scholarly journals OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes

2021 ◽  
Vol 4 (12) ◽  
pp. e202101167
Author(s):  
João Pedro Saraiva ◽  
Alexandre Bartholomäus ◽  
René Kallies ◽  
Marta Gomes ◽  
Marcos Bicalho ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source Software ◽  
Species Interactions ◽  
Functional Annotation ◽  
Ecosystem Processes ◽  
Independent Study ◽  
Microbial Interactions ◽  
High Complexity ◽  
Benzoate Degradation ◽  
User Friendly

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

scholarly journals OrtSuite – from genomes to prediction of microbial interactions within targeted ecosystem processes

2021 ◽  
Author(s):  
João Pedro Saraiva ◽  
Alexandre Bartholomäus ◽  
René Kallies ◽  
Marta Gomes ◽  
Marcos Bicalho ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source Software ◽  
Species Interactions ◽  
Functional Annotation ◽  
Ecosystem Processes ◽  
Independent Study ◽  
Microbial Interactions ◽  
High Complexity ◽  
Benzoate Degradation ◽  
User Friendly

Abstract The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96 % of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

scholarly journals OrtSuite – from genomes to prediction of microbial interactions within targeted ecosystem processes

2021 ◽  
Author(s):  
Joao Pedro Saraiva ◽  
Alexandre Bartholomäus ◽  
René Kallies ◽  
Marta Gomes ◽  
Marcos Vinicios Fleming Bicalho ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source Software ◽  
Species Interactions ◽  
Functional Annotation ◽  
Ecosystem Processes ◽  
Independent Study ◽  
Microbial Interactions ◽  
High Complexity ◽  
Benzoate Degradation ◽  
User Friendly

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96 % of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. Identification of putative synergistic species interactions was evaluated using the sequenced genomes of an independent study which had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy to use workflow that allows for rapid functional annotation based on a user curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

OrtSuite – a flexible pipeline for annotation of ecosystem processes and prediction of putative microbial interactions

2020 ◽  
Author(s):  
João Pedro Saraiva ◽  
Marta Gomes ◽  
René Kallies ◽  
Carsten Vogt ◽  
Antonis Chatzinotas ◽  
...  
Keyword(s):  
Functional Annotation ◽  
High Throughput Sequencing ◽  
Sequence Similarity ◽  
Microbial Community Composition ◽  
Ecosystem Processes ◽  
Microbial Interactions ◽  
Interspecies Interactions ◽  
Sequencing Data ◽  
Computer Clusters ◽  
Identity Threshold

Abstract Background: The exponential increase in high-throughput sequencing data and the development of computational sciences and bioinformatics pipelines has advanced our understanding of microbial community composition and distribution in complex ecosystems. Despite these advances, the identification of microbial interactions from genomic data remains a major bottleneck. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content. Results: OrtSuite combines ortholog clustering strategies with genome annotation based on a user-defined set of functions allowing for hypothesis-driven data analysis. OrtSuit allows users to install and run all workflow components and analyze the generated outputs using a simple pipeline consisting of 23 bash commands and one R command. Annotation is based on a two-stage process. First, only a subset of sequences from each ortholog cluster are aligned to all sequences in the Ortholog-Reaction Association database (ORAdb). Next, all sequences from clusters that meet a user-defined identity threshold are aligned to all sequence sets in ORAdb to which they had a hit. This approach results in a decrease in time needed for functional annotation. Further, OrtSuit identifies putative interspecies interactions based on their individual genomic content based on constrains given by the users. Additional control is afforded to the user at several stages of the workflow: 1) The construction of ORAdb only needs to be performed once for each specific process also allowing manual curation; 2) The identity and sequence similarity thresholds used during the annotation stage can be adjusted; and 3) Constraints related to pathway reaction composition and known species contributions to ecosystem processes can be defined. Conclusions: OrtSuit is an easy to use workflow that allows for rapid functional annotation based on a user curated database. Further, this novel workflow allows the identification of interspecies interactions through user-defined constrains. Due to its low computational demands, for small datasets (e.g. maximum 100 genomes) OrtSuit can run on a personal computer. For larger datasets (> 100 genomes), we suggest the use of computer clusters. OrtSuit is an open-source software available at https://github.com/mdsufz/OrtSuit .

scholarly journals How sample heterogeneity can obscure the signal of microbial interactions

2019 ◽  
Author(s):  
David W. Armitage ◽  
Stuart E. Jones
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Species Interactions ◽  
Dynamic Models ◽  
Spatial Scales ◽  
Microbial Interactions ◽  
Vital Rates ◽  
Ecological Processes ◽  
Correlation Networks ◽  
Community Data

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

scholarly journals Rewiring of peatland plant-microbe networks outpaces species turnover

2020 ◽  
Author(s):  
Bjorn J.M. Robroek ◽  
Magalí Martí ◽  
Bo H. Svensson ◽  
Marc G. Dumont ◽  
Annelies J. Veraart ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Species Turnover ◽  
Ecosystem Processes ◽  
Microbial Interactions ◽  
Ecosystem Dynamics ◽  
Bipartite Networks ◽  
Ecological Processes ◽  
Network Composition ◽  
The Impact ◽  
Network Rewiring

AbstractEnviro-climatological changes are thought to be causing alterations in ecosystem processes through shifts in plant and microbial communities; however, how links between plant and microbial communities change with enviro-climatological change is likely to be less straightforward but may be fundamental for many ecological processes. To address this, we assessed the composition of the plant community and the prokaryotic community –using amplicon-based sequencing– of three European peatlands that were distinct in enviro-climatological conditions. Bipartite networks were used to construct site-specific plant-prokaryote co-occurrence networks. Our data show that between sites, plant and prokaryotic communities differ and that turnover in interactions between the communities was complex. Essentially, turnover in plant-microbial interactions is much faster than turnover in the respective communities. Our findings suggest that network rewiring does largely result from novel associations between species that are common and shared across the networks. Turnover in network composition is largely driven by novel interactions between a core community of plants and microorganisms. Taken together our results indicate that plant-microbe associations are context dependent, and that changes in enviro-climatological conditions will likely lead to network rewiring. Integrating turnover in plant-microbe interactions into studies that assess the impact of enviro-climatological change on peatland ecosystems is essential to understand ecosystem dynamics and must be combined with studies on the impact of these changes on ecosystem processes.

scholarly journals Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data

2021 ◽  
Author(s):  
João Pedro Saraiva ◽  
Anja Worrich ◽  
Canan Karakoç ◽  
Rene Kallies ◽  
Antonis Chatzinotas ◽  
...  
Keyword(s):  
Species Interactions ◽  
Imaging Techniques ◽  
Research Question ◽  
Building Blocks ◽  
Search Space ◽  
Ecosystem Processes ◽  
Microbial Interactions ◽  
Complex Nature ◽  
Interspecies Interactions ◽  
Genetic Potential

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used to define the building blocks to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

scholarly journals Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data

2021 ◽  
Vol 9 (4) ◽  
pp. 840
Author(s):  
Joao Pedro Saraiva ◽  
Anja Worrich ◽  
Canan Karakoç ◽  
Rene Kallies ◽  
Antonis Chatzinotas ◽  
...  
Keyword(s):  
Species Interactions ◽  
Imaging Techniques ◽  
Research Question ◽  
Search Space ◽  
Ecosystem Processes ◽  
Microbial Interactions ◽  
Complex Nature ◽  
Interspecies Interactions ◽  
Genetic Potential ◽  
Starting Point

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

2020 ◽  
Vol 48 (2) ◽  
pp. 399-409
Author(s):  
Baizhen Gao ◽  
Rushant Sabnis ◽  
Tommaso Costantini ◽  
Robert Jinkerson ◽  
Qing Sun
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Environmental Remediation ◽  
Real Life ◽  
Design Principles ◽  
Microbial Interactions ◽  
Potential Applications ◽  
New Gene ◽  
Global Biogeochemical Cycles ◽  
Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

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