Constraint-based modeling of carbon fixation and the energetics of electron transfer in Geobacter metallireducens

Asynchronous characteristics of Feammox and iron reduction from paddy soils in Southern China

Recently, the newly discovered anaerobic ammonium oxidation coupled with iron reduction (i.e., Feammox) has been proven to be a widespread nitrogen (N) loss pathway in ecosystems and has an essential contribution to gaseous N loss in paddy soil. However, the mechanism of iron-nitrogen coupling transformation and the role of iron-reducing bacteria (IRB) in Feammox were poorly understood. This study investigated the Feammox and iron reduction changes and microbial community evolution in a long-term anaerobic incubation by 15N isotope labeling combined with molecular biological techniques. The average rates of Feammox and iron reduction during the whole incubation were 0.25 ± 0.04 μg N g-1 d-1 and 40.58 ± 3.28 μg Fe g-1 d-1, respectively. High iron oxide content increased the Feammox rate, but decreased the proportion of Feammox-N2 in three Feammox pathways. RBG-13-54-9, Brevundimonas, and Pelomonas played a vital role in the evolution of microbial communities. The characteristics of asynchronous changes between Feammox and iron reduction were found through long-term incubation. IRB might not be the key species directly driving Feammox, and it is necessary to reevaluate the role of IRB in Feammox process.

Community assembly and microbial interactions in an alkaline vanadium tailing pond

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

Fumarate disproportionation by Geobacter sulfurreducens and its involvement in biocorrosion and interspecies electron transfer

The model electroactive bacterium Geobacter sulfurreducens can acquire electrons directly from solid donors including metals and other species. Reports on this physiology concluding that solid donors are the only electron sources were conducted with fumarate believed to serve exclusively as the terminal electron acceptor (TEA). Here, G. sulfurreducens was repeatedly transferred for adaptation within a growth medium containing only fumarate and no other solid or soluble substrate. The resulting evolved strain grew efficiently with either the C4-dicarboxylate fumarate or malate acting simultaneously as electron donor, carbon source, and electron acceptor via disproportionation. Whole-genome sequencing identified 38 mutations including one in the regulator PilR known to repress the expression of the C4-dicarboxylate antiporter DcuB essential to G. sulfurreducens when growing with fumarate. Futhermore, the PilR mutation was identical to the sole mutation previously reported in an evolved G. sulfurreducens grown in a co-culture assumed to derive energy solely from direct interspecies electron transfer, but cultivated with fumarate as the TEA. When cultivating the fumarate-adapted strain in the presence of stainless steel and fumarate, biocorrosion was observed and bacterial growth was accelerated 2.3 times. These results suggest that G. sulfurreducens can conserve energy concomitantly from C4-dicarboxylate disproportionation and the oxidation of a solid electron donor. This co-metabolic capacity confers an advantage to Geobacter for survival and colonization and explains in part why these microbes are omnipresent in different anaerobic ecosystems.

Microbe Profile: Geobacter metallireducens: a model for novel physiologies of biogeochemical and technological significance

Geobacter metallireducens has served as the initial model for a substantial number of newly recognized microbial physiologies that play an important role in biogeochemical cycling of carbon, metals and nutrients. The strategies used by G. metallireducens for microbial interaction with minerals, contaminants, other microbes and electrodes have led to new technologies for bioremediation, bioenergy conversion and the sustainable production of 'green' electronics.

Identifying the essential nutritional requirements of the probiotic bacteria Bifidobacterium animalis and Bifidobacterium longum through genome-scale modeling

Although bifidobacteria are widely used as probiotics, their metabolism and physiology remain to be explored in depth. In this work, strain-specific genome-scale metabolic models were developed for two industrially and clinically relevant bifidobacteria, Bifidobacterium animalis subsp. lactis BB-12® and B. longum subsp. longum BB-46, and subjected to iterative cycles of manual curation and experimental validation. A constraint-based modeling framework was used to probe the metabolic landscape of the strains and identify their essential nutritional requirements. Both strains showed an absolute requirement for pantethine as a precursor for coenzyme A biosynthesis. Menaquinone-4 was found to be essential only for BB-46 growth, whereas nicotinic acid was only required by BB-12®. The model-generated insights were used to formulate a chemically defined medium that supports the growth of both strains to the same extent as a complex culture medium. Carbohydrate utilization profiles predicted by the models were experimentally validated. Furthermore, model predictions were quantitatively validated in the newly formulated medium in lab-scale batch fermentations. The models and the formulated medium represent valuable tools to further explore the metabolism and physiology of the two species, investigate the mechanisms underlying their health-promoting effects and guide the optimization of their industrial production processes.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

Background

Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.

Results

Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.

Conclusions

The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02357-1.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Comparative proteomics of Geobacter sulfurreducens PCAT in response to acetate, formate and/or hydrogen as electron donor

Geobacter sulfurreducens is a model bacterium to study the degradation of organic compounds coupled to the reduction of Fe(III). The response of G. sulfurreducens to the electron donors acetate, formate, hydrogen and a mixture of all three with Fe(III) citrate as electron acceptor was studied using comparative physiological and proteomic approaches. Variations in the supplied electron donors resulted in differential abundance of proteins involved in the citric acid cycle (CAC), gluconeogenesis, electron transport, and hydrogenases and formate dehydrogenase. Our results provided new insights into the electron donor metabolism of G. sulfurreducens. Remarkably, formate was the preferred electron donor compared to acetate, hydrogen, or acetate plus hydrogen. When hydrogen was the electron donor, formate was formed, which was associated with a high abundance of formate dehydrogenase. Notably, abundant proteins of two CO₂ fixation pathways (acetyl-CoA pathway and the reversed oxidative CAC) corroborated chemolithoautotrophic growth of G. sulfurreducens with formate or hydrogen and CO₂ , and provided novel insight into chemolithoautotrophic growth of G. sulfurreducens.

Providing new insights on the biphasic lifestyle of the predatory bacterium Bdellovibrio bacteriovorus through genome-scale metabolic modeling

In this study we analyze the growth-phase dependent metabolic states of Bdellovibrio bacteriovorus by constructing a fully compartmented, mass and charge-balanced genome-scale metabolic model of this predatory bacterium (iCH457). Considering the differences between life cycle phases driving the growth of this predator, growth-phase condition-specific models have been generated allowing the systematic study of its metabolic capabilities. Using these computational tools, we have been able to analyze, from a system level, the dynamic metabolism of the predatory bacteria as the life cycle progresses. We provide computational evidences supporting potential axenic growth of B. bacteriovorus's in a rich medium based on its encoded metabolic capabilities. Our systems-level analysis confirms the presence of "energy-saving" mechanisms in this predator as well as an abrupt metabolic shift between the attack and intraperiplasmic growth phases. Our results strongly suggest that predatory bacteria's metabolic networks have low robustness, likely hampering their ability to tackle drastic environmental fluctuations, thus being confined to stable and predictable habitats. Overall, we present here a valuable computational testbed based on predatory bacteria activity for rational design of novel and controlled biocatalysts in biotechnological/clinical applications.

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH₃). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.

Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

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Genome-scale metabolic model of Azotobacter vinelandii DJ achives over 90% accuracy.

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iDT1278 is the most comprehensive model to simulate diazotrophy.

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Determining the most suitable culture conditions to produce polymers A. vinelandii.

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Constraint-based modeling unravels links among nitrogen fixation and production of organic nitrogen.

The hidden chemolithoautotrophic metabolism of Geobacter sulfurreducens uncovered by adaptation to formate

Multiple Fe(III)-reducing Geobacter species including the model Geobacter sulfurreducens are thought to be incapable of carbon dioxide fixation. The discovery of the reversed oxidative tricarboxylic acid cycle (roTCA) for CO₂ reduction with citrate synthase as key enzyme raises the possibility that G. sulfurreducens harbors the metabolic potential for chemolithoautotrophic growth. We investigate this hypothesis by transferring G. sulfurreducens PCA serially with Fe(III) as electron acceptor and formate as electron donor and carbon source. The evolved strain T17-3 grew chemolithoautotrophically with a 2.7-fold population increase over 48 h and a Fe(III) reduction rate of 417.5 μM h^-1. T17-3 also grew with CO₂ as carbon source. Mutations in T17-3 and enzymatic assays point to an adaptation process where the succinyl-CoA synthetase, which is inactive in the wild-type, became active to complete the roTCA cycle. Deletion of the genes coding for the succinyl-CoA synthetase in T17-3 prevented growth with formate as substrate. Enzymatic assays also showed that the citrate synthase can perform the necessary cleavage of citrate for the functional roTCA cycle. These results demonstrate that G. sulfurreducens after adaptation reduced CO₂ via the roTCA cycle. This previously hidden metabolism can be harnessed for biotechnological applications and suggests hidden ecological functions for Geobacter.

Spotlight on the Energy Harvest of Electroactive Microorganisms: The Impact of the Applied Anode Potential

Electroactive microorganisms (EAM) harvest energy by reducing insoluble terminal electron acceptors (TEA) including electrodes via extracellular electron transfer (EET). Therefore, compared to microorganisms respiring soluble TEA, an adapted approach is required for thermodynamic analyses. In EAM, the thermodynamic frame (i.e., maximum available energy) is restricted as only a share of the energy difference between electron donor and TEA is exploited via the electron-transport chain to generate proton-motive force being subsequently utilized for ATP synthesis. However, according to a common misconception, the anode potential is suggested to co-determine the thermodynamic frame of EAM. By comparing the model organism Geobacter spp. and microorganisms respiring soluble TEA, we reason that a considerable part of the electron-transport chain of EAM performing direct EET does not contribute to the build-up of proton-motive force and thus, the anode potential does not co-determine the thermodynamic frame. Furthermore, using a modeling platform demonstrates that the influence of anode potential on energy harvest is solely a kinetic effect. When facing low anode potentials, NADH is accumulating due to a slow direct EET rate leading to a restricted exploitation of the thermodynamic frame. For anode potentials ≥ 0.2 V (vs. SHE), EET kinetics, NAD⁺/NADH ratio as well as exploitation of the thermodynamic frame are maximized, and a further potential increase does not result in higher energy harvest. Considering the limited influence of the anode potential on energy harvest of EAM is a prerequisite to improve thermodynamic analyses, microbial resource mining, and to transfer microbial electrochemical technologies (MET) into practice.

Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks

BACKGROUND

Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes' importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs.

RESULTS

We applied directed versions of centrality metrics to directed reaction-centric graphs of microbial metabolic networks. To investigate the local role of a node, we developed a novel metric, cascade number, considering how many nodes are closed off from information flow when a particular node is removed. High modularity and scale-freeness were found in the directed reaction-centric graphs and betweenness centrality tended to belong to densely connected modules. Cascade number and bridging centrality identified cascade subnetworks controlling local information flow and irreplaceable bridging nodes between functional modules, respectively. Reactions highly ranked with bridging centrality and cascade number tended to be essential, compared to reactions that other central metrics detected.

CONCLUSIONS

We demonstrate that cascade number and bridging centrality are useful to identify key reactions controlling local information flow in directed reaction-centric graphs of microbial metabolic networks. Knowledge about the local flow connectivity and connections between local modules will contribute to understand how metabolic pathways are assembled.

In Silico Metabolic Design of Two-Strain Biofilm Systems Predicts Enhanced Biomass Production and Biochemical Synthesis

Engineered biofilm consortia have the potential to solve important biotechnological problems that have proved difficult for monoculture biofilms and planktonic consortia, such as conversion of lignocellulosic material to useful biochemicals. While considerable experimental progress has been reported for engineering and characterizing biofilm consortia, the field still lacks in silico tools for simulation, design, and optimization of stable, robust, and productive designed consortia. We developed biofilm consortia metabolic models for two coculture systems centered around the ecological design motif of a primary cell type that utilizes a supplied electron donor and secretes acetate as a byproduct and a secondary cell type that consumes the acetate, relieving byproduct inhibition on the primary cell type and enhancing overall system biomass. The models presented in this paper predict that distinct metabolic niches for the two cell types could be established by supplying electron donors and acceptors at opposite ends of the biofilm and that acetate consumption by the secondary cell type could increase total biomass accumulation and the synthesis of valuable biochemicals, such as isobutanol, by the primary cell type. System tunability is enhanced when each cell type is supplied with a unique terminal electron acceptor at opposite ends of the biofilm rather than competing for a common electron acceptor. Our model provides good qualitative agreement with data for a synthetic Escherichia coli coculture system, suggesting that the proposed design rules may have wide applicability to engineered biofilm consortia.

A Genome-Scale Metabolic Model for Methylococcus capsulatus (Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.

Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature.

Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus.

Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice

The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use-efficient pathways exist widely among organisms; (iv) cofactor-use-efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.

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.

Balancing cellular redox metabolism in microbial electrosynthesis and electro fermentation - A chance for metabolic engineering

More and more microbes are discovered that are capable of extracellular electron transfer, a process in which they use external electrodes as electron donors or acceptors for metabolic reactions. This feature can be used to overcome cellular redox limitations and thus optimizing microbial production. The technologies, termed microbial electrosynthesis and electro-fermentation, have the potential to open novel bio-electro production platforms from sustainable energy and carbon sources. However, the performance of reported systems is currently limited by low electron transport rates between microbes and electrodes and our limited ability for targeted engineering of these systems due to remaining knowledge gaps about the underlying fundamental processes. Metabolic engineering offers many opportunities to optimize these processes, for instance by genetic engineering of pathways for electron transfer on the one hand and target product synthesis on the other hand. With this review, we summarize the status quo of knowledge and engineering attempts around chemical production in bio-electrochemical systems from a microbe perspective. Challenges associated with the introduction or enhancement of extracellular electron transfer capabilities into production hosts versus the engineering of target compound synthesis pathways in natural exoelectrogens are discussed. Recent advances of the research community in both directions are examined critically. Further, systems biology approaches, for instance using metabolic modelling, are examined for their potential to provide insight into fundamental processes and to identify targets for metabolic engineering.

Integrated In Silico Analysis of Pathway Designs for Synthetic Photo-Electro-Autotrophy

The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated in silico analysis of photo-electro-autotrophic pathway designs, consisting of natural and synthetic carbon fixation pathways, a proton-pumping rhodopsin photosystem for ATP regeneration and an electron uptake pathway. We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force). The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli_, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase–pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly thermodynamic favorable under conditions analysed. However, the most important challenge identified for improving photo-electro-autotrophic growth is increasing the proton-pumping rate of the rhodopsin photosystems, allowing for higher ATP regeneration. Alternatively, other designs of autotrophy may be considered, therefore the herein presented integrated modeling approach allows synthetic biologists to evaluate and compare complex pathway designs before experimental implementation.

Acidithiobacillus ferrooxidans's comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications

Acidithiobacillus ferrooxidans is a gram-negative chemolithoautotrophic γ-proteobacterium. It typically grows at an external pH of 2 using the oxidation of ferrous ions by oxygen, producing ferric ions and water, while fixing carbon dioxide from the environment. A. ferrooxidans is of great interest for biomining and environmental applications, as it can process mineral ores and alleviate the negative environmental consequences derived from the mining processes. In this study, the first genome-scale metabolic reconstruction of A. ferrooxidans ATCC 23270 was generated (iMC507). A total of 587 metabolic and transport/exchange reactions, 507 genes and 573 metabolites organized in over 42 subsystems were incorporated into the model. Based on a new genetic algorithm approach, that integrates flux balance analysis, chemiosmotic theory, and physiological data, the proton translocation stoichiometry for a number of enzymes and maintenance parameters under aerobic chemolithoautotrophic conditions using three different electron donors were estimated. Furthermore, a detailed electron transfer and carbon flux distributions during chemolithoautotrophic growth using ferrous ion, tetrathionate and thiosulfate were determined and reported. Finally, 134 growth-coupled designs were calculated that enables Extracellular Polysaccharide production. iMC507 serves as a knowledgebase for summarizing and categorizing the information currently available for A. ferrooxidans and enables the understanding and engineering of Acidithiobacillus and similar species from a comprehensive model-driven perspective for biomining applications.

ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks

MOTIVATION

Random sampling of the solution space has emerged as a popular tool to explore and infer properties of large metabolic networks. However, conventional sampling approaches commonly used do not eliminate thermodynamically unfeasible loops.

RESULTS

In order to overcome this limitation, we developed an efficient sampling algorithm called loopless Artificially Centered Hit-and-Run on a Box (ll-ACHRB). This algorithm is inspired by the Hit-and-Run on a Box algorithm for uniform sampling from general regions, but employs the directions of choice approach of Artificially Centered Hit-and-Run. A novel strategy for generating feasible warmup points improved both sampling efficiency and mixing. ll-ACHRB shows overall better performance than current strategies to generate feasible flux samples across several models. Furthermore, we demonstrate that a failure to eliminate unfeasible loops greatly affects sample statistics, in particular the correlation structure. Finally, we discuss recommendations for the interpretation of sampling results and possible algorithmic improvements.

AVAILABILITY AND IMPLEMENTATION

Source code for MATLAB and OCTAVE including examples are freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization runs can use Gurobi Optimizer (by default if available) or GLPK (included with the algorithm).

CONTACT

lars.nielsen@uq.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

The broad application of genome-scale metabolic modeling has made it a useful technique for tackling fundamental questions in biological research and engineering. Today over 100 models have been constructed for organisms that carry out a diverse array of metabolic activities spanning all three kingdoms of life. These models, however, have been curated independently following different conventions. The maintenance of model consistency has been challenging due to the lack of consensus in model representation and the absence of integrated modeling software for associating mathematical simulations with the annotation and biological interpretation of metabolic models. To solve this problem, we developed a new software package, PSAMM, and a new model format that incorporates heterogeneous, model-specific annotation information into modular representations of model definitions and simulation settings. PSAMM provides significant advances in standardizing the workflow of model annotation and consistency checking. Compared to existing tools, PSAMM supports more flexible configurations and is more efficient in running constraint-based simulations. All functions of PSAMM are freely available for academic users and can be downloaded from a public Git repository (https://zhanglab.github.io/psamm/) under the GNU General Public License.

A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production

Metabolic network models can be optimized for the production of desired materials like biofuels.

Electrifying microbes for the production of chemicals

Powering microbes with electrical energy to produce valuable chemicals such as biofuels has recently gained traction as a biosustainable strategy to reduce our dependence on oil. Microbial electrosynthesis (MES) is one of the bioelectrochemical approaches developed in the last decade that could have critical impact on the current methods of chemical synthesis. MES is a process in which electroautotrophic microbes use electrical current as electron source to reduce CO₂ to multicarbon organics. Electricity necessary for MES can be harvested from renewable resources such as solar energy, wind turbine, or wastewater treatment processes. The net outcome is that renewable energy is stored in the covalent bonds of organic compounds synthesized from greenhouse gas. This review will discuss the future of MES and the challenges that lie ahead for its development into a mature technology.

Critical assessment of genome-scale metabolic networks: the need for a unified standard

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.