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Liu Y, Xue B, Liu H, Wang S, Su H. Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform. Biotechnol Adv 2024; 72:108348. [PMID: 38531490 DOI: 10.1016/j.biotechadv.2024.108348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.
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Affiliation(s)
- Yu Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Boyuan Xue
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Hao Liu
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Shaojie Wang
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
| | - Haijia Su
- Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
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2
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Scott WT, Benito-Vaquerizo S, Zimmermann J, Bajić D, Heinken A, Suarez-Diez M, Schaap PJ. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 2023; 19:e1011363. [PMID: 37578975 PMCID: PMC10449394 DOI: 10.1371/journal.pcbi.1011363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
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Affiliation(s)
- William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
| | - Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Kiel, Germany
| | - Djordje Bajić
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Almut Heinken
- Inserm U1256 Laboratoire nGERE, Université de Lorraine, Nancy, France
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
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3
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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4
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Abstract
Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.
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Affiliation(s)
| | - Sean M. Gibbons
- Institute for Systems Biology, Seattle, Washington, USA
- Departments of Bioengineering and Genome Sciences, University of Washington, Seattle, Washington, USA
- eScience Institute, University of Washington, Seattle, Washington, USA
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5
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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Shi A, Broach JR. Microbial adaptive evolution. J Ind Microbiol Biotechnol 2021; 49:6407523. [PMID: 34673973 PMCID: PMC9118994 DOI: 10.1093/jimb/kuab076] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/27/2021] [Indexed: 01/08/2023]
Abstract
Bacterial species can adapt to significant changes in their environment by mutation followed by selection, a phenomenon known as “adaptive evolution.” With the development of bioinformatics and genetic engineering, research on adaptive evolution has progressed rapidly, as have applications of the process. In this review, we summarize various mechanisms of bacterial adaptive evolution, the technologies used for studying it, and successful applications of the method in research and industry. We particularly highlight the contributions of Dr. L. O. Ingram. Microbial adaptive evolution has significant impact on our society not only from its industrial applications, but also in the evolution, emergence, and control of various pathogens.
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Affiliation(s)
- Aiqin Shi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
| | - James R Broach
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
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7
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Qian Y, Lan F, Venturelli OS. Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models. Curr Opin Microbiol 2021; 62:84-92. [PMID: 34098512 PMCID: PMC8286325 DOI: 10.1016/j.mib.2021.05.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/15/2022]
Abstract
Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.
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Affiliation(s)
- Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Freeman Lan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States.
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8
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Wang J, Carper DL, Burdick LH, Shrestha HK, Appidi MR, Abraham PE, Timm CM, Hettich RL, Pelletier DA, Doktycz MJ. Formation, characterization and modeling of emergent synthetic microbial communities. Comput Struct Biotechnol J 2021; 19:1917-1927. [PMID: 33995895 PMCID: PMC8079826 DOI: 10.1016/j.csbj.2021.03.034] [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: 12/14/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/04/2023] Open
Abstract
Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form stable microbial communities. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains that depended on the medium. To unravel the underlying metabolic interactions, flux balance analysis was used to model microbial growth and identify potential metabolic exchanges involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. A fast growth rate is identified as one factor that can provide an advantage for maintaining presence in the community. Final community selection can also depend on selective antagonistic relationships and metabolic exchanges. Revealing the mechanisms of interaction among plant-associated microorganisms provides insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic communities with desired biological functions.
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Affiliation(s)
- Jia Wang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dana L. Carper
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Leah H. Burdick
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Him K. Shrestha
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Manasa R. Appidi
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Collin M. Timm
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert L. Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dale A. Pelletier
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
| | - Mitchel J. Doktycz
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
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9
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Nev OA, Lindsay RJ, Jepson A, Butt L, Beardmore RE, Gudelj I. Predicting microbial growth dynamics in response to nutrient availability. PLoS Comput Biol 2021; 17:e1008817. [PMID: 33735173 PMCID: PMC8009381 DOI: 10.1371/journal.pcbi.1008817] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 03/30/2021] [Accepted: 02/17/2021] [Indexed: 01/04/2023] Open
Abstract
Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker's yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.
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Affiliation(s)
- Olga A. Nev
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Richard J. Lindsay
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Alys Jepson
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Lisa Butt
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Robert E. Beardmore
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Ivana Gudelj
- Biosciences and Living Systems Institute, University of Exeter, Exeter, United Kingdom
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10
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Borer B, Or D. Spatiotemporal metabolic modeling of bacterial life in complex habitats. Curr Opin Biotechnol 2021; 67:65-71. [PMID: 33493977 DOI: 10.1016/j.copbio.2021.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 01/04/2023]
Abstract
The combination of genome-scale metabolic networks with spatially explicit representation of microbial habitats (spatiotemporal metabolic network modeling) paves the way to predict complex metabolic landscapes to a hitherto unparalleled detail, thus providing new insights into trophic interactions occurring at different scales. Placing detailed bacterial metabolism in realistic physical environment highlights the roles of physical barriers and diffusional bottlenecks on bacterial community interactions, structure and stability. We review recent advances in spatiotemporal metabolic network modeling using a few illustrative examples that highlight the immense potential of these novel approaches to interpret and design metabolic mediated interactions in structures (natural and engineered) environments.
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Affiliation(s)
- Benedict Borer
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; The Department for Earth, Atmospheric and Planetary Science, MIT, Boston, MA, USA.
| | - Dani Or
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; Div. of Hydrologic Sciences, Desert Research Institute, Reno, NV, USA
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11
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Smith NW, Shorten PR, Altermann E, Roy NC, McNabb WC. Examination of hydrogen cross-feeders using a colonic microbiota model. BMC Bioinformatics 2021; 22:3. [PMID: 33407079 PMCID: PMC7789523 DOI: 10.1186/s12859-020-03923-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/07/2020] [Indexed: 12/15/2022] Open
Abstract
Background Hydrogen cross-feeding microbes form a functionally important subset of the human colonic microbiota. The three major hydrogenotrophic functional groups of the colon: sulphate-reducing bacteria (SRB), methanogens and reductive acetogens, have been linked to wide ranging impacts on host physiology, health and wellbeing. Results An existing mathematical model for microbial community growth and metabolism was combined with models for each of the three hydrogenotrophic functional groups. The model was further developed for application to the colonic environment via inclusion of responsive pH, host metabolite absorption and the inclusion of host mucins. Predictions of the model, using two existing metabolic parameter sets, were compared to experimental faecal culture datasets. Model accuracy varied between experiments and measured variables and was most successful in predicting the growth of high relative abundance functional groups, such as the Bacteroides, and short chain fatty acid (SCFA) production. Two versions of the colonic model were developed: one representing the colon with sequential compartments and one utilising a continuous spatial representation. When applied to the colonic environment, the model predicted pH dynamics within the ranges measured in vivo and SCFA ratios comparable to those in the literature. The continuous version of the model simulated relative abundances of microbial functional groups comparable to measured values, but predictions were sensitive to the metabolic parameter values used for each functional group. Sulphate availability was found to strongly influence hydrogenotroph activity in the continuous version of the model, correlating positively with SRB and sulphide concentration and negatively with methanogen concentration, but had no effect in the compartmentalised model version. Conclusions Although the model predictions compared well to only some experimental measurements, the important features of the colon environment included make it a novel and useful contribution to modelling the colonic microbiota.
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Affiliation(s)
- Nick W Smith
- School of Food and Advanced Technology, Massey University, Palmerston North, New Zealand.,Riddet Institute, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.,AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240, New Zealand
| | - Paul R Shorten
- Riddet Institute, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand. .,AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240, New Zealand.
| | - Eric Altermann
- Riddet Institute, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.,AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442, New Zealand.,High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Nicole C Roy
- Riddet Institute, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.,High-Value Nutrition National Science Challenge, Auckland, New Zealand.,Department of Human Nutrition, University of Otago, Dunedin, New Zealand.,Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Warren C McNabb
- Riddet Institute, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.,High-Value Nutrition National Science Challenge, Auckland, New Zealand
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12
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García-Jiménez B, Torres-Bacete J, Nogales J. Metabolic modelling approaches for describing and engineering microbial communities. Comput Struct Biotechnol J 2020; 19:226-246. [PMID: 33425254 PMCID: PMC7773532 DOI: 10.1016/j.csbj.2020.12.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/17/2022] Open
Abstract
Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.
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Affiliation(s)
- Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223-Pozuelo de Alarcón, Madrid, Spain
| | - Jesús Torres-Bacete
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC), Madrid, Spain
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13
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Altamirano Á, Saa PA, Garrido D. Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools. Comput Struct Biotechnol J 2020; 18:3897-3904. [PMID: 33335687 PMCID: PMC7719866 DOI: 10.1016/j.csbj.2020.11.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 11/20/2020] [Accepted: 11/21/2020] [Indexed: 02/07/2023] Open
Abstract
The human gut hosts a complex community of microorganisms that directly influences gastrointestinal physiology, playing a central role in human health. Because of its importance, the metabolic interplay between the gut microbiome and host metabolism has gained special interest. While there has been great progress in the field driven by metagenomics and experimental studies, the mechanisms underpinning microbial composition and interactions in the microbiome remain poorly understood. Genome-scale metabolic models are mathematical structures capable of describing the metabolic potential of microbial cells. They are thus suitable tools for probing the metabolic properties of microbial communities. In this review, we discuss the most recent and relevant genome-scale metabolic modelling tools for inferring the composition, interactions, and ultimately, biological function of the constituent species of a microbial community with special emphasis in the gut microbiota. Particular attention is given to constraint-based metabolic modelling methods as well as hybrid agent-based methods for capturing the interactions and behavior of the community in time and space. Finally, we discuss the challenges hindering comprehensive modelling of complex microbial communities and its application for the in-silico design of microbial consortia with therapeutic functions.
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14
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Brunner JD, Chia N. Minimizing the number of optimizations for efficient community dynamic flux balance analysis. PLoS Comput Biol 2020; 16:e1007786. [PMID: 32991583 PMCID: PMC7546477 DOI: 10.1371/journal.pcbi.1007786] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 10/09/2020] [Accepted: 08/21/2020] [Indexed: 01/03/2023] Open
Abstract
Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.
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Affiliation(s)
- James D. Brunner
- Department of Surgery, Center for Individualized Medicine Microbiome Program, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Chia
- Department of Surgery, Center for Individualized Medicine Microbiome Program, Mayo Clinic, Rochester, MN, USA
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15
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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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16
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Altay O, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Systems biology perspective for studying the gut microbiota in human physiology and liver diseases. EBioMedicine 2019; 49:364-373. [PMID: 31636011 PMCID: PMC6945237 DOI: 10.1016/j.ebiom.2019.09.057] [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: 04/15/2019] [Revised: 09/21/2019] [Accepted: 09/23/2019] [Indexed: 02/06/2023] Open
Abstract
The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders.
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Affiliation(s)
- Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom.
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17
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García-Jiménez B, García JL, Nogales J. FLYCOP: metabolic modeling-based analysis and engineering microbial communities. Bioinformatics 2019; 34:i954-i963. [PMID: 30423096 PMCID: PMC6129290 DOI: 10.1093/bioinformatics/bty561] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Motivation Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account. Results In contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones. Availability and implementation Code reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOP. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain
| | - José Luis García
- Microbial and Plant Biotechnology Department, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain.,Applied System Biology and Synthetic Biology Department, Institute for Integrative Systems Biology (I2Sysbio-CSIC-UV), 46980 Paterna, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain
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18
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Barraclough TG. Species matter for predicting the functioning of evolving microbial communities - An eco-evolutionary model. PLoS One 2019; 14:e0218692. [PMID: 31425541 PMCID: PMC6699713 DOI: 10.1371/journal.pone.0218692] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 08/02/2019] [Indexed: 12/16/2022] Open
Abstract
Humans depend on microbial communities for numerous ecosystem services such as global nutrient cycles, plant growth and their digestive health. Yet predicting dynamics and functioning of these complex systems is hard, making interventions to enhance functioning harder still. One simplifying approach is to assume that functioning can be predicted from the set of enzymes present in a community. Alternatively, ecological and evolutionary dynamics of species, which depend on how enzymes are packaged among species, might be vital for predicting community functioning. I investigate these alternatives by extending classical chemostat models of bacterial growth to multiple species that evolve in their use of chemical resources. Ecological interactions emerge from patterns of resource use, which change as species evolve in their allocation of metabolic enzymes. Measures of community functioning derive in turn from metabolite concentrations and bacterial density. Although the model shows considerable functional redundancy, species packaging does matter by introducing constraints on whether enzyme levels can reach optimum levels for the whole system. Evolution can either promote or reduce functioning compared to purely ecological models, depending on the shape of trade-offs in resource use. The model provides baseline theory for interpreting emerging data on evolution and functioning in real bacterial communities.
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Affiliation(s)
- Timothy G. Barraclough
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, England, United Kingdom
- * E-mail:
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19
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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20
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Sieow BFL, Nurminen TJ, Ling H, Chang MW. Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism. Biotechnol J 2019; 14:e1800445. [PMID: 31144773 DOI: 10.1002/biot.201800445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/24/2019] [Indexed: 12/15/2022]
Abstract
The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.
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Affiliation(s)
- Brendan Fu-Long Sieow
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Toni Juhani Nurminen
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
| | - Hua Ling
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Matthew Wook Chang
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore.,NUS Graduate School of Integrative Sciences and Engineering (NGS), University Hall, Tan Chin Tuan Wing, National University of Singapore, Singapore, 119077, Singapore
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21
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Rodríguez-Verdugo A, Vulin C, Ackermann M. The rate of environmental fluctuations shapes ecological dynamics in a two-species microbial system. Ecol Lett 2019; 22:838-846. [PMID: 30790416 DOI: 10.1111/ele.13241] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 01/21/2019] [Accepted: 02/04/2019] [Indexed: 01/30/2023]
Abstract
Species interactions change when the external conditions change. How these changes affect microbial community properties is an open question. We address this question using a two-species consortium in which species interactions change from exploitation to competition depending on the carbon source provided. We built a mathematical model and calibrated it using single-species growth measurements. This model predicted that low frequencies of change between carbon sources lead to species loss, while intermediate and high frequencies of change maintained both species. We experimentally confirmed these predictions by growing co-cultures in fluctuating environments. These findings complement more established concepts of a diversity peak at intermediate disturbance frequencies. They also provide a mechanistic understanding for how the dynamics at the community level emerges from single-species behaviours and interspecific interactions. Our findings suggest that changes in species interactions can profoundly impact the ecological dynamics and properties of microbial systems.
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Affiliation(s)
- Alejandra Rodríguez-Verdugo
- Department of Environmental Systems Sciences, ETH Zürich, Zürich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland.,Adaptation to a Changing Environment, ETH Zürich, Zürich, Switzerland
| | - Clément Vulin
- Department of Environmental Systems Sciences, ETH Zürich, Zürich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Martin Ackermann
- Department of Environmental Systems Sciences, ETH Zürich, Zürich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
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22
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Sen P, Orešič M. Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites 2019; 9:E22. [PMID: 30695998 PMCID: PMC6410263 DOI: 10.3390/metabo9020022] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 01/23/2019] [Accepted: 01/24/2019] [Indexed: 12/18/2022] Open
Abstract
There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
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Affiliation(s)
- Partho Sen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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23
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Good BH, Hallatschek O. Effective models and the search for quantitative principles in microbial evolution. Curr Opin Microbiol 2018; 45:203-212. [PMID: 30530175 PMCID: PMC6599682 DOI: 10.1016/j.mib.2018.11.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/17/2018] [Accepted: 11/15/2018] [Indexed: 12/14/2022]
Abstract
Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.
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Affiliation(s)
- Benjamin H Good
- Department of Physics, University of California, Berkeley, United States; Department of Bioengineering, University of California, Berkeley, United States.
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, United States; Department of Integrative Biology, University of California, Berkeley, United States
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24
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Hynes WF, Chacón J, Segrè D, Marx CJ, Cady NC, Harcombe WR. Bioprinting microbial communities to examine interspecies interactions in time and space. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aad544] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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25
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Abstract
An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.
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Affiliation(s)
- Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
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26
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Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe. Methods 2018; 149:3-12. [PMID: 29715508 DOI: 10.1016/j.ymeth.2018.04.029] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 03/06/2018] [Accepted: 04/22/2018] [Indexed: 02/06/2023] Open
Abstract
It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.
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27
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28
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Vijayakumar S, Conway M, Lió P, Angione C. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. Methods Mol Biol 2018; 1716:389-408. [PMID: 29222764 DOI: 10.1007/978-1-4939-7528-0_18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
| | - Max Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK.
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29
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Magnúsdóttir S, Thiele I. Modeling metabolism of the human gut microbiome. Curr Opin Biotechnol 2017; 51:90-96. [PMID: 29258014 DOI: 10.1016/j.copbio.2017.12.005] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 12/15/2022]
Abstract
The human gut microbiome plays an important part in human health. The complexity of the microbiome makes it difficult to determine the detailed metabolic functions and cross-talk occurs between the individual species. In silico systems biology studies of the microbiome can help to identify metabolite exchanges among gut microbes. Constraint-based reconstruction and analysis methods use biochemically accurate genome-scale metabolic networks of microorganisms to simulate metabolism between species in a given microbiome and help generate novel hypotheses on microbial interactions. Here, we review metabolic modeling studies that have investigated metabolic functions of the gut microbiome.
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Affiliation(s)
- Stefanía Magnúsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
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30
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31
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Kreft JU, Plugge CM, Prats C, Leveau JHJ, Zhang W, Hellweger FL. From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality. Front Microbiol 2017; 8:2299. [PMID: 29230200 PMCID: PMC5711835 DOI: 10.3389/fmicb.2017.02299] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 11/07/2017] [Indexed: 01/04/2023] Open
Abstract
Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.
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Affiliation(s)
- Jan-Ulrich Kreft
- Centre for Computational Biology, Institute for Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Caroline M Plugge
- Laboratory of Microbiology, Wageningen University and Research, Wageningen, Netherlands
| | - Clara Prats
- Department of Physics, School of Agricultural Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Castelldefels, Spain
| | - Johan H J Leveau
- Department of Plant Pathology, University of California, Davis, Davis, CA, United States
| | - Weiwen Zhang
- Laboratory of Synthetic Microbiology, Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Ferdi L Hellweger
- Civil and Environmental Engineering Department, Marine and Environmental Sciences Department, Bioengineering Department, Northeastern University, Boston, MA, United States
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32
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Cavaliere M, Feng S, Soyer OS, Jiménez JI. Cooperation in microbial communities and their biotechnological applications. Environ Microbiol 2017; 19:2949-2963. [PMID: 28447371 PMCID: PMC5575505 DOI: 10.1111/1462-2920.13767] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 04/08/2017] [Accepted: 04/11/2017] [Indexed: 12/30/2022]
Abstract
Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross-feeding, which can be understood in the general framework of ecological and evolutionary (eco-evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of 'cheating' cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.
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Affiliation(s)
- Matteo Cavaliere
- School of Informatics, BBSRC/EPSRC/MRC Synthetic Biology Research CentreUniversity of EdinburghEdinburghEH8 9ABUK
| | - Song Feng
- Center for Nonlinear StudiesTheoretical Division (T‐6), Los Alamos National LaboratoryLos AlamosNM 87545USA
| | - Orkun S. Soyer
- School of Life Sciences, BBSRC/EPSRC Warwick Integrative Synthetic Biology CentreUniversity of WarwickCoventryCV4 7ALUK
| | - José I. Jiménez
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordGU2 7XHUK
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BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput Biol 2017; 13:e1005544. [PMID: 28531184 PMCID: PMC5460873 DOI: 10.1371/journal.pcbi.1005544] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 06/06/2017] [Accepted: 04/27/2017] [Indexed: 12/22/2022] Open
Abstract
Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena. In nature, organisms are typically found in near proximity to each other, forming symbiotic relationships. Particularly bacteria are often part of highly organized communities such as biofilms. In this study, we integrate the detailed knowledge about the metabolic capabilities of individual organisms into an individual-based modeling approach for simulating the dynamics of local interactions. We provide a fast and flexible framework, in which established computational models for individual organisms can be simulated in communities. Nutrients can diffuse in an area where cells move, divide, and die. The resulting spatial as well as temporal dynamics and metabolic interactions can be analyzed as well as visualized and subsequently compared to experimental findings. We demonstrate how our approach can be used to gain novel insights on dynamics in single species biofilm formation and multi-species intestinal microbial communities.
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34
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Hoek MJAV, Merks RMH. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC SYSTEMS BIOLOGY 2017; 11:56. [PMID: 28511646 PMCID: PMC5434578 DOI: 10.1186/s12918-017-0430-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 04/26/2017] [Indexed: 12/29/2022]
Abstract
Background The human gut contains approximately 1014 bacteria, belonging to hundreds of different species. Together, these microbial species form a complex food web that can break down nutrient sources that our own digestive enzymes cannot handle, including complex polysaccharides, producing short chain fatty acids and additional metabolites, e.g., vitamin K. Microbial diversity is important for colonic health: Changes in the composition of the microbiota have been associated with inflammatory bowel disease, diabetes, obesity and Crohn’s disease, and make the microbiota more vulnerable to infestation by harmful species, e.g., Clostridium difficile. To get a grip on the controlling factors of microbial diversity in the gut, we here propose a multi-scale, spatiotemporal dynamic flux-balance analysis model to study the emergence of metabolic diversity in a spatial gut-like, tubular environment. The model features genome-scale metabolic models (GEM) of microbial populations, resource sharing via extracellular metabolites, and spatial population dynamics and evolution. Results In this model, cross-feeding interactions emerge readily, despite the species’ ability to metabolize sugars autonomously. Interestingly, the community requires cross-feeding for producing a realistic set of short-chain fatty acids from an input of glucose, If we let the composition of the microbial subpopulations change during invasion of adjacent space, a complex and stratified microbiota evolves, with subspecies specializing on cross-feeding interactions via a mechanism of compensated trait loss. The microbial diversity and stratification collapse if the flux through the gut is enhanced to mimic diarrhea. Conclusions In conclusion, this in silico model is a helpful tool in systems biology to predict and explain the controlling factors of microbial diversity in the gut. It can be extended to include, e.g., complex nutrient sources, and host-microbiota interactions via the intestinal wall. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0430-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Milan J A van Hoek
- Life Sciences Group, Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, 1098 XG, The Netherlands
| | - Roeland M H Merks
- Life Sciences Group, Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, 1098 XG, The Netherlands. .,Mathematical Institute, Leiden University, Niels Bohrweg 1, Leiden, 2333, CA, The Netherlands.
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35
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Louca S, Doebeli M. Taxonomic variability and functional stability in microbial communities infected by phages. Environ Microbiol 2017; 19:3863-3878. [PMID: 28371143 DOI: 10.1111/1462-2920.13743] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 03/16/2017] [Accepted: 03/24/2017] [Indexed: 11/28/2022]
Abstract
Microbial communities can display large variation in taxonomic composition, yet this variation can coincide with stable metabolic functional structure and performance. The mechanisms driving the taxonomic variation within functional groups remain largely unknown. Biotic interactions, such as predation by phages, have been suggested as potential cause of taxonomic turnover, but the conditions for this scenario have not been rigorously examined. Further, it is unknown how predation by phages affects community function, and how these effects are modulated by functional redundancy in the communities. Here, we address these questions using a model for a methanogenic microbial community that includes several interacting metabolic functional groups. Each functional group comprises multiple competing clades, and each clade is attacked by a specialist lytic phage. Our model predicts that phages induce intense taxonomic turnover, resembling the variability observed in previous experiments. The functional structure and performance of the community are also disturbed by phage predation, but they become more stable as the functional redundancy in the community increases. The extent of this stabilization depends on the particular functions considered. Our model suggests mechanisms by which functional redundancy stabilizes community function and supports the interpretation that biotic interactions promote taxonomic turnover within microbial functional groups.
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Affiliation(s)
- Stilianos Louca
- Biodiversity Research Centre, University of British Columbia, Canada.,Department of Zoology, University of British Columbia, Canada
| | - Michael Doebeli
- Biodiversity Research Centre, University of British Columbia, Canada.,Department of Zoology, University of British Columbia, Canada.,Department of Mathematics, University of British Columbia, Canada
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36
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Belda I, Zarraonaindia I, Perisin M, Palacios A, Acedo A. From Vineyard Soil to Wine Fermentation: Microbiome Approximations to Explain the " terroir" Concept. Front Microbiol 2017; 8:821. [PMID: 28533770 PMCID: PMC5420814 DOI: 10.3389/fmicb.2017.00821] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 04/21/2017] [Indexed: 11/13/2022] Open
Abstract
Wine originally emerged as a serendipitous mix of chemistry and biology, where microorganisms played a decisive role. From these ancient fermentations to the current monitored industrial processes, winegrowers and winemakers have been continuously changing their practices according to scientific knowledge and advances. A new enology direction is emerging and aiming to blend the complexity of spontaneous fermentations with industrial safety of monitored fermentations. In this context, wines with distinctive autochthonous peculiarities have a great acceptance among consumers, causing important economic returns. The concept of terroir, far from being a rural term, conceals a wide range of analytical parameters that are the basis of the knowledge-based enology trend. In this sense, the biological aspect of soils has been underestimated for years, when actually it contains a great microbial diversity. This soil-associated microbiota has been described as determinant, not only for the chemistry and nutritional properties of soils, but also for health, yield, and quality of the grapevine. Additionally, recent works describe the soil microbiome as the reservoir of the grapevine associated microbiota, and as a contributor to the final sensory properties of wines. To understand the crucial roles of microorganisms on the entire wine making process, we must understand their ecological niches, population dynamics, and relationships between ‘microbiome- vine health’ and ‘microbiome-wine metabolome.’ These are critical steps for designing precision enology practices. For that purpose, current metagenomic techniques are expanding from laboratories, to the food industry. This review focuses on the current knowledge about vine and wine microbiomes, with emphasis on their biological roles and the technical basis of next-generation sequencing pipelines. An overview of molecular and informatics tools is included and new directions are proposed, highlighting the importance of –omics technologies in wine research and industry.
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Affiliation(s)
- Ignacio Belda
- Biome Makers Inc., San FranciscoCA, USA.,Department of Microbiology, Biology Faculty, Complutense University of MadridMadrid, Spain
| | - Iratxe Zarraonaindia
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque CountryLeioa, Spain.,IKERBASQUE - Basque Foundation for ScienceBilbao, Spain
| | | | - Antonio Palacios
- Biome Makers Inc., San FranciscoCA, USA.,Laboratorios Excell IbericaLogroño, Spain
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37
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Kim WJ, Kim HU, Lee SY. Current state and applications of microbial genome-scale metabolic models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.03.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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38
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Dick GJ. Embracing the mantra of modellers and synthesizing omics, experiments and models. ENVIRONMENTAL MICROBIOLOGY REPORTS 2017; 9:18-20. [PMID: 27775862 DOI: 10.1111/1758-2229.12491] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Gregory J Dick
- Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA
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40
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Großkopf T, Consuegra J, Gaffé J, Willison JC, Lenski RE, Soyer OS, Schneider D. Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment. BMC Evol Biol 2016; 16:163. [PMID: 27544664 PMCID: PMC4992563 DOI: 10.1186/s12862-016-0733-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 08/04/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications. Systems biology has enabled the development of genome-scale metabolic models. However, analysing these models via flux balance analysis (FBA) cannot predict many evolutionary outcomes including adaptive diversification, whereby an ancestral lineage diverges to fill multiple niches. Here we combine in silico evolution with FBA and apply this modelling framework, evoFBA, to a long-term evolution experiment with Escherichia coli. RESULTS Simulations predicted the adaptive diversification that occurred in one experimental population and generated hypotheses about the mechanisms that promoted coexistence of the diverged lineages. We experimentally tested and, on balance, verified these mechanisms, showing that diversification involved niche construction and character displacement through differential nutrient uptake and altered metabolic regulation. CONCLUSION The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.
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Affiliation(s)
- Tobias Großkopf
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Jessika Consuegra
- University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France
- Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France
| | - Joël Gaffé
- University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France
- Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France
| | - John C Willison
- University of Grenoble Alpes, Institut de recherches en technologies et sciences pour le vivant - Laboratoire de chimie et biologie des métaux (iRTSV-LCBM), Grenoble, F-38000, France
- CNRS, iRTSV-LCBM, F-38000, Grenoble, France
- Commissariat à l'énergie atomique (CEA), iRTSV-LCBM, F-38000, Grenoble, France
| | - Richard E Lenski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, 48824, USA
| | - Orkun S Soyer
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Dominique Schneider
- University of Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000, Grenoble, France.
- Centre National de la Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.
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Perez-Garcia O, Lear G, Singhal N. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems. Front Microbiol 2016; 7:673. [PMID: 27242701 PMCID: PMC4870247 DOI: 10.3389/fmicb.2016.00673] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 04/25/2016] [Indexed: 12/14/2022] Open
Abstract
We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.
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Affiliation(s)
- Octavio Perez-Garcia
- Department of Civil and Environmental Engineering, University of Auckland Auckland, New Zealand
| | - Gavin Lear
- School of Biological Sciences, The University of Auckland Auckland, New Zealand
| | - Naresh Singhal
- Department of Civil and Environmental Engineering, University of Auckland Auckland, New Zealand
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Granger BR, Chang YC, Wang Y, DeLisi C, Segrè D, Hu Z. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0. PLoS Comput Biol 2016; 12:e1004875. [PMID: 27081850 PMCID: PMC4833320 DOI: 10.1371/journal.pcbi.1004875] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 03/21/2016] [Indexed: 01/04/2023] Open
Abstract
The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.
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Affiliation(s)
- Brian R. Granger
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
| | - Yi-Chien Chang
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Center for Advanced Genomic Technology, Boston University, Boston, Massachusetts, United States of America
| | - Yan Wang
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Center for Advanced Genomic Technology, Boston University, Boston, Massachusetts, United States of America
| | - Charles DeLisi
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Center for Advanced Genomic Technology, Boston University, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Department of Biology, Boston University, Boston, Massachusetts, United States of America
| | - Zhenjun Hu
- Center for Advanced Genomic Technology, Boston University, Boston, Massachusetts, United States of America
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43
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Beyond the Black Queen Hypothesis. ISME JOURNAL 2016; 10:2085-91. [PMID: 26953598 DOI: 10.1038/ismej.2016.22] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 01/06/2016] [Accepted: 01/08/2016] [Indexed: 11/08/2022]
Abstract
The Black Queen Hypothesis, recently proposed to explain an evolution of dependency based on gene loss, is gaining ground. This paper focuses on how the evolution of dependency transforms interactions and the community. Using agent-based modeling we suggest that species specializing in the consumption of a common good escape competition and therefore favor coexistence. This evolutionary trajectory could open the way for novel long-lasting interactions and a need to revisit the classically accepted assembly rules. Such evolutionary events also reshape the structure and dynamics of communities, depending on the spatial heterogeneity of the common good production. Let Black be the new black!
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44
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Louca S, Doebeli M. Transient dynamics of competitive exclusion in microbial communities. Environ Microbiol 2015; 18:1863-74. [DOI: 10.1111/1462-2920.13058] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 09/15/2015] [Indexed: 12/23/2022]
Affiliation(s)
- Stilianos Louca
- Biodiversity Research Centre; University of British Columbia; Vancouver BC V6T 1Z4 Canada
| | - Michael Doebeli
- Department of Zoology; University of British Columbia; Vancouver BC V6T 1Z4 Canada
- Department of Mathematics; University of British Columbia; Vancouver BC V6T 1Z2 Canada
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