1
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Derosa L, Iebba V, Silva CAC, Piccinno G, Wu G, Lordello L, Routy B, Zhao N, Thelemaque C, Birebent R, Marmorino F, Fidelle M, Messaoudene M, Thomas AM, Zalcman G, Friard S, Mazieres J, Audigier-Valette C, Sibilot DM, Goldwasser F, Scherpereel A, Pegliasco H, Ghiringhelli F, Bouchard N, Sow C, Darik I, Zoppi S, Ly P, Reni A, Daillère R, Deutsch E, Lee KA, Bolte LA, Björk JR, Weersma RK, Barlesi F, Padilha L, Finzel A, Isaksen ML, Escudier B, Albiges L, Planchard D, André F, Cremolini C, Martinez S, Besse B, Zhao L, Segata N, Wojcik J, Kroemer G, Zitvogel L. Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome. Cell 2024; 187:3373-3389.e16. [PMID: 38906102 DOI: 10.1016/j.cell.2024.05.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/16/2024] [Accepted: 05/14/2024] [Indexed: 06/23/2024]
Abstract
The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.
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Affiliation(s)
- Lisa Derosa
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France.
| | - Valerio Iebba
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Carolina Alves Costa Silva
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | | | - Guojun Wu
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA; Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA; Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA
| | - Leonardo Lordello
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Bertrand Routy
- Centre Hospitalier de l'Université de Montréal (CHUM), Hematology-Oncology Division, Department of Medicine, Montréal, QC, Canada; Centre de Recherche du CHUM (CRCHUM), Montréal, QC, Canada
| | - Naisi Zhao
- Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Cassandra Thelemaque
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Roxanne Birebent
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Federica Marmorino
- Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Marine Fidelle
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | | | | | - Gerard Zalcman
- Université Paris Cité, Thoracic Oncology Department-CIC1425/CLIP2 Paris-Nord, Bichat-Claude Bernard Hospital, AP-HP, Paris, France
| | - Sylvie Friard
- Pneumology Department, Foch Hospital, Suresnes, France
| | - Julien Mazieres
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | | | - Denis Moro- Sibilot
- Department of Thoracic Oncology, Centre Hospitalier Universitaire, Grenoble, France
| | - François Goldwasser
- INSERM U1016-CNRS UMR8104, Paris Cité University, Paris, France; Department of Medical Oncology, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Immunomodulatory Therapies Multidisciplinary Study Group (CERTIM), Paris, France
| | - Arnaud Scherpereel
- Department of Pulmonary and Thoracic Oncology, University of Lille, University Hospital (CHU), Lille, France
| | | | - François Ghiringhelli
- Cancer Biology Transfer Platform, Centre Georges-François Leclerc, Dijon, France; Centre de Recherche INSERM LNC-UMR1231, Dijon, France; Department of Medical Oncology, Centre Georges-François Leclerc, Dijon, France
| | | | - Cissé Sow
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Ines Darik
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Silvia Zoppi
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Pierre Ly
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Anna Reni
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Section of Oncology, Department of Medicine, University of Verona School of Medicine and Verona University Hospital Trust, Verona, Italy
| | | | - Eric Deutsch
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Radiation Oncology, Gustave Roussy, Villejuif, France; INSERM U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Villejuif, France
| | - Karla A Lee
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Laura A Bolte
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Johannes R Björk
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Fabrice Barlesi
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Lucas Padilha
- Bio-Me AS, Oslo Science Park, Gaustadalléen 21, Oslo, Norway
| | - Ana Finzel
- Bio-Me AS, Oslo Science Park, Gaustadalléen 21, Oslo, Norway
| | | | - Bernard Escudier
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Laurence Albiges
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - David Planchard
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Fabrice André
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Chiara Cremolini
- Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Stéphanie Martinez
- Service des Maladies Respiratoires, Centre Hospitalier d'Aix-en-Provence, Aix-en-Provence, France
| | - Benjamin Besse
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Liping Zhao
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA; Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA; Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA; State Key Laboratory of Microbial Metabolism, Ministry of Education Laboratory of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Guido Kroemer
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée-Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Institut Universitaire de France, Inserm U1138, Paris, France; Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France; Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Laurence Zitvogel
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Center of Clinical Investigations in Biotherapies of Cancer (BIOTHERIS) 1428, Villejuif, France.
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2
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Diaz-Colunga J, Skwara A, Vila JCC, Bajic D, Sanchez A. Global epistasis and the emergence of function in microbial consortia. Cell 2024; 187:3108-3119.e30. [PMID: 38776921 DOI: 10.1016/j.cell.2024.04.016] [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: 06/09/2023] [Revised: 12/06/2023] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biotechnology, Delft University of Technology, Delft 2628 CD, the Netherlands.
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
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3
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Chodkowski JL, Shade A. Bioactive exometabolites drive maintenance competition in simple bacterial communities. mSystems 2024; 9:e0006424. [PMID: 38470039 PMCID: PMC11019792 DOI: 10.1128/msystems.00064-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/19/2024] [Indexed: 03/13/2024] Open
Abstract
During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532, and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure. IMPORTANCE Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.
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Affiliation(s)
- John L. Chodkowski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA
| | - Ashley Shade
- Universite Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, Villeurbanne, France
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4
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Srinivasan S, Jnana A, Murali TS. Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions. MICROBIAL ECOLOGY 2024; 87:56. [PMID: 38587642 PMCID: PMC11001700 DOI: 10.1007/s00248-024-02370-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.
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Affiliation(s)
- Shanchana Srinivasan
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Apoorva Jnana
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Thokur Sreepathy Murali
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India.
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5
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Yang R, Zhou F, Liu B, Lü L. A generalized simplicial model and its application. CHAOS (WOODBURY, N.Y.) 2024; 34:043113. [PMID: 38572946 DOI: 10.1063/5.0195423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.g., 2-simplex) and vital functions of complex networks by comparing the original network with its simplicial model. We provide a simplicial model that can regulate the quantity of 2-simplices and simultaneously fix the degree sequence. Although the algorithm is proposed to control the quantity of 2-simplices, results indicate it can also indirectly control simplexes more than 2-order. Experiments on spreading dynamics, pinning control, network robustness, and community detection have shown that regulating the quantity of 2-simplices changes network performance significantly. In conclusion, the proposed framework is a general and effective tool for linking higher-order structures with network functions. It can be regarded as a reference object in other applications and can deepen our understanding of the correlation between micro-level network structures and global network functions.
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Affiliation(s)
- Rongmei Yang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
| | - Fang Zhou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, People's Republic of China
| | - Bo Liu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, People's Republic of China
| | - Linyuan Lü
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, People's Republic of China
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6
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Chen X, Wang M, Luo L, An L, Liu X, Fang Y, Huang T, Nie Y, Wu XL. High immigration rates critical for establishing emigration-driven diversity in microbial communities. Cell Syst 2024; 15:275-285.e4. [PMID: 38401538 DOI: 10.1016/j.cels.2024.02.001] [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: 07/05/2023] [Revised: 12/03/2023] [Accepted: 02/02/2024] [Indexed: 02/26/2024]
Abstract
Unraveling the mechanisms governing the diversity of ecological communities is a central goal in ecology. Although microbial dispersal constitutes an important ecological process, the effect of dispersal on microbial diversity is poorly understood. Here, we sought to fill this gap by combining a generalized Lotka-Volterra model with experimental investigations. Our model showed that emigration increases the diversity of the community when the immigration rate crosses a defined threshold, which we identified as Ineutral. We also found that at high immigration rates, emigration weakens the relative abundance of fast-growing species and thus enhances the mass effect and increases the diversity. We experimentally confirmed this finding using co-cultures of 20 bacterial strains isolated from the soil. Our model further showed that Ineutral decreases with the increase of species pool size, growth rate, and interspecies interaction. Our work deepens the understanding of the effects of dispersal on the diversity of natural communities.
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Affiliation(s)
- Xiaoli Chen
- College of Engineering, Peking University, Beijing 100871, China; Institute of Ocean Research, Peking University, Beijing 100871, China
| | - Miaoxiao Wang
- Department of Environmental Systems Science, ETH Zürich, Zürich 8092, Switzerland; Department of Environmental Microbiology, Eawag, Dübendorf 8600, Switzerland
| | - Laipeng Luo
- College of Engineering, Peking University, Beijing 100871, China
| | - Liyun An
- College of Architecture and Environment, Sichuan University, Chengdu 610000, China
| | - Xiaonan Liu
- College of Engineering, Peking University, Beijing 100871, China
| | - Yuan Fang
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230000, China
| | - Ting Huang
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230000, China
| | - Yong Nie
- College of Engineering, Peking University, Beijing 100871, China.
| | - Xiao-Lei Wu
- College of Engineering, Peking University, Beijing 100871, China; Institute of Ocean Research, Peking University, Beijing 100871, China; Institute of Ecology, Peking University, Beijing 100871, China.
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7
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Wu L, Wang XW, Tao Z, Wang T, Zuo W, Zeng Y, Liu YY, Dai L. Data-driven prediction of colonization outcomes for complex microbial communities. Nat Commun 2024; 15:2406. [PMID: 38493186 PMCID: PMC10944475 DOI: 10.1038/s41467-024-46766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.
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Affiliation(s)
- Lu Wu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zining Tao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shandong Agricultural University, Tai'an, China
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wenlong Zuo
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yu Zeng
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
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8
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Ogbunugafor CB, Yitbarek S. Towards a fundamental theory of taxon transitions in microbial communities. Proc Natl Acad Sci U S A 2024; 121:e2400433121. [PMID: 38422064 PMCID: PMC10945776 DOI: 10.1073/pnas.2400433121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Affiliation(s)
- C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT06520
- Santa Fe Institute, Santa Fe, NM87501
| | - Senay Yitbarek
- Department of Biology, University of North Carolina, Chapel Hill, NC27599-3280
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9
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Long C, Deng J, Nguyen J, Liu YY, Alm EJ, Solé R, Saavedra S. Structured community transitions explain the switching capacity of microbial systems. Proc Natl Acad Sci U S A 2024; 121:e2312521121. [PMID: 38285940 PMCID: PMC10861894 DOI: 10.1073/pnas.2312521121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/29/2023] [Indexed: 01/31/2024] Open
Abstract
Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.
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Affiliation(s)
- Chengyi Long
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Jie Deng
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Jen Nguyen
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA02115
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL61801
| | - Eric J. Alm
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Ricard Solé
- Complex Systems Lab, Universitat Pompeu Fabra, Barcelona08003, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona08010, Spain
- Institute of Evolutionary Biology, Spanish National Research Council (CSIC)-Universitat Pompeu Fabra, Barcelona08003, Spain
- Santa Fe Institute, Santa Fe, NM87501
| | - Serguei Saavedra
- Institució Catalana de Recerca i Estudis Avançats, Barcelona08010, Spain
- Santa Fe Institute, Santa Fe, NM87501
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10
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Dooley KD, Bergelson J. Richness and density jointly determine context dependence in bacterial interactions. iScience 2024; 27:108654. [PMID: 38188527 PMCID: PMC10770726 DOI: 10.1016/j.isci.2023.108654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/30/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024] Open
Abstract
Pairwise interactions are often used to predict features of complex microbial communities due to the challenge of measuring multi-species interactions in high dimensional contexts. This assumes that interactions are unaffected by community context. Here, we used synthetic bacterial communities to investigate that assumption by observing how interactions varied across contexts. Interactions were most often weakly negative and showed a phylogenetic signal among genera. Community richness and total density emerged as strong predictors of interaction strength and contributed to an attenuation of interactions as richness increased. Population level and per-capita measures of interactions both displayed such attenuation, suggesting factors beyond systematic changes in population size were involved; namely, changes to the interactions themselves. Nevertheless, pairwise interactions retained some explanatory power across contexts, provided those contexts were not substantially divergent in richness. These results suggest that understanding the emergent properties of microbial interactions can improve our ability to predict the features of microbial communities.
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Affiliation(s)
- Keven D. Dooley
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA
| | - Joy Bergelson
- Center for Genomics and System Biology, Department of Biology, New York University, New York, NY 10003, USA
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11
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Honjo M, Suzuki K, Katai J, Tashiro Y, Aoyagi T, Hori T, Okada T, Saito Y, Futamata H. Stable States of a Microbial Community Are Formed by Dynamic Metabolic Networks with Members Functioning to Achieve Both Robustness and Plasticity. Microbes Environ 2024; 39:ME23091. [PMID: 38538313 PMCID: PMC10982111 DOI: 10.1264/jsme2.me23091] [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: 11/09/2023] [Accepted: 12/19/2023] [Indexed: 04/04/2024] Open
Abstract
A more detailed understanding of the mechanisms underlying the formation of microbial communities is essential for the efficient management of microbial ecosystems. The stable states of microbial communities are commonly perceived as static and, thus, have not been extensively examined. The present study investigated stabilizing mechanisms, minority functions, and the reliability of quantitative ana-lyses, emphasizing a metabolic network perspective. A bacterial community, formed by batch transferred cultures supplied with phenol as the sole carbon and energy source and paddy soil as the inoculum, was analyzed using a principal coordinate ana-lysis (PCoA), mathematical models, and quantitative parameters defined as growth activity, community-changing activity, community-forming activity, vulnerable force, and resilience force depending on changes in the abundance of operational taxonomic units (OTUs) using 16S rRNA gene amplicon sequences. PCoA showed succession states until the 3rd transferred cultures and stable states from the 5th to 10th transferred cultures. Quantitative parameters indicated that the bacterial community was dynamic irrespective of the succession and stable states. Three activities fluctuated under stable states. Vulnerable and resilience forces were detected under the succession and stable states, respectively. Mathematical models indicated the construction of metabolic networks, suggesting the stabilizing mechanism of the community structure. Thirteen OTUs coexisted during stable states, and were recognized as core OTUs consisting of majorities, middle-class, and minorities. The abundance of the middle-class changed, whereas that of the others did not, which indicated that core OTUs maintained metabolic networks. Some extremely low abundance OTUs were consistently exchanged, suggesting a role for scavengers. These results indicate that stable states were formed by dynamic metabolic networks with members functioning to achieve robustness and plasticity.
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Affiliation(s)
- Masahiro Honjo
- Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Hamamatsu 432–8011, Japan
| | - Kenshi Suzuki
- Microbial Ecotechnology, Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 111 Yayoi, Bunkyo-ku, Tokyo, Japan
| | - Junya Katai
- Department of Applied Chemistry and Biochemical Engineering, Graduate School of Engineering, Shizuoka University, Hamamatsu, 432–8011, Japan
| | - Yosuke Tashiro
- Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Hamamatsu 432–8011, Japan
- Department of Applied Chemistry and Biochemical Engineering, Graduate School of Engineering, Shizuoka University, Hamamatsu, 432–8011, Japan
| | - Tomo Aoyagi
- Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 16–1 Onogawa, Tsukuba, Ibaraki 305–8569, Japan
| | - Tomoyuki Hori
- Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 16–1 Onogawa, Tsukuba, Ibaraki 305–8569, Japan
| | - Takashi Okada
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606–8507, Japan
| | - Yasuhisa Saito
- Department of Mathematics, Shimane University, Matsue, 690–8504, Japan
| | - Hiroyuki Futamata
- Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Hamamatsu 432–8011, Japan
- Department of Applied Chemistry and Biochemical Engineering, Graduate School of Engineering, Shizuoka University, Hamamatsu, 432–8011, Japan
- Research Institution of Green Science and Technology, Shizuoka University, Shizuoka 422–8529, Japan
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12
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Gralka M. Searching for Principles of Microbial Ecology Across Levels of Biological Organization. Integr Comp Biol 2023; 63:1520-1531. [PMID: 37280177 PMCID: PMC10755194 DOI: 10.1093/icb/icad060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/21/2023] [Accepted: 06/01/2023] [Indexed: 06/08/2023] Open
Abstract
Microbial communities play pivotal roles in ecosystems across different scales, from global elemental cycles to household food fermentations. These complex assemblies comprise hundreds or thousands of microbial species whose abundances vary over time and space. Unraveling the principles that guide their dynamics at different levels of biological organization, from individual species, their interactions, to complex microbial communities, is a major challenge. To what extent are these different levels of organization governed by separate principles, and how can we connect these levels to develop predictive models for the dynamics and function of microbial communities? Here, we will discuss recent advances that point towards principles of microbial communities, rooted in various disciplines from physics, biochemistry, and dynamical systems. By considering the marine carbon cycle as a concrete example, we demonstrate how the integration of levels of biological organization can offer deeper insights into the impact of increasing temperatures, such as those associated with climate change, on ecosystem-scale processes. We argue that by focusing on principles that transcend specific microbiomes, we can pave the way for a comprehensive understanding of microbial community dynamics and the development of predictive models for diverse ecosystems.
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Affiliation(s)
- Matti Gralka
- Systems Biology lab, Amsterdam Institute for Life and Environment (A-LIFE), Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, 1081 HV, The Netherlands
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13
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Eble H, Joswig M, Lamberti L, Ludington WB. Master regulators of biological systems in higher dimensions. Proc Natl Acad Sci U S A 2023; 120:e2300634120. [PMID: 38096409 PMCID: PMC10743376 DOI: 10.1073/pnas.2300634120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023] Open
Abstract
A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.
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Affiliation(s)
- Holger Eble
- Chair of Discrete Mathematics/Geometry, Technical University Berlin, Berlin10623, Germany
| | - Michael Joswig
- Chair of Discrete Mathematics/Geometry, Technical University Berlin, Berlin10623, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig04103, Germany
| | - Lisa Lamberti
- Department of Biosystems Science and Engineering, Federal Institute of Technology (ETH Zürich), Basel4058, Switzerland
- Swiss Institute of Bioinformatics, Basel4058, Switzerland
| | - William B. Ludington
- Department of Biosphere Sciences and Engineering, Carnegie Institution for Science, Baltimore, MD21218
- Department of Biology, Johns Hopkins University, Baltimore, MD21218
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14
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Li Z, Selim A, Kuehn S. Statistical prediction of microbial metabolic traits from genomes. PLoS Comput Biol 2023; 19:e1011705. [PMID: 38113208 PMCID: PMC10729968 DOI: 10.1371/journal.pcbi.1011705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.
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Affiliation(s)
- Zeqian Li
- Center for the Physics of Evolving Systems, The University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology and Evolution, The University of Chicago, Chicago, Illinois, United States of America
- Department of Physics, The University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Ahmed Selim
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, Illinois, United States of America
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, The University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology and Evolution, The University of Chicago, Chicago, Illinois, United States of America
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15
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Arya S, George AB, O’Dwyer JP. Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes. Proc Natl Acad Sci U S A 2023; 120:e2307313120. [PMID: 37991947 PMCID: PMC10691334 DOI: 10.1073/pnas.2307313120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/16/2023] [Indexed: 11/24/2023] Open
Abstract
Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.
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Affiliation(s)
- Shreya Arya
- Department of Physics, University of Illinois, Urbana-Champaign, Urbana, IL61801
| | - Ashish B. George
- Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL61801
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA0214
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL61801
| | - James P. O’Dwyer
- Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL61801
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL61801
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16
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Papkou A, Garcia-Pastor L, Escudero JA, Wagner A. A rugged yet easily navigable fitness landscape. Science 2023; 382:eadh3860. [PMID: 37995212 DOI: 10.1126/science.adh3860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/29/2023] [Indexed: 11/25/2023]
Abstract
Fitness landscape theory predicts that rugged landscapes with multiple peaks impair Darwinian evolution, but experimental evidence is limited. In this study, we used genome editing to map the fitness of >260,000 genotypes of the key metabolic enzyme dihydrofolate reductase in the presence of the antibiotic trimethoprim, which targets this enzyme. The resulting landscape is highly rugged and harbors 514 fitness peaks. However, its highest peaks are accessible to evolving populations via abundant fitness-increasing paths. Different peaks share large basins of attraction that render the outcome of adaptive evolution highly contingent on chance events. Our work shows that ruggedness need not be an obstacle to Darwinian evolution but can reduce its predictability. If true in general, the complexity of optimization problems on realistic landscapes may require reappraisal.
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Affiliation(s)
- Andrei Papkou
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Lucia Garcia-Pastor
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - José Antonio Escudero
- Departamento de Sanidad Animal and VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid, Spain
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, NM, USA
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17
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Skwara A, Gowda K, Yousef M, Diaz-Colunga J, Raman AS, Sanchez A, Tikhonov M, Kuehn S. Statistically learning the functional landscape of microbial communities. Nat Ecol Evol 2023; 7:1823-1833. [PMID: 37783827 PMCID: PMC11088814 DOI: 10.1038/s41559-023-02197-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/11/2023] [Indexed: 10/04/2023]
Abstract
Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.
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Affiliation(s)
- Abigail Skwara
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Karna Gowda
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Mahmoud Yousef
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Juan Diaz-Colunga
- Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Arjun S Raman
- Department of Pathology, University of Chicago, Chicago, IL, USA
- Duchossois Family Institute, University of Chicago, Chicago, IL, USA
| | - Alvaro Sanchez
- Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University in St. Louis, St. Louis, MO, USA.
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA.
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
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18
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Li J, Wang J, Liu H, Macdonald CA, Singh BK. Microbial inoculants with higher capacity to colonize soils improved wheat drought tolerance. Microb Biotechnol 2023; 16:2131-2144. [PMID: 37815273 PMCID: PMC10616649 DOI: 10.1111/1751-7915.14350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023] Open
Abstract
Microbial inoculants have gained increasing attention worldwide as an eco-friendly solution for improving agriculture productivity. Several studies have demonstrated their potential benefits, such as enhanced resistance to drought, salinity, and pathogens. However, the beneficial impacts of inoculants remain inconsistent. This variability is attributed to limited knowledge of the mechanisms by which microbial inoculants affect crop growth and a lack of ecological characteristics of these inoculants that limit our ability to predict their beneficial effects. The first important step is believed to be the evaluation of the inoculant's ability to colonize new habitats (soils and plant roots), which could provide crops with beneficial functions and improve the consistency and efficiency of the inoculants. In this study, we aimed to investigate the impact of three microbial inoculants (two bacterial: P1 and P2, and one fungal: P3) on the growth and stress responses of three wheat varieties in two different soil types under drought conditions. Furthermore, we investigated the impact of microbial inoculants on soil microbial communities. Plant biomass and traits were measured, and high-throughput sequencing was used to characterize bulk and rhizosphere soil microbiomes after exposure to drought stress. Under drought conditions, plant shoot weight significantly increased (11.37%) under P1 treatments compared to uninoculated controls. In addition, total nitrogen enzyme activity increased significantly under P1 in sandy soil but not in clay soil. Importantly, network analyses revealed that P1, consisting of Bacillus paralicheniformis and Bacillus subtilis, emerged as the keystone taxa in sandy soil. Conversely, P2 and P3 failed to establish as keystone taxa, which may explain their insignificant impact on wheat performance under drought conditions. In conclusion, our study emphasizes the importance of effective colonization by microbial inoculants in promoting crop growth under drought conditions. Our findings support the development of microbial inoculants that robustly colonize plant roots for improved agricultural productivity.
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Affiliation(s)
- Jiayu Li
- Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia
| | - Juntao Wang
- Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia
- Global Centre for Land-Based Innovation, Western Sydney University, New South Wales, Penrith, Australia
- School of Science, Western Sydney University, Penrith, New South Wales, Australia
| | - Hongwei Liu
- Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia
| | - Catriona A Macdonald
- Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia
| | - Brajesh K Singh
- Hawkesbury Institute for the Environment, Western Sydney University, New South Wales, Penrith, Australia
- Global Centre for Land-Based Innovation, Western Sydney University, New South Wales, Penrith, Australia
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19
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Yitbarek S, Guittar J, Knutie SA, Ogbunugafor CB. Deconstructing taxa x taxa xenvironment interactions in the microbiota: A theoretical examination. iScience 2023; 26:107875. [PMID: 37860776 PMCID: PMC10583047 DOI: 10.1016/j.isci.2023.107875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 03/21/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023] Open
Abstract
A major objective of microbial ecology is to identify how the composition of microbial taxa shapes host phenotypes. However, most studies focus on pairwise interactions and ignore the potentially significant effects of higher-order microbial interactions.Here, we quantify the effects of higher-order interactions among taxa on host infection risk. We apply our approach to an in silico dataset that is built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite across a breadth of nutrient environmental contexts.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Furthermore, we show that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes.
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Affiliation(s)
- Senay Yitbarek
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John Guittar
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA
| | - Sarah A. Knutie
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
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20
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Ghosh R, Verma UK, Jalan S, Shrimali MD. First-order transition to oscillation death in coupled oscillators with higher-order interactions. Phys Rev E 2023; 108:044207. [PMID: 37978677 DOI: 10.1103/physreve.108.044207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/11/2023] [Indexed: 11/19/2023]
Abstract
We investigate the dynamical evolution of Stuart-Landau oscillators globally coupled through conjugate or dissimilar variables on simplicial complexes. We report a first-order explosive phase transition from an oscillatory state to oscillation death, with higher-order (2-simplex triadic) interactions, as opposed to the second-order transition with only pairwise (1-simplex) interactions. Moreover, the system displays four distinct homogeneous steady states in the presence of triadic interactions, in contrast to the two homogeneous steady states observed with dyadic interactions. We calculate the backward transition point analytically, confirming the numerical results and providing the origin of the dynamical states in the transition region. The results are robust against the application of noise. The study will be useful in understanding complex systems, such as ecological and epidemiological, having higher-order interactions and coupling through conjugate variables.
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Affiliation(s)
- Richita Ghosh
- Department of Physics, Central University of Rajasthan, Rajasthan, Ajmer-305 817, India
| | - Umesh Kumar Verma
- Complex Systems Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore-453 552, India
| | - Sarika Jalan
- Complex Systems Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore-453 552, India
| | - Manish Dev Shrimali
- Department of Physics, Central University of Rajasthan, Rajasthan, Ajmer-305 817, India
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21
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Ruiz J, de Celis M, Diaz‐Colunga J, Vila JCC, Benitez‐Dominguez B, Vicente J, Santos A, Sanchez A, Belda I. Predictability of the community-function landscape in wine yeast ecosystems. Mol Syst Biol 2023; 19:e11613. [PMID: 37548146 PMCID: PMC10495813 DOI: 10.15252/msb.202311613] [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: 02/21/2023] [Revised: 07/26/2023] [Accepted: 07/26/2023] [Indexed: 08/08/2023] Open
Abstract
Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most ecologically and industrially relevant traits exhibit phylogenetic signal, allowing functional traits in wine yeast communities to be predicted from taxonomy. Furthermore, we demonstrate that the quantitative contributions of individual wine yeast strains to the function of complex communities followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing theoretical questions in functional ecology, our results and methodologies can provide a blueprint for rationally managing microbial processes of biotechnological relevance.
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Affiliation(s)
- Javier Ruiz
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
- Department of Microbial and Plant BiotechnologyCentre for Biological Research (CIB‐CSIC)MadridSpain
| | - Miguel de Celis
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
- Department of Soil, Plant and Environmental QualityInstitute of Agricultural Sciences (ICA‐CSIC)MadridSpain
| | - Juan Diaz‐Colunga
- Department of Ecology & Evolutionary BiologyYale UniversityNew HavenCTUSA
- Department of Microbial BiotechnologyNational Centre for Biotechnology (CNB‐CSIC)MadridSpain
| | - Jean CC Vila
- Department of Ecology & Evolutionary BiologyYale UniversityNew HavenCTUSA
- Department of BiologyStanford UniversityStanfordCAUSA
| | - Belen Benitez‐Dominguez
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
| | - Javier Vicente
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
| | - Antonio Santos
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary BiologyYale UniversityNew HavenCTUSA
- Department of Microbial BiotechnologyNational Centre for Biotechnology (CNB‐CSIC)MadridSpain
| | - Ignacio Belda
- Department of Genetics, Physiology and Microbiology, Biology FacultyComplutense University of MadridMadridSpain
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22
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Dundore-Arias JP, Michalska-Smith M, Millican M, Kinkel LL. More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes. ANNUAL REVIEW OF PHYTOPATHOLOGY 2023; 61:403-423. [PMID: 37217203 DOI: 10.1146/annurev-phyto-021021-041457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.
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Affiliation(s)
- J P Dundore-Arias
- Department of Biology and Chemistry, California State University, Monterey Bay, Seaside, California, USA
| | - M Michalska-Smith
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, USA;
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, USA
| | | | - L L Kinkel
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, USA;
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23
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Thompson JC, Zavala VM, Venturelli OS. Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions. PLoS Comput Biol 2023; 19:e1011436. [PMID: 37773951 PMCID: PMC10540976 DOI: 10.1371/journal.pcbi.1011436] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 08/16/2023] [Indexed: 10/01/2023] Open
Abstract
Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.
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Affiliation(s)
- Jaron C. Thompson
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Victor M. Zavala
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Ophelia S. Venturelli
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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24
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Amit G, Bashan A. Top-down identification of keystone taxa in the microbiome. Nat Commun 2023; 14:3951. [PMID: 37402745 DOI: 10.1038/s41467-023-39459-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 06/14/2023] [Indexed: 07/06/2023] Open
Abstract
Keystone taxa in ecological communities are native taxa that play an especially important role in the stability of their ecosystem. However, we still lack an effective framework for identifying these taxa from the available high-throughput sequencing without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. In addition, while most microbial interaction models assume pair-wise relationships, it is yet unclear whether pair-wise interactions dominate the system, or whether higher-order interactions are relevant. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the taxa. Our method does not assume a priori knowledge of pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real high-throughput sequencing of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module - multiple candidate keystone species with correlated occurrence. The keystone analysis of single-time-point cross-sectional data is also later verified by the evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities.
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Affiliation(s)
- Guy Amit
- Department of Physics, Bar-Ilan University, Ramat-Gan, 590002, Israel
- Department of Natural Sciences, The Open University of Israel, Raanana, 4353701, Israel
| | - Amir Bashan
- Department of Physics, Bar-Ilan University, Ramat-Gan, 590002, Israel.
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25
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Wang M, Osborn LJ, Jain S, Meng X, Weakley A, Yan J, Massey WJ, Varadharajan V, Horak A, Banerjee R, Allende DS, Chan ER, Hajjar AM, Wang Z, Dimas A, Zhao A, Nagashima K, Cheng AG, Higginbottom S, Hazen SL, Brown JM, Fischbach MA. Strain dropouts reveal interactions that govern the metabolic output of the gut microbiome. Cell 2023; 186:2839-2852.e21. [PMID: 37352836 PMCID: PMC10299816 DOI: 10.1016/j.cell.2023.05.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 04/10/2023] [Accepted: 05/26/2023] [Indexed: 06/25/2023]
Abstract
The gut microbiome is complex, raising questions about the role of individual strains in the community. Here, we address this question by constructing variants of a complex defined community in which we eliminate strains that occupy the bile acid 7α-dehydroxylation niche. Omitting Clostridium scindens (Cs) and Clostridium hylemonae (Ch) eliminates secondary bile acid production and reshapes the community in a highly specific manner: eight strains change in relative abundance by >100-fold. In single-strain dropout communities, Cs and Ch reach the same relative abundance and dehydroxylate bile acids to a similar extent. However, Clostridium sporogenes increases >1,000-fold in the ΔCs but not ΔCh dropout, reshaping the pool of microbiome-derived phenylalanine metabolites. Thus, strains that are functionally redundant within a niche can have widely varying impacts outside the niche, and a strain swap can ripple through the community in an unpredictable manner, resulting in a large impact on an unrelated community-level phenotype.
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Affiliation(s)
- Min Wang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Lucas J Osborn
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Sunit Jain
- ChEM-H Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Xiandong Meng
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Allison Weakley
- ChEM-H Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Jia Yan
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - William J Massey
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Venkateshwari Varadharajan
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Anthony Horak
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Rakhee Banerjee
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Daniela S Allende
- Department of Anatomical Pathology, Cleveland Clinic, Cleveland, OH 44195, USA
| | - E Ricky Chan
- Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Adeline M Hajjar
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zeneng Wang
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Alejandra Dimas
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Aishan Zhao
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Kazuki Nagashima
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Alice G Cheng
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Steven Higginbottom
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Stanley L Hazen
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - J Mark Brown
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Michael A Fischbach
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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26
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Aguilar-Salinas B, Olmedo-Álvarez G. A three-species synthetic community model whose rapid response to antagonism allows the study of higher-order dynamics and emergent properties in minutes. Front Microbiol 2023; 14:1057883. [PMID: 37333661 PMCID: PMC10272403 DOI: 10.3389/fmicb.2023.1057883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 05/02/2023] [Indexed: 06/20/2023] Open
Abstract
Microbial communities can be considered complex adaptive systems. Understanding how these systems arise from different components and how the dynamics of microbial interactions allow for species coexistence are fundamental questions in ecology. To address these questions, we built a three-species synthetic community, called BARS (Bacillota A + S + R). Each species in this community exhibits one of three ecological roles: Antagonistic, Sensitive, or Resistant, assigned in the context of a sediment community. We show that the BARS community reproduces features of complex communities and exhibits higher-order interaction (HOI) dynamics. In paired interactions, the majority of the S species (Sutcliffiella horikoshii 20a) population dies within 5 min when paired with the A species (Bacillus pumilus 145). However, an emergent property appears upon adding the third interactor, as antagonism of species A over S is not observed in the presence of the R species (Bacillus cereus 111). For the paired interaction, within the first 5 min, the surviving population of the S species acquires tolerance to species A, and species A ceases antagonism. This qualitative change reflects endogenous dynamics leading to the expression for tolerance to an antagonistic substance. The stability reached in the triple interaction exhibits a nonlinear response, highly sensitive to the density of the R species. In summary, our HOI model allows the study of the assembly dynamics of a three-species community and evaluating the immediate outcome within a 30 min frame. The BARS has features of a complex system where the paired interactions do not predict the community dynamics. The model is amenable to mechanistic dissection and to modeling how the parts integrate to achieve collective properties.
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27
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Diaz-Colunga J, Skwara A, Gowda K, Diaz-Uriarte R, Tikhonov M, Bajic D, Sanchez A. Global epistasis on fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220053. [PMID: 37004717 PMCID: PMC10067270 DOI: 10.1098/rstb.2022.0053] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns—ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Karna Gowda
- Department of Ecology & Evolution & Center for the Physics of Evolving Systems, The University of Chicago, Chicago, IL 60637, USA
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, School of Medicine, Universidad Autónoma de Madrid, Madrid 28029, Spain
- Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (UAM-CSIC), Madrid 28029, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University of St Louis, St Louis, MO 63130, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Microbial Biotechnology, Campus de Cantoblanco, CNB-CSIC, Madrid 28049, Spain
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28
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Jo C, Bernstein DB, Vaisman N, Frydman HM, Segrè D. Construction and Modeling of a Coculture Microplate for Real-Time Measurement of Microbial Interactions. mSystems 2023; 8:e0001721. [PMID: 36802169 PMCID: PMC10134821 DOI: 10.1128/msystems.00017-21] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 01/24/2023] [Indexed: 02/23/2023] Open
Abstract
The dynamic structures of microbial communities emerge from the complex network of interactions between their constituent microorganisms. Quantitative measurements of these interactions are important for understanding and engineering ecosystem structure. Here, we present the development and application of the BioMe plate, a redesigned microplate device in which pairs of wells are separated by porous membranes. BioMe facilitates the measurement of dynamic microbial interactions and integrates easily with standard laboratory equipment. We first applied BioMe to recapitulate recently characterized, natural symbiotic interactions between bacteria isolated from the Drosophila melanogaster gut microbiome. Specifically, the BioMe plate allowed us to observe the benefit provided by two Lactobacillus strains to an Acetobacter strain. We next explored the use of BioMe to gain quantitative insight into the engineered obligate syntrophic interaction between a pair of Escherichia coli amino acid auxotrophs. We integrated experimental observations with a mechanistic computational model to quantify key parameters associated with this syntrophic interaction, including metabolite secretion and diffusion rates. This model also allowed us to explain the slow growth observed for auxotrophs growing in adjacent wells by demonstrating that, under the relevant range of parameters, local exchange between auxotrophs is essential for efficient growth. The BioMe plate provides a scalable and flexible approach for the study of dynamic microbial interactions. IMPORTANCE Microbial communities participate in many essential processes from biogeochemical cycles to the maintenance of human health. The structure and functions of these communities are dynamic properties that depend on poorly understood interactions among different species. Unraveling these interactions is therefore a crucial step toward understanding natural microbiota and engineering artificial ones. Microbial interactions have been difficult to measure directly, largely due to limitations of existing methods to disentangle the contribution of different organisms in mixed cocultures. To overcome these limitations, we developed the BioMe plate, a custom microplate-based device that enables direct measurement of microbial interactions, by detecting the abundance of segregated populations of microbes that can exchange small molecules through a membrane. We demonstrated the possible application of the BioMe plate for studying both natural and artificial consortia. BioMe is a scalable and accessible platform that can be used to broadly characterize microbial interactions mediated by diffusible molecules.
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Affiliation(s)
- Charles Jo
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - David B. Bernstein
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Natalie Vaisman
- Department of Biology, Boston University, Boston, Massachusetts, USA
- CAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil
| | | | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
- Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
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29
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Wu L, Wang XW, Tao Z, Wang T, Zuo W, Zeng Y, Liu YY, Dai L. Data-driven prediction of colonization outcomes for complex microbial communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.19.537502. [PMID: 37131715 PMCID: PMC10153232 DOI: 10.1101/2023.04.19.537502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.
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30
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Gao Z, Ghosh D, Harrington HA, Restrepo JG, Taylor D. Dynamics on networks with higher-order interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:040401. [PMID: 37097941 DOI: 10.1063/5.0151265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Affiliation(s)
- Z Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - D Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - H A Harrington
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - J G Restrepo
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado 80309, USA
| | - D Taylor
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
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31
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Pilosof S. Conceptualizing microbe-plasmid communities as complex adaptive systems. Trends Microbiol 2023:S0966-842X(23)00025-2. [PMID: 36822952 DOI: 10.1016/j.tim.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/29/2022] [Accepted: 01/23/2023] [Indexed: 02/24/2023]
Abstract
Plasmids shape microbial communities' diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe-plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe-plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.
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Affiliation(s)
- Shai Pilosof
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er-Sheva, Israel.
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32
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Sanchez A, Bajic D, Diaz-Colunga J, Skwara A, Vila JCC, Kuehn S. The community-function landscape of microbial consortia. Cell Syst 2023; 14:122-134. [PMID: 36796331 DOI: 10.1016/j.cels.2022.12.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/17/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.
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Affiliation(s)
- Alvaro Sanchez
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA; Department of Microbial Biotechnology, CNB-CSIC, Campus de Cantoblanco, Madrid, Spain.
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology & Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, The Unviersity of Chicago, Chicago, IL, USA; Department of Ecology and Evolution, The University of Chicago, Chicago, IL, USA
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33
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Morales Moreira ZP, Chen MY, Yanez Ortuno DL, Haney CH. Engineering plant microbiomes by integrating eco-evolutionary principles into current strategies. CURRENT OPINION IN PLANT BIOLOGY 2023; 71:102316. [PMID: 36442442 DOI: 10.1016/j.pbi.2022.102316] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/30/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Engineering plant microbiomes has the potential to improve plant health in a rapid and sustainable way. Rapidly changing climates and relatively long timelines for plant breeding make microbiome engineering an appealing approach to improving food security. However, approaches that have shown promise in the lab have not resulted in wide-scale implementation in the field. Here, we suggest the use of an integrated approach, combining mechanistic molecular and genetic knowledge, with ecological and evolutionary theory, to target knowledge gaps in plant microbiome engineering that may facilitate translatability of approaches into the field. We highlight examples where understanding microbial community ecology is essential for a holistic understanding of the efficacy and consequences of microbiome engineering. We also review examples where understanding plant-microbe evolution could facilitate the design of plants able to recruit specific microbial communities. Finally, we discuss possible trade-offs in plant-microbiome interactions that should be considered during microbiome engineering efforts so as not to introduce off-target negative effects. We include classic and emergent approaches, ranging from microbial inoculants to plant breeding to host-driven microbiome engineering, and address areas that would benefit from multidisciplinary approaches.
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Affiliation(s)
- Zayda P Morales Moreira
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
| | - Melissa Y Chen
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
| | - Daniela L Yanez Ortuno
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
| | - Cara H Haney
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.
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George AB, Korolev KS. Ecological landscapes guide the assembly of optimal microbial communities. PLoS Comput Biol 2023; 19:e1010570. [PMID: 36626403 PMCID: PMC9831326 DOI: 10.1371/journal.pcbi.1010570] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 09/13/2022] [Indexed: 01/11/2023] Open
Abstract
Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.
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Affiliation(s)
- Ashish B. George
- Department of Physics and Biological Design Center, Boston University, Boston, Massachusetts, United States of America
- Carl R. Woese Institute for Genomic Biology and Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- * E-mail: (ABG); (KSK)
| | - Kirill S. Korolev
- Department of Physics and Biological Design Center, Boston University, Boston, Massachusetts, United States of America
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
- * E-mail: (ABG); (KSK)
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Morin MA, Morrison AJ, Harms MJ, Dutton RJ. Higher-order interactions shape microbial interactions as microbial community complexity increases. Sci Rep 2022; 12:22640. [PMID: 36587027 PMCID: PMC9805437 DOI: 10.1038/s41598-022-25303-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 11/28/2022] [Indexed: 01/01/2023] Open
Abstract
Non-pairwise interactions, or higher-order interactions (HOIs), in microbial communities have been described as significant drivers of emergent features in microbiomes. Yet, the re-organization of microbial interactions between pairwise cultures and larger communities remains largely unexplored from a molecular perspective but is central to our understanding and further manipulation of microbial communities. Here, we used a bottom-up approach to investigate microbial interaction mechanisms from pairwise cultures up to 4-species communities from a simple microbiome (Hafnia alvei, Geotrichum candidum, Pencillium camemberti and Escherichia coli). Specifically, we characterized the interaction landscape for each species combination involving E. coli by identifying E. coli's interaction-associated mutants using an RB-TnSeq-based interaction assay. We observed a deep reorganization of the interaction-associated mutants, with very few 2-species interactions conserved all the way up to a 4-species community and the emergence of multiple HOIs. We further used a quantitative genetics strategy to decipher how 2-species interactions were quantitatively conserved in higher community compositions. Epistasis-based analysis revealed that, of the interactions that are conserved at all levels of complexity, 82% follow an additive pattern. Altogether, we demonstrate the complex architecture of microbial interactions even within a simple microbiome, and provide a mechanistic and molecular explanation of HOIs.
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Affiliation(s)
- Manon A. Morin
- grid.266100.30000 0001 2107 4242School of Biological Science, University of California San Diego, San Diego, 92093 USA
| | - Anneliese J. Morrison
- grid.170202.60000 0004 1936 8008Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR USA ,grid.170202.60000 0004 1936 8008Institute of Molecular Biology, University of Oregon, Eugene, OR USA
| | - Michael J. Harms
- grid.170202.60000 0004 1936 8008Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR USA ,grid.170202.60000 0004 1936 8008Institute of Molecular Biology, University of Oregon, Eugene, OR USA
| | - Rachel J. Dutton
- grid.266100.30000 0001 2107 4242School of Biological Science, University of California San Diego, San Diego, 92093 USA
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A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem. mSphere 2022; 7:e0043622. [PMID: 36259715 PMCID: PMC9769528 DOI: 10.1128/msphere.00436-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Nonlinear ecological interactions within microbial ecosystems and their contribution to ecosystem functioning remain largely unexplored. Higher-order interactions, or interactions in systems comprised of more than two members that cannot be explained by cumulative pairwise interactions, are particularly understudied, especially in eukaryotic microorganisms. The wine fermentation ecosystem presents an ideal model to study yeast ecosystem establishment and functioning. Some pairwise ecological interactions between wine yeast species have been characterized, but very little is known about how more complex, multispecies systems function. Here, we evaluated nonlinear ecosystem properties by determining the transcriptomic response of Saccharomyces cerevisiae to pairwise versus tri-species culture. The transcriptome revealed that genes expressed during pairwise coculture were enriched in the tri-species data set but also that just under half of the data set comprised unique genes attributed to a higher-order response. Through interactive protein-association network visualizations, a holistic cell-wide view of the gene expression data was generated, which highlighted known stress response and metabolic adaptation mechanisms which were specifically activated during tri-species growth. Further, extracellular metabolite data corroborated that the observed differences were a result of a biotic stress response. This provides exciting new evidence showing the presence of higher-order interactions within a model microbial ecosystem. IMPORTANCE Higher-order interactions are one of the major blind spots in our understanding of microbial ecosystems. These systems remain largely unpredictable and are characterized by nonlinear dynamics, in particular when the system is comprised of more than two entities. By evaluating the transcriptomic response of S. cerevisiae to an increase in culture complexity from a single species to two- and three-species systems, we were able to confirm the presence of a unique response in the more complex setting that could not be explained by the responses observed at the pairwise level. This is the first data set that provides molecular targets for further analysis to explain unpredictable ecosystem dynamics in yeast.
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Gibbs T, Levin SA, Levine JM. Coexistence in diverse communities with higher-order interactions. Proc Natl Acad Sci U S A 2022; 119:e2205063119. [PMID: 36252042 PMCID: PMC9618036 DOI: 10.1073/pnas.2205063119] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/14/2022] [Indexed: 11/18/2022] Open
Abstract
A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However, two species may interactively affect the growth of a focal species. Although interactions among three or more species, called higher-order interactions, have the potential to modify our theoretical understanding of coexistence, ecologists lack clear expectations for how these interactions shape community structure. Here we analytically predict and numerically confirm how the variability and strength of higher-order interactions affect species coexistence. We found that as higher-order interaction strengths became more variable across species, fewer species could coexist, echoing the behavior of pairwise models. If interspecific higher-order interactions became too harmful relative to self-regulation, coexistence in diverse communities was destabilized, but coexistence was also lost when these interactions were too weak and mutualistic higher-order effects became prevalent. This behavior depended on the functional form of the interactions as the destabilizing effects of the mutualistic higher-order interactions were ameliorated when their strength saturated with species' densities. Last, we showed that more species-rich communities structured by higher-order interactions lose species more readily than their species-poor counterparts, generalizing classic results for community stability. Our work provides needed theoretical expectations for how higher-order interactions impact species coexistence in diverse communities.
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Affiliation(s)
- Theo Gibbs
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
| | - Jonathan M. Levine
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
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38
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Deter HS, Lu T. Engineering microbial consortia with rationally designed cellular interactions. Curr Opin Biotechnol 2022; 76:102730. [PMID: 35609504 PMCID: PMC10129393 DOI: 10.1016/j.copbio.2022.102730] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/22/2022] [Accepted: 04/03/2022] [Indexed: 12/14/2022]
Abstract
Synthetic microbial consortia represent a frontier of synthetic biology that promises versatile engineering of cellular functions. They are primarily developed through the design and construction of cellular interactions that coordinate individual dynamics and generate collective behaviors. Here we review recent advances in the engineering of synthetic communities through cellular-interaction programming. We first examine fundamental building blocks for intercellular communication and unidirectional positive and negative interactions. We then recap the assembly of the building blocks for creating bidirectional interactions in two-species ecosystems, which is followed by the discussion of engineering toward complex communities with increasing species numbers, under spatial contexts, and via model-guided design. We conclude by summarizing major challenges and future opportunities of engineered microbial ecosystems.
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Affiliation(s)
- Heather S Deter
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA; Intelligence Community Postdoctoral Research Fellowship Program, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Ting Lu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA; Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA; Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA; National Center for Supercomputing Applications, Urbana, IL, USA.
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39
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van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
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Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
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40
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Baranwal M, Clark RL, Thompson J, Sun Z, Hero AO, Venturelli OS. Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics. eLife 2022; 11:e73870. [PMID: 35736613 PMCID: PMC9225007 DOI: 10.7554/elife.73870] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/22/2022] [Indexed: 12/26/2022] Open
Abstract
Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.
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Affiliation(s)
- Mayank Baranwal
- Department of Systems and Control Engineering, Indian Institute of TechnologyBombayIndia
- Division of Data & Decision Sciences, Tata Consultancy Services ResearchMumbaiIndia
| | - Ryan L Clark
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Jaron Thompson
- Department of Chemical & Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
| | - Zeyu Sun
- Department of Electrical Engineering & Computer Science, University of MichiganAnn ArborUnited States
| | - Alfred O Hero
- Department of Electrical Engineering & Computer Science, University of MichiganAnn ArborUnited States
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Department of Statistics, University of MichiganAnn ArborUnited States
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical & Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
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41
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Wagner A. Competition for nutrients increases invasion resistance during assembly of microbial communities. Mol Ecol 2022; 31:4188-4203. [PMID: 35713370 PMCID: PMC9542400 DOI: 10.1111/mec.16565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 12/02/2022]
Abstract
The assembly of microbial communities through sequential invasions of microbial species is challenging to study experimentally. Here, I used genome‐scale metabolic models of multiple species to model community assembly. Each such model represents all known biochemical reactions that a species uses to build biomass from nutrients in the environment. Species interactions in such models emerge from first biochemical principles, either through competition for environmental nutrients, or through cross‐feeding on metabolic by‐products excreted by resident species. I used these models to study 250 community assembly sequences. In each such sequence, a community changes through successive species invasions. During the 250 assembly sequences, communities become more species‐rich and invasion‐resistant. Resistance against both constructive and destructive invasions – those that entail species extinction – is associated with high community productivity, high biomass, and low concentrations of unused carbon. Competition for nutrients outweighs the influence of cross‐feeding on the growth rate of individual species. In a community assembly network of all communities that arise during the 250 assembly sequences, some communities occur more often than expected by chance. These include invasion resistant “attractor” communities with high biomass that arise late in community assembly and persist preferentially because of their invasion resistance. Genome‐scale metabolic models can reveal generic properties of microbial communities that are independent of the resident species and the environment.
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Affiliation(s)
- Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland.,The Santa Fe Institute, Santa Fe, New Mexico, USA.,Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
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42
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Rafieenia R, Atkinson E, Ledesma-Amaro R. Division of labor for substrate utilization in natural and synthetic microbial communities. Curr Opin Biotechnol 2022; 75:102706. [DOI: 10.1016/j.copbio.2022.102706] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 01/30/2023]
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Reyes-González D, De Luna-Valenciano H, Utrilla J, Sieber M, Peña-Miller R, Fuentes-Hernández A. Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia. ROYAL SOCIETY OPEN SCIENCE 2022; 9:212008. [PMID: 35592760 PMCID: PMC9066302 DOI: 10.1098/rsos.212008] [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: 01/11/2022] [Accepted: 03/25/2022] [Indexed: 05/03/2023]
Abstract
Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.
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Affiliation(s)
- D. Reyes-González
- Synthetic Biology Program, Center for Genomic Sciences, Universidad Autónoma de México, 62220 Cuernavaca, Mexico
| | - H. De Luna-Valenciano
- Synthetic Biology Program, Center for Genomic Sciences, Universidad Autónoma de México, 62220 Cuernavaca, Mexico
- Systems Biology Program, Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210 Cuernavaca, Mexico
| | - J. Utrilla
- Synthetic Biology Program, Center for Genomic Sciences, Universidad Autónoma de México, 62220 Cuernavaca, Mexico
| | - M. Sieber
- Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
| | - R. Peña-Miller
- Systems Biology Program, Center for Genomic Sciences, Universidad Nacional Autónoma de México, 62210 Cuernavaca, Mexico
| | - A. Fuentes-Hernández
- Synthetic Biology Program, Center for Genomic Sciences, Universidad Autónoma de México, 62220 Cuernavaca, Mexico
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44
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Ma Y, Ma Y, Jiang X. Hypergraph Clustering Based on Modularity Feature Projection for High-order Relationship Community Detection of Microorganisms. Methods 2022; 203:604-613. [DOI: 10.1016/j.ymeth.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/05/2022] [Accepted: 05/17/2022] [Indexed: 10/18/2022] Open
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Berrios L, Rentsch JD. Linking Reactive Oxygen Species (ROS) to Abiotic and Biotic Feedbacks in Plant Microbiomes: The Dose Makes the Poison. Int J Mol Sci 2022; 23:ijms23084402. [PMID: 35457220 PMCID: PMC9030523 DOI: 10.3390/ijms23084402] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/13/2022] [Accepted: 04/13/2022] [Indexed: 12/13/2022] Open
Abstract
In nature, plants develop in complex, adaptive environments. Plants must therefore respond efficiently to environmental stressors to maintain homeostasis and enhance their fitness. Although many coordinated processes remain integral for achieving homeostasis and driving plant development, reactive oxygen species (ROS) function as critical, fast-acting orchestrators that link abiotic and biotic responses to plant homeostasis and development. In addition to the suite of enzymatic and non-enzymatic ROS processing pathways that plants possess, they also rely on their microbiota to buffer and maintain the oxidative window needed to balance anabolic and catabolic processes. Strong evidence has been communicated recently that links ROS regulation to the aggregated function(s) of commensal microbiota and plant-growth-promoting microbes. To date, many reports have put forth insightful syntheses that either detail ROS regulation across plant development (independent of plant microbiota) or examine abiotic–biotic feedbacks in plant microbiomes (independent of clear emphases on ROS regulation). Here we provide a novel synthesis that incorporates recent findings regarding ROS and plant development in the context of both microbiota regulation and plant-associated microbes. Specifically, we discuss various roles of ROS across plant development to strengthen the links between plant microbiome functioning and ROS regulation for both basic and applied research aims.
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Affiliation(s)
- Louis Berrios
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Correspondence:
| | - Jeremy D. Rentsch
- Department of Biology, Francis Marion University, Florence, SC 29502, USA;
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46
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Ludington WB. Higher-order microbiome interactions and how to find them. Trends Microbiol 2022; 30:618-621. [DOI: 10.1016/j.tim.2022.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022]
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Ponte-Fernandez C, Gonzalez-Dominguez J, Carvajal-Rodriguez A, Martin MJ. Evaluation of Existing Methods for High-Order Epistasis Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:912-926. [PMID: 33055017 DOI: 10.1109/tcbb.2020.3030312] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive.
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Gopalakrishnappa C, Gowda K, Prabhakara KH, Kuehn S. An ensemble approach to the structure-function problem in microbial communities. iScience 2022; 25:103761. [PMID: 35141504 PMCID: PMC8810406 DOI: 10.1016/j.isci.2022.103761] [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: 11/30/2022] Open
Abstract
The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.
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Affiliation(s)
| | - Karna Gowda
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA
| | - Kaumudi H. Prabhakara
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA
| | - Seppe Kuehn
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA
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Exploring the roles of microbes in facilitating plant adaptation to climate change. Biochem J 2022; 479:327-335. [PMID: 35119455 PMCID: PMC8883484 DOI: 10.1042/bcj20210793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/30/2022]
Abstract
Plants benefit from their close association with soil microbes which assist in their response to abiotic and biotic stressors. Yet much of what we know about plant stress responses is based on studies where the microbial partners were uncontrolled and unknown. Under climate change, the soil microbial community will also be sensitive to and respond to abiotic and biotic stressors. Thus, facilitating plant adaptation to climate change will require a systems-based approach that accounts for the multi-dimensional nature of plant-microbe-environment interactions. In this perspective, we highlight some of the key factors influencing plant-microbe interactions under stress as well as new tools to facilitate the controlled study of their molecular complexity, such as fabricated ecosystems and synthetic communities. When paired with genomic and biochemical methods, these tools provide researchers with more precision, reproducibility, and manipulability for exploring plant-microbe-environment interactions under a changing climate.
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Genomic structure predicts metabolite dynamics in microbial communities. Cell 2022; 185:530-546.e25. [PMID: 35085485 DOI: 10.1016/j.cell.2021.12.036] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/16/2021] [Accepted: 12/21/2021] [Indexed: 12/21/2022]
Abstract
The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.
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