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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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2
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Burz SD, Causevic S, Dal Co A, Dmitrijeva M, Engel P, Garrido-Sanz D, Greub G, Hapfelmeier S, Hardt WD, Hatzimanikatis V, Heiman CM, Herzog MKM, Hockenberry A, Keel C, Keppler A, Lee SJ, Luneau J, Malfertheiner L, Mitri S, Ngyuen B, Oftadeh O, Pacheco AR, Peaudecerf F, Resch G, Ruscheweyh HJ, Sahin A, Sanders IR, Slack E, Sunagawa S, Tackmann J, Tecon R, Ugolini GS, Vacheron J, van der Meer JR, Vayena E, Vonaesch P, Vorholt JA. From microbiome composition to functional engineering, one step at a time. Microbiol Mol Biol Rev 2023; 87:e0006323. [PMID: 37947420 PMCID: PMC10732080 DOI: 10.1128/mmbr.00063-23] [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] [Indexed: 11/12/2023] Open
Abstract
SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.
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Affiliation(s)
- Sebastian Dan Burz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Senka Causevic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Alma Dal Co
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marija Dmitrijeva
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Philipp Engel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Daniel Garrido-Sanz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Gilbert Greub
- Institut de microbiologie, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | | | | | - Clara Margot Heiman
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | | | - Christoph Keel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Soon-Jae Lee
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Julien Luneau
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Bidong Ngyuen
- Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | | | | | - Grégory Resch
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | - Asli Sahin
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Ian R. Sanders
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | | | - Janko Tackmann
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Robin Tecon
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Jordan Vacheron
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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3
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Salicylate or Phthalate: The Main Intermediates in the Bacterial Degradation of Naphthalene. Processes (Basel) 2021. [DOI: 10.3390/pr9111862] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are widely presented in the environment and pose a serious environmental threat due to their toxicity. Among PAHs, naphthalene is the simplest compound. Nevertheless, due to its high toxicity and presence in the waste of chemical and oil processing industries, naphthalene is one of the most critical pollutants. Similar to other PAHs, naphthalene is released into the environment via the incomplete combustion of organic compounds, pyrolysis, oil spills, oil processing, household waste disposal, and use of fumigants and deodorants. One of the main ways to detoxify such compounds in the natural environment is through their microbial degradation. For the first time, the pathway of naphthalene degradation was investigated in pseudomonades. The salicylate was found to be a key intermediate. For some time, this pathway was considered the main, if not the only one, in the bacterial destruction of naphthalene. However, later, data emerged which indicated that gram-positive bacteria in the overwhelming majority of cases are not capable of the formation/destruction of salicylate. The obtained data made it possible to reveal that protocatechoate, phthalate, and cinnamic acids are predominant intermediates in the destruction of naphthalene by rhodococci. Pathways of naphthalene degradation, the key enzymes, and genetic regulation are the main subjects of the present review, representing an attempt to summarize the current knowledge about the mechanism of the microbial degradation of PAHs. Modern molecular methods are also discussed in the context of the development of “omics” approaches, namely genomic, metabolomic, and proteomic, used as tools for studying the mechanisms of microbial biodegradation. Lastly, a comprehensive understanding of the mechanisms of the formation of specific ecosystems is also provided.
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4
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Dahal S, Zhao J, Yang L. Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications. BIOTECHNOL BIOPROC E 2021. [DOI: 10.1007/s12257-020-0061-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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García-Jiménez B, Torres-Bacete J, Nogales J. Metabolic modelling approaches for describing and engineering microbial communities. Comput Struct Biotechnol J 2020; 19:226-246. [PMID: 33425254 PMCID: PMC7773532 DOI: 10.1016/j.csbj.2020.12.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/17/2022] Open
Abstract
Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.
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Affiliation(s)
- Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223-Pozuelo de Alarcón, Madrid, Spain
| | - Jesús Torres-Bacete
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC), Madrid, Spain
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6
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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Aguirre de Cárcer D. Experimental and computational approaches to unravel microbial community assembly. Comput Struct Biotechnol J 2020; 18:4071-4081. [PMID: 33363703 PMCID: PMC7736701 DOI: 10.1016/j.csbj.2020.11.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.
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8
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Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics 2020; 20:e1900282. [PMID: 32579720 PMCID: PMC7501203 DOI: 10.1002/pmic.201900282] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/13/2020] [Indexed: 12/18/2022]
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
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Affiliation(s)
- Sanjeev Dahal
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | | | - Hao Xu
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
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9
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Antoniewicz MR. A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities. Curr Opin Biotechnol 2020; 64:230-237. [PMID: 32711357 DOI: 10.1016/j.copbio.2020.07.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 01/21/2023]
Abstract
Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.
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Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Michigan, Ann Arbor, MI 48109, USA.
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10
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Tefagh M, Boyd SP. SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks. BMC Bioinformatics 2020; 21:140. [PMID: 32293238 PMCID: PMC7158141 DOI: 10.1186/s12859-020-3440-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 03/04/2020] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND High-throughput omics technologies have enabled the comprehensive reconstructions of genome-scale metabolic networks for many organisms. However, only a subset of reactions is active in each cell which differs from tissue to tissue or from patient to patient. Reconstructing a subnetwork of the generic metabolic network from a provided set of context-specific active reactions is a demanding computational task. RESULTS We propose SWIFTCC and SWIFTCORE as effective methods for flux consistency checking and the context-specific reconstruction of genome-scale metabolic networks which consistently outperform the previous approaches. CONCLUSIONS We have derived an approximate greedy algorithm which efficiently scales to increasingly large metabolic networks. SWIFTCORE is freely available for non-commercial use in the GitHub repository at https://mtefagh.github.io/swiftcore/.
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Affiliation(s)
- Mojtaba Tefagh
- Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, US
| | - Stephen P. Boyd
- Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, US
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11
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Fuertes A, Pérez-Burillo S, Apaolaza I, Vallès Y, Francino MP, Rufián-Henares JÁ, Planes FJ. Adaptation of the Human Gut Microbiota Metabolic Network During the First Year After Birth. Front Microbiol 2019; 10:848. [PMID: 31105659 PMCID: PMC6491881 DOI: 10.3389/fmicb.2019.00848] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 04/02/2019] [Indexed: 12/11/2022] Open
Abstract
Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.
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Affiliation(s)
- Alvaro Fuertes
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
| | - Sergio Pérez-Burillo
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain
| | - Iñigo Apaolaza
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
| | - Yvonne Vallès
- Unitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.,Department of Biological and Chemical Sciences, The University of the West Indies, Cave Hill, Barbados
| | - M Pilar Francino
- Unitat Mixta d'Investigació en Genòmica i Salut, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública/Instituto de Biología Integrativa de Sistemas, Universitat de València, Valencia, Spain.,CIBER en Epidemiología y Salud Pública, Madrid, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain.,Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, Granada, Spain
| | - Francisco J Planes
- Bioinformatics Group, TECNUN School of Engineering, Department of Biomedical Engineering and Sciences, Universidad de Navarra, San Sebastián, Spain.,Bioinformatics Group, CEIT, Water and Health Division, Universidad de Navarra, San Sebastián, Spain
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12
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Ang KS, Lakshmanan M, Lee NR, Lee DY. Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications. Curr Genomics 2018; 19:712-722. [PMID: 30532650 PMCID: PMC6225453 DOI: 10.2174/1389202919666180911144055] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Revised: 11/08/2017] [Accepted: 11/11/2017] [Indexed: 02/08/2023] Open
Abstract
In nature, microbes do not exist in isolation but co-exist in a variety of ecological and biological environments and on various host organisms. Due to their close proximity, these microbes interact among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrinsic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial communities. If detailed species-specific information is available, segregated models of individual organisms can be constructed and connected via metabolite exchanges; otherwise, the community may be represented as a lumped network of metabolic reactions. The constructed models can then be simulated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments. More importantly, such community models have been developed to study microbial interactions in various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights into the natural and synthetic microbial communities, and design interventions to achieve specific goals. Finally, potential directions for future development in metabolic modeling of microbial communities were also discussed.
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Affiliation(s)
- Kok Siong Ang
- 1Bioprocessing Technology Institute (BTI), ASTAR, Singapore 138668, Singapore; 2Department of Chemical and Biomolecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do16419, Republic of Korea
| | - Meiyappan Lakshmanan
- 1Bioprocessing Technology Institute (BTI), ASTAR, Singapore 138668, Singapore; 2Department of Chemical and Biomolecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do16419, Republic of Korea
| | - Na-Rae Lee
- 1Bioprocessing Technology Institute (BTI), ASTAR, Singapore 138668, Singapore; 2Department of Chemical and Biomolecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do16419, Republic of Korea
| | - Dong-Yup Lee
- 1Bioprocessing Technology Institute (BTI), ASTAR, Singapore 138668, Singapore; 2Department of Chemical and Biomolecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do16419, Republic of Korea
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13
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Kreft JU, Plugge CM, Prats C, Leveau JHJ, Zhang W, Hellweger FL. From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality. Front Microbiol 2017; 8:2299. [PMID: 29230200 PMCID: PMC5711835 DOI: 10.3389/fmicb.2017.02299] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 11/07/2017] [Indexed: 01/04/2023] Open
Abstract
Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.
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Affiliation(s)
- Jan-Ulrich Kreft
- Centre for Computational Biology, Institute for Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Caroline M Plugge
- Laboratory of Microbiology, Wageningen University and Research, Wageningen, Netherlands
| | - Clara Prats
- Department of Physics, School of Agricultural Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Castelldefels, Spain
| | - Johan H J Leveau
- Department of Plant Pathology, University of California, Davis, Davis, CA, United States
| | - Weiwen Zhang
- Laboratory of Synthetic Microbiology, Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Ferdi L Hellweger
- Civil and Environmental Engineering Department, Marine and Environmental Sciences Department, Bioengineering Department, Northeastern University, Boston, MA, United States
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14
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Ofaim S, Ofek-Lalzar M, Sela N, Jinag J, Kashi Y, Minz D, Freilich S. Analysis of Microbial Functions in the Rhizosphere Using a Metabolic-Network Based Framework for Metagenomics Interpretation. Front Microbiol 2017; 8:1606. [PMID: 28878756 PMCID: PMC5572346 DOI: 10.3389/fmicb.2017.01606] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Accepted: 08/07/2017] [Indexed: 12/25/2022] Open
Abstract
Advances in metagenomics enable high resolution description of complex bacterial communities in their natural environments. Consequently, conceptual approaches for community level functional analysis are in high need. Here, we introduce a framework for a metagenomics-based analysis of community functions. Environment-specific gene catalogs, derived from metagenomes, are processed into metabolic-network representation. By applying established ecological conventions, network-edges (metabolic functions) are assigned with taxonomic annotations according to the dominance level of specific groups. Once a function-taxonomy link is established, prediction of the impact of dominant taxa on the overall community performances is assessed by simulating removal or addition of edges (taxa associated functions). This approach is demonstrated on metagenomic data describing the microbial communities from the root environment of two crop plants – wheat and cucumber. Predictions for environment-dependent effects revealed differences between treatments (root vs. soil), corresponding to documented observations. Metabolism of specific plant exudates (e.g., organic acids, flavonoids) was linked with distinct taxonomic groups in simulated root, but not soil, environments. These dependencies point to the impact of these metabolite families as determinants of community structure. Simulations of the activity of pairwise combinations of taxonomic groups (order level) predicted the possible production of complementary metabolites. Complementation profiles allow formulating a possible metabolic role for observed co-occurrence patterns. For example, production of tryptophan-associated metabolites through complementary interactions is unique to the tryptophan-deficient cucumber root environment. Our approach enables formulation of testable predictions for species contribution to community activity and exploration of the functional outcome of structural shifts in complex bacterial communities. Understanding community-level metabolism is an essential step toward the manipulation and optimization of microbial function. Here, we introduce an analysis framework addressing three key challenges of such data: producing quantified links between taxonomy and function; contextualizing discrete functions into communal networks; and simulating environmental impact on community performances. New technologies will soon provide a high-coverage description of biotic and a-biotic aspects of complex microbial communities such as these found in gut and soil. This framework was designed to allow the integration of high-throughput metabolomic and metagenomic data toward tackling the intricate associations between community structure, community function, and metabolic inputs.
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Affiliation(s)
- Shany Ofaim
- Newe Ya'ar Research Center, Agricultural Research OrganizationRamat Yishay, Israel.,Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of TechnologyHaifa, Israel
| | - Maya Ofek-Lalzar
- Institute of Soil, Water and Environmental Sciences, Agricultural Research OrganizationBeit Dagan, Israel
| | - Noa Sela
- Department of Plant Pathology and Weed Research, Agricultural Research Organization, The Volcani CenterBeit Dagan, Israel
| | - Jiandong Jinag
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural UniversityNanjing, China
| | - Yechezkel Kashi
- Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of TechnologyHaifa, Israel
| | - Dror Minz
- Institute of Soil, Water and Environmental Sciences, Agricultural Research OrganizationBeit Dagan, Israel
| | - Shiri Freilich
- Newe Ya'ar Research Center, Agricultural Research OrganizationRamat Yishay, Israel
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15
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Heyer R, Schallert K, Zoun R, Becher B, Saake G, Benndorf D. Challenges and perspectives of metaproteomic data analysis. J Biotechnol 2017; 261:24-36. [PMID: 28663049 DOI: 10.1016/j.jbiotec.2017.06.1201] [Citation(s) in RCA: 117] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 06/20/2017] [Accepted: 06/23/2017] [Indexed: 02/07/2023]
Abstract
In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail.
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Affiliation(s)
- Robert Heyer
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Kay Schallert
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Roman Zoun
- Otto von Guericke University, Institute for Technical and Business Information Systems, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Beatrice Becher
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Gunter Saake
- Otto von Guericke University, Institute for Technical and Business Information Systems, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Dirk Benndorf
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany; Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, Sandtorstraße 1, 39106, Magdeburg, Germany.
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16
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Bosi E, Bacci G, Mengoni A, Fondi M. Perspectives and Challenges in Microbial Communities Metabolic Modeling. Front Genet 2017; 8:88. [PMID: 28680442 PMCID: PMC5478693 DOI: 10.3389/fgene.2017.00088] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 06/09/2017] [Indexed: 01/31/2023] Open
Abstract
Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These "super-organisms" play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future.
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Affiliation(s)
| | | | - Alessio Mengoni
- Department of Biology, University of FlorenceFlorence, Italy
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17
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Abstract
Network reconstruction procedures based on meta-"omics" data are an invaluable tool for inferring total and active set of reactions mediated by different members in a microbial community. Within them, network-based methods for automatic analysis of catabolic capacities in metagenomes are currently limited. Here, we describe the complete workflow, scripts, and commands allowing the automatic reconstruction of biodegradation networks using as an input meta-sequences generated by direct DNA sequencing.
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Affiliation(s)
- Rafael Bargiela
- Institute of Catalysis, Consejo Superior de Investigaciones Científicas (CSIC), Marie Curie 2, 28049, Madrid, Spain
| | - Manuel Ferrer
- Institute of Catalysis, Consejo Superior de Investigaciones Científicas (CSIC), Marie Curie 2, 28049, Madrid, Spain.
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18
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Noecker C, McNally CP, Eng A, Borenstein E. High-resolution characterization of the human microbiome. Transl Res 2017; 179:7-23. [PMID: 27513210 PMCID: PMC5164958 DOI: 10.1016/j.trsl.2016.07.012] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022]
Abstract
The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.
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Affiliation(s)
- Cecilia Noecker
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Colin P McNally
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Alexander Eng
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Elhanan Borenstein
- Department of Genome Sciences, University of Washington, Seattle, WA
- Department of Computer Science and Engineering, University of Washington, Seattle, WA
- Santa Fe Institute, Santa Fe, NM
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19
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Tobalina L, Pey J, Rezola A, Planes FJ. Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method. PLoS One 2016; 11:e0154583. [PMID: 27145226 PMCID: PMC4856428 DOI: 10.1371/journal.pone.0154583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 04/15/2016] [Indexed: 01/28/2023] Open
Abstract
MOTIVATION Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date. RESULTS In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Jon Pey
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Alberto Rezola
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
| | - Francisco J. Planes
- CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain
- * E-mail:
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20
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Vogt C, Lueders T, Richnow HH, Krüger M, von Bergen M, Seifert J. Stable Isotope Probing Approaches to Study Anaerobic Hydrocarbon Degradation and Degraders. J Mol Microbiol Biotechnol 2016; 26:195-210. [DOI: 10.1159/000440806] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Stable isotope probing (SIP) techniques have become state-of-the-art in microbial ecology over the last 10 years, allowing for the targeted detection and identification of organisms, metabolic pathways and elemental fluxes active in specific processes within complex microbial communities. For studying anaerobic hydrocarbon-degrading microbial communities, four stable isotope techniques have been used so far: DNA/RNA-SIP, PLFA (phospholipid-derived fatty acids)-SIP, protein-SIP, and single-cell-SIP by nanoSIMS (nanoscale secondary ion mass spectrometry) or confocal Raman microscopy. DNA/RNA-SIP techniques are most frequently applied due to their most meaningful phylogenetic resolution. Especially using <sup>13</sup>C-labeled benzene and toluene as model substrates, many new hydrocarbon degraders have been identified by SIP under various electron acceptor conditions. This has extended the current perspective of the true diversity of anaerobic hydrocarbon degraders relevant in the environment. Syntrophic hydrocarbon degradation was found to be a common mechanism for various electron acceptors. Fundamental concepts and recent advances in SIP are reflected here. A discussion is presented concerning how these techniques generate direct insights into intrinsic hydrocarbon degrader populations in environmental systems and how useful they are for more integrated approaches in the monitoring of contaminated sites and for bioremediation.
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21
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Herbst FA, Lünsmann V, Kjeldal H, Jehmlich N, Tholey A, von Bergen M, Nielsen JL, Hettich RL, Seifert J, Nielsen PH. Enhancing metaproteomics--The value of models and defined environmental microbial systems. Proteomics 2016; 16:783-98. [PMID: 26621789 DOI: 10.1002/pmic.201500305] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 11/03/2015] [Accepted: 11/26/2015] [Indexed: 12/24/2022]
Abstract
Metaproteomics--the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time--has provided new features to study complex microbial communities in order to unravel these "black boxes." New technical challenges arose that were not an issue for classical proteome analytics before that could be tackled by the application of different model systems. Here, we review different current and future model systems for metaproteome analysis. Following a short introduction to microbial communities and metaproteomics, we introduce model systems for clinical and biotechnological research questions including acid mine drainage, anaerobic digesters, and activated sludge. Model systems are useful to evaluate the challenges encountered within (but not limited to) metaproteomics, including species complexity and coverage, biomass availability, or reliable protein extraction. The implementation of model systems can be considered as a step forward to better understand microbial community responses and ecological functions of single member organisms. In the future, improvements are necessary to fully explore complex environmental systems by metaproteomics.
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Affiliation(s)
- Florian-Alexander Herbst
- Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark
| | - Vanessa Lünsmann
- Department of Proteomics, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.,Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Henrik Kjeldal
- Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark
| | - Nico Jehmlich
- Department of Proteomics, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Andreas Tholey
- Systematic Proteome Research and Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Martin von Bergen
- Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark.,Department of Proteomics, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Jeppe Lund Nielsen
- Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark
| | - Robert L Hettich
- Chemical Sciences Division, Oak Ridge National Lab, Oak Ridge, TN, USA
| | - Jana Seifert
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Per Halkjaer Nielsen
- Department of Chemistry and Bioscience, Center for Microbial Communities, Aalborg University, Aalborg, Denmark
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22
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El Amrani A, Dumas AS, Wick LY, Yergeau E, Berthomé R. "Omics" Insights into PAH Degradation toward Improved Green Remediation Biotechnologies. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:11281-91. [PMID: 26352597 DOI: 10.1021/acs.est.5b01740] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
This review summarizes recent knowledge of polycyclic aromatic hydrocarbons (PAHs) biotransformation by microorganisms and plants. Whereas most research has focused on PAH degradation either by plants or microorganisms separately, this review specifically addresses the interactions of plants with their rhizosphere microbial communities. Indeed, plant roots release exudates that contain various nutritional and signaling molecules that influence bacterial and fungal populations. The complex interactions of these populations play a pivotal role in the biodegradation of high-molecular-weight PAHs and other complex molecules. Emerging integrative approaches, such as (meta-) genomics, (meta-) transcriptomics, (meta-) metabolomics, and (meta-) proteomics studies are discussed, emphasizing how "omics" approaches bring new insight into decipher molecular mechanisms of PAH degradation both at the single species and community levels. Such knowledge address new pictures on how organic molecules are cometabolically degraded in a complex ecosystem and should help in setting up novel decontamination strategies based on the rhizosphere interactions between plants and their microbial associates.
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Affiliation(s)
- Abdelhak El Amrani
- University of Rennes 1 , CNRS/UMR 6553/OSUR, Ecosystems - Biodiversity - Evolution, 35042 Rennes Cedex, France
| | - Anne-Sophie Dumas
- University of Rennes 1 , CNRS/UMR 6553/OSUR, Ecosystems - Biodiversity - Evolution, 35042 Rennes Cedex, France
| | - Lukas Y Wick
- UFZ, Department of Environmental Microbiology, Helmholtz Centre for Environmental Research , Permoserstraße 15, D-04318 Leipzig, Germany
| | - Etienne Yergeau
- National Research Council Canada, Energy, Mining and Environment, Montreal, Quebec Canada
| | - Richard Berthomé
- Plant Genomics Research Unit, UMR INRA 1165 - CNRS 8114 - UEVE , 2, Gaston Crémieux St., CP5708, 91057 Evry Cedex, France
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23
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Biggs MB, Medlock GL, Kolling GL, Papin JA. Metabolic network modeling of microbial communities. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:317-34. [PMID: 26109480 DOI: 10.1002/wsbm.1308] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 05/07/2015] [Accepted: 05/13/2015] [Indexed: 12/15/2022]
Abstract
Genome-scale metabolic network reconstructions and constraint-based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome-scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the 'enzyme-soup' approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high-level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering.
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Affiliation(s)
- Matthew B Biggs
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Gregory L Medlock
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Glynis L Kolling
- Department of Medicine, Infectious Diseases, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
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