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Chen X, Wang M, Luo L, Liu X, An L, Nie Y, Wu XL. The evolution of autonomy from two cooperative specialists in fluctuating environments. Proc Natl Acad Sci U S A 2024; 121:e2317182121. [PMID: 39172793 PMCID: PMC11363282 DOI: 10.1073/pnas.2317182121] [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: 10/04/2023] [Accepted: 07/24/2024] [Indexed: 08/24/2024] Open
Abstract
From microbes to humans, organisms perform numerous tasks for their survival, including food acquisition, migration, and reproduction. A complex biological task can be performed by either an autonomous organism or by cooperation among several specialized organisms. However, it remains unclear how autonomy and cooperation evolutionarily switch. Specifically, it remains unclear whether and how cooperative specialists can repair deleted genes through direct genetic exchange, thereby regaining metabolic autonomy. Here, we address this question by experimentally evolving a mutualistic microbial consortium composed of two specialists that cooperatively degrade naphthalene. We observed that autonomous genotypes capable of performing the entire naphthalene degradation pathway evolved from two cooperative specialists and dominated the community. This evolutionary transition was driven by the horizontal gene transfer (HGT) between the two specialists. However, this evolution was exclusively observed in the fluctuating environment alternately supplied with naphthalene and pyruvate, where mutualism and competition between the two specialists alternated. The naphthalene-supplied environment exerted selective pressure that favors the expansion of autonomous genotypes. The pyruvate-supplied environment promoted the coexistence and cell density of the cooperative specialists, thereby increasing the likelihood of HGT. Using a mathematical model, we quantitatively demonstrate that environmental fluctuations facilitate the evolution of autonomy through HGT when the relative growth rate and carrying capacity of the cooperative specialists allow enhanced coexistence and higher cell density in the competitive environment. Together, our results demonstrate that cooperative specialists can repair deleted genes through a direct genetic exchange under specific conditions, thereby regaining metabolic autonomy.
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Affiliation(s)
- Xiaoli Chen
- College of Engineering, Peking University, Beijing100871, China
- Institute of Ocean Research, Peking University, Beijing100871, China
| | - Miaoxiao Wang
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Laipeng Luo
- College of Engineering, Peking University, Beijing100871, China
| | - Xiaonan Liu
- College of Engineering, Peking University, Beijing100871, China
| | - Liyun An
- College of Architecture and Environment, Sichuan University, Chengdu610000, China
| | - Yong Nie
- College of Engineering, Peking University, Beijing100871, China
| | - Xiao-Lei Wu
- College of Engineering, Peking University, Beijing100871, China
- Institute of Ocean Research, Peking University, Beijing100871, China
- Institute of Ecology, Peking University, Beijing100871, China
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2
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Doulcier G, Lambert A. Neutral diversity in experimental metapopulations. Theor Popul Biol 2024; 158:89-108. [PMID: 38493997 DOI: 10.1016/j.tpb.2024.02.011] [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: 07/12/2023] [Revised: 02/07/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024]
Abstract
New automated and high-throughput methods allow the manipulation and selection of numerous bacterial populations. In this manuscript we are interested in the neutral diversity patterns that emerge from such a setup in which many bacterial populations are grown in parallel serial transfers, in some cases with population-wide extinction and splitting events. We model bacterial growth by a birth-death process and use the theory of coalescent point processes. We show that there is a dilution factor that optimises the expected amount of neutral diversity for a given number of cycles, and study the power law behaviour of the mutation frequency spectrum for different experimental regimes. We also explore how neutral variation diverges between two recently split populations by establishing a new formula for the expected number of shared and private mutations. Finally, we show the interest of such a setup to select a phenotype of interest that requires multiple mutations.
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Affiliation(s)
- Guilhem Doulcier
- Macquarie University, Department of Philosophy, Sydney, Australia; Max Planck Institute for Evolutionary Biology, Department of Theoretical Biology, Plön, Germany.
| | - Amaury Lambert
- SMILE - Stochastic Models for the Inference of Life Evolution, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS UMR8197, INSERM U1024, France; Centre Interdisciplinaire de Recherche en Biologie (CIRB), Collège de France, CNRS UMR7241, INSERM U1050, PSL Université, Paris, France.
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3
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Wang J, Xu C, Zhang W, Hong Y, Shen G, Wang W, Tang H, Zhang S, Pan J, Wang W. Synergistic effect of two bacterial strains promoting anaerobic digestion of rice straw to produce methane. ENVIRONMENTAL RESEARCH 2024; 252:118974. [PMID: 38649016 DOI: 10.1016/j.envres.2024.118974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024]
Abstract
A large amount of agricultural waste causes global environmental pollution. Biogas production by microbial pretreatment is an important way to utilize agricultural waste resources. In this study, Sporocytophaga CG-1 (A, cellulolytic strain) was co-cultured with Bacillus clausii HP-1 (B, non-cellulolytic strain) to analyze the effect of pretreatment of rice straw on methanogenic capacity of anaerobic digestion (AD). The results showed that weight loss rate of filter paper of co-culture combination is 53.38%, which is 29.37% higher than that of A. The synergistic effect of B on A can promote its degradation of cellulose. The cumulative methane production rate of the co-culture combination was the highest (93.04 mL/g VS substrate), which was significantly higher than that of A, B and the control group (82.38, 67.28 and 67.70 mL/g VS substrate). Auxiliary bacteria can improve cellulose degradation rate by promoting secondary product metabolism. These results provide data support for the application of co-culture strategies in the field of anaerobic digestion practices.
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Affiliation(s)
- Jinghong Wang
- Key Laboratory of Low -Carbon Green Agriculture in Northeast China, Ministry of Agriculture and Rural Affairs, College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China; College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China
| | - Congfeng Xu
- Key Laboratory of Low -Carbon Green Agriculture in Northeast China, Ministry of Agriculture and Rural Affairs, College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China; College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China
| | - Wei Zhang
- Key Laboratory of Low -Carbon Green Agriculture in Northeast China, Ministry of Agriculture and Rural Affairs, College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China
| | - Yanhua Hong
- Key Laboratory of Low -Carbon Green Agriculture in Northeast China, Ministry of Agriculture and Rural Affairs, College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China
| | - Guinan Shen
- College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China
| | - Weiwei Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Hongzhi Tang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Shenglong Zhang
- College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China; Heilongjiang Guohong Environmental Co., LTD, Harbin, 150028, PR China
| | - Junting Pan
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, PR China
| | - Weidong Wang
- Key Laboratory of Low -Carbon Green Agriculture in Northeast China, Ministry of Agriculture and Rural Affairs, College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China; College of Life Science, Northeast Forestry University, Harbin, 150040, PR China; College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing, 163319, PR China.
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4
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Castle SD, Stock M, Gorochowski TE. Engineering is evolution: a perspective on design processes to engineer biology. Nat Commun 2024; 15:3640. [PMID: 38684714 PMCID: PMC11059173 DOI: 10.1038/s41467-024-48000-1] [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: 09/11/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.
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Affiliation(s)
- Simeon D Castle
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol, UK.
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK.
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5
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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.
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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.
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6
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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.
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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.
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7
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Jing J, Garbeva P, Raaijmakers JM, Medema MH. Strategies for tailoring functional microbial synthetic communities. THE ISME JOURNAL 2024; 18:wrae049. [PMID: 38537571 PMCID: PMC11008692 DOI: 10.1093/ismejo/wrae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/26/2024] [Indexed: 04/12/2024]
Abstract
Natural ecosystems harbor a huge reservoir of taxonomically diverse microbes that are important for plant growth and health. The vast diversity of soil microorganisms and their complex interactions make it challenging to pinpoint the main players important for the life support functions microbes can provide to plants, including enhanced tolerance to (a)biotic stress factors. Designing simplified microbial synthetic communities (SynComs) helps reduce this complexity to unravel the molecular and chemical basis and interplay of specific microbiome functions. While SynComs have been successfully employed to dissect microbial interactions or reproduce microbiome-associated phenotypes, the assembly and reconstitution of these communities have often been based on generic abundance patterns or taxonomic identities and co-occurrences but have only rarely been informed by functional traits. Here, we review recent studies on designing functional SynComs to reveal common principles and discuss multidimensional approaches for community design. We propose a strategy for tailoring the design of functional SynComs based on integration of high-throughput experimental assays with microbial strains and computational genomic analyses of their functional capabilities.
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Affiliation(s)
- Jiayi Jing
- Bioinformatics Group, Department of Plant Science, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Paolina Garbeva
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Jos M Raaijmakers
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Marnix H Medema
- Bioinformatics Group, Department of Plant Science, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
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8
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Lässig M, Mustonen V, Nourmohammad A. Steering and controlling evolution - from bioengineering to fighting pathogens. Nat Rev Genet 2023; 24:851-867. [PMID: 37400577 PMCID: PMC11137064 DOI: 10.1038/s41576-023-00623-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
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Affiliation(s)
- Michael Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany.
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA, USA.
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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9
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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.
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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
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10
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Zaccaria M, Sandlin N, Soen Y, Momeni B. Partner-assisted artificial selection of a secondary function for efficient bioremediation. iScience 2023; 26:107632. [PMID: 37694149 PMCID: PMC10484969 DOI: 10.1016/j.isci.2023.107632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/17/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.
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Affiliation(s)
- Marco Zaccaria
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
| | - Natalie Sandlin
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
| | - Yoav Soen
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7670001, Israel
| | - Babak Momeni
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
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11
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Burkart T, Willeke J, Frey E. Periodic temporal environmental variations induce coexistence in resource competition models. Phys Rev E 2023; 108:034404. [PMID: 37849086 DOI: 10.1103/physreve.108.034404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/13/2023] [Indexed: 10/19/2023]
Abstract
Natural ecosystems, in particular on the microbial scale, are inhabited by a large number of species. The population size of each species is affected by interactions of individuals with each other and by spatial and temporal changes in environmental conditions, such as resource abundance. Here, we use a generic population dynamics model to study how, and under what conditions, a periodic temporal environmental variation can alter an ecosystem's composition and biodiversity. We demonstrate that using timescale separation allows one to qualitatively predict the long-term population dynamics of interacting species in varying environments. We show that the notion of Tilman's R* rule, a well-known principle that applies for constant environments, can be extended to periodically varying environments if the timescale of environmental changes (e.g., seasonal variations) is much faster than the timescale of population growth (doubling time in bacteria). When these timescales are similar, our analysis shows that a varying environment deters the system from reaching a steady state, and stable coexistence between multiple species becomes possible. Our results posit that biodiversity can in part be attributed to natural environmental variations.
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Affiliation(s)
- Tom Burkart
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universität München, Theresienstraße 37, D-80333 München, Germany
| | - Jan Willeke
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universität München, Theresienstraße 37, D-80333 München, Germany
| | - Erwin Frey
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universität München, Theresienstraße 37, D-80333 München, Germany
- Max Planck School Matter to Life, Hofgartenstraße 8, D-80539 München, Germany
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12
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Fraboul J, Biroli G, De Monte S. Artificial selection of communities drives the emergence of structured interactions. J Theor Biol 2023; 571:111557. [PMID: 37302465 DOI: 10.1016/j.jtbi.2023.111557] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/07/2023] [Accepted: 06/05/2023] [Indexed: 06/13/2023]
Abstract
Species-rich communities, such as the microbiota or microbial ecosystems, provide key functions for human health and climatic resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. If numerical simulations started to explore the evolutionary dynamics of this complex, multi-scale system, a comprehensive theoretical understanding of the process of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of communities composed of a large number of interacting species, described by disordered generalised Lotka-Volterra equations. Our analytical and numerical results reveal that selection for scalar community functions leads to the emergence, along an evolutionary trajectory, of a low-dimensional structure in an initially featureless interaction matrix. Such structure reflects the combination of the properties of the ancestral community and of the selective pressure. Our analysis determines how the speed of adaptation scales with the system parameters and the abundance distribution of the evolved communities. Artificial selection for larger total abundance is thus shown to drive increased levels of mutualism and interaction diversity. Inference of the interaction matrix is proposed as a method to assess the emergence of structured interactions from experimentally accessible measures.
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Affiliation(s)
- Jules Fraboul
- Laboratoire de Physique de l'École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, F-75005, France.
| | - Giulio Biroli
- Laboratoire de Physique de l'École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, F-75005, France
| | - Silvia De Monte
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, Ecole normale supérieure, CNRS, INSERM, Université PSL, Paris, 75005, France; Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany
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13
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Gokhale CS, Velasque M, Denton JA. Ecological Drivers of Community Cohesion. mSystems 2023; 8:e0092922. [PMID: 36656037 PMCID: PMC9948702 DOI: 10.1128/msystems.00929-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/16/2022] [Indexed: 01/20/2023] Open
Abstract
From protocellular to societal, networks of living systems are complex and multiscale. Discerning the factors that facilitate assembly of these intricate interdependencies using pairwise interactions can be nearly impossible. To facilitate a greater understanding, we developed a mathematical and computational model based on a synthetic four-strain Saccharomyces cerevisiae interdependent system. Specifically, we aimed to provide a greater understanding of how ecological factors influence community dynamics. By leveraging transiently structured ecologies, we were able to drive community cohesion. We show how ecological interventions could reverse or slow the extinction rate of a cohesive community. An interconnected system first needs to persist long enough to be a subject of natural selection. Our emulation of Darwin's "warm little ponds" with an ecology governed by transient compartmentalization provided the necessary persistence. Our results reveal utility across scales of organization, stressing the importance of cyclic processes in major evolutionary transitions, engineering of synthetic microbial consortia, and conservation biology. IMPORTANCE We are facing unprecedented disruption and collapse of ecosystems across the globe. To have any hope of mitigating this phenomenon, a much greater understanding of ecosystem dynamics is required. However, ecosystems are typically composed of highly dynamic networks of individual species. These interactions are further modulated by abiotic and biotic factors that vary temporally and spatially. Thus, ecological dynamics are obfuscated by this complexity. Here, we developed a theoretical model, informed by a synthetic experimental system, of Darwin's "warm little ponds." This cycling four-species system seeks to elucidate the ecological factors that drive or inhibit interaction. We show that these factors could provide an essential tool for avoiding the accelerating ecological collapse. Our study also provides a starting point to develop a more encompassing model to inform conservation efforts.
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Affiliation(s)
- Chaitanya S. Gokhale
- Research Group for Theoretical Models of Eco-evolutionary Dynamics, Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Mariana Velasque
- Genomics and Regulatory Systems Unit, Okinawa Institute of Science and Technology, Onna-son, Japan
- Experimental Evolutionary Biology Lab, Monash University, Clayton, Australia
| | - Jai A. Denton
- Genomics and Regulatory Systems Unit, Okinawa Institute of Science and Technology, Onna-son, Japan
- World Mosquito Program, Institute of Vector-borne Disease, Monash University, Clayton, Australia
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14
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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: 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.
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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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15
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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
| | - 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
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16
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Jiang Y, Qin X, Zhu F, Zhang Y, Zhang X, Hartley W, Xue S. Halving gypsum dose by Penicillium oxalicum on alkaline neutralization and microbial community reconstruction in bauxite residue. CHEMICAL ENGINEERING JOURNAL 2023; 451:139008. [DOI: 10.1016/j.cej.2022.139008] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
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17
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Hu H, Wang M, Huang Y, Xu Z, Xu P, Nie Y, Tang H. Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use. MLIFE 2022; 1:382-398. [PMID: 38818482 PMCID: PMC10989833 DOI: 10.1002/mlf2.12043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2024]
Abstract
Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.
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Affiliation(s)
- Haiyang Hu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Miaoxiao Wang
- Department of Environmental Systems ScienceETH ZürichZürichSwitzerland
- Department of Environmental MicrobiologyETH ZürichEawagSwitzerland
| | - Yiqun Huang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Zhaoyong Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
| | - Yong Nie
- College of EngineeringPeking UniversityBeijingChina
| | - Hongzhi Tang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences & BiotechnologyShanghai Jiao Tong UniversityShanghaiChina
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18
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Yuan AE, Shou W. Data-driven causal analysis of observational biological time series. eLife 2022; 11:e72518. [PMID: 35983746 PMCID: PMC9391047 DOI: 10.7554/elife.72518] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/23/2022] [Indexed: 11/28/2022] Open
Abstract
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called 'model-free' causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AIV0ttQrjK8).
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Affiliation(s)
- Alex Eric Yuan
- Molecular and Cellular Biology PhD program, University of WashingtonSeattleUnited States
- Basic Sciences Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Wenying Shou
- Centre for Life’s Origins and Evolution, Department of Genetics, Evolution and Environment, University College LondonLondonUnited Kingdom
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19
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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.
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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
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20
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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.
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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.
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21
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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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22
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Zachar I, Boza G. The Evolution of Microbial Facilitation: Sociogenesis, Symbiogenesis, and Transition in Individuality. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.798045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Metabolic cooperation is widespread, and it seems to be a ubiquitous and easily evolvable interaction in the microbial domain. Mutual metabolic cooperation, like syntrophy, is thought to have a crucial role in stabilizing interactions and communities, for example biofilms. Furthermore, cooperation is expected to feed back positively to the community under higher-level selection. In certain cases, cooperation can lead to a transition in individuality, when freely reproducing, unrelated entities (genes, microbes, etc.) irreversibly integrate to form a new evolutionary unit. The textbook example is endosymbiosis, prevalent among eukaryotes but virtually lacking among prokaryotes. Concerning the ubiquity of syntrophic microbial communities, it is intriguing why evolution has not lead to more transitions in individuality in the microbial domain. We set out to distinguish syntrophy-specific aspects of major transitions, to investigate why a transition in individuality within a syntrophic pair or community is so rare. We review the field of metabolic communities to identify potential evolutionary trajectories that may lead to a transition. Community properties, like joint metabolic capacity, functional profile, guild composition, assembly and interaction patterns are important concepts that may not only persist stably but according to thought-provoking theories, may provide the heritable information at a higher level of selection. We explore these ideas, relating to concepts of multilevel selection and of informational replication, to assess their relevance in the debate whether microbial communities may inherit community-level information or not.
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