Comparisons of Shewanella strains based on genome annotations, modeling, and experiments

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how the adoption of robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology in particular.

Flux-Balance Analysis and Mobile CRISPRi-Guided Deletion of a Conditionally Essential Gene in Shewanella oneidensis MR-1

Carbon-neutral production of valuable bioproducts is critical to sustainable development but remains limited by the slow engineering of photosynthetic organisms. Improving existing synthetic biology tools to engineer model organisms to fix carbon dioxide is one route to overcoming the limitations of photosynthetic organisms. In this work, we describe a pipeline that enabled the deletion of a conditionally essential gene from the Shewanella oneidensis MR-1 genome. S. oneidensis is a simple bacterial host that could be used for electricity-driven conversion of carbon dioxide in the future with further genetic engineering. We used Flux Balance Analysis (FBA) to model carbon and energy flows in central metabolism and assess the effects of single and double gene deletions. We modeled the growth of deletion strains under several alternative conditions to identify substrates that restore viability to an otherwise lethal gene knockout. These predictions were tested in vivo using a Mobile-CRISPRi gene knockdown system. The information learned from FBA and knockdown experiments informed our strategy for gene deletion, allowing us to successfully delete an "expected essential" gene, gpmA. FBA predicted, knockdown experiments supported, and deletion confirmed that the "essential" gene gpmA is not needed for survival, dependent on the medium used. Removal of gpmA is a first step toward driving electrode-powered CO2 fixation via RuBisCO. This work demonstrates the potential for broadening the scope of genetic engineering in S. oneidensis as a synthetic biology chassis. By combining computational analysis with a CRISPRi knockdown system in this way, one can systematically assess the impact of conditionally essential genes and use this knowledge to generate mutations previously thought unachievable.

Reconstruction and Analysis of Thermodynamically Constrained Models Reveal Metabolic Responses of a Deep-Sea Bacterium to Temperature Perturbations

Microbial acclimation to different temperature conditions can involve broad changes in cell composition and metabolic efficiency. A systems-level view of these metabolic responses in nonmesophilic organisms, however, is currently missing. In this study, thermodynamically constrained genome-scale models were applied to simulate the metabolic responses of a deep-sea psychrophilic bacterium, Shewanella psychrophila WP2, under suboptimal (4°C), optimal (15°C), and supraoptimal (20°C) growth temperatures. The models were calibrated with experimentally determined growth rates of WP2. Gibbs free energy change of reactions (Δ_rG'), metabolic fluxes, and metabolite concentrations were predicted using random simulations to characterize temperature-dependent changes in the metabolism. The modeling revealed the highest metabolic efficiency at the optimal temperature, and it suggested distinct patterns of ATP production and consumption that could lead to lower metabolic efficiency under suboptimal or supraoptimal temperatures. The modeling also predicted rearrangement of fluxes through multiple metabolic pathways, including the glycolysis pathway, Entner-Doudoroff pathway, tricarboxylic acid (TCA) cycle, and electron transport system, and these predictions were corroborated through comparisons to WP2 transcriptomes. Furthermore, predictions of metabolite concentrations revealed the potential conservation of reducing equivalents and ATP in the suboptimal temperature, consistent with experimental observations from other psychrophiles. Taken together, the WP2 models provided mechanistic insights into the metabolism of a psychrophile in response to different temperatures. IMPORTANCE Metabolic flexibility is a central component of any organism's ability to survive and adapt to changes in environmental conditions. This study represents the first application of thermodynamically constrained genome-scale models in simulating the metabolic responses of a deep-sea psychrophilic bacterium to various temperatures. The models predicted differences in metabolic efficiency that were attributed to changes in metabolic pathway utilization and metabolite concentration during growth under optimal and nonoptimal temperatures. Experimental growth measurements were used for model calibration, and temperature-dependent transcriptomic changes corroborated the model-predicted rearrangement of metabolic fluxes. Overall, this study highlights the utility of modeling approaches in studying the temperature-driven metabolic responses of an extremophilic organism.

Reconstruction of a Genome-Scale Metabolic Network for Shewanella oneidensis MR-1 and Analysis of its Metabolic Potential for Bioelectrochemical Systems

Bioelectrochemical systems (BESs) based on Shewanella oneidensis MR-1 offer great promise for sustainable energy/chemical production, but the low rate of electron generation remains a crucial bottleneck preventing their industrial application. Here, we reconstructed a genome-scale metabolic model of MR-1 to provide a strong theoretical basis for novel BES applications. The model iLJ1162, comprising 1,162 genes, 1,818 metabolites and 2,084 reactions, accurately predicted cellular growth using a variety of substrates with 86.9% agreement with experimental results, which is significantly higher than the previously published models iMR1_799 and iSO783. The simulation of microbial fuel cells indicated that expanding the substrate spectrum of MR-1 to highly reduced feedstocks, such as glucose and glycerol, would be beneficial for electron generation. In addition, 31 metabolic engineering targets were predicted to improve electricity production, three of which have been experimentally demonstrated, while the remainder are potential targets for modification. Two potential electron transfer pathways were identified, which could be new engineering targets for increasing the electricity production capacity of MR-1. Finally, the iLJ1162 model was used to simulate the optimal biosynthetic pathways for six platform chemicals based on the MR-1 chassis in microbial electrosynthesis systems. These results offer guidance for rational design of novel BESs.

Engineering S. oneidensis for Performance Improvement of Microbial Fuel Cell-a Mini Review

Microbial fuel cell (MFC) is a promising technology that utilizes exoelectrogens cultivated in the form of biofilm to generate power from various types of sources supplied. A metal-reducing pathway is utilized by these organisms to transfer electrons obtained from the metabolism of substrate from anaerobic respiration extracellularly. A widely established model organism that is capable of extracellular electron transfer (EET) is Shewanella oneidensis. This review highlights the strategies used in the transformation of S. oneidensis and the recent development of MFC in terms of intervention through genetic modifications. S. oneidensis was genetically engineered for several aims including the study on the underlying mechanisms of EET, and the enhancement of power generation and wastewater treating potential when used in an MFC. Through engineering S. oneidensis, genes responsible for EET are identified and strategies on enhancing the EET efficiency are studied. Overexpressing genes related to EET to enhance biofilm formation, mediator biosynthesis, and respiration appears as one of the common approaches.

Isolation of two iron-reducing facultative anaerobic electricigens and probing the application performance in eutrophication water

Purpose

Sediment microbial fuel cell (SMFC) is a promising bioremediation technology in which microbes play an important role. Electricigens as the bio-catalysts have effect on pollution control and electricity generation. It is of great significance to screen the microorganisms with the ability of generating electricity.

Methods

The SMFC anode biofilm was used as microbiological source to study the feasibility of electricigens with iron-reducing property for eutrophication water treatment. Preliminarily, we isolated 20 facultative anaerobic pure bacteria and evaluated their cyclic voltammogram (CV) through the three-electrode system and electrochemical workstation. The power generation performance of strains was verified by air-cathode microbial fuel cells (AC-MFCs) under different single carbon sources.

Result

According to its morphological, physiological, and biochemical characteristics, along with phylogenetic analysis, the two strains (SMFC-7 and SMFC-17) with electrical characteristics were identified as Bacillus cereus. Compared with SMFC-7, SMFC-17 exhibited efficient NH4+-N and NO3−-N removal and PO43−-P accumulation from eutrophic solution with a removal rate of 79.91 ± 6.34% and 81.26 ± 1.11% and accumulation rate of 57.68 ± 4.36%, respectively.

Conclusion

The isolated bacteria SMFC-17 showed a good performance in eutrophic solution, and it might be a useful biocatalyst to enable the industrialized application of SMFC in eutrophic water treatment.

Reconstruction and Validation of a Genome-Scale Metabolic Model of Streptococcus oralis (iCJ415), a Human Commensal and Opportunistic Pathogen

The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.

ssbio: a Python framework for structural systems biology

Summary

Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.

Availability and implementation

ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb.

Supplementary information

Supplementary data are available at Bioinformatics online.

Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora

BACKGROUND

There is little published regarding metabolism of Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440T has been built despite the role of Salinispora strains in drug discovery.

RESULTS

Here, we updated, and expanded the scope of the model of Salinispora tropica CNB-440T, and GSMs were constructed for two sequenced type strains covering the three-identified species. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains and a few non-conserved genes associated with essential reactions. The models predicted no auxotrophies for essential amino acids, which was corroborated experimentally using a defined minimal medium (DMM). Experimental observations suggest possible sulfur accumulation. The Core metabolic content shows that the biosynthesis of specialised metabolites is the less conserved subsystem. Sets of reactions were analyzed to explore the differences between the reconstructions. Unique reactions associated to each GSM were mainly due to genome sequence data except for the ST-CNB440 reconstruction. In this case, additional reactions were added from experimental evidence. This reveals that by reaction content the ST-CNB440 model is different from the other species models. The differences identified in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species in DMM. Furthermore, models were used to evaluate in silico single gene knockouts under DMM and complex medium. Cluster analysis of these results shows that ST-CNB440, and SP-CNR114 models are more similar when considering predicted essential genes.

CONCLUSIONS

Models were built for each of the three currently identified Salinispora species, and a core model representing the conserved metabolic capabilities of Salinispora was constructed. Models will allow in silico metabolism studies of Salinispora strains, and help researchers to guide and increase the production of specialised metabolites. Also, models can be used as templates to build GSMs models of closely related organisms with high biotechnology potential.

A Comparative Study of Network Motifs in the Integrated Transcriptional Regulation and Protein Interaction Networks of Shewanella

The Shewanella species shows a remarkable respiratory versatility with a great variety of extracellular electron acceptors (termed Extracellular Electron Transfer, EET). To explore relevant mechanisms from the network motif view, we constructed the integrated networks that combined transcriptional regulation interactions (TRIs) and protein-protein interactions (PPIs) for 13 Shewanella species, identified and compared the network motifs in these integrated networks. We found that the network motifs were evolutionary conserved in these integrated networks. The functional significance of the highly conserved motifs was discussed, especially the important ones that were potentially involved in the Shewanella EET processes. More importantly, we found that: 1) the motif co-regulated PPI took a role in the "standby mode" of protein utilization, which will be helpful for cells to rapidly response to environmental changes; and 2) the type II cofactors, which involved in the motif TRI interacting with a third protein, mainly carried out a signalling role in Shewanella oneidensis MR-1.

Weak electricigens: A new avenue for bioelectrochemical research

Electroactivity appears to be a phylogenetically diverse trait independent of cell wall classification, with both Gram-negative and Gram-positive electricigens reported. While numerous electricigens have been observed, the majority of research focuses on a select group of highly electroactive species. Under favorable conditions, many microorganisms can be considered electroactive, either through their own mechanisms or exogenously-added mediators, producing a weak current. Such microbes should not be dismissed based on their modest electroactivity. Rather, they may be key to understanding what drives extracellular electron transfer in response to transient limitations of electron acceptor or donor, with implications for the study of pathogens and industrial bioprocesses. Due to their low electroactivity, such populations are difficult to grow in bioelectrochemical systems and characterise with electrochemistry. Here, a critical review of recent research on weak electricigens is provided, with a focus on the methodology and the overall relevance to microbial ecology and bioelectrochemical systems.

Whole-genome sequencing reveals that Shewanella haliotis Kim et al. 2007 can be considered a later heterotypic synonym of Shewanella algae Simidu et al. 1990

Previously, experimental DNA-DNA hybridization (DDH) between Shewanellahaliotis JCM 14758^T and Shewanellaalgae JCM 21037^T had suggested that the two strains could be considered different species, despite minimal phenotypic differences. The recent isolation of Shewanella sp. MN-01, with 99 % 16S rRNA gene identity to S. algae and S. haliotis, revealed a potential taxonomic problem between these two species. In this study, we reassessed the nomenclature of S. haliotis and S. algae using available whole-genome sequences. The whole-genome sequence of S. haliotis JCM 14758^T and ten S. algae strains showed ≥97.7 % average nucleotide identity and >78.9 % digital DDH, clearly above the recommended species thresholds. According to the rules of priority and in view of the results obtained, S. haliotis is to be considered a later heterotypic synonym of S. algae. Because the whole-genome sequence of Shewanella sp. strain MN-01 shares >99 % ANI with S. algae JCM 14758^T, it can be confidently identified as S. algae.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.

Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria. Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements. In this study, GeMMs of multiple Plasmodium species are used to study metabolic similarities and differences across the Plasmodium genus. In silico gene-knock out simulations across species and stages uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s life-cycle stages. These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the organism.

Interaction and Regulation of Carbon, Nitrogen, and Phosphorus Metabolisms in Root Nodules of Legumes

Members of the plant family Leguminosae (Fabaceae) are unique in that they have evolved a symbiotic relationship with rhizobia (a group of soil bacteria that can fix atmospheric nitrogen). Rhizobia infect and form root nodules on their specific host plants before differentiating into bacteroids, the symbiotic form of rhizobia. This complex relationship involves the supply of C₄-dicarboxylate and phosphate by the host plants to the microsymbionts that utilize them in the energy-intensive process of fixing atmospheric nitrogen into ammonium, which is in turn made available to the host plants as a source of nitrogen, a macronutrient for growth. Although nitrogen-fixing bacteroids are no longer growing, they are metabolically active. The symbiotic process is complex and tightly regulated by both the host plants and the bacteroids. The metabolic pathways of carbon, nitrogen, and phosphate are heavily regulated in the host plants, as they need to strike a fine balance between satisfying their own needs as well as those of the microsymbionts. A network of transporters for the various metabolites are responsible for the trafficking of these essential molecules between the two partners through the symbiosome membrane (plant-derived membrane surrounding the bacteroid), and these are in turn regulated by various transcription factors that control their expressions under different environmental conditions. Understanding this complex process of symbiotic nitrogen fixation is vital in promoting sustainable agriculture and enhancing soil fertility.

Pan-genome analyses of 24 Shewanella strains re-emphasize the diversification of their functions yet evolutionary dynamics of metal-reducing pathway

BACKGROUND

Shewanella strains are important dissimilatory metal-reducing bacteria which are widely distributed in diverse habitats. Despite efforts to genomically characterize Shewanella, knowledge of the molecular components, functional information and evolutionary patterns remain lacking, especially for their compatibility in the metal-reducing pathway. The increasing number of genome sequences of Shewanella strains offers a basis for pan-genome studies.

RESULTS

A comparative pan-genome analysis was conducted to study genomic diversity and evolutionary relationships among 24 Shewanella strains. Results revealed an open pan-genome of 13,406 non-redundant genes and a core-genome of 1878 non-redundant genes. Selective pressure acted on the invariant members of core genome, in which purifying selection drove evolution in the housekeeping mechanisms. Shewanella strains exhibited extensive genome variability, with high levels of gene gain and loss during the evolution, which affected variable gene sets and facilitated the rapid evolution. Additionally, genes related to metal reduction were diversely distributed in Shewanella strains and evolved under purifying selection, which highlighted the basic conserved functionality and specificity of respiratory systems.

CONCLUSIONS

The diversity of genes present in the accessory and specific genomes of Shewanella strains indicates that each strain uses different strategies to adapt to diverse environments. Horizontal gene transfer is an important evolutionary force in shaping Shewanella genomes. Purifying selection plays an important role in the stability of the core-genome and also drives evolution in mtr-omc cluster of different Shewanella strains.

Growth Trade-Offs Accompany the Emergence of Glycolytic Metabolism in Shewanella oneidensis MR-1

Bacteria increase their metabolic capacity via the acquisition of genetic material or by the mutation of genes already present in the genome. Here, we explore the mechanisms and trade-offs involved when Shewanella oneidensis, a bacterium that typically consumes small organic and amino acids, rapidly evolves to expand its metabolic capacity to catabolize glucose after a short period of adaptation to a glucose-rich environment. Using whole-genome sequencing and genetic approaches, we discovered that deletions in a region including the transcriptional repressor (nagR) that regulates the expression of genes associated with catabolism of N-acetylglucosamine are the common basis for evolved glucose metabolism across populations. The loss of nagR results in the constitutive expression of genes for an N-acetylglucosamine permease (nagP) and kinase (nagK). We demonstrate that promiscuous activities of both NagP and NagK toward glucose allow for the transport and phosphorylation of glucose to glucose-6-phosphate, the initial events of glycolysis otherwise thought to be absent in S. oneidensis. ¹³C-based metabolic flux analysis uncovered that subsequent utilization was mediated by the Entner-Doudoroff pathway. This is an example whereby gene loss and preexisting enzymatic promiscuity, and not gain-of-function mutations, were the drivers of increased metabolic capacity. However, we observed a significant decrease in the growth rate on lactate after adaptation to glucose catabolism, suggesting that trade-offs may explain why glycolytic function may not be readily observed in S. oneidensis in natural environments despite it being readily accessible through just a single mutational event.

IMPORTANCE Gains in metabolic capacity are frequently associated with the acquisition of novel genetic material via natural or engineered horizontal gene transfer events. Here, we explored how a bacterium that typically consumes small organic acids and amino acids expands its metabolic capacity to include glucose via a loss of genetic material, a process frequently associated with a deterioration of metabolic function. Our findings highlight how the natural promiscuity of transporters and enzymes can be a key driver in expanding metabolic diversity and that many bacteria may possess a latent metabolic capacity accessible through one or a few mutations that remove regulatory functions. Our discovery of trade-offs between growth on lactate and on glucose suggests why this easily gained trait is not observed in nature.

Model-enabled gene search (MEGS) allows fast and direct discovery of enzymatic and transport gene functions in the marine bacterium Vibrio fischeri

Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here a model-enabled gene search (MEGS) approach that (i) identifies metabolic functions either missing from an organism's genome annotation or incorrectly assigned to an ORF by using discrepancies between metabolic model predictions and experimental culturing data; (ii) designs functional selection experiments for these specific metabolic functions; and (iii) selects a candidate gene(s) responsible for these functions from a genomic library and directly interrogates this gene's function experimentally. To discover gene functions, MEGS uses genomic functional selections instead of relying on correlations across large experimental datasets or sequence similarity as do other approaches. When applied to the bioluminescent marine bacterium Vibrio fischeri, MEGS successfully identified five genes that are responsible for four metabolic and transport reactions whose absence from a draft metabolic model of V. fischeri caused inaccurate modeling of high-throughput experimental data. This work demonstrates that MEGS provides a rapid and efficient integrated computational and experimental approach for annotating metabolic genes, including those that have previously been uncharacterized or misannotated.

A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea.

IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

Isolation and Characterization of a Shewanella Phage-Host System from the Gut of the Tunicate, Ciona intestinalis

Outnumbering all other biological entities on earth, bacteriophages (phages) play critical roles in structuring microbial communities through bacterial infection and subsequent lysis, as well as through horizontal gene transfer. While numerous studies have examined the effects of phages on free-living bacterial cells, much less is known regarding the role of phage infection in host-associated biofilms, which help to stabilize adherent microbial communities. Here we report the cultivation and characterization of a novel strain of Shewanella fidelis from the gut of the marine tunicate Ciona intestinalis, inducible prophages from the S. fidelis genome, and a strain-specific lytic phage recovered from surrounding seawater. In vitro biofilm assays demonstrated that lytic phage infection affects biofilm formation in a process likely influenced by the accumulation and integration of the extracellular DNA released during cell lysis, similar to the mechanism that has been previously shown for prophage induction.

Rapid construction of a whole-genome transposon insertion collection for Shewanella oneidensis by Knockout Sudoku

Whole-genome knockout collections are invaluable for connecting gene sequence to function, yet traditionally, their construction has required an extraordinary technical effort. Here we report a method for the construction and purification of a curated whole-genome collection of single-gene transposon disruption mutants termed Knockout Sudoku. Using simple combinatorial pooling, a highly oversampled collection of mutants is condensed into a next-generation sequencing library in a single day, a 30- to 100-fold improvement over prior methods. The identities of the mutants in the collection are then solved by a probabilistic algorithm that uses internal self-consistency within the sequencing data set, followed by rapid algorithmically guided condensation to a minimal representative set of mutants, validation, and curation. Starting from a progenitor collection of 39,918 mutants, we compile a quality-controlled knockout collection of the electroactive microbe Shewanella oneidensis MR-1 containing representatives for 3,667 genes that is functionally validated by high-throughput kinetic measurements of quinone reduction.

Knockout collections provide a valuable tool to explore gene function, yet are expensive and technically challenging to produce at a genome-wide scale. Here Baym et al. devise a cost-effective transposon-based method to quickly develop a knockout collection for the electroactive microbe Shewanella oneidensis.

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

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.

Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis

Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentiality assignments; and, 2) flux balance analysis (FBA) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.