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Zhu SB, Jiang QH, Chen ZG, Zhou X, Jin YT, Deng Z, Guo FB. Mslar: Microbial synthetic lethal and rescue database. PLoS Comput Biol 2023; 19:e1011218. [PMID: 37289843 DOI: 10.1371/journal.pcbi.1011218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/26/2023] [Indexed: 06/10/2023] Open
Abstract
Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.
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Affiliation(s)
- Sen-Bin Zhu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Qian-Hu Jiang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi-Guo Chen
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Zhou
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan-Ting Jin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zixin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Feng-Biao Guo
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
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2
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Data-driven and model-guided systematic framework for media development in CHO cell culture. Metab Eng 2022; 73:114-123. [DOI: 10.1016/j.ymben.2022.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 06/20/2022] [Accepted: 07/01/2022] [Indexed: 11/21/2022]
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3
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Chen Y, Li F, Nielsen J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 2022; 22:foac003. [PMID: 35094064 PMCID: PMC8862083 DOI: 10.1093/femsyr/foac003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 11/30/2022] Open
Abstract
Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
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4
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Oftadeh O, Salvy P, Masid M, Curvat M, Miskovic L, Hatzimanikatis V. A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics. Nat Commun 2021; 12:4790. [PMID: 34373465 PMCID: PMC8352978 DOI: 10.1038/s41467-021-25158-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.
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Affiliation(s)
- Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pierre Salvy
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Cambrium GmbH, Berlin, Germany
| | - Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Curvat
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Quotient Suisse SA, Eysins, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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5
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Magazzù G, Zampieri G, Angione C. Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data. Bioinformatics 2021; 37:3546-3552. [PMID: 33974036 DOI: 10.1093/bioinformatics/btab324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 01/06/2021] [Accepted: 04/27/2021] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modelling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multi-source and multi-omic nature of these data types while preserving mechanistic interpretation. RESULTS Here we investigate different regularisation techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularisation frameworks including group, view-specific and principal component regularisation, and experimentally compare them using data from 1,143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularisation employed. In multi-omic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularised linear models compared to data-hungry methods based on neural networks. AVAILABILITY All data, models, and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giuseppe Magazzù
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.,Department of Biology, University of Padova, Padova, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.,Healthcare Innovation Centre, Teesside University, Middlesbrough, UK.,Centre for Digital Innovation, Teesside University, Middlesbrough, UK
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6
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Domenzain I, Li F, Kerkhoven EJ, Siewers V. Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. FEMS Yeast Res 2021; 21:foab002. [PMID: 33428734 PMCID: PMC7943257 DOI: 10.1093/femsyr/foab002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/08/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
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7
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Suthers PF, Dinh HV, Fatma Z, Shen Y, Chan SHJ, Rabinowitz JD, Zhao H, Maranas CD. Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production. Metab Eng Commun 2020; 11:e00148. [PMID: 33134082 PMCID: PMC7586132 DOI: 10.1016/j.mec.2020.e00148] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/08/2020] [Accepted: 10/05/2020] [Indexed: 12/18/2022] Open
Abstract
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. Genome-scale metabolic model iIsor850 describes metabolism of I. orientalis SD108. Customized biomass reaction highlights differences with S. cerevisiae. Chemostat data elucidate growth-associated ATP maintenance. Substrate utilization and CRISPR/Cas9 gene knockout phenotypes validate model. Model pinpoints candidate gene deletions coupling succinic acid production to growth.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Siu Hung Joshua Chan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champagne, Urbana, IL, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
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8
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Schroeder WL, Saha R. Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis. STAR Protoc 2020; 1:100105. [PMID: 32935086 PMCID: PMC7484705 DOI: 10.1016/j.xpro.2020.100105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Exophiala dermatitidis is a polyextremotolerant fungus with a small genome, thus suitable as a model system for melanogenesis and carotenogensis. A genome-scale model, iEde2091, is reconstructed to increase metabolic understanding and used in a shadow price analysis of pigments, as detailed here. Important to this reconstruction is OptFill, a recently developed alternative gap-filling method useful in the holistic and conservative reconstruction of genome-scale models of metabolism, particularly for understudied organisms like E. dermatitidis where gaps in metabolic knowledge are abundant. For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020).
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Affiliation(s)
- Wheaton L. Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska 68526, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska 68526, USA
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9
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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc Natl Acad Sci U S A 2020; 117:18869-18879. [PMID: 32675233 DOI: 10.1073/pnas.2002959117] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.
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10
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Sarkar D, Maranas CD. SNPeffect: identifying functional roles of SNPs using metabolic networks. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:512-531. [PMID: 32167625 PMCID: PMC9328443 DOI: 10.1111/tpj.14746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/20/2020] [Indexed: 05/04/2023]
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
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11
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Schroeder WL, Harris SD, Saha R. Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications. iScience 2020; 23:100980. [PMID: 32240950 PMCID: PMC7115120 DOI: 10.1016/j.isci.2020.100980] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA
| | - Steven D Harris
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA.
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12
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Schroeder WL, Saha R. OptFill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models. iScience 2019; 23:100783. [PMID: 31954977 PMCID: PMC6970165 DOI: 10.1016/j.isci.2019.100783] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 12/03/2019] [Accepted: 12/12/2019] [Indexed: 02/06/2023] Open
Abstract
Stoichiometric metabolic modeling, particularly genome-scale models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions but cannot avoid thermodynamically infeasible cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill, which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle-free gapfilling solutions. In addition, OptFill can be adapted to automate inherent TICs identification in any GSM. Overall, OptFill can address critical issues in automated development of high-quality GSMs. This work presents an alternative to state-of-the-art methods for gapfilling Unlike current methods, this method is holistic and infeasible cycle free This method is applied to three tests and one published model This method might also be used to address infeasible cycling
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
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13
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Dinh HV, Suthers PF, Chan SHJ, Shen Y, Xiao T, Deewan A, Jagtap SS, Zhao H, Rao CV, Rabinowitz JD, Maranas CD. A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data. Metab Eng Commun 2019; 9:e00101. [PMID: 31720216 PMCID: PMC6838544 DOI: 10.1016/j.mec.2019.e00101] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.
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Affiliation(s)
- Hoang V. Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Patrick F. Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Siu Hung Joshua Chan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Tianxia Xiao
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Anshu Deewan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
| | - Sujit S. Jagtap
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Christopher V. Rao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Joshua D. Rabinowitz
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
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14
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Chowdhury R, Maranas CD. From directed evolution to computational enzyme engineering—A review. AIChE J 2019. [DOI: 10.1002/aic.16847] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Ratul Chowdhury
- Department of Chemical Engineering The Pennsylvania State University University Park Pennsylvania
| | - Costas D. Maranas
- Department of Chemical Engineering The Pennsylvania State University University Park Pennsylvania
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15
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Abstract
Abstract
Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.
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16
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Lu H, Li F, Sánchez BJ, Zhu Z, Li G, Domenzain I, Marcišauskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 2019; 10:3586. [PMID: 31395883 PMCID: PMC6687777 DOI: 10.1038/s41467-019-11581-3] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/17/2019] [Indexed: 01/06/2023] Open
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.
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Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Benjamín J Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Zhengming Zhu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
- School of Biotechnology, Jiangnan University, 1800 Lihu Road, 214122, Wuxi, Jiangsu, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Simonas Marcišauskas
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Petre Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Dimitra Lappa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Moritz Emanuel Beber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
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17
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Kremkow BG, Lee KH. Glyco-Mapper: A Chinese hamster ovary (CHO) genome-specific glycosylation prediction tool. Metab Eng 2018. [PMID: 29522825 DOI: 10.1016/j.ymben.2018.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes.
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Affiliation(s)
- Benjamin G Kremkow
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA; Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
| | - Kelvin H Lee
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA; Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA.
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18
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Stolfa G, Smonskey MT, Boniface R, Hachmann AB, Gulde P, Joshi AD, Pierce AP, Jacobia SJ, Campbell A. CHO-Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction. Biotechnol J 2017; 13:e1700227. [PMID: 29072373 DOI: 10.1002/biot.201700227] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 10/12/2017] [Accepted: 10/16/2017] [Indexed: 01/07/2023]
Abstract
CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.
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Affiliation(s)
- Gino Stolfa
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | | | - Ryan Boniface
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | | | - Paul Gulde
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | - Atul D Joshi
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | - Anson P Pierce
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | - Scott J Jacobia
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
| | - Andrew Campbell
- Bioproduction R&D, Thermo Fisher Scientific, Grand Island, USA
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19
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Suástegui M, Yu Ng C, Chowdhury A, Sun W, Cao M, House E, Maranas CD, Shao Z. Multilevel engineering of the upstream module of aromatic amino acid biosynthesis in Saccharomyces cerevisiae for high production of polymer and drug precursors. Metab Eng 2017. [DOI: 10.1016/j.ymben.2017.06.008] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides. mSystems 2017; 2:mSystems00015-17. [PMID: 28744485 PMCID: PMC5513736 DOI: 10.1128/msystems.00015-17] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 05/16/2017] [Indexed: 12/14/2022] Open
Abstract
Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival. Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosynthetic growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species. IMPORTANCE Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival. Author Video: An author video summary of this article is available.
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21
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Kim WJ, Kim HU, Lee SY. Current state and applications of microbial genome-scale metabolic models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.03.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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22
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Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis. Proc Natl Acad Sci U S A 2016; 113:E8344-E8353. [PMID: 27911809 DOI: 10.1073/pnas.1613446113] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.
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23
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Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D, Martinez VS, Kyriakopoulos S, Jiménez NE, Zielinski DC, Quek LE, Wulff T, Arnsdorf J, Li S, Lee JS, Paglia G, Loira N, Spahn PN, Pedersen LE, Gutierrez JM, King ZA, Lund AM, Nagarajan H, Thomas A, Abdel-Haleem AM, Zanghellini J, Kildegaard HF, Voldborg BG, Gerdtzen ZP, Betenbaugh MJ, Palsson BO, Andersen MR, Nielsen LK, Borth N, Lee DY, Lewis NE. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism. Cell Syst 2016; 3:434-443.e8. [PMID: 27883890 PMCID: PMC5132346 DOI: 10.1016/j.cels.2016.10.020] [Citation(s) in RCA: 138] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 06/16/2016] [Accepted: 10/21/2016] [Indexed: 12/22/2022]
Abstract
Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.
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Affiliation(s)
- Hooman Hefzi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kok Siong Ang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Michael Hanscho
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Aarash Bordbar
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - David Ruckerbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Camila A Orellana
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Deniz Baycin-Hizal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yingxiang Huang
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Daniel Ley
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Veronica S Martinez
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Sarantos Kyriakopoulos
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Natalia E Jiménez
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Tune Wulff
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Johnny Arnsdorf
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Shangzhong Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jae Seong Lee
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Giuseppe Paglia
- Center for Systems Biology, University of Iceland, 101 Reykjavik, Iceland
| | - Nicolas Loira
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Philipp N Spahn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lasse E Pedersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Jahir M Gutierrez
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zachary A King
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anne Mathilde Lund
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Harish Nagarajan
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Alex Thomas
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Alyaa M Abdel-Haleem
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Juergen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Helene F Kildegaard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Bjørn G Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Ziomara P Gerdtzen
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mikael R Andersen
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria.
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore.
| | - Nathan E Lewis
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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24
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Dash S, Ng CY, Maranas CD. Metabolic modeling of clostridia: current developments and applications. FEMS Microbiol Lett 2016; 363:fnw004. [PMID: 26755502 DOI: 10.1093/femsle/fnw004] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2016] [Indexed: 12/12/2022] Open
Abstract
Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.
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Affiliation(s)
- Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802-1503, USA
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25
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Gopalakrishnan S, Maranas CD. Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations. Metabolites 2015; 5:521-35. [PMID: 26393660 PMCID: PMC4588810 DOI: 10.3390/metabo5030521] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 09/04/2015] [Indexed: 12/11/2022] Open
Abstract
Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.
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Affiliation(s)
- Saratram Gopalakrishnan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
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