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

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.

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Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets

Journal of Bacteriology ◽

10.1128/jb.185.21.6392-6399.2003 ◽

2003 ◽

Vol 185 (21) ◽

pp. 6392-6399 ◽

Cited By ~ 53

Author(s):

Timothy E. Allen ◽

Markus J. Herrgård ◽

Mingzhu Liu ◽

Yu Qiu ◽

Jeremy D. Glasner ◽

...

Keyword(s):

Escherichia Coli ◽

Protein Synthesis ◽

In Silico ◽

Half Life ◽

Data Sets ◽

Expression Data ◽

E Coli ◽

Model Driven ◽

Life Data ◽

Genome Scale

ABSTRACT The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

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OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

10.1101/2021.12.10.472123 ◽

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.

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Engineered Synthetic Pathway for Isopropanol Production in Escherichia coli

Applied and Environmental Microbiology ◽

10.1128/aem.01140-07 ◽

2007 ◽

Vol 73 (24) ◽

pp. 7814-7818 ◽

Cited By ~ 195

Author(s):

T. Hanai ◽

S. Atsumi ◽

J. C. Liao

Keyword(s):

Escherichia Coli ◽

Secondary Alcohol ◽

Clostridium Beijerinckii ◽

Synthetic Pathway ◽

E Coli ◽

Coa Transferase ◽

Secondary Alcohol Dehydrogenase ◽

Acetoacetate Decarboxylase ◽

Shake Flasks ◽

K 12

ABSTRACT A synthetic pathway was engineered in Escherichia coli to produce isopropanol by expressing various combinations of genes from Clostridium acetobutylicum ATCC 824, E. coli K-12 MG1655, Clostridium beijerinckii NRRL B593, and Thermoanaerobacter brockii HTD4. The strain with the combination of C. acetobutylicum thl (acetyl-coenzyme A [CoA] acetyltransferase), E. coli atoAD (acetoacetyl-CoA transferase), C. acetobutylicum adc (acetoacetate decarboxylase), and C. beijerinckii adh (secondary alcohol dehydrogenase) achieved the highest titer. This strain produced 81.6 mM isopropanol in shake flasks with a yield of 43.5% (mol/mol) in the production phase. To our knowledge, this work is the first to produce isopropanol in E. coli, and the titer exceeded that from the native producers.

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Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica

BMC Systems Biology ◽

10.1186/s12918-018-0542-5 ◽

2018 ◽

Vol 12 (S2) ◽

Cited By ~ 18

Author(s):

Pranjul Mishra ◽

Na-Rae Lee ◽

Meiyappan Lakshmanan ◽

Minsuk Kim ◽

Byung-Gee Kim ◽

...

Keyword(s):

Dicarboxylic Acid ◽

Yarrowia Lipolytica ◽

Acid Production ◽

Scale Model ◽

Model Driven ◽

Strain Design ◽

Genome Scale ◽

Genome Scale Model

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Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content

Metabolites ◽

10.3390/metabo10050177 ◽

2020 ◽

Vol 10 (5) ◽

pp. 177 ◽

Cited By ~ 4

Author(s):

Ahmad Ahmad ◽

Ruchi Pathania ◽

Shireesh Srivastava

Keyword(s):

Biomass Accumulation ◽

Metabolic Model ◽

Metabolic Characteristics ◽

Protein Levels ◽

A Genome ◽

Lower Protein ◽

Balance Analysis ◽

Synechococcus Sp ◽

The Difference ◽

Genome Scale

Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.

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

10.1101/536011 ◽

2019 ◽

Cited By ~ 1

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.

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Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.00772 ◽

2020 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Sergio Garcia ◽

R. Adam Thompson ◽

Richard J. Giannone ◽

Satyakam Dash ◽

Costas D. Maranas ◽

...

Keyword(s):

Clostridium Thermocellum ◽

Metabolic Model ◽

Strain Design ◽

A Genome ◽

Genome Scale

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New insights into the adaptive transcriptional response to nitrogen starvation in Escherichia coli

Biochemical Society Transactions ◽

10.1042/bst20180502 ◽

2018 ◽

Vol 46 (6) ◽

pp. 1721-1728 ◽

Cited By ~ 6

Author(s):

Amy Switzer ◽

Daniel R. Brown ◽

Sivaramesh Wigneshweraraj

Keyword(s):

Escherichia Coli ◽

Adaptive Response ◽

Large Scale ◽

Nitrogen Starvation ◽

Transcriptional Response ◽

Adaptive Responses ◽

Nitrogenous Compounds ◽

E Coli ◽

N Starvation ◽

Genome Scale

Bacterial adaptive responses to biotic and abiotic stresses often involve large-scale reprogramming of the transcriptome. Since nitrogen is an essential component of the bacterial cell, the transcriptional basis of the adaptive response to nitrogen starvation has been well studied. The adaptive response to N starvation in Escherichia coli is primarily a ‘scavenging response’, which results in the transcription of genes required for the transport and catabolism of nitrogenous compounds. However, recent genome-scale studies have begun to uncover and expand some of the intricate regulatory complexities that underpin the adaptive transcriptional response to nitrogen starvation in E. coli. The purpose of this review is to highlight some of these new developments.

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

Genes ◽

10.3390/genes9070364 ◽

2018 ◽

Vol 9 (7) ◽

pp. 364 ◽

Cited By ~ 6

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.

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