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Kees2018 - Genome-scale constraint-based model of the mucin-degrader Akkermansia muciniphila
Kees2018 - Genome-scale constraint-based
model of the mucin-degrader Akkermansia
This model is described in the article:
Model-driven design of a
minimal medium for Akkermansia muciniphila confirms mucus
van der Ark KCH, Aalvink S,
Suarez-Diez M, Schaap PJ, de Vos WM, Belzer C.
Microb Biotechnol 2018 Jan; :
The abundance of the human intestinal symbiont Akkermansia
muciniphila has found to be inversely correlated with several
diseases, including metabolic syndrome and obesity.
A. muciniphila is known to use mucin as sole carbon and
nitrogen source. To study the physiology and the potential for
therapeutic applications of this bacterium, we designed a
defined minimal medium. The composition of the medium was based
on the genome-scale metabolic model of A. muciniphila and
the composition of mucin. Our results indicate that
A. muciniphila does not code for GlmS, the enzyme that
mediates the conversion of fructose-6-phosphate (Fru6P) to
glucosamine-6-phosphate (GlcN6P), which is essential in
peptidoglycan formation. The only annotated enzyme that could
mediate this conversion is Amuc-NagB on locus Amuc_1822. We
found that Amuc-NagB was unable to form GlcN6P from Fru6P at
physiological conditions, while it efficiently catalyzed the
reverse reaction. To overcome this inability,
N-acetylglucosamine needs to be present in the medium for
A. muciniphila growth. With these findings, the
genome-scale metabolic model was updated and used to accurately
predict growth of A. muciniphila on synthetic media. The
finding that A. muciniphila has a necessity for GlcNAc,
which is present in mucin further prompts the adaptation to its
This model is hosted on
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To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
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Project description:The abundance of the human intestinal symbiont Akkermansia muciniphila has found to be inversely correlated with several diseases, including metabolic syndrome and obesity. A. muciniphila is known to use mucin as sole carbon and nitrogen source. To study the physiology and the potential for therapeutic applications of this bacterium, we designed a defined minimal medium. The composition of the medium was based on the genome-scale metabolic model of A. muciniphila and the composition of mucin. Our results indicate that A. muciniphila does not code for GlmS, the enzyme that mediates the conversion of fructose-6-phosphate (Fru6P) to glucosamine-6-phosphate (GlcN6P), which is essential in peptidoglycan formation. The only annotated enzyme that could mediate this conversion is Amuc-NagB on locus Amuc_1822. We found that Amuc-NagB was unable to form GlcN6P from Fru6P at physiological conditions, while it efficiently catalyzed the reverse reaction. To overcome this inability, N-acetylglucosamine needs to be present in the medium for A. muciniphila growth. With these findings, the genome-scale metabolic model was updated and used to accurately predict growth of A. muciniphila on synthetic media. The finding that A. muciniphila has a necessity for GlcNAc, which is present in mucin further prompts the adaptation to its mucosal niche.
Project description:Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.
Project description:BACKGROUND: Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904) remains significantly less predictive than the latest E. coli model (i.e., iAF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model. RESULTS: In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium. CONCLUSIONS: Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.
Project description:BACKGROUND: Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years. RESULTS: Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested successfully in batch and chemostat cultures. CONCLUSION: The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature.
Project description:BACKGROUND: Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. RESULTS: To investigate the metabolism of N. meningitidis we generated and then selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogen's biology we constructed a genome-scale metabolic network: Nmb_iTM560. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the Nmb_iTM560 model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium, such as a requirement for phosphoenolpyruvate carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. CONCLUSIONS: This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight into the pathogen's metabolism both in vitro and during infection.
Project description:The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.
Project description:Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb's vulnerability and metabolic adaptation have been carried out.A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports.This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.
Project description:The pyruvate dehydrogenase regulator protein (PdhR) of Escherichia coli acts as a transcriptional regulator in a pyruvate dependent manner to control central metabolic fluxes. However, the complete PdhR regulon has not yet been uncovered. To achieve an extended understanding of its gene regulation network, we combined large-scale network inference and experimental verification of results obtained by a systems biology approach. Overall design: We determined the gene expression of four different strains of E. coli on three different media. The four strains corresponded to the wild-type E. coli (LJ110), a PdhR knockout mutant (LJ110deltapdhR), a strain carrying an empty plasmid (LJ110/pTM30) and a PdhR overexpression strain (LJ110/pTM30PdhRhis). These strains were cultivated on Luria-Bertani broth (LBo), standard phosphate minimal medium supplemented with acetate and standard phosphate minimal medium supplemented with pyruvate. We obtained an overall 24 microarray experiments from two replicates of each of these cultivations.
Project description:Understanding microbial nutritional requirements is a key challenge in microbiology. Here we leverage the recent availability of thousands of automatically generated genome-scale metabolic models to develop a predictor of microbial minimal medium requirements, which we apply to thousands of species to study the relationship between their nutritional requirements and their ecological and genomic traits. We first show that nutritional requirements are more similar among species that co-habit many ecological niches. We then reveal three fundamental characteristics of microbial fastidiousness (i.e., complex and specific nutritional requirements): (1) more fastidious microorganisms tend to be more ecologically limited; (2) fastidiousness is positively associated with smaller genomes and smaller metabolic networks; and (3) more fastidious species grow more slowly and have less ability to cooperate with other species than more metabolically versatile organisms. These associations reflect the adaptation of fastidious microorganisms to unique niches with few cohabitating species. They also explain how non-fastidious species inhabit many ecological niches with high abundance rates. Taken together, these results advance our understanding microbial nutrition on a large scale, by presenting new nutrition-related associations that govern the distribution of microorganisms in nature.
Project description:With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.