1
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Hoosein S, Neuenkamp L, Trivedi P, Paschke MW. AM fungal-bacterial relationships: what can they tell us about ecosystem sustainability and soil functioning? FRONTIERS IN FUNGAL BIOLOGY 2023; 4:1141963. [PMID: 37746131 PMCID: PMC10512368 DOI: 10.3389/ffunb.2023.1141963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/05/2023] [Indexed: 09/26/2023]
Abstract
Considering our growing population and our continuous degradation of soil environments, understanding the fundamental ecology of soil biota and plant microbiomes will be imperative to sustaining soil systems. Arbuscular mycorrhizal (AM) fungi extend their hyphae beyond plant root zones, creating microhabitats with bacterial symbionts for nutrient acquisition through a tripartite symbiotic relationship along with plants. Nonetheless, it is unclear what drives these AM fungal-bacterial relationships and how AM fungal functional traits contribute to these relationships. By delving into the literature, we look at the drivers and complexity behind AM fungal-bacterial relationships, describe the shift needed in AM fungal research towards the inclusion of interdisciplinary tools, and discuss the utilization of bacterial datasets to provide contextual evidence behind these complex relationships, bringing insights and new hypotheses to AM fungal functional traits. From this synthesis, we gather that interdependent microbial relationships are at the foundation of understanding microbiome functionality and deciphering microbial functional traits. We suggest using pattern-based inference tools along with machine learning to elucidate AM fungal-bacterial relationship trends, along with the utilization of synthetic communities, functional gene analyses, and metabolomics to understand how AM fungal and bacterial communities facilitate communication for the survival of host plant communities. These suggestions could result in improving microbial inocula and products, as well as a better understanding of complex relationships in terrestrial ecosystems that contribute to plant-soil feedbacks.
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Affiliation(s)
- Shabana Hoosein
- Department of Forest and Rangeland Stewardship/Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Lena Neuenkamp
- Institute of Landscape Ecology, Münster University, Münster, Germany
- Department of Ecology and Multidisciplinary Institute for Environment Studies “Ramon Margalef,” University of Alicante, Alicante, Spain
| | - Pankaj Trivedi
- Microbiome Network, Department of Agricultural Biology, Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Mark W. Paschke
- Department of Forest and Rangeland Stewardship/Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
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2
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Liu YY. Controlling the human microbiome. Cell Syst 2023; 14:135-159. [PMID: 36796332 PMCID: PMC9942095 DOI: 10.1016/j.cels.2022.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.
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Affiliation(s)
- Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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3
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Decomposing predictability to identify dominant causal drivers in complex ecosystems. Proc Natl Acad Sci U S A 2022; 119:e2204405119. [PMID: 36215500 PMCID: PMC9586263 DOI: 10.1073/pnas.2204405119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
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4
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Sarkar I, Sen G, Bhattacharyya S, Gtari M, Sen A. Inter-cluster competition and resource partitioning may govern the ecology of Frankia. Arch Microbiol 2022; 204:326. [PMID: 35576077 DOI: 10.1007/s00203-022-02910-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/09/2022] [Accepted: 04/10/2022] [Indexed: 11/25/2022]
Abstract
Microbes live in a complex communal ecosystem. The structural complexity of microbial community reflects diversity, functionality, as well as habitat type. Delineation of ecologically important microbial populations along with exploration of their roles in environmental adaptation or host-microbe interaction has a crucial role in modern microbiology. In this scenario, reverse ecology (the use of genomics to study ecology) plays a pivotal role. Since the co-existence of two different genera in one small niche should maintain a strict direct interaction, it will be interesting to utilize the concept of reverse ecology in this scenario. Here, we exploited an 'R' package, the RevEcoR, to resolve the issue of co-existing microbes which are proven to be a crucial tool for identifying the nature of their relationship (competition or complementation) persisting among them. Our target organism here is Frankia, a nitrogen-fixing actinobacterium popular for its genetic and host-specific nature. According to their plant host, Frankia has already been sub-divided into four clusters C-I, C-II, C-III, and C-IV. Our results revealed a strong competing nature of CI Frankia. Among the clusters of Frankia studied, the competition index between C-I and C-III was the largest. The other interesting result was the co-occurrence of C-II and C-IV groups. It was revealed that these two groups follow the theory of resource partitioning in their lifestyle. Metabolic analysis along with their differential transporter machinery validated our hypothesis of resource partitioning among C-II and C-IV groups.
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Affiliation(s)
- I Sarkar
- Bioinformatics Facility, University of North Bengal, Siliguri, West Bengal, India
- Department of Botany, University of North Bengal, Siliguri, West Bengal, India
| | - G Sen
- Bioinformatics Facility, University of North Bengal, Siliguri, West Bengal, India
| | - S Bhattacharyya
- Biswa Bangla Genome Centre, Univ. of North Bengal, Siliguri, West Bengal, India
| | - M Gtari
- Unité de Bactériologie Moléculaire and Génomique, Département de Génie Biologique and Chimique, Institut National Des Sciences Appliquéeset de Technologie, Université de Carthage, Carthage, Tunisia
| | - A Sen
- Bioinformatics Facility, University of North Bengal, Siliguri, West Bengal, India.
- Biswa Bangla Genome Centre, Univ. of North Bengal, Siliguri, West Bengal, India.
- Department of Botany, University of North Bengal, Siliguri, West Bengal, India.
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5
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Kodera SM, Das P, Gilbert JA, Lutz HL. Conceptual strategies for characterizing interactions in microbial communities. iScience 2022; 25:103775. [PMID: 35146390 PMCID: PMC8819398 DOI: 10.1016/j.isci.2022.103775] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.
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Affiliation(s)
- Sho M Kodera
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
| | - Promi Das
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jack A Gilbert
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Holly L Lutz
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA.,Negaunee Integrative Collections Center, Field Museum of Natural History, Chicago, IL 60605, USA
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6
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Suzuki K, Abe MS, Kumakura D, Nakaoka S, Fujiwara F, Miyamoto H, Nakaguma T, Okada M, Sakurai K, Shimizu S, Iwata H, Masuya H, Nihei N, Ichihashi Y. Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:1228. [PMID: 35162258 PMCID: PMC8834966 DOI: 10.3390/ijerph19031228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
Network-based assessments are important for disentangling complex microbial and microbial-host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka-Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.
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Affiliation(s)
- Kenta Suzuki
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
| | - Masato S. Abe
- Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan; (M.S.A.); (S.S.)
| | - Daiki Kumakura
- Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan; (D.K.); (S.N.)
| | - Shinji Nakaoka
- Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan; (D.K.); (S.N.)
- Laboratory of Mathematical Biology, Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Fuki Fujiwara
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Hirokuni Miyamoto
- Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan; (H.M.); (T.N.)
- RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Sermas Co., Ltd., Ichikawa 272-0015, Japan
| | - Teruno Nakaguma
- Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan; (H.M.); (T.N.)
- Sermas Co., Ltd., Ichikawa 272-0015, Japan
| | - Mashiro Okada
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Kengo Sakurai
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Shohei Shimizu
- Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan; (M.S.A.); (S.S.)
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Hiroshi Masuya
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
| | - Naoto Nihei
- Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 960-1296, Japan;
| | - Yasunori Ichihashi
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
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7
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Beck AE, Kleiner M, Garrell AK. Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities. FRONTIERS IN PLANT SCIENCE 2022; 13:910377. [PMID: 35795346 PMCID: PMC9251461 DOI: 10.3389/fpls.2022.910377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/16/2022] [Indexed: 05/10/2023]
Abstract
With a growing world population and increasing frequency of climate disturbance events, we are in dire need of methods to improve plant productivity, resilience, and resistance to both abiotic and biotic stressors, both for agriculture and conservation efforts. Microorganisms play an essential role in supporting plant growth, environmental response, and susceptibility to disease. However, understanding the specific mechanisms by which microbes interact with each other and with plants to influence plant phenotypes is a major challenge due to the complexity of natural communities, simultaneous competition and cooperation effects, signalling interactions, and environmental impacts. Synthetic communities are a major asset in reducing the complexity of these systems by simplifying to dominant components and isolating specific variables for controlled experiments, yet there still remains a large gap in our understanding of plant microbiome interactions. This perspectives article presents a brief review discussing ways in which metabolic modelling can be used in combination with synthetic communities to continue progress toward understanding the complexity of plant-microbe-environment interactions. We highlight the utility of metabolic models as applied to a community setting, identify different applications for both flux balance and elementary flux mode simulation approaches, emphasize the importance of ecological theory in guiding data interpretation, and provide ideas for how the integration of metabolic modelling techniques with big data may bridge the gap between simplified synthetic communities and the complexity of natural plant-microbe systems.
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Affiliation(s)
- Ashley E. Beck
- Department of Biological and Environmental Sciences, Carroll College, Helena, MT, United States
- *Correspondence: Ashley E. Beck,
| | - Manuel Kleiner
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
| | - Anna-Katharina Garrell
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
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8
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Mayerhofer MM, Eigemann F, Lackner C, Hoffmann J, Hellweger FL. Dynamic carbon flux network of a diverse marine microbial community. ISME COMMUNICATIONS 2021; 1:50. [PMID: 37938646 PMCID: PMC9723560 DOI: 10.1038/s43705-021-00055-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 08/19/2021] [Accepted: 09/10/2021] [Indexed: 11/09/2023]
Abstract
The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton-heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.
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Affiliation(s)
| | - Falk Eigemann
- Water Quality Engineering, Technical University of Berlin, Berlin, Germany
| | - Carsten Lackner
- Water Quality Engineering, Technical University of Berlin, Berlin, Germany
| | - Jutta Hoffmann
- Water Quality Engineering, Technical University of Berlin, Berlin, Germany
| | - Ferdi L Hellweger
- Water Quality Engineering, Technical University of Berlin, Berlin, Germany.
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9
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Toju H, Abe MS, Ishii C, Hori Y, Fujita H, Fukuda S. Scoring Species for Synthetic Community Design: Network Analyses of Functional Core Microbiomes. Front Microbiol 2020; 11:1361. [PMID: 32676061 PMCID: PMC7333532 DOI: 10.3389/fmicb.2020.01361] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/27/2020] [Indexed: 11/16/2022] Open
Abstract
Constructing biological communities is a major challenge in both basic and applied sciences. Although model synthetic communities with a few species have been constructed, designing systems consisting of tens or hundreds of species remains one of the most difficult goals in ecology and microbiology. By utilizing high-throughput sequencing data of interspecific association networks, we here propose a framework for exploring “functional core” species that have great impacts on whole community processes and functions. The framework allows us to score each species within a large community based on three criteria: namely, topological positions, functional portfolios, and functional balance within a target network. The criteria are measures of each species’ roles in maximizing functional benefits at the community or ecosystem level. When species with potentially large contributions to ecosystem-level functions are screened, the framework also helps us design “functional core microbiomes” by focusing on properties of species groups (modules) within a network. When embedded into agroecosystems or human gut, such functional core microbiomes are expected to organize whole microbiome processes and functions. An application to a plant-associated microbiome dataset actually highlighted potential functional core microbes that were known to control rhizosphere microbiomes by suppressing pathogens. Meanwhile, an example of application in mouse gut microbiomes called attention to poorly investigated bacterial species, whose potential roles within gut microbiomes deserve future experimental studies. The framework for gaining “bird’s-eye” views of functional cores within networks is applicable not only to agricultural and medical data but also to datasets produced in food processing, brewing, waste water purification, and biofuel production.
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Affiliation(s)
- Hirokazu Toju
- Center for Ecological Research, Kyoto University, Kyoto, Japan.,Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Masato S Abe
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Chiharu Ishii
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
| | - Yoshie Hori
- Center for Ecological Research, Kyoto University, Kyoto, Japan
| | - Hiroaki Fujita
- Center for Ecological Research, Kyoto University, Kyoto, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan.,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
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10
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Garcia J, Kao‐Kniffin J. Can dynamic network modelling be used to identify adaptive microbiomes? Funct Ecol 2019. [DOI: 10.1111/1365-2435.13491] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Joshua Garcia
- School of Integrative Plant Science Cornell University Ithaca NY USA
| | - Jenny Kao‐Kniffin
- School of Integrative Plant Science Cornell University Ithaca NY USA
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11
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Matsuzaki SIS, Suzuki K, Kadoya T, Nakagawa M, Takamura N. Bottom-up linkages between primary production, zooplankton, and fish in a shallow, hypereutrophic lake. Ecology 2018; 99:2025-2036. [DOI: 10.1002/ecy.2414] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/06/2018] [Accepted: 05/21/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Shin-ichiro S. Matsuzaki
- Center for Environmental Biology and Ecosystem Studies; National Institute for Environmental Studies; 16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
- Lake Biwa Branch Office; National Institute for Environmental Studies; 5-34 Yanagasaki Otsu Shiga 520-0022 Japan
| | - Kenta Suzuki
- Center for Environmental Biology and Ecosystem Studies; National Institute for Environmental Studies; 16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
| | - Taku Kadoya
- Center for Environmental Biology and Ecosystem Studies; National Institute for Environmental Studies; 16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
| | - Megumi Nakagawa
- Center for Environmental Biology and Ecosystem Studies; National Institute for Environmental Studies; 16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
| | - Noriko Takamura
- Center for Environmental Biology and Ecosystem Studies; National Institute for Environmental Studies; 16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
- Lake Biwa Branch Office; National Institute for Environmental Studies; 5-34 Yanagasaki Otsu Shiga 520-0022 Japan
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12
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Certain G, Barraquand F, Gårdmark A. How do MAR(1) models cope with hidden nonlinearities in ecological dynamics? Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Grégoire Certain
- MARBEC, Ifremer Laboratoire Halieutique MéditerranéeUniversity of MontpellierCNRS, IRD Sète France
- Department of Aquatic ResourcesSwedish University of Agricultural Sciences Öregrund Sweden
| | - Frédéric Barraquand
- Institute of Mathematics of BordeauxCNRS Talence France
- Integrative and Theoretical Ecology ChairLabEx COTEUniversity of Bordeaux Pessac France
| | - Anna Gårdmark
- Department of Aquatic ResourcesSwedish University of Agricultural Sciences Öregrund Sweden
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13
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Toju H, Peay KG, Yamamichi M, Narisawa K, Hiruma K, Naito K, Fukuda S, Ushio M, Nakaoka S, Onoda Y, Yoshida K, Schlaeppi K, Bai Y, Sugiura R, Ichihashi Y, Minamisawa K, Kiers ET. Core microbiomes for sustainable agroecosystems. NATURE PLANTS 2018; 4:247-257. [PMID: 29725101 DOI: 10.1038/s41477-018-0139-4] [Citation(s) in RCA: 378] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 03/23/2018] [Indexed: 05/18/2023]
Abstract
In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of 'core microbiomes', which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.
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Affiliation(s)
- Hirokazu Toju
- Center for Ecological Research, Kyoto University, Otsu, Shiga, Japan.
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan.
| | - Kabir G Peay
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Masato Yamamichi
- Department of General Systems Studies, University of Tokyo, Meguro, Tokyo, Japan
| | - Kazuhiko Narisawa
- Department of Bioresource Science, College of Agriculture, Ibaraki University, Ami, Ibaraki, Japan
| | - Kei Hiruma
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Department of Biological Sciences, Nara Institute of Science and Technology, Nara, Japan
| | - Ken Naito
- Genetic Resource Center, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan
| | - Shinji Fukuda
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Masayuki Ushio
- Center for Ecological Research, Kyoto University, Otsu, Shiga, Japan
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
| | - Shinji Nakaoka
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Institute of Industrial Sciences, The University of Tokyo, Tokyo, Japan
| | - Yusuke Onoda
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Kentaro Yoshida
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Graduate School of Agricultural Science, Kobe University, Nada-ku, Kobe, Japan
| | - Klaus Schlaeppi
- Institute of Plant Sciences, University of Bern, Bern, Switzerland
- Department of Agroecology and Environment, Agroscope, Zurich, Switzerland
| | - Yang Bai
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Science, Beijing, China
- Centre of Excellence for Plant and Microbial Sciences (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Science & John Innes Centre, Beijing, China
| | - Ryo Sugiura
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Hokkaido Agricultural Research Center, NARO (National Agriculture and Food Research Organization), Memuro, Hokkaido, Japan
| | - Yasunori Ichihashi
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
- RIKEN BioResource Research Center, Tsukuba, Ibaraki, Japan
| | - Kiwamu Minamisawa
- Graduate School of Life Sciences, Tohoku University, Katahira, Sendai, Japan
| | - E Toby Kiers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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