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Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

TOXICOLOGICAL SCIENCES 2019年第2期172卷 279-291页

作者： Rawls, Kristopher D. Blais, Edik M. Dougherty, Bonnie, V Vinnakota, Kalyan C. Pannala, Venkat R. Wallqvist, Anders Kolling, Glynis L. Papin, Jason A. Univ Virginia Dept Biomed Engn Charlottesville VA 22908 USA Henry M Jackson Fdn Adv Mil Med Inc HJF Bethesda MD 20817 USA US Army Med Res & Dev Command Dept Def Biotechnol High Performance Comp Softwar Telemed & Adv Technol Res Ctr Ft Detrick MD 21702 USA Univ Virginia Dept Med Div Infect Dis & Int Hlth Charlottesville VA 22908 USA Univ Virginia Dept Biochem & Mol Genet Box 800759 Charlottesville VA 22908 USA

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for sixhour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

关键词： metabolism constraint-based modeling toxicology transcriptomics metabolomics

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Energy metabolism controls phenotypes by protein efficiency and allocation

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2019年第35期116卷 17592-17597页

作者： Chen, Yu Nielsen, Jens Chalmers Univ Technol Dept Biol & Biol Engn SE-41296 Gothenburg Sweden Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark BioInnovat Inst DK-2200 Copenhagen N Denmark

Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.

关键词： constraint-based modeling Escherichia coli Saccharomyces cerevisiae metabolic switch growth rate

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Challenges of cultivated meat production and applications of genome-scale metabolic modeling

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AICHE JOURNAL 2020年第6期66卷 e16235-e16235页

作者： Suthers, Patrick F. Maranas, Costas D. Penn State Univ Dept Chem Engn University Pk PA 16802 USA

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Different Routes of Protein Folding Contribute to Improved Protein Production in Saccharomyces cerevisiae

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MBIO 2020年第6期11卷 e02743-20页

作者： Qi, Qi Li, Feiran Yu, Rosemary Engqvist, Martin K. M. Siewers, Verena Fuchs, Johannes Nielsen, Jens Chalmers Univ Technol Dept Biol & Biol Engn Gothenburg Sweden Chalmers Univ Technol Novo Nordisk Fdn Ctr Biosustainabil Gothenburg Sweden Gothenburg Univ Sahlgrenska Acad Prote Core Facil Gothenburg Sweden Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Lyngby Denmark BioInnovat Inst Copenhagen N Denmark

Protein folding is often considered the flux controlling process in protein synthesis and secretion. Here, two previously isolated Saccharomyces cerevisiae strains with increased alpha-amylase productivity were analyzed in chemostat cultures at different dilution rates using multi-omics data. based on the analysis, we identified different routes of the protein folding pathway to improve protein production. In the first strain, the increased abundance of proteins working on the folding process, coordinated with upregulated glycogen metabolism and trehalose metabolism, helped increase alpha-amylase productivity 1.95-fold compared to the level in the original strain in chemostat culture at a dilution rate of 0.2/h. The second strain further strengthened the folding precision to improve protein production. More precise folding helps the cell improve protein production efficiency and reduce the expenditure of energy on the handling of misfolded proteins. As calculated using an enzyme-constrained genome-scale metabolic model, the second strain had an increased productivity of 2.36-fold with lower energy expenditure than that of the original under the same condition. Further study revealed that the regulation of N-glycans played an important role in the folding precision control and that overexpression of the glucosidase Cwh41p can significantly improve protein production, especially for the strains with improved folding capacity but lower folding precision. Our findings elucidated in detail the mechanisms in two strains having improved protein productivity and thereby provided novel insights for industrial recombinant protein production as well as demonstrating how multi-omics analysis can be used for identification of novel strain-engineering targets. IMPORTANCE Protein folding plays an important role in protein maturation and secretion. In recombinant protein production, many studies have focused on the folding pathway to improve productivity. Here, we identified two di

关键词： protein secretory pathway protein folding precision multi-omics analysis protein production constraint-based modeling

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A Graph based Approach to Analyse Metabolic Networks for Strain Engineering 27

A Graph Based Approach to Analyse Metabolic Networks for Str...

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27th Iranian Conference on Electrical Engineering (ICEE)

作者： Hassani, Leila Moosavi, Mohammad R. Setoodeh, Payam Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz Iran Shiraz Univ Sch Chem & Petr Engn Dept Chem Engn Shiraz Iran

ISBN: (纸本)9781728115085

Recently, there has been great interest in the field of metabolic engineering. Engineered microorganisms such as bacteria can be exploited to synthesize value-added bioproducts. Conversely, optimal genetic manipulations to reach to operative engineered cells is very costly;hence, regarding whole-cell functionalities, a number of proficient simulation algorithms have been developed and widely applied for systems-level analyses, and as a consequence, systems metabolic engineering. However, most of the currently-used in silico algorithms are usually time-consuming. In other words, it is requisite that computer scientists gain prior knowledge of molecular biology and biochemistry to be able to develop more effective algorithms in this field. Here, in the current study, we regard the metabolic network of interest as a general graph to present algorithmic perspective of the complex metabolic systems. A cell's metabolism is modeled in the form of a metabolic network. This way, the major problems in the field of metabolic engineering can be tackled using graph theory and graph mining algorithms. For this purpose, we reconstruct the metabolic network as a general graph, redefine the metabolic engineering problems accordingly, and finally, solve one of them as a sample.

关键词： In-silico strain engineering Network Flow Graph mining constraint-based modeling

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Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects

Metabolic Network Reconstructions to Predict Drug Targets an...

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作者： Kristopher Rawls Bonnie V. Dougherty Jason Papin

The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions. less

关键词： Machine Learning Homo sapiens Drug targets constraint-based modeling Genome-scale metabolic network reconstruction (GENRE) Off-target effects Flux balance analysis (FBA)

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In Silico Predictions for Fucoxanthin Production by the Diatom Phaeodactylum Tricornutum 12th

In Silico Predictions for Fucoxanthin Production by the Diat...

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12th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Bauer, Claudia M. Vilaca, Paulo Ramlov, Fernanda de Oliveira, Eva Regina Cabral, Debora Q. Schmitz, Caroline Correa, Rafaela Gordo Rocha, Miguel Maraschin, Marcelo Univ Fed Santa Catarina Plant Morphogenesis & Biochem Lab Florianopolis SC Brazil Silico Life Lda Braga Portugal Univ Fed Santa Catarina Lab Phycol Florianopolis SC Brazil Univ Minho Sch Engn Ctr Biol Engn Braga Portugal

ISBN: (纸本)9783319987026;9783319987019

Diatoms and brown seaweeds are the main producers of fucoxanthin, an oxy-carotenoid with important biological functions related to its antioxidative properties. The diatom Phaeodactylum tricornutum appears in this scenario as a good source for fucoxanthin production. Its whole genome sequence was published in 2008, and some genome-scale metabolic models are currently available. This work intends to make use of the two most recent genome-scale metabolic models published to predict ways to increase fucoxanthin production, using constraint-based modeling and flux balance analysis. One of the models was completed with 31 downstream reactions of the methylerythritol 4-phosphate plastidic (MEP) pathway. Simulations and optimizations were performed regarding inorganic carbon and nitrogen sources in the two models and comparisons were made between them. Biomass growth was predicted to increase in all sources tested, i.e., CO2, HCO3-, NO3- and urea. However, the best results were obtained by combining CO2 plus HCO3- regarding inorganic carbon, and for urea as a nitrogen source, in both models tested. As a result of optimizations for fucoxanthin production, many of the knockout reactions brought on are involved in the metabolism of pyruvate, glutamine/glutamate and nitrogen assimilation.

关键词： Fucoxanthin P. tricornutum Metabolic engineering constraint-based modeling

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Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using constraint-based modeling

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Procedia Computer Science 2012年 11卷 24-35页

作者： Iyarest Tawornsamretkit Rattana Thanasomboon Jittrawan Thaiprasit Dujduan Waraho Supapon Cheevadhanarak Asawin Meechai Biological Engineering Program King Mongkut's University of Technology Thonburi Bangkok 10140 Thailand Pilot Plant Development and Training Institute King Mongkut's University of Technology Thonburi (Bangkuntien) Bangkok 10150 Thailand School of Bioresources and Technology King Mongkut's University of Technology Thonburi Bangkok 10140 Thailand Department of Chemical Engineering King Mongkut's University of Technology Thonburi Bangkok 10140 Thailand

Linalool is a major volatile compound used frequently as an ingredient of perfumes and fragrances. Typically, it is produced by chemical routes that are not eco-friendly. We have previously succeeded to construct a synthetic Escherchia coli producing linalool. However, the linalool productivity of this strain is not economically feasible. In this work, the metabolic network of this synthetic E. coli was analyzed by using constraint-based modeling through flux balance analysis (FBA) and single gene deletion analysis to enhance linalool production. The results suggest that a deletion of gdh gene can improve the production of linalool by 14 folds with no growth reduction. The analysis of metabolic flux distribution reveals that the fluxes around glyceraldehyde-3-phosphate and pyruvate, which are the intermediates for the biosynthesis of DXP and linalool, are markedly increased in gdh -deleted strain.

关键词： linalool synthetic E. coli constraint-based modeling

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Inferring Metabolic Flux from Time-Course Metabolomics

Inferring Metabolic Flux from Time-Course Metabolomics

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作者： Scott Campit Sriram Chandrasekaran

The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines. less

关键词： Metabolomics Transcriptomics Proteomics MATLAB Gene Knockout Cell And Tissue Culture Python Homo sapiens Cancer metabolism Flux balance analysis Dynamic flux activity constraint-based modeling Genome-scale metabolic models

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Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production

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METABOLIC ENGINEERING COMMUNICATIONS 2020年 11卷 e00148页

作者： Suthers, Patrick F. Dinh, Hoang V. Fatma, Zia Shen, Yihui Chan, Siu Hung Joshua Rabinowitz, Joshua D. Zhao, Huimin Maranas, Costas D. Penn State Univ Dept Chem Engn University Pk PA 16802 USA Univ Illinois Dept Chem & Biomol Engn Urbana IL USA Princeton Univ Dept Chem Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Univ Illinois Urbana Champagne Carl R Woese Inst Genom Biol Urbana IL USA

Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model's quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism's production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction.

关键词： Genome-scale model Non-model yeast constraint-based modeling Metabolic engineering

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