Metabolic model to help in search for biofuel algae source

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression

Metabolites ◽

10.3390/metabo11070456 ◽

2021 ◽

Vol 11 (7) ◽

pp. 456

Author(s):

Pejman Salahshouri ◽

Modjtaba Emadi-Baygi ◽

Mahdi Jalili ◽

Faiz M. Khan ◽

Olaf Wolkenhauer ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Progression ◽

Gut Microbiome ◽

Metabolic Model ◽

System Level ◽

Mathematical Framework ◽

Bacterial Populations ◽

Colorectal Cancer Progression ◽

Genome Scale ◽

High Throughput Data Analysis

The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA, as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.

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A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Metabolites ◽

10.3390/metabo11030168 ◽

2021 ◽

Vol 11 (3) ◽

pp. 168

Author(s):

John I. Hendry ◽

Hoang V. Dinh ◽

Debolina Sarkar ◽

Lin Wang ◽

Anindita Bandyopadhyay ◽

...

Keyword(s):

Electron Transport ◽

Pyruvate Decarboxylase ◽

Metabolic Model ◽

Cell Types ◽

Economic Feasibility ◽

Nitrogen Fixing ◽

Design Algorithm ◽

A Genome ◽

Anabaena Sp ◽

Genome Scale

Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors.

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High‐quality genome‐scale metabolic model of Aurantiochytrium sp. T66

Biotechnology and Bioengineering ◽

10.1002/bit.27726 ◽

2021 ◽

Author(s):

Vetle Simensen ◽

André Voigt ◽

Eivind Almaas

Keyword(s):

Metabolic Model ◽

High Quality ◽

Genome Scale ◽

High Quality Genome

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Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria

Microorganisms ◽

10.3390/microorganisms7040101 ◽

2019 ◽

Vol 7 (4) ◽

pp. 101 ◽

Cited By ~ 5

Author(s):

Sabina Zoledowska ◽

Luana Presta ◽

Marco Fondi ◽

Francesca Decorosi ◽

Luciana Giovannetti ◽

...

Keyword(s):

In Silico ◽

Pathogenic Bacteria ◽

Metabolic Modeling ◽

Metabolic Model ◽

Metabolic Adaptation ◽

Ecological Niches ◽

Plant Colonization ◽

Phenotypic Data ◽

Plant Pathogenic Bacteria ◽

Metabolic Versatility

Understanding plant–microbe interactions is crucial for improving plants’ productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.

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Metagenomics-guided analysis of microbial chemolithoautotrophic phosphite oxidation yields evidence of a seventh natural CO2fixation pathway

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1715549114 ◽

2017 ◽

Vol 115 (1) ◽

pp. E92-E101 ◽

Cited By ~ 52

Author(s):

Israel A. Figueroa ◽

Tyler P. Barnum ◽

Pranav Y. Somasekhar ◽

Charlotte I. Carlström ◽

Anna L. Engelbrektson ◽

...

Keyword(s):

Wastewater Treatment ◽

16S Rrna ◽

Carbon Fixation ◽

Community Analysis ◽

Metabolic Model ◽

Rrna Gene ◽

Autotrophic Growth ◽

Anaerobic Wastewater Treatment ◽

Natural Carbon ◽

Environmental Relevance

Dissimilatory phosphite oxidation (DPO), a microbial metabolism by which phosphite (HPO32−) is oxidized to phosphate (PO43−), is the most energetically favorable chemotrophic electron-donating process known. Only one DPO organism has been described to date, and little is known about the environmental relevance of this metabolism. In this study, we used 16S rRNA gene community analysis and genome-resolved metagenomics to characterize anaerobic wastewater treatment sludge enrichments performing DPO coupled to CO2reduction. We identified an uncultivated DPO bacterium,CandidatusPhosphitivorax (Ca.P.) anaerolimi strain Phox-21, that belongs to candidate order GW-28 within theDeltaproteobacteria, which has no known cultured isolates. Genes for phosphite oxidation and for CO2reduction to formate were found in the genome ofCa.P. anaerolimi, but it appears to lack any of the known natural carbon fixation pathways. These observations led us to propose a metabolic model for autotrophic growth byCa.P. anaerolimi whereby DPO drives CO2reduction to formate, which is then assimilated into biomass via the reductive glycine pathway.

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