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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024:S1043-2760(24)00062-6. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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2
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Gelbach PE, Cetin H, Finley SD. Flux sampling in genome-scale metabolic modeling of microbial communities. BMC Bioinformatics 2024; 25:45. [PMID: 38287239 PMCID: PMC10826046 DOI: 10.1186/s12859-024-05655-3] [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: 06/21/2023] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates. RESULTS In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux. CONCLUSIONS Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Handan Cetin
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Stacey D Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.
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3
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Walsh LH, Coakley M, Walsh AM, O'Toole PW, Cotter PD. Bioinformatic approaches for studying the microbiome of fermented food. Crit Rev Microbiol 2023; 49:693-725. [PMID: 36287644 DOI: 10.1080/1040841x.2022.2132850] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/11/2022] [Accepted: 09/28/2022] [Indexed: 11/03/2022]
Abstract
High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.
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Affiliation(s)
- Liam H Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- School of Microbiology, University College Cork, Ireland
| | - Mairéad Coakley
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Aaron M Walsh
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
| | - Paul W O'Toole
- School of Microbiology, University College Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
| | - Paul D Cotter
- Teagasc Food Research Centre, Moorepark, Fermoy, Cork, Ireland
- APC Microbiome Ireland, University College Cork, Ireland
- VistaMilk SFI Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
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4
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Hellal J, Barthelmebs L, Bérard A, Cébron A, Cheloni G, Colas S, Cravo-Laureau C, De Clerck C, Gallois N, Hery M, Martin-Laurent F, Martins J, Morin S, Palacios C, Pesce S, Richaume A, Vuilleumier S. Unlocking secrets of microbial ecotoxicology: recent achievements and future challenges. FEMS Microbiol Ecol 2023; 99:fiad102. [PMID: 37669892 PMCID: PMC10516372 DOI: 10.1093/femsec/fiad102] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/21/2023] [Accepted: 09/04/2023] [Indexed: 09/07/2023] Open
Abstract
Environmental pollution is one of the main challenges faced by humanity. By their ubiquity and vast range of metabolic capabilities, microorganisms are affected by pollution with consequences on their host organisms and on the functioning of their environment. They also play key roles in the fate of pollutants through the degradation, transformation, and transfer of organic or inorganic compounds. Thus, they are crucial for the development of nature-based solutions to reduce pollution and of bio-based solutions for environmental risk assessment of chemicals. At the intersection between microbial ecology, toxicology, and biogeochemistry, microbial ecotoxicology is a fast-expanding research area aiming to decipher the interactions between pollutants and microorganisms. This perspective paper gives an overview of the main research challenges identified by the Ecotoxicomic network within the emerging One Health framework and in the light of ongoing interest in biological approaches to environmental remediation and of the current state of the art in microbial ecology. We highlight prevailing knowledge gaps and pitfalls in exploring complex interactions among microorganisms and their environment in the context of chemical pollution and pinpoint areas of research where future efforts are needed.
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Affiliation(s)
| | - Lise Barthelmebs
- Université de Perpignan Via Domitia, Biocapteurs – Analyse-Environnement, Perpignan, France
- Laboratoire de Biodiversité et Biotechnologies Microbiennes, USR 3579 Sorbonne Universités (UPMC) Paris 6 et CNRS Observatoire Océanologique, Banyuls-sur-Mer, France
| | - Annette Bérard
- UMR EMMAH INRAE/AU – équipe SWIFT, 228, route de l'Aérodrome, 84914 Avignon Cedex 9, France
| | | | - Giulia Cheloni
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
| | - Simon Colas
- Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, IPREM, Pau, France
| | | | - Caroline De Clerck
- AgricultureIsLife, Gembloux Agro-Bio Tech (Liege University), Passage des Déportés 2, 5030 Gembloux, Belgium
| | | | - Marina Hery
- HydroSciences Montpellier, Université de Montpellier, CNRS, IRD, Montpellier, France
| | - Fabrice Martin-Laurent
- Institut Agro Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, Agroécologie, 21065 Dijon, France
| | - Jean Martins
- IGE, UMR 5001, Université Grenoble Alpes, CNRS, G-INP, INRAE, IRD Grenoble, France
| | | | - Carmen Palacios
- Université de Perpignan Via Domitia, CEFREM, F-66860 Perpignan, France
- CNRS, CEFREM, UMR5110, F-66860 Perpignan, France
| | | | - Agnès Richaume
- Université de Lyon, Université Claude Bernard Lyon 1, CNRS, UMR 5557, Ecologie Microbienne, Villeurbanne, France
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5
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Kothari A, Kherdekar R, Mago V, Uniyal M, Mamgain G, Kalia RB, Kumar S, Jain N, Pandey A, Omar BJ. Age of Antibiotic Resistance in MDR/XDR Clinical Pathogen of Pseudomonas aeruginosa. Pharmaceuticals (Basel) 2023; 16:1230. [PMID: 37765038 PMCID: PMC10534605 DOI: 10.3390/ph16091230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/15/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
Antibiotic resistance in Pseudomonas aeruginosa remains one of the most challenging phenomena of everyday medical science. The universal spread of high-risk clones of multidrug-resistant/extensively drug-resistant (MDR/XDR) clinical P. aeruginosa has become a public health threat. The P. aeruginosa bacteria exhibits remarkable genome plasticity that utilizes highly acquired and intrinsic resistance mechanisms to counter most antibiotic challenges. In addition, the adaptive antibiotic resistance of P. aeruginosa, including biofilm-mediated resistance and the formation of multidrug-tolerant persisted cells, are accountable for recalcitrance and relapse of infections. We highlighted the AMR mechanism considering the most common pathogen P. aeruginosa, its clinical impact, epidemiology, and save our souls (SOS)-mediated resistance. We further discussed the current therapeutic options against MDR/XDR P. aeruginosa infections, and described those treatment options in clinical practice. Finally, other therapeutic strategies, such as bacteriophage-based therapy and antimicrobial peptides, were described with clinical relevance.
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Affiliation(s)
- Ashish Kothari
- Department of Microbiology, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Radhika Kherdekar
- Department of Dentistry, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Vishal Mago
- Department of Burn and Plastic Surgery, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Madhur Uniyal
- Department of Trauma Surgery, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Garima Mamgain
- Department of Biochemistry, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Roop Bhushan Kalia
- Department of Orthopaedics, All India Institute of Medical Sciences, Rishikesh 249203, India;
| | - Sandeep Kumar
- Department of Cellular Biology and Anatomy, Augusta University, Augusta, GA 30912, USA;
| | - Neeraj Jain
- Department of Medical Oncology, All India Institute of Medical Sciences, Rishikesh 249203, India
- Division of Cancer Biology, Central Drug Research Institute, Lucknow 226031, India
| | - Atul Pandey
- Department of Entomology, University of Kentucky, Lexington, KY 40503, USA
| | - Balram Ji Omar
- Department of Microbiology, All India Institute of Medical Sciences, Rishikesh 249203, India;
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Gelbach PE, Finley SD. Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537368. [PMID: 37197028 PMCID: PMC10173371 DOI: 10.1101/2023.04.18.537368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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7
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A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01. mSystems 2023; 8:e0037722. [PMID: 36853050 PMCID: PMC10134806 DOI: 10.1128/msystems.00377-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described. IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.
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8
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Kenefake D, Armingol E, Lewis NE, Pistikopoulos EN. An improved algorithm for flux variability analysis. BMC Bioinformatics 2022; 23:550. [PMID: 36536290 PMCID: PMC9761963 DOI: 10.1186/s12859-022-05089-9] [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: 09/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving [Formula: see text] linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than [Formula: see text] LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than [Formula: see text] LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.
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Affiliation(s)
- Dustin Kenefake
- grid.264756.40000 0004 4687 2082Texas A &M Energy Institute, Texas A &M University, College Station, TX 77843 USA ,grid.264756.40000 0004 4687 2082Department of Chemical Engineering, Texas A &M University, College Station, TX 77843 USA
| | - Erick Armingol
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093 USA
| | - Nathan E. Lewis
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093 USA
| | - Efstratios N. Pistikopoulos
- grid.264756.40000 0004 4687 2082Texas A &M Energy Institute, Texas A &M University, College Station, TX 77843 USA ,grid.264756.40000 0004 4687 2082Department of Chemical Engineering, Texas A &M University, College Station, TX 77843 USA
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9
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [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: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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10
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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11
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Manipur I, Manzo M, Granata I, Giordano M, Maddalena L, Guarracino MR. Netpro2vec: A Graph Embedding Framework for Biomedical Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:729-740. [PMID: 33961560 DOI: 10.1109/tcbb.2021.3078089] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.
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12
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Kang H, Park B, Oh S, Pathiraja D, Kim JY, Jung S, Jeong J, Cha M, Park ZY, Choi IG, Chang IS. Metabolism perturbation Causedby the overexpression of carbon monoxide dehydrogenase/Acetyl-CoA synthase gene complex accelerated gas to acetate conversion rate ofEubacterium limosumKIST612. BIORESOURCE TECHNOLOGY 2021; 341:125879. [PMID: 34523550 DOI: 10.1016/j.biortech.2021.125879] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
Microbial conversion of carbon monoxide (CO) to acetate is a promising upcycling strategy for carbon sequestration. Herein, we demonstrate that CO conversion and acetate production rates of Eubacterium limosum KIST612 strain can be improved by in silico prediction and in vivo assessment. The mimicked CO metabolic model of KIST612 predicted that overexpressing the CO dehydrogenase (CODH) increases CO conversion and acetate production rates. To validate the prediction, we constructed mutant strains overexpressing CODH gene cluster and measured their CO conversion and acetate production rates. A mutant strain (ELM031) co-overexpressing CODH, coenzyme CooC2 and ACS showed a 3.1 × increased specific CO oxidation rate as well as 1.4 × increased specific acetate production rate, compared to the wild type strain. The transcriptional and translational data with redox balance analysis showed that ELM031 has enhanced reducing potential from up-regulation of ferredoxin and related metabolism directly linked to energy conservation.
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Affiliation(s)
- Hyunsoo Kang
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Byeonghyeok Park
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Soyoung Oh
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Duleepa Pathiraja
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - Ji-Yeon Kim
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Seunghyeon Jung
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Jiyeong Jeong
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Minseok Cha
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Zee-Yong Park
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - In-Geol Choi
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Republic of Korea
| | - In Seop Chang
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.
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13
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Khaleghi MK, Savizi ISP, Lewis NE, Shojaosadati SA. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J 2021; 16:e2100212. [PMID: 34390201 DOI: 10.1002/biot.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/06/2022]
Abstract
Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mohammad Karim Khaleghi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, USA.,Department of Pediatrics, University of California, San Diego, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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14
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Norena-Caro DA, Zuniga C, Pete AJ, Saemundsson SA, Donaldson MR, Adams AJ, Dooley KM, Zengler K, Benton MG. Analysis of the cyanobacterial amino acid metabolism with a precise genome-scale metabolic reconstruction of Anabaena sp. UTEX 2576. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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15
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Haiman ZB, Zielinski DC, Koike Y, Yurkovich JT, Palsson BO. MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics. PLoS Comput Biol 2021; 17:e1008208. [PMID: 33507922 PMCID: PMC7872247 DOI: 10.1371/journal.pcbi.1008208] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/09/2021] [Accepted: 12/21/2020] [Indexed: 01/01/2023] Open
Abstract
Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
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Affiliation(s)
- Zachary B. Haiman
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Yuko Koike
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - James T. Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, 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, Kongens Lyngby, Denmark
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16
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Rivas-Astroza M, Conejeros R. Metabolic flux configuration determination using information entropy. PLoS One 2020; 15:e0243067. [PMID: 33275628 PMCID: PMC7717585 DOI: 10.1371/journal.pone.0243067] [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: 05/30/2020] [Accepted: 11/14/2020] [Indexed: 11/20/2022] Open
Abstract
Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli’s glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.
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Affiliation(s)
- Marcelo Rivas-Astroza
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
- * E-mail:
| | - Raúl Conejeros
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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17
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Kuang E, Marney M, Cuevas D, Edwards RA, Forsberg EM. Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics. Metabolites 2020; 10:metabo10040156. [PMID: 32316423 PMCID: PMC7240944 DOI: 10.3390/metabo10040156] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/13/2020] [Accepted: 04/13/2020] [Indexed: 11/21/2022] Open
Abstract
Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.
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Affiliation(s)
- Ellen Kuang
- Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182, USA
| | - Matthew Marney
- Department of Biomedical Informatics, San Diego State University, San Diego, CA 92182, USA
| | - Daniel Cuevas
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Robert A. Edwards
- Department of Biomedical Informatics, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
- Department of Biology, San Diego State University, San Diego, CA 92182, USA
| | - Erica M. Forsberg
- Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182, USA
- Department of Biomedical Informatics, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
- Correspondence: ; Tel.: +1-619-594-5806
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18
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Blanco-Míguez A, Fdez-Riverola F, Sánchez B, Lourenço A. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Brief Bioinform 2020; 20:1032-1056. [PMID: 29186315 DOI: 10.1093/bib/bbx156] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 10/23/2017] [Indexed: 12/18/2022] Open
Abstract
The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.
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Affiliation(s)
| | | | | | - Anália Lourenço
- Dpto. de Informática - Universidade de Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
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19
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Norman RO, Millat T, Schatschneider S, Henstra AM, Breitkopf R, Pander B, Annan FJ, Piatek P, Hartman HB, Poolman MG, Fell DA, Winzer K, Minton NP, Hodgman C. Genome‐scale model of
C. autoethanogenum
reveals optimal bioprocess conditions for high‐value chemical production from carbon monoxide. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2018.5003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- Rupert O.J. Norman
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- School of BiosciencesUniversity of NottinghamSutton Bonington Campus, Sutton BoningtonLeicestershireLE12 5RDUK
| | - Thomas Millat
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Sarah Schatschneider
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- Evonik Nutrition and Care GmbHKantstr. 233798Halle‐KinsbeckGermany
| | - Anne M. Henstra
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Ronja Breitkopf
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Bart Pander
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Florence J. Annan
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Pawel Piatek
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Hassan B. Hartman
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
- Public Health England61 Colindale AvenueLondonNW9 5EQUK
| | - Mark G. Poolman
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
| | - David A. Fell
- Department of Biology and Medical SciencesOxford Brookes UniversityOxfordOX3 0BPUK
| | - Klaus Winzer
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Nigel P. Minton
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Charlie Hodgman
- Synthetic Biology Research CentreUniversity of Nottingham, University ParkNottinghamNG7 2RDUK
- School of BiosciencesUniversity of NottinghamSutton Bonington Campus, Sutton BoningtonLeicestershireLE12 5RDUK
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20
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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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21
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Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, Gruden K, Stitt M, Bolger ME, Usadel B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. MOLECULAR PLANT 2019; 12:879-892. [PMID: 30639314 DOI: 10.1016/j.molp.2019.01.003] [Citation(s) in RCA: 239] [Impact Index Per Article: 47.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 12/14/2018] [Accepted: 01/01/2019] [Indexed: 05/18/2023]
Abstract
Genome sequences from over 200 plant species have already been published, with this number expected to increase rapidly due to advances in sequencing technologies. Once a new genome has been assembled and the genes identified, the functional annotation of their putative translational products, proteins, using ontologies is of key importance as it places the sequencing data in a biological context. Furthermore, to keep pace with rapid production of genome sequences, this functional annotation process must be fully automated. Here we present a redesigned and significantly enhanced MapMan4 framework, together with a revised version of the associated online Mercator annotation tool. Compared with the original MapMan, the new ontology has been expanded almost threefold and enforces stricter assignment rules. This framework was then incorporated into Mercator4, which has been upgraded to reflect current knowledge across the land plant group, providing protein annotations for all embryophytes with a comparably high quality. The annotation process has been optimized to allow a plant genome to be annotated in a matter of minutes. The output results continue to be compatible with the established MapMan desktop application.
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Affiliation(s)
- Rainer Schwacke
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Gabriel Y Ponce-Soto
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kirsten Krause
- Department of Arctic and Marine Biology, The Arctic University of Norway, Biology Building, 9037 Tromsø, Norway
| | - Anthony M Bolger
- Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
| | - Borjana Arsova
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Asis Hallab
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany
| | - Kristina Gruden
- National Institute of Biology, Department of Biotechnology and Systems Biology, Večna Pot 111, 1000 Ljubljana, Slovenia
| | - Mark Stitt
- Max Planck Institute for Molecular Plant Physiology, Department of Systems Regulation, 14476 Potsdam-Golm, Germany
| | - Marie E Bolger
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany.
| | - Björn Usadel
- Institute for Bio- and Geosciences (IBG-2: Plant Sciences), Forschungszentrum Jülich, Wilhelm Johnen Straße, Jülich, Germany; Institute for Botany and Molecular Genetics, BioEconomy Science Center, Worringer Weg, RWTH Aachen University, 52074 Aachen, Germany
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22
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Metabolic modelling of mixed culture anaerobic microbial processes. Curr Opin Biotechnol 2019; 57:137-144. [DOI: 10.1016/j.copbio.2019.03.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 03/14/2019] [Accepted: 03/17/2019] [Indexed: 01/22/2023]
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23
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Presnell KV, Alper HS. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering. Biotechnol J 2019; 14:e1800416. [PMID: 30927499 DOI: 10.1002/biot.201800416] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/20/2019] [Indexed: 12/30/2022]
Abstract
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.
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Affiliation(s)
- Kristin V Presnell
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, 100 E 24 St., Austin, TX, 78712, USA
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24
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Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance. mSystems 2019; 4:mSystems00026-19. [PMID: 31020043 PMCID: PMC6478966 DOI: 10.1128/msystems.00026-19] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/29/2019] [Indexed: 01/08/2023] Open
Abstract
Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies. Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy. IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.
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25
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Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling. Metabolites 2019; 9:metabo9020028. [PMID: 30736318 PMCID: PMC6409771 DOI: 10.3390/metabo9020028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 01/24/2019] [Accepted: 01/31/2019] [Indexed: 12/22/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.
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26
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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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27
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Dunphy LJ, Papin JA. Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. Curr Opin Biotechnol 2018; 51:70-79. [PMID: 29223465 PMCID: PMC5991985 DOI: 10.1016/j.copbio.2017.11.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/22/2017] [Accepted: 11/24/2017] [Indexed: 12/14/2022]
Abstract
The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22903, USA.
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28
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Cuevas DA, Edwards RA. PMAnalyzer: a new web interface for bacterial growth curve analysis. Bioinformatics 2018; 33:1905-1906. [PMID: 28200078 PMCID: PMC5870709 DOI: 10.1093/bioinformatics/btx084] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 02/09/2016] [Indexed: 11/14/2022] Open
Abstract
Summary Bacterial growth curves are essential representations for characterizing bacteria metabolism within a variety of media compositions. Using high-throughput, spectrophotometers capable of processing tens of 96-well plates, quantitative phenotypic information can be easily integrated into the current data structures that describe a bacterial organism. The PMAnalyzer pipeline performs a growth curve analysis to parameterize the unique features occurring within microtiter wells containing specific growth media sources. We have expanded the pipeline capabilities and provide a user-friendly, online implementation of this automated pipeline. PMAnalyzer version 2.0 provides fast automatic growth curve parameter analysis, growth identification and high resolution figures of sample-replicate growth curves and several statistical analyses. Availability and Implementation PMAnalyzer v2.0 can be found at https://edwards.sdsu.edu/pmanalyzer/. Source code for the pipeline can be found on GitHub at https://github.com/dacuevas/PMAnalyzer. Source code for the online implementation can be found on GitHub at https://github.com/dacuevas/PMAnalyzerWeb. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel A Cuevas
- Computational Science Research Center, San Diego State University, San Diego, CA, USA
| | - Robert A Edwards
- Computational Science Research Center, San Diego State University, San Diego, CA, USA.,Department of Computer Science, San Diego State University, San Diego, CA, USA
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Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018; 46:249-260. [PMID: 29588387 PMCID: PMC5906707 DOI: 10.1042/bst20170268] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/01/2018] [Accepted: 03/02/2018] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.
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30
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The soil microbiome-from metagenomics to metaphenomics. Curr Opin Microbiol 2018; 43:162-168. [PMID: 29454931 DOI: 10.1016/j.mib.2018.01.013] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/24/2018] [Accepted: 01/26/2018] [Indexed: 11/21/2022]
Abstract
Soil microorganisms carry out important processes, including support of plant growth and cycling of carbon and other nutrients. However, the majority of soil microbes have not yet been isolated and their functions are largely unknown. Although metagenomic sequencing reveals microbial identities and functional gene information, it includes DNA from microbes with vastly varying physiological states. Therefore, metagenomics is only predictive of community functional potential. We posit that the next frontier lies in understanding the metaphenome, the product of the combined genetic potential of the microbiome and available resources. Here we describe examples of opportunities towards gaining understanding of the soil metaphenome.
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Methane utilization in Methylomicrobium alcaliphilum 20Z R: a systems approach. Sci Rep 2018; 8:2512. [PMID: 29410419 PMCID: PMC5802761 DOI: 10.1038/s41598-018-20574-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 01/22/2018] [Indexed: 12/20/2022] Open
Abstract
Biological methane utilization, one of the main sinks of the greenhouse gas in nature, represents an attractive platform for production of fuels and value-added chemicals. Despite the progress made in our understanding of the individual parts of methane utilization, our knowledge of how the whole-cell metabolic network is organized and coordinated is limited. Attractive growth and methane-conversion rates, a complete and expert-annotated genome sequence, as well as large enzymatic, 13C-labeling, and transcriptomic datasets make Methylomicrobium alcaliphilum 20ZR an exceptional model system for investigating methane utilization networks. Here we present a comprehensive metabolic framework of methane and methanol utilization in M. alcaliphilum 20ZR. A set of novel metabolic reactions governing carbon distribution across central pathways in methanotrophic bacteria was predicted by in-silico simulations and confirmed by global non-targeted metabolomics and enzymatic evidences. Our data highlight the importance of substitution of ATP-linked steps with PPi-dependent reactions and support the presence of a carbon shunt from acetyl-CoA to the pentose-phosphate pathway and highly branched TCA cycle. The diverged TCA reactions promote balance between anabolic reactions and redox demands. The computational framework of C1-metabolism in methanotrophic bacteria can represent an efficient tool for metabolic engineering or ecosystem modeling.
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Collins DA, Kalyuzhnaya MG. Navigating methane metabolism: Enzymes, compartments, and networks. Methods Enzymol 2018; 613:349-383. [DOI: 10.1016/bs.mie.2018.10.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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