as a field aimed at improving cellular properties for the production of useful chemicals and for broader biomedical applications. As such, ME defined and developed the tools to address the most challenging questions to accomplish these goals, most notably the identification of gene targets where modulation was crucial in achieving a desirable phenotype. In this endeavor, ME assumed a strategic role in examining the various biosynthetic pathways in their entirety and considering the global cellular effects of single-gene modulations, a central tenet of modern-day systems biology research. This approach was successful in engineering cells with remarkable properties in terms of their ability to synthesize useful chemical and pharmaceutical products; it also helped to define fundamental relationships between gene expression and cell function, which is of central interest to the field of functional genomics. It has become increasingly clear that further cellular improvements and the modulation of generic multigenic cellular properties will require the simultaneous modulation of multiple genes. The general problem that ME initially defined, thus, becomes more difficult as the vastness of genome-wide modifications of multigene combinations amply demonstrate. New concepts and tools will be required to accomplish this goal, and ideas from systems biology and omics technologies will certainly help in this endeavor. At the same time, methods from protein engineering and synthetic biology will enhance the toolkit through which the control of gene expression, enzyme activity and the regulatory control of key enzymes and other proteins are accomplished. ME will pursue these goals through a combination of rational and combinatorial approaches. In the former, system-wide rational pathway analysis will define the targets for gene modulation, aided by cell-wide models that will be developed by the computational arm of systems biology. In parallel, combinatorial methods, such as gTME in combination with efficient screening, will enable the efficient probing of the gene– protein space in the quest for phenotypes that have been impossible to elicit using traditional methods. These approaches hold great promise for the potential of ME to meet some pressing industrial, pharmaceutical and biomedical challenges. Acknowledgements This work was supported by the Department of Energy grant DEFG02– 94ER14487 and the National Science Foundation grant 0331364.
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Elsevier celebrates two anniversaries with a gift to university libraries in the developing world In 1580, the Elzevir family began their printing and bookselling business in the Netherlands, publishing works by scholars such as John Locke, Galileo Galilei and Hugo Grotius. On 4 March 1880, Jacobus George Robbers founded the modern Elsevier company intending, just like the original Elzevir family, to reproduce fine editions of literary classics for the edification of others who shared his passion, other ‘Elzevirians’. Robbers co-opted the Elzevir family printer’s mark, stamping the new Elsevier products with a classic symbol of the symbiotic relationship between publisher and scholar. Elsevier has since become a leader in the dissemination of scientific, technical and medical (STM) information, building a reputation for excellence in publishing, new product innovation and commitment to its STM communities. In celebration of the House of Elzevir’s 425th anniversary and the 125th anniversary of the modern Elsevier company, Elsevier donated books to ten university libraries in the developing world. Entitled ‘A Book in Your Name’, each of the 6700 Elsevier employees worldwide was invited to select one of the chosen libraries to receive a book donated by Elsevier. The core gift collection contains the company’s most important and widely used STM publications, including Gray’s Anatomy, Dorland’s Illustrated Medical Dictionary, Essential Medical Physiology, Cecil Essentials of Medicine, Mosby’s Medical, Nursing and Allied Health Dictionary, The Vaccine Book, Fundamentals of Neuroscience, and Myles Textbook for Midwives. The ten beneficiary libraries are located in Africa, South America and Asia. They include the Library of the Sciences of the University of Sierra Leone; the library of the Muhimbili University College of Health Sciences of the University of Dar es Salaam, Tanzania; the library of the College of Medicine of the University of Malawi; and the University of Zambia; Universite du Mali; Universidade Eduardo Mondlane, Mozambique; Makerere University, Uganda; Universidad San Francisco de Quito, Ecuador; Universidad Francisco Marroquin, Guatemala; and the National Centre for Scientific and Technological Information (NACESTI), Vietnam. Through ‘A Book in Your Name’, these libraries received books with a total retail value of approximately one million US dollars.
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