Phosphoglycerate kinase acts as a futile cycle at high temperature.

In (hyper)thermophilic organisms metabolic processes have to be adapted to function optimally at high temperature. We compared the gluconeogenic conversion of 3-phosphoglycerate via 1,3-bisphosphoglycerate to glyceraldehyde-3-phosphate at 30 °C and at 70 °C. At 30 °C it was possible to produce 1,3-bisphosphoglycerate from 3-phosphoglycerate with phosphoglycerate kinase, but at 70 °C, 1,3-bisphosphoglycerate was dephosphorylated rapidly to 3-phosphoglycerate, effectively turning the phosphoglycerate kinase into a futile cycle. When phosphoglycerate kinase was incubated together with glyceraldehyde 3-phosphate dehydrogenase it was possible to convert 3-phosphoglycerate to glyceraldehyde 3-phosphate, both at 30 °C and at 70 °C, however, at 70 °C only low concentrations of product were observed due to thermal instability of glyceraldehyde 3-phosphate. Thus, thermolabile intermediates challenge central metabolic reactions and require special adaptation strategies for life at high temperature.

The JWS online simulation database.

SUMMARY: JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments. AVAILABILITY AND IMPLEMENTATION: The Simulation Database is available on line at https://jjj.bio.vu.nl/models/experiments/ . CONTACT: jls@sun.ac.za .

Determining enzyme kinetics for systems biology with nuclear magnetic resonance spectroscopy.

Enzyme kinetics for systems biology should ideally yield information about the enzyme's activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in the literature is based on the uni-substrate Michaelis-Menten equation, which makes unnatural assumptions about enzymatic reactions (e.g., irreversibility), and its application in systems biology models is therefore limited. To overcome this limitation, we have utilised NMR time-course data in a combined theoretical and experimental approach to parameterize the generic reversible Hill equation, which is capable of describing enzymatic reactions in terms of all the properties mentioned above and has fewer parameters than detailed mechanistic kinetic equations; these parameters are moreover defined operationally. Traditionally, enzyme kinetic data have been obtained from initial-rate studies, often using assays coupled to NAD(P)H-producing or NAD(P)H-consuming reactions. However, these assays are very labour-intensive, especially for detailed characterisation of multi-substrate reactions. We here present a cost-effective and relatively rapid method for obtaining enzyme-kinetic parameters from metabolite time-course data generated using NMR spectroscopy. The method requires fewer runs than traditional initial-rate studies and yields more information per experiment, as whole time-courses are analyzed and used for parameter fitting. Additionally, this approach allows real-time simultaneous quantification of all metabolites present in the assay system (including products and allosteric modifiers), which demonstrates the superiority of NMR over traditional spectrophotometric coupled enzyme assays. The methodology presented is applied to the elucidation of kinetic parameters for two coupled glycolytic enzymes from Escherichia coli (phosphoglucose isomerase and phosphofructokinase). 31P-NMR time-course data were collected by incubating cell extracts with substrates, products and modifiers at different initial concentrations. NMR kinetic data were subsequently processed using a custom software module written in the Python programming language, and globally fitted to appropriately modified Hill equations.