Optimization of extraction conditions for LC-ToF-MS analysis of mevalonate pathway metabolites in engineered E. coli strain via statistical experimental designs.

Isoprenoids give rise to many functional products used today such as flavours, fragrances and even pharmaceutical compounds. Mevalonate pathway metabolites are the key intermediates that affect the production yield of isoprenoids. With increasing demand and benefit of isoprenoids, the present study adopts Analytical Quality-by-Design (AQbD) approach to establish an efficacious extraction protocol prior to the determination of mevalonate pathway metabolites in an engineered Escherichia coli model. The statistical experimental design approach, described in this work, has successfully validated an optimised sample preparation method i.e., using acetonitrile: 50 mM ammonium formate (pH 9.5) (7:3) (ACN73) at -20 °C for 10 min without solvent evaporation to retain the targeted mevalonate metabolites in engineered E. coli strain. The study also demonstrates the use of liquid chromatography paired with a Time-of-Flight Mass Spectrometer (LC-ToF-MS) for the quantitative analysis of the mevalonate pathway metabolites in E. coli. The analytical method was validated in accordance with guidelines in Metabolomics Standards Initiative and ICH Q2 (R1) with analyte spike recoveries at 80% and above. In short, the present study overcomes the one-variable-at-a-time (OVAT) limitations in analytical development, minimises metabolite losses and gives better cost and time efficiencies by eliminating the solvent evaporation and swapping process. This work highlights the importance of analytical methods development in microbial metabolomics studies.

Metabolomics and modelling approaches for systems metabolic engineering.

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.