Статті в журналах: "Genome-Scale Metabolic Network (GSMN)"

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Moretti, Sébastien, Van Du T. Tran, Florence Mehl, Mark Ibberson, and Marco Pagni. "MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models." Nucleic Acids Research 49, no. D1 (November 6, 2020): D570—D574. http://dx.doi.org/10.1093/nar/gkaa992.

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Abstract MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

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Nègre, Delphine, Abdelhalim Larhlimi, and Samuel Bertrand. "Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks–What about specialised metabolism?" PLOS ONE 18, no. 8 (August 30, 2023): e0289757. http://dx.doi.org/10.1371/journal.pone.0289757.

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In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54–1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.

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Thananusak, Roypim, Kobkul Laoteng, Nachon Raethong, Yu Zhang, and Wanwipa Vongsangnak. "Metabolic Responses of Carotenoid and Cordycepin Biosynthetic Pathways in Cordyceps militaris under Light-Programming Exposure through Genome-Wide Transcriptional Analysis." Biology 9, no. 9 (August 21, 2020): 242. http://dx.doi.org/10.3390/biology9090242.

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Cordyceps militaris is currently exploited for commercial production of specialty products as its biomass constituents are enriched in bioactive compounds, such as cordycepin. The rational process development is important for economically feasible production of high quality bioproducts. Light is an abiotic factor affecting the cultivation process of this entomopathogenic fungus, particularly in its carotenoid formation. To uncover the cell response to light exposure, this study aimed to systematically investigate the metabolic responses of C. militaris strain TBRC6039 using integrative genome-wide transcriptome and genome-scale metabolic network (GSMN)-driven analysis. The genome-wide transcriptome analysis showed 8747 expressed genes in the glucose and sucrose cultures grown under light-programming and dark conditions. Of them, 689 differentially expressed genes were significant in response to the light-programming exposure. Through integration with the GSMN-driven analysis using the improved network (iRT1467), the reporter metabolites, e.g., adenosine-5′-monophosphate (AMP) and 2-oxoglutarate, were identified when cultivated under the carotenoid-producing condition controlled by light-programming exposure, linking to up-regulations of the metabolic genes involved in glyoxalase system, as well as cordycepin and carotenoid biosynthesis. These results indicated that C. militaris had a metabolic control in acclimatization to light exposure through transcriptional co-regulation, which supported the cell growth and cordycepin production in addition to the accumulation of carotenoid as a photo-protective bio-pigment. This study provides a perspective in manipulating the metabolic fluxes towards the target metabolites through either genetic or physiological approaches.

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Nègre, Aite, Belcour, Frioux, Brillet-Guéguen, Liu, Bordron, et al. "Genome–Scale Metabolic Networks Shed Light on the Carotenoid Biosynthesis Pathway in the Brown Algae Saccharina japonica and Cladosiphon okamuranus." Antioxidants 8, no. 11 (November 16, 2019): 564. http://dx.doi.org/10.3390/antiox8110564.

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Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome–Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.

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Borah, Khushboo, Jacque-Lucca Kearney, Ruma Banerjee, Pankaj Vats, Huihai Wu, Sonal Dahale, Sunitha Manjari Kasibhatla, et al. "GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae." PLOS Neglected Tropical Diseases 14, no. 7 (July 6, 2020): e0007871. http://dx.doi.org/10.1371/journal.pntd.0007871.

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Rodríguez-Mier, Pablo, Nathalie Poupin, Carlo de Blasio, Laurent Le Cam, and Fabien Jourdan. "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks." PLOS Computational Biology 17, no. 2 (February 11, 2021): e1008730. http://dx.doi.org/10.1371/journal.pcbi.1008730.

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The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

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7

Liu, Lili, Qian Mei, Zhenning Yu, Tianhao Sun, Zijun Zhang, and Ming Chen. "An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice." Journal of Integrative Bioinformatics 10, no. 2 (June 1, 2013): 94–102. http://dx.doi.org/10.1515/jib-2013-223.

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Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

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Gupta, Ankit, Ahmad Ahmad, Dipesh Chothwe, Midhun K. Madhu, Shireesh Srivastava, and Vineet K. Sharma. "Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath." PeerJ 7 (June 14, 2019): e6685. http://dx.doi.org/10.7717/peerj.6685.

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The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph,Methylococcus capsulatusstr. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is namediMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found thatM. capsulatuscan grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotrophMethylococcus capsulatusstr. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential ofM. capsulatusBath.

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Brunner, James D., Laverne A. Gallegos-Graves, and Marie E. Kroeger. "Inferring microbial interactions with their environment from genomic and metagenomic data." PLOS Computational Biology 19, no. 11 (November 13, 2023): e1011661. http://dx.doi.org/10.1371/journal.pcbi.1011661.

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Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

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10

Li, Jian, Renliang Sun, Xinjuan Ning, Xinran Wang, and Zhuo Wang. "Genome-Scale Metabolic Model of Actinosynnema pretiosum ATCC 31280 and Its Application for Ansamitocin P-3 Production Improvement." Genes 9, no. 7 (July 20, 2018): 364. http://dx.doi.org/10.3390/genes9070364.

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Actinosynnema pretiosum ATCC 31280 is the producer of antitumor agent ansamitocin P-3 (AP-3). Understanding of the AP-3 biosynthetic pathway and the whole metabolic network in A. pretiosum is important for the improvement of AP-3 titer. In this study, we reconstructed the first complete Genome-Scale Metabolic Model (GSMM) Aspm1282 for A. pretiosum ATCC 31280 based on the newly sequenced genome, with 87% reactions having definite functional annotation. The model has been validated by effectively predicting growth and the key genes for AP-3 biosynthesis. Then we built condition-specific models for an AP-3 high-yield mutant NXJ-24 by integrating Aspm1282 model with time-course transcriptome data. The changes of flux distribution reflect the metabolic shift from growth-related pathway to secondary metabolism pathway since the second day of cultivation. The AP-3 and methionine metabolisms were both enriched in active flux for the last two days, which uncovered the relationships among cell growth, activation of methionine metabolism, and the biosynthesis of AP-3. Furthermore, we identified four combinatorial gene modifications for overproducing AP-3 by in silico strain design, which improved the theoretical flux of AP-3 biosynthesis from 0.201 to 0.372 mmol/gDW/h. Upregulation of methionine metabolic pathway is a potential strategy to improve the production of AP-3.

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Zhang, Dandan, Jinyu Chen, Zihui Wang, and Cheng Wang. "Integrated Metabolomic and Network Analysis to Explore the Potential Mechanism of Three Chemical Elicitors in Rapamycin Overproduction." Microorganisms 10, no. 11 (November 8, 2022): 2205. http://dx.doi.org/10.3390/microorganisms10112205.

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Rapamycin is a polyketide macrocyclic antibiotic with exceptional pharmacological potential. To explore the potential mechanism of rapamycin overproduction, the intracellular metabolic differences of three chemical elicitor treatments were first investigated by combining them with dynamic metabolomics and network analysis. The metabolic response characteristics of each chemical elicitor treatment were identified by a weighted gene co-expression network analysis (WGCNA) model. According to the analysis of the identified metabolic modules, the changes in the cell membrane permeability might play a key role in rapamycin overproduction for dimethyl sulfoxide (DMSO) treatment. The enhancement of the starter unit of 4,5-dihydroxycyclohex-1-ene carboxylic acid (DHCHC) and the nicotinamide adenine dinucleotide phosphate (NADPH) availability were the main functions in the LaCl3 treatment. However, for sodium butyrate (SB), the improvement of the methylmalonyl-CoA and NADPH availability was a potential reason for the rapamycin overproduction. Further, the responsive metabolic pathways after chemical elicitor treatments were selected to predict the potential key limiting steps in rapamycin accumulation using a genome-scale metabolic network model (GSMM). Based on the prediction results, the targets within the reinforcement of the DHCHC and NADPH supply were selected to verify their effects on rapamycin production. The highest rapamycin yield improved 1.62 fold in the HT-aroA/zwf2 strain compared to the control.

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12

Beste, Dany JV, Tracy Hooper, Graham Stewart, Bhushan Bonde, Claudio Avignone-Rossa, Michael E. Bushell, Paul Wheeler, Steffen Klamt, Andrzej M. Kierzek, and Johnjoe McFadden. "GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism." Genome Biology 8, no. 5 (2007): R89. http://dx.doi.org/10.1186/gb-2007-8-5-r89.

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13

Belcour, Arnaud, Jeanne Got, Méziane Aite, Ludovic Delage, Jonas Collén, Clémence Frioux, Catherine Leblanc, et al. "Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe." Genome Research 33, no. 6 (June 2023): 972–87. http://dx.doi.org/10.1101/gr.277056.122.

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Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

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Witting, Michael. "Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model." Metabolites 10, no. 4 (March 28, 2020): 130. http://dx.doi.org/10.3390/metabo10040130.

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Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic networks in silico or used for analysis of omics data. Beside correct mass and charge balance, correct structural annotation of metabolites represents an important factor for analysis of these metabolic networks. However, several metabolites in different GSMs have no or only partial structural information associated with them. Here, a new systematic nomenclature for acyl-based metabolites such as fatty acids, acyl-carnitines, acyl-coenzymes A or acyl-carrier proteins is presented. This nomenclature enables one to encode structural details in the metabolite identifiers and improves human readability of reactions. As proof of principle, it was applied to the fatty acid biosynthesis and degradation in the Caenorhabditis elegans consensus model WormJam.

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15

Cooke, Juliette, Maxime Delmas, Cecilia Wieder, Pablo Rodríguez Mier, Clément Frainay, Florence Vinson, Timothy Ebbels, Nathalie Poupin, and Fabien Jourdan. "Genome scale metabolic network modelling for metabolic profile predictions." PLOS Computational Biology 20, no. 2 (February 22, 2024): e1011381. http://dx.doi.org/10.1371/journal.pcbi.1011381.

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Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

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Kim, Byoungjin, Won Jun Kim, Dong In Kim, and Sang Yup Lee. "Applications of genome-scale metabolic network model in metabolic engineering." Journal of Industrial Microbiology & Biotechnology 42, no. 3 (December 3, 2014): 339–48. http://dx.doi.org/10.1007/s10295-014-1554-9.

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17

Manna, Bharat, and Amit Ghosh. "Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila." Canadian Journal of Biotechnology 1, Special Issue (October 5, 2017): 134. http://dx.doi.org/10.24870/cjb.2017-a120.

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18

Singh, Dipali, and Martin J. Lercher. "Network reduction methods for genome-scale metabolic models." Cellular and Molecular Life Sciences 77, no. 3 (November 20, 2019): 481–88. http://dx.doi.org/10.1007/s00018-019-03383-z.

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19

Yilmaz, L. Safak, and Albertha J. M. Walhout. "A Caenorhabditis elegans Genome-Scale Metabolic Network Model." Cell Systems 2, no. 5 (May 2016): 297–311. http://dx.doi.org/10.1016/j.cels.2016.04.012.

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20

Yu, Hai-Long, Xiao-Long Liang, Zhen-Yang Ge, Zhi Zhang, Yao Ruan, Hao Tang, and Qing-Ye Zhang. "Metabolic Flux Analysis of Xanthomonas oryzae Treated with Bismerthiazol Revealed Glutathione Oxidoreductase in Glutathione Metabolism Serves as an Effective Target." International Journal of Molecular Sciences 25, no. 22 (November 14, 2024): 12236. http://dx.doi.org/10.3390/ijms252212236.

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Bacterial blight (BB) of rice caused by Xanthomonas oryzae pathovar oryzae (Xoo) is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti-Xoo targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti-Xoo target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.

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Sarathy, Chaitra, Marian Breuer, Martina Kutmon, Michiel E. Adriaens, Chris T. Evelo, and Ilja C. W. Arts. "Comparison of metabolic states using genome-scale metabolic models." PLOS Computational Biology 17, no. 11 (November 8, 2021): e1009522. http://dx.doi.org/10.1371/journal.pcbi.1009522.

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Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

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Sigurdsson, Martin, Neema Jamshidi, Jon J. Jonsson, and Bernhard Ø. Palsson. "Genome-scale network analysis of imprinted human metabolic genes." Epigenetics 4, no. 1 (January 2009): 43–46. http://dx.doi.org/10.4161/epi.4.1.7603.

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Serrano, M. Ángeles, and Francesc Sagués. "Network-based scoring system for genome-scale metabolic reconstructions." BMC Systems Biology 5, no. 1 (2011): 76. http://dx.doi.org/10.1186/1752-0509-5-76.

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Burgard, A. P. "Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions." Genome Research 14, no. 2 (February 1, 2004): 301–12. http://dx.doi.org/10.1101/gr.1926504.

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25

Forster, J. "Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network." Genome Research 13, no. 2 (February 1, 2003): 244–53. http://dx.doi.org/10.1101/gr.234503.

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26

Han, N. S., Y. J. Kim, and D. Y. Lee. "Genome-scale reconstruction of metabolic network in Leuconostoc mesenteroides." New Biotechnology 25 (September 2009): S358. http://dx.doi.org/10.1016/j.nbt.2009.06.865.

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Christian, Nils, Oliver Ebenhöh, and Patrick May. "Improving the genome-scale metabolic network of Arabidopsis thaliana." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 153, no. 2 (June 2009): S227—S228. http://dx.doi.org/10.1016/j.cbpa.2009.04.570.

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Fell, David A., Mark G. Poolman, and Albert Gevorgyan. "Building and analysing genome-scale metabolic models." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1197–201. http://dx.doi.org/10.1042/bst0381197.

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Reconstructing a model of the metabolic network of an organism from its annotated genome sequence would seem, at first sight, to be one of the most straightforward tasks in functional genomics, even if the various data sources required were never designed with this application in mind. The number of genome-scale metabolic models is, however, lagging far behind the number of sequenced genomes and is likely to continue to do so unless the model-building process can be accelerated. Two aspects that could usefully be improved are the ability to find the sources of error in a nascent model rapidly, and the generation of tenable hypotheses concerning solutions that would improve a model. We will illustrate these issues with approaches we have developed in the course of building metabolic models of Streptococcus agalactiae and Arabidopsis thaliana.

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Vargas, C., A. García-Yoldi, J. M. Rodríguez, M. Argandoña, M. Cánovas, J. M. P. Hernández, and J. J. Nieto. "Genome-scale reconstruction of the metabolic network in Chromohalobacter salexigens." New Biotechnology 25 (September 2009): S333. http://dx.doi.org/10.1016/j.nbt.2009.06.806.

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Portela, Carla, Silas Villas-Bôas, Isabel Rocha, and Eugénio C. Ferreira. "Genome scale metabolic network reconstruction of the pathogen Enterococcus faecalis." IFAC Proceedings Volumes 46, no. 31 (2013): 131–36. http://dx.doi.org/10.3182/20131216-3-in-2044.00067.

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31

Lertampaiporn, Supatcha, Jittisak Senachak, Wassana Taenkaew, Chiraphan Khannapho, and Apiradee Hongsthong. "Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis." Cells 9, no. 9 (September 15, 2020): 2097. http://dx.doi.org/10.3390/cells9092097.

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This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

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32

Schilling, Christophe H., Markus W. Covert, Iman Famili, George M. Church, Jeremy S. Edwards, and Bernhard O. Palsson. "Genome-Scale Metabolic Model of Helicobacter pylori 26695." Journal of Bacteriology 184, no. 16 (August 15, 2002): 4582–93. http://dx.doi.org/10.1128/jb.184.16.4582-4593.2002.

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ABSTRACT A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.

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33

Chang, Roger L., Kathleen Andrews, Donghyuk Kim, Zhanwen Li, Adam Godzik, and Bernhard O. Palsson. "Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli." Science 340, no. 6137 (June 6, 2013): 1220–23. http://dx.doi.org/10.1126/science.1234012.

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Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.

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34

Oberhardt, Matthew A., Jacek Puchałka, Kimberly E. Fryer, Vítor A. P. Martins dos Santos, and Jason A. Papin. "Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1." Journal of Bacteriology 190, no. 8 (January 11, 2008): 2790–803. http://dx.doi.org/10.1128/jb.01583-07.

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ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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Hosseini, Sayed-Rzgar, Olivier C. Martin, and Andreas Wagner. "Phenotypic innovation through recombination in genome-scale metabolic networks." Proceedings of the Royal Society B: Biological Sciences 283, no. 1839 (September 28, 2016): 20161536. http://dx.doi.org/10.1098/rspb.2016.1536.

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Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype–phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

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Richards, Matthew A., Thomas J. Lie, Juan Zhang, Stephen W. Ragsdale, John A. Leigh, and Nathan D. Price. "Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis." Journal of Bacteriology 198, no. 24 (October 10, 2016): 3379–90. http://dx.doi.org/10.1128/jb.00571-16.

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ABSTRACTHydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus onMethanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes ofM. maripaludisstrain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.IMPORTANCEUnderstanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network ofMethanococcus maripaludisthat explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

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37

Schuster, Stefan, Luís F. de Figueiredo, and Christoph Kaleta. "Predicting novel pathways in genome-scale metabolic networks." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1202–5. http://dx.doi.org/10.1042/bst0381202.

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Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.

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38

Salehabadi, Ehsan, Ehsan Motamedian, and Seyed Abbas Shojaosadati. "Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers." PLOS ONE 17, no. 3 (March 22, 2022): e0254270. http://dx.doi.org/10.1371/journal.pone.0254270.

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Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.

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39

Yang, Yi, Xiao-Pan Hu, and Bin-Guang Ma. "Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states." Molecular BioSystems 13, no. 3 (2017): 607–20. http://dx.doi.org/10.1039/c6mb00553e.

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Kim, Hyun Uk, and Sang Yup Lee. "Applications of genome-scale metabolic network models in the biopharmaceutical industry." Pharmaceutical Bioprocessing 1, no. 4 (October 2013): 337–39. http://dx.doi.org/10.4155/pbp.13.37.

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41

Imam, Saheed, Safak Yilmaz, Ugur Sohmen, Alexander S. Gorzalski, Jennifer L. Reed, Daniel R. Noguera, and Timothy J. Donohue. "iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network." BMC Systems Biology 5, no. 1 (2011): 116. http://dx.doi.org/10.1186/1752-0509-5-116.

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42

Wu, Xinsen, Xiaoyang Wang, and Wenyu Lu. "Genome-scale reconstruction of a metabolic network for Gluconobacter oxydans 621H." Biosystems 117 (March 2014): 10–14. http://dx.doi.org/10.1016/j.biosystems.2014.01.001.

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43

Babaei, Parizad, Sayed-Amir Marashi, and Sedigheh Asad. "Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501." Molecular BioSystems 11, no. 11 (2015): 3022–32. http://dx.doi.org/10.1039/c5mb00086f.

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Martínez, Verónica S., Lake-Ee Quek, and Lars K. Nielsen. "Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models." Biophysical Journal 107, no. 2 (July 2014): 493–503. http://dx.doi.org/10.1016/j.bpj.2014.05.029.

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45

Dunphy, Laura J., and Jason A. Papin. "Biomedical applications of genome-scale metabolic network reconstructions of human pathogens." Current Opinion in Biotechnology 51 (June 2018): 70–79. http://dx.doi.org/10.1016/j.copbio.2017.11.014.

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46

Damiani, Andrew L., Q. Peter He, Thomas W. Jeffries, and Jin Wang. "Comprehensive evaluation of two genome-scale metabolic network models forScheffersomyces stipitis." Biotechnology and Bioengineering 112, no. 6 (March 21, 2015): 1250–62. http://dx.doi.org/10.1002/bit.25535.

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47

Barona-Gómez, Francisco, Pablo Cruz-Morales, and Lianet Noda-García. "What can genome-scale metabolic network reconstructions do for prokaryotic systematics?" Antonie van Leeuwenhoek 101, no. 1 (October 21, 2011): 35–43. http://dx.doi.org/10.1007/s10482-011-9655-1.

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48

Li, Gaoyang, Huansheng Cao, and Ying Xu. "Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes." Briefings in Bioinformatics 20, no. 4 (March 27, 2018): 1590–603. http://dx.doi.org/10.1093/bib/bby022.

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Abstract We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average ∼56, ∼31 and ∼13% of a network’s nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.

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McCubbin, Tim, R. Axayacatl Gonzalez-Garcia, Robin W. Palfreyman, Chris Stowers, Lars K. Nielsen, and Esteban Marcellin. "A Pan-Genome Guided Metabolic Network Reconstruction of Five Propionibacterium Species Reveals Extensive Metabolic Diversity." Genes 11, no. 10 (September 23, 2020): 1115. http://dx.doi.org/10.3390/genes11101115.

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Propionibacteria have been studied extensively since the early 1930s due to their relevance to industry and importance as human pathogens. Still, their unique metabolism is far from fully understood. This is partly due to their signature high GC content, which has previously hampered the acquisition of quality sequence data, the accurate annotation of the available genomes, and the functional characterization of genes. The recent completion of the genome sequences for several species has led researchers to reassess the taxonomical classification of the genus Propionibacterium, which has been divided into several new genres. Such data also enable a comparative genomic approach to annotation and provide a new opportunity to revisit our understanding of their metabolism. Using pan-genome analysis combined with the reconstruction of the first high-quality Propionibacterium genome-scale metabolic model and a pan-metabolic model of current and former members of the genus Propionibacterium, we demonstrate that despite sharing unique metabolic traits, these organisms have an unexpected diversity in central carbon metabolism and a hidden layer of metabolic complexity. This combined approach gave us new insights into the evolution of Propionibacterium metabolism and led us to propose a novel, putative ferredoxin-linked energy conservation strategy. The pan-genomic approach highlighted key differences in Propionibacterium metabolism that reflect adaptation to their environment. Results were mathematically captured in genome-scale metabolic reconstructions that can be used to further explore metabolism using metabolic modeling techniques. Overall, the data provide a platform to explore Propionibacterium metabolism and a tool for the rational design of strains.

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Paley, Suzanne, Richard Billington, James Herson, Markus Krummenacker, and Peter D. Karp. "Pathway Tools Visualization of Organism-Scale Metabolic Networks." Metabolites 11, no. 2 (January 22, 2021): 64. http://dx.doi.org/10.3390/metabo11020064.

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Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.

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