(378g) Designing Synthetic Genes for Recombinant Expression in Synthetic Biotechnology

Abstract

System-wide data, such as genomics, transcriptomics, proteomics and metabolomics, generated by high-throughput experimental techniques have fueled the rapid advancement in systems biology research over the past decade. This wealth of information generated in systems biology has equipped us with the knowledge to challenge the new frontier of synthetic biology. Hence, in this study, we demonstrate a methodology for employing omics data to computationally design synthetic genes with enhanced recombinant expression capabilities. In the biotechnology industry, recombinant DNA technology is commonly used to achieve high productivity of certain desired biochemical in host organisms such as Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Chinese hamster ovary (CHO) cells. Accordingly, high heterologous expression levels of the recombinant genes in these host organisms are required. Nonetheless, these foreign genes are typically poorly expressed because they have not been evolved for efficient expression in the selected host organism. Consequently, molecular biology techniques have to be employed to either genetically engineer the host organism or modify the recombinant genes for enhanced heterologous protein expression. However, since the former approach is more challenging to implement and may adversely affect the host cells’ physiology, the latter approach of redesigning the recombinant genes to generate synthetic genes is usually adopted. In this aspect, the issue of codon usage bias has been well-known to be a key factor affecting recombinant protein expressivity, more specifically at the step of translating mRNA to peptide chain (Gustafsson et al., 2004). Several codon optimization software tools, such as Gene Designer (Villalobos et al., 2006), JCat (Grote et al., 2005) and OPTIMIZER (Fuglsang, 2003), have been developed to generate synthetic genes with improved codon usage bias. Most of these earlier algorithms optimize the genetic sequence based on individual codon usage (ICU). Yet, the pairing of codons, sometimes referred to as codon context (CC), is also known to significantly affect mRNA translation efficiency (Hatfield and Roth, 2007; Coleman et al., 2008). Thus, it is relevant to incorporate both ICU and CC as design parameters under a multi-objective optimization framework to examine the relevant importance of these two parameters. Towards this end, we have developed a computational tool based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the synthetic gene design with respect to both ICU and CC (Chung and Lee, 2012). This tool has been applied to optimize recombinant genes for heterologous expression in P. pastoris (Ahn et al., 2013) and CHO cells (Chung et al., 2013). The in vivoexpression of the optimized synthetic genes in these two expression hosts has consistently revealed that CC-optimized genes were capable of higher expression levels than the ICU-optimized ones. This suggests that CC may be a more important design criterion than the widely considered ICU. Henceforth, the incorporation of the CC parameter into the codon optimization algorithm is expected to improve the efficacy of existing tools. In addition, the developed multi-objective codon optimization framework based on NSGA-II will be a good implementation strategy for future applications since it can be easily expanded to include more design parameters.

Acknowledgements

This work was supported by the National University of Singapore, Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore, and a grant from the Next-Generation BioGreen 21 Program (SSAC, No. PJ009520), Rural Development Administration, Republic of Korea.

References

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