scholarly journals Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
R. Adam Thompson ◽  
Sanjeev Dahal ◽  
Sergio Garcia ◽  
Intawat Nookaew ◽  
Cong T. Trinh
2020 ◽  
Author(s):  
Sergio Garcia ◽  
R. Adam Thompson ◽  
Richard J. Giannone ◽  
Satyakam Dash ◽  
Costas D. Maranas ◽  
...  

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.


2019 ◽  
Author(s):  
Shany Ofaim ◽  
Raphy Zarecki ◽  
Seema Porob ◽  
Daniella Gat ◽  
Tamar Lahav ◽  
...  

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.


Genes ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 364 ◽  
Author(s):  
Jian Li ◽  
Renliang Sun ◽  
Xinjuan Ning ◽  
Xinran Wang ◽  
Zhuo Wang

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.


2021 ◽  
Author(s):  
Shouyong Jiang

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.


2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  

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.


2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  

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.


2017 ◽  
Vol 6 (2) ◽  
pp. 149-160 ◽  
Author(s):  
P. Chellapandi ◽  
M. Bharathi ◽  
R. Prathiviraj ◽  
R. Sasikala ◽  
M. Vikraman

2021 ◽  
Vol 412 ◽  
pp. 115390
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Kalyan C. Vinnakota ◽  
Venkat R. Pannala ◽  
Anders Wallqvist ◽  
...  

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