Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Plant Heterotrophic Cultures: No Food, No Growth

Plant cells are capable of uptaking exogenous organic substances. This inherited trait allows the development of heterotrophic cell cultures in various plants. The most common of them are Nicotiana tabacum and Arabidopsis thaliana. Plant cells are widely used in academic studies and as factories for valuable substance production. The repertoire of compounds supporting the heterotrophic growth of plant cells is limited. The best growth of cultures is ensured by oligosaccharides and their cleavage products. Primarily, these are sucrose, raffinose, glucose and fructose. Other molecules such as glycerol, carbonic acids, starch, and mannitol have the ability to support growth occasionally, or in combination with another substrate. Culture growth is accompanied by processes of specialization, such as elongation growth. This determines the pattern of the carbon budget. Culture ageing is closely linked to substrate depletion, changes in medium composition, and cell physiological rearrangements. A lack of substrate leads to starvation, which results in a decrease in physiological activity and the mobilization of resources, and finally in the loss of viability. The cause of the instability of cultivated cells may be the non-optimal metabolism under cultural conditions or the insufficiency of internal regulation.

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how the adoption of robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology in particular.