Systematic applications of metabolomics in metabolic engineering

Abstract

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Figure 1

Examples of data analysis techniques for metabolomics. The effects of glucose deprivation on a cancer cell line were measured with GC-MS and analyzed in MetaboAnalyst [78] (unpublished data). (a) Pairwise t-tests of metabolites identify statistical significance of differences in individual compounds between control and experiment. The dotted line indicates p < 0.05 (no multiple hypotheses testing corrections). (b) PCA score plot reveals separation between control and experiment samples in components 2, 3, and 4. Component 1 (not shown) corresponds to analytical batch separation. (c) HCA (Ward method, Pearson’s correlation) and heatmap using the 150 most significant compounds as determined by t-test. Compounds along top, samples along left side. (d) PLS-DA score plot shows separation achieved using components 1 and 2. Dashed circles indicate the 95% confidence interval for each class. (e) Leave-one-out cross-validation shows that the majority of the predictive capacity is derived from the first two PLS-DA components. R² and Q² denote, respectively, the goodness of fit and goodness of prediction statistics. (f) Contribution of individual compounds to PLS-DA component 1. The 30 most important compounds and their relative abundance in control and experiment are shown, sorted by the Variable Importance in the Projection (VIP) [91] for the first component.

Figure 2

Applications of various techniques to understanding and manipulating cellular metabolism. Solid lines represent widely used strategies, dashed lines represent underused strategies. Both metabolomics and transcriptional profiling provide a direct readout that helps enable a deeper understanding of cellular metabolism, but only transcriptional profiling has seen widespread application to enhance standard computational modeling and metabolic engineering strategies. Integrating metabolomics data into metabolic engineering and computational modeling strategies would help bridge gaps in biochemical knowledge and improve our ability to control cellular metabolism.