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Burz SD, Causevic S, Dal Co A, Dmitrijeva M, Engel P, Garrido-Sanz D, Greub G, Hapfelmeier S, Hardt WD, Hatzimanikatis V, Heiman CM, Herzog MKM, Hockenberry A, Keel C, Keppler A, Lee SJ, Luneau J, Malfertheiner L, Mitri S, Ngyuen B, Oftadeh O, Pacheco AR, Peaudecerf F, Resch G, Ruscheweyh HJ, Sahin A, Sanders IR, Slack E, Sunagawa S, Tackmann J, Tecon R, Ugolini GS, Vacheron J, van der Meer JR, Vayena E, Vonaesch P, Vorholt JA. From microbiome composition to functional engineering, one step at a time. Microbiol Mol Biol Rev 2023; 87:e0006323. [PMID: 37947420 PMCID: PMC10732080 DOI: 10.1128/mmbr.00063-23] [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] [Indexed: 11/12/2023] Open
Abstract
SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.
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Affiliation(s)
- Sebastian Dan Burz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Senka Causevic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Alma Dal Co
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marija Dmitrijeva
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Philipp Engel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Daniel Garrido-Sanz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Gilbert Greub
- Institut de microbiologie, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | | | | | - Clara Margot Heiman
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | | | - Christoph Keel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Soon-Jae Lee
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Julien Luneau
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Bidong Ngyuen
- Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | | | | | - Grégory Resch
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | - Asli Sahin
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Ian R. Sanders
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | | | - Janko Tackmann
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Robin Tecon
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Jordan Vacheron
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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Picot A, Shibasaki S, Meacock OJ, Mitri S. Microbial interactions in theory and practice: when are measurements compatible with models? Curr Opin Microbiol 2023; 75:102354. [PMID: 37421708 DOI: 10.1016/j.mib.2023.102354] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 07/10/2023]
Abstract
Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.
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Affiliation(s)
- Aurore Picot
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France; Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Shota Shibasaki
- Department of Biology, University of North Carolina at Greensboro, Greensboro, NC, USA; Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Oliver J Meacock
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
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Kishore D, Birzu G, Hu Z, DeLisi C, Korolev KS, Segrè D. Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation. mSystems 2023; 8:e0096122. [PMID: 37338270 PMCID: PMC10469762 DOI: 10.1128/msystems.00961-22] [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: 10/25/2022] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.
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Affiliation(s)
- Dileep Kishore
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Gabriel Birzu
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Applied Physics, Stanford University, Stanford, California, USA
| | - Zhenjun Hu
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Charles DeLisi
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Kirill S. Korolev
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
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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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