scholarly journals Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽  
2014 ◽  
Vol 160 (6) ◽  
pp. 1252-1266 ◽  
Author(s):  
Hassan B. Hartman ◽  
David A. Fell ◽  
Sergio Rossell ◽  
Peter Ruhdal Jensen ◽  
Martin J. Woodward ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Energy Demand ◽  
Drug Targets ◽  
Minimal Medium ◽  
Model Organism ◽  
Metabolic Model ◽  
Scale Model ◽  
Glucose Minimal Medium ◽  
A Genome ◽  
Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

scholarly journals Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

2021 ◽  
Author(s):  
Christopher E. Lawson ◽  
Aniela B. Mundinger ◽  
Hanna Koch ◽  
Tyler B. Jacobson ◽  
Coty A. Weathersby ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Scale Model ◽  
Nitrifying Bacteria ◽  
Metabolomic Analysis ◽  
A Genome ◽  
Nitrite Oxidizing Bacteria ◽  
Genome Scale ◽  
Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

scholarly journals Genome-Scale Model of Streptococcus thermophilus LMG18311 for Metabolic Comparison of Lactic Acid Bacteria

2009 ◽  
Vol 75 (11) ◽  
pp. 3627-3633 ◽  
Author(s):  
Margreet I. Pastink ◽  
Bas Teusink ◽  
Pascal Hols ◽  
Sanne Visser ◽  
Willem M. de Vos ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Amino Acid ◽  
Lactic Acid Bacteria ◽  
Streptococcus Thermophilus ◽  
Metabolic Model ◽  
Pentose Phosphate ◽  
Scale Model ◽  
A Genome ◽  
Broad Variety ◽  
Genome Scale

ABSTRACT In this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum. Through the construction of a genome-scale metabolic model of S. thermophilus, the metabolic differences between the three bacteria were visualized by direct projection on a metabolic map. The comparative analysis revealed the minimal amino acid auxotrophy (only histidine and methionine or cysteine) of S. thermophilus LMG18311 and the broad variety of volatiles produced from amino acids compared to the other two bacteria. It also revealed the limited number of pyruvate branches, forcing this strain to use the homofermentative metabolism for growth optimization. In addition, some industrially relevant features could be identified in S. thermophilus, such as the unique pathway for acetaldehyde (yogurt flavor) production and the absence of a complete pentose phosphate pathway.

Pantograph: A template-based method for genome-scale metabolic model reconstruction

2015 ◽  
Vol 13 (02) ◽  
pp. 1550006 ◽  
Author(s):  
Nicolas Loira ◽  
Anna Zhukova ◽  
David James Sherman
Keyword(s):  
Metabolic Model ◽  
Scale Model ◽  
Model Reconstruction ◽  
Specific Knowledge ◽  
Manual Curation ◽  
A Genome ◽  
Iterative Improvement ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Template Reaction

Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr .

scholarly journals Genome-Scale Model and Omics Analysis of Metabolic Capacities of Akkermansia muciniphila Reveal a Preferential Mucin-Degrading Lifestyle

2017 ◽  
Vol 83 (18) ◽  
Author(s):  
Noora Ottman ◽  
Mark Davids ◽  
Maria Suarez-Diez ◽  
Sjef Boeren ◽  
Peter J. Schaap ◽  
...  
Keyword(s):  
Proteome Analysis ◽  
Metabolic Model ◽  
Scale Model ◽  
Bacterial Cells ◽  
Akkermansia Muciniphila ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale ◽  
Genome Scale Model

ABSTRACT The composition and activity of the microbiota in the human gastrointestinal tract are primarily shaped by nutrients derived from either food or the host. Bacteria colonizing the mucus layer have evolved to use mucin as a carbon and energy source. One of the members of the mucosa-associated microbiota is Akkermansia muciniphila, which is capable of producing an extensive repertoire of mucin-degrading enzymes. To further study the substrate utilization abilities of A. muciniphila, we constructed a genome-scale metabolic model to test amino acid auxotrophy, vitamin biosynthesis, and sugar-degrading capacities. The model-supported predictions were validated by in vitro experiments, which showed A. muciniphila to be able to utilize the mucin-derived monosaccharides fucose, galactose, and N-acetylglucosamine. Growth was also observed on N-acetylgalactosamine, even though the metabolic model did not predict this. The uptake of these sugars, as well as the nonmucin sugar glucose, was enhanced in the presence of mucin, indicating that additional mucin-derived components are needed for optimal growth. An analysis of whole-transcriptome sequencing (RNA-Seq) comparing the gene expression of A. muciniphila grown on mucin with that of the same bacterium grown on glucose confirmed the activity of the genes involved in mucin degradation and revealed most of these to be upregulated in the presence of mucin. The transcriptional response was confirmed by a proteome analysis, altogether revealing a hierarchy in the use of sugars and reflecting the adaptation of A. muciniphila to the mucosal environment. In conclusion, these findings provide molecular insights into the lifestyle of A. muciniphila and further confirm its role as a mucin specialist in the gut. IMPORTANCE Akkermansia muciniphila is among the most abundant mucosal bacteria in humans and in a wide range of other animals. Recently, A. muciniphila has attracted considerable attention because of its capacity to protect against diet-induced obesity in mouse models. However, the physiology of A. muciniphila has not been studied in detail. Hence, we constructed a genome-scale model and describe its validation by transcriptomic and proteomic approaches on bacterial cells grown on mucus and glucose, a nonmucus sugar. The results provide detailed molecular insight into the mucus-degrading lifestyle of A. muciniphila and further confirm the role of this mucin specialist in producing propionate and acetate under conditions of the intestinal tract.

scholarly journals First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 232
Author(s):  
Alina Renz ◽  
Lina Widerspick ◽  
Andreas Dräger
Keyword(s):  
Minimal Medium ◽  
Laboratory Experiments ◽  
Carbon Sources ◽  
Metabolic Model ◽  
High Quality ◽  
High Importance ◽  
Manual Curation ◽  
A Genome ◽  
Severe Infections ◽  
Genome Scale

Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources’ growth capacities. This model will pave the way to better understand D. pigrum’s role in the fight against S. aureus.

scholarly journals Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

2021 ◽  
Vol 9 ◽  
Author(s):  
Kusum Dhakar ◽  
Raphy Zarecki ◽  
Daniella van Bommel ◽  
Nadav Knossow ◽  
Shlomit Medina ◽  
...  
Keyword(s):  
Genetic Manipulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Scale Model ◽  
Promoting Effect ◽  
Metabolic Potential ◽  
Carbon And Nitrogen ◽  
A Genome ◽  
Genome Scale

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

scholarly journals Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

2014 ◽  
Vol 81 (5) ◽  
pp. 1622-1633 ◽  
Author(s):  
Nadine Veith ◽  
Margrete Solheim ◽  
Koen W. A. van Grinsven ◽  
Brett G. Olivier ◽  
Jennifer Levering ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Metabolic Network ◽  
Drug Targets ◽  
Metabolic Model ◽  
Content Type ◽  
A Genome ◽  
Genome Scale ◽  
Novel Drug ◽  
Novel Drug Targets

ABSTRACTIncreasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogenEnterococcus faecalisV583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles ofE. faecalisgrown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especiallyl-glutamine andl-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

scholarly journals EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Oliver Hädicke ◽  
Steffen Klamt
Keyword(s):  
Escherichia Coli ◽  
Reference Model ◽  
Model Organism ◽  
Solution Space ◽  
Scale Model ◽  
Reference Network ◽  
Central Metabolism ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

Abstract Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

scholarly journals All driven by energy demand? Integrative comparison of metabolism of Enterococcus faecalis wildtype and a glutamine synthase mutant

2021 ◽  
Author(s):  
Seyed Babak Loghmani ◽  
Eric Zitzow ◽  
Gene Ching-Chiek Koh ◽  
Andreas Ulmer ◽  
Nadine Veith ◽  
...  
Keyword(s):  
Enterococcus Faecalis ◽  
Energy Demand ◽  
Food Industry ◽  
Metabolic Model ◽  
Amino Acid Transporters ◽  
Proteomic Data ◽  
A Genome ◽  
Scale Models ◽  
Human Infections ◽  
Genome Scale

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g. food industry, but also in human health. Many LAB genera have developed a multidrug resistance in the past few years, becoming a serious problem in controlling hospital germs all around the world. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important. In this study, we investigated the effect of glutamine auxotrophy (ΔglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of its natural environment in the human body, but also play a role in food industry. We compared the results to those of the wildtype. Our integrative method, using a genome-scale metabolic model, constrained by metabolic and proteomic data allows us to understand the bigger picture of adaptation strategies in this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that ΔglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the non-specificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models since it affects energy balance.

scholarly journals Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli

2006 ◽  
Vol 188 (23) ◽  
pp. 8259-8271 ◽  
Author(s):  
Andrew R. Joyce ◽  
Jennifer L. Reed ◽  
Aprilfawn White ◽  
Robert Edwards ◽  
Andrei Osterman ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Minimal Medium ◽  
Essential Genes ◽  
Integrative Model ◽  
Scale Model ◽  
Data Sets ◽  
Data Set ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

ABSTRACT Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in ∼91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

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