scholarly journals gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs

2021 ◽  
Author(s):  
Laurence Legon ◽  
Christophe Corre ◽  
Declan G. Bates ◽  
Ahmad A. Mannan
Keyword(s):  
Cell Growth ◽  
Coupling Strength ◽  
Rational Design ◽  
Pareto Front ◽  
Software Tool ◽  
Cell Factory ◽  
Knock Out ◽  
Cell Factories ◽  
Growth Product ◽  
Genome Scale

Motivation: A widely applicable strategy for developing evolutionarily robust cell factories is to knock out (KO) genes or reactions to couple chemical synthesis with cell growth. Genome-scale metabolic models enable their rational design, but KOs that provide growth-coupling (gc) are rare in the immense design space, making searching difficult and slow, and though several measures determine the utility of those strains, few drive the search. Results: To address these issues we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise key performance objectives: cell growth, product synthesis, and coupling strength. Our measure of coupling strength facilitates the search, so gcFront not only finds a gc-design in minutes but also outputs many alternative Pareto optimal gc-designs from a single run - granting users freedom to select designs to take to the lab.

scholarly journals Development of an Efficient Gene Editing Tool in Schizochytrium sp. and Improving Its Lipid and Terpenoid Biosynthesis

2021 ◽  
Vol 8 ◽  
Author(s):  
Peng-Wei Huang ◽  
Ying-Shuang Xu ◽  
Xiao-Man Sun ◽  
Tian-Qiong Shi ◽  
Yang Gu ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Homologous Recombination ◽  
Gene Editing ◽  
Saturated Fatty Acids ◽  
Cell Factory ◽  
Terpenoid Biosynthesis ◽  
Acetyl Coa ◽  
Knock Out ◽  
Cell Factories ◽  
Efficient Gene

Schizochytrium sp. HX-308 is a marine microalga with fast growth and high lipid content, which has potential as microbial cell factories for lipid compound biosynthesis. It is significant to develop efficient genetic editing tool and discover molecular target in Schizochytrium sp. HX-308 for lipid compound biosynthesis. In this study, we developed an efficient gene editing tool in HX-308 which was mediated by Agrobacterium tumefaciens AGL-1. Results showed that the random integration efficiency reached 100%, and the homologous recombination efficiency reached about 30%. Furthermore, the metabolic pathway of lipid and terpenoid biosynthesis were engineered. Firstly, the acetyl-CoA c-acetyltransferase was overexpressed in HX-308 with a strong constitutive promoter. With the overexpression of acetyl-CoA c-acetyltransferase, more acetyl-CoA was used to synthesize terpenoids, and the production of squalene, β-carotene and astaxanthin was increased 5.4, 1.8, and 2.4 times, respectively. Interestingly, the production of saturated fatty acids and polyunsaturated fatty acids also changed. Moreover, three Acyl-CoA oxidase genes which catalyze the first step of β-oxidation were knocked out using homologous recombination. Results showed that the production of lipids increased in the three knock-out strains. Our results demonstrated that the A. tumefaciens-mediated transformation method will be of great use for the study of function genes, as well as developing Schizochytrium sp. as a strong cell factory for producing high value products.

scholarly journals Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

2017 ◽  
Author(s):  
João G. R. Cardoso ◽  
Kristian Jensen ◽  
Christian Lieven ◽  
Anne Sofie Lærke Hansen ◽  
Svetlana Galkina ◽  
...  
Keyword(s):  
Open Source Software ◽  
Rational Design ◽  
Software Project ◽  
Computational Systems Biology ◽  
Knock Out ◽  
Over Expression ◽  
Link Type ◽  
Cell Factories ◽  
Regulation Strategies ◽  
Computational Systems

ABSTRACTComputational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods’ implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enablesin silicodesign of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found athttp://cameo.bio. Users can also give cameo a try athttp://try.cameo.bio.

scholarly journals Uncovering novel pathways for enhancing hyaluronan synthesis in recombinantLactococcus lactis: Genome-scale metabolic modelling and experimental validation

2018 ◽  
Author(s):  
Abinaya Badri ◽  
Karthik Raman ◽  
Guhan Jayaraman
Keyword(s):  
Metabolic Network ◽  
Fold Increase ◽  
Experimental Results ◽  
Metabolic Modelling ◽  
Knock Out ◽  
Cell Factories ◽  
Naturally Occurring ◽  
A Genome ◽  
Recombinant Cell ◽  
Genome Scale

AbstractHyaluronan (HA) is a naturally occurring high-value polysaccharide with important medical applications. HA is commercially produced from pathogenic microbial sources. HA-producing recombinant cell factories that are being developed with GRAS organisms are comparatively less productive than the best natural producers. The metabolism of these recombinant systems needs to be more strategically engineered to achieve significant improvement. Here, we use a genome-scale metabolic network model to account for the entire metabolic network of the cell to predict strategies for improving HA production. We here analyze the metabolic network ofLactococcus lactisadapted to produce HA, and identify non-conventional overexpression and knock-out strategies to enhance HA flux.To experimentally validate our predictions, we identify an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, which has the capacity to function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report of the use of a nucleoside to improve HA production, our study shows how experimental results enable model refinement. Overall, we point out that well-constructed genome-scale metabolic models could be very potent tools to derive efficient strategies to improve biosynthesis of important high-value products.

scholarly journals Nanocellulose-based mechanically stable immobilization matrix for enhanced ethylene production: A framework for photosynthetic solid-state cell factories

2021 ◽  
Author(s):  
Ville Rissanen ◽  
Sindhujaa Vajravel ◽  
Sergey Kosourov ◽  
Suvi Arola ◽  
Eero Kontturi ◽  
...  
Keyword(s):  
Solid State ◽  
Ethylene Production ◽  
Cell Immobilization ◽  
Cell Factory ◽  
Cell Factories ◽  
Solid State Cell ◽  
Immobilization Matrix

Cell immobilization is a promising approach to create efficient photosynthetic cell factories for sustainable chemicals production. Here, we demonstrate a novel photosynthetic solid-state cell factory design for sustainable biocatalytic ethylene...

scholarly journals Computational Analysis of Dynamic Light Exposure of Unicellular Algal Cells in a Flat-Panel Photobioreactor to Support Light-Induced CO2 Bioprocess Development

2021 ◽  
Vol 12 ◽  
Author(s):  
Nicolò S. Vasile ◽  
Alessandro Cordara ◽  
Giulia Usai ◽  
Angela Re
Keyword(s):  
Light Transmission ◽  
Light Exposure ◽  
Model Organism ◽  
Cell Factory ◽  
Bioprocess Development ◽  
Cultivation Conditions ◽  
Flat Panel ◽  
Modeling Framework ◽  
Cell Factories ◽  
Cell Trajectories

Cyanobacterial cell factories trace a vibrant pathway to climate change neutrality and sustainable development owing to their ability to turn carbon dioxide-rich waste into a broad portfolio of renewable compounds, which are deemed valuable in green chemistry cross-sectorial applications. Cell factory design requires to define the optimal operational and cultivation conditions. The paramount parameter in biomass cultivation in photobioreactors is the light intensity since it impacts cellular physiology and productivity. Our modeling framework provides a basis for the predictive control of light-limited, light-saturated, and light-inhibited growth of the Synechocystis sp. PCC 6803 model organism in a flat-panel photobioreactor. The model here presented couples computational fluid dynamics, light transmission, kinetic modeling, and the reconstruction of single cell trajectories in differently irradiated areas of the photobioreactor to relate key physiological parameters to the multi-faceted processes occurring in the cultivation environment. Furthermore, our analysis highlights the need for properly constraining the model with decisive qualitative and quantitative data related to light calibration and light measurements both at the inlet and outlet of the photobioreactor in order to boost the accuracy and extrapolation capabilities of the model.

Engineering the CBB cycle and hydrogen utilization pathway of Ralstonia eutropha H16 for improved autotrophic growth and PHB production

2020 ◽  
Author(s):  
Zhongkang Li ◽  
Muzi Hu ◽  
Bin Xiong ◽  
Dongdong Zhao ◽  
Chunzhi Zhang ◽  
...  
Keyword(s):  
Carbon Fixation ◽  
Ralstonia Eutropha ◽  
Biological Research ◽  
Industrial Biotechnology ◽  
Cell Factory ◽  
Autotrophic Growth ◽  
Microbial Cell Factory ◽  
Knock Out ◽  
Ralstonia Eutropha H16 ◽  
Living Organisms

Abstract CO 2 is fixed by all living organisms with an autotrophic metabolism, among which the Calvin-Benson-Bassham ( CBB) cycle is the most important and widespread carbon fixation pathway. Thus, studying and engineering the CBB cycle with the associated energy providing pathways to increase the CO 2 fixation efficiency of cells is an important subject of biological research with significant application potential. In this work, the autotrophic microbe Ralstonia eutropha H16 was selected as a research platform for CBB cycle optimization engineering. By knocking out either CBB operon genes on the operon or mega-plasmid of R. eutropha , we found that both CBB operons were active and contributed almost equally to the carbon fixation process. With similar knock-out experiments, we found while both soluble and membrane-bound hydrogenases (SH and MBH), belonging to the energy providing hydrogenase module, were f unctional d uring autotrophic growth of R. eutropha. And SH played a more significant role. By introducing a heterologous cyanobacterial RuBisCO with the endogenous GroES/EL chaperone system and RbcX, the culture OD 600 of engineered strain increased 89.15% after 72 hours of autotrophic growth, indicating cyanobacterial RuBisCO with a higher activity was functional in R. eutropha and improved upon original CBB pathway. Meanwhile, expression of hydrogenases were optimized by modulating the expression of MBH and SH, which could further increase the R. eutropha H16 culture OD 600 to 93.4% at 72 hours. Moreover, the autotrophic yield of its major industrially relevant product, polyhydroxybutyrate (PHB), was increased by 99.71%. To our best knowledge, this is the first report of successfully engineering the CBB pathway of R. eutropha for improved activity , and is one of only a few cases where the efficiency of CO 2 assimilation pathway was improved. Our work demonstrates that R. eutropha is an extremely useful platform for studying and engineering the CBB for applications in more important organisms, such as agricultural crops, and a potential microbial cell factory to develop industrial biotechnology for sequestrating CO 2 .

scholarly journals Genome-Scale Metabolic Network Reconstruction and In Silico Analysis of Hexanoic acid Producing Megasphaera elsdenii

2020 ◽  
Vol 8 (4) ◽  
pp. 539 ◽  
Author(s):  
Na-Rae Lee ◽  
Choong Hwan Lee ◽  
Dong-Yup Lee ◽  
Jin-Byung Park
Keyword(s):  
Cell Growth ◽  
Acid Production ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Flux Ratio ◽  
Value Added ◽  
Tca Cycle ◽  
Hexanoic Acid ◽  
Acetyl Coa ◽  
Megasphaera Elsdenii ◽  
Genome Scale

Hexanoic acid and its derivatives have been recently recognized as value-added materials and can be synthesized by several microbes. Of them, Megasphaera elsdenii has been considered as an interesting hexanoic acid producer because of its capability to utilize a variety of carbons sources. However, the cellular metabolism and physiology of M. elsdenii still remain uncharacterized. Therefore, in order to better understand hexanoic acid synthetic metabolism in M. elsdenii, we newly reconstructed its genome-scale metabolic model, iME375, which accounts for 375 genes, 521 reactions, and 443 metabolites. A constraint-based analysis was then employed to evaluate cell growth under various conditions. Subsequently, a flux ratio analysis was conducted to understand the mechanism of bifurcated hexanoic acid synthetic pathways, including the typical fatty acid synthetic pathway via acetyl-CoA and the TCA cycle in a counterclockwise direction through succinate. The resultant metabolic states showed that the highest hexanoic acid production could be achieved when the balanced fractional contribution via acetyl-CoA and succinate in reductive TCA cycle was formed in various cell growth rates. The highest hexanoic acid production was maintained in the most perturbed flux ratio, as phosphoenolpyruvate carboxykinase (pck) enables the bifurcated pathway to form consistent fluxes. Finally, organic acid consuming simulations suggested that succinate can increase both biomass formation and hexanoic acid production.

scholarly journals A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hongzhong Lu ◽  
Feiran Li ◽  
Benjamín J. Sánchez ◽  
Zhengming Zhu ◽  
Gang Li ◽  
...  
Keyword(s):  
Model Organism ◽  
Metabolic Model ◽  
Phenotypic Traits ◽  
Cell Factory ◽  
Scale Level ◽  
Single Nucleotide ◽  
Multi Scale ◽  
Yeast Strains ◽  
Single Nucleotide Variations ◽  
Genome Scale

Abstract Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

scholarly journals Primary effusion lymphoma enhancer connectome links super-enhancers to dependency factors

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chong Wang ◽  
Luyao Zhang ◽  
Liangru Ke ◽  
Weiyue Ding ◽  
Sizun Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Growth ◽  
Cell Line ◽  
Poor Prognosis ◽  
Target Gene ◽  
Primary Effusion Lymphoma ◽  
Knock Out ◽  
Growth And Survival ◽  
Super Enhancer ◽  
Genome Wide

AbstractPrimary effusion lymphoma (PEL) has a very poor prognosis. To evaluate the contributions of enhancers/promoters interactions to PEL cell growth and survival, here we produce H3K27ac HiChIP datasets in PEL cells. This allows us to generate the PEL enhancer connectome, which links enhancers and promoters in PEL genome-wide. We identify more than 8000 genomic interactions in each PEL cell line. By incorporating HiChIP data with H3K27ac ChIP-seq data, we identify interactions between enhancers/enhancers, enhancers/promoters, and promoters/promoters. HiChIP further links PEL super-enhancers to PEL dependency factors MYC, IRF4, MCL1, CCND2, MDM2, and CFLAR. CRISPR knock out of MEF2C and IRF4 significantly reduces MYC and IRF4 super-enhancer H3K27ac signal. Knock out also reduces MYC and IRF4 expression. CRISPRi perturbation of these super-enhancers by tethering transcription repressors to enhancers significantly reduces target gene expression and reduces PEL cell growth. These data provide insights into PEL molecular pathogenesis.

scholarly journals A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Thordis Kristjansdottir ◽  
Elleke F. Bosma ◽  
Filipe Branco dos Santos ◽  
Emre Özdemir ◽  
Markus J. Herrgård ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Engineering ◽  
Growth Rates ◽  
Lactobacillus Reuteri ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Cell Factory ◽  
A Genome ◽  
A Cell ◽  
Genome Scale

Abstract Background Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. Results A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. Conclusion We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

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