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Atasoy M, Scott WT, Regueira A, Mauricio-Iglesias M, Schaap PJ, Smidt H. Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches. Biotechnol Adv 2024; 73:108363. [PMID: 38657743 DOI: 10.1016/j.biotechadv.2024.108363] [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: 12/07/2023] [Revised: 04/03/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024]
Abstract
In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.
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Affiliation(s)
- Merve Atasoy
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Department of Environmental Technology, Wageningen University & Research, Wageningen, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
| | - William T Scott
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Alberte Regueira
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium; Center for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Frieda Saeysstraat 1, Ghent, Belgium.
| | - Miguel Mauricio-Iglesias
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
| | - Peter J Schaap
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Hauke Smidt
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
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2
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Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Exp Mol Med 2024:10.1038/s12276-024-01262-7. [PMID: 38945961 DOI: 10.1038/s12276-024-01262-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/06/2024] [Accepted: 03/25/2024] [Indexed: 07/02/2024] Open
Abstract
Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.
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Affiliation(s)
- Nayeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Junyeong Ma
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Wonjong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jungyeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Peter Belenky
- Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, 02912, USA.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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3
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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4
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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; 35:533-548. [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] [MESH Headings] [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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5
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Fonseca LL, Böttcher L, Mehrad B, Laubenbacher RC. Surrogate modeling and control of medical digital twins. ARXIV 2024:arXiv:2402.05750v2. [PMID: 38827450 PMCID: PMC11142319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.
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Affiliation(s)
- Luis L. Fonseca
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Lucas Böttcher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
- Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany
| | - Borna Mehrad
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Reinhard C. Laubenbacher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
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6
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Choudhary R, Mahadevan R. DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities. Biophys J 2024:S0006-3495(24)00320-5. [PMID: 38733081 DOI: 10.1016/j.bpj.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/22/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.
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Affiliation(s)
- Ruhi Choudhary
- University of Toronto, Department of Chemical Engineering and Applied Chemistry, Toronto, ON, Canada
| | - Radhakrishnan Mahadevan
- University of Toronto, Department of Chemical Engineering and Applied Chemistry, Toronto, ON, Canada.
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7
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Zimmermann J, Piecyk A, Sieber M, Petersen C, Johnke J, Moitinho-Silva L, Künzel S, Bluhm L, Traulsen A, Kaleta C, Schulenburg H. Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life. mBio 2024; 15:e0001224. [PMID: 38634692 PMCID: PMC11077962 DOI: 10.1128/mbio.00012-24] [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: 01/02/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
The microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome's composition, which can change during the host's lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm's lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function. IMPORTANCE The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.
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Affiliation(s)
- Johannes Zimmermann
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
- Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Ploen, Germany
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, Kiel, Germany
| | - Agnes Piecyk
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Michael Sieber
- Department for Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Carola Petersen
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Julia Johnke
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Lucas Moitinho-Silva
- />Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | - Sven Künzel
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Lena Bluhm
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Arne Traulsen
- Department for Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, Kiel, Germany
| | - Hinrich Schulenburg
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
- Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Ploen, Germany
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8
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Blasco T, Balzerani F, Valcárcel LV, Larrañaga P, Bielza C, Francino MP, Rufián-Henares JÁ, Planes FJ, Pérez-Burillo S. BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities. Bioinformatics 2024; 40:btae266. [PMID: 38688585 PMCID: PMC11082422 DOI: 10.1093/bioinformatics/btae266] [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: 08/29/2023] [Revised: 03/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.
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Affiliation(s)
- Telmo Blasco
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Francesco Balzerani
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - María Pilar Francino
- Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia 46020, Spain
- CIBER en Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Centro de Investigación Biomédica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, Granada 18016, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Universidad de Granada, Granada 18012, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Sergio Pérez-Burillo
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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9
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Liu Y, Xue B, Liu H, Wang S, Su H. Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform. Biotechnol Adv 2024; 72:108348. [PMID: 38531490 DOI: 10.1016/j.biotechadv.2024.108348] [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: 02/04/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.
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Affiliation(s)
- Yu Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Boyuan Xue
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Hao Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Shaojie Wang
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
| | - Haijia Su
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
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10
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Kuper TJ, Islam MM, Peirce-Cottler SM, Papin JA, Ford RM. Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment. PLoS Comput Biol 2024; 20:e1012031. [PMID: 38669236 PMCID: PMC11051585 DOI: 10.1371/journal.pcbi.1012031] [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: 01/12/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.
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Affiliation(s)
- Tracy J. Kuper
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Mohammad Mazharul Islam
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Shayn M. Peirce-Cottler
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Roseanne M Ford
- Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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11
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Böttcher L, Fonseca LL, Laubenbacher RC. Control of Medical Digital Twins with Artificial Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585589. [PMID: 38562787 PMCID: PMC10983973 DOI: 10.1101/2024.03.18.585589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.
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Luo M, Zhu J, Jia J, Zhang H, Zhao J. Progress on network modeling and analysis of gut microecology: a review. Appl Environ Microbiol 2024; 90:e0009224. [PMID: 38415584 PMCID: PMC11207142 DOI: 10.1128/aem.00092-24] [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] [Indexed: 02/29/2024] Open
Abstract
The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.
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Affiliation(s)
- Meng Luo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jiajia Jia
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, China
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Law SR, Mathes F, Paten AM, Alexandre PA, Regmi R, Reid C, Safarchi A, Shaktivesh S, Wang Y, Wilson A, Rice SA, Gupta VVSR. Life at the borderlands: microbiomes of interfaces critical to One Health. FEMS Microbiol Rev 2024; 48:fuae008. [PMID: 38425054 PMCID: PMC10977922 DOI: 10.1093/femsre/fuae008] [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: 07/26/2023] [Revised: 02/12/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.
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Affiliation(s)
- Simon R Law
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Canberra, ACT 2601, Australia
| | - Falko Mathes
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Floreat, WA 6014, Australia
| | - Amy M Paten
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Canberra, ACT 2601, Australia
| | - Pamela A Alexandre
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, St Lucia, Qld 4072, Australia
| | - Roshan Regmi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
| | - Cameron Reid
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Urrbrae, SA 5064, Australia
| | - Azadeh Safarchi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Westmead, NSW 2145, Australia
| | - Shaktivesh Shaktivesh
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Data 61, Clayton, Vic 3168, Australia
| | - Yanan Wang
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Adelaide SA 5000, Australia
| | - Annaleise Wilson
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Geelong, Vic 3220, Australia
| | - Scott A Rice
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture, and Food, Westmead, NSW 2145, Australia
| | - Vadakattu V S R Gupta
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
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Marinos G, Hamerich IK, Debray R, Obeng N, Petersen C, Taubenheim J, Zimmermann J, Blackburn D, Samuel BS, Dierking K, Franke A, Laudes M, Waschina S, Schulenburg H, Kaleta C. Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics. Microbiol Spectr 2024; 12:e0114423. [PMID: 38230938 PMCID: PMC10846184 DOI: 10.1128/spectrum.01144-23] [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: 03/17/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024] Open
Abstract
While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.
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Affiliation(s)
- Georgios Marinos
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Inga K. Hamerich
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Reena Debray
- Department of Integrative Biology, University of California, Berkeley, California, USA
| | - Nancy Obeng
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Carola Petersen
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Jan Taubenheim
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Johannes Zimmermann
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
- Max-Planck Institute for Evolutionary Biology, Ploen, Schleswig-Holstein, Germany
| | - Dana Blackburn
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - Buck S. Samuel
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - Katja Dierking
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Matthias Laudes
- Institute of Diabetes and Clinical Metabolic Research, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Silvio Waschina
- Nutriinformatics, Institute for Human Nutrition and Food Science, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Hinrich Schulenburg
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
- Max-Planck Institute for Evolutionary Biology, Ploen, Schleswig-Holstein, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
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15
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Forero-Rodríguez J, Zimmermann J, Taubenheim J, Arias-Rodríguez N, Caicedo-Narvaez JD, Best L, Mendieta CV, López-Castiblanco J, Gómez-Muñoz LA, Gonzalez-Santos J, Arboleda H, Fernandez W, Kaleta C, Pinzón A. Changes in Bacterial Gut Composition in Parkinson's Disease and Their Metabolic Contribution to Disease Development: A Gut Community Reconstruction Approach. Microorganisms 2024; 12:325. [PMID: 38399728 PMCID: PMC10893096 DOI: 10.3390/microorganisms12020325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/25/2024] Open
Abstract
Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with the major symptoms comprising loss of movement coordination (motor dysfunction) and non-motor dysfunction, including gastrointestinal symptoms. Alterations in the gut microbiota composition have been reported in PD patients vs. controls. However, it is still unclear how these compositional changes contribute to disease etiology and progression. Furthermore, most of the available studies have focused on European, Asian, and North American cohorts, but the microbiomes of PD patients in Latin America have not been characterized. To address this problem, we obtained fecal samples from Colombian participants (n = 25 controls, n = 25 PD idiopathic cases) to characterize the taxonomical community changes during disease via 16S rRNA gene sequencing. An analysis of differential composition, diversity, and personalized computational modeling was carried out, given the fecal bacterial composition and diet of each participant. We found three metabolites that differed in dietary habits between PD patients and controls: carbohydrates, trans fatty acids, and potassium. We identified six genera that changed significantly in their relative abundance between PD patients and controls, belonging to the families Lachnospiraceae, Lactobacillaceae, Verrucomicrobioaceae, Peptostreptococcaceae, and Streptococcaceae. Furthermore, personalized metabolic modeling of the gut microbiome revealed changes in the predicted production of seven metabolites (Indole, tryptophan, fructose, phenylacetic acid, myristic acid, 3-Methyl-2-oxovaleric acid, and N-Acetylneuraminic acid). These metabolites are associated with the metabolism of aromatic amino acids and their consumption in the diet. Therefore, this research suggests that each individual's diet and intestinal composition could affect host metabolism. Furthermore, these findings open the door to the study of microbiome-host interactions and allow us to contribute to personalized medicine.
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Affiliation(s)
- Johanna Forero-Rodríguez
- Bioinformatics and Systems Biology Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (J.F.-R.); (J.D.C.-N.); (J.L.-C.)
- Medical Systems Biology Research Group, Institute of Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany (J.T.)
| | - Johannes Zimmermann
- Medical Systems Biology Research Group, Institute of Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany (J.T.)
| | - Jan Taubenheim
- Medical Systems Biology Research Group, Institute of Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany (J.T.)
| | - Natalia Arias-Rodríguez
- Bioinformatics and Systems Biology Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (J.F.-R.); (J.D.C.-N.); (J.L.-C.)
| | - Juan David Caicedo-Narvaez
- Bioinformatics and Systems Biology Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (J.F.-R.); (J.D.C.-N.); (J.L.-C.)
- Neurosciences Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Lena Best
- Medical Systems Biology Research Group, Institute of Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany (J.T.)
| | - Cindy V. Mendieta
- PhD Program in Clinical Epidemiology, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogotá 110231, Colombia;
- Department of Nutrition and Biochemistry, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Julieth López-Castiblanco
- Bioinformatics and Systems Biology Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (J.F.-R.); (J.D.C.-N.); (J.L.-C.)
| | - Laura Alejandra Gómez-Muñoz
- Neurosciences Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
- Cell Death Research Group, Medical School and Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Janneth Gonzalez-Santos
- Structural Biochemistry and Bioinformatics Laboratory, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Humberto Arboleda
- Cell Death Research Group, Medical School and Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - William Fernandez
- Neurosciences Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
- Cell Death Research Group, Medical School and Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Christoph Kaleta
- Medical Systems Biology Research Group, Institute of Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany (J.T.)
| | - Andrés Pinzón
- Bioinformatics and Systems Biology Research Group, Genetic Institute, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (J.F.-R.); (J.D.C.-N.); (J.L.-C.)
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Cockx BJR, Foster T, Clegg RJ, Alden K, Arya S, Stekel DJ, Smets BF, Kreft JU. Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. PLoS Comput Biol 2024; 20:e1011303. [PMID: 38422165 PMCID: PMC10947719 DOI: 10.1371/journal.pcbi.1011303] [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: 06/27/2023] [Revised: 03/18/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.
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Affiliation(s)
- Bastiaan J. R. Cockx
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Tim Foster
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Robert J. Clegg
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Kieran Alden
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Sankalp Arya
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Dov J. Stekel
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Barth F. Smets
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Jan-Ulrich Kreft
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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Liu X, Tong X, Zou L, Ju Y, Liu M, Han M, Lu H, Yang H, Wang J, Zong Y, Liu W, Xu X, Jin X, Xiao L, Jia H, Guo R, Zhang T. A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome. Commun Biol 2024; 7:139. [PMID: 38291185 PMCID: PMC10828421 DOI: 10.1038/s42003-024-05822-5] [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: 06/27/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024] Open
Abstract
The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.
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Affiliation(s)
- Xiaomin Liu
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xin Tong
- BGI Research, Shenzhen, 518083, China
| | | | - Yanmei Ju
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Mo Han
- BGI Research, Shenzhen, 518083, China
| | - Haorong Lu
- China National Genebank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Huanming Yang
- BGI Research, Shenzhen, 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jian Wang
- BGI Research, Shenzhen, 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Yang Zong
- BGI Research, Shenzhen, 518083, China
| | | | - Xun Xu
- BGI Research, Shenzhen, 518083, China
| | - Xin Jin
- BGI Research, Shenzhen, 518083, China
| | - Liang Xiao
- BGI Research, Shenzhen, 518083, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen, 518083, China
| | - Huijue Jia
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China.
- School of Life Sciences, Fudan University, Shanghai, China.
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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19
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Joseph C, Zafeiropoulos H, Bernaerts K, Faust K. Predicting microbial interactions with approaches based on flux balance analysis: an evaluation. BMC Bioinformatics 2024; 25:36. [PMID: 38262921 PMCID: PMC10804772 DOI: 10.1186/s12859-024-05651-7] [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: 03/23/2023] [Accepted: 01/11/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. RESULTS Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. CONCLUSIONS Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.
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Affiliation(s)
- Clémence Joseph
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Haris Zafeiropoulos
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium
| | - Kristel Bernaerts
- Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), KU Leuven, 3001, Leuven, Belgium
| | - Karoline Faust
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, KU Leuven, 3000, Leuven, Belgium.
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20
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Ghadermazi P, Chan SHJ. Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution. Bioinformatics 2024; 40:btae003. [PMID: 38212999 PMCID: PMC10799744 DOI: 10.1093/bioinformatics/btae003] [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: 08/04/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. RESULTS Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. AVAILABILITY AND IMPLEMENTATION The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.
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Affiliation(s)
- Parsa Ghadermazi
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
| | - Siu Hung Joshua Chan
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
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21
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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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22
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Ismaeel A, Valentino TR, Burke B, Goh J, Saliu TP, Albathi F, Owen A, McCarthy JJ, Wen Y. Acetate and succinate benefit host muscle energetics as exercise-associated post-biotics. Physiol Rep 2023; 11:e15848. [PMID: 37940330 PMCID: PMC10632089 DOI: 10.14814/phy2.15848] [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: 10/13/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023] Open
Abstract
Recently, the gut microbiome has emerged as a potent modulator of exercise-induced systemic adaptation and appears to be crucial for mediating some of the benefits of exercise. This study builds upon previous evidence establishing a gut microbiome-skeletal muscle axis, identifying exercise-induced changes in microbiome composition. Metagenomics sequencing of fecal samples from non-exercise-trained controls or exercise-trained mice was conducted. Biodiversity indices indicated exercise training did not change alpha diversity. However, there were notable differences in beta-diversity between trained and untrained microbiomes. Exercise significantly increased the level of the bacterial species Muribaculaceae bacterium DSM 103720. Computation simulation of bacterial growth was used to predict metabolites that accumulate under in silico culture of exercise-responsive bacteria. We identified acetate and succinate as potential gut microbial metabolites that are produced by Muribaculaceae bacterium, which were then administered to mice during a period of mechanical overload-induced muscle hypertrophy. Although no differences were observed for the overall muscle growth response to succinate or acetate administration during the first 5 days of mechanical overload-induced hypertrophy, acetate and succinate increased skeletal muscle mitochondrial respiration. When given as post-biotics, succinate or acetate treatment may improve oxidative metabolism during muscle hypertrophy.
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Affiliation(s)
- Ahmed Ismaeel
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | | | - Benjamin Burke
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Jensen Goh
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Tolulope P. Saliu
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Fatmah Albathi
- Department of Pharmacology and Nutritional Sciences, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Allison Owen
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
- Department of Athletic TrainingCollege of Health SciencesUniversity of KentuckyLexingtonKentuckyUSA
| | - John J. McCarthy
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Yuan Wen
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Center for Muscle BiologyUniversity of KentuckyLexingtonKentuckyUSA
- Division of Biomedical Informatics, Department of Internal Medicine, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
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23
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Ponce-de-Leon M, Montagud A, Noël V, Meert A, Pradas G, Barillot E, Calzone L, Valencia A. PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks. NPJ Syst Biol Appl 2023; 9:54. [PMID: 37903760 PMCID: PMC10616087 DOI: 10.1038/s41540-023-00314-4] [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: 03/31/2023] [Accepted: 10/11/2023] [Indexed: 11/01/2023] Open
Abstract
In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.
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Affiliation(s)
- Miguel Ponce-de-Leon
- Life Science, Barcelona Supercomputing Center (BSC), 1-3 Plaça Eusebi Güell, 08034, Barcelona, Spain
| | - Arnau Montagud
- Life Science, Barcelona Supercomputing Center (BSC), 1-3 Plaça Eusebi Güell, 08034, Barcelona, Spain
| | - Vincent Noël
- Institut Curie, Université PSL, 26 rue d'Ulm, 75248, Paris, France
- INSERM U900, Paris, France
- Mines ParisTech, Université PSL, Paris, France
| | - Annika Meert
- Life Science, Barcelona Supercomputing Center (BSC), 1-3 Plaça Eusebi Güell, 08034, Barcelona, Spain
| | - Gerard Pradas
- Life Science, Barcelona Supercomputing Center (BSC), 1-3 Plaça Eusebi Güell, 08034, Barcelona, Spain
| | - Emmanuel Barillot
- Institut Curie, Université PSL, 26 rue d'Ulm, 75248, Paris, France
- INSERM U900, Paris, France
- Mines ParisTech, Université PSL, Paris, France
| | - Laurence Calzone
- Institut Curie, Université PSL, 26 rue d'Ulm, 75248, Paris, France
- INSERM U900, Paris, France
- Mines ParisTech, Université PSL, Paris, France
| | - Alfonso Valencia
- Life Science, Barcelona Supercomputing Center (BSC), 1-3 Plaça Eusebi Güell, 08034, Barcelona, Spain.
- ICREA, 23 Passeig Lluís Companys, 08010, Barcelona, Spain.
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24
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Farkas C, Retamal-Fredes E, Ávila A, Fehlings MG, Vidal PM. Degenerative Cervical Myelopathy induces sex-specific dysbiosis in mice. Front Microbiol 2023; 14:1229783. [PMID: 37928672 PMCID: PMC10623434 DOI: 10.3389/fmicb.2023.1229783] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Degenerative Cervical Myelopathy (DCM) is the most common cause of spinal cord impairment in elderly populations. It describes a spectrum of disorders that cause progressive spinal cord compression, neurological impairment, loss of bladder and bowel functions, and gastrointestinal dysfunction. The gut microbiota has been recognized as an environmental factor that can modulate both the function of the central nervous system and the immune response through the microbiota-gut-brain axis. Changes in gut microbiota composition or microbiota-producing factors have been linked to the progression and development of several pathologies. However, little is known about the potential role of the gut microbiota in the pathobiology of DCM. Here, DCM was induced in C57BL/6 mice by implanting an aromatic polyether material underneath the C5-6 laminae. The extent of DCM-induced changes in microbiota composition was assessed by 16S rRNA sequencing of the fecal samples. The immune cell composition was assessed using flow cytometry. To date, several bacterial members have been identified using BLAST against the largest collection of metagenome-derived genomes from the mouse gut. In both, female and males DCM caused gut dysbiosis compared to the sham group. However, dysbiosis was more pronounced in males than in females, and several bacterial members of the families Lachnospiraceae and Muribaculaceae were significantly altered in the DCM group. These changes were also associated with altered microbe-derived metabolic changes in propionate-, butyrate-, and lactate-producing bacterial members. Our results demonstrate that DCM causes dynamic changes over time in the gut microbiota, reducing the abundance of butyrate-producing bacteria, and lactate-producing bacteria to a lesser extent. Genome-scale metabolic modeling using gapseq successfully identified pyruvate-to-butanoate and pyruvate-to-propionate reactions involving genes such as Buk and ACH1, respectively. These results provide a better understanding of the sex-specific molecular effects of changes in the gut microbiota on DCM pathobiology.
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Affiliation(s)
- Carlos Farkas
- Biomedical Science Research Laboratory, Department of Basic Sciences, Faculty of Medicine, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Eduardo Retamal-Fredes
- Biomedical Science Research Laboratory, Developmental Neurobiology Unit, Department of Basic Sciences, Faculty of Medicine, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Ariel Ávila
- Biomedical Science Research Laboratory, Developmental Neurobiology Unit, Department of Basic Sciences, Faculty of Medicine, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Michael G Fehlings
- Department of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Pia M Vidal
- Biomedical Science Research Laboratory, Neuroimmunology and Regeneration of the Central Nervous System Unit, Department of Basic Sciences, Faculty of Medicine, Universidad Católica de la Santísima Concepción, Concepción, Chile
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25
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Scott WT, Benito-Vaquerizo S, Zimmermann J, Bajić D, Heinken A, Suarez-Diez M, Schaap PJ. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 2023; 19:e1011363. [PMID: 37578975 PMCID: PMC10449394 DOI: 10.1371/journal.pcbi.1011363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
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Affiliation(s)
- William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
| | - Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Kiel, Germany
| | - Djordje Bajić
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Almut Heinken
- Inserm U1256 Laboratoire nGERE, Université de Lorraine, Nancy, France
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
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26
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Guex I, Mazza C, Dubey M, Batsch M, Li R, van der Meer JR. Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding. PLoS Comput Biol 2023; 19:e1011402. [PMID: 37603551 PMCID: PMC10470959 DOI: 10.1371/journal.pcbi.1011402] [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: 02/13/2023] [Revised: 08/31/2023] [Accepted: 07/31/2023] [Indexed: 08/23/2023] Open
Abstract
When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.
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Affiliation(s)
- Isaline Guex
- Department of Mathematics, University of Fribourg, Fribourg, Switzerland
| | - Christian Mazza
- Department of Mathematics, University of Fribourg, Fribourg, Switzerland
| | - Manupriyam Dubey
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Maxime Batsch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Renyi Li
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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27
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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28
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Schäfer M, Pacheco AR, Künzler R, Bortfeld-Miller M, Field CM, Vayena E, Hatzimanikatis V, Vorholt JA. Metabolic interaction models recapitulate leaf microbiota ecology. Science 2023; 381:eadf5121. [PMID: 37410834 DOI: 10.1126/science.adf5121] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding the degree to which this dependency determines interspecies interactions may advance efforts to control host-microbiome relationships. We combined synthetic community experiments with computational models to predict interaction outcomes between plant-associated bacteria. We mapped the metabolic capabilities of 224 leaf isolates from Arabidopsis thaliana by assessing the growth of each strain on 45 environmentally relevant carbon sources in vitro. We used these data to build curated genome-scale metabolic models for all strains, which we combined to simulate >17,500 interactions. The models recapitulated outcomes observed in planta with >89% accuracy, highlighting the role of carbon utilization and the contributions of niche partitioning and cross-feeding in the assembly of leaf microbiomes.
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Affiliation(s)
- Martin Schäfer
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Alan R Pacheco
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Rahel Künzler
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | | | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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29
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Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73:102292. [PMID: 36931094 DOI: 10.1016/j.mib.2023.102292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.
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Affiliation(s)
- Gonçalo Ds Correia
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julian R Marchesi
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK; Centre for Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, Imperial College London, London W2 1NY, UK
| | - David A MacIntyre
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
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30
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Sakkos JK, Santos-Merino M, Kokarakis EJ, Li B, Fuentes-Cabrera M, Zuliani P, Ducat DC. Predicting partner fitness based on spatial structuring in a light-driven microbial community. PLoS Comput Biol 2023; 19:e1011045. [PMID: 37134119 PMCID: PMC10184905 DOI: 10.1371/journal.pcbi.1011045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/15/2023] [Accepted: 03/22/2023] [Indexed: 05/04/2023] Open
Abstract
Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.
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Affiliation(s)
- Jonathan K Sakkos
- Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America
| | - María Santos-Merino
- Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America
| | - Emmanuel J Kokarakis
- Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America
- Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America
| | - Bowen Li
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Miguel Fuentes-Cabrera
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - Paolo Zuliani
- Dipartimento di Informatica, Università di Roma "La Sapienza", Rome, Italy
| | - Daniel C Ducat
- Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
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31
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Abstract
Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.
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Affiliation(s)
| | - Sean M. Gibbons
- Institute for Systems Biology, Seattle, Washington, USA
- Departments of Bioengineering and Genome Sciences, University of Washington, Seattle, Washington, USA
- eScience Institute, University of Washington, Seattle, Washington, USA
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32
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Lee CY, Dillard LR, Papin JA, Arnold KB. New perspectives into the vaginal microbiome with systems biology. Trends Microbiol 2023; 31:356-368. [PMID: 36272885 DOI: 10.1016/j.tim.2022.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/19/2022] [Accepted: 09/21/2022] [Indexed: 10/28/2022]
Abstract
The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.
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Affiliation(s)
- Christina Y Lee
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Lillian R Dillard
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kelly B Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
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33
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Archambault L, Koshy-Chenthittayil S, Thompson A, Dongari-Bagtzoglou A, Laubenbacher R, Mendes P. Corrected and Republished from: "Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling". mSphere 2023; 8:e0065622. [PMID: 36942961 DOI: 10.1128/msphere.00656-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023] Open
Abstract
As common commensals residing on mucosal tissues, Lactobacillus species are known to promote health, while some Streptococcus species act to enhance the pathogenicity of other organisms in those environments. In this study we used a combination of in vitro imaging of live biofilms and computational modeling to explore biofilm interactions between Streptococcus oralis, an accessory pathogen in oral candidiasis, and Lactobacillus paracasei, an organism with known probiotic properties. A computational agent-based model was created where the two species interact only by competing for space, oxygen, and glucose. Quantification of bacterial growth in live biofilms indicated that S. oralis biomass and cell numbers were much lower than predicted by the model. Two subsequent models were then created to examine more complex interactions between these species, one where L. paracasei secretes a surfactant and another where L. paracasei secretes an inhibitor of S. oralis growth. We observed that the growth of S. oralis could be affected by both mechanisms. Further biofilm experiments support the hypothesis that L. paracasei may secrete an inhibitor of S. oralis growth, although they do not exclude that a surfactant could also be involved. This contribution shows how agent-based modeling and experiments can be used in synergy to address multiple-species biofilm interactions, with important roles in mucosal health and disease. IMPORTANCE We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms. Here, we used an iterative collaborative process between experimentation and modeling to reveal aspects of the mostly unexplored relationship between S. oralis and L. paracasei in biofilm growth. The inhibitory nature of L. paracasei on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.
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Affiliation(s)
- Linda Archambault
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, Connecticut, USA
- Department of Oral Health and Diagnostic Sciences, University of Connecticut School of Dental Medicine, Farmington, Connecticut, USA
- Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Sherli Koshy-Chenthittayil
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, Connecticut, USA
- Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Angela Thompson
- Department of Oral Health and Diagnostic Sciences, University of Connecticut School of Dental Medicine, Farmington, Connecticut, USA
| | - Anna Dongari-Bagtzoglou
- Department of Oral Health and Diagnostic Sciences, University of Connecticut School of Dental Medicine, Farmington, Connecticut, USA
| | | | - Pedro Mendes
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, Connecticut, USA
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut, USA
- Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, USA
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34
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Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions. Metab Eng 2023; 76:120-132. [PMID: 36720400 DOI: 10.1016/j.ymben.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/13/2022] [Accepted: 01/21/2023] [Indexed: 01/29/2023]
Abstract
Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.
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35
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Computational Insight into Intraspecies Distinctions in Pseudoalteromonas distincta: Carotenoid-like Synthesis Traits and Genomic Heterogeneity. Int J Mol Sci 2023; 24:ijms24044158. [PMID: 36835570 PMCID: PMC9966250 DOI: 10.3390/ijms24044158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Advances in the computational annotation of genomes and the predictive potential of current metabolic models, based on more than thousands of experimental phenotypes, allow them to be applied to identify the diversity of metabolic pathways at the level of ecophysiology differentiation within taxa and to predict phenotypes, secondary metabolites, host-associated interactions, survivability, and biochemical productivity under proposed environmental conditions. The significantly distinctive phenotypes of members of the marine bacterial species Pseudoalteromonas distincta and an inability to use common molecular markers make their identification within the genus Pseudoalteromonas and prediction of their biotechnology potential impossible without genome-scale analysis and metabolic reconstruction. A new strain, KMM 6257, of a carotenoid-like phenotype, isolated from a deep-habituating starfish, emended the description of P. distincta, particularly in the temperature growth range from 4 to 37 °C. The taxonomic status of all available closely related species was elucidated by phylogenomics. P. distincta possesses putative methylerythritol phosphate pathway II and 4,4'-diapolycopenedioate biosynthesis, related to C30 carotenoids, and their functional analogues, aryl polyene biosynthetic gene clusters (BGC). However, the yellow-orange pigmentation phenotypes in some strains coincide with the presence of a hybrid BGC encoding for aryl polyene esterified with resorcinol. The alginate degradation and glycosylated immunosuppressant production, similar to brasilicardin, streptorubin, and nucleocidines, are the common predicted features. Starch, agar, carrageenan, xylose, lignin-derived compound degradation, polysaccharide, folate, and cobalamin biosynthesis are all strain-specific.
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36
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Marinos G, Hamerich IK, Debray R, Obeng N, Petersen C, Taubenheim J, Zimmermann J, Blackburn D, Samuel BS, Dierking K, Franke A, Laudes M, Waschina S, Schulenburg H, Kaleta C. Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.528811. [PMID: 36824941 PMCID: PMC9949166 DOI: 10.1101/2023.02.17.528811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.
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Affiliation(s)
- Georgios Marinos
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Inga K Hamerich
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Reena Debray
- Department of Integrative Biology, University of California, Berkeley, California, USA
| | - Nancy Obeng
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Carola Petersen
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Jan Taubenheim
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Johannes Zimmermann
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
- Max-Planck Institute for Evolutionary Biology, Ploen, Schleswig-Holstein, Germany
| | - Dana Blackburn
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - Buck S Samuel
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - Katja Dierking
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Matthias Laudes
- Institute of Diabetes and Clinical Metabolic Research, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Schleswig-Holstein, Germany
| | - Silvio Waschina
- Nutriinformatics, Institute for Human Nutrition and Food Science, Kiel University, Kiel, Schleswig-Holstein, Germany
| | - Hinrich Schulenburg
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Schleswig-Holstein, Germany
- Max-Planck Institute for Evolutionary Biology, Ploen, Schleswig-Holstein, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, Kiel University, Kiel, Schleswig-Holstein, Germany
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37
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Smythe P, Wilkinson HN. The Skin Microbiome: Current Landscape and Future Opportunities. Int J Mol Sci 2023; 24:ijms24043950. [PMID: 36835363 PMCID: PMC9963692 DOI: 10.3390/ijms24043950] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 02/18/2023] Open
Abstract
Our skin is the largest organ of the body, serving as an important barrier against the harsh extrinsic environment. Alongside preventing desiccation, chemical damage and hypothermia, this barrier protects the body from invading pathogens through a sophisticated innate immune response and co-adapted consortium of commensal microorganisms, collectively termed the microbiota. These microorganisms inhabit distinct biogeographical regions dictated by skin physiology. Thus, it follows that perturbations to normal skin homeostasis, as occurs with ageing, diabetes and skin disease, can cause microbial dysbiosis and increase infection risk. In this review, we discuss emerging concepts in skin microbiome research, highlighting pertinent links between skin ageing, the microbiome and cutaneous repair. Moreover, we address gaps in current knowledge and highlight key areas requiring further exploration. Future advances in this field could revolutionise the way we treat microbial dysbiosis associated with skin ageing and other pathologies.
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Affiliation(s)
- Paisleigh Smythe
- Centre for Biomedicine, Hull York Medical School, University of Hull, Hull HU6 7RX, UK
- Skin Research Centre, Hull York Medical School, University of York, York YO10 5DD, UK
| | - Holly N. Wilkinson
- Centre for Biomedicine, Hull York Medical School, University of Hull, Hull HU6 7RX, UK
- Skin Research Centre, Hull York Medical School, University of York, York YO10 5DD, UK
- Correspondence:
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38
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Liu YY. Controlling the human microbiome. Cell Syst 2023; 14:135-159. [PMID: 36796332 PMCID: PMC9942095 DOI: 10.1016/j.cels.2022.12.010] [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: 06/06/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.
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Affiliation(s)
- Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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39
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Dal Co A, Ackermann M, van Vliet S. Spatial self-organization of metabolism in microbial systems: A matter of enzymes and chemicals. Cell Syst 2023; 14:98-108. [PMID: 36796335 DOI: 10.1016/j.cels.2022.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/14/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
Most bacteria live in dense, spatially structured communities such as biofilms. The high density allows cells to alter the local microenvironment, whereas the limited mobility can cause species to become spatially organized. Together, these factors can spatially organize metabolic processes within microbial communities so that cells in different locations perform different metabolic reactions. The overall metabolic activity of a community depends both on how metabolic reactions are arranged in space and on how they are coupled, i.e., how cells in different regions exchange metabolites. Here, we review mechanisms that lead to the spatial organization of metabolic processes in microbial systems. We discuss factors that determine the length scales over which metabolic activities are arranged in space and highlight how the spatial organization of metabolic processes affects the ecology and evolution of microbial communities. Finally, we define key open questions that we believe should be the main focus of future research.
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Affiliation(s)
- Alma Dal Co
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Martin Ackermann
- Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland; Department of Environmental Microbiology, Eawag, 8600 Duebendorf, Switzerland.
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40
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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41
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Rodriguez A, Hirakawa MP, Geiselman GM, Tran-Gyamfi MB, Light YK, George A, Sale KL. Prospects for utilizing microbial consortia for lignin conversion. FRONTIERS IN CHEMICAL ENGINEERING 2023. [DOI: 10.3389/fceng.2023.1086881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Naturally occurring microbial communities are able to decompose lignocellulosic biomass through the concerted production of a myriad of enzymes that degrade its polymeric components and assimilate the resulting breakdown compounds by members of the community. This process includes the conversion of lignin, the most recalcitrant component of lignocellulosic biomass and historically the most difficult to valorize in the context of a biorefinery. Although several fundamental questions on microbial conversion of lignin remain unanswered, it is known that some fungi and bacteria produce enzymes to break, internalize, and assimilate lignin-derived molecules. The interest in developing efficient biological lignin conversion approaches has led to a better understanding of the types of enzymes and organisms that can act on different types of lignin structures, the depolymerized compounds that can be released, and the products that can be generated through microbial biosynthetic pathways. It has become clear that the discovery and implementation of native or engineered microbial consortia could be a powerful tool to facilitate conversion and valorization of this underutilized polymer. Here we review recent approaches that employ isolated or synthetic microbial communities for lignin conversion to bioproducts, including the development of methods for tracking and predicting the behavior of these consortia, the most significant challenges that have been identified, and the possibilities that remain to be explored in this field.
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42
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Kiti MC, Aguolu OG, Zelaya A, Chen HY, Ahmed N, Battross J, Liu CY, Nelson KN, Jenness SM, Melegaro A, Ahmed F, Malik F, Omer SB, Lopman BA. Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.19.22283700. [PMID: 36597545 PMCID: PMC9810228 DOI: 10.1101/2022.12.19.22283700] [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: 12/24/2022]
Abstract
Non-pharmaceutical interventions minimize social contacts, hence the spread of SARS-CoV-2. We quantified two-day contact patterns among USA employees from 2020-2021 during the COVID-19 pandemic. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic diaries. Mean (standard deviation) contacts reported by 1,456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We modeled SARS-CoV-2 transmission at home, work, and community. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained very low in work settings. Contact data are important to parameterize models of infection transmission and control. Teaser Changes in social contact patterns shape disease dynamics at workplaces in the USA.
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Affiliation(s)
- Moses C. Kiti
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Obianuju G. Aguolu
- Yale Institute for Global Health, Yale University, Connecticut, USA
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Alana Zelaya
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Holin Y. Chen
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Noureen Ahmed
- Yale Institute for Global Health, Yale University, Connecticut, USA
| | | | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Georgia, USA
| | | | | | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | - Faruque Ahmed
- Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Fauzia Malik
- Yale Institute for Global Health, Yale University, Connecticut, USA
| | - Saad B. Omer
- Yale Institute for Global Health, Yale University, Connecticut, USA
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Ben A. Lopman
- Rollins School of Public Health, Emory University, Georgia, USA
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43
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Zacharias HU, Kaleta C, Cossais F, Schaeffer E, Berndt H, Best L, Dost T, Glüsing S, Groussin M, Poyet M, Heinzel S, Bang C, Siebert L, Demetrowitsch T, Leypoldt F, Adelung R, Bartsch T, Bosy-Westphal A, Schwarz K, Berg D. Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets. Metabolites 2022; 12:metabo12121222. [PMID: 36557259 PMCID: PMC9786685 DOI: 10.3390/metabo12121222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.
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Affiliation(s)
- Helena U. Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | | | - Eva Schaeffer
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Henry Berndt
- Research Group Comparative Immunobiology, Zoological Institute, Kiel University, 24118 Kiel, Germany
| | - Lena Best
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Thomas Dost
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Svea Glüsing
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
| | - Mathieu Groussin
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Mathilde Poyet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sebastian Heinzel
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Leonard Siebert
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Frank Leypoldt
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Neuroimmunology, Institute of Clinical Chemistry, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Rainer Adelung
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Thorsten Bartsch
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Anja Bosy-Westphal
- Institute of Human Nutrition and Food Science, Kiel University, 24107 Kiel, Germany
| | - Karin Schwarz
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Daniela Berg
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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González-Plaza JJ, Furlan C, Rijavec T, Lapanje A, Barros R, Tamayo-Ramos JA, Suarez-Diez M. Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels. Front Microbiol 2022; 13:1006946. [PMID: 36519168 PMCID: PMC9744117 DOI: 10.3389/fmicb.2022.1006946] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/02/2022] [Indexed: 08/31/2023] Open
Abstract
The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.
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Affiliation(s)
- Juan José González-Plaza
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | - Cristina Furlan
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
| | - Tomaž Rijavec
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Aleš Lapanje
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Rocío Barros
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | | | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
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45
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A Multiscale Spatiotemporal Model Including a Switch from Aerobic to Anaerobic Metabolism Reproduces Succession in the Early Infant Gut Microbiota. mSystems 2022; 7:e0044622. [PMID: 36047700 PMCID: PMC9600552 DOI: 10.1128/msystems.00446-22] [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] [Indexed: 12/24/2022] Open
Abstract
The human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally dominate, such as Enterobacteriaceae. These are succeeded by strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium species is associated with health benefits; for example, Bifidobacterium species repress growth of pathogenic competitors and modulate the immune response. Succession to Bifidobacterium is thought to be due to consumption of intracolonic oxygen present in newborns by facultative anaerobes, including Enterobacteriaceae. To study if oxygen depletion suffices for the transition to Bifidobacterium species, here we introduced a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics, and cross-feeding. Using publicly available metabolic network data from the AGORA collection, the model simulates ab initio the competition of strictly and facultatively anaerobic species in a gut-like environment under the influence of lactose and oxygen. The model predicts that individual differences in intracolonic oxygen in newborn infants can explain the observed individual variation in succession to anaerobic species, in particular Bifidobacterium species. Bifidobacterium species became dominant in the model by their use of the bifid shunt, which allows Bifidobacterium to switch to suboptimal yield metabolism with fast growth at high lactose concentrations, as predicted here using flux balance analysis. The computational model thus allows us to test the internal plausibility of hypotheses for bacterial colonization and succession in the infant colon. IMPORTANCE The composition of the infant microbiota has a great impact on infant health, but its controlling factors are still incompletely understood. The frequently dominant anaerobic Bifidobacterium species benefit health, e.g., they can keep harmful competitors under control and modulate the intestinal immune response. Controlling factors could include nutritional composition and intestinal mucus composition, as well as environmental factors, such as antibiotics. We introduce a modeling framework of a metabolically realistic intestinal microbial ecology in which hypothetical scenarios can be tested and compared. We present simulations that suggest that greater levels of intraintestinal oxygenation more strongly delay the dominance of Bifidobacterium species, explaining the observed variety of microbial composition and demonstrating the use of the model for hypothesis generation. The framework allowed us to test a variety of controlling factors, including intestinal mixing and transit time. Future versions will also include detailed modeling of oligosaccharide and mucin metabolism.
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Mostolizadeh R, Glöckler M, Dräger A. Towards the human nasal microbiome: Simulating D. pigrum and S. aureus. Front Cell Infect Microbiol 2022; 12:925215. [PMID: 36605126 PMCID: PMC9810029 DOI: 10.3389/fcimb.2022.925215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023] Open
Abstract
The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions.
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Affiliation(s)
- Reihaneh Mostolizadeh
- 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,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany,*Correspondence: Reihaneh Mostolizadeh,
| | - Manuel Glöckler
- Department of Computer Science, University of Tübingen, Tübingen, 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,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany
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47
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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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48
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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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Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties. THE ISME JOURNAL 2022; 16:2144-2159. [PMID: 35717467 PMCID: PMC9381525 DOI: 10.1038/s41396-022-01255-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/30/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022]
Abstract
AbstractMicrobe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.
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50
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Martínez-López YE, Esquivel-Hernández DA, Sánchez-Castañeda JP, Neri-Rosario D, Guardado-Mendoza R, Resendis-Antonio O. Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era. Gut Microbes 2022; 14:2111952. [PMID: 36004400 PMCID: PMC9423831 DOI: 10.1080/19490976.2022.2111952] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.
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Affiliation(s)
- Yoscelina Estrella Martínez-López
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | | | - Jean Paul Sánchez-Castañeda
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México
| | - Rodolfo Guardado-Mendoza
- Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México,Research Department, Hospital Regional de Alta Especialidad del Bajío. León, Guanajuato, México,Rodolfo Guardado-Mendoza Metabolic Research Laboratory, Department of Medicine and Nutrition. University of Guanajuato. León, Guanajuato, México
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN). México City, México,Coordinación de la Investigación Científica – Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM). Ciudad de México, México,CONTACT Osbaldo Resendis-Antonio Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Ciudad de México, CDMX
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