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Enuh BM, Aytar Çelik P, Angione C. Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments. Biotechnol J 2024; 19:e202400267. [PMID: 39380500 DOI: 10.1002/biot.202400267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 08/22/2024] [Accepted: 09/09/2024] [Indexed: 10/10/2024]
Abstract
Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.
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Affiliation(s)
- Blaise Manga Enuh
- Wisconsin Energy Institute, University of Wisconsin, Madison, Wisconsin, USA
- Biotechnology and Biosafety Department, Graduate and Natural Applied Science, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Pınar Aytar Çelik
- Biotechnology and Biosafety Department, Graduate and Natural Applied Science, Eskişehir Osmangazi University, Eskişehir, Turkey
- Environmental Protection and Control Program, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
- National Horizons Centre, Darlington, UK
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2
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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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Hsieh YE, Tandon K, Verbruggen H, Nikoloski Z. Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches. NPJ Syst Biol Appl 2024; 10:54. [PMID: 38783065 PMCID: PMC11116368 DOI: 10.1038/s41540-024-00384-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.
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Affiliation(s)
- Yunli Eric Hsieh
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Kshitij Tandon
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Heroen Verbruggen
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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4
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Noirungsee N, Changkhong S, Phinyo K, Suwannajak C, Tanakul N, Inwongwan S. Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments. ENVIRONMENTAL MICROBIOLOGY REPORTS 2024; 16:e13231. [PMID: 38192220 PMCID: PMC10866088 DOI: 10.1111/1758-2229.13231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 12/19/2023] [Indexed: 01/10/2024]
Abstract
Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.
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Affiliation(s)
- Nuttapol Noirungsee
- Department of Biology, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
| | - Sakunthip Changkhong
- Department of Biology, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
- Department of Thoracic SurgeryUniversity Hospital ZurichZurichSwitzerland
| | - Kittiya Phinyo
- Department of Biology, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
- Research group on Earth—Space Ecology (ESE), Faculty of ScienceChiang Mai UniversityChiang MaiThailand
- Office of Research AdministrationChiang Mai UniversityChiang MaiThailand
| | | | - Nahathai Tanakul
- National Astronomical Research Institute of ThailandChiang MaiThailand
| | - Sahutchai Inwongwan
- Department of Biology, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of ScienceChiang Mai UniversityChiang MaiThailand
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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Starke S, Harris DMM, Zimmermann J, Schuchardt S, Oumari M, Frank D, Bang C, Rosenstiel P, Schreiber S, Frey N, Franke A, Aden K, Waschina S. Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability. THE ISME JOURNAL 2023; 17:2370-2380. [PMID: 37891427 PMCID: PMC10689445 DOI: 10.1038/s41396-023-01537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.
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Affiliation(s)
- Svenja Starke
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany
| | - Danielle M M Harris
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Johannes Zimmermann
- Zoological Institute, Research Group Evolutionary Ecology and Genetics, Kiel University, Kiel, Germany
- Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Sven Schuchardt
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Hanover, Germany
| | - Mhmd Oumari
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Derk Frank
- Department of Internal Medicine III, University Medical Center Schleswig-Holstein, Kiel, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Hamburg, Kiel, Lübeck, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Norbert Frey
- Department of Internal Medicine III, University Medical Center Schleswig-Holstein, Kiel, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Hamburg, Kiel, Lübeck, Germany
- Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Konrad Aden
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany.
| | - Silvio Waschina
- Institute of Human Nutrition and Food Science, Nutriinformatics, Kiel University, Kiel, Germany.
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Cunha E, Silva M, Chaves I, Demirci H, Lagoa DR, Lima D, Rocha M, Rocha I, Dias O. The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Comput Biol 2023; 19:e1011499. [PMID: 37729340 PMCID: PMC10545120 DOI: 10.1371/journal.pcbi.1011499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/02/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.
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Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Silva
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
| | - Huseyin Demirci
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- SnT/University of Luxembourg, Luxembourg
| | | | - Diogo Lima
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
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8
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Potter AD, Baiocco CM, Papin JA, Criss AK. Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture. mSystems 2023; 8:e0126522. [PMID: 37387581 PMCID: PMC10470122 DOI: 10.1128/msystems.01265-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/19/2023] [Indexed: 07/01/2023] Open
Abstract
The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.
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Affiliation(s)
- Aimee D. Potter
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M. Baiocco
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alison K. Criss
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
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9
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Hessler T, Huddy RJ, Sachdeva R, Lei S, Harrison STL, Diamond S, Banfield JF. Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms. Nat Commun 2023; 14:4768. [PMID: 37553333 PMCID: PMC10409787 DOI: 10.1038/s41467-023-40360-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/20/2023] [Indexed: 08/10/2023] Open
Abstract
Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.
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Affiliation(s)
- Tomas Hessler
- The Innovative Genomics Institute at the University of California, Berkeley, CA, USA
- The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Robert J Huddy
- Reasearch Office, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Rohan Sachdeva
- The Innovative Genomics Institute at the University of California, Berkeley, CA, USA
- The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Shufei Lei
- The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Susan T L Harrison
- The Center for Bioprocess Engineering Research, University of Cape Town, Cape Town, South Africa
- The Future Water Institute, University of Cape Town, Cape Town, South Africa
- Department of Chemical Engineering, University of Cape Town, Cape Town, South Africa
| | - Spencer Diamond
- The Innovative Genomics Institute at the University of California, Berkeley, CA, USA
- The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Jillian F Banfield
- The Innovative Genomics Institute at the University of California, Berkeley, CA, USA.
- The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA.
- The Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA.
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10
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Choi YM, Choi DH, Lee YQ, Koduru L, Lewis NE, Lakshmanan M, Lee DY. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. Comput Struct Biotechnol J 2023; 21:3736-3745. [PMID: 37547082 PMCID: PMC10400880 DOI: 10.1016/j.csbj.2023.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.
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Affiliation(s)
- Yoon-Mi Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Yi Qing Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Lokanand Koduru
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, La Jolla, San Diego, USA
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bitwinners Pte. Ltd., Singapore
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11
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Composition, structure, and functional shifts of prokaryotic communities in response to co-composting of various nitrogenous green feedstocks. BMC Microbiol 2023; 23:50. [PMID: 36859170 PMCID: PMC9979578 DOI: 10.1186/s12866-023-02798-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 02/17/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Thermophilic composting is a promising method of sanitizing pathogens in manure and a source of agriculturally important thermostable enzymes and microorganisms from organic wastes. Despite the extensive studies on compost prokaryotes, shifts in microbial profiles under the influence of various green materials and composting days are still not well understood, considering the complexity of the green material sources. Here, the effect of regimens of green composting material on the diversity, abundance, and metabolic capacity of prokaryotic communities in a thermophilic compost environment was examined. METHODS Total community 16S rRNA was recovered from triplicate compost samples of Lantana-based, Tithonia-based, Grass-based, and mixed (Lantana + Tithonia + Grass)- based at 21, 42, 63, and 84 days of composting. The 16S rRNA was sequenced using the Illumina Miseq platform. Bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 (DADA2) R version 4.1 and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) pipelines for community structure and metabolic profiles, respectively. In DADA2, prokaryotic classification was done using the Refseq-ribosomal database project (RDP) and SILVA version 138 databases. RESULTS Our results showed apparent differences in prokaryotic community structure for total diversity and abundance within the four compost regimens and composting days. The study showed that the most prevalent phyla during composting included Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, and Proteobacteria. Additionally, there were differences in the overall diversity of metabolic pathways but no significant differences among the various compost treatments on major metabolic pathways like carbohydrate biosynthesis, carbohydrate degradation, and nitrogen biosynthesis. CONCLUSION Various sources of green material affect the succession of compost nutrients and prokaryotic communities. The similarity of amounts of nutrients, such as total Nitrogen, at the end of the composting process, despite differences in feedstock material, indicates a significant influence of composting days on the stability of nutrients during composting.
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12
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Genome-scale reconstruction and metabolic modelling of the fast-growing thermophile Geobacillus sp. LC300. Metab Eng Commun 2022; 15:e00212. [DOI: 10.1016/j.mec.2022.e00212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022] Open
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13
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Mejía-Gomez CE, Rios-Estepa R, Gonzalez-Lopez LA, Balcazar-Morales N. An experimental and in silico analysis of Lacticaseibacillus paracasei isolated from whey shows an association between lactate production and amino acid catabolism. AN ACAD BRAS CIENC 2022; 94:e20211071. [PMID: 35946647 DOI: 10.1590/0001-3765202220211071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/07/2021] [Indexed: 11/22/2022] Open
Abstract
The production of lactic acid from agroindustry waste products, such as whey, heavily relies on microorganisms within the genusLactobacillus. In this work, a genome-scale metabolic model was implemented from Vinay-Lara (iLca334_548), improved adding some enzymatic reactions and used to analyse metabolic fluxes ofLacticaseibacillus paracasei, which is aLactobacillusstrain isolated from whey used in the large-scale production of lactic acid. Overall, the highest rate of lactic acid productivity was 2.9 g l-1h-1, which equates to a dilution rate of 0.125 h-1, when continuous culture conditions were established. Restrictions on lactic acid production caused by exchange reactions, complex culture medium and intracellular metabolite concentrations were considered and included in the model. In total, theiLca334_548 model consisted of 1046 reactions and 959 metabolites, and flow balance analysis better predicted lactate flux than biomass. The distribution of fluxes exhibited an increase in lactate formation as biomass decreased. This finding is supported by the reactions carried out by glyceraldehyde 3-phosphate dehydrogenase, pyruvate formate lyase and ribose-5-phosphate isomerase, corroborating the modelled phenotype with experimental data. In conclusion, there is potential for the improvement of lactate production in a complex media by amino acid catabolism, especially when lactate is derived from pyruvate.
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Affiliation(s)
- Carlos Eduardo Mejía-Gomez
- Grupo de Biotransformación, Escuela de Microbiología, Universidad de Antioquia, Calle 70, N° 52-21, 050010 Medellin, Colombia
| | - Rigoberto Rios-Estepa
- Grupo de Bioprocesos, Facultad de Ingeniería, Universidad de Calle 70, N° 52-21, 050010 Medellin, Colombia
| | - Luis Alberto Gonzalez-Lopez
- Grupo de Química Orgánica de Productos Naturales, Facultad de Ciencias Exactas y Naturales, Universidad de Antioquia, Calle 70, N° 52-21, 050010 Medellin, Colombia
| | - Norman Balcazar-Morales
- Grupo de Genética Molecular y Departamento de Fisiología y Bioquímica, Facultad de Medicina, Universidad de Antioquia, Calle 62 N° 52-59, 050010 Medellín, Colombia
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14
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Xavier JC, Kauffman S. Small-molecule autocatalytic networks are universal metabolic fossils. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210244. [PMID: 35599556 DOI: 10.1098/rsta.2021.0244] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Life and the genetic code are self-referential and so are autocatalytic networks made of simpler, small molecules. Several origins of life theories postulate autocatalytic chemical networks preceding the primordial genetic code, yet demonstration with biochemical systems is lacking. Here, small-molecule reflexively autocatalytic food-generated networks (RAFs) ranging in size from 3 to 619 reactions were found in all of 6683 prokaryotic metabolic networks searched. The average maximum RAF size is 275 reactions for a rich organic medium and 93 for a medium with a single organic cofactor, NAD. In the rich medium, all universally essential metabolites are produced with the exception of glycerol-1-p (archaeal lipid precursor), phenylalanine, histidine and arginine. The 300 most common reactions, present in at least 2732 RAFs, are mostly involved in amino acid biosynthesis and the metabolism of carbon, 2-oxocarboxylic acid and purines. ATP and NAD are central in generating network complexity, and because ATP is also one of the monomers of RNA, autocatalytic networks producing redox and energy currencies are a strong candidate niche of the origin of a primordial information-processing system. The wide distribution of small-molecule autocatalytic networks indicates that molecular reproduction may be much more prevalent in the Universe than hitherto predicted. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Joana C Xavier
- Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK
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15
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de Raad M, Li YV, Kuehl JV, Andeer PF, Kosina SM, Hendrickson A, Saichek NR, Golini AN, Han LZ, Wang Y, Bowen BP, Deutschbauer AM, Arkin AP, Chakraborty R, Northen TR. A Defined Medium for Cultivation and Exometabolite Profiling of Soil Bacteria. Front Microbiol 2022; 13:855331. [PMID: 35694313 PMCID: PMC9174792 DOI: 10.3389/fmicb.2022.855331] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC–MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.
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Affiliation(s)
- Markus de Raad
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Yifan V. Li
- Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jennifer V. Kuehl
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Peter F. Andeer
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Suzanne M. Kosina
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Andrew Hendrickson
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Nicholas R. Saichek
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Amber N. Golini
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - La Zhen Han
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Ying Wang
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Benjamin P. Bowen
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Adam M. Deutschbauer
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
| | - Adam P. Arkin
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States
| | - Romy Chakraborty
- Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Trent R. Northen
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, United States
- Lawrence Berkeley National Laboratory, Joint Genome Institute, Berkeley, CA, United States
- *Correspondence: Trent R. Northen,
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16
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Wendering P, Nikoloski Z. COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions. PLoS Comput Biol 2022; 18:e1009906. [PMID: 35320266 PMCID: PMC8942231 DOI: 10.1371/journal.pcbi.1009906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/09/2022] [Indexed: 11/18/2022] Open
Abstract
Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- * E-mail:
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17
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Carey MA, Medlock GL, Stolarczyk M, Petri WA, Guler JL, Papin JA. Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models. PLoS Comput Biol 2022; 18:e1009870. [PMID: 35196325 PMCID: PMC8901074 DOI: 10.1371/journal.pcbi.1009870] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/07/2022] [Accepted: 01/27/2022] [Indexed: 01/01/2023] Open
Abstract
Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.
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Affiliation(s)
- Maureen A. Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
| | - Gregory L. Medlock
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Michał Stolarczyk
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - William A. Petri
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Jennifer L. Guler
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
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Abstract
Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses. IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.
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Feierabend M, Renz A, Zelle E, Nöh K, Wiechert W, Dräger A. High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032. Front Microbiol 2021; 12:750206. [PMID: 34867870 PMCID: PMC8634658 DOI: 10.3389/fmicb.2021.750206] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/19/2021] [Indexed: 11/30/2022] Open
Abstract
Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.
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Affiliation(s)
- Martina Feierabend
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Elisabeth Zelle
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
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20
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Quantifying the propagation of parametric uncertainty on flux balance analysis. Metab Eng 2021; 69:26-39. [PMID: 34718140 DOI: 10.1016/j.ymben.2021.10.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/21/2021] [Accepted: 10/24/2021] [Indexed: 12/27/2022]
Abstract
Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.
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21
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Hemkemeyer M, Schwalb SA, Heinze S, Joergensen RG, Wichern F. Functions of elements in soil microorganisms. Microbiol Res 2021; 252:126832. [PMID: 34508963 DOI: 10.1016/j.micres.2021.126832] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
The soil microbial community fulfils various functions, such as nutrient cycling and carbon (C) sequestration, therefore contributing to maintenance of soil fertility and mitigation of global warming. In this context, a major focus of research has been on C, nitrogen (N) and phosphorus (P) cycling. However, from aquatic and other environments, it is well known that other elements beyond C, N, and P are essential for microbial functioning. Nonetheless, for soil microorganisms this knowledge has not yet been synthesised. To gain a better mechanistic understanding of microbial processes in soil systems, we aimed at summarising the current knowledge on the function of a range of essential or beneficial elements, which may affect the efficiency of microbial processes in soil. This knowledge is discussed in the context of microbial driven nutrient and C cycling. Our findings may support future investigations and data evaluation, where other elements than C, N, and P affect microbial processes.
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Affiliation(s)
- Michael Hemkemeyer
- Department of Soil Science and Plant Nutrition, Institute of Biogenic Resources in Sustainable Food Systems - From Farm to Function, Rhine-Waal University of Applied Sciences, Marie-Curie-Str. 1, 47533 Kleve, Germany.
| | - Sanja A Schwalb
- Department of Soil Science and Plant Nutrition, Institute of Biogenic Resources in Sustainable Food Systems - From Farm to Function, Rhine-Waal University of Applied Sciences, Marie-Curie-Str. 1, 47533 Kleve, Germany
| | - Stefanie Heinze
- Department of Soil Science & Soil Ecology, Ruhr-University Bochum, Universitätsstr. 150, 44801 Bochum, Germany
| | - Rainer Georg Joergensen
- Department of Soil Biology and Plant Nutrition, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany
| | - Florian Wichern
- Department of Soil Science and Plant Nutrition, Institute of Biogenic Resources in Sustainable Food Systems - From Farm to Function, Rhine-Waal University of Applied Sciences, Marie-Curie-Str. 1, 47533 Kleve, Germany
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22
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Moyer D, Pacheco AR, Bernstein DB, Segrè D. Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure. J Mol Evol 2021; 89:472-483. [PMID: 34230992 PMCID: PMC8318951 DOI: 10.1007/s00239-021-10018-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 06/12/2021] [Indexed: 11/15/2022]
Abstract
Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.
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Affiliation(s)
- Devlin Moyer
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Alan R Pacheco
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - David B Bernstein
- Biological Design Center, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Biological Design Center, Boston University, Boston, MA, 02215, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Department of Physics, Boston University, Boston, MA, 02215, USA.
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23
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Lachance J, Matteau D, Brodeur J, Lloyd CJ, Mih N, King ZA, Knight TF, Feist AM, Monk JM, Palsson BO, Jacques P, Rodrigue S. Genome-scale metabolic modeling reveals key features of a minimal gene set. Mol Syst Biol 2021; 17:e10099. [PMID: 34288418 PMCID: PMC8290834 DOI: 10.15252/msb.202010099] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 12/19/2022] Open
Abstract
Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.
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Affiliation(s)
| | - Dominick Matteau
- Département de BiologieUniversité de SherbrookeSherbrookeQCCanada
| | - Joëlle Brodeur
- Département de BiologieUniversité de SherbrookeSherbrookeQCCanada
| | - Colton J Lloyd
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Nathan Mih
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Zachary A King
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
| | | | - Adam M Feist
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
- Department of PediatricsUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Jonathan M Monk
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Bernhard O Palsson
- Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaCAUSA
- Department of PediatricsUniversity of CaliforniaSan Diego, La JollaCAUSA
- Bioinformatics and Systems Biology ProgramUniversity of CaliforniaSan Diego, La JollaCAUSA
- Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
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Lloyd CJ, Monk J, Yang L, Ebrahim A, Palsson BO. Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth. PLoS Comput Biol 2021; 17:e1007817. [PMID: 34161321 PMCID: PMC8259983 DOI: 10.1371/journal.pcbi.1007817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/06/2021] [Accepted: 05/31/2021] [Indexed: 11/21/2022] Open
Abstract
Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles. Escherichia coli is capable of growing in many environments, each of which requires a different collection of enzymes to metabolize the nutrients within that environment. Each individual enzyme requires its own set of amino acids and oftentimes cofactors, which are accessory molecules essential for the enzyme to function. Thus, the composition of the micronutrients (amino acids, cofactors, etc.) within a cell will differ depending on its metabolic needs. The presented work is the first effort to employ metabolic models to probe the connection between E. coli’s diverse growth environments and its biomass composition. We first show how differences in model-predicted enzyme use for aerobic or anaerobic growth results in distinct amino acid and cofactor usage. Alternatively, we show that the metabolic models can predict how modifying the cell’s biomass composition will affect growth. For example, by modeling the exposure of E. coli to trimethoprim or sulfamethoxazole—two antibiotics that target folate (vitamin B9) synthesis—we predicted how E. coli could adapt to grow under folate-limited conditions. This work demonstrates how models can be used to study antibiotic resistance of drugs that target amino acid or cofactor synthesis.
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Affiliation(s)
- Colton J. Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Jonathan Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Laurence Yang
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- * E-mail:
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25
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Schulz C, Kumelj T, Karlsen E, Almaas E. Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition. PLoS Comput Biol 2021; 17:e1008528. [PMID: 34029317 PMCID: PMC8177628 DOI: 10.1371/journal.pcbi.1008528] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/04/2021] [Accepted: 04/27/2021] [Indexed: 11/29/2022] Open
Abstract
Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments. Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments. In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell. Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.
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Affiliation(s)
- Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tjasa Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
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26
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Renz A, Widerspick L, Dräger A. First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies. Metabolites 2021; 11:232. [PMID: 33918864 PMCID: PMC8069353 DOI: 10.3390/metabo11040232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/21/2021] [Accepted: 04/06/2021] [Indexed: 12/11/2022] Open
Abstract
Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources' growth capacities. This model will pave the way to better understand D. pigrum's role in the fight against S. aureus.
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Affiliation(s)
- Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
| | - Lina Widerspick
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
- German Center for Infection Research (DZIF), Partner site Tübingen, 72076 Tübingen, Germany
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27
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Xavier JC, Gerhards RE, Wimmer JLE, Brueckner J, Tria FDK, Martin WF. The metabolic network of the last bacterial common ancestor. Commun Biol 2021; 4:413. [PMID: 33772086 PMCID: PMC7997952 DOI: 10.1038/s42003-021-01918-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 02/26/2021] [Indexed: 02/03/2023] Open
Abstract
Bacteria are the most abundant cells on Earth. They are generally regarded as ancient, but due to striking diversity in their metabolic capacities and widespread lateral gene transfer, the physiology of the first bacteria is unknown. From 1089 reference genomes of bacterial anaerobes, we identified 146 protein families that trace to the last bacterial common ancestor, LBCA, and form the conserved predicted core of its metabolic network, which requires only nine genes to encompass all universal metabolites. Our results indicate that LBCA performed gluconeogenesis towards cell wall synthesis, and had numerous RNA modifications and multifunctional enzymes that permitted life with low gene content. In accordance with recent findings for LUCA and LACA, analyses of thousands of individual gene trees indicate that LBCA was rod-shaped and the first lineage to diverge from the ancestral bacterial stem was most similar to modern Clostridia, followed by other autotrophs that harbor the acetyl-CoA pathway.
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Affiliation(s)
- Joana C Xavier
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany.
| | - Rebecca E Gerhards
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Jessica L E Wimmer
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Julia Brueckner
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Fernando D K Tria
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - William F Martin
- Institute for Molecular Evolution, Heinrich-Heine-University, 40225, Düsseldorf, Germany
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28
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The Autotrophic Core: An Ancient Network of 404 Reactions Converts H 2, CO 2, and NH 3 into Amino Acids, Bases, and Cofactors. Microorganisms 2021; 9:microorganisms9020458. [PMID: 33672143 PMCID: PMC7926472 DOI: 10.3390/microorganisms9020458] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/11/2021] [Accepted: 02/19/2021] [Indexed: 01/03/2023] Open
Abstract
The metabolism of cells contains evidence reflecting the process by which they arose. Here, we have identified the ancient core of autotrophic metabolism encompassing 404 reactions that comprise the reaction network from H2, CO2, and ammonia (NH3) to amino acids, nucleic acid monomers, and the 19 cofactors required for their synthesis. Water is the most common reactant in the autotrophic core, indicating that the core arose in an aqueous environment. Seventy-seven core reactions involve the hydrolysis of high-energy phosphate bonds, furthermore suggesting the presence of a non-enzymatic and highly exergonic chemical reaction capable of continuously synthesizing activated phosphate bonds. CO2 is the most common carbon-containing compound in the core. An abundance of NADH and NADPH-dependent redox reactions in the autotrophic core, the central role of CO2, and the circumstance that the core’s main products are far more reduced than CO2 indicate that the core arose in a highly reducing environment. The chemical reactions of the autotrophic core suggest that it arose from H2, inorganic carbon, and NH3 in an aqueous environment marked by highly reducing and continuously far from equilibrium conditions. Such conditions are very similar to those found in serpentinizing hydrothermal systems.
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29
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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30
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Viana R, Dias O, Lagoa D, Galocha M, Rocha I, Teixeira MC. Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction. J Fungi (Basel) 2020; 6:E171. [PMID: 32932905 PMCID: PMC7559133 DOI: 10.3390/jof6030171] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022] Open
Abstract
Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.
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Affiliation(s)
- Romeu Viana
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
| | - Davide Lagoa
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
| | - Mónica Galocha
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
| | - Isabel Rocha
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), 2780-157 Oeiras, Portugal
| | - Miguel Cacho Teixeira
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
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31
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Széliová D, Ruckerbauer DE, Galleguillos SN, Petersen LB, Natter K, Hanscho M, Troyer C, Causon T, Schoeny H, Christensen HB, Lee DY, Lewis NE, Koellensperger G, Hann S, Nielsen LK, Borth N, Zanghellini J. What CHO is made of: Variations in the biomass composition of Chinese hamster ovary cell lines. Metab Eng 2020; 61:288-300. [DOI: 10.1016/j.ymben.2020.06.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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32
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Chu XY, Zhang HY. Cofactors as Molecular Fossils To Trace the Origin and Evolution of Proteins. Chembiochem 2020; 21:3161-3168. [PMID: 32515532 DOI: 10.1002/cbic.202000027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 06/03/2020] [Indexed: 12/16/2022]
Abstract
Due to their early origin and extreme conservation, cofactors are valuable molecular fossils for tracing the origin and evolution of proteins. First, as the order of protein folds binding with cofactors roughly coincides with protein-fold chronology, cofactors are considered to have facilitated the origin of primitive proteins by selecting them from pools of random amino acid sequences. Second, in the subsequent evolution of proteins, cofactors still played an important role. More interestingly, as metallic cofactors evolved with geochemical variations, some geochemical events left imprints in the chronology of protein architecture; this provides further evidence supporting the coevolution of biochemistry and geochemistry. In this paper, we attempt to review the molecular fossils used in tracing the origin and evolution of proteins, with a special focus on cofactors.
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Affiliation(s)
- Xin-Yi Chu
- Hubei Key Laboratory of Agricultural Bioinformatics College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
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33
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Frioux C, Singh D, Korcsmaros T, Hildebrand F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput Struct Biotechnol J 2020; 18:1722-1734. [PMID: 32670511 PMCID: PMC7347713 DOI: 10.1016/j.csbj.2020.06.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.
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Affiliation(s)
- Clémence Frioux
- Inria, CNRS, INRAE Bordeaux, France
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Dipali Singh
- Microbes in the Food Chain, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
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34
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López-Agudelo VA, Mendum TA, Laing E, Wu H, Baena A, Barrera LF, Beste DJV, Rios-Estepa R. A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks. PLoS Comput Biol 2020; 16:e1007533. [PMID: 32542021 PMCID: PMC7316355 DOI: 10.1371/journal.pcbi.1007533] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 06/25/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023] Open
Abstract
Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.
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Affiliation(s)
- Víctor A. López-Agudelo
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín, Colombia
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Tom A. Mendum
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Emma Laing
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - HuiHai Wu
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Andres Baena
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
- Departamento de Microbiología y Parasitología, Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Luis F. Barrera
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
- Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Dany J. V. Beste
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Rigoberto Rios-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín, Colombia
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35
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Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, King Z. BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res 2020; 48:D402-D406. [PMID: 31696234 PMCID: PMC7145653 DOI: 10.1093/nar/gkz1054] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/21/2019] [Accepted: 10/24/2019] [Indexed: 01/04/2023] Open
Abstract
The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.
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Affiliation(s)
- Charles J Norsigian
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Neha Pusarla
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - John Luke McConn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany.,Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany.,German Center for Infection Research (DZIF), 72076 Tübingen, Germany
| | - 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, Kemitorvet, Building 220, 2800 Kongens Lyngby, Denmark
| | - Zachary King
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
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36
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Jacoby RP, Succurro A, Kopriva S. Nitrogen Substrate Utilization in Three Rhizosphere Bacterial Strains Investigated Using Proteomics. Front Microbiol 2020; 11:784. [PMID: 32411116 PMCID: PMC7198800 DOI: 10.3389/fmicb.2020.00784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 04/01/2020] [Indexed: 12/25/2022] Open
Abstract
Nitrogen metabolism in the rhizosphere microbiome plays an important role in mediating plant nutrition, particularly under low inputs of mineral fertilizers. However, there is relatively little mechanistic information about which genes and metabolic pathways are induced by rhizosphere bacterial strains to utilize diverse nitrogen substrates. Here we investigate nitrogen substrate utilization in three taxonomically diverse bacterial strains previously isolated from Arabidopsis roots. The three strains represent taxa that are consistently detected as core members of the plant microbiome: Pseudomonas, Streptomyces, and Rhizobium. We use phenotype microarrays to determine the nitrogen substrate preferences of these strains, and compare the experimental results vs. computational simulations of genome-scale metabolic network models obtained with EnsembleFBA. Results show that all three strains exhibit generalistic nitrogen substrate preferences, with substrate utilization being well predicted by EnsembleFBA. Using label-free quantitative proteomics, we document hundreds of proteins in each strain that exhibit differential abundance values following cultivation on five different nitrogen sources: ammonium, glutamate, lysine, serine, and urea. The proteomic response to these nitrogen sources was strongly strain-dependent, with lysine nutrition eliciting widespread protein-level changes in Pseudomonas sp. Root9, whereas Rhizobium sp. Root491 showed relatively stable proteome composition across different nitrogen sources. Our results give new protein-level information about the specific transporters and enzymes induced by diverse rhizosphere bacterial strains to utilize organic nitrogen substrates.
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Affiliation(s)
- Richard P. Jacoby
- Institute for Plant Sciences and Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
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37
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Jensen CS, Norsigian CJ, Fang X, Nielsen XC, Christensen JJ, Palsson BO, Monk JM. Reconstruction and Validation of a Genome-Scale Metabolic Model of Streptococcus oralis (iCJ415), a Human Commensal and Opportunistic Pathogen. Front Genet 2020; 11:116. [PMID: 32194617 PMCID: PMC7063969 DOI: 10.3389/fgene.2020.00116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/31/2020] [Indexed: 11/22/2022] Open
Abstract
The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.
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Affiliation(s)
- Christian S Jensen
- The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark
| | - Charles J Norsigian
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - Xin Fang
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - Xiaohui C Nielsen
- The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark
| | - Jens Jørgen Christensen
- The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark.,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
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38
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Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M, Babaei P, Bartell JA, Blank LM, Chauhan S, Correia K, Diener C, Dräger A, Ebert BE, Edirisinghe JN, Faria JP, Feist AM, Fengos G, Fleming RMT, García-Jiménez B, Hatzimanikatis V, van Helvoirt W, Henry CS, Hermjakob H, Herrgård MJ, Kaafarani A, Kim HU, King Z, Klamt S, Klipp E, Koehorst JJ, König M, Lakshmanan M, Lee DY, Lee SY, Lee S, Lewis NE, Liu F, Ma H, Machado D, Mahadevan R, Maia P, Mardinoglu A, Medlock GL, Monk JM, Nielsen J, Nielsen LK, Nogales J, Nookaew I, Palsson BO, Papin JA, Patil KR, Poolman M, Price ND, Resendis-Antonio O, Richelle A, Rocha I, Sánchez BJ, Schaap PJ, Malik Sheriff RS, Shoaie S, Sonnenschein N, Teusink B, Vilaça P, Vik JO, Wodke JAH, Xavier JC, Yuan Q, Zakhartsev M, Zhang C. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol 2020; 38:272-276. [PMID: 32123384 PMCID: PMC7082222 DOI: 10.1038/s41587-020-0446-y] [Citation(s) in RCA: 241] [Impact Index Per Article: 60.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Christian Lieven
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Brett G Olivier
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Meric Ataman
- Ecole Polytechnique Fédérale de Lausanne, Laboratory of Computational Systems Biotechnology, Lausanne, Switzerland
| | - Parizad Babaei
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Jennifer A Bartell
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Lars M Blank
- iAMB-Institute of Applied Microbiology, ABBt-Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Siddharth Chauhan
- Department of Bioengineering, University of California, La Jolla, CA, USA
| | - Kevin Correia
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Christian Diener
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genomica & Coordinación de la Investigación Científica-Red de Apoyo a la Investigación, UNAM, Mexico City, Mexico
- Institute for Systems Biology, Seattle, WA, USA
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI), Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
| | - Birgitta E Ebert
- iAMB-Institute of Applied Microbiology, ABBt-Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia
| | | | | | - Adam M Feist
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Bioengineering, University of California, La Jolla, CA, USA
| | - Georgios Fengos
- Ecole Polytechnique Fédérale de Lausanne, Laboratory of Computational Systems Biotechnology, Lausanne, Switzerland
| | - Ronan M T Fleming
- Analytical Biosciences, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Vassily Hatzimanikatis
- Ecole Polytechnique Fédérale de Lausanne, Laboratory of Computational Systems Biotechnology, Lausanne, Switzerland
| | - Wout van Helvoirt
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Oslo, Norway
- Hanze University of Applied Sciences, Groningen, the Netherlands
| | | | - Henning Hermjakob
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Ali Kaafarani
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, BioInformatics Research Center, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Zachary King
- Department of Bioengineering, University of California, La Jolla, CA, USA
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg, Magdeburg, Germany
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jasper J Koehorst
- Department of Agrotechnology and Food Sciences, Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Matthias König
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- School of Chemical Engineering Sungkyunkwan University, Jangan-gu Suwon, Gyeonggi-do, Republic of Korea
| | - Sang Yup Lee
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, BioInformatics Research Center, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, CA, USA
- Department of Pediatrics and Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Filipe Liu
- Argonne National Laboratory, Lemont, IL, USA
| | - Hongwu Ma
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P.R. China
| | - Daniel Machado
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Gregory L Medlock
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, La Jolla, CA, USA
| | - Jens Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Chalmers University of Technology, Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Göteborg, Sweden
| | - Lars Keld Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid, Spain
| | - Intawat Nookaew
- Chalmers University of Technology, Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Göteborg, Sweden
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, USA
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Department of Bioengineering, University of California, La Jolla, CA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Kiran R Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | | | | | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genomica & Coordinación de la Investigación Científica-Red de Apoyo a la Investigación, UNAM, Mexico City, Mexico
| | - Anne Richelle
- Department of Pediatrics and Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal
| | - Benjamín J Sánchez
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Chalmers University of Technology, Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Göteborg, Sweden
| | - Peter J Schaap
- Department of Agrotechnology and Food Sciences, Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Rahuman S Malik Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Saeed Shoaie
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Nikolaus Sonnenschein
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Jon Olav Vik
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Oslo, Norway
| | - Judith A H Wodke
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Joana C Xavier
- Institute for Molecular Evolution, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Qianqian Yuan
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P.R. China
| | - Maksim Zakhartsev
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Oslo, Norway
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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39
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Cesur MF, Siraj B, Uddin R, Durmuş S, Çakır T. Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale. Front Cell Infect Microbiol 2020; 9:447. [PMID: 31993376 PMCID: PMC6970976 DOI: 10.3389/fcimb.2019.00447] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 12/12/2019] [Indexed: 01/28/2023] Open
Abstract
Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.
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Affiliation(s)
- Müberra Fatma Cesur
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Bushra Siraj
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
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40
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Dinh HV, Suthers PF, Chan SHJ, Shen Y, Xiao T, Deewan A, Jagtap SS, Zhao H, Rao CV, Rabinowitz JD, Maranas CD. A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data. Metab Eng Commun 2019; 9:e00101. [PMID: 31720216 PMCID: PMC6838544 DOI: 10.1016/j.mec.2019.e00101] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.
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Affiliation(s)
- Hoang V. Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Patrick F. Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Siu Hung Joshua Chan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Tianxia Xiao
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Anshu Deewan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
| | - Sujit S. Jagtap
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Christopher V. Rao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champagne, 114 Roger Adams Laboratory MC 712, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Joshua D. Rabinowitz
- Department of Chemistry, Princeton University, 285 Frick Laboratory, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08540, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, 306 Chemical and Biomedical Engineering Building, PA, 16802-4400, USA
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Dias O, Saraiva J, Faria C, Ramirez M, Pinto F, Rocha I. iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6. Front Microbiol 2019; 10:1283. [PMID: 31293525 PMCID: PMC6603136 DOI: 10.3389/fmicb.2019.01283] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/23/2019] [Indexed: 11/13/2022] Open
Abstract
A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.
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Affiliation(s)
- Oscar Dias
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - João Saraiva
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Cristiana Faria
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Mario Ramirez
- Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Francisco Pinto
- BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal
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42
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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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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Karlsen E, Schulz C, Almaas E. Automated generation of genome-scale metabolic draft reconstructions based on KEGG. BMC Bioinformatics 2018; 19:467. [PMID: 30514205 PMCID: PMC6280343 DOI: 10.1186/s12859-018-2472-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 11/06/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Constraint-based modeling is a widely used and powerful methodology to assess the metabolic phenotypes and capabilities of an organism. The starting point and cornerstone of all such modeling is a genome-scale metabolic network reconstruction. The creation, further development, and application of such networks is a growing field of research thanks to a plethora of readily accessible computational tools. While the majority of studies are focused on single-species analyses, typically of a microbe, the computational study of communities of organisms is gaining attention. Similarly, reconstructions that are unified for a multi-cellular organism have gained in popularity. Consequently, the rapid generation of genome-scale metabolic reconstructed networks is crucial. While multiple web-based or stand-alone tools are available for automated network reconstruction, there is, however, currently no publicly available tool that allows the swift assembly of draft reconstructions of community metabolic networks and consolidated metabolic networks for a specified list of organisms. RESULTS Here, we present AutoKEGGRec, an automated tool that creates first draft metabolic network reconstructions of single organisms, community reconstructions based on a list of organisms, and finally a consolidated reconstruction for a list of organisms or strains. AutoKEGGRec is developed in Matlab and works seamlessly with the COBRA Toolbox v3, and it is based on only using the KEGG database as external input. The generated first draft reconstructions are stored in SBML files and consist of all reactions for a KEGG organism ID and corresponding linked genes. This provides a comprehensive starting point for further refinement and curation using the host of COBRA toolbox functions or other preferred tools. Through the data structures created, the tool also facilitates a comparative analysis of metabolic content in any given number of organisms present in the KEGG database. CONCLUSION AutoKEGGRec provides a first step in a metabolic network reconstruction process, filling a gap for tools creating community and consolidated metabolic networks. Based only on KEGG data as external input, the generated reconstructions consist of data with a directly traceable foundation and pedigree. With AutoKEGGRec, this kind of modeling is made accessible to a wider part of the genome-scale metabolic analysis community.
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Affiliation(s)
- Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
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Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes. PLoS Comput Biol 2018; 14:e1006556. [PMID: 30444863 PMCID: PMC6283598 DOI: 10.1371/journal.pcbi.1006556] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 12/06/2018] [Accepted: 10/09/2018] [Indexed: 01/13/2023] Open
Abstract
Essential metabolic reactions are shaping constituents of metabolic networks, enabling viable and distinct phenotypes across diverse life forms. Here we analyse and compare modelling predictions of essential metabolic functions with experimental data and thereby identify core metabolic pathways in prokaryotes. Simulations of 15 manually curated genome-scale metabolic models were integrated with 36 large-scale gene essentiality datasets encompassing a wide variety of species of bacteria and archaea. Conservation of metabolic genes was estimated by analysing 79 representative genomes from all the branches of the prokaryotic tree of life. We find that essentiality patterns reflect phylogenetic relations both for modelling and experimental data, which correlate highly at the pathway level. Genes that are essential for several species tend to be highly conserved as opposed to non-essential genes which may be conserved or not. The tRNA-charging module is highlighted as ancestral and with high centrality in the networks, followed closely by cofactor metabolism, pointing to an early information processing system supplied by organic cofactors. The results, which point to model improvements and also indicate faults in the experimental data, should be relevant to the study of centrality in metabolic networks and ancient metabolism but also to metabolic engineering with prokaryotes. If we tried to list every known chemical reaction within an organism–human, plant or even bacteria–we would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular species–prokaryotes–to understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.
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Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 2018; 46:7542-7553. [PMID: 30192979 PMCID: PMC6125623 DOI: 10.1093/nar/gky537] [Citation(s) in RCA: 311] [Impact Index Per Article: 51.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 05/17/2018] [Accepted: 05/29/2018] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.
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Affiliation(s)
- Daniel Machado
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Sergej Andrejev
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Melanie Tramontano
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
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Dias O, Rocha M, Ferreira EC, Rocha I. Reconstructing High-Quality Large-Scale Metabolic Models with merlin. Methods Mol Biol 2018; 1716:1-36. [PMID: 29222747 DOI: 10.1007/978-1-4939-7528-0_1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Here, the basic principles of reconstructing genome-scale metabolic models with merlin are described. This tool covers the basic stages of this process, providing several tools that allow assembling models, using the sequenced genome as a starting point. merlin has two main modules, separating the process of annotating (enzymes, transporters, and compartments) on the genome from the process of model assembly, though information from the former is integrated in the latter after curation. Moreover, merlin provides several tools to curate the model, including tools for generating reactions' gene rules and placeholder entities for biomass precursors, such as proteins (e-protein) or nucleotides (e-DNA and e-RNA) among others.This tutorial covers each feature of merlin in detail, including the assessment of experimental data for the validation of the model.
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Affiliation(s)
- Oscar Dias
- Centre of Biological Engineering, University of Minho, Braga, Portugal.
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | | | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
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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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Lopes H, Rocha I. Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res 2017; 17:3950252. [PMID: 28899034 PMCID: PMC5812505 DOI: 10.1093/femsyr/fox050] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/07/2017] [Indexed: 01/21/2023] Open
Abstract
Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.
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Affiliation(s)
- Helder Lopes
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
| | - Isabel Rocha
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
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50
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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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