Effective and rapid inactivation of cellular metabolism is a prerequisite for accurate metabolome analysis. Cold methanol quenching is commonly applied to stop any metabolic activity and, at the same time remaining the cells' integrity. However, it is reported that especially prokaryotic cells like Escherichia coli and Corynebacterium glutamicum tend to leak intracellular metabolites during cold methanol quenching. In this work leakage of adenylates is quantified for different quenching fluids. Further, a methanol/glycerol based quenching fluid is proposed, which reduces leakage drastically compared to the commonly applied methanol/water solution (16% ATP leakage compared to more than 70%).
Commonly steady state analysis of microbial metabolism is performed under well defined physiological conditions in continuous cultures with fixed external rates. However, most industrial bioprocesses are operated in fed-batch mode under non-stationary conditions, which cannot be realized in chemostat cultures. A novel experimental setup-rapid media transition-enables steady state perturbation of metabolism on a time scale of several minutes in parallel to operating bioprocesses. For this purpose, cells are separated from the production process and transferred into a lab-scale stirred-tank reactor with modified environmental conditions. This new approach was evaluated experimentally in four rapid media transition experiments with Escherichia coli from a fed-batch process. We tested the reaction to different carbon sources entering at various points of central metabolism. In all cases, the applied substrates (glucose, succinate, acetate, and pyruvate) were immediately utilized by the cells. Extracellular rates and metabolome data indicate a metabolic steady state during the short-term cultivation. Stoichiometric analysis revealed distribution of intracellular fluxes, which differs drastically subject to the applied carbon source. For some reactions, the variation of flux could be correlated to changes of metabolite concentrations.
Ursodeoxycholic acid is an important pharmaceutical so far chemically synthesized from cholic acid. Various biocatalytic alternatives have already been discussed with hydroxysteroid dehydrogenases (HSDH) playing a crucial role. Several whole-cell biocatalysts based on a 7α-HSDH-knockout strain of Escherichia coli overexpressing a recently identified 7β-HSDH from Collinsella aerofaciens and a NAD(P)-bispecific formate dehydrogenase mutant from Mycobacterium vaccae for internal cofactor regeneration were designed and characterized. A strong pH dependence of the whole-cell bioreduction of dehydrocholic acid to 3,12-diketo-ursodeoxycholic acid was observed with the selected recombinant E. coli strain. In the optimal, slightly acidic pH range dehydrocholic acid is partly undissolved and forms a suspension in the aqueous solution. The batch process was optimized making use of a second-order polynomial to estimate conversion as function of initial pH, initial dehydrocholic acid concentration, and initial formate concentration. Complete conversion of 72 mM dehydrocholic acid was thus made possible at pH 6.4 in a whole-cell batch process within a process time of 1 h without cofactor addition. Finally, a NADH-dependent 3α-HSDH from Comamonas testosteroni was expressed additionally in the E. coli production strain overexpressing the 7β-HSDH and the NAD(P)-bispecific formate dehydrogenase mutant. It was shown that this novel whole-cell biocatalyst was able to convert 50 mM dehydrocholic acid directly to 12-keto-ursodeoxycholic acid with the formation of only small amounts of intermediate products. This approach may be an efficient process alternative which avoids the costly chemical epimerization at C-7 in the production of ursodeoxycholic acid.
Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74-100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes.
Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem-specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D-optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (~1.400 U g(-1) refolded activity), which were 3.4-fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two-step statistical DOE.
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