scholarly journals Correction to: Genome-scale metabolic model integrated with RNAseq data to identify metabolic states of Clostridium thermocellum

2010 ◽  
Vol 5 (9) ◽  
pp. 994-994 ◽  
Author(s):  
Christopher M. Gowen ◽  
Stephen S. Fong
Keyword(s):  
Clostridium Thermocellum ◽  
Metabolic Model ◽  
Metabolic States ◽  
Rnaseq Data ◽  
Genome Scale

scholarly journals Development of a genome-scale metabolic model of Clostridium thermocellum and its applications for integration of multi-omics datasets and strain design

2020 ◽  
Author(s):  
Sergio Garcia ◽  
R. Adam Thompson ◽  
Richard J. Giannone ◽  
Satyakam Dash ◽  
Costas D. Maranas ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Lignocellulosic Biomass ◽  
Clostridium Thermocellum ◽  
System Analysis ◽  
Plant Biomass ◽  
Metabolic Model ◽  
Efficient Production ◽  
Branch Points ◽  
A Genome ◽  
Genome Scale

AbstractSolving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as catalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade untreated lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

scholarly journals Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction

2021 ◽  
Vol 9 ◽  
Author(s):  
Thora Bjorg Sigmarsdottir ◽  
Sarah McGarrity ◽  
James T. Yurkovich ◽  
Óttar Rolfsson ◽  
Ólafur Eysteinn Sigurjónsson
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Therapeutic Potential ◽  
Cell Lineage ◽  
Human Mesenchymal Stem Cells ◽  
Metabolic Model ◽  
Metabolic Function ◽  
Metabolic States ◽  
Initial Discovery ◽  
Genome Scale

Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage (proliferation, osteogenesis, and adipogenesis) that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs.

Genome-Scale Metabolic Model as a Virtual Platform to Reveal the Environmental Contribution of Methanogens

2017 ◽  
Vol 6 (2) ◽  
pp. 149-160 ◽  
Author(s):  
P. Chellapandi ◽  
M. Bharathi ◽  
R. Prathiviraj ◽  
R. Sasikala ◽  
M. Vikraman
Keyword(s):  
Metabolic Model ◽  
Environmental Contribution ◽  
Virtual Platform ◽  
Genome Scale

scholarly journals Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

2021 ◽  
Vol 412 ◽  
pp. 115390
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Kalyan C. Vinnakota ◽  
Venkat R. Pannala ◽  
Anders Wallqvist ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Renal Metabolism ◽  
A Genome ◽  
Compound Exposure ◽  
Genome Scale

scholarly journals Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jingru Zhou ◽  
Yingping Zhuang ◽  
Jianye Xia
Keyword(s):  
Aspergillus Niger ◽  
Large Scale ◽  
Measurement Techniques ◽  
Metabolic Model ◽  
System Level ◽  
Metabolic Phenotype ◽  
Omics Data ◽  
Prediction Ability ◽  
Phenotype Prediction ◽  
Genome Scale

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.

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

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
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.

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

Metabolites ◽  
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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