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Aliev TA, Lavrentev FV, Dyakonov AV, Diveev DA, Shilovskikh VV, Skorb EV. Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods. Biosens Bioelectron 2024; 259:116377. [PMID: 38776798 DOI: 10.1016/j.bios.2024.116377] [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: 02/29/2024] [Revised: 04/24/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approach is based on a system which includes gallium-indium (eGaIn) alloy to provide conductivity and a hydrogel system to preserve bacteria and their metabolic species during the analysis. The work is dedicated to accurate and fast detection of Escherichia coli bacteria in different environments with the supply of machine learning methods. Electrochemical data obtained during the analysis is processed via multilayer perceptron model to identify i.e. predict bacterial concentration in the samples. The performed approach provides the effectiveness of bacteria identification in the range of 102-109 colony forming units per ml with the average accuracy of 97%. The proposed bioelectrochemical system combined with machine learning model is prospective for food analysis, agriculture, biomedicine.
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Affiliation(s)
- Timur A Aliev
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia
| | - Filipp V Lavrentev
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia
| | - Alexandr V Dyakonov
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia
| | - Daniil A Diveev
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia
| | - Vladimir V Shilovskikh
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia; Saint Petersburg State University, Universitetskaya Embankment 7-9, Saint-Petersburg, 199034, Russia
| | - Ekaterina V Skorb
- Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia.
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2
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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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3
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Shtossel O, Koren O, Shai I, Rinott E, Louzoun Y. Gut microbiome-metabolome interactions predict host condition. MICROBIOME 2024; 12:24. [PMID: 38336867 PMCID: PMC10858481 DOI: 10.1186/s40168-023-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 12/10/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment. RESULTS We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]). CONCLUSION These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
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Affiliation(s)
- Oshrit Shtossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Omry Koren
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Iris Shai
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ehud Rinott
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
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4
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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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5
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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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6
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Muñoz-Tamayo R, Davoudkhani M, Fakih I, Robles-Rodriguez CE, Rubino F, Creevey CJ, Forano E. Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies. Animal 2023; 17 Suppl 5:100984. [PMID: 37821326 DOI: 10.1016/j.animal.2023.100984] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 09/01/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.
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Affiliation(s)
- R Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France.
| | - M Davoudkhani
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - I Fakih
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France; Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
| | | | - F Rubino
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - C J Creevey
- Institute of Global Food Security, School of Biological Sciences, Queen's University Belfast, BT9 5DL Northern Ireland, UK
| | - E Forano
- Université Clermont Auvergne, INRAE, UMR 454 MEDIS, Clermont-Ferrand, France
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7
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Timofeeva AM, Galyamova MR, Sedykh SE. Plant Growth-Promoting Bacteria of Soil: Designing of Consortia Beneficial for Crop Production. Microorganisms 2023; 11:2864. [PMID: 38138008 PMCID: PMC10745983 DOI: 10.3390/microorganisms11122864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/23/2023] [Accepted: 11/25/2023] [Indexed: 12/24/2023] Open
Abstract
Plant growth-promoting bacteria are commonly used in agriculture, particularly for seed inoculation. Multispecies consortia are believed to be the most promising form of these bacteria. However, designing and modeling bacterial consortia to achieve desired phenotypic outcomes in plants is challenging. This review aims to address this challenge by exploring key antimicrobial interactions. Special attention is given to approaches for developing soil plant growth-promoting bacteria consortia. Additionally, advanced omics-based methods are analyzed that allow soil microbiomes to be characterized, providing an understanding of the molecular and functional aspects of these microbial communities. A comprehensive discussion explores the utilization of bacterial preparations in biofertilizers for agricultural applications, focusing on the intricate design of synthetic bacterial consortia with these preparations. Overall, the review provides valuable insights and strategies for intentionally designing bacterial consortia to enhance plant growth and development.
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Affiliation(s)
- Anna M. Timofeeva
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
| | - Maria R. Galyamova
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
| | - Sergey E. Sedykh
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia;
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8
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Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion: from black to glass box. Curr Opin Microbiol 2023; 75:102363. [PMID: 37542746 DOI: 10.1016/j.mib.2023.102363] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.
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Affiliation(s)
- Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Adam Kovalovszki
- Department of Environmental and Resource Engineering, Technical University of Denmark, Building 115, Bygningstorvet, 2800 Kgs. Lyngby, Denmark
| | - Behzad Karkaria
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
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9
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Theorell A, Stelling J. Assumptions on decision making and environment can yield multiple steady states in microbial community models. BMC Bioinformatics 2023; 24:262. [PMID: 37349675 DOI: 10.1186/s12859-023-05325-w] [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: 02/28/2022] [Accepted: 05/05/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. RESULTS Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism. CONCLUSIONS Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.
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Affiliation(s)
- Axel Theorell
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
| | - Jörg Stelling
- Department of Biosystems Science and Engineering (D-BSSE) and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058, Basel, Switzerland.
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10
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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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11
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Couvillion SP, Mostoller KE, Williams JE, Pace RM, Stohel IL, Peterson HK, Nicora CD, Nakayasu ES, Webb-Robertson BJM, McGuire MA, McGuire MK, Metz TO. Interrogating the role of the milk microbiome in mastitis in the multi-omics era. Front Microbiol 2023; 14:1105675. [PMID: 36819069 PMCID: PMC9932517 DOI: 10.3389/fmicb.2023.1105675] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.
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Affiliation(s)
- Sneha P. Couvillion
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Sneha P. Couvillion, ✉
| | - Katie E. Mostoller
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Janet E. Williams
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Ryan M. Pace
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, ID, United States
| | - Izabel L. Stohel
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Haley K. Peterson
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Carrie D. Nicora
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Ernesto S. Nakayasu
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Mark A. McGuire
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, ID, United States
| | - Michelle K. McGuire
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, ID, United States
| | - Thomas O. Metz
- Pacific Northwest National Laboratory, Earth and Biological Sciences Directorate, Richland, WA, United States,Thomas O. Metz, ✉
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12
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Hu H, Wang M, Huang Y, Xu Z, Xu P, Nie Y, Tang H. Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use. MLIFE 2022; 1:382-398. [PMID: 38818482 PMCID: PMC10989833 DOI: 10.1002/mlf2.12043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2024]
Abstract
Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.
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Affiliation(s)
- Haiyang Hu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Miaoxiao Wang
- Department of Environmental Systems ScienceETH ZürichZürichSwitzerland
- Department of Environmental MicrobiologyETH ZürichEawagSwitzerland
| | - Yiqun Huang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Zhaoyong Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Yong Nie
- College of EngineeringPeking UniversityBeijingChina
| | - Hongzhi Tang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
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13
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Liang Y, Ma A, Zhuang G. Construction of Environmental Synthetic Microbial Consortia: Based on Engineering and Ecological Principles. Front Microbiol 2022; 13:829717. [PMID: 35283862 PMCID: PMC8905317 DOI: 10.3389/fmicb.2022.829717] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/31/2022] [Indexed: 01/30/2023] Open
Abstract
In synthetic biology, engineering principles are applied to system design. The development of synthetic microbial consortia represents the intersection of synthetic biology and microbiology. Synthetic community systems are constructed by co-cultivating two or more microorganisms under certain environmental conditions, with broad applications in many fields including ecological restoration and ecological theory. Synthetic microbial consortia tend to have high biological processing efficiencies, because the division of labor reduces the metabolic burden of individual members. In this review, we focus on the environmental applications of synthetic microbial consortia. Although there are many strategies for the construction of synthetic microbial consortia, we mainly introduce the most widely used construction principles based on cross-feeding. Additionally, we propose methods for constructing synthetic microbial consortia based on traits and spatial structure from the perspective of ecology to provide a basis for future work.
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Affiliation(s)
- Yu Liang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Anzhou Ma
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Guoqiang Zhuang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing, China
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14
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Zafeiropoulos H, Paragkamian S, Ninidakis S, Pavlopoulos GA, Jensen LJ, Pafilis E. PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types. Microorganisms 2022; 10:microorganisms10020293. [PMID: 35208748 PMCID: PMC8879827 DOI: 10.3390/microorganisms10020293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.
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Affiliation(s)
- Haris Zafeiropoulos
- Department of Biology, University of Crete, Voutes University Campus, P.O. Box 2208, 70013 Heraklion, Crete, Greece; (H.Z.); (S.P.)
- 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;
| | - Savvas Paragkamian
- Department of Biology, University of Crete, Voutes University Campus, P.O. Box 2208, 70013 Heraklion, Crete, Greece; (H.Z.); (S.P.)
- 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;
| | - Stelios Ninidakis
- 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;
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece;
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Evangelos Pafilis
- 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;
- Correspondence: or ; Tel.: +30-2810-337748
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15
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Fournier P, Pellan L, Barroso-Bergadà D, Bohan DA, Candresse T, Delmotte F, Dufour MC, Lauvergeat V, Le Marrec C, Marais A, Martins G, Masneuf-Pomarède I, Rey P, Sherman D, This P, Frioux C, Labarthe S, Vacher C. The functional microbiome of grapevine throughout plant evolutionary history and lifetime. ADV ECOL RES 2022. [DOI: 10.1016/bs.aecr.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Ankrah NYD, Bernstein DB, Biggs M, Carey M, Engevik M, García-Jiménez B, Lakshmanan M, Pacheco AR, Sulheim S, Medlock GL. Enhancing Microbiome Research through Genome-Scale Metabolic Modeling. mSystems 2021; 6:e0059921. [PMID: 34904863 PMCID: PMC8670372 DOI: 10.1128/msystems.00599-21] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.
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Affiliation(s)
- Nana Y. D. Ankrah
- State University of New York at Plattsburgh, Plattsburgh, New York, USA
| | | | | | - Maureen Carey
- University of Virginia, Charlottesville, Virginia, USA
| | - Melinda Engevik
- Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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17
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Ansari AF, Reddy YBS, Raut J, Dixit NM. An efficient and scalable top-down method for predicting structures of microbial communities. NATURE COMPUTATIONAL SCIENCE 2021; 1:619-628. [PMID: 38217133 DOI: 10.1038/s43588-021-00131-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 08/13/2021] [Indexed: 01/15/2024]
Abstract
Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.
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Affiliation(s)
- Aamir Faisal Ansari
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India
| | | | | | - Narendra M Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India.
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bengaluru, India.
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18
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Ibrahim M, Raajaraam L, Raman K. Modelling microbial communities: Harnessing consortia for biotechnological applications. Comput Struct Biotechnol J 2021; 19:3892-3907. [PMID: 34584635 PMCID: PMC8441623 DOI: 10.1016/j.csbj.2021.06.048] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023] Open
Abstract
Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.
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Affiliation(s)
- Maziya Ibrahim
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lavanya Raajaraam
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
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19
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Nazem-Bokaee H, Hom EFY, Warden AC, Mathews S, Gueidan C. Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling. Front Microbiol 2021; 12:667864. [PMID: 34012428 PMCID: PMC8126723 DOI: 10.3389/fmicb.2021.667864] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022] Open
Abstract
Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.
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Affiliation(s)
- Hadi Nazem-Bokaee
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia.,CSIRO Land and Water, Canberra, ACT, Australia.,CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Erik F Y Hom
- Department of Biology and Center for Biodiversity and Conservation Research, The University of Mississippi, University City, MS, United States
| | | | - Sarah Mathews
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Cécile Gueidan
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia
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20
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Reiman D, Layden BT, Dai Y. MiMeNet: Exploring microbiome-metabolome relationships using neural networks. PLoS Comput Biol 2021; 17:e1009021. [PMID: 33999922 PMCID: PMC8158931 DOI: 10.1371/journal.pcbi.1009021] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 05/27/2021] [Accepted: 04/28/2021] [Indexed: 12/31/2022] Open
Abstract
The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics. The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.
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Affiliation(s)
- Derek Reiman
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Brian T. Layden
- Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, University of Illinois at Chicago, Chicago, Illinois, United States of America
- Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, United States of America
| | - Yang Dai
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America
- * E-mail:
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21
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Saraiva JP, Worrich A, Karakoç C, Kallies R, Chatzinotas A, Centler F, Nunes da Rocha U. Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data. Microorganisms 2021; 9:microorganisms9040840. [PMID: 33920040 PMCID: PMC8070991 DOI: 10.3390/microorganisms9040840] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/13/2021] [Accepted: 04/08/2021] [Indexed: 11/24/2022] Open
Abstract
Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.
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Affiliation(s)
- Joao Pedro Saraiva
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
| | - Anja Worrich
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
| | - Canan Karakoç
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
| | - Rene Kallies
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
| | - Antonis Chatzinotas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
- Institute of Biology, Leipzig University, 04103 Leipzig, Germany
| | - Florian Centler
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
| | - Ulisses Nunes da Rocha
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; (J.P.S.); (A.W.); (C.K.); (R.K.); (A.C.); (F.C.)
- Correspondence:
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22
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Abstract
Culture-independent studies have revealed that chronic lung infections in persons with cystic fibrosis (pwCF) are rarely limited to one microbial species. Interactions among bacterial members of these polymicrobial communities in the airways of pwCF have been reported to modulate clinically relevant phenotypes. Furthermore, it is clear that a single polymicrobial community in the context of CF airway infections cannot explain the diversity of clinical outcomes. While large 16S rRNA gene-based studies have allowed us to gain insight into the microbial composition and predicted functional capacities of communities found in the CF lung, here we argue that in silico approaches can help build clinically relevant in vitro models of polymicrobial communities that can in turn be used to experimentally test and validate computationally generated hypotheses. Furthermore, we posit that combining computational and experimental approaches will enhance our understanding of mechanisms that drive microbial community function and identify new therapeutics to target polymicrobial infections.
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23
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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24
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Jean-Pierre F, Henson MA, O'Toole GA. Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists. Front Mol Biosci 2021; 8:634479. [PMID: 33681294 PMCID: PMC7930556 DOI: 10.3389/fmolb.2021.634479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.
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Affiliation(s)
- Fabrice Jean-Pierre
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Michael A Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States
| | - George A O'Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
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25
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Cocco N, Llabrés M, Reyes-Prieto M, Simeoni M. MetNet: A two-level approach to reconstructing and comparing metabolic networks. PLoS One 2021; 16:e0246962. [PMID: 33577575 PMCID: PMC7880445 DOI: 10.1371/journal.pone.0246962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/28/2021] [Indexed: 11/28/2022] Open
Abstract
Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.
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Affiliation(s)
- Nicoletta Cocco
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venice, Italy
| | - Mercè Llabrés
- Mathematics and Computer Science Department, University of the Balearic Islands, Palma, Spain
| | - Mariana Reyes-Prieto
- Evolutionary Systems Biology of Symbionts, Institute for Integrative Systems Biology (I 2 SysBio), Universitat de Valencia, Paterna, Valencia, Spain
- Sequencing and Bioinformatics Service, Foundation for the Promotion of Sanitary and Biomedical Research of the Valencia Region (FISABIO), València, Spain
| | - Marta Simeoni
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venice, Italy
- European Centre for Living Technology (ECLT), Venice, Italy
- * E-mail:
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26
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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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27
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Karkaria BD, Treloar NJ, Barnes CP, Fedorec AJH. From Microbial Communities to Distributed Computing Systems. Front Bioeng Biotechnol 2020; 8:834. [PMID: 32793576 PMCID: PMC7387671 DOI: 10.3389/fbioe.2020.00834] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/29/2020] [Indexed: 12/15/2022] Open
Abstract
A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.
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Affiliation(s)
- Behzad D. Karkaria
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Neythen J. Treloar
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Chris P. Barnes
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
| | - Alex J. H. Fedorec
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
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28
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Popp D, Centler F. μBialSim: Constraint-Based Dynamic Simulation of Complex Microbiomes. Front Bioeng Biotechnol 2020; 8:574. [PMID: 32656192 PMCID: PMC7325871 DOI: 10.3389/fbioe.2020.00574] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/12/2020] [Indexed: 02/05/2023] Open
Abstract
Microbial communities are pervasive in the natural environment, associated with many hosts, and of increasing importance in biotechnological applications. The complexity of these microbial systems makes the underlying mechanisms driving their dynamics difficult to identify. While experimental meta-OMICS techniques are routinely applied to record the inventory and activity of microbiomes over time, it remains difficult to obtain quantitative predictions based on such data. Mechanistic, quantitative mathematical modeling approaches hold the promise to both provide predictive power and shed light on cause-effect relationships driving these dynamic systems. We introduce μbialSim (pronounced "microbial sim"), a dynamic Flux-Balance-Analysis-based (dFBA) numerical simulator which is able to predict the time course in terms of composition and activity of microbiomes containing 100s of species in batch or chemostat mode. Activity of individual species is simulated by using separate FBA models which have access to a common pool of compounds, allowing for metabolite exchange. A novel augmented forward Euler method ensures numerical accuracy by temporarily reducing the time step size when compound concentrations decrease rapidly due to high compound affinities and/or the presence of many consuming species. We present three exemplary applications of μbialSim: a batch culture of a hydrogenotrophic archaeon, a syntrophic methanogenic biculture, and a 773-species human gut microbiome which exhibits a complex and dynamic pattern of metabolite exchange. Focusing on metabolite exchange as the main interaction type, μbialSim allows for the mechanistic simulation of microbiomes at their natural complexity. Simulated trajectories can be used to contextualize experimental meta-OMICS data and to derive hypotheses on cause-effect relationships driving community dynamics based on scenario simulations. μbialSim is implemented in Matlab and relies on the COBRA Toolbox or CellNetAnalyzer for FBA calculations. The source code is available under the GNU General Public License v3.0 at https://git.ufz.de/UMBSysBio/microbialsim.
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Affiliation(s)
| | - Florian Centler
- UFZ – Helmholtz Centre for Environmental Research, Department of Environmental Microbiology, Leipzig, Germany
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29
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Jaiswal S, Shukla P. Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology. Front Microbiol 2020; 11:808. [PMID: 32508759 PMCID: PMC7249858 DOI: 10.3389/fmicb.2020.00808] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 04/06/2020] [Indexed: 12/13/2022] Open
Abstract
Continuous contamination of the environment with xenobiotics and related recalcitrant compounds has emerged as a serious pollution threat. Bioremediation is the key to eliminating persistent contaminants from the environment. Traditional bioremediation processes show limitations, therefore it is necessary to discover new bioremediation technologies for better results. In this review we provide an outlook of alternative strategies for bioremediation via synthetic biology, including exploring the prerequisites for analysis of research data for developing synthetic biological models of microbial bioremediation. Moreover, cell coordination in synthetic microbial community, cell signaling, and quorum sensing as engineered for enhanced bioremediation strategies are described, along with promising gene editing tools for obtaining the host with target gene sequences responsible for the degradation of recalcitrant compounds. The synthetic genetic circuit and two-component regulatory system (TCRS)-based microbial biosensors for detection and bioremediation are also briefly explained. These developments are expected to increase the efficiency of bioremediation strategies for best results.
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30
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Regueira A, Bevilacqua R, Lema JM, Carballa M, Mauricio-Iglesias M. A metabolic model for targeted volatile fatty acids production by cofermentation of carbohydrates and proteins. BIORESOURCE TECHNOLOGY 2020; 298:122535. [PMID: 31865254 DOI: 10.1016/j.biortech.2019.122535] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/28/2019] [Accepted: 11/29/2019] [Indexed: 06/10/2023]
Abstract
Anaerobic mixed-culture fermentations are interesting processes to valorise organic wastes by converting them to volatile fatty acids. One of the main issues is that certain operational conditions (e.g. pH or different substrate concentrations) can vary significantly the product spectrum. So far, there are no tools that take into the account the characteristic features of cofermentation processes, which hinders the possibility of designing processes that use real wastes as substrates. In this work a mathematical model was developed for the production of volatile fatty acids from organic wastes with a high concentration of carbohydrates and proteins. The model reproduces satisfactorily experimental results and is also able of giving mechanistic insight into the interactions between carbohydrates and proteins that explain the observed changes in the product spectrum. We envision this model as the core of an early-stage design tool for anaerobic cofermentation processes, as shown in this work with different examples.
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Affiliation(s)
- Alberte Regueira
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
| | - Riccardo Bevilacqua
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Juan Manuel Lema
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Marta Carballa
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Miguel Mauricio-Iglesias
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
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31
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Chowdhury S, Fong SS. Computational Modeling of the Human Microbiome. Microorganisms 2020; 8:microorganisms8020197. [PMID: 32023941 PMCID: PMC7074762 DOI: 10.3390/microorganisms8020197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/23/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284 USA;
| | - Stephen S. Fong
- Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, VA 23284, USA
- Correspondence:
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32
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Ansari AF, Acharya NIS, Kumaran S, Ravindra K, Reddy YBS, Dixit NM, Raut J. 110th Anniversary: High-Order Interactions Can Eclipse Pairwise Interactions in Shaping the Structure of Microbial Communities. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Aamir Faisal Ansari
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
| | | | | | | | | | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Janhavi Raut
- Unilever R&D India Pvt, Ltd., Bangalore 560066, India
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33
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Metabolic modeling for predicting VFA production from protein‐rich substrates by mixed‐culture fermentation. Biotechnol Bioeng 2019; 117:73-84. [DOI: 10.1002/bit.27177] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 08/27/2019] [Accepted: 09/17/2019] [Indexed: 11/07/2022]
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34
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Ul-Hasan S, Rodríguez-Román E, Reitzel AM, Adams RMM, Herzig V, Nobile CJ, Saviola AJ, Trim SA, Stiers EE, Moschos SA, Keiser CN, Petras D, Moran Y, Colston TJ. The emerging field of venom-microbiomics for exploring venom as a microenvironment, and the corresponding Initiative for Venom Associated Microbes and Parasites (iVAMP). Toxicon X 2019; 4:100016. [PMID: 32550573 PMCID: PMC7286055 DOI: 10.1016/j.toxcx.2019.100016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Venom is a known source of novel antimicrobial natural products. The substantial, increasing number of these discoveries have unintentionally culminated in the misconception that venom and venom-producing glands are largely sterile environments. Culture-dependent and -independent studies on the microbial communities in venom microenvironments reveal the presence of archaea, algae, bacteria, fungi, protozoa, and viruses. Venom-centric microbiome studies are relatively sparse to date with the adaptive advantages that venom-associated microbes might offer to their hosts, or that hosts might provide to venom-associated microbes, remaining largely unknown. We highlight the potential for the discovery of venom microbiomes within the adaptive landscape of venom systems. The considerable number of convergently evolved venomous animals, juxtaposed with the comparatively few known studies to identify microbial communities in venom, provides new possibilities for both biodiversity and therapeutic discoveries. We present an evidence-based argument for integrating microbiology as part of venomics (i.e., venom-microbiomics) and introduce iVAMP, the Initiative for Venom Associated Microbes and Parasites (https://ivamp-consortium.github.io/), as a growing collaborative consortium. We express commitment to the diversity, inclusion and scientific collaboration among researchers interested in this emerging subdiscipline through expansion of the iVAMP consortium. Venom-microbiome studies as an integrative field of venomics and microbiology. Argument for multi-omics-based discovery through a microenvironment framework. Introduction of a venom-microbiome research consortium (iVAMP).
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Affiliation(s)
- Sabah Ul-Hasan
- Quantitative and Systems Biology Graduate Program, University of California Merced, Merced, CA, 95343, USA.,Department of Molecular and Cell Biology, School of Natural Sciences, University of California Merced, Merced, CA, 95343, USA
| | - Eduardo Rodríguez-Román
- Center for Microbiology and Cell Biology, Venezuelan Institute for Scientific Research. Caracas, 1020A, Venezuela
| | - Adam M Reitzel
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Rachelle M M Adams
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, 43212, USA
| | - Volker Herzig
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
| | - Clarissa J Nobile
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California Merced, Merced, CA, 95343, USA
| | - Anthony J Saviola
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Steven A Trim
- Venomtech Ltd, Discovery Park, Sandwich, Kent, CT13 9ND, UK
| | - Erin E Stiers
- Department of Biological Science, Clemson University, Clemson, SC, 29634, USA
| | - Sterghios A Moschos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, Tyne and Wear, NE1 8ST, UK
| | - Carl N Keiser
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, USA.,Scripps Institution of Oceanography, University of California, San Diego, USA
| | - Yehu Moran
- Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Timothy J Colston
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
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35
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Mallick H, Franzosa EA, Mclver LJ, Banerjee S, Sirota-Madi A, Kostic AD, Clish CB, Vlamakis H, Xavier RJ, Huttenhower C. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat Commun 2019; 10:3136. [PMID: 31316056 PMCID: PMC6637180 DOI: 10.1038/s41467-019-10927-1] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 06/06/2019] [Indexed: 02/07/2023] Open
Abstract
Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.
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Affiliation(s)
- Himel Mallick
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Eric A Franzosa
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Lauren J Mclver
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Soumya Banerjee
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alexandra Sirota-Madi
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Aleksandar D Kostic
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
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36
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Müller J, Spriewald S, Stecher B, Stadler E, Fuchs TM. Evolutionary Stability of Salmonella Competition with the Gut Microbiota: How the Environment Fosters Heterogeneity in Exploitative and Interference Competition. J Mol Biol 2019; 431:4732-4748. [PMID: 31260689 DOI: 10.1016/j.jmb.2019.06.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/19/2019] [Accepted: 06/19/2019] [Indexed: 11/27/2022]
Abstract
Following ingestion, gastrointestinal pathogens compete against the gastrointestinal microbiota and overcome host immune defenses in order to cause infections. Besides employing direct killing mechanisms, the commensal microbiota occupies metabolic niches to outcompete invading pathogens. Salmonella enterica serovar Typhimurium (S. Typhimurium) uses several strategies to successfully colonize the gut and establish infection, of which an increasing number is based on phenotypic heterogeneity within the S. Typhimurium population. The utilization of myo-inositol (MI) and the production of colicin confer a selective advantage over the microbiota in terms of exploitative and interference competition, respectively. In this review, we summarize the genetic basis underlying bistability of MI catabolism and colicin production. As demonstrated by single-cell analyses, a stochastic switch in the expression of the genes responsible for colicin production and MI degradation constitutes the heterogeneity of the two phenotypes. Both genetic systems are tightly regulated to avoid their expression under non-appropriate conditions and possible detrimental effects on bacterial fitness. Moreover, evolutionary mechanisms underlying formation and stability of these phenotypes in S. Typhimurium are discussed. We propose that both MI catabolism and colicin production create a bet-hedging strategy, which provides an adaptive benefit for S. Typhimurium in the fluctuating environment of the mammalian gut.
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Affiliation(s)
- Johannes Müller
- Technische Universität München, Centre for Mathematical Sciences, Boltzmannstr. 3, 85747 Garching, Germany; Institute for Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany
| | - Stefanie Spriewald
- Max von Pettenkofer-Institute, LMU Munich, Pettenkoferstr. 9a, 80336 Munich, Germany
| | - Bärbel Stecher
- Max von Pettenkofer-Institute, LMU Munich, Pettenkoferstr. 9a, 80336 Munich, Germany
| | - Eva Stadler
- Technische Universität München, Centre for Mathematical Sciences, Boltzmannstr. 3, 85747 Garching, Germany
| | - Thilo M Fuchs
- Friedrich-Loeffler-Institut, Institut für Molekulare Pathogenese, Naumburger Str. 96a, 07743 Jena, Germany.
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37
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Melkonian C, Gottstein W, Blasche S, Kim Y, Abel-Kistrup M, Swiegers H, Saerens S, Edwards N, Patil KR, Teusink B, Molenaar D. Finding Functional Differences Between Species in a Microbial Community: Case Studies in Wine Fermentation and Kefir Culture. Front Microbiol 2019; 10:1347. [PMID: 31293529 PMCID: PMC6603220 DOI: 10.3389/fmicb.2019.01347] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 05/31/2019] [Indexed: 12/20/2022] Open
Abstract
Microbial life usually takes place in a community where individuals interact, by competition for nutrients, cross-feeding, inhibition by end-products, but also by their spatial distribution. Lactic acid bacteria are prominent members of microbial communities responsible for food fermentations. Their niche in a community depends on their own properties as well as those of the other species. Here, we apply a computational approach, which uses only genomic and metagenomic information and functional annotation of genes, to find properties that distinguish a species from others in the community, as well as to follow individual species in a community. We analyzed isolated and sequenced strains from a kefir community, and metagenomes from wine fermentations. We demonstrate how the distinguishing properties of an organism lead to experimentally testable hypotheses concerning the niche and the interactions with other species. We observe, for example, that L. kefiranofaciens, a dominant organism in kefir, stands out among the Lactobacilli because it potentially has more amino acid auxotrophies. Using metagenomic analysis of industrial wine fermentations we investigate the role of an inoculated L. plantarum in malolactic fermentation. We observed that L. plantarum thrives better on white than on red wine fermentations and has the largest number of phosphotransferase system among the bacteria observed in the wine communities. Also, L. plantarum together with Pantoea, Erwinia, Asaia, Gluconobacter, and Komagataeibacter genera had the highest number of genes involved in biosynthesis of amino acids.
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Affiliation(s)
- Chrats Melkonian
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands
| | - Willi Gottstein
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands
| | - Sonja Blasche
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Yongkyu Kim
- European Molecular Biology Laboratory, Heidelberg, Germany
| | | | | | | | | | - Kiran R. Patil
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bas Teusink
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands
| | - Douwe Molenaar
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands
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38
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Bernstein DB, Dewhirst FE, Segrè D. Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome. eLife 2019; 8:39733. [PMID: 31194675 PMCID: PMC6609349 DOI: 10.7554/elife.39733] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 06/13/2019] [Indexed: 12/18/2022] Open
Abstract
The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering, Boston University, Boston, United States.,Biological Design Center, Boston University, Boston, United States
| | - Floyd E Dewhirst
- The Forsyth Institute, Cambridge, United States.,Harvard School of Dental Medicine, Boston, United States
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, United States.,Biological Design Center, Boston University, Boston, United States.,Bioinformatics Program, Boston University, Boston, United States.,Department of Biology, Boston University, Boston, United States.,Department of Physics, Boston University, Boston, United States
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39
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Thommes M, Wang T, Zhao Q, Paschalidis IC, Segrè D. Designing Metabolic Division of Labor in Microbial Communities. mSystems 2019; 4:e00263-18. [PMID: 30984871 PMCID: PMC6456671 DOI: 10.1128/msystems.00263-18] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 03/15/2019] [Indexed: 12/19/2022] Open
Abstract
Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A "gedanken" (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using in silico genome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities of Escherichia coli strains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor. IMPORTANCE Understanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia.
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Affiliation(s)
- Meghan Thommes
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Taiyao Wang
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - Qi Zhao
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - Ioannis C. Paschalidis
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
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Koch S, Kohrs F, Lahmann P, Bissinger T, Wendschuh S, Benndorf D, Reichl U, Klamt S. RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion. PLoS Comput Biol 2019; 15:e1006759. [PMID: 30707687 PMCID: PMC6373973 DOI: 10.1371/journal.pcbi.1006759] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 02/13/2019] [Accepted: 01/05/2019] [Indexed: 11/18/2022] Open
Abstract
Constraint-based modeling (CBM) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology, biomedicine, and various biotechnological processes. While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models, CBM of communities with balanced growth is more complicated, not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models. Moreover, the solution space of these community models often contains biologically unrealistic solutions, which, even with model linearization and under application of certain objective functions, cannot easily be excluded. Here we present RedCom, a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks. By discarding (single-species) net conversions that violate a minimality criterion in the exchange fluxes, it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts (instead of biomass) to fulfill the requirements of other species. We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants. Compared to full (bilinear and linearized) community models, we found that the reduced community models obtained with RedCom are not only much smaller but allow, also in the largest model with nine species, extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates. Furthermore, the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures. For an enrichment culture for growth on ethanol, we also used metaproteomic data to further constrain the solution space of the community models. Both model and proteomic data indicated a dominance of acetoclastic methanogens (Methanosarcinales) and Desulfovibrionales being the least abundant group in this microbial community. Microbial communities are involved in many fundamental processes in nature, health and biotechnology. The elucidation of interdependencies between the involved players of microbial communities and how the interactions shape the composition, behavior and characteristic features of the consortium has become an important branch of microbiology research. Many communities are based on the exchange of metabolites between the species and constraint-based metabolic modeling has become an important approach for a formal description and quantitative analysis of these metabolic dependencies. However, the complexity of the models rises quickly with a growing number of organisms and the space of predicted feasible behaviors often includes unrealistic solutions. Here we present RedCom, a new approach to build reduced stoichiometric models of balanced microbial communities based on net conversions of the single-species models. We demonstrate the applicability of our RedCom approach by modeling communities of up to nine organisms involved in degradation steps of anaerobic digestion in biogas plants. As one of the first studies in this field, we compare simulation results from the community models with experimental data of laboratory-scale biogas reactors for growth on ethanol and glucose-cellulose media. The results also demonstrate a higher predictive power of the RedCom vs. the full models.
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Affiliation(s)
- Sabine Koch
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Fabian Kohrs
- Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany
| | - Patrick Lahmann
- Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany
| | - Thomas Bissinger
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stefan Wendschuh
- Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany
| | - Dirk Benndorf
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany
| | - Udo Reichl
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- * E-mail:
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Abstract
After nearly two decades of subsidized and energy crop-oriented development, agricultural biogas production in Germany is standing at a crossroads. Fundamental challenges need to be met. In this article we sketch a vision of a future agricultural biogas plant that is an integral part of the circular bioeconomy and works mainly on the base of residues. It is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. It is modular in design and its operation is knowledge-based, information-driven and largely automated. It will be competitive with fossil energies and other renewable energies, profitable for farmers and plant operators and favorable for the national economy. In this paper we discuss the required contribution of research to achieve these aims.
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Cao X, Hamilton JJ, Venturelli OS. Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities. Biochemistry 2019; 58:94-107. [PMID: 30457843 PMCID: PMC6733022 DOI: 10.1021/acs.biochem.8b01006] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Microbiomes impact nearly every environment on Earth by modulating the molecular composition of the environment. Temporally changing environmental stimuli and spatial organization are major variables shaping the structure and function of microbiomes. The web of interactions among members of these communities and between the organisms and the environment dictates microbiome functions. Microbial interactions are major drivers of microbiomes and are modulated by spatiotemporal parameters. A mechanistic and quantitative understanding of ecological, molecular, and environmental forces shaping microbiomes could inform strategies to control microbiome dynamics and functions. Major challenges for harnessing the potential of microbiomes for diverse applications include the development of predictive modeling frameworks and tools for precise manipulation of microbiome behaviors.
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Affiliation(s)
| | | | - Ophelia S. Venturelli
- Department of Biochemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
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Abstract
Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.
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Affiliation(s)
- Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.
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Haruta S, Yamamoto K. Model Microbial Consortia as Tools for Understanding Complex Microbial Communities. Curr Genomics 2018; 19:723-733. [PMID: 30532651 PMCID: PMC6225455 DOI: 10.2174/1389202919666180911131206] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/19/2018] [Accepted: 09/03/2018] [Indexed: 02/08/2023] Open
Abstract
A major biological challenge in the postgenomic era has been untangling the composition and functions of microbes that inhabit complex communities or microbiomes. Multi-omics and modern bioinformatics have provided the tools to assay molecules across different cellular and community scales; however, mechanistic knowledge over microbial interactions often remains elusive. This is due to the immense diversity and the essentially undiminished volume of not-yet-cultured microbes. Simplified model communities hold some promise in enabling researchers to manage complexity so that they can mechanistically understand the emergent properties of microbial community interactions. In this review, we surveyed several approaches that have effectively used tractable model consortia to elucidate the complex behavior of microbial communities. We go further to provide some perspectives on the limitations and new opportunities with these approaches and highlight where these efforts are likely to lead as advances are made in molecular ecology and systems biology.
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Affiliation(s)
- Shin Haruta
- Address correspondence to this author at the Department of Biological Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan; Tel: +81-42-677-2580; Fax: +81-42-677-2559; E-mail:
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Henson MA, Phalak P. Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota. PLoS Comput Biol 2018; 14:e1006558. [PMID: 30376571 PMCID: PMC6226200 DOI: 10.1371/journal.pcbi.1006558] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 11/09/2018] [Accepted: 10/09/2018] [Indexed: 12/12/2022] Open
Abstract
The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.
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Affiliation(s)
- Michael A. Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, USA
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, USA
- * E-mail:
| | - Poonam Phalak
- Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, USA
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, USA
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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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Abstract
Current understanding of many animal-microbial symbioses involving unculturable bacterial symbionts with much-reduced genomes derives almost entirely from nonquantitative inferences from genome data. To overcome this limitation, we reconstructed multipartner metabolic models that quantify both the metabolic fluxes within and between three xylem-feeding insects and their bacterial symbionts. This revealed near-complete metabolic segregation between cooccurring bacterial symbionts, despite extensive metabolite exchange between each symbiont and the host, suggestive of strict host controls over the metabolism of its symbionts. We extended the model analysis to investigate metabolic costs. The positive relationship between symbiont genome size and the metabolic cost incurred by the host points to fitness benefits to the host of bearing symbionts with small genomes. The multicompartment metabolic models developed here can be applied to other symbioses that are not readily tractable to experimental approaches. Various intracellular bacterial symbionts that provide their host with essential nutrients have much-reduced genomes, attributed largely to genomic decay and relaxed selection. To obtain quantitative estimates of the metabolic function of these bacteria, we reconstructed genome- and transcriptome-informed metabolic models of three xylem-feeding insects that bear two bacterial symbionts with complementary metabolic functions: a primary symbiont, Sulcia, that has codiversified with the insects, and a coprimary symbiont of distinct taxonomic origin and with different degrees of genome reduction in each insect species (Hodgkinia in a cicada, Baumannia in a sharpshooter, and Sodalis in a spittlebug). Our simulations reveal extensive bidirectional flux of multiple metabolites between each symbiont and the host, but near-complete metabolic segregation (i.e., near absence of metabolic cross-feeding) between the two symbionts, a likely mode of host control over symbiont metabolism. Genome reduction of the symbionts is associated with an increased number of host metabolic inputs to the symbiont and also reduced metabolic cost to the host. In particular, Sulcia and Hodgkinia with genomes of ≤0.3 Mb are calculated to recycle ∼30 to 80% of host-derived nitrogen to essential amino acids returned to the host, while Baumannia and Sodalis with genomes of ≥0.6 Mb recycle 10 to 15% of host nitrogen. We hypothesize that genome reduction of symbionts may be driven by selection for increased host control and reduced host costs, as well as by the stochastic process of genomic decay and relaxed selection.
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Huws SA, Creevey CJ, Oyama LB, Mizrahi I, Denman SE, Popova M, Muñoz-Tamayo R, Forano E, Waters SM, Hess M, Tapio I, Smidt H, Krizsan SJ, Yáñez-Ruiz DR, Belanche A, Guan L, Gruninger RJ, McAllister TA, Newbold CJ, Roehe R, Dewhurst RJ, Snelling TJ, Watson M, Suen G, Hart EH, Kingston-Smith AH, Scollan ND, do Prado RM, Pilau EJ, Mantovani HC, Attwood GT, Edwards JE, McEwan NR, Morrisson S, Mayorga OL, Elliott C, Morgavi DP. Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future. Front Microbiol 2018; 9:2161. [PMID: 30319557 PMCID: PMC6167468 DOI: 10.3389/fmicb.2018.02161] [Citation(s) in RCA: 181] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/23/2018] [Indexed: 12/24/2022] Open
Abstract
The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.
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Affiliation(s)
- Sharon A Huws
- Institute for Global Food Security, Queen's University of Belfast, Belfast, United Kingdom
| | - Christopher J Creevey
- Institute for Global Food Security, Queen's University of Belfast, Belfast, United Kingdom
| | - Linda B Oyama
- Institute for Global Food Security, Queen's University of Belfast, Belfast, United Kingdom
| | - Itzhak Mizrahi
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Stuart E Denman
- Commonwealth Scientific and Industrial Research Organisation Agriculture and Food, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Milka Popova
- Institute National de la Recherche Agronomique, UMR1213 Herbivores, Clermont Université, VetAgro Sup, UMR Herbivores, Clermont-Ferrand, France
| | - Rafael Muñoz-Tamayo
- UMR Modélisation Systémique Appliquée aux Ruminants, INRA, AgroParisTech, Université Paris-Saclay, Paris, France
| | - Evelyne Forano
- UMR 454 MEDIS, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Sinead M Waters
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Grange, Ireland
| | - Matthias Hess
- College of Agricultural and Environmental Sciences, University of California, Davis, Davis, CA, United States
| | - Ilma Tapio
- Natural Resources Institute Finland, Jokioinen, Finland
| | - Hauke Smidt
- Department of Agrotechnology and Food Sciences, Wageningen, Netherlands
| | - Sophie J Krizsan
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - David R Yáñez-Ruiz
- Estacion Experimental del Zaidin, Consejo Superior de Investigaciones Cientificas, Granada, Spain
| | - Alejandro Belanche
- Estacion Experimental del Zaidin, Consejo Superior de Investigaciones Cientificas, Granada, Spain
| | - Leluo Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Robert J Gruninger
- Lethbridge Research Centre, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
| | - Tim A McAllister
- Lethbridge Research Centre, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
| | | | - Rainer Roehe
- Scotland's Rural College, Edinburgh, United Kingdom
| | | | - Tim J Snelling
- The Rowett Institute, University of Aberdeen, Aberdeen, United Kingdom
| | - Mick Watson
- The Roslin Institute and the Royal (Dick) School of Veterinary Studies (R(D)SVS), University of Edinburgh, Edinburgh, United Kingdom
| | - Garret Suen
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, United States
| | - Elizabeth H Hart
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Alison H Kingston-Smith
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Nigel D Scollan
- Institute for Global Food Security, Queen's University of Belfast, Belfast, United Kingdom
| | - Rodolpho M do Prado
- Laboratório de Biomoléculas e Espectrometria de Massas-Labiomass, Departamento de Química, Universidade Estadual de Maringá, Maringá, Brazil
| | - Eduardo J Pilau
- Laboratório de Biomoléculas e Espectrometria de Massas-Labiomass, Departamento de Química, Universidade Estadual de Maringá, Maringá, Brazil
| | | | - Graeme T Attwood
- AgResearch Limited, Grasslands Research Centre, Palmerston North, New Zealand
| | - Joan E Edwards
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, Netherlands
| | - Neil R McEwan
- School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, United Kingdom
| | - Steven Morrisson
- Sustainable Livestock, Agri-Food and Bio-Sciences Institute, Hillsborough, United Kingdom
| | - Olga L Mayorga
- Colombian Agricultural Research Corporation, Mosquera, Colombia
| | - Christopher Elliott
- Institute for Global Food Security, Queen's University of Belfast, Belfast, United Kingdom
| | - Diego P Morgavi
- Institute National de la Recherche Agronomique, UMR1213 Herbivores, Clermont Université, VetAgro Sup, UMR Herbivores, Clermont-Ferrand, France
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McClure RS, Overall CC, Hill EA, Song HS, Charania M, Bernstein HC, McDermott JE, Beliaev AS. Species-specific transcriptomic network inference of interspecies interactions. THE ISME JOURNAL 2018; 12:2011-2023. [PMID: 29795448 PMCID: PMC6052077 DOI: 10.1038/s41396-018-0145-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 02/22/2018] [Accepted: 03/26/2018] [Indexed: 12/25/2022]
Abstract
The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.
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Affiliation(s)
- Ryan S McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Christopher C Overall
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Eric A Hill
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Moiz Charania
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Hans C Bernstein
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
| | - Jason E McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, OR, USA
| | - Alexander S Beliaev
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
- Institute for Future Environments, Queensland University of Technology, Brisbane, Australia.
- Center for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, Australia.
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Bauer E, Thiele I. From metagenomic data to personalized in silico microbiotas: predicting dietary supplements for Crohn's disease. NPJ Syst Biol Appl 2018; 4:27. [PMID: 30083388 PMCID: PMC6068170 DOI: 10.1038/s41540-018-0063-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 05/17/2018] [Accepted: 05/30/2018] [Indexed: 12/13/2022] Open
Abstract
Crohn's disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient's SCFA levels. The underlying modeling approach could aid clinical practice to find dietary treatment and guide recovery by rationally proposing food aliments.
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Affiliation(s)
- Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg, L-4362 Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg, L-4362 Luxembourg
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