Towards systems metabolic engineering of microorganisms for amino acid production

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Microorganisms capable of efficient production of amino acids have traditionally been developed by random mutation and selection method, which might cause unwanted physiological changes in cellular metabolism. Rational genome-wide metabolic engineering based on systems and synthetic biology tools, which is termed ‘systems metabolic engineering’, is rising as an alternative to overcome these problems. Recently, several amino acid producers have been successfully developed by systems metabolic engineering, where the metabolic engineering procedures were performed within a systems biology framework, and entire metabolic networks, including complex regulatory circuits, were engineered in an integrated manner. Here we review the current status of systems metabolic engineering successfully applied for developing amino acid producing strains and discuss future prospects.

Introduction

The current annual production of amino acid is estimated to be more than two million tons. Amino acids are used in a number of applications such as food additives, pharmaceuticals, animal feed supplements, cosmetics and polymer materials [1]. Recently, some amino acids have been used as important intermediate precursors for the production of biofuels [2••] and antibiotics [3]. As the market of amino acids grows rapidly but is accompanied by increasing competition, researchers have begun to direct their efforts to improve the efficiency of amino acid production to the highest possible levels.

Traditionally, multiple rounds of random mutation and selection have been performed to develop amino acid producing strains [4, 5]. However, this approach often causes unwanted alterations in the genome, the consequences of which cannot be easily identified. Owing to these unknown characteristics present in the random mutants, further improvement of cellular performance by rational metabolic engineering becomes difficult. For this reason, approaches for strain development have shifted to targeted engineering strategies, which purposefully modify genes and pathways towards enhanced production of a desired amino acid [6, 7]. Despite being targeted and site-specific, this strategy also has some problems. The engineered strain might have limitations in the extent of improvement because the scope of engineering the cell is often local rather than genome-wide, and consideration of the entire metabolic network is often neglected. The emergence of systems biology allows us to overcome this limitation through the use of genome-wide high-throughput omics data and genome-scale computational analysis [8, 9, 10].

Here, we suggest that systems metabolic engineering is an ideal way of developing strains for the production of amino acids and other bioproducts. In systems metabolic engineering, engineering targets are determined by considering the entire metabolic and regulatory networks together with midstream (fermentation) and downstream (recovery and purification) processes. During the actual metabolic engineering, the impact of altering these targets on the entire metabolism is examined to provide feedback [11]. Through the iterative mode of operation of metabolic engineering based on the performance of the strain developed, the ultimate metabolic phenotype desired can be obtained. In this paper, we briefly introduce the tools for systems metabolic engineering, review the recent papers employing systems metabolic engineering approaches for the production of amino acids and provide future prospects.

Section snippets

Paradigm shift towards systems metabolic engineering

Strategies for developing amino acid producers are now in transition towards systems metabolic engineering from random mutagenesis (Figure 1). Some representative examples employing these strategies are described in this section. One of the classical strategies is selecting an amino acid analogue-resistant mutant showing normal growth after treating the wild-type strain with mutagens. The resulting strain undergoes multiple rounds of mutation and selection until it shows the desirable amino

Tools for systems metabolic engineering

For systems metabolic engineering of microorganisms, multi-level high-throughput omics data such as genome, transcriptome, proteome, metabolome and fluxome are required. Availability of the complete genome sequences of numerous microorganisms has opened a new avenue towards understanding cellular physiology at the systems level. Genomic information serves as a basis for performing functional genomic studies; for example, performing transcriptome and proteome profiling and analyzing their data

Application of systems metabolic engineering for amino acid production

There have recently been several reports on the use of omics analysis for the development of strains for amino acid production. Comparative genome analysis has been performed for the development of an efficient l-lysine producer by Ohnishi et al. [40]. Genome comparisons were made between a wild-type strain and an l-lysine producing C. glutamicum strain constructed by random mutation and selection to identify the mutations in three genes that might be beneficial for the efficient production of l

Conclusions

Amino acid biosynthetic pathways are complex and tightly regulated. Thus, microorganisms normally do not produce the desired levels of amino acids. A traditionally used method of random mutation and selection for the development of amino acid producers is now complemented by rational metabolic engineering, which became more powerful as omics tools have become available. Furthermore, systems metabolic engineering based on integrated analysis of omics data and genome-scale metabolic model allows

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank Seung Bum Sohn for his help in manuscript preparation. This work was supported by the Korean Systems Biology Project from the Ministry of Education, Science and Technology through the Korea Science and Engineering Foundation. Further supports by LG Chem Chair Professorship and Microsoft are appreciated.

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