A new Zymomonas mobilis platform strain for the efficient production of chemicals.

BACKGROUND: Zymomonas mobilis is well known for its outstanding ability to produce ethanol with both high specific productivity and with high yield close to the theoretical maximum. The key enzyme in the ethanol production pathway is the pyruvate decarboxylase (PDC) which is converting pyruvate to acetaldehyde. Since it is widely considered that its gene pdc is essential, metabolic engineering strategies aiming to produce other compounds derived from pyruvate need to find ways to reduce PDC activity. RESULTS: Here, we present a new platform strain (sGB027) of Z. mobilis in which the native promoter of pdc was replaced with the IPTG-inducible PT7A1, allowing for a controllable expression of pdc. Expression of lactate dehydrogenase from E. coli in sGB027 allowed the production of D-lactate with, to the best of our knowledge, the highest reported specific productivity of any microbial lactate producer as well as with the highest reported lactate yield for Z. mobilis so far. Additionally, by expressing the L-alanine dehydrogenase of Geobacillus stearothermophilus in sGB027 we produced L-alanine, further demonstrating the potential of sGB027 as a base for the production of compounds other than ethanol. CONCLUSION: We demonstrated that our new platform strain can be an excellent starting point for the efficient production of various compounds derived from pyruvate with Z. mobilis and can thus enhance the establishment of this organism as a workhorse for biotechnological production processes.

OptMSP: A toolbox for designing optimal multi-stage (bio)processes.

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Growth-coupled anaerobic production of isobutanol from glucose in minimal medium with Escherichia coli.

BACKGROUND: The microbial production of isobutanol holds promise to become a sustainable alternative to fossil-based synthesis routes for this important chemical. Escherichia coli has been considered as one production host, however, due to redox imbalance, growth-coupled anaerobic production of isobutanol from glucose in E. coli is only possible if complex media additives or small amounts of oxygen are provided. These strategies have a negative impact on product yield, productivity, reproducibility, and production costs. RESULTS: In this study, we propose a strategy based on acetate as co-substrate for resolving the redox imbalance. We constructed the E. coli background strain SB001 (ΔldhA ΔfrdA ΔpflB) with blocked pathways from glucose to alternative fermentation products but with an enabled pathway for acetate uptake and subsequent conversion to ethanol via acetyl-CoA. This strain, if equipped with the isobutanol production plasmid pIBA4, showed robust exponential growth (µ = 0.05 h-1) under anaerobic conditions in minimal glucose medium supplemented with small amounts of acetate. In small-scale batch cultivations, the strain reached a glucose uptake rate of 4.8 mmol gDW-1 h-1, a titer of 74 mM and 89% of the theoretical maximal isobutanol/glucose yield, while secreting only small amounts of ethanol synthesized from acetate. Furthermore, we show that the strain keeps a high metabolic activity also in a pulsed fed-batch bioreactor cultivation, even if cell growth is impaired by the accumulation of isobutanol in the medium. CONCLUSIONS: This study showcases the beneficial utilization of acetate as a co-substrate and redox sink to facilitate growth-coupled production of isobutanol under anaerobic conditions. This approach holds potential for other applications with different production hosts and/or substrate-product combinations.

Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis.

MOTIVATION: Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty. RESULTS: In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes. AVAILABILITY AND IMPLEMENTATION: The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions.

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli. As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

A Cell-Free Multi-enzyme Cascade Reaction for the Synthesis of CDP-Glycerol.

CDP-glycerol is a nucleotide-diphosphate-activated version of glycerol. In nature, it is required for the biosynthesis of teichoic acid in Gram-positive bacteria, which is an appealing target epitope for the development of new vaccines. Here, a cell-free multi-enzyme cascade was developed to synthetize nucleotide-activated glycerol from the inexpensive and readily available substrates cytidine and glycerol. The cascade comprises five recombinant enzymes expressed in Escherichia coli that were purified by immobilized metal affinity chromatography. As part of the cascade, ATP is regenerated in situ from polyphosphate to reduce synthesis costs. The enzymatic cascade was characterized at the laboratory scale, and the products were analyzed by high-performance anion-exchange chromatography (HPAEC)-UV and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). After the successful synthesis had been confirmed, a design-of-experiments approach was used to screen for optimal operation conditions (temperature, pH value and MgCl2 concentration). Overall, a substrate conversion of 89 % was achieved with respect to the substrate cytidine.

Characterizing and utilizing oxygen-dependent promoters for efficient dynamic metabolic engineering.

Promoters adjust cellular gene expression in response to internal or external signals and are key elements for implementing dynamic metabolic engineering concepts in fermentation processes. One useful signal is the dissolved oxygen content of the culture medium, since production phases often proceed in anaerobic conditions. Although several oxygen-dependent promoters have been described, a comprehensive and comparative study is missing. The goal of this work is to systematically test and characterize 15 promoter candidates that have been previously reported to be induced upon oxygen depletion in Escherichia coli. For this purpose, we developed a microtiter plate-level screening using an algal oxygen-independent flavin-based fluorescent protein and additionally employed flow cytometry analysis for verification. Various expression levels and dynamic ranges could be observed, and six promoters (nar-strong, nar-medium, nar-weak, nirB-m, yfiD-m, and fnrF8) appear particularly suited for dynamic metabolic engineering applications. We demonstrate applicability of these candidates for dynamic induction of enforced ATP wasting, a metabolic engineering approach to increase productivity of microbial strains that requires a narrow level of ATPase expression for optimal function. The selected candidates exhibited sufficient tightness under aerobic conditions while, under complete anaerobiosis, driving expression of the cytosolic F1-subunit of the ATPase from E. coli to levels that resulted in unprecedented specific glucose uptake rates. We finally utilized the nirB-m promoter to demonstrate the optimization of a two-stage lactate production process by dynamically enforcing ATP wasting, which is automatically turned on in the anaerobic (growth-arrested) production phase to boost the volumetric productivity. Our results are valuable for implementing metabolic control and bioprocess design concepts that use oxygen as signal for regulation and induction.

Zymo-Parts: A Golden Gate Modular Cloning Toolbox for Heterologous Gene Expression in Zymomonas mobilis.

Zymomonas mobilis is a microorganism with extremely high sugar consumption and ethanol production rates and is generally considered to hold great potential for biotechnological applications. However, its genetic engineering is still difficult, hampering the efficient construction of genetically modified strains. In this work, we present Zymo-Parts, a modular toolbox based on Golden-Gate cloning offering a collection of promoters (including native, inducible, and synthetic constitutive promoters of varying strength), an array of terminators and several synthetic ribosomal binding sites and reporter genes. All these parts can be combined in an efficient and flexible way to achieve a desired level of gene expression, either from plasmids or via genome integration. Use of the GoldenBraid-based system also enables an assembly of operons consisting of up to five genes. We present the basic structure of the Zymo-Parts cloning system, characterize several constitutive and inducible promoters, and exemplify the construction of an operon and of chromosomal integration of a reporter gene. Finally, we demonstrate the power and utility of the Zymo-Parts toolbox for metabolic engineering applications by overexpressing a heterologous gene encoding for the lactate dehydrogenase of Escherichia coli to achieve different levels of lactate production in Z. mobilis.

StrainDesign: a comprehensive Python package for computational design of metabolic networks.

SUMMARY: Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. AVAILABILITY AND IMPLEMENTATION: The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.

Analyzing and Resolving Infeasibility in Flux Balance Analysis of Metabolic Networks.

Flux balance analysis (FBA) is a key method for the constraint-based analysis of metabolic networks. A technical problem may occur in FBA when known (e.g., measured) fluxes of certain reactions are integrated into an FBA scenario rendering the underlying linear program (LP) infeasible, for example, due to inconsistencies between some of the measured fluxes causing a violation of the steady-state or other constraints. Here, we present and compare two methods, one based on an LP and one on a quadratic program (QP), to find minimal corrections for the given flux values so that the FBA problem becomes feasible. We provide a general guide on how to treat infeasible FBA systems in practice and discuss relevant examples of potentially infeasible scenarios in core and genome-scale metabolic models. Finally, we also highlight and clarify the relationships to classical metabolic flux analysis, where solely algebraic approaches are used to compute unknown metabolic rates from measured fluxes and to balance infeasible flux scenarios.

Enabling anaerobic growth of Escherichia coli on glycerol in defined minimal medium using acetate as redox sink.

Glycerol has become an attractive substrate for bio-based production processes. However, Escherichia coli, an established production organism in the biotech industry, is not able to grow on glycerol under strictly anaerobic conditions in defined minimal medium due to redox imbalance. Despite extensive research efforts aiming to overcome these limitations, anaerobic growth of wild-type E. coli on glycerol always required either the addition of electron acceptors for anaerobic respiration (e.g. fumarate) or the supplementation with complex and relatively expensive additives (tryptone or yeast extract). In the present work, driven by model-based calculations, we propose and validate a novel and simple strategy to enable fermentative growth of E. coli on glycerol in defined minimal medium. We show that redox balance could be achieved by uptake of small amounts of acetate with subsequent reduction to ethanol via acetyl-CoA. Using a directed laboratory evolution approach, we were able to confirm this hypothesis and to generate an E. coli strain that shows, under anaerobic conditions with glycerol as the main substrate and acetate as co-substrate, robust growth (µ = 0.06 h-1), a high specific glycerol uptake rate (10.2 mmol/gDW/h) and an ethanol yield close to the theoretical maximum (0.92 mol per mol glycerol). Using further stoichiometric calculations, we also clarify why complex additives such as tryptone used in previous studies enable E. coli to achieve redox balance. Our results provide new biological insights regarding the fermentative metabolism of E. coli and offer new perspectives for sustainable production processes based on glycerol.

Cell-Free Multi-Enzyme Synthesis and Purification of Uridine Diphosphate Galactose.

High costs and low availability of UDP-galactose hampers the enzymatic synthesis of valuable oligosaccharides such as human milk oligosaccharides. Here, we report the development of a platform for the scalable, biocatalytic synthesis and purification of UDP-galactose. UDP-galactose was produced with a titer of 48 mM (27.2 g/L) in a small-scale batch process (200 µL) within 24 h using 0.02 genzyme /gproduct . Through in-situ ATP regeneration, the amount of ATP (0.6 mM) supplemented was around 240-fold lower than the stoichiometric equivalent required to achieve the final product yield. Chromatographic purification using porous graphic carbon adsorbent yielded UDP-galactose with a purity of 92 %. The synthesis was transferred to 1 L preparative scale production in a stirred tank bioreactor. To further reduce the synthesis costs here, the supernatant of cell lysates was used bypassing expensive purification of enzymes. Here, 23.4 g/L UDP-galactose were produced within 23 h with a synthesis yield of 71 % and a biocatalyst load of 0.05 gtotal_protein /gproduct . The costs for substrates per gram of UDP-galactose synthesized were around 0.26 €/g.

CNApy: a CellNetAnalyzer GUI in Python for analyzing and designing metabolic networks.

SUMMARY: Constraint-based reconstruction and analysis (COBRA) is a widely used modeling framework for analyzing and designing metabolic networks. Here, we present CNApy, an open-source cross-platform desktop application written in Python, which offers a state-of-the-art graphical front-end for the intuitive analysis of metabolic networks with COBRA methods. While the basic look-and-feel of CNApy is similar to the user interface of the MATLAB toolbox CellNetAnalyzer, it provides various enhanced features by using components of the powerful Qt library. CNApy supports a number of standard and advanced COBRA techniques and further functionalities can be easily embedded in its GUI facilitating modular extension in the future. AVAILABILITY AND IMPLEMENTATION: CNApy can be installed via conda and its source code is freely available at https://github.com/cnapy-org/CNApy under the Apache 2 license.

Deciphering the physiological response of Escherichia coli under high ATP demand.

One long-standing question in microbiology is how microbes buffer perturbations in energy metabolism. In this study, we systematically analyzed the impact of different levels of ATP demand in Escherichia coli under various conditions (aerobic and anaerobic, with and without cell growth). One key finding is that, under all conditions tested, the glucose uptake increases with rising ATP demand, but only to a critical level beyond which it drops markedly, even below wild-type levels. Focusing on anaerobic growth and using metabolomics and proteomics data in combination with a kinetic model, we show that this biphasic behavior is induced by the dual dependency of the phosphofructokinase on ATP (substrate) and ADP (allosteric activator). This mechanism buffers increased ATP demands by a higher glycolytic flux but, as shown herein, it collapses under very low ATP concentrations. Model analysis also revealed two major rate-controlling steps in the glycolysis under high ATP demand, which could be confirmed experimentally. Our results provide new insights on fundamental mechanisms of bacterial energy metabolism and guide the rational engineering of highly productive cell factories.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs.

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals.

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Increasing ATP turnover boosts productivity of 2,3-butanediol synthesis in Escherichia coli.

BACKGROUND: The alcohol 2,3-butanediol (2,3-BDO) is an important chemical and an Escherichia coli producer strain was recently engineered for bio-based production of 2,3-BDO. However, further improvements are required for realistic applications. RESULTS: Here we report that enforced ATP wasting, implemented by overexpressing the genes of the ATP-hydrolyzing F1-part of the ATPase, leads to significant increases of yield and especially of productivity of 2,3-BDO synthesis in an E. coli producer strain under various cultivation conditions. We studied aerobic and microaerobic conditions as well as growth-coupled and growth-decoupled production scenarios. In all these cases, the specific substrate uptake and 2,3-BDO synthesis rate (up to sixfold and tenfold higher, respectively) were markedly improved in the ATPase strain compared to a control strain. However, aerobic conditions generally enable higher productivities only with reduced 2,3-BDO yields while high product yields under microaerobic conditions are accompanied with low productivities. Based on these findings we finally designed and validated a three-stage process for optimal conversion of glucose to 2,3-BDO, which enables a high productivity in combination with relatively high yield. The ATPase strain showed again superior performance and finished the process twice as fast as the control strain and with higher 2,3-BDO yield. CONCLUSIONS: Our results demonstrate the high potential of enforced ATP wasting as a generic metabolic engineering strategy and we expect more applications to come in the future.

Blending industrial blast furnace gas with H2 enables Acetobacterium woodii to efficiently co-utilize CO, CO2 and H2.

In this study, the impact of gas composition (i.e. CO, CO2 and H2 partial pressures) on CO2 utilization, growth, and acetate production was investigated in batch and continuous cultures of A. woodii. Based on an industrial blast furnace gas, H2 blending was used to study the impact of H2 availability on CO2 fixation alone and together with CO using idealized gas streams. With H2 available as an additional energy source, net CO2 fixation and CO, CO2 and H2 co-utilization was achieved in gas-limited fermentations. Using industrial blast furnace gas, up to 15.1 g l-1 acetate were produced in continuous cultures. Flux balance analysis showed that intracellular fluxes and total ATP production were dependent on the availability of H2 and CO. Overall, H2 blending was shown to be a suitable control strategy for gas fermentations and demonstrated that A. woodii is an interesting host for CO2 fixation from industrial gas streams.

ATPase-based implementation of enforced ATP wasting in Saccharomyces cerevisiae for improved ethanol production.

BACKGROUND: Enforced ATP wasting has been recognized as a promising metabolic engineering strategy to enhance the microbial production of metabolites that are coupled to ATP generation. It also appears to be a suitable approach to improve production of ethanol by Saccharomyces cerevisiae. In the present study, we constructed different S. cerevisiae strains with heterologous expression of genes of the ATP-hydrolyzing F1-part of the ATPase enzyme to induce enforced ATP wasting and quantify the resulting effect on biomass and ethanol formation. RESULTS: In contrast to genomic integration, we found that episomal expression of the αßγ subunits of the F1-ATPase genes of Escherichia coli in S. cerevisiae resulted in significantly increased ATPase activity, while neither genomic integration nor episomal expression of the ß subunit from Trichoderma reesei could enhance ATPase activity. When grown in minimal medium under anaerobic growth-coupled conditions, the strains expressing E. coli's F1-ATPase genes showed significantly improved ethanol yield (increase of 10% compared to the control strain). However, elevated product formation reduces biomass formation and, therefore, volumetric productivity. We demonstrate that this negative effect can be overcome under growth-decoupled (nitrogen-starved) operation with high and constant biomass concentration. Under these conditions, which mimic the second (production) phase of a two-stage fermentation process, the ATPase-expressing strains showed significant improvement in volumetric productivity (up to 111%) compared to the control strain. CONCLUSIONS: Our study shows that expression of genes of the F1-portion of E. coli's ATPase induces ATPase activity in S. cerevisiae and can be a promising way to improve ethanol production. This ATP-wasting strategy can be easily applied to other metabolites of interest, whose formation is coupled to ATP generation.