Functional analysis of isoprenoid precursors biosynthesis by quantitative metabolomics and isotopologue profiling

Quantifying Isoprenoids in the Ergosterol Biosynthesis by Gas Chromatography-Mass Spectrometry

The ergosterol pathway is a promising target for the development of new antifungals since its enzymes are essential for fungal cell growth. Appropriate screening assays are therefore needed that allow the identification of potential inhibitors. We developed a whole-cell screening method, which can be used to identify compounds interacting with the enzymes of isoprenoid biosynthesis, an important part of the ergosterol biosynthesis pathway. The method was validated according to the EMEA guideline on bioanalytical method validation. Aspergillus fumigatus hyphae and Saccharomyces cerevisiae cells were lysed mechanically in an aqueous buffer optimized for the enzymatic deconjugation of isoprenoid pyrophosphates. The residual alcohols were extracted, silylated and analyzed by GC-MS. The obtained isoprenoid pattern provides an indication of the inhibited enzyme, due to the accumulation of specific substrates. By analyzing terbinafine-treated A. fumigatus and mutant strains containing tunable gene copies of erg9 or erg1, respectively, the method was verified. Downregulation of erg9 resulted in a high accumulation of intracellular farnesol as well as elevated levels of geranylgeraniol and isoprenol. The decreased expression of erg1 as well as terbinafine treatment led to an increased squalene content. Additional analysis of growth medium revealed high farnesyl pyrophosphate levels extruded during erg9 downregulation.

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.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.