Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Partners for life: building microbial consortia for the future

New technologies have allowed researchers to better design, build, and analyze complex consortia. These developments are fueling a wider implementation of consortium-based bioprocessing by leveraging synthetic biology, delivering on the field's multitudinous promises of higher efficiencies, superior resiliency, augmented capabilities, and modular bioprocessing. Here we chronicle current progress by presenting a range of screening, computational, and biomolecular tools enabling robust population control, efficient division of labor, and programmatic spatial organization; furthermore, we detail corresponding advancements in areas including machine learning, biocontainment, and standardization. Additionally, we show applications in myriad sectors, including medicine, energy and waste sustainability, chemical production, agriculture, and biosensors. Concluding remarks outline areas of growth that will promote the utilization of complex community structures across the biotechnology spectrum.

Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

Insights into Ammonia Adaptation and Methanogenic Precursor Oxidation by Genome-Centric Analysis

Ammonia released from the degradation of protein and/or urea usually leads to suboptimal anaerobic digestion (AD) when N-rich organic waste is used. However, the insights behind the differential ammonia tolerance of anaerobic microbiomes remain an enigma. In this study, the cultivation in synthetic medium with different carbon sources (acetate, methanol, formate, and H2/CO2) shaped a common initial inoculum into four unique ammonia-tolerant syntrophic populations. Specifically, various levels of ammonia tolerance were observed: consortia fed with methanol and H2/CO2 could grow at ammonia levels up to 7.25 g NH+-N/L, whereas the other two groups (formate and acetate) only thrived at 5.25 and 4.25 g NH+-N/L, respectively. Metabolic reconstruction highlighted that this divergent microbiome might be achieved by complementary metabolisms to maximize biomethane recovery from carbon sources, thus indicating the importance of the syntrophic community in the AD of N-rich substrates. Besides, sodium/proton antiporter operon, osmoprotectant/K+ regulator, and osmoprotectant synthesis operon may function as the main drivers of adaptation to the ammonia stress. Moreover, energy from the substrate-level phosphorylation and multiple energy-converting hydrogenases (e.g., Ech and Eha) could aid methanogens to balance the energy request for anabolic activities and contribute to thriving when exposed to high ammonia levels.

EnzyMine: a comprehensive database for enzyme function annotation with enzymatic reaction chemical feature

Addition of chemical structural information in enzymatic reactions has proven to be significant for accurate enzyme function prediction. However, such chemical data lack systematic feature mining and hardly exist in enzyme-related databases. Therefore, global mining of enzymatic reactions will offer a unique landscape for researchers to understand the basic functional mechanisms of natural bioprocesses and facilitate enzyme function annotation. Here, we established a new knowledge base called EnzyMine, through which we propose to elucidate enzymatic reaction features and then link them with sequence and structural annotations. EnzyMine represents an advanced database that extends enzyme knowledge by incorporating reaction chemical feature strategies, strengthening the connectivity between enzyme and metabolic reactions. Therefore, it has the potential to reveal many new metabolic pathways involved with given enzymes, as well as expand enzyme function annotation. Database URL: http://www.rxnfinder.org/enzymine/.

Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species

Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut associated bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models

Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults.

Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

Antimicrobial resistance (AMR) threatens the effective prevention and treatment of a wide range of infections. Governments around the world are beginning to devote effort for innovative treatment development to treat these resistant bacteria. Systems biology methods have been applied extensively to provide valuable insights into metabolic processes at system level. Genome-scale metabolic models serve as platforms for constraint-based computational techniques which aid in novel drug discovery. Tools for automated reconstruction of metabolic models have been developed to support system level metabolic analysis. We discuss features of such software platforms for potential users to best fit their purpose of research. In this work, we focus to review the development of genome-scale metabolic models of Gram-negative pathogens and also metabolic network approach for identification of antimicrobial drugs targets.

Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis

Anaerobic digestion is a key biological process for renewable energy, yet the mechanistic knowledge on its hidden microbial dynamics is still limited. The present work charted the interaction network in the anaerobic digestion microbiome via the full characterization of pairwise interactions and the associated metabolite exchanges. To this goal, a novel collection of 836 genome-scale metabolic models was built to represent the functional capabilities of bacteria and archaea species derived from genome-centric metagenomics. Dominant microbes were shown to prefer mutualistic, parasitic and commensalistic interactions over neutralism, amensalism and competition, and are more likely to behave as metabolite importers and profiteers of the coexistence. Additionally, external hydrogen injection positively influences microbiome dynamics by promoting commensalism over amensalism. Finally, exchanges of glucogenic amino acids were shown to overcome auxotrophies caused by an incomplete tricarboxylic acid cycle. Our novel strategy predicted the most favourable growth conditions for the microbes, overall suggesting strategies to increasing the biogas production efficiency. In principle, this approach could also be applied to microbial populations of biomedical importance, such as the gut microbiome, to allow a broad inspection of the microbial interplays.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

Genome scale metabolic models and analysis for evaluating probiotic potentials

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

A Genome-Scale Insight into the Effect of Shear Stress During the Fed-Batch Production of Clavulanic Acid by Streptomyces Clavuligerus

Streptomyces clavuligerus is a filamentous Gram-positive bacterial producer of the β-lactamase inhibitor clavulanic acid. Antibiotics biosynthesis in the Streptomyces genus is usually triggered by nutritional and environmental perturbations. In this work, a new genome scale metabolic network of Streptomyces clavuligerus was reconstructed and used to study the experimentally observed effect of oxygen and phosphate concentrations on clavulanic acid biosynthesis under high and low shear stress. A flux balance analysis based on experimental evidence revealed that clavulanic acid biosynthetic reaction fluxes are favored in conditions of phosphate limitation, and this is correlated with enhanced activity of central and amino acid metabolism, as well as with enhanced oxygen uptake. In silico and experimental results show a possible slowing down of tricarboxylic acid (TCA) due to reduced oxygen availability in low shear stress conditions. In contrast, high shear stress conditions are connected with high intracellular oxygen availability favoring TCA activity, precursors availability and clavulanic acid (CA) production.

Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality

A detailed understanding of the cheese microbiome is key to the optimization of flavour, appearance, quality and safety. Accordingly, we conducted a high-resolution meta-analysis of cheese microbiomes and corresponding volatilomes. Using 77 new samples from 55 artisanal cheeses from 27 Irish producers combined with 107 publicly available cheese metagenomes, we recovered 328 metagenome-assembled genomes, including 47 putative new species that could influence taste or colour through the secretion of volatiles or biosynthesis of pigments. Additionally, from a subset of samples, we found that differences in the abundances of strains corresponded with levels of volatiles. Genes encoding bacteriocins and other antimicrobials, such as pseudoalterin, were common, potentially contributing to the control of undesirable microorganisms. Although antibiotic-resistance genes were detected, evidence suggested they are not of major concern with respect to dissemination to other microbiomes. Phages, a potential cause of fermentation failure, were abundant and evidence for phage-mediated gene transfer was detected. The anti-phage defence mechanism CRISPR was widespread and analysis thereof, and of anti-CRISPR proteins, revealed a complex interaction between phages and bacteria. Overall, our results provide new and substantial technological and ecological insights into the cheese microbiome that can be applied to further improve cheese production.

A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities

Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

Our natural "makeup" reveals more than it hides: Modeling the skin and its microbiome

Skin is our primary interface with the environment. A structurally and functionally complex organ that hosts a dynamic ecosystem of microbes, and synthesizes many compounds that affect our well-being and psychosocial interactions. It is a natural platform of signal exchange between internal organs, skin resident microbes, and the environment. These interactions have gained a great deal of attention due to the increased prevalence of atopic diseases, and the co-occurrence of multiple allergic diseases related to allergic sensitization in early life. Despite significant advances in experimentally characterizing the skin, its microbial ecology, and disease phenotypes, high-levels of variability in these characteristics even for the same clinical phenotype are observed. Addressing this variability and resolving the relevant biological processes requires a systems approach. This review presents some of our current understanding of the skin, skin-immune, skin-neuroendocrine, skin-microbiome interactions, and computer-based modeling approaches to simulate this ecosystem in the context of health and disease. The review highlights the need for a systems-based understanding of this sophisticated ecosystem. This article is categorized under: Infectious Diseases > Computational Models.

An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data

Photocatalytic disinfection has long been used to combat pathogenic bacteria. However, the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear. In this study, an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data. Two sets of RNA-seq data (wild type and a photocatalysis-resistant mutant mediated by titanium dioxide (TiO₂)) were processed to constrain the genome scale metabolic models (GSMM) of E. coli. By analyzing the metabolic network, the differential metabolic flux of every reaction was computed in constrained GSMM, and several significantly differential metabolic fluxes in reactions were extracted and analyzed. Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane. These results, which are consistent with the reported information, validated our analysis process. In addition, our work also identified other new and valuable metabolic pathways, such as the reaction ALCD2x, which has a great effect on the energy production process under bacterial anaerobic conditions. The DHAK reaction is also related to the metabolic process of ATP. These reactions with large differential metabolic fluxes merit further research. Additionally, to provide a strategy to address photocatalysis-resistant mutant bacteria, a metabolic compensation analysis was also performed. The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria. This method could also be used to explore the mechanisms of drug resistance in other microorganisms.

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.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

Understanding the metabolism of the tetralin degrader Sphingopyxis granuli strain TFA through genome-scale metabolic modelling

Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA.

Distinct actions of the fermented beverage kefir on host behaviour, immunity and microbiome gut-brain modules in the mouse

BACKGROUND

Mounting evidence suggests a role for the gut microbiota in modulating brain physiology and behaviour, through bi-directional communication, along the gut-brain axis. As such, the gut microbiota represents a potential therapeutic target for influencing centrally mediated events and host behaviour. It is thus notable that the fermented milk beverage kefir has recently been shown to modulate the composition of the gut microbiota in mice. It is unclear whether kefirs have differential effects on microbiota-gut-brain axis and whether they can modulate host behaviour per se.

METHODS

To address this, two distinct kefirs (Fr1 and UK4), or unfermented milk control, were administered to mice that underwent a battery of tests to characterise their behavioural phenotype. In addition, shotgun metagenomic sequencing of ileal, caecal and faecal matter was performed, as was faecal metabolome analysis. Finally, systemic immunity measures and gut serotonin levels were assessed. Statistical analyses were performed by ANOVA followed by Dunnett's post hoc test or Kruskal-Wallis test followed by Mann-Whitney U test.

RESULTS

Fr1 ameliorated the stress-induced decrease in serotonergic signalling in the colon and reward-seeking behaviour in the saccharin preference test. On the other hand, UK4 decreased repetitive behaviour and ameliorated stress-induced deficits in reward-seeking behaviour. Furthermore, UK4 increased fear-dependent contextual memory, yet decreased milk gavage-induced improvements in long-term spatial learning. In the peripheral immune system, UK4 increased the prevalence of Treg cells and interleukin 10 levels, whereas Fr1 ameliorated the milk gavage stress-induced elevation in neutrophil levels and CXCL1 levels. Analysis of the gut microbiota revealed that both kefirs significantly changed the composition and functional capacity of the host microbiota, where specific bacterial species were changed in a kefir-dependent manner. Furthermore, both kefirs increased the capacity of the gut microbiota to produce GABA, which was linked to an increased prevalence in Lactobacillus reuteri.

CONCLUSIONS

Altogether, these data show that kefir can signal through the microbiota-gut-immune-brain axis and modulate host behaviour. In addition, different kefirs may direct the microbiota toward distinct immunological and behavioural modulatory effects. These results indicate that kefir can positively modulate specific aspects of the microbiota-gut-brain axis and support the broadening of the definition of psychobiotic to include kefir fermented foods. Video abstract.

Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions

Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.

The engineering of spatially linked microbial consortia - potential and perspectives

Traditional biotechnological applications of microorganisms employ mono-cultivation or co-cultivation in well-mixed vessels disregarding the potential of spatially organized cultures. Metabolic specialization and guided species interactions facilitated through spatial isolation would enable consortia of microbes to accomplish more complex functions than currently possible, for bioproduction as well as biodegradation processes. Here, we review concepts of spatially linked microbial consortia in which spatial arrangement is optimized to increase control and facilitate new species combinations. We highlight that genome-scale metabolic network models can inform the design and tuning of synthetic microbial consortia and suggest that a standardized assembly of such systems allows the combination of 'incompatibles', potentially leading to countless novel applications.

Genome-Scale Investigation of the Metabolic Determinants Generating Bacterial Fastidious Growth

Xylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa.

High proliferation rate and robustness are vital characteristics of bacterial pathogens that successfully colonize their hosts. The observation of drastically slow growth in some pathogens is thus paradoxical and remains unexplained. In this study, we sought to understand the slow (fastidious) growth of the plant pathogen Xylella fastidiosa. Using genome-scale metabolic network reconstruction, modeling, and experimental validation, we explored its metabolic capabilities. Despite genome reduction and slow growth, the pathogen’s metabolic network is complete but strikingly minimalist and lacking in robustness. Most alternative reactions were missing, especially those favoring fast growth, and were replaced by less efficient paths. We also found that the production of some virulence factors imposes a heavy burden on growth. Interestingly, some specific determinants of fastidious growth were also found in other slow-growing pathogens, enriching the view that these metabolic peculiarities are a pathogenicity strategy to remain at a low population level.

IMPORTANCEXylella fastidiosa is one of the most important threats to plant health worldwide, causing disease in the Americas on a range of agricultural crops and trees, and recently associated with a critical epidemic affecting olive trees in Europe. A main challenge for the detection of the pathogen and the development of physiological studies is its fastidious growth, as the generation time can vary from 10 to 100 h for some strains. This physiological peculiarity is shared with several human pathogens and is poorly understood. We performed an analysis of the metabolic capabilities of X. fastidiosa through a genome-scale metabolic model of the bacterium. This model was reconstructed and manually curated using experiments and bibliographical evidence. Our study revealed that fastidious growth most probably results from different metabolic specificities such as the absence of highly efficient enzymes or a global inefficiency in virulence factor production. These results support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa.

Genome-Scale Metabolic Model Reconstruction and in Silico Investigations of Methane Metabolism in Methylosinus trichosporium OB3b

Methylosinus trichosporium OB3b is an obligate aerobic methane-utilizing alpha-proteobacterium. Since its isolation, M. trichosporium OB3b has been established as a model organism to study methane metabolism in type II methanotrophs. M. trichosporium OB3b utilizes soluble and particulate methane monooxygenase (sMMO and pMMO respectively) for methane oxidation. While the source of electrons is known for sMMO, there is less consensus regarding electron donor to pMMO. To investigate this and other questions regarding methane metabolism, the genome-scale metabolic model for M. trichosporium OB3b (model ID: iMsOB3b) was reconstructed. The model accurately predicted oxygen: methane molar uptake ratios and specific growth rates on nitrate-supplemented medium with methane as carbon and energy source. The redox-arm mechanism which links methane oxidation with complex I of electron transport chain has been found to be the most optimal mode of electron transfer. The model was also qualitatively validated on ammonium-supplemented medium indicating its potential to accurately predict methane metabolism in different environmental conditions. Finally, in silico investigations regarding flux distribution in central carbon metabolism of M. trichosporium OB3b were performed. Overall, iMsOB3b can be used as an organism-specific knowledgebase and a platform for hypothesis-driven theoretical investigations of methane metabolism.

Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models

MOTIVATION

The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.

RESULTS

Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements.

AVAILABILITY AND IMPLEMENTATION

The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An adjustable algal chloroplast plug-and-play model for genome-scale metabolic models

The chloroplast is a central part of plant cells, as this is the organelle where the photosynthesis, fixation of inorganic carbon, and other key functions related to fatty acid synthesis and amino acid synthesis occur. Since this organelle should be an integral part of any genome-scale metabolic model for a microalgae or a higher plant, it is of great interest to generate a detailed and standardized chloroplast model. Additionally, we see the need for a novel type of sub-model template, or organelle model, which could be incorporated into a larger, less specific genome-scale metabolic model, while allowing for minor differences between chloroplast-containing organisms. The result of this work is the very first standardized chloroplast model, iGR774, consisting of 788 reactions, 764 metabolites, and 774 genes. The model is currently able to run in three different modes, mimicking the chloroplast metabolism of three photosynthetic microalgae-Nannochloropsis gaditana, Chlamydomonas reinhardtii and Phaeodactylum tricornutum. In addition to developing the chloroplast metabolic network reconstruction, we have developed multiple software tools for working with this novel type of sub-model in the COBRA Toolbox for MATLAB, including tools for connecting the chloroplast model to a genome-scale metabolic reconstruction in need of a chloroplast, for switching the model between running in different organism modes, and for expanding it by introducing more reactions either related to one of the current organisms included in the model, or to a new organism.

Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review

Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases.

Complete genome sequence and genome-scale metabolic modelling of Acinetobacter baumannii type strain ATCC 19606

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to global health. The type strain ATCC 19606 has been widely used in studying the virulence, pathogenesis and mechanisms of antimicrobial resistance in A. baumannii. However, the lack of a complete genome sequence is a hindrance towards detailed bioinformatic studies. Here we report the generation of a complete genome for ATCC 19606 using PacBio sequencing. ATCC 19606 genome consists of a 3,980,848-bp chromosome and a 9,450-bp plasmid pMAC, and harbours a chromosomal dihydropteroate synthase gene sul2 conferring resistance to sulphonamides and a plasmid-borne ohr gene conferring resistance to peroxides. The genome also contains 69 virulence genes involved in surface adherence, biofilm formation, extracellular phospholipase, iron uptake, immune evasion and quorum sensing. Insertion sequences ISCR2 and ISAba11 are embedded in a 36.1-Kb genomic island, suggesting an IS-mediated large-scale DNA recombination. Furthermore, a genome-scale metabolic model (GSMM) iATCC19606v2 was constructed using the complete genome annotation. The model iATCC19606v2 incorporated a periplasmic compartment, 1,422 metabolites, 2,114 reactions and 1,009 genes, and a set of protein crowding constraints taking into account enzyme abundance limitation. The prediction of bacterial growth on 190 carbon and 95 nitrogen sources achieved a high accuracy of 85.6% compared to Biolog experiment results. Based upon two transposon mutant libraries of AB5075 and ATCC 17978, the predictions of essential genes reached the accuracy of 87.6% and 82.1%, respectively. Together, the complete genome sequence and high-quality GSMM iATCC19606v2 provide valuable tools for antimicrobial systems pharmacological investigations on A. baumannii.

Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism

Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.

Future impacts and trends in treatment of hospital wastewater

The world’s population growth and economic development result in the increased requirement of land, water, and energy. This increased demand leads to the deforestation, loss in biodiversity, imbalance in agriculture and food supply, climate change, and increase in food and travel trade, which result in emergence and reemergence of infectious diseases. This chapter discussed various emerging infectious diseases and their causative agents (Buruli ulcer and Bunyvirus). Furthermore, this chapter further illustrates the emergence of superbugs and the associated threat due to the presence of pharmaceutical compounds in the environment. The prevalence of pharmaceuticals in the environment exerts ecotoxic effects on living organisms and causes thousands of death every year. The threats associated with the pharmaceutical presence in the environment were briefly discussed in this chapter. Finally, this chapter provides the alternative methods to avoid the use of antibiotics and to develop novel treatment technologies (such as Phage therapy) to degrade and remove the pharmaceutical compounds.

Genome-Scale Metabolic Reconstruction of Acetobacter pasteurianus 386B, a Candidate Functional Starter Culture for Cocoa Bean Fermentation

Acetobacter pasteurianus 386B is a candidate functional starter culture for the cocoa bean fermentation process. To allow in silico simulations of its related metabolism in response to different environmental conditions, a genome-scale metabolic model for A. pasteurianus 386B was reconstructed. This is the first genome-scale metabolic model reconstruction for a member of the genus Acetobacter. The metabolic network reconstruction process was based on extensive genome re-annotation and comparative genomics analyses. The information content related to the functional annotation of metabolic enzymes and transporters was placed in a metabolic context by exploring and curating a Pathway/Genome Database of A. pasteurianus 386B using the Pathway Tools software. Metabolic reactions and curated gene-protein-reaction associations were bundled into a genome-scale metabolic model of A. pasteurianus 386B, named iAp386B454, containing 454 genes, 322 reactions, and 296 metabolites embedded in two cellular compartments. The reconstructed model was validated by performing growth experiments in a defined medium, which revealed that lactic acid as the sole carbon source could sustain growth of this strain. Further, the reconstruction of the A. pasteurianus 386B genome-scale metabolic model revealed knowledge gaps concerning the metabolism of this strain, especially related to the biosynthesis of its cell envelope and the presence or absence of metabolite transporters.

The ecology and evolution of microbial metabolic strategies

Free-living microbes are generally capable of growing on multiple different nutrients. Some of those nutrients are used simultaneously, while others are used sequentially. The pattern of nutrient preferences and co-utilization defines the metabolic strategy of a microorganism. Metabolic strategies can substantially affect ecological interactions between species, but their evolution and distribution across the tree of life remain poorly characterized. We discuss how the confluence of better computational models of genotype-phenotype maps and high-throughput experimental tools can help us fill gaps in our knowledge and incorporate metabolic strategies into quantitative predictive models of microbial consortia.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

MOTIVATION

Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.

RESULTS

As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.

AVAILABILITY AND IMPLEMENTATION

The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae--an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

A systematic assessment of current genome-scale metabolic reconstruction tools

BACKGROUND

Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research.

RESULTS

In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool's output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features.

CONCLUSIONS

Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software.

The Role of the Microbiome in Drug Response

The microbiome is known to regulate many aspects of host health and disease and is increasingly being recognized as a key mediator of drug action. However, investigating the complex multidirectional relationships between drugs, the microbiota, and the host is a challenging endeavor, and the biological mechanisms that underpin these interactions are often not well understood. In this review, we outline the current evidence that supports a role for the microbiota as a contributor to both the therapeutic benefits and side effects of drugs, with a particular focus on those used to treat mental disorders, type 2 diabetes, and cancer. We also provide a snapshot of the experimental and computational tools that are currently available for the dissection of drug-microbiota-host interactions. The advancement of knowledge in this area may ultimately pave the way for the development of novel microbiota-based strategies that can be used to improve treatment outcomes.

Modelling approaches for studying the microbiome

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.