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Chen C, Liao C, Liu YY. Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning. Nat Commun 2023; 14:2375. [PMID: 37185345 PMCID: PMC10130184 DOI: 10.1038/s41467-023-38110-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method - CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) - to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.
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Affiliation(s)
- Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
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2
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Strain B, Morrissey J, Antonakoudis A, Kontoravdi C. Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems. Comput Struct Biotechnol J 2023; 21:1543-1549. [PMID: 36879884 PMCID: PMC9984296 DOI: 10.1016/j.csbj.2023.02.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.
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Affiliation(s)
- Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - James Morrissey
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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3
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Past, Present, and Future of Genome Modification in Escherichia coli. Microorganisms 2022; 10:microorganisms10091835. [PMID: 36144436 PMCID: PMC9504249 DOI: 10.3390/microorganisms10091835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/05/2022] [Accepted: 09/05/2022] [Indexed: 12/04/2022] Open
Abstract
Escherichia coli K-12 is one of the most well-studied species of bacteria. This species, however, is much more difficult to modify by homologous recombination (HR) than other model microorganisms. Research on HR in E. coli has led to a better understanding of the molecular mechanisms of HR, resulting in technical improvements and rapid progress in genome research, and allowing whole-genome mutagenesis and large-scale genome modifications. Developments using λ Red (exo, bet, and gam) and CRISPR-Cas have made E. coli as amenable to genome modification as other model microorganisms, such as Saccharomyces cerevisiae and Bacillus subtilis. This review describes the history of recombination research in E. coli, as well as improvements in techniques for genome modification by HR. This review also describes the results of large-scale genome modification of E. coli using these technologies, including DNA synthesis and assembly. In addition, this article reviews recent advances in genome modification, considers future directions, and describes problems associated with the creation of cells by design.
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Network Reconstruction and Modelling Made Reproducible with moped. Metabolites 2022; 12:metabo12040275. [PMID: 35448462 PMCID: PMC9032245 DOI: 10.3390/metabo12040275] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/24/2022] [Accepted: 03/15/2022] [Indexed: 11/23/2022] Open
Abstract
Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses.
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Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers. PLoS One 2022; 17:e0254270. [PMID: 35316277 PMCID: PMC8939822 DOI: 10.1371/journal.pone.0254270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 03/08/2022] [Indexed: 11/23/2022] Open
Abstract
Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.
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Palsson BO. Genome‐Scale Models. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Leggieri PA, Liu Y, Hayes M, Connors B, Seppälä S, O'Malley MA, Venturelli OS. Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng 2021; 23:169-201. [PMID: 33781078 PMCID: PMC8277735 DOI: 10.1146/annurev-bioeng-082120-022836] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.
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Affiliation(s)
- Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Yiyi Liu
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Madeline Hayes
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
| | - Bryce Connors
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Susanna Seppälä
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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IDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape. Metabolites 2021; 11:metabo11050300. [PMID: 34066448 PMCID: PMC8148105 DOI: 10.3390/metabo11050300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 04/29/2021] [Indexed: 12/21/2022] Open
Abstract
Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.
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Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications. Methods Mol Biol 2021; 2189:217-229. [PMID: 33180304 DOI: 10.1007/978-1-0716-0822-7_16] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Genome-scale metabolic modeling is and will continue to play a central role in computational systems metabolic engineering and synthetic biology applications for the productions of chemicals and antibiotics. To that end, a survey and workflows of methods used for the development of high-quality genome-scale metabolic models (GEMs) and chassis design for synthetic biology are described here. The chapter consists of two parts (a) the methods of development of GEMs (Escherichia coli as a case study) and (b) E. coli chassis design for synthetic production of 1,4-butanediol (BDO). The methods described here can guide existing and future development of GEMs coupled with host chassis design for synthetic productions of novel antibiotics.
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10
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Fang X, Lloyd CJ, Palsson BO. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 2020; 18:731-743. [PMID: 32958892 PMCID: PMC7981288 DOI: 10.1038/s41579-020-00440-4] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
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Affiliation(s)
- Xin Fang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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ElBanna SA, Moneib NA, Aziz RK, Samir R. Genomics-guided identification of a conserved CptBA-like toxin-antitoxin system in Acinetobacter baumannii. J Adv Res 2020; 30:159-170. [PMID: 34026293 PMCID: PMC8132199 DOI: 10.1016/j.jare.2020.11.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/18/2020] [Accepted: 11/19/2020] [Indexed: 01/07/2023] Open
Abstract
Introduction Toxin-antitoxin (TA) systems are widespread among bacteria, archaea and fungi. They are classified into six types (I-VI) and have recently been proposed as novel drug targets. Objectives This study aimed to screen the pathogen Acinetobacter baumannii, known for its alarming antimicrobial resistance, for TA systems and identified a CptBA-like type IV TA, one of the least characterized systems. Methods In silico methods included secondary structure prediction, comparative genomics, multiple sequence alignment, and phylogenetic analysis, while in vitro strategies included plasmid engineering and expression of the TA system in Escherichia coli BL21, growth measurement, and transcription analysis with quantitative reverse-transcription polymerase chain reaction. Results Comparative genomics demonstrated the distribution of CptBA-like systems among Gram-negative bacteria, while phylogenetic analysis delineated two major groups, in each of which Acinetobacter spp. proteins clustered together. Sequence alignment indicated the conservation of cptA and cptB in 4,732 strains of A. baumannii in the same syntenic order. Using A. baumannii recombinant cptA and cptB, cloned under different promoters, confirmed their TA nature, as cptB expression was able to reverse growth inhibition by CptA in a dose-time dependent manner. Furthermore, transcriptional analysis of cptBA in clinical and standard A. baumannii strains demonstrated the downregulation of this system under oxidative and antibiotic stress. Conclusion Combining in silico and in vitro studies confirmed the predicted TA nature of a cptBA-like system in A. baumannii . Transcriptional analysis suggests a possible role of cptBA in response to antibiotics and stress factors in A. baumannii, making it a promising drug target.
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Affiliation(s)
- Shahira A ElBanna
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt
| | - Nayera A Moneib
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt
| | - Ramy K Aziz
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt.,The Center for Genome and Microbiome Research, Cairo University, Cairo, Egypt
| | - Reham Samir
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, 11562 Cairo, Egypt.,The Center for Genome and Microbiome Research, Cairo University, Cairo, Egypt
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12
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Systematically gap-filling the genome-scale metabolic model of CHO cells. Biotechnol Lett 2020; 43:73-87. [PMID: 33040240 DOI: 10.1007/s10529-020-03021-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/03/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. RESULTS We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. CONCLUSIONS The present CHO model is an important step towards more complete metabolic models of CHO cells.
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Marinos G, Kaleta C, Waschina S. Defining the nutritional input for genome-scale metabolic models: A roadmap. PLoS One 2020; 15:e0236890. [PMID: 32797084 PMCID: PMC7428157 DOI: 10.1371/journal.pone.0236890] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/15/2020] [Indexed: 12/13/2022] Open
Abstract
The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.
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Affiliation(s)
- Georgios Marinos
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, University Medical Center Schleswig-Holstein, Kiel, Schleswig-Holstein, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, University Medical Center Schleswig-Holstein, Kiel, Schleswig-Holstein, Germany
| | - Silvio Waschina
- Division of Nutriinformatics, Institute for Human Nutrition and Food Sciences, Kiel University, Kiel, Schleswig-Holstein, Germany
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Rana P, Berry C, Ghosh P, Fong SS. Recent advances on constraint-based models by integrating machine learning. Curr Opin Biotechnol 2020; 64:85-91. [DOI: 10.1016/j.copbio.2019.11.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 01/06/2023]
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15
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Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
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16
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Stanway RR, Bushell E, Chiappino-Pepe A, Roques M, Sanderson T, Franke-Fayard B, Caldelari R, Golomingi M, Nyonda M, Pandey V, Schwach F, Chevalley S, Ramesar J, Metcalf T, Herd C, Burda PC, Rayner JC, Soldati-Favre D, Janse CJ, Hatzimanikatis V, Billker O, Heussler VT. Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage. Cell 2020; 179:1112-1128.e26. [PMID: 31730853 PMCID: PMC6904910 DOI: 10.1016/j.cell.2019.10.030] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 07/23/2019] [Accepted: 10/23/2019] [Indexed: 12/11/2022]
Abstract
Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development. 1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes Life-stage-specific metabolic models reveal reprogramming of cellular function High agreement between blood/liver stage metabolic models and genetic screening data Essential metabolic pathways for parasite development and mechanistic origin revealed
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Affiliation(s)
- Rebecca R Stanway
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | - Ellen Bushell
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland
| | - Magali Roques
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | - Theo Sanderson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Blandine Franke-Fayard
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Reto Caldelari
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland
| | | | - Mary Nyonda
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Vikash Pandey
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden
| | - Frank Schwach
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Séverine Chevalley
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Jai Ramesar
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Tom Metcalf
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Colin Herd
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Paul-Christian Burda
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland; Bernhard Nocht Institute for Tropical Medicine, Hamburg 20359, Germany
| | - Julian C Rayner
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2, 0XY, UK
| | - Dominique Soldati-Favre
- Department of Microbiology & Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva 1211, Switzerland
| | - Chris J Janse
- Leiden Malaria Research Group, Parasitology, Center of Infectious Diseases, Leiden University Medical Center (LUMC), Leiden 2333ZA, the Netherlands
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne 1015, Switzerland
| | - Oliver Billker
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Infection Medicine Sweden (MIMS), Department of Molecular Biology, Umeå University, Umeå 901 87, Sweden.
| | - Volker T Heussler
- Institute of Cell Biology, University of Bern, Bern 3012, Switzerland.
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A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4. Sci Rep 2020; 10:7782. [PMID: 32385302 PMCID: PMC7210963 DOI: 10.1038/s41598-020-64721-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 04/21/2020] [Indexed: 01/09/2023] Open
Abstract
Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications.
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18
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Ong WK, Midford PE, Karp PD. Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models. Bioinformatics 2020; 36:1823-1830. [PMID: 31688932 PMCID: PMC7523652 DOI: 10.1093/bioinformatics/btz813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/29/2019] [Accepted: 10/31/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions. RESULTS Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements. AVAILABILITY AND IMPLEMENTATION The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wai Kit Ong
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA
| | - Peter E Midford
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA
| | - Peter D Karp
- Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA
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19
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Chowdhury S, Fong SS. Computational Modeling of the Human Microbiome. Microorganisms 2020; 8:microorganisms8020197. [PMID: 32023941 PMCID: PMC7074762 DOI: 10.3390/microorganisms8020197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/23/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284 USA;
| | - Stephen S. Fong
- Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, VA 23284, USA
- Correspondence:
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20
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Norsigian CJ, Fang X, Seif Y, Monk JM, Palsson BO. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. Nat Protoc 2020; 15:1-14. [PMID: 31863076 PMCID: PMC7017905 DOI: 10.1038/s41596-019-0254-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 10/08/2019] [Indexed: 11/09/2022]
Abstract
Genome-scale models (GEMs) of bacterial strains' metabolism have been formulated and used over the past 20 years. Recently, with the number of genome sequences exponentially increasing, multi-strain GEMs have proved valuable to define the properties of a species. Here, through four major stages, we extend the original Protocol used to generate a GEM for a single strain to enable multi-strain GEMs: (i) obtain or generate a high-quality model of a reference strain; (ii) compare the genome sequence between a reference strain and target strains to generate a homology matrix; (iii) generate draft strain-specific models from the homology matrix; and (iv) manually curate draft models. These multi-strain GEMs can be used to study pan-metabolic capabilities and strain-specific differences across a species, thus providing insights into its range of lifestyles. Unlike the original Protocol, this procedure is scalable and can be partly automated with the Supplementary Jupyter notebook Tutorial. This Protocol Extension joins the ranks of other comparable methods for generating models such as CarveMe and KBase. This extension of the original Protocol takes on the order of weeks to multiple months to complete depending on the availability of a suitable reference model.
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Affiliation(s)
- Charles J Norsigian
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Xin Fang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Yara Seif
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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21
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Shaffer M, Thurimella K, Quinn K, Doenges K, Zhang X, Bokatzian S, Reisdorph N, Lozupone CA. AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data. BMC Bioinformatics 2019; 20:614. [PMID: 31779604 PMCID: PMC6883642 DOI: 10.1186/s12859-019-3176-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 10/28/2019] [Indexed: 12/26/2022] Open
Abstract
Background Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment. Results We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps. Conclusions AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.
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Affiliation(s)
- M Shaffer
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - K Thurimella
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - K Quinn
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA
| | - K Doenges
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA
| | - X Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA.,Present address: BioElectron Technology Corporation, Mountain View, CA, 94043, USA
| | - S Bokatzian
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA
| | - N Reisdorph
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA
| | - C A Lozupone
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
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22
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Lillington SP, Leggieri PA, Heom KA, O'Malley MA. Nature's recyclers: anaerobic microbial communities drive crude biomass deconstruction. Curr Opin Biotechnol 2019; 62:38-47. [PMID: 31593910 DOI: 10.1016/j.copbio.2019.08.015] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 08/25/2019] [Accepted: 08/29/2019] [Indexed: 12/13/2022]
Abstract
Microbial communities within anaerobic ecosystems have evolved to degrade and recycle carbon throughout the earth. A number of strains have been isolated from anaerobic microbial communities, which are rich in carbohydrate active enzymes (CAZymes) to liberate fermentable sugars from crude plant biomass (lignocellulose). However, natural anaerobic communities host a wealth of microbial diversity that has yet to be harnessed for biotechnological applications to hydrolyze crude biomass into sugars and value-added products. This review highlights recent advances in 'omics' techniques to sequence anaerobic microbial genomes, decipher microbial membership, and characterize CAZyme diversity in anaerobic microbiomes. With a focus on the herbivore rumen, we further discuss methods to discover new CAZymes, including those found within multi-enzyme fungal cellulosomes. Emerging techniques to characterize the interwoven metabolism and spatial interactions between anaerobes are also reviewed, which will prove critical to developing a predictive understanding of anaerobic communities to guide in microbiome engineering.
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Affiliation(s)
- Stephen P Lillington
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Kellie A Heom
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, United States.
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23
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Sieow BFL, Nurminen TJ, Ling H, Chang MW. Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism. Biotechnol J 2019; 14:e1800445. [PMID: 31144773 DOI: 10.1002/biot.201800445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/24/2019] [Indexed: 12/15/2022]
Abstract
The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.
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Affiliation(s)
- Brendan Fu-Long Sieow
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Toni Juhani Nurminen
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Hua Ling
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Matthew Wook Chang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
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24
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 2019; 15:e1006971. [PMID: 31009451 PMCID: PMC6497307 DOI: 10.1371/journal.pcbi.1006971] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 03/21/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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25
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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A 2019; 116:7298-7307. [PMID: 30910961 PMCID: PMC6462048 DOI: 10.1073/pnas.1818877116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations. Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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26
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Monk JM, Lloyd CJ, Brunk E, Mih N, Sastry A, King Z, Takeuchi R, Nomura W, Zhang Z, Mori H, Feist AM, Palsson BO. iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 2019; 35:904-908. [PMID: 29020004 DOI: 10.1038/nbt.3956] [Citation(s) in RCA: 283] [Impact Index Per Article: 56.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Nathan Mih
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Anand Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Zachary King
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Rikiya Takeuchi
- Graduate School of Biological Sciences Nara Institute of Science and Technology Ikoma, Nara, Japan
| | - Wataru Nomura
- Graduate School of Biological Sciences Nara Institute of Science and Technology Ikoma, Nara, Japan
| | - Zhen Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Hirotada Mori
- Graduate School of Biological Sciences Nara Institute of Science and Technology Ikoma, Nara, Japan
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.,The NNF Center for Biosustainability, The Technical University of Denmark, Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.,The NNF Center for Biosustainability, The Technical University of Denmark, Lyngby, Denmark
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27
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Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, Magnusdóttir S, Ng CY, Preciat G, Žagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Valcarcel LV, Apaolaza I, Ghaderi S, Ahookhosh M, Ben Guebila M, Kostromins A, Sompairac N, Le HM, Ma D, Sun Y, Wang L, Yurkovich JT, Oliveira MAP, Vuong PT, El Assal LP, Kuperstein I, Zinovyev A, Hinton HS, Bryant WA, Aragón Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 2019; 14:639-702. [PMID: 30787451 PMCID: PMC6635304 DOI: 10.1038/s41596-018-0098-2] [Citation(s) in RCA: 595] [Impact Index Per Article: 119.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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Affiliation(s)
- Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sylvain Arreckx
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Sebastián N Mendoza
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jacek Wachowiak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Vanja Vlasov
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefania Magnusdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Siu H J Chan
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine M Clancy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - John T Sauls
- Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Benjamin Cousins
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diana C El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luis V Valcarcel
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Iñigo Apaolaza
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Susan Ghaderi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Masoud Ahookhosh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Andrejs Kostromins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Hoai M Le
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuekai Sun
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - James T Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Miguel A P Oliveira
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Phan T Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lemmer P El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - H Scott Hinton
- Utah State University Research Foundation, North Logan, UT, USA
| | - William A Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK
| | | | - Francisco J Planes
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Alejandro Maass
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Santosh Vempala
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Michael A Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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28
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Yang L, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO. DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. BMC SYSTEMS BIOLOGY 2019; 13:2. [PMID: 30626386 PMCID: PMC6327497 DOI: 10.1186/s12918-018-0675-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 12/21/2018] [Indexed: 01/09/2023]
Abstract
BACKGROUND Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. RESULTS We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. CONCLUSIONS Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
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Affiliation(s)
- Laurence Yang
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Ali Ebrahim
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Colton J. Lloyd
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
| | - Michael A. Saunders
- Department of Management Science and Engineering, Stanford University, 475 Via Ortega, Stanford, 94305 CA USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093 CA USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Kongens Lyngby, 2800 Denmark
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29
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Abstract
Flux coupling analysis (FCA) aims to describe the functional dependencies among reactions in a metabolic network. Currently studied coupling relations are qualitative in the sense that they identify pairs of reactions for which the activity of one reaction necessitates the activity of the other one, but without giving any numerical bounds relating the possible activity rates. The potential applications of FCA are heavily investigated, however apart from some trivial cases there is no clue of what bottleneck in the metabolic network causes each dependency. In this article, we introduce a quantitative approach to the same flux coupling problem named quantitative flux coupling analysis (QFCA). It generalizes the current concepts as we show that all the qualitative information provided by FCA is readily available in the quantitative flux coupling equations of QFCA, without the need for any additional analysis. Moreover, we design a simple algorithm to efficiently identify these flux coupling equations which scales up to the genome-scale metabolic networks with thousands of reactions and metabolites in an effective way. Furthermore, this framework enables us to quantify the "strength" of the flux coupling relations. We also provide different biologically meaningful interpretations, including one which gives an intuitive certificate of precisely which metabolites in the network enforce each flux coupling relation. Eventually, we conclude by suggesting the probable application of QFCA to the metabolic gap-filling problem, which we only begin to address here and is left for future research to further investigate.
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30
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Tamura T, Lu W, Song J, Akutsu T. Computing Minimum Reaction Modifications in a Boolean Metabolic Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1853-1862. [PMID: 29989991 DOI: 10.1109/tcbb.2017.2777456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In metabolic network modification, we newly add enzymes or/and knock-out genes to maximize the biomass production with minimum side-effect. Although this problem has been studied for various problem settings via mathematical models including flux balance analysis, elementary mode, and Boolean models, some important problem settings still remain to be studied. In this paper, we consider the Boolean Reaction Modification (BRM) problem, where a host metabolic network and a reference metabolic network are given in the Boolean model. The host network initially produces some toxic compounds and cannot produce some necessary compounds, but the reference network can produce the necessary compounds, and we should minimize the total number of removed reactions from the host network and added reactions from the reference network so that the toxic compounds are not producible, but the necessary compounds are producible in the resulting host network. We developed integer linear programming (ILP)-based methods for BRM, and compared them with OptStrain and SimOptStrain. The results show that our method performed better for reducing the total number of added and removed reactions, while OptStrain and SimOptStrain performed better for optimizing the production of the target compound. Our developed software is freely available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/solBRM/solBRM.html ".
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31
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Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018; 46:249-260. [PMID: 29588387 PMCID: PMC5906707 DOI: 10.1042/bst20170268] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/01/2018] [Accepted: 03/02/2018] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.
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32
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Botero D, Alvarado C, Bernal A, Danies G, Restrepo S. Network Analyses in Plant Pathogens. Front Microbiol 2018; 9:35. [PMID: 29441045 PMCID: PMC5797656 DOI: 10.3389/fmicb.2018.00035] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/09/2018] [Indexed: 11/14/2022] Open
Abstract
Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Camilo Alvarado
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Adriana Bernal
- Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Department of Design, Universidad de Los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
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33
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Piubeli F, Salvador M, Argandoña M, Nieto JJ, Bernal V, Pastor JM, Cánovas M, Vargas C. Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model. Microb Cell Fact 2018; 17:2. [PMID: 29316921 PMCID: PMC5759318 DOI: 10.1186/s12934-017-0852-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 12/20/2017] [Indexed: 01/08/2023] Open
Abstract
Background The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached. Results We generated and validated a high quality genome-based C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems. Conclusions Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production. Electronic supplementary material The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Francine Piubeli
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain
| | - Manuel Salvador
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain
| | - Montserrat Argandoña
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain
| | - Joaquín J Nieto
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain
| | - Vicente Bernal
- Department of Biochemistry and Molecular Biology B and Immunology, Faculty of Chemistry, Campus Regional de Excelencia Internacional "Campus Mare Nostrum", University of Murcia, 30100, Murcia, Spain.,Centro de Tecnología de Repsol, REPSOL S.A. Calle Agustín de Betancourt, s/n. 28935, Móstoles, Madrid, Spain
| | - Jose M Pastor
- Department of Biochemistry and Molecular Biology B and Immunology, Faculty of Chemistry, Campus Regional de Excelencia Internacional "Campus Mare Nostrum", University of Murcia, 30100, Murcia, Spain
| | - Manuel Cánovas
- Department of Biochemistry and Molecular Biology B and Immunology, Faculty of Chemistry, Campus Regional de Excelencia Internacional "Campus Mare Nostrum", University of Murcia, 30100, Murcia, Spain
| | - Carmen Vargas
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain.
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34
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Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. Curr Opin Biotechnol 2017; 51:103-108. [PMID: 29278837 DOI: 10.1016/j.copbio.2017.12.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 12/18/2022]
Abstract
With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.
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35
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van der Ark KCH, van Heck RGA, Martins Dos Santos VAP, Belzer C, de Vos WM. More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes. MICROBIOME 2017; 5:78. [PMID: 28705224 PMCID: PMC5512848 DOI: 10.1186/s40168-017-0299-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 07/05/2017] [Indexed: 05/14/2023]
Abstract
The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.
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Affiliation(s)
- Kees C H van der Ark
- Laboratory of Microbiology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Ruben G A van Heck
- Laboratory of Systems and Synthetic Biology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
- LifeGlimmer GmbH, Markelstrasse 38, 12163, Berlin, Germany
| | - Clara Belzer
- Laboratory of Microbiology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Willem M de Vos
- Laboratory of Microbiology, Wageningen University, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.
- RPU Immunobiology, Department of Bacteriology and Immunology, University of Helsinki, Haartmanikatu 4, 002940, Helsinki, Finland.
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36
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Oyetunde T, Zhang M, Chen Y, Tang Y, Lo C. BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods. Bioinformatics 2017; 33:608-611. [PMID: 27797784 DOI: 10.1093/bioinformatics/btw684] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/25/2016] [Indexed: 11/13/2022] Open
Abstract
Motivation Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability. Results We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools. Availability and Implementation MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ). Contacts toyetunde@wustl.edu or muhan@wustl.edu . Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Muhan Zhang
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA
| | - Yinjie Tang
- Department of Energy, Environmental and Chemical Engineering
| | - Cynthia Lo
- Department of Energy, Environmental and Chemical Engineering
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37
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Hosseini Z, Marashi SA. Discovering missing reactions of metabolic networks by using gene co-expression data. Sci Rep 2017; 7:41774. [PMID: 28150713 PMCID: PMC5288723 DOI: 10.1038/srep41774] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 12/30/2016] [Indexed: 11/23/2022] Open
Abstract
Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.
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Affiliation(s)
- Zhaleh Hosseini
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
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38
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Liu L, Zhang Z, Sheng T, Chen M. DEF: an automated dead-end filling approach based on quasi-endosymbiosis. Bioinformatics 2017; 33:405-413. [PMID: 28171511 DOI: 10.1093/bioinformatics/btw604] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Revised: 06/27/2016] [Accepted: 09/16/2016] [Indexed: 11/15/2022] Open
Abstract
Motivation Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model. Results We present an automated dead-end filling (DEF) approach, which is derived from the wisdom of endosymbiosis theory, to fill gaps by finding the most efficient dead-end utilization paths in a constructed quasi-endosymbiosis model. The recalls of reactions and dead ends of DEF reach around 73% and 86%, respectively. This method is capable of finding indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model. In the E. coli iJR904 model, for instance, about 42% of the dead-end metabolites were fixed by our proposed method. Availabilty and Implementaion DEF is publicly available at http://bis.zju.edu.cn/DEF/. Contact mchen@zju.edu.cn Supplimentary Information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lili Liu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Zijun Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China.,Department of Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Taotao Sheng
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, China
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39
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Saito R, Rocanin-Arjo A, You YH, Darshi M, Van Espen B, Miyamoto S, Pham J, Pu M, Romoli S, Natarajan L, Ju W, Kretzler M, Nelson R, Ono K, Thomasova D, Mulay SR, Ideker T, D'Agati V, Beyret E, Belmonte JCI, Anders HJ, Sharma K. Systems biology analysis reveals role of MDM2 in diabetic nephropathy. JCI Insight 2016; 1:e87877. [PMID: 27777973 DOI: 10.1172/jci.insight.87877] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
To derive new insights in diabetic complications, we integrated publicly available human protein-protein interaction (PPI) networks with global metabolic networks using metabolomic data from patients with diabetic nephropathy. We focused on the participating proteins in the network that were computationally predicted to connect the urine metabolites. MDM2 had the highest significant number of PPI connections. As validation, significant downregulation of MDM2 gene expression was found in both glomerular and tubulointerstitial compartments of kidney biopsy tissue from 2 independent cohorts of patients with diabetic nephropathy. In diabetic mice, chemical inhibition of MDM2 with Nutlin-3a led to reduction in the number of podocytes, increased blood urea nitrogen, and increased mortality. Addition of Nutlin-3a decreased WT1+ cells in embryonic kidneys. Both podocyte- and tubule-specific MDM2-knockout mice exhibited severe glomerular and tubular dysfunction, respectively. Interestingly, the only 2 metabolites that were reduced in both podocyte and tubule-specific MDM2-knockout mice were 3-methylcrotonylglycine and uracil, both of which were also reduced in human diabetic kidney disease. Thus, our bioinformatics tool combined with multi-omics studies identified an important functional role for MDM2 in glomeruli and tubules of the diabetic nephropathic kidney and links MDM2 to a reduction in 2 key metabolite biomarkers.
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Affiliation(s)
- Rintaro Saito
- Institute of Metabolomic Medicine.,Center for Renal Translational Medicine, Division of Nephrology-Hypertension.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Anaïs Rocanin-Arjo
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU Munich, Munich, Germany
| | - Young-Hyun You
- Center for Renal Translational Medicine, Division of Nephrology-Hypertension.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Manjula Darshi
- Institute of Metabolomic Medicine.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Benjamin Van Espen
- Institute of Metabolomic Medicine.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Satoshi Miyamoto
- Center for Renal Translational Medicine, Division of Nephrology-Hypertension.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Jessica Pham
- Center for Renal Translational Medicine, Division of Nephrology-Hypertension.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Minya Pu
- Institute of Metabolomic Medicine.,Department of Family Medicine and Epidemiology, UCSD, San Diego, California, USA
| | - Simone Romoli
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU Munich, Munich, Germany
| | - Loki Natarajan
- Institute of Metabolomic Medicine.,Department of Family Medicine and Epidemiology, UCSD, San Diego, California, USA
| | - Wenjun Ju
- Department of Internal Medicine, Nephrology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Department of Internal Medicine, Nephrology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert Nelson
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA
| | - Keiichiro Ono
- Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Dana Thomasova
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU Munich, Munich, Germany
| | - Shrikant R Mulay
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU Munich, Munich, Germany
| | - Trey Ideker
- Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA
| | - Vivette D'Agati
- Renal Pathology Laboratory, Columbia University, College of Physicians and Surgeons, Department of Pathology, New York, New York, USA
| | - Ergin Beyret
- Salk Institute for Biological Studies, San Diego, California, USA
| | | | - Hans Joachim Anders
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU Munich, Munich, Germany
| | - Kumar Sharma
- Institute of Metabolomic Medicine.,Center for Renal Translational Medicine, Division of Nephrology-Hypertension.,Division of Medical Genetics, Department of Medicine, UCSD, San Diego, California, USA.,Veterans Affairs Health Systems, San Diego, California, USA
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40
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Missing gene identification using functional coherence scores. Sci Rep 2016; 6:31725. [PMID: 27552989 PMCID: PMC4995438 DOI: 10.1038/srep31725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/22/2016] [Indexed: 11/18/2022] Open
Abstract
Reconstructing metabolic and signaling pathways is an effective way of interpreting a genome sequence. A challenge in a pathway reconstruction is that often genes in a pathway cannot be easily found, reflecting current imperfect information of the target organism. In this work, we developed a new method for finding missing genes, which integrates multiple features, including gene expression, phylogenetic profile, and function association scores. Particularly, for considering function association between candidate genes and neighboring proteins to the target missing gene in the network, we used Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), which are designed for capturing functional coherence of proteins. We showed that adding CAS and PAS substantially improve the accuracy of identifying missing genes in the yeast enzyme-enzyme network compared to the cases when only the conventional features, gene expression, phylogenetic profile, were used. Finally, it was also demonstrated that the accuracy improves by considering indirect neighbors to the target enzyme position in the network using a proper network-topology-based weighting scheme.
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41
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Mienda BS. Genome-scale metabolic models as platforms for strain design and biological discovery. J Biomol Struct Dyn 2016; 35:1863-1873. [DOI: 10.1080/07391102.2016.1197153] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Bashir Sajo Mienda
- Faculty of Biosciences and Medical Engineering, Department of Biosciences and Health Sciences, Bioinformatics Research Group (BIRG), Universiti Teknologi Malaysia, Johor Bahru, Skudai 81310, Malaysia
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42
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Ryu JY, Kim HU, Lee SY. Reconstruction of genome-scale human metabolic models using omics data. Integr Biol (Camb) 2016; 7:859-68. [PMID: 25730289 DOI: 10.1039/c5ib00002e] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.
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Affiliation(s)
- Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
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43
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Martins Conde PDR, Sauter T, Pfau T. Constraint Based Modeling Going Multicellular. Front Mol Biosci 2016; 3:3. [PMID: 26904548 PMCID: PMC4748834 DOI: 10.3389/fmolb.2016.00003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 01/25/2016] [Indexed: 12/31/2022] Open
Abstract
Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches.
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Affiliation(s)
- Patricia do Rosario Martins Conde
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of Luxembourg Luxembourg, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of Luxembourg Luxembourg, Luxembourg
| | - Thomas Pfau
- Systems Biology Group, Life Sciences Research Unit, Faculty of Sciences, Technology and Communications, University of LuxembourgLuxembourg, Luxembourg; Department of Physics, Institute of Complex Systems and Mathematical Biology, University of AberdeenAberdeen, UK
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44
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Mienda BS, Shamsir MS, Md. Illias R. Model-assisted formate dehydrogenase-O (fdoH) gene knockout for enhanced succinate production in Escherichia coli from glucose and glycerol carbon sources. J Biomol Struct Dyn 2016; 34:2305-16. [DOI: 10.1080/07391102.2015.1113387] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Bashir Sajo Mienda
- Bioinformatics Research Group (BIRG), Faculty of Biosciences and Medical Engineering, Department of Biosciences and Health Sciences, Universiti Teknologi Malaysia, Skudai Johor Bahru 81310, Malaysia
| | - Mohd Shahir Shamsir
- Bioinformatics Research Group (BIRG), Faculty of Biosciences and Medical Engineering, Department of Biosciences and Health Sciences, Universiti Teknologi Malaysia, Skudai Johor Bahru 81310, Malaysia
| | - Rosli Md. Illias
- Faculty of Chemical Engineering, Department of Bioprocess Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
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45
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Ponce-de-Leon M, Calle-Espinosa J, Peretó J, Montero F. Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach. PLoS One 2015; 10:e0143626. [PMID: 26629901 PMCID: PMC4668087 DOI: 10.1371/journal.pone.0143626] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 11/06/2015] [Indexed: 01/10/2023] Open
Abstract
Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.
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Affiliation(s)
- Miguel Ponce-de-Leon
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain
- * E-mail:
| | - Jorge Calle-Espinosa
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain
| | - Juli Peretó
- Departament de Bioquímica i Biologia Molecular and Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, C/José Beltrán 2, Paterna 46980, Spain
| | - Francisco Montero
- Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain
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46
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Ganter M, Kaltenbach HM, Stelling J. Predicting network functions with nested patterns. Nat Commun 2015; 5:3006. [PMID: 24398547 DOI: 10.1038/ncomms4006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 11/24/2013] [Indexed: 12/20/2022] Open
Abstract
Identifying suitable patterns in complex biological interaction networks helps understanding network functions and allows for predictions at the pattern level: by recognizing a known pattern, one can assign its previously established function. However, current approaches fail for previously unseen patterns, when patterns overlap and when they are embedded into a new network context. Here we show how to conceptually extend pattern-based approaches. We define metabolite patterns in metabolic networks that formalize co-occurrences of metabolites. Our probabilistic framework decodes the implicit information in the networks' metabolite patterns to predict metabolic functions. We demonstrate the predictive power by identifying 'indicator patterns', for instance, for enzyme classification, by predicting directions of novel reactions and of known reactions in new network contexts, and by ranking candidate network extensions for gap filling. Beyond their use in improving genome annotations and metabolic network models, we expect that the concepts transfer to other network types.
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Affiliation(s)
- Mathias Ganter
- 1] Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland [2]
| | - Hans-Michael Kaltenbach
- 1] Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland [2]
| | - Jörg Stelling
- Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland
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47
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Lubitz T, Welkenhuysen N, Shashkova S, Bendrioua L, Hohmann S, Klipp E, Krantz M. Network reconstruction and validation of the Snf1/AMPK pathway in baker's yeast based on a comprehensive literature review. NPJ Syst Biol Appl 2015; 1:15007. [PMID: 28725459 PMCID: PMC5516868 DOI: 10.1038/npjsba.2015.7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Revised: 06/19/2015] [Accepted: 07/14/2015] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND/OBJECTIVES The SNF1/AMPK protein kinase has a central role in energy homeostasis in eukaryotic cells. It is activated by energy depletion and stimulates processes leading to the production of ATP while it downregulates ATP-consuming processes. The yeast SNF1 complex is best known for its role in glucose derepression. METHODS We performed a network reconstruction of the Snf1 pathway based on a comprehensive literature review. The network was formalised in the rxncon language, and we used the rxncon toolbox for model validation and gap filling. RESULTS We present a machine-readable network definition that summarises the mechanistic knowledge of the Snf1 pathway. Furthermore, we used the known input/output relationships in the network to identify and fill gaps in the information transfer through the pathway, to produce a functional network model. Finally, we convert the functional network model into a rule-based model as a proof-of-principle. CONCLUSIONS The workflow presented here enables large scale reconstruction, validation and gap filling of signal transduction networks. It is analogous to but distinct from that established for metabolic networks. We demonstrate the workflow capabilities, and the direct link between the reconstruction and dynamic modelling, with the Snf1 network. This network is a distillation of the knowledge from all previous publications on the Snf1/AMPK pathway. The network is a knowledge resource for modellers and experimentalists alike, and a template for similar efforts in higher eukaryotes. Finally, we envisage the workflow as an instrumental tool for reconstruction of large signalling networks across Eukaryota.
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Affiliation(s)
- Timo Lubitz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Niek Welkenhuysen
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Sviatlana Shashkova
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Loubna Bendrioua
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Stefan Hohmann
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
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48
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O'Brien EJ, Monk JM, Palsson BO. Using Genome-scale Models to Predict Biological Capabilities. Cell 2015; 161:971-987. [PMID: 26000478 DOI: 10.1016/j.cell.2015.05.019] [Citation(s) in RCA: 438] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Indexed: 11/29/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.
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Affiliation(s)
- Edward J O'Brien
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark.
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49
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Fouladiha H, Marashi SA, Shokrgozar MA. Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells. Cell Prolif 2015; 48:475-85. [PMID: 26132591 DOI: 10.1111/cpr.12197] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 04/14/2015] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Over recent years, constraint-based modelling of metabolic networks has become increasingly popular; the models are suitable for system-level modelling of cell physiology. The goal of the present work was to reconstruct a constraint-based metabolic network model of bone marrow-derived mesenchymal stem cells (BMMSCs). MATERIALS AND METHODS To reconstruct a BMMSC-specific metabolic model, transcriptomic data of BMMSCs, and additionally, the human generic metabolic network model (Recon1) were used. Then, using the mCADRE algorithm, a draft metabolic network was reconstructed. Literature and proteomic data were subsequently used to refine and improve the draft. From this, iMSC1255 was derived to be the metabolic network model of BMMSCs. RESULTS iMSC1255 has 1255 genes, 1850 metabolites and 2288 reactions. After including additional constraints based on previously reported experimental results, our model successfully predicted BMMSC growth rate and metabolic phenotypes. CONCLUSIONS Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.
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Affiliation(s)
- H Fouladiha
- Department of Biotechnology, College of Science, University of Tehran, Tehran, 1417614411, Iran
| | - S-A Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, 1417614411, Iran
| | - M A Shokrgozar
- National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, 1316943551, Iran
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50
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Bailey LB, Stover PJ, McNulty H, Fenech MF, Gregory JF, Mills JL, Pfeiffer CM, Fazili Z, Zhang M, Ueland PM, Molloy AM, Caudill MA, Shane B, Berry RJ, Bailey RL, Hausman DB, Raghavan R, Raiten DJ. Biomarkers of Nutrition for Development-Folate Review. J Nutr 2015; 145:1636S-1680S. [PMID: 26451605 PMCID: PMC4478945 DOI: 10.3945/jn.114.206599] [Citation(s) in RCA: 296] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 12/11/2014] [Accepted: 04/14/2015] [Indexed: 12/13/2022] Open
Abstract
The Biomarkers of Nutrition for Development (BOND) project is designed to provide evidence-based advice to anyone with an interest in the role of nutrition in health. Specifically, the BOND program provides state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. To accomplish this objective, expert panels are recruited to evaluate the literature and to draft comprehensive reports on the current state of the art with regard to specific nutrient biology and available biomarkers for assessing nutrients in body tissues at the individual and population level. Phase I of the BOND project includes the evaluation of biomarkers for 6 nutrients: iodine, iron, zinc, folate, vitamin A, and vitamin B-12. This review represents the second in the series of reviews and covers all relevant aspects of folate biology and biomarkers. The article is organized to provide the reader with a full appreciation of folate's history as a public health issue, its biology, and an overview of available biomarkers (serum folate, RBC folate, and plasma homocysteine concentrations) and their interpretation across a range of clinical and population-based uses. The article also includes a list of priority research needs for advancing the area of folate biomarkers related to nutritional health status and development.
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Affiliation(s)
- Lynn B Bailey
- Department of Foods and Nutrition, University of Georgia, Athens, GA;
| | - Patrick J Stover
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Helene McNulty
- Northern Ireland Centre for Food and Health, Biomedical Sciences Research Institute, University of Ulster, Londonderry, United Kingdom
| | - Michael F Fenech
- Genome Health Nutrigenomics Laboratory, Food, Nutrition, and Bioproducts Flagship, Commonwealth Scientific and Industrial Research Organization, Adelaide, Australia
| | - Jesse F Gregory
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL
| | - James L Mills
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | | | - Zia Fazili
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Mindy Zhang
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Per M Ueland
- Department of Clinical Science, Univeristy of Bergen, Bergen, Norway
| | - Anne M Molloy
- Institute of Molecular Medicine, Trinity College, Dublin, Ireland
| | - Marie A Caudill
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Barry Shane
- Department of Nutritional Sciences and Toxicology, University of California-Berkeley, Berkeley, CA
| | - Robert J Berry
- National Center on Birth Defects and Developmental Disabilities, CDC, Atlanta, GA; and
| | | | - Dorothy B Hausman
- Department of Foods and Nutrition, University of Georgia, Athens, GA
| | - Ramkripa Raghavan
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | - Daniel J Raiten
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD;
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