Target Selection for Strain Improvement
To demonstrate that the combined metabolomics/MVDA approach results in targets that are important for strain improvement, we studied the production of phenylalanine by E. coli. A patented phenylalanine-producing strain (ATCC 31884) was obtained that had already been optimized by eight or more steps of rational design. This strain was cultivated in a batch fermentor under different environmental conditions to achieve large variations in the amount of phenylalanine produced.
Samples were taken from these controlled fermentations at different time points, quenched to halt cellular metabolism, and worked-up for metabolome analysis using GC and LC-MS. Subsequently, the raw GC and LC-MS output data files were preprocessed using home-made software. The resulting clean data set was analyzed using Partial Least Squares (PLS). This regression tool results in a model that predicted the phenylalanine titer ([P]), based on all metabolites (A,B,C,) measured:
[P] = b1A + b2B + b3C + ...
The relative statistical importance of all the metabolites toward the phenylalanine titer is determined by their weight factors (regression values [b1, b2, b3]). When the metabolites are subsequently ordered, based on the absolute value of the regression value in the PLS model, those metabolites that contribute most to the phenylalanine titer can be identified and ranked.
Results showed that approximately one-half of the metabolites that strongly correlate with phenylalanine titer were intermediates or side-products of the phenylalanine biosynthesis route. Subsequently, the biological interpretation of the results allowed the identification of genes that should be knocked-out or overexpressed to achieve higher phenylalanine titers (Figure 2). Several of these leads were validated resulting in a phenylalanine titer increase of up to 50% (Figure 3). The entire improvement effort took less than nine months.