scholarly journals Genome-Scale Metabolic Model of Actinosynnema pretiosum ATCC 31280 and Its Application for Ansamitocin P-3 Production Improvement

Genes ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 364 ◽  
Author(s):  
Jian Li ◽  
Renliang Sun ◽  
Xinjuan Ning ◽  
Xinran Wang ◽  
Zhuo Wang
Keyword(s):  
Time Course ◽  
Antitumor Agent ◽  
Metabolic Model ◽  
High Yield ◽  
Strain Design ◽  
Production Improvement ◽  
Actinosynnema Pretiosum ◽  
Growth Activation ◽  
Potential Strategy ◽  
Genome Scale

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.

scholarly journals Genome-Scale reconstruction ofPaenarthrobacter aurescensTC1 metabolic model towards the study of atrazine bioremediation

2019 ◽  
Author(s):  
Shany Ofaim ◽  
Raphy Zarecki ◽  
Seema Porob ◽  
Daniella Gat ◽  
Tamar Lahav ◽  
...  
Keyword(s):  
Environmental Concern ◽  
Subsequent Development ◽  
Biomass Accumulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Strain Design ◽  
A Genome ◽  
Genome Scale ◽  
Atrazine Degradation

ABSTRACTAtrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as anin silicotool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model,iRZ960, forPaenarthrobacter aurescensTC1 – a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence ofP. aurescensTC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verifiedin vitro. Experimental validations support the reliability of the model’s predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

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.

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

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.

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