scholarly journals In Silico Design for Systems-Based Metabolic Engineering In Escherichia Coli for The Bioconversion of Aerobic Glycerol to Succinic Acid

2020 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Lorena Rios Guzman ◽  
Carmen Elvira Ojeda Cuella ◽  
Daissy Esther Mejia Perez ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Rational Design ◽  
Industrial Biotechnology ◽  
E Coli ◽  
Cell Factories ◽  
Transcriptomics Data

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

scholarly journals In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

2021 ◽  
Vol 12 ◽  
Author(s):  
Albert Enrique Tafur Rangel ◽  
Wendy Ríos ◽  
Daisy Mejía ◽  
Carmen Ojeda ◽  
Ross Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Engineering ◽  
Succinic Acid ◽  
In Silico ◽  
Industrial Biotechnology ◽  
Cell Factory ◽  
Cell Factories ◽  
Transcriptomics Data ◽  
Valuable Compounds ◽  
By Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

scholarly journals Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation

2005 ◽  
Vol 71 (12) ◽  
pp. 7880-7887 ◽  
Author(s):  
Sang Jun Lee ◽  
Dong-Yup Lee ◽  
Tae Yong Kim ◽  
Byung Hun Kim ◽  
Jinwon Lee ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Succinic Acid ◽  
In Silico ◽  
Acid Production ◽  
Gene Knockout ◽  
Fermentation Products ◽  
E Coli ◽  
Metabolic Analysis ◽  
Enhanced Production ◽  
Succinic Acid Production

ABSTRACT Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli. This resulted in the identification of five genes or operons, including ptsG, pykF, sdhA, mqo, and aceBA, which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli. However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli. Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG, pykF, and pykA, allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.

Escherichia coli as a platform microbial host for systems metabolic engineering

2021 ◽  
Author(s):  
Dongsoo Yang ◽  
Cindy Pricilia Surya Prabowo ◽  
Hyunmin Eun ◽  
Seon Young Park ◽  
In Jin Cho ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Natural Products ◽  
Metabolic Engineering ◽  
Food Ingredients ◽  
E Coli ◽  
Renewable Biomass ◽  
Microbial Cell Factories ◽  
Cell Factories ◽  
Systems Metabolic Engineering ◽  
Microbial Host

Abstract Bio-based production of industrially important chemicals and materials from non-edible and renewable biomass has become increasingly important to resolve the urgent worldwide issues including climate change. Also, bio-based production, instead of chemical synthesis, of food ingredients and natural products has gained ever increasing interest for health benefits. Systems metabolic engineering allows more efficient development of microbial cell factories capable of sustainable, green, and human-friendly production of diverse chemicals and materials. Escherichia coli is unarguably the most widely employed host strain for the bio-based production of chemicals and materials. In the present paper, we review the tools and strategies employed for systems metabolic engineering of E. coli. Next, representative examples and strategies for the production of chemicals including biofuels, bulk and specialty chemicals, and natural products are discussed, followed by discussion on materials including polyhydroxyalkanoates (PHAs), proteins, and nanomaterials. Lastly, future perspectives and challenges remaining for systems metabolic engineering of E. coli are discussed.

scholarly journals Metabolic Engineering Escherichia coli for the Production of Lycopene

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3136 ◽  
Author(s):  
Zhaobao Wang ◽  
JingXin Sun ◽  
Qun Yang ◽  
Jianming Yang
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Regulatory Networks ◽  
Carbon Sources ◽  
Operation Technique ◽  
Mva Pathway ◽  
E Coli ◽  
Microbial Cell Factories ◽  
Cell Factories ◽  
Lycopene Production

Lycopene, a potent antioxidant, has been widely used in the fields of pharmaceuticals, nutraceuticals, and cosmetics. However, the production of lycopene extracted from natural sources is far from meeting the demand. Consequently, synthetic biology and metabolic engineering have been employed to develop microbial cell factories for lycopene production. Due to the advantages of rapid growth, complete genetic background, and a reliable genetic operation technique, Escherichia coli has become the preferred host cell for microbial biochemicals production. In this review, the recent advances in biological lycopene production using engineered E. coli strains are summarized: First, modification of the endogenous MEP pathway and introduction of the heterogeneous MVA pathway for lycopene production are outlined. Second, the common challenges and strategies for lycopene biosynthesis are also presented, such as the optimization of other metabolic pathways, modulation of regulatory networks, and optimization of auxiliary carbon sources and the fermentation process. Finally, the future prospects for the improvement of lycopene biosynthesis are also discussed.

SATP (YaaH), a succinate–acetate transporter protein in Escherichia coli

2013 ◽  
Vol 454 (3) ◽  
pp. 585-595 ◽  
Author(s):  
Joana Sá-Pessoa ◽  
Sandra Paiva ◽  
David Ribas ◽  
Inês Jesus Silva ◽  
Sandra Cristina Viegas ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Acetic Acid ◽  
Succinic Acid ◽  
Critical Role ◽  
Amino Acid Residues ◽  
E Coli ◽  
Transporter Protein ◽  
Affinity Constants ◽  
In Trans

In the present paper we describe a new carboxylic acid transporter in Escherichia coli encoded by the gene yaaH. In contrast to what had been described for other YaaH family members, the E. coli transporter is highly specific for acetic acid (a monocarboxylate) and for succinic acid (a dicarboxylate), with affinity constants at pH 6.0 of 1.24±0.13 mM for acetic acid and 1.18±0.10 mM for succinic acid. In glucose-grown cells the ΔyaaH mutant is compromised for the uptake of both labelled acetic and succinic acids. YaaH, together with ActP, described previously as an acetate transporter, affect the use of acetic acid as sole carbon and energy source. Both genes have to be deleted simultaneously to abolish acetate transport. The uptake of acetate and succinate was restored when yaaH was expressed in trans in ΔyaaH ΔactP cells. We also demonstrate the critical role of YaaH amino acid residues Leu131 and Ala164 on the enhanced ability to transport lactate. Owing to its functional role in acetate and succinate uptake we propose its assignment as SatP: the Succinate–Acetate Transporter Protein.

scholarly journals Metabolic engineering of Escherichia coli for de novo production of 3-phenylpropanol via retrobiosynthesis approach

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenning Liu ◽  
Xue Zhang ◽  
Dengwei Lei ◽  
Bin Qiao ◽  
Guang-Rong Zhao
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
De Novo ◽  
Microbial Cell ◽  
Cell Factory ◽  
Phosphopantetheinyl Transferase ◽  
Chromosome Engineering ◽  
Microbial Cell Factory ◽  
E Coli ◽  
Artificial Pathway

Abstract Background 3-Phenylpropanol with a pleasant odor is widely used in foods, beverages and cosmetics as a fragrance ingredient. It also acts as the precursor and reactant in pharmaceutical and chemical industries. Currently, petroleum-based manufacturing processes of 3-phenypropanol is environmentally unfriendly and unsustainable. In this study, we aim to engineer Escherichia coli as microbial cell factory for de novo production of 3-phenypropanol via retrobiosynthesis approach. Results Aided by in silico retrobiosynthesis analysis, we designed a novel 3-phenylpropanol biosynthetic pathway extending from l-phenylalanine and comprising the phenylalanine ammonia lyase (PAL), enoate reductase (ER), aryl carboxylic acid reductase (CAR) and phosphopantetheinyl transferase (PPTase). We screened the enzymes from plants and microorganisms and reconstructed the artificial pathway for conversion of 3-phenylpropanol from l-phenylalanine. Then we conducted chromosome engineering to increase the supply of precursor l-phenylalanine and combined the upstream l-phenylalanine pathway and downstream 3-phenylpropanol pathway. Finally, we regulated the metabolic pathway strength and optimized fermentation conditions. As a consequence, metabolically engineered E. coli strain produced 847.97 mg/L of 3-phenypropanol at 24 h using glucose-glycerol mixture as co-carbon source. Conclusions We successfully developed an artificial 3-phenylpropanol pathway based on retrobiosynthesis approach, and highest titer of 3-phenylpropanol was achieved in E. coli via systems metabolic engineering strategies including enzyme sources variety, chromosome engineering, metabolic strength balancing and fermentation optimization. This work provides an engineered strain with industrial potential for production of 3-phenylpropanol, and the strategies applied here could be practical for bioengineers to design and reconstruct the microbial cell factory for high valuable chemicals.

Detecting antimicrobial resistance in Escherichia coli using benchtop attenuated total reflectance-Fourier transform infrared spectroscopy and machine learning

The Analyst ◽  
2021 ◽  
Vol 146 (20) ◽  
pp. 6211-6219
Author(s):  
Hewa G. S. Wijesinghe ◽  
Dominic J. Hare ◽  
Ahmed Mohamed ◽  
Alok K. Shah ◽  
Patrick N. A. Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Antimicrobial Resistance ◽  
Fourier Transform Infrared Spectroscopy ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
E Coli ◽  
Machine Learning Model

ATR–FTIR with a machine learning model predicts ESBL genotype of unknown E. coli strains with 86.5% AUC.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 220
Author(s):  
Wubliker Dessie ◽  
Zongcheng Wang ◽  
Xiaofang Luo ◽  
Meifeng Wang ◽  
Zuodong Qin
Keyword(s):  
Succinic Acid ◽  
In Silico ◽  
Value Added ◽  
Research Progress ◽  
Efficient Production ◽  
Actinobacillus Succinogenes ◽  
Cell Factories ◽  
In Silico Studies ◽  
Current Scenario ◽  
Critical Problems

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

scholarly journals Bacillus subtilis and Escherichia coli essential genes and minimal cell factories after one decade of genome engineering

Microbiology ◽  
2014 ◽  
Vol 160 (11) ◽  
pp. 2341-2351 ◽  
Author(s):  
Mario Juhas ◽  
Daniel R. Reuß ◽  
Bingyao Zhu ◽  
Fabian M. Commichau
Keyword(s):  
Escherichia Coli ◽  
Bacillus Subtilis ◽  
Essential Gene ◽  
Building Blocks ◽  
Essential Genes ◽  
Minimal Cell ◽  
E Coli ◽  
Cell Factories ◽  
Gene Sets ◽  
K 12

Investigation of essential genes, besides contributing to understanding the fundamental principles of life, has numerous practical applications. Essential genes can be exploited as building blocks of a tightly controlled cell ‘chassis’. Bacillus subtilis and Escherichia coli K-12 are both well-characterized model bacteria used as hosts for a plethora of biotechnological applications. Determination of the essential genes that constitute the B. subtilis and E. coli minimal genomes is therefore of the highest importance. Recent advances have led to the modification of the original B. subtilis and E. coli essential gene sets identified 10 years ago. Furthermore, significant progress has been made in the area of genome minimization of both model bacteria. This review provides an update, with particular emphasis on the current essential gene sets and their comparison with the original gene sets identified 10 years ago. Special attention is focused on the genome reduction analyses in B. subtilis and E. coli and the construction of minimal cell factories for industrial applications.

scholarly journals Efficient production of myo-inositol in Escherichia coli through metabolic engineering

2020 ◽  
Author(s):  
Ran You ◽  
Lei Wang ◽  
Congrong Shi ◽  
Hao Chen ◽  
Shasha Zhang ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Carbon Flux ◽  
Value Added ◽  
Efficient Production ◽  
Wild Type ◽  
Food Industries ◽  
E Coli ◽  
Mixed Carbon Source

Abstract Background: The biosynthesis of high value-added compounds using metabolically engineered strains has received wide attention in recent years. Myo-inositol (inositol), an important compound in the pharmaceutics, cosmetics and food industries, is usually produced from phytate via a harsh set of chemical reactions. Recombinant Escherichia coli strains have been constructed by metabolic engineering strategies to produce inositol, but with a low yield. The proper distribution of carbon flux between cell growth and inositol production is a major challenge for constructing an efficient inositol-synthesis pathway in bacteria. Construction of metabolically engineered E. coli strains with high stoichiometric yield of inositol is desirable.Results: In the present study, we designed an inositol-synthesis pathway from glucose with a theoretical stoichiometric yield of 1 mol inositol/mol glucose. Recombinant E. coli strains with high stoichiometric yield (>0.7 mol inositol/mol glucose) were obtained. Inositol was successfully biosynthesized after introducing two crucial enzymes: inositol-3-phosphate synthase (IPS) from Trypanosoma brucei, and inositol monophosphatase (IMP) from E. coli. Based on starting strains E. coli BW25113 (wild-type) and SG104 (ΔptsG::glk, ΔgalR::zglf, ΔpoxB::acs), a series of engineered strains for inositol production was constructed by deleting the key genes pgi, pfkA and pykF. Plasmid-based expression systems for IPS and IMP were optimized, and expression of the gene zwf was regulated to enhance the stoichiometric yield of inositol. The highest stoichiometric yield (0.96 mol inositol/mol glucose) was achieved from recombinant strain R15 (SG104, Δpgi, Δpgm, and RBSL5-zwf). Strain R04 (SG104 and Δpgi) reached high-density in a 1-L fermenter when using glucose and glycerol as a mixed carbon source. In scaled-up fed-batch bioconversion in situ using strain R04, 0.82 mol inositol/mol glucose was produced within 23 h, corresponding to a titer of 106.3 g/L (590.5 mM) inositol.Conclusions: The biosynthesis of inositol from glucose in recombinant E. coli was optimized by metabolic engineering strategies. The metabolically engineered E. coli strains represent a promising method for future inositol production. This study provides an essential reference to obtain a suitable distribution of carbon flux between glycolysis and inositol synthesis.

Sign in / Sign up

Export Citation Format

Share Document