Computational identification of gene over-expression targets for metabolic engineering of taxadiene production

Abstract

Taxadiene is the first dedicated intermediate in the biosynthetic pathway of the anticancer compound Taxol. Recent studies have taken advantage of heterologous hosts to produce taxadiene and other isoprenoid compounds, and such ventures now offer research opportunities that take advantage of the engineering tools associated with the surrogate host. In this study, metabolic engineering was applied in the context of over-expression targets predicted to improve taxadiene production. Identified targets included genes both within and outside of the isoprenoid precursor pathway. These targets were then tested for experimental over-expression in a heterologous Escherichia coli host designed to support isoprenoid biosynthesis. Results confirmed the computationally predicted improvements and indicated a synergy between targets within the expected isoprenoid precursor pathway and those outside this pathway. The presented algorithm is broadly applicable to other host systems and/or product choices.

Fig. 1

The pathway from central metabolites pyruvate and glyceraldehyde-3-phosphate to taxadiene

Fig. 1

The pathway from central metabolites pyruvate and glyceraldehyde-3-phosphate to taxadiene

Fig. 2

An overview of the proposed algorithm for identifying over-expression targets to improve product titer

Fig. 3

Calculated taxadiene production flux (left-hand y-axis) and f_PH (right-hand y-axis) as a function of gene over-expressed

Fig. 4

Experimental specific taxadiene titer data for the computationally identified gene over-expression targets and corresponding controls. The “Control” sample is YW22 (pTrcHis2B-TXS-GGPPS). Error bars represent ± one standard deviation of three independent replicates. *Statistically significant (p<0.05, as determined by paired Student’s t-test) difference from the YW22 (pTrcHis2B-TXS-GGPPS) control

Fig. 5

Experimental specific taxadiene titer data for the isoprenoid precursor pathway gene over-expression targets. The “Control” sample is YW22 (pTrcHis2B-TXS-GGPPS). Error bars represent ± one standard deviation of three independent replicates. *Statistically significant (p<0.05, as determined by paired Student’s t-test) difference from the YW22 (pTrcHis2B-TXS-GGPPS) control. †Taxadiene was not detected by GC–MS

Fig. 6

Experimental specific taxadiene titer data for the computationally identified targets combined with over-expression of idi. The “Control” sample is YW22 (pTrcHis2B-TXS-GGPPS). Error bars represent ± one standard deviation of three independent replicates. *Statistically significant (p<0.05, as determined by paired Student’s t-test) difference from the YW22 (pTrcHis2B-TXS-GGPPS) control