Stephen Gang Wu

An ancient Chinese wisdom for metabolic engineering: Yin-Yang

AbstractIn ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell “powerhouse” prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysis model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create “minimal cells” or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

WUFlux: an open-source platform for 13 C metabolic flux analysis of bacterial metabolism

BackgroundFlux analyses, including flux balance analysis (FBA) and 13C-metabolic flux analysis (13C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the 13C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB®.ResultsWe constructed an open-source platform for steady-state 13C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results.ConclusionOpen-source software for 13C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.

Dual-cycle immobilization to reuse both enzyme and support by reblossoming enzyme–inorganic hybrid nanoflowers

To achieve dual-reuse of both enzyme and support in enzyme immobilization, hybrid nanoflowers (hNFs) were synthesized and crystallized in aqueous solution using calcium phosphate as inorganic component and enzyme as organic component. When hNFs lost their catalytic activity after reuse for times, they underwent dissolution and recrystallization to achieve the dual-cycle of enzyme and support. Six enzymes including papain, bromelain, trypsin, Lipase from Porcine Pancreas (PPL), Lipase from Thermomyces lanuginosus (TLL) and Lipase B from Candida antarctica (CALB) were chose as model enzymes and the obtained hNFs all presented high catalytic activity and thermal stability. The highest catalytic efficiency (Kcat/Km) of TLL-hNFs was 38.52 mM−1 s−1, 21.7 folds than that of free enzyme. Moreover, after heating for 6 h at 70 °C, the residual activities of TLL-hNFs, PPL-hNFs, and CALB-hNFs, were 78.3%, 72.9% and 84.3%, which were 4.57, 2.61 2.35 folds of that of their corresponding free one. Furthermore, the recovery rate of Ca3(PO4)2 were above 95% by recrystallizing the calcium phosphate with fresh enzymes after dissolving the used hNFs and removing the denatured enzyme. The recrystallized hNFs using the recovered phosphate salts and fresh enzymes all gave the consistent catalytic activities. This sustainable dual-cycle method depending on calcium phosphate crystallization, dissolution and recrystallization, was facile and efficient and can also be applied to other enzymes immobilization for industrial biocatalysis.