Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans' robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%-65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.IMPORTANCEThe robustness of B. coagulans makes it a valuable microorganism for biotechnology applications; yet, this phenotype is not well understood at the cellular level. Through phenotypic characterization and systems analysis, this study elucidates the functional genomics and robustness of a novel, undomesticated strain, B. coagulans B-768, capable of utilizing inhibitory switchgrass biomass hydrolysates. The genome of B-768, enriched with carbohydrate metabolism genes, demonstrates high regulatory capacity. The coordination of proteome reallocation in Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) is critical for effective cell growth, sugar utilization, and lactate production via overflow metabolism. Overall, B-768 is a novel, robust, and promising B. coagulans strain that can be harnessed as a microbial biomanufacturing platform to produce chemicals and fuels from biomass hydrolysates.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BACKGROUND

Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions.

RESULTS

We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies.

CONCLUSIONS

We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

Engineering a Synthetic Escherichia coli Coculture for Compartmentalized de novo Biosynthesis of Isobutyl Butyrate from Mixed Sugars

Short-chain esters are versatile chemicals that can be used as flavors, fragrances, solvents, and fuels. The de novo ester biosynthesis consists of diverging and converging pathway submodules, which is challenging to engineer to achieve optimal metabolic fluxes and selective product synthesis. Compartmentalizing the pathway submodules into specialist cells that facilitate pathway modularization and labor division is a promising solution. Here, we engineered a synthetic Escherichia coli coculture with the compartmentalized sugar utilization and ester biosynthesis pathways to produce isobutyl butyrate from a mixture of glucose and xylose. To compartmentalize the sugar-utilizing pathway submodules, we engineered a xylose-utilizing E. coli specialist that selectively consumes xylose over glucose and bypasses carbon catabolite repression (CCR) while leveraging the native CCR machinery to activate a glucose-utilizing E. coli specialist. We found that the compartmentalization of sugar catabolism enabled simultaneous co-utilization of glucose and xylose by a coculture of the two E. coli specialists, improving the stability of the coculture population. Next, we modularized the isobutyl butyrate pathway into the isobutanol, butyl-CoA, and ester condensation submodules, where we distributed the isobutanol submodule to the glucose-utilizing specialist and the other submodules to the xylose-utilizing specialist. Upon compartmentalization of the isobutyl butyrate pathway submodules into these sugar-utilizing specialist cells, a robust synthetic coculture was engineered to selectively produce isobutyl butyrate, reduce the biosynthesis of unwanted ester byproducts, and improve the production titer as compared to the monoculture.

OptEnvelope: A target point guided method for growth-coupled production using knockouts

Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.

Unravelling the hidden power of esterases for biomanufacturing of short-chain esters

Microbial production of esters has recently garnered wide attention, but the current production metrics are low. Evidently, the ester precursors (organic acids and alcohols) can be accumulated at higher titers by microbes like Escherichia coli. Hence, we hypothesized that their 'direct esterification' using esterases will be efficient. We engineered esterases from various microorganisms into E. coli, along with overexpression of ethanol and lactate pathway genes. High cell density fermentation exhibited the strains possessing esterase-A (SSL76) and carbohydrate esterase (SSL74) as the potent candidates. Fed-batch fermentation at pH 7 resulted in 80 mg/L of ethyl acetate and 10 mg/L of ethyl lactate accumulation by SSL76. At pH 6, the total ester titer improved by 2.5-fold, with SSL76 producing 225 mg/L of ethyl acetate, and 18.2 mg/L of ethyl lactate, the highest reported titer in E. coli. To our knowledge, this is the first successful demonstration of short-chain ester production by engineering 'esterases' in E. coli.

A First Marine Vibrio Biocatalyst to Produce Ethanol from Alginate, which is a Rich Polysaccharide in Brown Macroalgal Biomass

The use of un-utilized feedstock and seawater for material and/or energy production using marine microbial catalysts is one potential option toward contributing to the development of a more sustainable society. Ethanol production from alginate, which is an oxidized polysaccharide present in brown seaweed, is extremely difficult due to the imbalance of reducing power in the microbial cells. Production of ethanol by such means has so far been unsuccessful using marine microbial biocatalysts. To produce ethanol from alginate, an alternative pathway consisting of a pyruvate decarboxylase gene (pdc) and an alcohol dehydrogenase II gene (adhII) derived from Zymomonas mobilis strain ZM4 was implemented into a metabolically engineered bacterium, Vibrio halioticoli, which is a representative marine alginate decomposer. No ethanol from alginate was produced in the wild-type V. halioticoli; however, the engineered V. halioticoli harboring the pdc and adhII operon (Pet operon), designated to the V. halioticoli (Pet), was able to produce 880 mg/L ethanol in maximum from 1.5% alginate for 72 h. The Pet operon also worked on the other marine alginolytic vibrios for ethanol production from alginate. This is the first case of ethanol production from alginate using marine bacterial biocatalysts under seawater-based media.

Efficient Large-Scale and Scarless Genome Engineering Enables the Construction and Screening of Bacillus subtilis Biofuel Overproducers

Bacillus subtilis is a versatile microbial cell factory that can produce valuable proteins and value-added chemicals. Long fragment editing techniques are of great importance for accelerating bacterial genome engineering to obtain desirable and genetically stable host strains. Herein, we develop an efficient CRISPR-Cas9 method for large-scale and scarless genome engineering in the Bacillus subtilis genome, which can delete up to 134.3 kb DNA fragments, 3.5 times as long as the previous report, with a positivity rate of 100%. The effects of using a heterologous NHEJ system, linear donor DNA, and various donor DNA length on the engineering efficiencies were also investigated. The CRISPR-Cas9 method was then utilized for Bacillus subtilis genome simplification and construction of a series of individual and cumulative deletion mutants, which are further screened for overproducer of isobutanol, a new generation biofuel. These results suggest that the method is a powerful genome engineering tool for constructing and screening engineered host strains with enhanced capabilities, highlighting the potential for synthetic biology and metabolic engineering.

Controlling selectivity of modular microbial biosynthesis of butyryl-CoA-derived designer esters

Short-chain esters have broad utility as flavors, fragrances, solvents, and biofuels. Controlling selectivity of ester microbial biosynthesis has been an outstanding metabolic engineering problem. In this study, we enabled the de novo fermentative microbial biosynthesis of butyryl-CoA-derived designer esters (e.g., butyl acetate, ethyl butyrate, butyl butyrate) in Escherichia coli with controllable selectivity. Using the modular design principles, we generated the butyryl-CoA-derived ester pathways as exchangeable production modules compatible with an engineered chassis cell for anaerobic production of designer esters. We designed these modules derived from an acyl-CoA submodule (e.g., acetyl-CoA, butyryl-CoA), an alcohol submodule (e.g., ethanol, butanol), a cofactor regeneration submodule (e.g., NADH), and an alcohol acetyltransferase (AAT) submodule (e.g., ATF1, SAAT) for rapid module construction and optimization by manipulating replication (e.g., plasmid copy number), transcription (e.g., promoters), translation (e.g., codon optimization), pathway enzymes, and pathway induction conditions. To further enhance production of designer esters with high selectivity, we systematically screened various strategies of protein solubilization using protein fusion tags and chaperones to improve the soluble expression of multiple pathway enzymes. Finally, our engineered ester-producing strains could achieve 19-fold increase in butyl acetate production (0.64 g/L, 96% selectivity), 6-fold increase in ethyl butyrate production (0.41 g/L, 86% selectivity), and 13-fold increase in butyl butyrate production (0.45 g/L, 54% selectivity) as compared to the initial strains. Overall, this study presented a generalizable framework to engineer modular microbial platforms for anaerobic production of butyryl-CoA-derived designer esters from renewable feedstocks.

EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search

Background

Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed mplrs—a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487, 2017). Here we test its applicability for EFM enumeration.

Results

We developed EFMlrs, a Python package that gives users access to the enumeration capabilities of mplrs. EFMlrs uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for mplrs as well as efmtool, providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, EFMlrs also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows.

Conclusion

We show that due to mplrs’s properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. EFMlrs is an open-source program that comes together with a designated workflow and can be easily installed via pip.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04417-9.

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes.

In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

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.

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all of 161 production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

Engineering of xylose metabolism in Escherichia coli for the production of valuable compounds

The lignocellulosic sugar d-xylose has recently gained prominence as an inexpensive alternative substrate for the production of value-added compounds using genetically modified organisms. Among the prokaryotes, Escherichia coli has become the de facto host for the development of engineered microbial cell factories. The favored status of E. coli resulted from a century of scientific explorations leading to a deep understanding of its systems. However, there are limited literature reviews that discuss engineered E. coli as a platform for the conversion of d-xylose to any target compounds. Additionally, available critical review articles tend to focus on products rather than the host itself. This review aims to provide relevant and current information about significant advances in the metabolic engineering of d-xylose metabolism in E. coli. This focusses on unconventional and synthetic d-xylose metabolic pathways as several review articles have already discussed the engineering of native d-xylose metabolism. This paper, in particular, is essential to those who are working on engineering of d-xylose metabolism using E. coli as the host.

Answer Set Programming for Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism

Elementary Flux Modes (EFMs) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of EFMs typically grows exponentially with the size of the metabolic network, leading to excessive computational demands, and unfortunately, a large fraction of these EFMs are not biologically feasible due to system constraints. This combinatorial explosion often prevents the complete analysis of genome-scale metabolic models. Traditionally, EFMs are computed by the double description method, an efficient algorithm based on matrix calculation; however, only a few constraints can be integrated into this computation. They must be monotonic with regard to the set inclusion of the supports; otherwise, they must be treated in post-processing and thus do not save computational time. We present aspefm, a hybrid computational tool based on Answer Set Programming (ASP) and Linear Programming (LP) that permits the computation of EFMs while implementing many different types of constraints. We apply our methodology to the Escherichia coli core model, which contains 226×106 EFMs. In considering transcriptional and environmental regulation, thermodynamic constraints, and resource usage considerations, the solution space is reduced to 1118 EFMs that can be computed directly with aspefm. The solution set, for E. coli growth on O2 gradients spanning fully aerobic to anaerobic, can be further reduced to four optimal EFMs using post-processing and Pareto front analysis.

Harnessing Natural Modularity of Metabolism with Goal Attainment Optimization to Design a Modular Chassis Cell for Production of Diverse Chemicals

Modular design is key to achieve efficient and robust systems across engineering disciplines. Modular design potentially offers advantages to engineer microbial systems for biocatalysis, bioremediation, and biosensing, overcoming the slow and costly design-build-test-learn cycles in the conventional cell engineering approach. These systems consist of a modular (chassis) cell compatible with exchangeable modules that enable programmed functions such as overproduction of a desirable chemical. We previously proposed a multiobjective optimization framework coupled with metabolic flux models to design modular cells and solved it using multiobjective evolutionary algorithms. However, such approach might not achieve solution optimality and hence limits design options for experimental implementation. In this study, we developed the goal attainment formulation compatible with optimization algorithms that guarantee solution optimality. We applied goal attainment to design an Escherichia coli modular cell capable of synthesizing all molecules in a biochemically diverse library at high yields and rates with only a few genetic manipulations. To elucidate modular organization of the designed cells, we developed a flux variance clustering (FVC) method by identifying reactions with high flux variance and clustering them to identify metabolic modules. Using FVC, we identified reaction usage patterns for different modules in the modular cell, revealing that its broad pathway compatibility is enabled by the natural modularity and flexible flux capacity of endogenous core metabolism. Overall, this study not only sheds light on modularity in metabolic networks from their topology and metabolic functions but also presents a useful synthetic biology toolbox to design modular cells with broad applications.

Chromosome-free bacterial cells are safe and programmable platforms for synthetic biology

A type of chromosome-free cell called SimCells (simple cells) has been generated from Escherichia coli, Pseudomonas putida, and Ralstonia eutropha. The removal of the native chromosomes of these bacteria was achieved by double-stranded breaks made by heterologous I-CeuI endonuclease and the degradation activity of endogenous nucleases. We have shown that the cellular machinery remained functional in these chromosome-free SimCells and was able to process various genetic circuits. This includes the glycolysis pathway (composed of 10 genes) and inducible genetic circuits. It was found that the glycolysis pathway significantly extended longevity of SimCells due to its ability to regenerate ATP and NADH/NADPH. The SimCells were able to continuously express synthetic genetic circuits for 10 d after chromosome removal. As a proof of principle, we demonstrated that SimCells can be used as a safe agent (as they cannot replicate) for bacterial therapy. SimCells were used to synthesize catechol (a potent anticancer drug) from salicylic acid to inhibit lung, brain, and soft-tissue cancer cells. SimCells represent a simplified synthetic biology chassis that can be programmed to manufacture and deliver products safely without interference from the host genome.

EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models

Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.

Towards scaling elementary flux mode computation

While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

Microbial biosynthesis of lactate esters

BACKGROUND

Green organic solvents such as lactate esters have broad industrial applications and favorable environmental profiles. Thus, manufacturing and use of these biodegradable solvents from renewable feedstocks help benefit the environment. However, to date, the direct microbial biosynthesis of lactate esters from fermentable sugars has not yet been demonstrated.

RESULTS

In this study, we present a microbial conversion platform for direct biosynthesis of lactate esters from fermentable sugars. First, we designed a pyruvate-to-lactate ester module, consisting of a lactate dehydrogenase (ldhA) to convert pyruvate to lactate, a propionate CoA-transferase (pct) to convert lactate to lactyl-CoA, and an alcohol acyltransferase (AAT) to condense lactyl-CoA and alcohol(s) to make lactate ester(s). By generating a library of five pyruvate-to-lactate ester modules with divergent AATs, we screened for the best module(s) capable of producing a wide range of linear, branched, and aromatic lactate esters with an external alcohol supply. By co-introducing a pyruvate-to-lactate ester module and an alcohol (i.e., ethanol, isobutanol) module into a modular Escherichia coli (chassis) cell, we demonstrated for the first time the microbial biosynthesis of ethyl and isobutyl lactate esters directly from glucose. In an attempt to enhance ethyl lactate production as a proof-of-study, we re-modularized the pathway into (1) the upstream module to generate the ethanol and lactate precursors and (2) the downstream module to generate lactyl-CoA and condense it with ethanol to produce the target ethyl lactate. By manipulating the metabolic fluxes of the upstream and downstream modules through plasmid copy numbers, promoters, ribosome binding sites, and environmental perturbation, we were able to probe and alleviate the metabolic bottlenecks by improving ethyl lactate production by 4.96-fold. We found that AAT is the most rate-limiting step in biosynthesis of lactate esters likely due to its low activity and specificity toward the non-natural substrate lactyl-CoA and alcohols.

CONCLUSIONS

We have successfully established the biosynthesis pathway of lactate esters from fermentable sugars and demonstrated for the first time the direct fermentative production of lactate esters from glucose using an E. coli modular cell. This study defines a cornerstone for the microbial production of lactate esters as green solvents from renewable resources with novel industrial applications.

Determination of growth-coupling strategies and their underlying principles

BACKGROUND

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.

RESULTS

We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.

CONCLUSION

Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Xylose-glucose co-fermentation to ethanol by Escherichia coli strain MS04 using single- and two-stage continuous cultures under micro-aerated conditions

Background

Simultaneous co-fermentation of mixed sugars is an important feature to consider in the production of ethanol from lignocellulosic biomass hydrolysates because it enhances the overall ethanol yield and volumetric productivity during fermentation. Continuous cultures can be used during ethanol production from lignocellulosic hydrolysates to prevent catabolite repression by glucose on other sugars, such as xylose, and thus promote the simultaneous and total consumption of sugars and reduce fermentation time. The use of single- and two-stage continuous cultures under micro-aerated conditions for simultaneous consumption of xylose and glucose, and fermentation to ethanol by ethanologenic Escherichia coli strain MS04 was studied. Mineral medium supplemented with glucose, xylose and sodium acetate, was used to compare continuous cultures performance to batch cultures.

Results

Single-stage continuous cultures under micro-aerated conditions allowed the total co-consumption of a mixture of glucose and xylose (7.5 and 42.5 g/L, respectively) in mineral medium, with steady state ethanol production of 18 g/L, and a volumetric ethanol productivity of 0.9 g/L h, when low dilution rates (0.05 h⁻¹) were used. However, the volumetric productivity was lower than the batch process under similar conditions (1.3 g/L h). Conversely, micro-aerated two-stage continuous culture enhanced the volumetric productivity up to 1.6 g/L h at a dilution rate of 0.15 h⁻¹, with a total consumption of sugars and a slight reduction of the overall ethanol yield.

Conclusions

The total and simultaneous consumption of glucose and xylose by the ethanologenic E. coli strain MS04 was accomplished by using two-stage continuous culture under micro-aerated conditions with an increase in the volumetric ethanol productivity of 23% and 78% when compared to batch and single-stage continuous cultures, respectively. Multi-stage continuous cultivation can be used to promote the simultaneous consumption of all sugars contained in biomass hydrolysates, and thus increase the volumetric ethanol productivity of the fermentation process.

Systems Metabolic Engineering of Escherichia coli Improves Coconversion of Lignocellulose-Derived Sugars

Currently, microbial conversion of lignocellulose-derived glucose and xylose to biofuels is hindered by the fact that most microbes (including Escherichia coli [E. coli], Saccharomyces cerevisiae, and Zymomonas mobilis) preferentially consume glucose first and consume xylose slowly after glucose is depleted in lignocellulosic hydrolysates. In this study, E. coli strains are developed that simultaneously utilize glucose and xylose in lignocellulosic biomass hydrolysate using genome-scale models and adaptive laboratory evolution. E. coli strains are designed and constructed that coutilize glucose and xylose and adaptively evolve them to improve glucose and xylose utilization. Whole-genome resequencing of the evolved strains find relevant mutations in metabolic and regulatory genes and the mutations' involvement in sugar coutilization is investigated. The developed strains show significantly improved coconversion of sugars in lignocellulosic biomass hydrolysates and provide a promising platform for producing next-generation biofuels.

Engineering Escherichia coli for the production of butyl octanoate from endogenous octanoyl-CoA

Medium chain esters produced from fruits and flowering plants have a number of commercial applications including use as flavour and fragrance ingredients, biofuels, and in pharmaceutical formulations. These esters are typically made via the activity of an alcohol acyl transferase (AAT) enzyme which catalyses the condensation of an alcohol and an acyl-CoA. Developing a microbial platform for medium chain ester production using AAT activity presents several obstacles, including the low product specificity of these enzymes for the desired ester and/or low endogenous substrate availability. In this study, we engineered Escherichia coli for the production of butyl octanoate from endogenously produced octanoyl-CoA. This was achieved through rational protein engineering of an AAT enzyme from Actinidia chinensis for improved octanoyl-CoA substrate specificity and metabolic engineering of E. coli fatty acid metabolism for increased endogenous octanoyl-CoA availability. This resulted in accumulation of 3.3 + 0.1 mg/L butyl octanoate as the sole product from E. coli after 48 h. This study represents a preliminary examination of the feasibility of developing E. coli platforms for the synthesis single medium chain esters from endogenous fatty acids.

Modular design: Implementing proven engineering principles in biotechnology

Modular design is at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We then discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular cell engineering in biotechnology.

Harnessing a P450 fatty acid decarboxylase from Macrococcus caseolyticus for microbial biosynthesis of odd chain terminal alkenes

Alkenes are industrially important platform chemicals with broad applications. In this study, we report a direct microbial biosynthesis of terminal alkenes from fermentable sugars by harnessing a P450 fatty acid (FA) decarboxylase from Macrococcus caseolyticus (OleTMC). We first characterized OleTMC and demonstrated its in vitro H2O2-independent activities towards linear C10:0-C18:0 FAs, with higher activity for C16:0-C18:0 FAs. Next, we engineered a de novo alkene biosynthesis pathway, consisting of OleTMC and an engineered E. coli thioesterase (TesA) with compatible substrate specificities, and introduced this pathway into E. coli for terminal alkene biosynthesis from glucose. The recombinant E. coli EcNN101 produced a total of 17.78 ± 0.63 mg/L odd-chain terminal alkenes, comprising of 0.9% ± 0.5% C11 alkene, 12.7% ± 2.2% C13 alkene, 82.7% ± 1.7% C15 alkene, and 3.7% ± 0.8% C17 alkene, and a yield of 0.87 ± 0.03 (mg/g) on glucose. To improve alkene production, we identified and overcame the electron transfer limitation in OleTMC, by introducing a two-component redox system, consisting of a putidaredoxin reductase (CamA) and a putidaredoxin (CamB) from Pseudomonas putida, into EcNN101, and demonstrated the alkene production increased ~2.8 fold. Finally, to better understand the substrate specificities of OleTMC observed, we employed in silico protein modeling to illuminate the functional role of FA binding pocket.

Characterizing the effect of expression of an acetyl-CoA synthetase insensitive to acetylation on co-utilization of glucose and acetate in batch and continuous cultures of E. coli W

BACKGROUND

Due to its high stress tolerance and low acetate secretion, Escherichia coli W is reported to be a good production host for several metabolites and recombinant proteins. However, simultaneous co-utilization of glucose and other substrates such as acetate remains a challenge. The activity of acetyl-CoA-synthetase, one of the key enzymes involved in acetate assimilation is tightly regulated on a transcriptional and post-translational level. The aim of this study was to engineer E. coli W for overexpression of an acetylation insensitive acetyl-CoA-synthetase and to characterize this strain in batch and continuous cultures using glucose, acetate and during co-utilization of both substrates.

RESULTS

Escherichia coli W engineered to overexpress an acetylation-insensitive acetyl-CoA synthetase showed a 2.7-fold increase in acetate uptake in a batch process containing glucose and high concentrations of acetate compared to a control strain, indicating more efficient co-consumption of glucose and acetate. When acetate was used as the carbon source, batch duration could significantly be decreased in the overexpression strain, possibly due to alleviation of acetate toxicity. Chemostat cultivations with different dilution rates using glucose revealed only minor differences between the overexpression and control strain. Accelerostat cultivations using dilution rates between 0.20 and 0.70 h-1 indicated that E. coli W is naturally capable of efficiently co-utilizing glucose and acetate over a broad range of specific growth rates. Expression of acetyl-CoA synthetase resulted in acetate and glucose accumulation at lower dilution rates compared to the control strain. This observation can possibly be attributed to a higher ratio between acs and pta-ackA in the overexpression strain as revealed by gene expression analysis. This would result in enhanced energy dissipation caused by an imbalance in the Pta-AckA-Acs cycle. Furthermore, yjcH and actP, genes co-transcribed with acetyl-CoA synthetase showed significant down-regulation at elevated dilution rates.

CONCLUSIONS

Escherichia coli W expressing an acetylation-insensitive acetyl-CoA synthetase was shown to be a promising candidate for mixed feed processes using glucose and acetate. Comparison between batch and continuous cultures revealed distinct differences in glucose-acetate co-utilization behavior, requiring additional investigations such as multi-omics analysis and further engineering towards even more efficient co-utilization strains of E. coli W.

Using metabolic charge production in the tricarboxylic acid cycle (QTCA) to evaluate the extracellular-electron-transfer performances of Shewanella spp

Using an electrochemical cell equipped with carbon felt electrodes (poised at +0.63 V vs. SHE), the current production capabilities of two Shewanella strains-NTOU1 and KR-12-were examined under various conditions with lactate as an electron donor. The metabolic charge produced in the tricarboxylic acid cycle (Q_TCA) was calculated by mass-balance. The data showed a linear relation between the electric coulomb production (Q_EL) and Q_TCA with an R² of 0.65. In addition, a large amount of pyruvate accumulation was observed at pH = 6, rendering Q_TCA negative. The results indicate an occurrence of an undesired cataplerotic reaction. It was also found that Q_TCA provides important information showing the oxygen-boosting TCA cycle and anodic-current generation of Shewanella spp. Linear dependence of the change in charge for biomass growth (4.52FΔn_Cell) on Q_TCA was also found as expressed by 4.52FΔn_Cell = 1.0428 Q_TCA + 0.0442, indicating that these two charge quantities are inherently identical under most of the experimental conditions. In the mediator-spiked experiments, the external addition of the mediators (ferricyanide, anthraquinone-2, 6-disulfonate, and riboflavin) beyond certain concentrations inhibited the activity of the TCA cycle, indicating that the oxidative phosphorylation is deactivated by excessive amounts of mediators, yet Shewanella spp. are constrained with regard to carrying out the substrate-level phosphorylation.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering

BACKGROUND

The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing.

RESULTS

We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli.

CONCLUSIONS

We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

Motivation

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP).

Results

Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

Availability and Implementation

The software is implemented in Matlab, and is provided as supplementary information .

Contact

hyunseob.song@pnnl.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolic Engineering of Escherichia coli K12 for Homofermentative Production of L-Lactate from Xylose

The efficient utilization of xylose is regarded as a technical barrier to the commercial production of bulk chemicals from biomass. Due to the desirable mechanical properties of polylactic acid (PLA) depending on the isomeric composition of lactate, biotechnological production of lactate with high optical pure has been increasingly focused in recent years. The main objective of this work was to construct an engineered Escherichia coli for the optically pure L-lactate production from xylose. Six chromosomal deletions (pflB, ldhA, ackA, pta, frdA, adhE) and a chromosomal integration of L-lactate dehydrogenase-encoding gene (ldhL) from Bacillus coagulans was involved in construction of E. coli KSJ316. The recombinant strain could produce L-lactate from xylose resulting in a yield of 0.91 g/g xylose. The chemical purity of L-lactate was 95.52%, and the optical purity was greater than 99%. Moreover, three strategies, including overexpression of L-lactate dehydrogenase, intensification of xylose catabolism, and addition of additives to medium, were designed to enhance the production. The results showed that they could increase the concentration of L-lactate by 32.90, 20.13, and 233.88% relative to the control, respectively. This was the first report that adding formate not only could increase the xylose utilization but also led to the fewer by-product levels.

A Prototype for Modular Cell Engineering

When aiming to produce a target chemical at high yield, titer, and productivity, various combinations of genetic parts available to build the target pathway can generate a large number of strains for characterization. This engineering approach will become increasingly laborious and expensive when seeking to develop desirable strains for optimal production of a large space of biochemicals due to extensive screening. Our recent theoretical development of modular cell (MODCELL) design principles can offer a promising solution for rapid generation of optimal strains by coupling a modular cell with exchangeable production modules in a plug-and-play fashion. In this study, we experimentally validated some design properties of MODCELL by demonstrating the following: (i) a modular (chassis) cell is required to couple with a production module, a heterologous ethanol pathway, as a testbed, (ii) degree of coupling between the modular cell and production modules can be modulated to enhance growth and product synthesis, (iii) a modular cell can be used as a host to select an optimal pyruvate decarboxylase (PDC) of the ethanol production module and to help identify a hypothetical PDC protein, and (iv) adaptive laboratory evolution based on growth selection of the modular cell can enhance growth and product synthesis rates. We envision that the MODCELL design provides a powerful prototype for modular cell engineering to rapidly create optimal strains for synthesis of a large space of biochemicals.

Synthetic Consortium of Escherichia coli for n-Butanol Production by Fermentation of the Glucose-Xylose Mixture

The microbial production of n-butanol using glucose and xylose, the major components of plant biomass, can provide a sustainable and renewable fuel as crude oil replacement. However, Escherichia coli prefers glucose to xylose as programmed by carbohydrate catabolite repression (CCR). In this study, a synthetic consortium consisting of two strains was developed by transforming the CCR-insensitive strain into a glucose-selective strain and a xylose-selective strain. Furthermore, the dual culture was reshaped by distribution of the synthetic pathway of n-butanol into two strains. Consequently, the co-culture system enabled effective co-utilization of both sugars and production of 5.2 g/L n-butanol at 30 h. The result leads to the conversion yield and productivity accounting for 63% of the theoretical yield and 0.17 g L^-1 h^-1, respectively. Overall, the technology platform as proposed is useful for production of other value-added chemicals, which require complicated pathways for their synthesis by microbial fermentation of a sugar mixture.

Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

Overflow metabolism and growth cessation in Clostridium thermocellum DSM1313 during high cellulose loading fermentations

As a model thermophilic bacterium for the production of second-generation biofuels, the metabolism of Clostridium thermocellum has been widely studied. However, most studies have characterized C. thermocellum metabolism for growth at relatively low substrate concentrations. This outlook is not industrially relevant, however, as commercial viability requires substrate loadings of at least 100 g/L cellulosic materials. Recently, a wild-type C. thermocellum DSM1313 was cultured on high cellulose loading batch fermentations and reported to produce a wide range of fermentative products not seen at lower substrate concentrations, opening the door for a more in-depth analysis of how this organism will behave in industrially relevant conditions. In this work, we elucidated the interconnectedness of overflow metabolism and growth cessation in C. thermocellum during high cellulose loading batch fermentations (100 g/L). Metabolic flux and thermodynamic analyses suggested that hydrogen and formate accumulation perturbed the complex redox metabolism and limited conversion of pyruvate to acetyl-CoA conversion, likely leading to overflow metabolism and growth cessation in C. thermocellum. Pyruvate formate lyase (PFL) acts as an important redox valve and its flux is inhibited by formate accumulation. Finally, we demonstrated that manipulation of fermentation conditions to alleviate hydrogen accumulation could dramatically alter the fate of pyruvate, providing valuable insight into process design for enhanced C. thermocellum production of chemicals and biofuels. Biotechnol. Bioeng. 2017;114: 2592-2604. © 2017 Wiley Periodicals, Inc.

The contribution of bacterial genome engineering to sustainable development

The United Nations’ Sustainable Development Goals define important challenges for the prosperous development of mankind. To reach several of these goals, among them the production of value‐added compounds, improved economic and ecologic balance of production processes, prevention of climate change and protection of ecosystems, the use of engineered bacteria can make valuable contributions. We discuss the strategies for genome engineering and how they can be applied to meet the United Nations’ goals for sustainable development.

Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms

Computational modelling of metabolic networks has become an established procedure in the metabolic engineering of production strains. One key principle that is frequently used to guide the rational design of microbial cell factories is the stoichiometric coupling of growth and product synthesis, which makes production of the desired compound obligatory for growth. Here we show that the coupling of growth and production is feasible under appropriate conditions for almost all metabolites in genome-scale metabolic models of five major production organisms. These organisms comprise eukaryotes and prokaryotes as well as heterotrophic and photoautotrophic organisms, which shows that growth coupling as a strain design principle has a wide applicability. The feasibility of coupling is proven by calculating appropriate reaction knockouts, which enforce the coupling behaviour. The study presented here is the most comprehensive computational investigation of growth-coupled production so far and its results are of fundamental importance for rational metabolic engineering.

Fermentation of lactose to ethanol in cheese whey permeate and concentrated permeate by engineered Escherichia coli

BACKGROUND

Whey permeate is a lactose-rich effluent remaining after protein extraction from milk-resulting cheese whey, an abundant dairy waste. The lactose to ethanol fermentation can complete whey valorization chain by decreasing dairy waste polluting potential, due to its nutritional load, and producing a biofuel from renewable source at the same time. Wild type and engineered microorganisms have been proposed as fermentation biocatalysts. However, they present different drawbacks (e.g., nutritional supplements requirement, high transcriptional demand of recombinant genes, precise oxygen level, and substrate inhibition) which limit the industrial attractiveness of such conversion process. In this work, we aim to engineer a new bacterial biocatalyst, specific for dairy waste fermentation.

RESULTS

We metabolically engineered eight Escherichia coli strains via a new expression plasmid with the pyruvate-to-ethanol conversion genes, and we carried out the selection of the best strain among the candidates, in terms of growth in permeate, lactose consumption and ethanol formation. We finally showed that the selected engineered microbe (W strain) is able to efficiently ferment permeate and concentrated permeate, without nutritional supplements, in pH-controlled bioreactor. In the conditions tested in this work, the selected biocatalyst could complete the fermentation of permeate and concentrated permeate in about 50 and 85 h on average, producing up to 17 and 40 g/l of ethanol, respectively.

CONCLUSIONS

To our knowledge, this is the first report showing efficient ethanol production from the lactose contained in whey permeate with engineered E. coli. The selected strain is amenable to further metabolic optimization and represents an advance towards efficient biofuel production from industrial waste stream.

Model-based biotechnological potential analysis of Kluyveromyces marxianus central metabolism

The non-conventional yeast Kluyveromyces marxianus is an emerging industrial producer for many biotechnological processes. Here, we show the application of a biomass-linked stoichiometric model of central metabolism that is experimentally validated, and mass and charge balanced for assessing the carbon conversion efficiency of wild type and modified K. marxianus. Pairs of substrates (lactose, glucose, inulin, xylose) and products (ethanol, acetate, lactate, glycerol, ethyl acetate, succinate, glutamate, phenylethanol and phenylalanine) are examined by various modelling and optimisation methods. Our model reveals the organism's potential for industrial application and metabolic engineering. Modelling results imply that the aeration regime can be used as a tool to optimise product yield and flux distribution in K. marxianus. Also rebalancing NADH and NADPH utilisation can be used to improve the efficiency of substrate conversion. Xylose is identified as a biotechnologically promising substrate for K. marxianus.