Design of Terpenoid Producing Synthetic Microbial Cell Factories By Integrative in silico Modeling and “-Omics” Data Analysis
Terpenoids are a set of diverse, low molecular-mass plant secondary metabolites with several important applications in human health and nutrition. Nevertheless, its low yields from the natural sources, i.e. plants, have demanded the search for an effective alternate production strategy. In this regard, metabolic engineering is an effective tool to synthesize terpenoids in bulk quantities using fast-growing microbes such as E. coli and S. cerevisiae by creatively customizing their innate metabolic capabilities by up-, down-regulating, inserting and/or deleting numerous metabolic genes at the same time. To this end, we herein present an in silico model-driven systematic framework for the rational design of microbial cell factories for synthetic terpenoids production. Guided by the genome-scale metabolic modeling and “-omics” data profiling of plants, Arabidopsis and rice, we first analyzed the metabolic organization, gene expression pattern and the putative transcriptional mechanisms involved in terpenoids synthesis. These studies include the comparison of terpenoid-backbone synthetic pathways, 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway and mevalonic acid (MVA) pathway, effect of different colored lights in terpenoid synthetic gene expression and their relevant regulatory mechanisms. These analyses revealed that the MEP pathway is slightly better than MVA pathway in terms of IPP yield and the stimulating effects of light, especially blue color, in terpenoid gene expression. Based on these observations, we chose to manifest these characteristics into E. coli for synthetic terpenoid production as it has MEP pathway naturally and well characterized genetic tools. Further, again with the guide of genome-scale model, identified relevant gene knockout targets to enhance the carbon flux through MEP pathway. Additionally, we also found that terpenoids synthesis in host organisms is often limited due to the limited availability of intracellular NADP(H) concentrations through in silico simulations. To resolve this issue, we propose a novel computational algorithm, cofactor modification analysis (CMA), which identifies the plausible enzyme targets for cofactor engineering. The identified enzyme target was subsequently modified for cofactor specificity from NAD(H) to NADP(H) using the mutational targets identified from computational molecular modeling and analysis. Apart from engineering the innate metabolic capabilities of host organism to synthesize terpenoids, expressing the foreign plant genes in microbes at an optimal level is also a major challenge. In the proposed framework, this bottleneck can be fully addressed by resorting to the CC optimization framework, optimizing the individual codon usage and codon context of the host organism. In general, this study outlines an in silico model-driven approach for designing microbial cell factories to synthesize non-native compounds optimally.