With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome.
The extensive subcellular compartmentalization of metabolites and metabolism in eukaryotic cells is widely acknowledged and represents a key factor of metabolic activity and functionality. In striking contrast, the knowledge of actual compartmental distribution of metabolites from experimental studies is surprisingly low. However, a precise knowledge of, possibly all, metabolites and their subcellular distributions remains a key prerequisite for the understanding of any cellular function.
Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment. To demonstrate the relevance of these six parameters, we analyzed three cyanobacteria with greatly different morphologies, comprising a unicellular, a filamentous, and a filamentous biofilm-forming strain. Comparative analysis of GC/MS data from cultures grown under standardized conditions indicated that adjustment of the corresponding metabolite profiles by any of the measured cellular constituents or chosen intrinsic parameters led to similar results with respect to sample cohesion and strain separation. Twenty-one metabolites significantly enriched for the carbohydrate and amine superclasses are mainly responsible for strain separation, with a majority of the remaining metabolites contributing to sample group cohesion. Therefore, we conclude that any of the parameters tested in this study can be used for spectral data adjustment of cyanobacterial strains grown under controlled conditions. However, their use for the differentiation between different stresses or physiological states within a strain remains to be shown. Interestingly, both the adjustment approaches and statistical tests applied effected the detection of metabolic differences and their patterns among the analyzed strains.
Metabolomics is the comprehensive analysis of the small molecules that compose an organisms metabolism. The main limiting step in microbial metabolomics is the requirement for fast and efficient separation of microbes from the culture medium under conditions in which metabolism is rapidly halted. In this article we compare three different sampling strategies, quenching, filtering, and centrifugation, for arresting the metabolic activities of two morphologically diverse cyanobacteria, the unicellular Synechocystis sp. PCC 6803 and the filamentous Nostoc sp. PCC 7120 for GC-MS analysis. We demonstrate that each sampling technique produces internally consistent and reproducible data, however, cold methanol-water quenching caused leakage and substantial loss of metabolites from various compound classes, while fast filtering and centrifugation produced quite similar metabolite pool sizes, even for metabolites with predicted high turnover. This indicates that cyanobacterial metabolic pools, as measured by GC-MS, do not show high turnover under standard growing conditions. As well, using stable (13)C labeling we show the biological origin of some of the consistently observed unknown analytes. With the development of these techniques, we establish the basis for broad scale comparative metabolite profiling of cyanobacteria.
Mitochondria are tightly linked to cellular nutrient sensing, and provide not only energy, but also intermediates for the de novo synthesis of cellular compounds including amino acids. Mitochondrial metabolic enzymes as generators and/or targets of signals are therefore important players in the distribution of intermediates between catabolic and anabolic pathways. The highly regulated 2-oxoglutarate dehydrogenase complex (OGDHC) participates in glucose oxidation via the tricarboxylic acid cycle. It occupies an amphibolic branch point in the cycle, where the energy-producing reaction of the 2-oxoglutarate degradation competes with glutamate (Glu) synthesis via nitrogen incorporation into 2-oxoglutarate. To characterize the specific impact of the OGDHC inhibition on amino acid metabolism in both plant and animal mitochondria, a synthetic analog of 2-oxoglutarate, namely succinyl phosphonate (SP), was applied to living systems from different kingdoms, both in situ and in vivo. Using a high-throughput mass spectrometry-based approach, we showed that organisms possessing OGDHC respond to SP by significantly changing their amino acid pools. By contrast, cyanobacteria which lack OGDHC do not show perturbations in amino acids following SP treatment. Increases in Glu, 4-aminobutyrate and alanine represent the most universal change accompanying the 2-oxoglutarate accumulation upon OGDHC inhibition. Other amino acids were affected in a species-specific manner, suggesting specific metabolic rearrangements and substrate availability mediating secondary changes. Strong perturbation in the relative abundance of amino acids due to the OGDHC inhibition was accompanied by decreased protein content. Our results provide specific evidence of a considerable role of OGDHC in amino acid metabolism.
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