scholarly journals Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

2019 ◽  
Author(s):  
Sara A. Amin ◽  
Elizabeth Chavez ◽  
Nikhil U. Nair ◽  
Soha Hassoun
Keyword(s):  
Metabolic Model ◽  
Enzyme Promiscuity ◽  
Metabolomics Data ◽  
Structural Modifications ◽  
E Coli ◽  
Cellular Metabolic Activity ◽  
Metabolic Models ◽  
Genome Scale ◽  
Primary Substrate ◽  
Model Annotation

AbstractBackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous – i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.ResultsOur workflow utilizes PROXIMAL – a tool that uses reactant-product transformation patterns from the KEGG database – to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

scholarly journals Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Sara A. Amin ◽  
Elizabeth Chavez ◽  
Vladimir Porokhin ◽  
Nikhil U. Nair ◽  
Soha Hassoun
Keyword(s):  
Metabolic Model ◽  
Enzyme Promiscuity ◽  
Metabolomics Data ◽  
E Coli

scholarly journals metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

2021 ◽  
Author(s):  
Francisco Zorrilla ◽  
Kiran R. Patil ◽  
Aleksej Zelezniak
Keyword(s):  
Microbial Communities ◽  
Metabolic Modeling ◽  
Large Degree ◽  
Metabolic Model ◽  
Community Level ◽  
Starting Point ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale ◽  
Reference Genomes

AbstractAdvances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

scholarly journals iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

2021 ◽  
Author(s):  
Emanuel Cunha ◽  
Miguel Silva ◽  
Ines Chaves ◽  
Huseyin Demirci ◽  
Davide Lagoa ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Tissue Level ◽  
Quercus Suber ◽  
Biomass Composition ◽  
Improve Model ◽  
Transcriptomics Data ◽  
Oak Tree ◽  
Metabolic Models ◽  
Genome Scale ◽  
Metabolic Behaviour

AbstractIn the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level in different environmental conditions. Quercus suber (Q. suber), also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6230 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen. Each tissue’s biomass composition was determined to improve model accuracy and merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights about other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

scholarly journals Methods for automated genome-scale metabolic model reconstruction

2018 ◽  
Vol 46 (4) ◽  
pp. 931-936 ◽  
Author(s):  
José P. Faria ◽  
Miguel Rocha ◽  
Isabel Rocha ◽  
Christopher S. Henry
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Data ◽  
Metabolic Model ◽  
Model Reconstruction ◽  
Next Generation ◽  
Genome Sequences ◽  
Metabolic Models ◽  
Genome Scale ◽  
Generation Sequencing

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

scholarly journals Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain of C. elegans

2017 ◽  
Author(s):  
Li Ma ◽  
Angelo Hoi Chung Chan ◽  
Jake Hattwell ◽  
Paul R. Ebert ◽  
Horst Joachim Schirra
Keyword(s):  
Systems Biology ◽  
Resistant Strain ◽  
Metabolic Model ◽  
Loss Of Function ◽  
Metabolic Resistance ◽  
Metabolomics Data ◽  
C Elegans ◽  
Phosphine Resistance ◽  
A Genome ◽  
Genome Scale

AbstractBackgroundPest insects are increasingly resistant to phosphine gas, which is used globally to protect grain reserves. The enzyme dihydrolipoamide dehydrogenase (DLD) is a phosphine resistance factor and participates in four key steps of core metabolism, making it a potential central metabolic regulator.ResultsHere we used microarray data and NMR-based metabolomics to characterize the phosphine response of wild-type C. elegans and the phosphine-resistant strain dld-1(wr4) which has a partial loss-of-function mutation in the gene for DLD. In addition, we have constructed CeCon, a C. elegans genome-scale metabolic model to facilitate integration of gene expression and metabolomics data.ConclusionsThe resulting systems biology analysis is consistent with the hypothesis that adaptation to a hypometabolic state is the most prominent mechanism of phosphine resistance in this nematode strain. The involvement of DLD in regulating and creating hypometabolic adaptation has implications for other biological phenomena involving hypometabolism, such as reperfusion injury and metabolic resistance.

scholarly journals Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4130
Author(s):  
Helena A. Herrmann ◽  
Mate Rusz ◽  
Dina Baier ◽  
Michael A. Jakupec ◽  
Bernhard K. Keppler ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Carcinoma Cell ◽  
Metabolic Model ◽  
Cellular Reprogramming ◽  
Metabolic Reprogramming ◽  
Metabolomics Data ◽  
Extracellular Metabolite ◽  
Metabolite Concentrations ◽  
Genome Scale ◽  
Resistant Cells

Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

scholarly journals On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

2019 ◽  
Author(s):  
Miguel Ponce-de-León ◽  
Iñigo Apaolaza ◽  
Alfonso Valencia ◽  
Francisco J. Planes
Keyword(s):  
Gene Expression ◽  
Metabolic Model ◽  
Specific Model ◽  
Omics Data ◽  
Model Reconstruction ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

scholarly journals Genome-scale metabolic reconstruction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

2018 ◽  
Author(s):  
Marzia Di Filippo ◽  
Raúl A. Ortiz-Merino ◽  
Chiara Damiani ◽  
Gianni Frascotti ◽  
Danilo Porro ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Metabolic Model ◽  
Cellular Systems ◽  
Metabolic Reconstruction ◽  
Genome Sequences ◽  
Metabolic Potential ◽  
Cell Factories ◽  
Metabolic Models ◽  
Constraint Based Modeling ◽  
Genome Scale

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

scholarly journals Metabolic Responses to Polymyxin Treatment in Acinetobacter baumannii ATCC 19606: Integrating Transcriptomics and Metabolomics with Genome-Scale Metabolic Modeling

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Yan Zhu ◽  
Jinxin Zhao ◽  
Mohd Hafidz Mahamad Maifiah ◽  
Tony Velkov ◽  
Falk Schreiber ◽  
...  
Keyword(s):  
Acinetobacter Baumannii ◽  
Metabolic Modeling ◽  
Metabolic Model ◽  
Metabolic Responses ◽  
Metabolomics Data ◽  
Gram Negative ◽  
Content Type ◽  
Integrative Modeling ◽  
Line Therapy ◽  
Genome Scale

ABSTRACT Multidrug-resistant (MDR) Acinetobacter baumannii has emerged as a very problematic pathogen over the past decades, with a high incidence in nosocomial infections. Discovered in the late 1940s but abandoned in the 1970s, polymyxins (i.e., polymyxin B and colistin) have been revived as the last-line therapy against Gram-negative “superbugs,” including MDR A. baumannii. Worryingly, resistance to polymyxins in A. baumannii has been increasingly reported, urging the development of novel antimicrobial therapies to rescue this last-line class of antibiotics. In the present study, we integrated genome-scale metabolic modeling with multiomics data to elucidate the mechanisms of cellular responses to colistin treatment in A. baumannii. A genome-scale metabolic model, iATCC19606, was constructed for strain ATCC 19606 based on the literature and genome annotation, containing 897 genes, 1,270 reactions, and 1,180 metabolites. After extensive curation, prediction of growth on 190 carbon sources using iATCC19606 achieved an overall accuracy of 84.3% compared to Biolog experimental results. Prediction of gene essentiality reached a high accuracy of 86.1% and 82.7% compared to two transposon mutant libraries of AB5075 and ATCC 17978, respectively. Further integrative modeling with our correlative transcriptomics and metabolomics data deciphered the complex regulation on metabolic responses to colistin treatment, including (i) upregulated fluxes through gluconeogenesis, the pentose phosphate pathway, and amino acid and nucleotide biosynthesis; (ii) downregulated TCA cycle and peptidoglycan and lipopolysaccharide biogenesis; and (iii) altered fluxes over respiratory chain. Our results elucidated the interplay of multiple metabolic pathways under colistin treatment in A. baumannii and provide key mechanistic insights into optimizing polymyxin combination therapy. IMPORTANCE Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative “superbugs”; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model iATCC19606 and the correlative multiomics data provide the fundamental understanding of the complex metabolic responses to polymyxin treatment in A. baumannii at the systems level. The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii.

scholarly journals OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

2021 ◽  
Author(s):  
Shouyong Jiang
Keyword(s):  
Metabolic Model ◽  
Strain Engineering ◽  
Design Strategies ◽  
E Coli ◽  
Success Stories ◽  
Strain Design ◽  
Optimality Principles ◽  
Biochemical Production ◽  
Genome Scale ◽  
Flux Difference

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximisation of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout) leading to high biochemical production. The usefulness 1and capabilities of OptDesign are demonstrated for the production of three biochemicals in E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

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