1
|
Jiang X, Peng Z, Zhang J. Starting with screening strains to construct synthetic microbial communities (SynComs) for traditional food fermentation. Food Res Int 2024; 190:114557. [PMID: 38945561 DOI: 10.1016/j.foodres.2024.114557] [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: 03/21/2024] [Revised: 05/16/2024] [Accepted: 05/26/2024] [Indexed: 07/02/2024]
Abstract
With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.
Collapse
Affiliation(s)
- Xinyi Jiang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Zheng Peng
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Juan Zhang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China.
| |
Collapse
|
2
|
Faller L, Leite MFA, Kuramae EE. Enhancing phosphate-solubilising microbial communities through artificial selection. Nat Commun 2024; 15:1649. [PMID: 38388537 PMCID: PMC10884399 DOI: 10.1038/s41467-024-46060-x] [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: 08/24/2023] [Accepted: 02/13/2024] [Indexed: 02/24/2024] Open
Abstract
Microbial communities, acting as key drivers of ecosystem processes, harbour immense potential for sustainable agriculture practices. Phosphate-solubilising microorganisms, for example, can partially replace conventional phosphate fertilisers, which rely on finite resources. However, understanding the mechanisms and engineering efficient communities poses a significant challenge. In this study, we employ two artificial selection methods, environmental perturbation, and propagation, to construct phosphate-solubilising microbial communities. To assess trait transferability, we investigate the community performance in different media and a hydroponic system with Chrysanthemum indicum. Our findings reveal a distinct subset of phosphate-solubilising bacteria primarily dominated by Klebsiella and Enterobacterales. The propagated communities consistently demonstrate elevated levels of phosphate solubilisation, surpassing the starting soil community by 24.2% in activity. The increased activity of propagated communities remains consistent upon introduction into the hydroponic system. This study shows the efficacy of community-level artificial selection, particularly through propagation, as a tool for successfully modifying microbial communities to enhance phosphate solubilisation.
Collapse
Affiliation(s)
- Lena Faller
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB, Wageningen, The Netherlands
- Utrecht University, Institute of Environmental Biology, Ecology and Biodiversity, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Marcio F A Leite
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB, Wageningen, The Netherlands
- Utrecht University, Institute of Environmental Biology, Ecology and Biodiversity, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Eiko E Kuramae
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB, Wageningen, The Netherlands.
- Utrecht University, Institute of Environmental Biology, Ecology and Biodiversity, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
| |
Collapse
|
3
|
Thomas JL, Rowland-Chandler J, Shou W. Artificial selection of microbial communities: what have we learnt and how can we improve? Curr Opin Microbiol 2024; 77:102400. [PMID: 38091857 DOI: 10.1016/j.mib.2023.102400] [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: 07/10/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 02/12/2024]
Abstract
Microbial communities are capable of performing diverse functions with important bioindustrial and medical applications. One approach to improving community function is to breed new communities by artificially selecting for those displaying high community function ('community selection'). Importantly, community selection can improve the function of interest without needing to understand how the function arises, just like in classical artificial selection of individuals. However, experimental studies of community selection have had varied and largely limited success. Here, we review a conceptual framework to help foster an understanding of community selection and its associated challenges, and provide broad insights for designing effective selection strategies.
Collapse
Affiliation(s)
- Joshua L Thomas
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, United Kingdom
| | - Jamila Rowland-Chandler
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, United Kingdom
| | - Wenying Shou
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, United Kingdom.
| |
Collapse
|
4
|
Ji X, Zhang L, Yu X, Chen F, Guo F, Wu Q, Xu Y. Selection of initial microbial community for the alcoholic fermentation of sesame flavor-type baijiu. Food Res Int 2023; 172:113141. [PMID: 37689904 DOI: 10.1016/j.foodres.2023.113141] [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: 03/09/2023] [Revised: 05/02/2023] [Accepted: 06/13/2023] [Indexed: 09/11/2023]
Abstract
The initial microbial community is critical for the production of volatile metabolites during traditional food fermentations. Selection of the initial community plays an important role in improving the quality of fermented foods. Here, we used high-throughput amplicon sequencing combined with multivariate statistical methods to explore the microbial succession in stacking and alcoholic fermentation stages in sesame flavor-type baijiu making. We proposed a selection strategy for the initial microbial community in the alcoholic fermentation stage, which determined the quality of baijiu. Results suggested that the microbial composition statistically differed between stacking and alcoholic fermentation stages (ANOSIM, Bacteria: R = 0.60, P = 0.001; Fungi: R = 0.53, P = 0.001). Microbial succession drove metabolic succession (Bacteria: r = 0.87, P < 0.05; Fungi: r = 0.56, P < 0.05) in alcoholic fermentation. The fermentation time of stacking fermentation determined the initial community for alcoholic fermentation, and it can be used as a criterion for selection of the initial microbial community for alcoholic fermentation. The succession distance of the microbial community was varied and reached the highest (Bacteria: 0.048, Fungi: 0.064) at 30 h in stacking fermentation. When we selected 30 h as stacking fermentation time, the concentration (4.58 mg/kg) and diversity (0.61) of volatile metabolites were highest at the end of alcoholic fermentation. This work developed a succession distance-guided approach to select the initial microbial community for the alcoholic fermentation of sesame flavor-type baijiu. This approach can be used to improve the quality of baijiu.
Collapse
Affiliation(s)
- Xueao Ji
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Longyun Zhang
- Suqian Yanghe Distillery Co. Ltd, Jiangsu 223800, China
| | - Xiaowei Yu
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Fujiang Chen
- Suqian Yanghe Distillery Co. Ltd, Jiangsu 223800, China
| | - Fengxue Guo
- Suqian Yanghe Distillery Co. Ltd, Jiangsu 223800, China
| | - Qun Wu
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
| |
Collapse
|
5
|
Yu SR, Zhang YY, Zhang QG. The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis. Front Microbiol 2023; 14:1257935. [PMID: 37840740 PMCID: PMC10570731 DOI: 10.3389/fmicb.2023.1257935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/18/2023] [Indexed: 10/17/2023] Open
Abstract
The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs.
Collapse
Affiliation(s)
- Shi-Rui Yu
- State Key Laboratory of Earth Surface Processes and Resource Ecology and MOE Key Laboratory for Biodiversity Science and Ecological Engineering, Beijing Normal University, Beijing, China
| | - Yuan-Ye Zhang
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian, China
| | - Quan-Guo Zhang
- State Key Laboratory of Earth Surface Processes and Resource Ecology and MOE Key Laboratory for Biodiversity Science and Ecological Engineering, Beijing Normal University, Beijing, China
| |
Collapse
|
6
|
Letten AD, Ludington WB. Pulsed, continuous or somewhere in between? Resource dynamics matter in the optimisation of microbial communities. THE ISME JOURNAL 2023; 17:641-644. [PMID: 36694008 PMCID: PMC10030971 DOI: 10.1038/s41396-023-01369-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
The optimisation of synthetic and natural microbial communities has vast potential for emerging applications in medicine, agriculture and industry. Realising this goal is contingent on a close correlation between theory, experiments, and the real world. Although the temporal pattern of resource supply can play a major role in microbial community assembly, resource dynamics are commonly treated inconsistently in theoretical and experimental research. Here we explore how the composition of communities varies under continuous resource supply, typical of theoretical approaches, versus pulsed resource supply, typical of experiments. Using simulations of classical resource competition models, we show that community composition diverges rapidly between the two regimes, with almost zero overlap in composition once the pulsing interval stretches beyond just four hours. The implication for the rapidly growing field of microbial community optimisation is that the resource supply regime must be tailored to the community being optimised. As such, we argue that resource supply dynamics should be considered both a constraint in the design of novel microbial communities and as a tuning mechanism for the optimisation of pre-existing communities like those found in the human gut.
Collapse
Affiliation(s)
- Andrew D Letten
- School of Biological Sciences, University of Queensland, Brisbane, QLD, 4072, Australia.
| | - William B Ludington
- Department of Embryology, Carnegie Institution of Washington, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
7
|
Yang S, Liu H, Xie P, Wen T, Shen Q, Yuan J. Emerging Pathways for Engineering the Rhizosphere Microbiome for Optimal Plant Health. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:4441-4449. [PMID: 36890647 DOI: 10.1021/acs.jafc.2c08758] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The increasing impacts of global climate change on crop performance pose a significant threat to global food security. The rhizosphere microbiomes intimately interact with the plant and can largely facilitate plants in growth promotion and stress resistance via multiple mechanisms. This review focuses on approaches for harnessing the rhizosphere microbiomes to produce beneficial effects toward enhanced crop productivity, including the use of organic and inorganic amendments, and microbial inoculants. Emerging methods, such as the utilization of synthetic microbial consortia, host-mediated microbiome engineering, prebiotics made from specific plant root exudates, and crop breeding to promote beneficial plant-microbiome interactions, are highlighted. Updating our knowledge in this field is critical for understanding and improving plant-microbiome interactions, thereby enhancing plant adaptiveness to changing environmental conditions.
Collapse
Affiliation(s)
- Shengdie Yang
- The Key Laboratory of Plant Immunity, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Hongwei Liu
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW 2753, Australia
| | - Penghao Xie
- The Key Laboratory of Plant Immunity, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Tao Wen
- The Key Laboratory of Plant Immunity, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Qirong Shen
- The Key Laboratory of Plant Immunity, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Jun Yuan
- The Key Laboratory of Plant Immunity, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| |
Collapse
|
8
|
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: 18] [Impact Index Per Article: 18.0] [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.
Collapse
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
| |
Collapse
|
9
|
Hu H, Wang M, Huang Y, Xu Z, Xu P, Nie Y, Tang H. Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use. MLIFE 2022; 1:382-398. [PMID: 38818482 PMCID: PMC10989833 DOI: 10.1002/mlf2.12043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2024]
Abstract
Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.
Collapse
Affiliation(s)
- Haiyang Hu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Miaoxiao Wang
- Department of Environmental Systems ScienceETH ZürichZürichSwitzerland
- Department of Environmental MicrobiologyETH ZürichEawagSwitzerland
| | - Yiqun Huang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Zhaoyong Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Yong Nie
- College of EngineeringPeking UniversityBeijingChina
| | - Hongzhi Tang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| |
Collapse
|
10
|
Lalejini A, Dolson E, Vostinar AE, Zaman L. Artificial selection methods from evolutionary computing show promise for directed evolution of microbes. eLife 2022; 11:79665. [PMID: 35916365 PMCID: PMC9444240 DOI: 10.7554/elife.79665] [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: 04/21/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods. Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs. Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective. Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations. In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications. While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.
Collapse
Affiliation(s)
| | - Emily Dolson
- Department of Computer Science and Engineering, Michigan State University, East Lansing, United States
| | - Anya E Vostinar
- Computer Science Department, Carleton College, Northfield, United States
| | - Luis Zaman
- University of Michigan-Ann Arbor, Ann Arbor, United States
| |
Collapse
|
11
|
Raynaud T, Devers-Lamrani M, Spor A, Blouin M. Community diversity determines the evolution of synthetic bacterial communities under artificial selection. Evolution 2022; 76:1883-1895. [PMID: 35789998 DOI: 10.1111/evo.14558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 05/20/2022] [Accepted: 06/17/2022] [Indexed: 01/22/2023]
Abstract
Artificial selection can be conducted at the community level in the laboratory through a differential propagation of the communities according to their level of expression of a targeted function. Working with communities instead of individuals as selection units brings in additional sources of variation in the considered function that can influence the outcome of the artificial selection. In this study, we wanted to assess the effect of manipulating the initial community richness on artificial selection efficiency, defined as the change in the targeted function over time. We applied artificial selection for a high productivity on synthetic bacterial communities varying for their richness (from one to 16 strains). Overall, the selected communities were 16% more productive than the control communities, but a convergence of community composition might have limited the effect of diversity on artificial selection efficiency. Community richness positively influenced community productivity and metabolic capacities and was a strong determinant of the dynamics of community evolution. We propose that applying artificial selection on communities varying for their diversity could be a way to find communities differing for their level of expression of a function but also for their responsiveness to artificial selection, provided that their initial composition is different enough.
Collapse
Affiliation(s)
- Tiffany Raynaud
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, F-21000, France
| | - Marion Devers-Lamrani
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, F-21000, France
| | - Aymé Spor
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, F-21000, France
| | - Manuel Blouin
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, F-21000, France
| |
Collapse
|
12
|
Mueller UG, Linksvayer TA. Microbiome breeding: conceptual and practical issues. Trends Microbiol 2022; 30:997-1011. [PMID: 35595643 DOI: 10.1016/j.tim.2022.04.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/30/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
Abstract
Microbiome breeding is a new artificial selection technique that seeks to change the genetic composition of microbiomes in order to benefit plant or animal hosts. Recent experimental and theoretical analyses have shown that microbiome breeding is possible whenever microbiome-encoded genetic factors affect host traits (e.g., health) and microbiomes are transmissible between hosts with sufficient fidelity, such as during natural microbiome transmission between individuals of social animals, or during experimental microbiome transplanting between plants. To address misunderstandings that stymie microbiome-breeding programs, we (i) clarify and visualize the corresponding elements of microbiome selection and standard selection; (ii) elucidate the eco-evolutionary processes underlying microbiome selection within a quantitative genetic framework to summarize practical guidelines that optimize microbiome breeding; and (iii) characterize the kinds of host species most amenable to microbiome breeding.
Collapse
Affiliation(s)
- Ulrich G Mueller
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.
| | - Timothy A Linksvayer
- Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA.
| |
Collapse
|
13
|
Xie L, Shou W. Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities. Nat Commun 2021; 12:6799. [PMID: 34815384 PMCID: PMC8611069 DOI: 10.1038/s41467-021-26647-4] [Citation(s) in RCA: 21] [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: 03/31/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022] Open
Abstract
Microbial communities often perform important functions that depend on inter-species interactions. To improve community function via artificial selection, one can repeatedly grow many communities to allow mutations to arise, and "reproduce" the highest-functioning communities by partitioning each into multiple offspring communities for the next cycle. Since improvement is often unimpressive in experiments, we study how to design effective selection strategies in silico. Specifically, we simulate community selection to improve a function that requires two species. With a "community function landscape", we visualize how community function depends on species and genotype compositions. Due to ecological interactions that promote species coexistence, the evolutionary trajectory of communities is restricted to a path on the landscape. This restriction can generate counter-intuitive evolutionary dynamics, prevent the attainment of maximal function, and importantly, hinder selection by trapping communities in locations of low community function heritability. We devise experimentally-implementable manipulations to shift the path to higher heritability, which speeds up community function improvement even when landscapes are high dimensional or unknown. Video walkthroughs: https://go.nature.com/3GWwS6j ; https://online.kitp.ucsb.edu/online/ecoevo21/shou2/ .
Collapse
Affiliation(s)
- Li Xie
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States.
| | - Wenying Shou
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.
| |
Collapse
|
14
|
Jacquiod S, Spor A, Wei S, Munkager V, Bru D, Sørensen SJ, Salon C, Philippot L, Blouin M. Artificial selection of stable rhizosphere microbiota leads to heritable plant phenotype changes. Ecol Lett 2021; 25:189-201. [PMID: 34749426 DOI: 10.1111/ele.13916] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/07/2021] [Accepted: 09/27/2021] [Indexed: 11/29/2022]
Abstract
Artificial selection of microbiota opens new avenues for improving plants. However, reported results lack consistency. We hypothesised that the success in artificial selection of microbiota depends on the stabilisation of community structure. In a ten-generation experiment involving 1,800 plants, we selected rhizosphere microbiota of Brachypodium distachyon associated with high or low leaf greenness, a proxy of plant performance. The microbiota structure showed strong fluctuations during an initial transitory phase, with no detectable leaf greenness heritability. After five generations, the microbiota structure stabilised, concomitantly with heritability in leaf greenness. Selection, initially ineffective, did successfully alter the selected property as intended, especially for high selection. We show a remarkable correlation between the variability in plant traits and selected microbiota structures, revealing two distinct sub-communities associated with high or low leaf greenness, whose abundance was significantly steered by directional selection. Understanding microbiota structure stabilisation will improve the reliability of artificial microbiota selection.
Collapse
Affiliation(s)
- Samuel Jacquiod
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| | - Aymé Spor
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| | - Shaodong Wei
- Section of Microbiology, University of Copenhagen, Copenhagen, Denmark
| | - Victoria Munkager
- Section of Terrestrial Ecology, University of Copenhagen, Copenhagen, Denmark
| | - David Bru
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| | - Søren J Sørensen
- Section of Microbiology, University of Copenhagen, Copenhagen, Denmark
| | - Christophe Salon
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| | - Laurent Philippot
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| | - Manuel Blouin
- Agroécologie laboratory, Université Bourgogne Franche-Comté, AgroSup Dijon, INRAE, Université Bourgogne, Dijon, France
| |
Collapse
|
15
|
Closed microbial communities self-organize to persistently cycle carbon. Proc Natl Acad Sci U S A 2021; 118:2013564118. [PMID: 34740965 PMCID: PMC8609437 DOI: 10.1073/pnas.2013564118] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 12/16/2022] Open
Abstract
Life on Earth depends on ecologically driven nutrient cycles to regenerate resources. Understanding how nutrient cycles emerge from a complex web of ecological processes is a central challenge in ecology. However, we lack model ecosystems that can be replicated, manipulated, and quantified in the laboratory, making it challenging to determine how changes in composition and the environment impact cycling. Enabled by a new high-precision method to quantify carbon cycling, we show that materially closed microbial ecosystems (CES) provided with only light self-organize to robustly cycle carbon. Studying replicate CES that support a carbon cycle reveals variable community composition but a conserved set of metabolic capabilities. Our study helps establish CES as model biospheres for studying how ecosystems persistently cycle nutrients. Cycles of nutrients (N, P, etc.) and resources (C) are a defining emergent feature of ecosystems. Cycling plays a critical role in determining ecosystem structure at all scales, from microbial communities to the entire biosphere. Stable cycles are essential for ecosystem persistence because they allow resources and nutrients to be regenerated. Therefore, a central problem in ecology is understanding how ecosystems are organized to sustain robust cycles. Addressing this problem quantitatively has proved challenging because of the difficulties associated with manipulating ecosystem structure while measuring cycling. We address this problem using closed microbial ecosystems (CES), hermetically sealed microbial consortia provided with only light. We develop a technique for quantifying carbon cycling in hermetically sealed microbial communities and show that CES composed of an alga and diverse bacterial consortia self-organize to robustly cycle carbon for months. Comparing replicates of diverse CES, we find that carbon cycling does not depend strongly on the taxonomy of the bacteria present. Moreover, despite strong taxonomic differences, self-organized CES exhibit a conserved set of metabolic capabilities. Therefore, an emergent carbon cycle enforces metabolic but not taxonomic constraints on ecosystem organization. Our study helps establish closed microbial communities as model ecosystems to study emergent function and persistence in replicate systems while controlling community composition and the environment.
Collapse
|
16
|
Chang CY, Vila JCC, Bender M, Li R, Mankowski MC, Bassette M, Borden J, Golfier S, Sanchez PGL, Waymack R, Zhu X, Diaz-Colunga J, Estrela S, Rebolleda-Gomez M, Sanchez A. Engineering complex communities by directed evolution. Nat Ecol Evol 2021; 5:1011-1023. [PMID: 33986540 PMCID: PMC8263491 DOI: 10.1038/s41559-021-01457-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 03/28/2021] [Indexed: 02/03/2023]
Abstract
Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.
Collapse
Affiliation(s)
- Chang-Yu Chang
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Madeline Bender
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Richard Li
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Madeleine C Mankowski
- Department of Immunobiology and Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Molly Bassette
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Julia Borden
- Department of Molecular & Cellular Biology, University of California Berkeley, Berkeley, CA, USA
| | - Stefan Golfier
- Max Planck Institute of Molecular Cell Biology and Genetics, and Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Paul Gerald L Sanchez
- European Molecular Biology Laboratory (EMBL), Developmental Biology Unit, Heidelberg, Germany
| | - Rachel Waymack
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA
| | - Xinwen Zhu
- Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, USA
| | - Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Sylvie Estrela
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Maria Rebolleda-Gomez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.
- Microbial Sciences Institute, Yale University, New Haven, CT, USA.
| |
Collapse
|
17
|
Pacheco AR, Segrè D. An evolutionary algorithm for designing microbial communities via environmental modification. J R Soc Interface 2021; 18:20210348. [PMID: 34157894 PMCID: PMC8220269 DOI: 10.1098/rsif.2021.0348] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.
Collapse
Affiliation(s)
- Alan R Pacheco
- Graduate Program in Bioinformatics and Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Daniel Segrè
- Graduate Program in Bioinformatics and Biological Design Center, Boston University, Boston, MA 02215, USA.,Department of Biology, Boston University, Boston, MA 02215, USA.,Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Department of Physics, Boston University, Boston, MA 02215, USA
| |
Collapse
|
18
|
Sánchez Á, Vila JCC, Chang CY, Diaz-Colunga J, Estrela S, Rebolleda-Gomez M. Directed Evolution of Microbial Communities. Annu Rev Biophys 2021; 50:323-341. [PMID: 33646814 PMCID: PMC8105285 DOI: 10.1146/annurev-biophys-101220-072829] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes.
Collapse
Affiliation(s)
- Álvaro Sánchez
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| | - Chang-Yu Chang
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| | - Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| | - Sylvie Estrela
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| | - María Rebolleda-Gomez
- Department of Ecology & Evolutionary Biology and Microbial Sciences Institute, Yale University, New Haven, Connecticut 06520, USA; , , , , ,
| |
Collapse
|
19
|
Estrela S, Sánchez Á, Rebolleda-Gómez M. Multi-Replicated Enrichment Communities as a Model System in Microbial Ecology. Front Microbiol 2021; 12:657467. [PMID: 33897672 PMCID: PMC8062719 DOI: 10.3389/fmicb.2021.657467] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/15/2021] [Indexed: 12/21/2022] Open
Abstract
Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.
Collapse
Affiliation(s)
- Sylvie Estrela
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States
| | - Álvaro Sánchez
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States
| | | |
Collapse
|
20
|
Wang J, Carper DL, Burdick LH, Shrestha HK, Appidi MR, Abraham PE, Timm CM, Hettich RL, Pelletier DA, Doktycz MJ. Formation, characterization and modeling of emergent synthetic microbial communities. Comput Struct Biotechnol J 2021; 19:1917-1927. [PMID: 33995895 PMCID: PMC8079826 DOI: 10.1016/j.csbj.2021.03.034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 01/04/2023] Open
Abstract
Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form stable microbial communities. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains that depended on the medium. To unravel the underlying metabolic interactions, flux balance analysis was used to model microbial growth and identify potential metabolic exchanges involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. A fast growth rate is identified as one factor that can provide an advantage for maintaining presence in the community. Final community selection can also depend on selective antagonistic relationships and metabolic exchanges. Revealing the mechanisms of interaction among plant-associated microorganisms provides insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic communities with desired biological functions.
Collapse
Affiliation(s)
- Jia Wang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dana L. Carper
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Leah H. Burdick
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Him K. Shrestha
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Manasa R. Appidi
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Collin M. Timm
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert L. Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Dale A. Pelletier
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
| | - Mitchel J. Doktycz
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Corresponding authors.
| |
Collapse
|
21
|
Aggarwal S. Digest: Optimization of microbial community function through propagule strategies. Evolution 2020; 75:200-201. [PMID: 33169378 DOI: 10.1111/evo.14123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/25/2020] [Indexed: 12/19/2022]
Abstract
How do microbial communities interact with each other? Chang et al. devised two artificial selection experiments to better characterize the function of propagule strategies in microbiomes. Consistent with previous results and with conventional evolutionary biology theory, the authors found that high group level selection was associated with high heritability, and as heritability declined, so did selection. These findings provide the necessary background to develop further artificial selection experimental protocols in microbiota.
Collapse
Affiliation(s)
- Saloni Aggarwal
- Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2R3, Canada
| |
Collapse
|
22
|
Chang CY, Osborne ML, Bajic D, Sanchez A. Artificially selecting bacterial communities using propagule strategies. Evolution 2020; 74:2392-2403. [PMID: 32888315 PMCID: PMC7942404 DOI: 10.1111/evo.14092] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/21/2020] [Accepted: 08/29/2020] [Indexed: 02/06/2023]
Abstract
Artificial selection is a promising approach to manipulate microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both used "propagule" selection strategies, whereby the best-performing communities are used as the inocula to form a new generation of communities. Both experiments were contrasted to a random selection control. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used multiple soil communities as the starting inocula, and the function under selection was the communities' cross-feeding potential. In both experiments, the selected communities reached a higher mean function than the control. In the first experiment, this was caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, this response was fueled by the large initial variance in function across communities, and stopped when the top-performing community "fixed" in the metacommunity. Our results are in agreement with basic expectations from breeding theory, pointing to some of the limitations of community-level selection experiments that can inform the design of future studies.
Collapse
Affiliation(s)
- Chang-Yu Chang
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.,Microbial Sciences Institute. Yale University, New Haven, CT, USA
| | - Melisa L. Osborne
- The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.,Graduate Program in Bioinformatics and Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.,Microbial Sciences Institute. Yale University, New Haven, CT, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.,Microbial Sciences Institute. Yale University, New Haven, CT, USA.,The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.,Corresponding author:
| |
Collapse
|