ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

Zhang, Cheng Cheng; Bidkhori, Gholamreza; Benfeitas, Rui; Lee, Sunjae; Arif, Muhammad; Uhlén, Mathias; Mardinoğlu, Adil

doi:10.3389/fphys.2018.01355

Cited by 10 publications

(11 citation statements)

References 30 publications

(35 reference statements)

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“…To evaluate the importance of nutrient transport for P. infestans while colonizing tomato, we assessed the essentiality of the host-pathogen transport reactions in each submodel (61). A host-pathogen transport reaction is essential when its deletion disables biomass production and is partially essential when deletion in combination with deletions of other reactions disables biomass production.…”

Section: Resultsmentioning

confidence: 99%

Metabolic Model of thePhytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

Rodenburg

Seidl

Judelson

et al. 2019

mBio

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The oomycete pathogen Phytophthora infestans causes potato and tomato late blight, a disease that is a serious threat to agriculture. P. infestans is a hemibiotrophic pathogen, and during infection, it scavenges nutrients from living host cells for its own proliferation. To date, the nutrient flux from host to pathogen during infection has hardly been studied, and the interlinked metabolisms of the pathogen and host remain poorly understood. Here, we reconstructed an integrated metabolic model of P. infestans and tomato (Solanum lycopersicum) by integrating two previously published models for both species. We used this integrated model to simulate metabolic fluxes from host to pathogen and explored the topology of the model to study the dependencies of the metabolism of P. infestans on that of tomato. This showed, for example, that P. infestans, a thiamine auxotroph, depends on certain metabolic reactions of the tomato thiamine biosynthesis. We also exploited dual-transcriptome data of a time course of a full late blight infection cycle on tomato leaves and integrated the expression of metabolic enzymes in the model. This revealed profound changes in pathogen-host metabolism during infection. As infection progresses, P. infestans performs less de novo synthesis of metabolites and scavenges more metabolites from tomato. This integrated metabolic model for the P. infestans-tomato interaction provides a framework to integrate data and generate hypotheses about in planta nutrition of P. infestans throughout its infection cycle. IMPORTANCE Late blight disease caused by the oomycete pathogen Phytophthora infestans leads to extensive yield losses in tomato and potato cultivation worldwide. To effectively control this pathogen, a thorough understanding of the mechanisms shaping the interaction with its hosts is paramount. While considerable work has focused on exploring host defense mechanisms and identifying P. infestans proteins contributing to virulence and pathogenicity, the nutritional strategies of the pathogen are mostly unresolved. Genome-scale metabolic models (GEMs) can be used to simulate metabolic fluxes and help in unravelling the complex nature of metabolism. We integrated a GEM of tomato with a GEM of P. infestans to simulate the metabolic fluxes that occur during infection. This yields insights into the nutrients that P. infestans obtains during different phases of the infection cycle and helps in generating hypotheses about nutrition in planta.

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Section: Resultsmentioning

confidence: 99%

Metabolic Model of thePhytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

Rodenburg

Seidl

Judelson

et al. 2019

mBio

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“…The mathematical representation of GEMs through the stoichiometric matrix 7 is amenable to methods such as flux balance analysis (FBA) 8 and thermodynamic-based flux balance analysis (TFA) [9][10][11][12][13] , which ensure that the modeled metabolic reactions retain feasible concentrations and their directionalities obey the rules of thermodynamics, to predict reaction rates and metabolite concentrations when optimizing for a cellular function, such as growth, energy maintenance, or a specific metabolic task. Additionally, GEMs can be used for gene essentiality 14 , drug off-target analysis 15 , metabolic engineering [16][17][18] , and the derivation of kinetic models [19][20][21][22] .…”

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confidence: 99%

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

2020

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Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.

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“…Beyond the capacity of models to give structure and chemical meaning to functional annotations in biology, these models are also valuable for their predictive capacity. Today, models can be used to predict a wide range of biological phenotypes, including: (i) respiration, photosynthesis, and fermentation types (Cheung et al 2014;de la Torre et al 2015;Marcellin et al 2016;Edirisinghe et al 2016;Meadows et al 2016;Mendoza et al 2017;Marshall et al 2017;Chen et al 2018;Shameer et al 2018;Lieven et al 2018); (ii) feasible growth conditions and Biolog phenotype array profiles (Plata et al 2015;Bosi et al 2016;diCenzo et al 2016;Hartleb et al 2016); (iii) essential genes and reactions (Ding et al 2016;Khodayari and Maranas 2016;Zhang et al 2018;Xavier et al 2018;Guzmán et al 2018); (iv) potential existing or engineerable by-product biosynthesis pathways (Alper et al 2005;Milne et al 2011;Park et al 2013;Chen and Henson 2016;Harder et al 2016); and (v) the yields and even titre available for those pathways (Zuñiga et al 2016;Wang et al 2017;Li et al 2018;Niu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

Seaver

Liu

Zhang

et al. 2020

Preprint

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Introduction: For over ten years, the ModelSEED has been a primary resource for researchers endeavoring to construct draft genome-scale metabolic models based on annotated microbial or plant genomes. As described here, and now being released, the ModelSEED biochemistry database serves as the foundation of biochemical data underlying the ModelSEED and KBase. Objectives: The ModelSEED biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by being: (i) a database to serve metabolic modeling by including compartmentalization, transport reactions, charged molecules, proton balancing on reactions, and templates for model species; (ii) extensible by the user community, with all data stored in GitHub; and (iii) designed as a biochemical "Rosetta Stone" to facilitate comparison and integration of annotations from many different tools and databases. Methods: The ModelSEED was constructed by combining chemistry from many resources, applying standard transformations to data, identifying overlapping compounds and reactions, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. We also develop ontologies designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. Results: The current ModelSEED includes 33,978 compounds and 36,645 reactions, made available in an extensible set of files on GitHub, and visualized via the web from the ModelSEED and KBase. Conclusion: This database serves as a transparent source of biochemistry data to broadly support mechanistic modeling and data integration.

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ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling

Cited by 10 publications

References 30 publications

Metabolic Model of thePhytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

Metabolic Model of thePhytophthora infestans-Tomato Interaction Reveals Metabolic Switches during Host Colonization

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

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