1
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Gaizer T, Juhász J, Pillér B, Szakadáti H, Pongor CI, Csikász-Nagy A. Integrative analysis of yeast colony growth. Commun Biol 2024; 7:511. [PMID: 38684888 PMCID: PMC11058853 DOI: 10.1038/s42003-024-06218-1] [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: 09/28/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Yeast colonies are routinely grown on agar plates in everyday experimental settings to understand basic molecular processes, produce novel drugs, improve health, and so on. Standardized conditions ensure these colonies grow in a reproducible fashion, while in nature microbes are under a constantly changing environment. Here we combine the power of computational simulations and laboratory experiments to investigate the impact of non-standard environmental factors on colony growth. We present the developement and parameterization of a quantitative agent-based model for yeast colony growth to reproduce measurements on colony size and cell number in a colony at non-standard environmental conditions. Specifically, we establish experimental conditions that mimic the effects of humidity changes and nutrient gradients. Our results show how colony growth is affected by moisture changes, nutrient availability, and initial colony inoculation conditions. We show that initial colony spread, not initial cell number have higher impact on the final size and cell number of colonies. Parameters of the model were identified by fitting these experiments and the fitted model gives guidance to establish conditions which enable unlimited growth of yeast colonies.
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Affiliation(s)
- Tünde Gaizer
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - János Juhász
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
- Semmelweis University, Institute of Medical Microbiology, Budapest, Hungary
| | - Bíborka Pillér
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Helga Szakadáti
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Csaba I Pongor
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Attila Csikász-Nagy
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary.
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2
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Pathak D, Suman A, Sharma P, Aswini K, Govindasamy V, Gond S, Anshika R. Community-forming traits play role in effective colonization of plant-growth-promoting bacteria and improved plant growth. FRONTIERS IN PLANT SCIENCE 2024; 15:1332745. [PMID: 38533409 PMCID: PMC10963436 DOI: 10.3389/fpls.2024.1332745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/21/2024] [Indexed: 03/28/2024]
Abstract
Community-forming traits (CFts) play an important role in the effective colonization of plant-growth-promoting bacterial communities that influence host plants positively by modulating their adaptive functions. In this study, by considering plant-growth-promoting traits (PGPts) and community-forming traits (CFts), three communities were constructed, viz., SM1 (PGPts), SM2 (CFts), and SM3 (PGPts+CFts). Each category isolates were picked up on the basis of their catabolic diversity of different carbon sources. Results revealed a distinctive pattern in the colonization of the communities possessed with CF traits. It was observed that the community with CFts colonized inside the plant in groups or in large aggregations, whereas the community with only PGPts colonized as separate individual and small colonies inside the plant root and leaf. The effect of SM3 in the microcosm experiment was more significant than the uninoculated control by 22.12%, 27.19%, and 9.11% improvement in germination percentage, chlorophyll content, and plant biomass, respectively. The significant difference shown by the microbial community SM3 clearly demonstrates the integrated effect of CFts and PGPts on effective colonization vis-à-vis positive influence on the host plant. Further detailed characterization of the interaction will take this technology ahead in sustainable agriculture.
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Affiliation(s)
| | - Archna Suman
- Division of Microbiology, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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3
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Chen Y, Lei X, Jiang J, Qin Y, Jiang L, Liu YL. Microbial diversity on grape epidermis and wine volatile aroma in spontaneous fermentation comprehensively driven by geography, subregion, and variety. Int J Food Microbiol 2023; 404:110315. [PMID: 37467530 DOI: 10.1016/j.ijfoodmicro.2023.110315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/15/2023] [Accepted: 07/05/2023] [Indexed: 07/21/2023]
Abstract
On their journey from the wine grape to the resulting wine, microbiota from grape surfaces controlled by multiple factors is transferred to wine spontaneous fermentation process with indisputable consequences for wine quality parameters. The associated microbiota was regionally distinct (defined to microbial terroir) but how these microbial patterns with significantly regional distinctiveness quantitatively drive the wine regional characteristics are not definite within a complete grape ecosystem at different geographical (> 300 km), subregional (< 10 km), and varietal scales. Here, we collected 24 samples (containing two grape varieties) from four subregions of two regions in Xinjiang wine production area to investigate fungal distribution patterns and the association with wine chemical composition at different evaluation scales. Meanwhile, the relationships were established between geographical, subregional, varietal community of fungi, and wine volatile aroma using partial least squares regression (PLSR) and structural equation modeling (SEM). Results show that microbial and volatile samples present the significantly regional difference inside the complete ecosystem. Microbiota showed a stronger heterogeneity at geography scales, which drove the distributions of subregional and varietal microbiota thereby influencing the volatile composition of finished wines. Moreover, geographical microbiota seems to weaken the effects of varietal community on wine aroma compounds. Microbial communities respond to environmental changes within a completely set grape-related ecosystem at different scales, and these responses resulted in the wine regional distinctiveness based on the volatile profiles. Our findings further confirmed the important role of microbial terroir in shaping wine styles and provided the new cerebration for the terroir drivers of microbiota.
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Affiliation(s)
- Yu Chen
- College of Enology, Northwest A & F University, Yangling, China
| | - Xingmeng Lei
- College of Enology, Northwest A & F University, Yangling, China
| | - Jiao Jiang
- College of Enology, Northwest A & F University, Yangling, China
| | - Yi Qin
- College of Enology, Northwest A & F University, Yangling, China
| | - Lei Jiang
- College of Life and Geographical Sciences, Kashi University, Kashi, China.
| | - Yan-Lin Liu
- College of Enology, Northwest A & F University, Yangling, China.
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4
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Sun X, Sanchez A. Synthesizing microbial biodiversity. Curr Opin Microbiol 2023; 75:102348. [PMID: 37352679 DOI: 10.1016/j.mib.2023.102348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/20/2023] [Accepted: 05/25/2023] [Indexed: 06/25/2023]
Abstract
The diversity of microbial ecosystems is linked to crucial ecological processes and functions. Despite its significance, the ecological mechanisms responsible for the initiation and maintenance of microbiome diversity are still not fully understood. The primary challenge lies in the difficulty of isolating, monitoring, and manipulating the complex and interrelated ecological processes that modulate the diversity of microbial communities in their natural habitats. Synthetic ecology experiments provide a suitable alternative for investigating the mechanisms behind microbial biodiversity in controlled laboratory settings, as the environment can be systematically and modularly manipulated by adding and removing components. This enables the testing of hypotheses and the advancement of predictive theories. In this review, we present an overview of recent progress toward achieving this goal.
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Affiliation(s)
- Xin Sun
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Alvaro Sanchez
- Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, Madrid, Spain.
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5
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Lee H, Bloxham B, Gore J. Resource competition can explain simplicity in microbial community assembly. Proc Natl Acad Sci U S A 2023; 120:e2212113120. [PMID: 37603734 PMCID: PMC10469513 DOI: 10.1073/pnas.2212113120] [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: 07/20/2022] [Accepted: 06/16/2023] [Indexed: 08/23/2023] Open
Abstract
Predicting the composition and diversity of communities is a central goal in ecology. While community assembly is considered hard to predict, laboratory microcosms often follow a simple assembly rule based on the outcome of pairwise competitions. This assembly rule predicts that a species that is excluded by another species in pairwise competition cannot survive in a multispecies community with that species. Despite the empirical success of this bottom-up prediction, its mechanistic origin has remained elusive. In this study, we elucidate how this simple pattern in community assembly can emerge from resource competition. Our geometric analysis of a consumer-resource model shows that trio community assembly is always predictable from pairwise outcomes when one species grows faster than another species on every resource. We also identify all possible trio assembly outcomes under three resources and find that only two outcomes violate the assembly rule. Simulations demonstrate that pairwise competitions accurately predict trio assembly with up to 100 resources and the assembly of larger communities containing up to twelve species. We then further demonstrate accurate quantitative prediction of community composition using the harmonic mean of pairwise fractions. Finally, we show that cross-feeding between species does not decrease assembly rule prediction accuracy. Our findings highlight that simple community assembly can emerge even in ecosystems with complex underlying dynamics.
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Affiliation(s)
- Hyunseok Lee
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Blox Bloxham
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Jeff Gore
- Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA02139
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6
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Silverstein M, Bhatnagar JM, Segrè D. Metabolic complexity drives divergence in microbial communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551516. [PMID: 37577626 PMCID: PMC10418233 DOI: 10.1101/2023.08.03.551516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.
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Affiliation(s)
- Michael Silverstein
- Bioinformatics Program, Boston University, Boston, MA
- Biological Design Center, Boston University, Boston, MA
| | - Jennifer M. Bhatnagar
- Bioinformatics Program, Boston University, Boston, MA
- Department of Biology, Boston University, Boston, MA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA
- Biological Design Center, Boston University, Boston, MA
- Department of Biology, Boston University, Boston, MA
- Department of Biomedical Engineering and Department of Physics, Boston University, Boston, MA
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7
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Guex I, Mazza C, Dubey M, Batsch M, Li R, van der Meer JR. Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding. PLoS Comput Biol 2023; 19:e1011402. [PMID: 37603551 PMCID: PMC10470959 DOI: 10.1371/journal.pcbi.1011402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 08/31/2023] [Accepted: 07/31/2023] [Indexed: 08/23/2023] Open
Abstract
When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.
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Affiliation(s)
- Isaline Guex
- Department of Mathematics, University of Fribourg, Fribourg, Switzerland
| | - Christian Mazza
- Department of Mathematics, University of Fribourg, Fribourg, Switzerland
| | - Manupriyam Dubey
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Maxime Batsch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Renyi Li
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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8
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Schäfer M, Pacheco AR, Künzler R, Bortfeld-Miller M, Field CM, Vayena E, Hatzimanikatis V, Vorholt JA. Metabolic interaction models recapitulate leaf microbiota ecology. Science 2023; 381:eadf5121. [PMID: 37410834 DOI: 10.1126/science.adf5121] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding the degree to which this dependency determines interspecies interactions may advance efforts to control host-microbiome relationships. We combined synthetic community experiments with computational models to predict interaction outcomes between plant-associated bacteria. We mapped the metabolic capabilities of 224 leaf isolates from Arabidopsis thaliana by assessing the growth of each strain on 45 environmentally relevant carbon sources in vitro. We used these data to build curated genome-scale metabolic models for all strains, which we combined to simulate >17,500 interactions. The models recapitulated outcomes observed in planta with >89% accuracy, highlighting the role of carbon utilization and the contributions of niche partitioning and cross-feeding in the assembly of leaf microbiomes.
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Affiliation(s)
- Martin Schäfer
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Alan R Pacheco
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Rahel Künzler
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | | | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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9
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Pacheco AR, Vorholt JA. Resolving metabolic interaction mechanisms in plant microbiomes. Curr Opin Microbiol 2023; 74:102317. [PMID: 37062173 DOI: 10.1016/j.mib.2023.102317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/10/2023] [Accepted: 03/16/2023] [Indexed: 04/18/2023]
Abstract
Metabolic interactions are fundamental to the assembly and functioning of microbiomes, including those of plants. However, disentangling the molecular basis of these interactions and their specific roles remains a major challenge. Here, we review recent applications of experimental and computational methods toward the elucidation of metabolic interactions in plant-associated microbiomes. We highlight studies that span various scales of taxonomic and environmental complexity, including those that test interaction outcomes in vitro and in planta by deconstructing microbial communities. We also discuss how the continued integration of multiple methods can further reveal the general ecological characteristics of plant microbiomes, as well as provide strategies for applications in areas such as improved plant protection, bioremediation, and sustainable agriculture.
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Affiliation(s)
- Alan R Pacheco
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
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10
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Manjunath M, Khokhar A, Chary GR, Singh M, Yadav SK, Gopinath KA, Jyothilakshmi N, Srinivas K, Prabhakar M, Singh VK. Microbial consortia enhance the yield of maize under sub-humid rainfed production system of India. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2023. [DOI: 10.3389/fsufs.2023.1108492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023] Open
Abstract
Plant beneficial microorganisms are being used to improve soil health and crop yield in different cropping systems. Maize is an important crop grown around the world for food, feed and raw material for various industries. The aim of the present study was to evaluate two microbial consortia viz., microbial consortia 1 (Pseudomonas putida P7 + Paenibacillus favisporus B30) and microbial consortia 2 (Pseudomonas putida P45 + Bacillus amyloliquefaciens B17) under field conditions for their suitability in improving maize yield under rainfed situations at Ballowal Saunkhri (Punjab) having sub-humid (Hot Dry) climatic conditions. Pooled analysis of three years field experiments data showed that, seed + soil application of microbial consortia 1 and 2 led to enhancement in grain yield of kharif maize by 27.78 and 23.21% respectively over uninoculated control. Likewise, significant increase in Benefit:Cost ratio as well as straw yield was also observed. The present investigation suggests that, microbial consortia would help in significantly improving the yield and economics of maize grown on inceptisols under rainfed conditions.
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11
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Phototroph-heterotroph interactions during growth and long-term starvation across Prochlorococcus and Alteromonas diversity. THE ISME JOURNAL 2023; 17:227-237. [PMID: 36335212 PMCID: PMC9860064 DOI: 10.1038/s41396-022-01330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022]
Abstract
Due to their potential impact on ecosystems and biogeochemistry, microbial interactions, such as those between phytoplankton and bacteria, have been studied intensively using specific model organisms. Yet, to what extent interactions differ between closely related organisms, or how these interactions change over time, or culture conditions, remains unclear. Here, we characterize the interactions between five strains each of two globally abundant marine microorganisms, Prochlorococcus (phototroph) and Alteromonas (heterotroph), from the first encounter between individual strains and over more than a year of repeated cycles of exponential growth and long-term nitrogen starvation. Prochlorococcus-Alteromonas interactions had little effect on traditional growth parameters such as Prochlorococcus growth rate, maximal fluorescence, or lag phase, affecting primarily the dynamics of culture decline, which we interpret as representing cell mortality and lysis. The shape of the Prochlorococcus decline curve and the carrying capacity of the co-cultures were determined by the phototroph and not the heterotroph strains involved. Comparing various mathematical models of culture mortality suggests that Prochlorococcus death rate increases over time in mono-cultures but decreases in co-cultures, with cells potentially becoming more resistant to stress. Our results demonstrate intra-species differences in ecologically relevant co-culture outcomes. These include the recycling efficiency of N and whether the interactions are mutually synergistic or competitive. They also highlight the information-rich growth and death curves as a useful readout of the interaction phenotype.
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12
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Midani FS, David LA. Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity. Front Microbiol 2023; 13:910390. [PMID: 36687598 PMCID: PMC9849913 DOI: 10.3389/fmicb.2022.910390] [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: 04/01/2022] [Accepted: 11/29/2022] [Indexed: 01/07/2023] Open
Abstract
Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.
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Affiliation(s)
- Firas S. Midani
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, United States
| | - Lawrence A. David
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States
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13
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Ostrem Loss E, Thompson J, Cheung PLK, Qian Y, Venturelli OS. Carbohydrate complexity limits microbial growth and reduces the sensitivity of human gut communities to perturbations. Nat Ecol Evol 2023; 7:127-142. [PMID: 36604549 DOI: 10.1038/s41559-022-01930-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/10/2022] [Indexed: 01/07/2023]
Abstract
Dietary fibre impacts the growth dynamics of human gut microbiota, yet we lack a detailed and quantitative understanding of how these nutrients shape microbial interaction networks and responses to perturbations. By building human gut communities coupled with computational modelling, we dissect the effects of fibres that vary in chemical complexity and each of their constituent sugars on community assembly and response to perturbations. We demonstrate that the degree of chemical complexity across different fibres limits microbial growth and the number of species that can utilize these nutrients. The prevalence of negative interspecies interactions is reduced in the presence of fibres compared with their constituent sugars. Carbohydrate chemical complexity enhances the reproducibility of community assembly and resistance of the community to invasion. We demonstrate that maximizing or minimizing carbohydrate competition between resident and invader species enhances resistance to invasion. In sum, the quantitative effects of carbohydrate chemical complexity on microbial interaction networks could be exploited to inform dietary and bacterial interventions to modulate community resistance to perturbations.
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Affiliation(s)
- Erin Ostrem Loss
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaron Thompson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.,Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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14
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Morin MA, Morrison AJ, Harms MJ, Dutton RJ. Higher-order interactions shape microbial interactions as microbial community complexity increases. Sci Rep 2022; 12:22640. [PMID: 36587027 PMCID: PMC9805437 DOI: 10.1038/s41598-022-25303-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 11/28/2022] [Indexed: 01/01/2023] Open
Abstract
Non-pairwise interactions, or higher-order interactions (HOIs), in microbial communities have been described as significant drivers of emergent features in microbiomes. Yet, the re-organization of microbial interactions between pairwise cultures and larger communities remains largely unexplored from a molecular perspective but is central to our understanding and further manipulation of microbial communities. Here, we used a bottom-up approach to investigate microbial interaction mechanisms from pairwise cultures up to 4-species communities from a simple microbiome (Hafnia alvei, Geotrichum candidum, Pencillium camemberti and Escherichia coli). Specifically, we characterized the interaction landscape for each species combination involving E. coli by identifying E. coli's interaction-associated mutants using an RB-TnSeq-based interaction assay. We observed a deep reorganization of the interaction-associated mutants, with very few 2-species interactions conserved all the way up to a 4-species community and the emergence of multiple HOIs. We further used a quantitative genetics strategy to decipher how 2-species interactions were quantitatively conserved in higher community compositions. Epistasis-based analysis revealed that, of the interactions that are conserved at all levels of complexity, 82% follow an additive pattern. Altogether, we demonstrate the complex architecture of microbial interactions even within a simple microbiome, and provide a mechanistic and molecular explanation of HOIs.
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Affiliation(s)
- Manon A. Morin
- grid.266100.30000 0001 2107 4242School of Biological Science, University of California San Diego, San Diego, 92093 USA
| | - Anneliese J. Morrison
- grid.170202.60000 0004 1936 8008Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR USA ,grid.170202.60000 0004 1936 8008Institute of Molecular Biology, University of Oregon, Eugene, OR USA
| | - Michael J. Harms
- grid.170202.60000 0004 1936 8008Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR USA ,grid.170202.60000 0004 1936 8008Institute of Molecular Biology, University of Oregon, Eugene, OR USA
| | - Rachel J. Dutton
- grid.266100.30000 0001 2107 4242School of Biological Science, University of California San Diego, San Diego, 92093 USA
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15
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Ryback B, Bortfeld-Miller M, Vorholt JA. Metabolic adaptation to vitamin auxotrophy by leaf-associated bacteria. THE ISME JOURNAL 2022; 16:2712-2724. [PMID: 35987782 PMCID: PMC9666465 DOI: 10.1038/s41396-022-01303-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 07/13/2022] [Accepted: 07/25/2022] [Indexed: 12/15/2022]
Abstract
Auxotrophs are unable to synthesize all the metabolites essential for their metabolism and rely on others to provide them. They have been intensively studied in laboratory-generated and -evolved mutants, but emergent adaptation mechanisms to auxotrophy have not been systematically addressed. Here, we investigated auxotrophies in bacteria isolated from Arabidopsis thaliana leaves and found that up to half of the strains have auxotrophic requirements for biotin, niacin, pantothenate and/or thiamine. We then explored the genetic basis of auxotrophy as well as traits that co-occurred with vitamin auxotrophy. We found that auxotrophic strains generally stored coenzymes with the capacity to grow exponentially for 1-3 doublings without vitamin supplementation; however, the highest observed storage was for biotin, which allowed for 9 doublings in one strain. In co-culture experiments, we demonstrated vitamin supply to auxotrophs, and found that auxotrophic strains maintained higher species richness than prototrophs upon external supplementation with vitamins. Extension of a consumer-resource model predicted that auxotrophs can utilize carbon compounds provided by other organisms, suggesting that auxotrophic strains benefit from metabolic by-products beyond vitamins.
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Affiliation(s)
- Birgitta Ryback
- grid.5801.c0000 0001 2156 2780Institute of Microbiology, ETH Zurich, 8093 Zurich, Switzerland
| | - Miriam Bortfeld-Miller
- grid.5801.c0000 0001 2156 2780Institute of Microbiology, ETH Zurich, 8093 Zurich, Switzerland
| | - Julia A. Vorholt
- grid.5801.c0000 0001 2156 2780Institute of Microbiology, ETH Zurich, 8093 Zurich, Switzerland
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16
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Aida H, Hashizume T, Ashino K, Ying BW. Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity. eLife 2022; 11:76846. [PMID: 36017903 PMCID: PMC9417415 DOI: 10.7554/elife.76846] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 08/15/2022] [Indexed: 12/30/2022] Open
Abstract
Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate, and serine for maximum growth, growth rate, and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.
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Affiliation(s)
- Honoka Aida
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Kazuha Ashino
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
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17
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van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
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Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
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18
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Reproducible Propagation of Species-Rich Soil Bacterial Communities Suggests Robust Underlying Deterministic Principles of Community Formation. mSystems 2022; 7:e0016022. [PMID: 35353008 PMCID: PMC9040596 DOI: 10.1128/msystems.00160-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Microbiomes are typically characterized by high species diversity but it is poorly understood how such system-level complexity can be generated and propagated. Here, we used soil microcosms as a model to study development of bacterial communities as a function of their starting complexity and environmental boundary conditions. Despite inherent stochastic variation in manipulating species-rich communities, both laboratory-mixed medium complexity (21 soil bacterial isolates in equal proportions) and high-diversity natural top-soil communities followed highly reproducible succession paths, maintaining 16S rRNA gene amplicon signatures prominent for known soil communities in general. Development trajectories and compositional states were different for communities propagated in soil microcosms than in liquid suspension. Compositional states were maintained over multiple renewed growth cycles but could be diverged by short-term pollutant exposure. The different but robust trajectories demonstrated that deterministic taxa-inherent characteristics underlie reproducible development and self-organized complexity of soil microbiomes within their environmental boundary conditions. Our findings also have direct implications for potential strategies to achieve controlled restoration of desertified land. IMPORTANCE There is now a great awareness of the high diversity of most environmental (“free-living”) and host-associated microbiomes, but exactly how diverse microbial communities form and maintain is still highly debated. A variety of theories have been put forward, but testing them has been problematic because most studies have been based on synthetic communities that fail to accurately mimic the natural composition (i.e., the species used are typically not found together in the same environment), the diversity (usually too low to be representative), or the environmental system itself (using designs with single carbon sources or solely mixed liquid cultures). In this study, we show how species-diverse soil bacterial communities can reproducibly be generated, propagated, and maintained, either from individual isolates (21 soil bacterial strains) or from natural microbial mixtures washed from top-soil. The high replicate consistency we achieve both in terms of species compositions and developmental trajectories demonstrates the strong inherent deterministic factors driving community formation from their species composition. Generating complex soil microbiomes may provide ways for restoration of damaged soils that are prevalent on our planet.
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19
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Pierce EC, Dutton RJ. Putting microbial interactions back into community contexts. Curr Opin Microbiol 2022; 65:56-63. [PMID: 34739927 DOI: 10.1016/j.mib.2021.10.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 08/31/2021] [Accepted: 10/08/2021] [Indexed: 02/05/2023]
Abstract
Microbial interactions are key aspects of the biology of microbiomes. Recently, there has been a shift in the field towards studying interactions in more representative contexts, whether using multispecies model microbial communities or by looking at interactions in situ. Across diverse microbial systems, these studies have begun to identify common interaction mechanisms. These mechanisms include interactions related to toxic molecules, nutrient competition and cross-feeding, access to metals, signaling pathways, pH changes, and interactions within biofilms. Leveraging technological innovations, many of these studies have used an interdisciplinary approach combining genetic, metabolomic, imaging, and/or microfluidic techniques to gain insight into mechanisms of microbial interactions and into the impact of these interactions on microbiomes.
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Affiliation(s)
- Emily C Pierce
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rachel J Dutton
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, USA.
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20
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Complementary resource preferences spontaneously emerge in diauxic microbial communities. Nat Commun 2021; 12:6661. [PMID: 34795267 PMCID: PMC8602314 DOI: 10.1038/s41467-021-27023-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 10/25/2021] [Indexed: 01/04/2023] Open
Abstract
Many microbes grow diauxically, utilizing the available resources one at a time rather than simultaneously. The properties of communities of microbes growing diauxically remain poorly understood, largely due to a lack of theory and models of such communities. Here, we develop and study a minimal model of diauxic microbial communities assembling in a serially diluted culture. We find that unlike co-utilizing communities, diauxic community assembly repeatably and spontaneously leads to communities with complementary resource preferences, namely communities where species prefer different resources as their top choice. Simulations and theory explain that the emergence of complementarity is driven by the disproportionate contribution of the top choice resource to the growth of a diauxic species. Additionally, we develop a geometric approach for analyzing serially diluted communities, with or without diauxie, which intuitively explains several additional emergent community properties, such as the apparent lack of species which grow fastest on a resource other than their most preferred resource. Overall, our work provides testable predictions for the assembly of natural as well as synthetic communities of diauxically shifting microbes.
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21
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Díaz-Pascual F, Lempp M, Nosho K, Jeckel H, Jo JK, Neuhaus K, Hartmann R, Jelli E, Hansen MF, Price-Whelan A, Dietrich LEP, Link H, Drescher K. Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies. eLife 2021; 10:e70794. [PMID: 34751128 PMCID: PMC8579308 DOI: 10.7554/elife.70794] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 10/18/2021] [Indexed: 12/17/2022] Open
Abstract
Bacteria commonly live in spatially structured biofilm assemblages, which are encased by an extracellular matrix. Metabolic activity of the cells inside biofilms causes gradients in local environmental conditions, which leads to the emergence of physiologically differentiated subpopulations. Information about the properties and spatial arrangement of such metabolic subpopulations, as well as their interaction strength and interaction length scales are lacking, even for model systems like Escherichia coli colony biofilms grown on agar-solidified media. Here, we use an unbiased approach, based on temporal and spatial transcriptome and metabolome data acquired during E. coli colony biofilm growth, to study the spatial organization of metabolism. We discovered that alanine displays a unique pattern among amino acids and that alanine metabolism is spatially and temporally heterogeneous. At the anoxic base of the colony, where carbon and nitrogen sources are abundant, cells secrete alanine via the transporter AlaE. In contrast, cells utilize alanine as a carbon and nitrogen source in the oxic nutrient-deprived region at the colony mid-height, via the enzymes DadA and DadX. This spatially structured alanine cross-feeding influences cellular viability and growth in the cross-feeding-dependent region, which shapes the overall colony morphology. More generally, our results on this precisely controllable biofilm model system demonstrate a remarkable spatiotemporal complexity of metabolism in biofilms. A better characterization of the spatiotemporal metabolic heterogeneities and dependencies is essential for understanding the physiology, architecture, and function of biofilms.
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Affiliation(s)
| | - Martin Lempp
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
| | - Kazuki Nosho
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
| | - Hannah Jeckel
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
- Department of Physics,
Philipps-Universität MarburgMarburgGermany
- Biozentrum, University of
BaselBaselSwitzerland
| | - Jeanyoung K Jo
- Department of Biological Sciences,
Columbia UniversityNew YorkUnited
States
| | - Konstantin Neuhaus
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
- Department of Physics,
Philipps-Universität MarburgMarburgGermany
- Biozentrum, University of
BaselBaselSwitzerland
| | - Raimo Hartmann
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
| | - Eric Jelli
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
- Department of Physics,
Philipps-Universität MarburgMarburgGermany
| | | | - Alexa Price-Whelan
- Department of Biological Sciences,
Columbia UniversityNew YorkUnited
States
| | - Lars EP Dietrich
- Department of Biological Sciences,
Columbia UniversityNew YorkUnited
States
| | - Hannes Link
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
- Interfaculty Institute for Microbiology
and Infection Medicine, Eberhard Karls Universität
TübingenTübingenGermany
| | - Knut Drescher
- Max Planck Institute for Terrestrial
MicrobiologyMarburgGermany
- Department of Physics,
Philipps-Universität MarburgMarburgGermany
- Biozentrum, University of
BaselBaselSwitzerland
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22
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Dukovski I, Bajić D, Chacón JM, Quintin M, Vila JCC, Sulheim S, Pacheco AR, Bernstein DB, Riehl WJ, Korolev KS, Sanchez A, Harcombe WR, Segrè D. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat Protoc 2021; 16:5030-5082. [PMID: 34635859 PMCID: PMC10824140 DOI: 10.1038/s41596-021-00593-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 06/16/2021] [Indexed: 02/08/2023]
Abstract
Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.
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Affiliation(s)
- Ilija Dukovski
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Djordje Bajić
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
| | - Jeremy M Chacón
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA
- BioTechnology Institute, University of Minnesota, St. Paul, MN, USA
| | - Michael Quintin
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Alan R Pacheco
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - David B Bernstein
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - William J Riehl
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kirill S Korolev
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Physics, Boston University, Boston, MA, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, West Haven, CT, USA
| | - William R Harcombe
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA
- BioTechnology Institute, University of Minnesota, St. Paul, MN, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Department of Physics, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
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23
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Tierney BT, Szymanski E, Henriksen JR, Kostic AD, Patel CJ. Using Cartesian Doubt To Build a Sequencing-Based View of Microbiology. mSystems 2021; 6:e0057421. [PMID: 34636670 PMCID: PMC8510522 DOI: 10.1128/msystems.00574-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/23/2021] [Indexed: 12/13/2022] Open
Abstract
The technological leap of DNA sequencing generated a tension between modern metagenomics and historical microbiology. We are forcibly harmonizing the output of a modern tool with centuries of experimental knowledge derived from culture-based microbiology. As a thought experiment, we borrow the notion of Cartesian doubt from philosopher Rene Descartes, who used doubt to build a philosophical framework from his incorrigible statement that "I think therefore I am." We aim to cast away preconceived notions and conceptualize microorganisms through the lens of metagenomic sequencing alone. Specifically, we propose funding and building analysis and engineering methods that neither search for nor rely on the assumption of independent genomes bound by lipid barriers containing discrete functional roles and taxonomies. We propose that a view of microbial communities based in sequencing will engender novel insights into metagenomic structure and may capture functional biology not reflected within the current paradigm.
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Affiliation(s)
- Braden T. Tierney
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Erika Szymanski
- Department of English, Colorado State University, Fort Collins, Colorado, USA
| | | | - Aleksandar D. Kostic
- Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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24
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Dal Bello M, Lee H, Goyal A, Gore J. Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat Ecol Evol 2021; 5:1424-1434. [PMID: 34413507 DOI: 10.1038/s41559-021-01535-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 07/14/2021] [Indexed: 02/06/2023]
Abstract
The relationship between the number of available nutrients and community diversity is a central question in ecological research that remains unanswered. Here we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found that, while single resources supported multispecies communities varying from 8 to 40 taxa, mean community richness increased only one-by-one with additional resources. Cross-feeding could reconcile these seemingly contrasting observations, with the metabolic network seeded by the supplied resources explaining the changes in richness due to both the identity and the number of resources, as well as the distribution of taxa across different communities. By using a consumer-resource model incorporating the inferred cross-feeding network, we provide further theoretical support to our observations and a framework to link the type and number of environmental resources to microbial community diversity.
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Affiliation(s)
- Martina Dal Bello
- Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Hyunseok Lee
- Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Akshit Goyal
- Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeff Gore
- Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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25
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Ansari AF, Reddy YBS, Raut J, Dixit NM. An efficient and scalable top-down method for predicting structures of microbial communities. NATURE COMPUTATIONAL SCIENCE 2021; 1:619-628. [PMID: 38217133 DOI: 10.1038/s43588-021-00131-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 08/13/2021] [Indexed: 01/15/2024]
Abstract
Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.
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Affiliation(s)
- Aamir Faisal Ansari
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India
| | | | | | - Narendra M Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India.
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bengaluru, India.
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26
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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: 5] [Impact Index Per Article: 1.7] [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.
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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
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