Geobacter sulfurreducens strain engineered for increased rates of respiration

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Quantifying Geobacter sulfurreducens growth: A mathematical model based on acetate concentration as an oxidizing substrate

We developed a mathematical model to simulate dynamics associated with the proliferation of Geobacter and ultimately optimize cellular operation by analyzing the interaction of its components. The model comprises two segments: an initial part comprising a logistic form and a subsequent segment that incorporates acetate oxidation as a saturation term for the microbial nutrient medium. Given that four parameters can be obtained by minimizing the square root of the mean square error between experimental Geobacter growth and the mathematical model, the model underscores the importance of incorporating nonlinear terms. The determined parameter values closely align with experimental data, providing insights into the mechanisms that govern Geobacter proliferation. Furthermore, the model has been transformed into a scaleless equation with only two parameters to simplify the exploration of qualitative properties. This allowed us to conduct stability analysis of the fixed point and construct a co-dimension two bifurcation diagram.

Efficient and precise control of gene expression in Geobacter sulfurreducens through new genetic elements and tools for pollutant conversion

Geobacter species, exhibiting exceptional extracellular electron transfer aptitude, hold great potential for applications in pollution remediation, bioenergy production, and natural elemental cycles. Nonetheless, a scarcity of well-characterized genetic elements and gene expression tools constrains the effective and precise fine-tuning of gene expression in Geobacter species, thereby limiting their applications. Here, we examined a suite of genetic elements and developed a new genetic editing tool in Geobacter sulfurreducens to enhance their pollutant conversion capacity. First, the performances of the widely used inducible promoters, constitutive promoters, and ribosomal binding sites (RBSs) elements in G. sulfurreducens were quantitatively evaluated. Also, six native promoters with superior expression levels than constitutive promoters were identified on the genome of G. sulfurreducens. Employing the characterized genetic elements, the clustered regularly interspaced short palindromic repeats interference (CRISPRi) system was constructed in G. sulfurreducens to achieve the repression of an essential gene-aroK and morphogenic genes-ftsZ and mreB. Finally, applying the engineered strain to the reduction of tungsten trioxide (WO3 ), methyl orange (MO), and Cr(VI), We found that morphological elongation through ftsZ repression amplified the extracellular electron transfer proficiency of G. sulfurreducens and facilitated its contaminant transformation efficiency. These new systems provide rapid, versatile, and scalable tools poised to expedite advancements in Geobacter genomic engineering to favor environmental and other biotechnological applications.

Multiple roles of released c-type cytochromes in tuning electron transport and physiological status of Geobacter sulfurreducens

Dissimilatory metal-reducing bacteria (DMRB) can transfer electrons to extracellular insoluble electron acceptors and play important roles in geochemical cycling, biocorrosion, environmental remediation, and bioenergy generation. c-type cytochromes (c-Cyts) are synthesized by DMRB and usually transported to the cell surface to form modularized electron transport conduits through protein assembly, while some of them are released as extracellularly free-moving electron carriers in growth to promote electron transport. However, the type of these released c-Cyts, the timing of their release, and the functions they perform have not been unrevealed yet. In this work, after characterizing the types of c-Cyts released by Geobacter sulfurreducens under a variety of cultivation conditions, we found that these c-Cyts accumulated up to micromolar concentrations in the surrounding medium and conserved their chemical activities. Further studies demonstrated that the presence of c-Cyts accelerated the process of microbial extracellular electron transfer and mediated long-distance electron transfer. In particular, the presence of c-Cyts promoted the microbial respiration and affected the physiological state of the microbial community. In addition, c-Cyts were observed to be adsorbed on the surface of insoluble electron acceptors and modify electron acceptors. These results reveal the overlooked multiple roles of the released c-Cyts in acting as public goods, delivering electrons, modifying electron acceptors, and even regulating bacterial community structure in natural and artificial environments.

Can polymer-degrading microorganisms solve the bottleneck of plastics' environmental challenges?

Increasing world population and industrial activities have enhanced anthropogenic pollution, plastic pollution being especially alarming. So, plastics should be recycled and/or make them biodegradable. Chemical and physical remediating methods are usually energy consuming and costly. In addition, they are not ecofriendly and usually produce toxic byproducts. Bioremediation is a proper option as it is cost-efficient and environmentally friendly. Plastic production and consumption are increasing daily, and, as a consequence, more microorganisms are exposed to these nonbiodegradable polymers. Therefore, investigating new efficient microorganisms and increasing the knowledge about their biology can pave the way for efficient and feasible plastic bioremediation processes. In this sense, omics, systems biology and bioinformatics are three important fields to analyze the biodegradation pathways in microorganisms. Based on the above-mentioned technologies, researchers can engineer microorganisms with specific desired properties to make bioremediation more efficient.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

Trehalose Recycling Promotes Energy-Efficient Biosynthesis of the Mycobacterial Cell Envelope

The mycomembrane layer of the mycobacterial cell envelope is a barrier to environmental, immune, and antibiotic insults. There is considerable evidence of mycomembrane plasticity during infection and in response to host-mimicking stresses.

The mycomembrane layer of the mycobacterial cell envelope is a barrier to environmental, immune, and antibiotic insults. There is considerable evidence of mycomembrane plasticity during infection and in response to host-mimicking stresses. Since mycobacteria are resource and energy limited under these conditions, it is likely that remodeling has distinct requirements from those of the well-characterized biosynthetic program that operates during unrestricted growth. Unexpectedly, we found that mycomembrane remodeling in nutrient-starved, nonreplicating mycobacteria includes synthesis in addition to turnover. Mycomembrane synthesis under these conditions occurs along the cell periphery, in contrast to the polar assembly of actively growing cells, and both liberates and relies on the nonmammalian disaccharide trehalose. In the absence of trehalose recycling, de novo trehalose synthesis fuels mycomembrane remodeling. However, mycobacteria experience ATP depletion, enhanced respiration, and redox stress, hallmarks of futile cycling and the collateral dysfunction elicited by some bactericidal antibiotics. Inefficient energy metabolism compromises the survival of trehalose recycling mutants in macrophages. Our data suggest that trehalose recycling alleviates the energetic burden of mycomembrane remodeling under stress. Cell envelope recycling pathways are emerging targets for sensitizing resource-limited bacterial pathogens to host and antibiotic pressure.

Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism’s phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation.

Microbial energy management-A product of three broad tradeoffs

Wherever thermodynamics allows, microbial life has evolved to transform and harness energy. Microbial life thus abounds in the most unexpected places, enabled by profound metabolic diversity. Within this diversity, energy is transformed primarily through variations on a few core mechanisms. Energy is further managed by the physiological processes of cell growth and maintenance that use energy. Some aspects of microbial physiology are streamlined for energetic efficiency while other aspects seem suboptimal or even wasteful. We propose that the energy that a microbe harnesses and devotes to growth and maintenance is a product of three broad tradeoffs: (i) economic, trading enzyme synthesis or operational cost for functional benefit, (ii) environmental, trading optimization for a single environment for adaptability to multiple environments, and (iii) thermodynamic, trading energetic yield for forward metabolic flux. Consideration of these tradeoffs allows one to reconcile features of microbial physiology that seem to opposingly promote either energetic efficiency or waste.

Effects of graphite and Mn ore media on electro-active bacteria enrichment and fate of antibiotic and corresponding resistance gene in up flow microbial fuel cell constructed wetland

This study assessed the influence of substrate type on pollutants removal, antibiotic resistance gene (ARG) fate and bacterial community evolution in up-flow microbial fuel cell constructed wetlands (UCW-MFC) with graphite and Mn ore electrode substrates. Better COD removal and higher bacterial community diversity and electricity generation performance were achieved in Mn ore constructed UCW-MFC (Mn). However, the lower concentration of sulfadiazine (SDZ) and the total abundances of ARGs were obtained in the effluent in the graphite constructed UCW-MFC (s), which may be related to higher graphite adsorption and filter capacity. Notably, both reactors can remove more than 97.8% of ciprofloxacin. In addition, significant negative correlations were observed between SDZ, COD concentration, ARG abundances and bacterial a-diversity indices. The LEfse analysis revealed significantly different bacterial communities due to the substrate differences in the two reactors, and Geobacter, a typical model electro-active bacteria (EAB), was greatly enriched on the anode of UCW-MFC (Mn). In contrast, the relative abundance of methanogens (Methanosaeta) was inhibited. PICRUSt analysis results further demonstrated that the abundance of extracellular electron transfer related functional genes was increased, but the methanogen function genes and multiple antibiotic resistance genes in UCW-MFC (Mn) anode were reduced. Redundancy analyses indicated that substrate type, antibiotic accumulation and bacterial community were the main factors affecting ARGs. Moreover, the potential ARG hosts and the co-occurrence of ARGs and intI1 were revealed by network analysis.

Less biomass and intracellular glutamate in anodic biofilms lead to efficient electricity generation by microbial fuel cells

BACKGROUND

Using a microbial fuel cell (MFC), we observed that a complex microbial community decomposed starch and transferred electrons to a graphite felt anode to generate current. In spite of the same reactor configuration, inoculum, substrate, temperature, and pH, MFCs produced different current and power density. To understand which factor(s) affected electricity generation, here, we analyzed a complex microbial community in an anodic biofilm and fermentation broth using Illumina MiSeq sequencing and metabolomics.

RESULTS

Microbial biomass on the anode was lower in MFCs generating more electricity (0.09-0.16 mg cm^-2-anode) than in those generating less electricity (0.60-2.80 mg cm^-2-anode), while being equal (3890-4196 mg L^-1-broth) in the fermentation broth over the same operational period. Chemical oxygen demand removal and acetate concentration were also similar in fermentation broths. MFCs generating more electricity had relatively more exoelectrogenic bacteria, such as Geobacter sp., but fewer acetate-utilizing Methanosarcina sp. and/or Lactococcus sp. in anodic biofilms. Accordingly, anodic biofilms generating more electricity presented higher levels of most intracellular metabolites related to the tricarboxylic acid cycle and a higher intracellular ATP/ADP ratio, but a lower intracellular NADH/NAD⁺ ratio. Moreover, the level of intracellular glutamate, an essential metabolite for microbial anabolic reactions, correlated negatively with current density.

CONCLUSION

Microbial growth on the anode and intracellular glutamate levels negatively affect electricity generation by MFCs. Reduced formation of anodic biofilm, in which intracellular glutamate concentration is 33.9 μmol g-cell^-1 or less, favors the growth of acetate-utilizing Geobacter sp. on the anode and improves current generation.

IN SILICO GENOME-SCALE RECONSTRUCTION AND ANALYSIS OF THE SHEWANELLA LOIHICA PV-4 METABOLIC NETWORK

Microbial Fuel Cells (MFCs) are devices that generate electricity directly from organic compounds with microbes (electricigens) serving as anodic catalysts. As a novel environment-friendly energy source, MFCs have extensive practical value. Since the biological features and metabolic mechanism of electricigens have a great effect on the electricity production of MFCs, it is a big deal to screen strains with high electricity productivity for improving the power output of MFC. Reconstructions and simulations of metabolic networks are of significant help in studying the metabolism of microorganisms so as to guide gene engineering and metabolic engineering to improve their power-generating efficiency. Herein, we reconstructed a genome-scale constraint-based metabolic network model of Shewanella loihica PV-4, an important electricigen, based on its genomic functional annotations, reaction databases and published metabolic network models of seven microorganisms. The resulting network model iGX790 consists of 902 reactions (including 71 exchange reactions), 798 metabolites and 790 genes, covering the main pathways such as carbon metabolism, energy metabolism, amino acid metabolism, nucleic acid metabolism and lipid metabolism. Using the model, we simulated the growth rate, the maximal synthetic rate of ATP, the flux variability analysis of metabolic network, gene deletion and so on to examine the metabolism of S. loihica PV-4.

Construction of a Geobacter Strain With Exceptional Growth on Cathodes

Insoluble extracellular electron donors are important sources of energy for anaerobic respiration in biogeochemical cycling and in diverse practical applications. The previous lack of a genetically tractable model microorganism that could be grown to high densities under anaerobic conditions in pure culture with an insoluble extracellular electron donor has stymied efforts to better understand this form of respiration. We report here on the design of a strain of Geobacter sulfurreducens, designated strain ACL, which grows as thick (ca. 35 μm) confluent biofilms on graphite cathodes poised at -500 mV (versus Ag/AgCl) with fumarate as the electron acceptor. Sustained maximum current consumption rates were >0.8 A/m², which is >10-fold higher than the current consumption of the wild-type strain. The improved function on the cathode was achieved by introducing genes for an ATP-dependent citrate lyase, completing the complement of enzymes needed for a reverse TCA cycle for the synthesis of biosynthetic precursors from carbon dioxide. Strain ACL provides an important model organism for elucidating the mechanisms for effective anaerobic growth with an insoluble extracellular electron donor and may offer unique possibilities as a chassis for the introduction of synthetic metabolic pathways for the production of commodities with electrons derived from electrodes.

Genetic switches and related tools for controlling gene expression and electrical outputs of Geobacter sulfurreducens

Physiological studies and biotechnology applications of Geobacter species have been limited by a lack of genetic tools. Therefore, potential additional molecular strategies for controlling metabolism were explored. When the gene for citrate synthase, or acetyl-CoA transferase, was placed under the control of a LacI/IPTG regulator/inducer system, cells grew on acetate only in the presence of IPTG. The TetR/AT system could also be used to control citrate synthase gene expression and acetate metabolism. A strain that required IPTG for growth on D-lactate was constructed by placing the gene for D-lactate dehydrogenase under the control of the LacI/IPTG system. D-Lactate served as an inducer in a strain in which a D-lactate responsive promoter and transcription repressor were used to control citrate synthase expression. Iron- and potassium-responsive systems were successfully incorporated to regulate citrate synthase expression and growth on acetate. Linking the appropriate degradation tags on the citrate synthase protein made it possible to control acetate metabolism with either the endogenous ClpXP or exogenous Lon protease and tag system. The ability to control current output from Geobacter biofilms and the construction of an AND logic gate for acetate metabolism suggested that the tools developed may be applicable for biosensor and biocomputing applications.

Hardwiring microbes via direct interspecies electron transfer: mechanisms and applications

Multicellular microbial communities are important catalysts in engineered systems designed to treat wastewater, remediate contaminated sediments, and produce energy from biomass. Understanding the interspecies interactions within them is therefore essential to design effective processes. The flow of electrons within these communities is especially important in the determination of reaction possibilities (thermodynamics) and rates (kinetics). Conventional models of electron transfer incorporate the diffusion of metabolites generated by one organism and consumed by a second, frequently referred to as mediated interspecies electron transfer (MIET). Evidence has emerged in the last decade that another method, called direct interspecies electron transfer (DIET), may occur between organisms or in conjunction with electrically conductive materials. Recent research has suggested that DIET can be stimulated in engineered systems to improve desired treatment goals and energy recovery in systems such as anaerobic digesters and microbial electrochemical technologies. In this review, we summarize the latest understanding of DIET mechanisms, the associated microorganisms, and the underlying thermodynamics. We also critically examine approaches to stimulate DIET in engineered systems and assess their effectiveness. We find that in most cases attempts to promote DIET in mixed culture systems do not yield the improvements expected based on defined culture studies. Uncertainties of other processes that may be co-occurring in real systems, such as contaminant sorption and biofilm promotion, need to be further investigated. We conclude by identifying areas of future research related to DIET and its application in biological treatment processes.

Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.

Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum

Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome-scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock-in transformants. Here we present a genome-scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

RNA Futile Cycling in Model Persisters Derived from MazF Accumulation

Metabolism plays an important role in the persister phenotype, as evidenced by the number of strategies that perturb metabolism to sabotage this troublesome subpopulation. However, the absence of techniques to isolate high-purity populations of native persisters has precluded direct measurement of persister metabolism. To address this technical challenge, we studied Escherichia coli populations whose growth had been inhibited by the accumulation of the MazF toxin, which catalyzes RNA cleavage, as a model system for persistence. Using chromosomally integrated, orthogonally inducible promoters to express MazF and its antitoxin MazE, bacterial populations that were almost entirely tolerant to fluoroquinolone and β-lactam antibiotics were obtained upon MazF accumulation, and these were subjected to direct metabolic measurements. While MazF model persisters were nonreplicative, they maintained substantial oxygen and glucose consumption. Metabolomic analysis revealed accumulation of all four ribonucleotide monophosphates (NMPs). These results are consistent with a MazF-catalyzed RNA futile cycle, where the energy derived from catabolism is dissipated through continuous transcription and MazF-mediated RNA degradation. When transcription was inhibited, oxygen consumption and glucose uptake decreased, and nucleotide triphosphates (NTPs) and NTP/NMP ratios increased. Interestingly, the MazF-inhibited cells were sensitive to aminoglycosides, and this sensitivity was blocked by inhibition of transcription. Thus, in MazF model persisters, futile cycles of RNA synthesis and degradation result in both significant metabolic demands and aminoglycoside sensitivity.

IMPORTANCE

Metabolism plays a critical role in controlling each stage of bacterial persistence (shutdown, stasis, and reawakening). In this work, we generated an E. coli strain in which the MazE antitoxin and MazF toxin were artificially and independently inducible, and we used this strain to generate model persisters and study their metabolism. We found that even though growth of the model persisters was inhibited, they remained highly metabolically active. We further uncovered a futile cycle driven by continued transcription and MazF-mediated transcript degradation that dissipated the energy derived from carbon catabolism. Interestingly, the existence of this futile cycle acted as an Achilles' heel for MazF model persisters, rendering them vulnerable to killing by aminoglycosides.

Co-evolution of strain design methods based on flux balance and elementary mode analysis

More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last 10 years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications.

•

Computational strain design methods are divided into two main families.

•

We trace the evolutionary history of these two families.

•

Surveyed successful cases of model-guided strain design for industrial applications.

•

Most proposed methods have not yet been tested in real applications.

•

Agreement between in silico and in vivo results shows potential of tested methods.

Futile cycling increases sensitivity toward oxidative stress in Escherichia coli

Reactive oxygen species (ROS) are toxic molecules utilized by the immune system to combat invading pathogens. Recent evidence suggests that inefficiencies in ATP production or usage can lead to increased endogenous ROS production and sensitivity to oxidative stress in bacteria. With this as inspiration, and knowledge that ATP is required for a number of DNA repair mechanisms, we hypothesized that futile cycling would be an effective way to increase sensitivity to oxidative stress. We developed a mixed integer linear optimization framework to identify experimentally-tractable futile cycles, and confirmed metabolic modeling predictions that futile cycling depresses growth rate, and increases both O2 consumption and ROS production per biomass generated. Further, intracellular ATP was decreased and sensitivity to oxidative stress increased in all actively cycling strains compared to their catalytically inactive controls. This research establishes a fundamental connection between ATP metabolism, endogenous ROS production, and tolerance toward oxidative stress in bacteria.

Computational approaches for microalgal biofuel optimization: a review

The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Metabolic modeling of spatial heterogeneity of biofilms in microbial fuel cells reveals substrate limitations in electrical current generation

Microbial fuel cells (MFCs) have been proposed as an alternative energy resource for the conversion of organic compounds to electricity. In an MFC, microorganisms such as Geobacter sulfurreducens form an anode-associated biofilm that can completely oxidize organic matter (electron donor) to carbon dioxide with direct electron transfer to the anode (electron acceptor). Mathematical models are useful in analyzing biofilm processes; however, existing models rely on Nernst-Monod type expressions, and evaluate extracellular processes separated from the intracellular metabolism of the microorganism. Thus, models that combine both extracellular and intracellular components, while addressing spatial heterogeneity, are essential for improved representation of biofilm processes. The goal of this work is to develop a model that integrates genome-scale metabolic models with the model of biofilm environment. This integrated model shows the variations of electrical current production and biofilm thickness under the presence/absence of NH4 in the bulk solution, and under varying maintenance energy demands. Further, sensitivity analysis suggested that conductivity is not limiting electrical current generation and that increasing cell density can lead to enhanced current generation. In addition, the modeling results also highlight instances such as the transformation into respiring cells, where the mechanism of electrical current generation during biofilm development is not yet clearly understood.

Toward quantitative understanding on microbial community structure and functioning: a modeling-centered approach using degradation of marine oil spills as example

Molecular ecology approaches are rapidly advancing our insights into the microorganisms involved in the degradation of marine oil spills and their metabolic potentials. Yet, many questions remain open: how do oil-degrading microbial communities assemble in terms of functional diversity, species abundances and organization and what are the drivers? How do the functional properties of microorganisms scale to processes at the ecosystem level? How does mass flow among species, and which factors and species control and regulate fluxes, stability and other ecosystem functions? Can generic rules on oil-degradation be derived, and what drivers underlie these rules? How can we engineer oil-degrading microbial communities such that toxic polycyclic aromatic hydrocarbons are degraded faster? These types of questions apply to the field of microbial ecology in general. We outline how recent advances in single-species systems biology might be extended to help answer these questions. We argue that bottom-up mechanistic modeling allows deciphering the respective roles and interactions among microorganisms. In particular constraint-based, metagenome-derived community-scale flux balance analysis appears suited for this goal as it allows calculating degradation-related fluxes based on physiological constraints and growth strategies, without needing detailed kinetic information. We subsequently discuss what is required to make these approaches successful, and identify a need to better understand microbial physiology in order to advance microbial ecology. We advocate the development of databases containing microbial physiological data. Answering the posed questions is far from trivial. Oil-degrading communities are, however, an attractive setting to start testing systems biology-derived models and hypotheses as they are relatively simple in diversity and key activities, with several key players being isolated and a high availability of experimental data and approaches.

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.

Analysis of enhanced current-generating mechanism of Geobacter sulfurreducens strain via model-driven metabolism simulation

Microbial fuel cells (MFCs) are a class of ideal technologies that function via anaerobic respiration of electricigens, which bring current generation and environmental restoration together. An in-depth understanding of microbial metabolism is of great importance in engineering microbes to further improve their respiration. We employed flux balance analysis and selected Fe(iii) as a substitute for the electrode to simulate current-generating metabolism of Geobacter sulfurreducens PCA with a fixed acetate uptake rate. Simulation results indicated the fluxes of reactions directing acetate towards dissimilation to generate electrons increased under the suboptimal growth condition, resulting in an increase in the respiration rate and a decrease in the growth rate. The results revealed the competitive relationship between oxidative respiration and cell growth during the metabolism of microbe current generation. The results helped us quantitatively understand why microbes growing slowly have the potential to make good use of fuel in MFCs. At the same time, slow growth does not necessarily result in speedy respiration. Alternative respirations may exist under the same growth state due to redundant pathways in the metabolic network. The big difference between the maximum and minimum respiration mainly results from the total formate secretion. With iterative flux variability analysis, a relatively ideal model of variant of G. sulfurreducens PCA was reconstructed by deleting several enzymes in the wild model, which could reach simultaneous suboptimal growth and maximum respiration. Under this ideal condition, flux towards extracellular electron transfer rather than for biosynthesis is beneficial for the conversion of organic matter to electricity without large accumulations of biomass and electricigens may maximize utilization of limited fuel. Our simulations will provide an insight into the enhanced current-generating mechanism and identify theoretical range of respiration rates for guiding strain improvement in MFCs.

MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks

Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

Comparative genomic analysis of Geobacter sulfurreducens KN400, a strain with enhanced capacity for extracellular electron transfer and electricity production

Background

A new strain of Geobacter sulfurreducens, strain KN400, produces more electrical current in microbial fuel cells and reduces insoluble Fe(III) oxides much faster than the wildtype strain, PCA. The genome of KN400 was compared to wildtype with the goal of discovering how the network for extracellular electron transfer has changed and how these two strains evolved.

Results

Both genomes were re-annotated, resulting in 14 fewer genes (net) in the PCA genome; 28 fewer (net) in the KN400 genome; and ca. 400 gene start and stop sites moved. 96% of genes in KN400 had clear orthologs with conserved synteny in PCA. Most of the remaining genes were in regions of genomic mobility and were strain-specific or conserved in other Geobacteraceae, indicating that the changes occurred post-divergence. There were 27,270 single nucleotide polymorphisms (SNP) between the genomes. There was significant enrichment for SNP locations in non-coding or synonymous amino acid sites, indicating significant selective pressure since the divergence. 25% of orthologs had sequence differences, and this set was enriched in phosphorylation and ATP-dependent enzymes. Substantial sequence differences (at least 12 non-synonymous SNP/kb) were found in 3.6% of the orthologs, and this set was enriched in cytochromes and integral membrane proteins. Genes known to be involved in electron transport, those used in the metabolic cell model, and those that exhibit changes in expression during growth in microbial fuel cells were examined in detail.

Conclusions

The improvement in external electron transfer in the KN400 strain does not appear to be due to novel gene acquisition, but rather to changes in the common metabolic network. The increase in electron transfer rate and yield in KN400 may be due to changes in carbon flux towards oxidation pathways and to changes in ATP metabolism, both of which indicate that the overall energy state of the cell may be different. The electrically conductive pili appear to be unchanged, but cytochrome folding, localization, and redox potentials may all be affected, which would alter the electrical connection between the cell and the substrate.

Real-time spatial gene expression analysis within current-producing biofilms

The expression of genes involved in central metabolism and extracellular electron transfer was examined in real-time in current-producing anode biofilms of Geobacter sulfurreducens. Strains of G. sulfurreducens were generated, in which the expression of the gene for a short half-life fluorescent protein was placed under control of the promoter of the genes of interest. Anode biofilms were grown in a chamber that permitted direct examination of the cell fluorescence with confocal scanning laser microscopy. Studies on nifD and citrate synthase expression in response to environmental changes demonstrated that the reporter system revealed initiation and termination of gene transcription. Uniform expression throughout the biofilms was noted for the genes for citrate synthase; PilA, the structural protein of the conductive pili; and OmcZ, a c-type cytochrome essential for optimal current production, which was localized at the anode-biofilm interface. These results demonstrate that even cells at great distance from the anode, or within expected low-pH zones, are metabolically active and likely to contribute to current production and that there are factors other than gene expression differences influencing the distribution of OmcZ. This real-time reporter approach is likely to be a useful tool in optimizing the design of technologies relying on microbe-electrode interactions.

Charge transport through Geobacter sulfurreducens biofilms grown on graphite rods

Biofilms of the electroactive bacterium Geobacter sulfurreducens were induced to grow on graphite-rod electrodes under a potential of 0 V (vs Ag/AgCl) in the presence of acetate as an electron donor. Increased anodic currents for bioelectrocatalytic oxidation of acetate were obtained when the electrodes were incubated for longer periods with periodic electron-donor feeding. The maximum current density for acetate oxidation increased 2.8-fold, and the biofilm thickness increased by 4.25-fold, over a time period of 83-147 h. Cyclic voltammetry in the presence of acetate supports a model of heterogeneous electron transfer, one electron at time, from biofilm to electrode through a dominant redox species centered at -0.41 V vs Ag/AgCl. Voltammetry performed under nonturnover conditions provided an estimate of the surface coverage of the redox species of 25 nmol/cm(2). This value was used to estimate a redox species concentration of 7.3 mM within the 34-μm-thick biofilm and a charge-transport diffusion coefficient of 3.6 × 10(-7) cm(2)/s. This value of diffusion coefficient is greater than that observed in traditional thin-film voltammetric studies with redox polymer films containing much higher surface concentrations of redox species and might be associated with proton transport to ensure electroneutrality within the biofilm upon electrolysis.

Enhanced electricity production by use of reconstituted artificial consortia of estuarine bacteria grown as biofilms

Microbial fuel cells (MFCs) can convert organic compounds directly into electricity by catalytic oxidation, and although MFCs have attracted considerable interest, there is little information on the electricity-generating potential of artificial bacterial biofilms. We have used acetate-fed MFCs inoculated with sediment, with two-chamber bottles and carbon cloth electrodes to deliver a maximum power output of ~175 mW · m(-2) and a stable power output of ~105 mW · m(-2). Power production was by direct transfer of electrons to the anode from bacterial consortia growing on the anode, as confirmed by cyclic voltammetry (CV) and scanning electron microscopy (SEM). Twenty different species (74 strains) of bacteria were isolated from the consortium under anaerobic conditions and cultured in the laboratory, of which 34% were found to be exoelectrogens in single-species studies. Exoelectrogenesis by members of the genera Vibrio , Enterobacter , and Citrobacter and by Bacillus stratosphericus was confirmed, by use of culture-based methods, for the first time. An MFC with a natural bacterial consortium showed higher power densities than those obtained with single strains. In addition, the maximum power output could be further increased to ~200 mW · m(-2) when an artificial consortium consisting of the best 25 exoelectrogenic isolates was used, demonstrating the potential for increased performance and underlying the importance of artificial biofilms for increasing power output.

Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments

The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.

100 years of microbial electricity production: three concepts for the future

Bioelectrochemical systems (BES) have been explored according to three main concepts: to produce energy from organic substrates, to generate products and to provide specific environmental services. In this work, by using an engineering approach, biological conversion rates are calculated for BES resp. anaerobic digestion. These rates are compared with currents produced by chemical batteries and chemical fuel cells in order to position BES in the 'energy'-market. To evaluate the potential of generating various products, the biochemistry behind the biological conversion rates is examined in relation to terminal electron transfer molecules. By comparing kinetics rather than thermodynamics, more insight is gained in the biological bottlenecks that hamper a BES. The short-term future for BES research and its possible application is situated in smart niches in sustainable environmental development, i.e. in processes where no large currents or investment cost intensive reactors are needed to obtain the desired results. Some specific examples are identified.

The application of flux balance analysis in systems biology

An increasing number of genome-scale reconstructions of intracellular biochemical networks are being generated. Coupled with these stoichiometric models, several systems-based approaches for probing these reconstructions in silico have been developed. One such approach, called flux balance analysis (FBA), has been effective at predicting systemic phenotypes in the form of fluxes through a reaction network. FBA employs a linear programming (LP) strategy to generate a flux distribution that is optimized toward a particular 'objective,' subject to a set of underlying physicochemical and thermodynamic constraints. Although classical FBA assumes steady-state conditions, several extensions have been proposed in recent years to constrain the allowable flux distributions and enable characterization of dynamic profiles even with minimal kinetic information. Furthermore, FBA coupled with techniques for measuring fluxes in vivo has facilitated integration of computational and experimental approaches, and is allowing pursuit of rational hypothesis-driven research. Ultimately, as we will describe in this review, studying intracellular reaction fluxes allows us to understand network structure and function and has broad applications ranging from metabolic engineering to drug discovery.

Genome-scale metabolic networks

During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.

Geobacter: the microbe electric's physiology, ecology, and practical applications

Geobacter species specialize in making electrical contacts with extracellular electron acceptors and other organisms. This permits Geobacter species to fill important niches in a diversity of anaerobic environments. Geobacter species appear to be the primary agents for coupling the oxidation of organic compounds to the reduction of insoluble Fe(III) and Mn(IV) oxides in many soils and sediments, a process of global biogeochemical significance. Some Geobacter species can anaerobically oxidize aromatic hydrocarbons and play an important role in aromatic hydrocarbon removal from contaminated aquifers. The ability of Geobacter species to reductively precipitate uranium and related contaminants has led to the development of bioremediation strategies for contaminated environments. Geobacter species produce higher current densities than any other known organism in microbial fuel cells and are common colonizers of electrodes harvesting electricity from organic wastes and aquatic sediments. Direct interspecies electron exchange between Geobacter species and syntrophic partners appears to be an important process in anaerobic wastewater digesters. Functional and comparative genomic studies have begun to reveal important aspects of Geobacter physiology and regulation, but much remains unexplored. Quantifying key gene transcripts and proteins of subsurface Geobacter communities has proven to be a powerful approach to diagnose the in situ physiological status of Geobacter species during groundwater bioremediation. The growth and activity of Geobacter species in the subsurface and their biogeochemical impact under different environmental conditions can be predicted with a systems biology approach in which genome-scale metabolic models are coupled with appropriate physical/chemical models. The proficiency of Geobacter species in transferring electrons to insoluble minerals, electrodes, and possibly other microorganisms can be attributed to their unique "microbial nanowires," pili that conduct electrons along their length with metallic-like conductivity. Surprisingly, the abundant c-type cytochromes of Geobacter species do not contribute to this long-range electron transport, but cytochromes are important for making the terminal electrical connections with Fe(III) oxides and electrodes and also function as capacitors, storing charge to permit continued respiration when extracellular electron acceptors are temporarily unavailable. The high conductivity of Geobacter pili and biofilms and the ability of biofilms to function as supercapacitors are novel properties that might contribute to the field of bioelectronics. The study of Geobacter species has revealed a remarkable number of microbial physiological properties that had not previously been described in any microorganism. Further investigation of these environmentally relevant and physiologically unique organisms is warranted.