Measuring intracellular metabolism has increasingly led to important insights in biomedical research. 13C tracer analysis, although less information-rich than quantitative 13C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting 13C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

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Strains belonging to the yeast species Kluyveromyces marxianus have been isolated from a great variety of habitats, which results in a high metabolic diversity and a substantial degree of intraspecific polymorphism. As a consequence, several different biotechnological applications have been investigated with this yeast: production of enzymes (beta-galactosidase, beta-glucosidase, inulinase, and polygalacturonases, among others), of single-cell protein, of aroma compounds, and of ethanol (including high-temperature and simultaneous saccharification-fermentation processes); reduction of lactose content in food products; production of bioingredients from cheese-whey; bioremediation; as an anticholesterolemic agent; and as a host for heterologous protein production. Compared to its congener and model organism, Kluyveromyces lactis, the accumulated knowledge on K. marxianus is much smaller and spread over a number of different strains. Although there is no publicly available genome sequence for this species, 20% of the CBS 712 strain genome was randomly sequenced (Llorente et al. in FEBS Lett 487:71-75, 2000). In spite of these facts, K. marxianus can envisage a great biotechnological future because of some of its qualities, such as a broad substrate spectrum, thermotolerance, high growth rates, and less tendency to ferment when exposed to sugar excess, when compared to K. lactis. To increase our knowledge on the biology of this species and to enable the potential applications to be converted into industrial practice, a more systematic approach, including the careful choice of (a) reference strain(s) by the scientific community, would certainly be of great value.

In the present work we investigated the most commonly applied methods used for sampling of microorganisms in the field of metabolomics in order to unravel potential sources of error previously ignored but of utmost importance for accurate metabolome analysis. To broaden the significance of our study, we investigated different Gram-negative and Gram-positive bacteria, i.e., Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Gluconobacter oxydans, Pseudomonas putida, and Zymononas mobilis, and analyzed metabolites from different catabolic and anabolic intracellular pathways. Quenching of cells with cold methanol prior to cell separation and extraction led to drastic loss (>60%) of all metabolites tested due to unspecific leakage. Using fast filtration, Gram-negative bacteria also revealed a significant loss (>80%) when inappropriate washing solutions with low ionic strength were applied. Adapting the ionic strength of the washing solution to that of the cultivation medium could almost completely avoid this problem. Gram-positive strains did not show significant leakage independent of the washing solution. Fast filtration with sampling times of several seconds prior to extraction appears to be a suitable approach for metabolites with relatively high intracellular level and low turnover such as amino acids or TCA cycle intermediates. Comparison of metabolite levels in the culture supernatant and the cell interior revealed that the common assumption of whole broth quenching protocols attributing the metabolites found exclusively to the intracellular pools may not be valid in many cases. In such cases a differential approach correcting for medium-contained metabolites is required.