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Meroz N, Livny T, Friedman J. Quantifying microbial interactions: concepts, caveats, and applications. Curr Opin Microbiol 2024; 80:102511. [PMID: 39002491 DOI: 10.1016/j.mib.2024.102511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/10/2024] [Accepted: 06/25/2024] [Indexed: 07/15/2024]
Abstract
Microbial communities are fundamental to every ecosystem on Earth and hold great potential for biotechnological applications. However, their complex nature hampers our ability to study and understand them. A common strategy to tackle this complexity is to abstract the community into a network of interactions between its members - a phenomenological description that captures the overall effects of various chemical and physical mechanisms that underpin these relationships. This approach has proven useful for numerous applications in microbial ecology, including predicting community dynamics and stability and understanding community assembly and evolution. However, care is required in quantifying and interpreting interactions. Here, we clarify the concept of an interaction and discuss when interaction measurements are useful despite their context-dependent nature. Furthermore, we categorize different approaches for quantifying interactions, highlighting the research objectives each approach is best suited for.
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Affiliation(s)
- Nittay Meroz
- Institute of Environmental Sciences, Hebrew University, Rehovot
| | - Tal Livny
- Institute of Environmental Sciences, Hebrew University, Rehovot; Department of Immunology and Regenerative Biology, Weizmann Institute, Rehovot
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2
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Lemmen KD, Pennekamp F. Food web context modifies predator foraging and weakens trophic interaction strength. Ecol Lett 2024; 27:e14475. [PMID: 39060898 DOI: 10.1111/ele.14475] [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: 03/11/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Trophic interaction modifications (TIM) are widespread in natural systems and occur when a third species indirectly alters the strength of a trophic interaction. Past studies have focused on documenting the existence and magnitude of TIMs; however, the underlying processes and long-term consequences remain elusive. To address this gap, we experimentally quantified the density-dependent effect of a third species on a predator's functional response. We conducted short-term experiments with ciliate communities composed of a predator, prey and non-consumable 'modifier' species. In both communities, increasing modifier density weakened the trophic interaction strength, due to a negative effect on the predator's space clearance rate. Simulated long-term dynamics indicate quantitative differences between models that account for TIMs or include only pairwise interactions. Our study demonstrates that TIMs are important to understand and predict community dynamics and highlights the need to move beyond focal species pairs to understand the consequences of species interactions in communities.
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Affiliation(s)
- Kimberley D Lemmen
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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3
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Diaz-Colunga J, Skwara A, Vila JCC, Bajic D, Sanchez A. Global epistasis and the emergence of function in microbial consortia. Cell 2024; 187:3108-3119.e30. [PMID: 38776921 DOI: 10.1016/j.cell.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/06/2023] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA
| | - Jean C C Vila
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Biotechnology, Delft University of Technology, Delft 2628 CD, the Netherlands.
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA; Microbial Sciences Institute, Yale University, New Haven, CT 06511, USA; Department of Microbial Biotechnology, National Center for Biotechnology CNB-CSIC, 28049 Madrid, Spain; Institute of Functional Biology and Genomics IBFG-CSIC, University of Salamanca, 37007 Salamanca, Spain.
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4
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Ho PY, Huang KC. Challenges in quantifying functional redundancy and selection in microbial communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586891. [PMID: 38586050 PMCID: PMC10996602 DOI: 10.1101/2024.03.26.586891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Microbiomes can exhibit large variations in species abundances but high reproducibility in abundances of functional units, an observation often considered evidence for functional redundancy. Based on such reduction in functional variability, selection is hypothesized to act on functional units in these ecosystems. However, the link between functional redundancy and selection remains unclear. Here, we show that reduction in functional variability does not always imply selection on functional profiles. We propose empirical null models to account for the confounding effects of statistical averaging and bias toward environment-independent beneficial functions. We apply our models to existing data sets, and find that the abundances of metabolic groups within microbial communities from bromeliad foliage do not exhibit any evidence of the previously hypothesized functional selection. By contrast, communities of soil bacteria or human gut commensals grown in vitro are selected for metabolic capabilities. By separating the effects of averaging and functional bias on functional variability, we find that the appearance of functional selection in gut microbiome samples from the Human Microbiome Project is artifactual, and that there is no evidence of selection for any molecular function represented by KEGG orthology. These concepts articulate a basic framework for quantifying functional redundancy and selection, advancing our understanding of the mapping between microbiome taxonomy and function.
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5
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Wu L, Wang XW, Tao Z, Wang T, Zuo W, Zeng Y, Liu YY, Dai L. Data-driven prediction of colonization outcomes for complex microbial communities. Nat Commun 2024; 15:2406. [PMID: 38493186 PMCID: PMC10944475 DOI: 10.1038/s41467-024-46766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.
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Affiliation(s)
- Lu Wu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zining Tao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shandong Agricultural University, Tai'an, China
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wenlong Zuo
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yu Zeng
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
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6
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Geesink P, ter Horst J, Ettema TJG. More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities. FEMS Microbiol Ecol 2024; 100:fiae029. [PMID: 38444203 PMCID: PMC10950044 DOI: 10.1093/femsec/fiae029] [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: 11/15/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
Microbial communities are not only shaped by the diversity of microorganisms and their individual metabolic potential, but also by the vast amount of intra- and interspecies interactions that can occur pairwise interactions among microorganisms, we suggest that more attention should be drawn towards the effects on the entire microbiome that emerge from individual interactions between community members. The production of certain metabolites that can be tied to a specific microbe-microbe interaction might subsequently influence the physicochemical parameters of the habitat, stimulate a change in the trophic network of the community or create new micro-habitats through the formation of biofilms, similar to the production of antimicrobial substances which might negatively affect only one microorganism but cause a ripple effect on the abundance of other community members. Here, we argue that combining established as well as innovative laboratory and computational methods is needed to predict novel interactions and assess their secondary effects. Such efforts will enable future microbiome studies to expand our knowledge on the dynamics of complex microbial communities.
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Affiliation(s)
- Patricia Geesink
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Jolanda ter Horst
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Thijs J G Ettema
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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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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Srinivasan A, Sajeevan A, Rajaramon S, David H, Solomon AP. Solving polymicrobial puzzles: evolutionary dynamics and future directions. Front Cell Infect Microbiol 2023; 13:1295063. [PMID: 38145044 PMCID: PMC10748482 DOI: 10.3389/fcimb.2023.1295063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Polymicrobial infections include various microorganisms, often necessitating different treatment methods than a monomicrobial infection. Scientists have been puzzled by the complex interactions within these communities for generations. The presence of specific microorganisms warrants a chronic infection and impacts crucial factors such as virulence and antibiotic susceptibility. Game theory is valuable for scenarios involving multiple decision-makers, but its relevance to polymicrobial infections is limited. Eco-evolutionary dynamics introduce causation for multiple proteomic interactions like metabolic syntropy and niche segregation. The review culminates both these giants to form evolutionary dynamics (ED). There is a significant amount of literature on inter-bacterial interactions that remain unsynchronised. Such raw data can only be moulded by analysing the ED involved. The review culminates the inter-bacterial interactions in multiple clinically relevant polymicrobial infections like chronic wounds, CAUTI, otitis media and dental carries. The data is further moulded with ED to analyse the niche colonisation of two notoriously competitive bacteria: S.aureus and P.aeruginosa. The review attempts to develop a future trajectory for polymicrobial research by following recent innovative strategies incorporating ED to curb polymicrobial infections.
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Affiliation(s)
| | | | | | | | - Adline Princy Solomon
- Quorum Sensing Laboratory, Centre for Research in Infectious Diseases (CRID), School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India
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Doran BA, Chen RY, Giba H, Behera V, Barat B, Sundararajan A, Lin H, Sidebottom A, Pamer EG, Raman AS. An evolution-based framework for describing human gut bacteria. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.04.569969. [PMID: 38105970 PMCID: PMC10723311 DOI: 10.1101/2023.12.04.569969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The human gut microbiome contains many bacterial strains of the same species ('strain-level variants'). Describing strains in a biologically meaningful way rather than purely taxonomically is an important goal but challenging due to the genetic complexity of strain-level variation. Here, we measured patterns of co-evolution across >7,000 strains spanning the bacterial tree-of-life. Using these patterns as a prior for studying hundreds of gut commensal strains that we isolated, sequenced, and metabolically profiled revealed widespread structure beneath the phylogenetic level of species. Defining strains by their co-evolutionary signatures enabled predicting their metabolic phenotypes and engineering consortia from strain genome content alone. Our findings demonstrate a biologically relevant organization to strain-level variation and motivate a new schema for describing bacterial strains based on their evolutionary history.
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Affiliation(s)
- Benjamin A. Doran
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637
| | - Robert Y. Chen
- Department of Psychiatry, University of Washington, Seattle, WA, 98195
| | - Hannah Giba
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
- Department of Pathology, University of Chicago, Chicago, IL, 60637
| | - Vivek Behera
- Department of Medicine, University of Chicago, Chicago, IL, 60637
| | - Bidisha Barat
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
| | | | - Huaiying Lin
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
| | - Ashley Sidebottom
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
| | - Eric G. Pamer
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
- Department of Medicine, University of Chicago, Chicago, IL, 60637
| | - Arjun S. Raman
- Duchossois Family Institute, University of Chicago, Chicago, IL, 60637
- Department of Pathology, University of Chicago, Chicago, IL, 60637
- Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, 60637
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10
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Amor DR. Smooth functional landscapes in microcosms. Nat Ecol Evol 2023; 7:1754-1755. [PMID: 37783828 DOI: 10.1038/s41559-023-02214-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Affiliation(s)
- Daniel R Amor
- Laboratoire de Physique, Ecole normale supérieure, Université PSL, CNRS, Paris, France.
- IAME, Université de Paris Cité, Université Sorbonne Paris Nord, INSERM, Paris, France.
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