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

2018 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M.T. Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Link Type ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale ◽  
Cobra Toolbox

MotivationThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.ResultsTo address this shortage, 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 COBRA Toolbox.AvailabilityThe Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

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.

scholarly journals Pathway-based and phylogenetically adjusted quantification of metabolic interaction between microbial species

2020 ◽  
Author(s):  
Tony J. Lam ◽  
Moses Stamboulian ◽  
Wontack Han ◽  
Yuzhen Ye
Keyword(s):  
Microbial Communities ◽  
Bacterial Species ◽  
Microbial Interactions ◽  
Phylogenetic Distance ◽  
Phylogenetic Relatedness ◽  
Bacterial Interactions ◽  
Competition And Cooperation ◽  
Microbial Species ◽  
Metabolic Interactions ◽  
Genome Scale

AbstractMicrobial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of metabolic interactions, such as competition and cooperation, between bacterial species. By nature, phylogenetically similar microbial species are likely to share common functional profiles or biological pathways due to their genomics similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation based on functional/pathway profiles may bias downstream applications.To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.Author summaryMicrobial communities, also known as microbiomes, are formed through the interactions of various microbial species. Utilizing genomic sequencing, it is possible to infer the compositional make-up of communities as well as predict their metabolic interactions. However, because some species are more similarly related to each other, while others are more distantly related, one cannot directly compare metabolic relationships without first accounting for their phylogenetic relatedness. Here we developed a computational pipeline which predicts complimentary and competitive metabolic relationships between bacterial species, while normalizing for their phylogenetic relatedness. Our results show that phylogenetic distances are correlated with metabolic interactions, and factoring out such relationships can help better understand microbial interactions which drive community formation.

scholarly journals Metabolic dependencies drive species co-occurrence in diverse microbial communities

2015 ◽  
Vol 112 (20) ◽  
pp. 6449-6454 ◽  
Author(s):  
Aleksej Zelezniak ◽  
Sergej Andrejev ◽  
Olga Ponomarova ◽  
Daniel R. Mende ◽  
Peer Bork ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Resource Competition ◽  
Critical Role ◽  
Metabolic Modeling ◽  
Cooperative Groups ◽  
Cooperative Interactions ◽  
A Genome ◽  
Group Survival ◽  
Metabolic Interactions ◽  
Genome Scale

Microbial communities populate most environments on earth and play a critical role in ecology and human health. Their composition is thought to be largely shaped by interspecies competition for the available resources, but cooperative interactions, such as metabolite exchanges, have also been implicated in community assembly. The prevalence of metabolic interactions in microbial communities, however, has remained largely unknown. Here, we systematically survey, by using a genome-scale metabolic modeling approach, the extent of resource competition and metabolic exchanges in over 800 communities. We find that, despite marked resource competition at the level of whole assemblies, microbial communities harbor metabolically interdependent groups that recur across diverse habitats. By enumerating flux-balanced metabolic exchanges in these co-occurring subcommunities we also predict the likely exchanged metabolites, such as amino acids and sugars, that can promote group survival under nutritionally challenging conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence and hint at cooperative groups as recurring modules of microbial community architecture.

scholarly journals A gap-filling algorithm for prediction of metabolic interactions in microbial communities

2021 ◽  
Vol 17 (11) ◽  
pp. e1009060
Author(s):  
Dafni Giannari ◽  
Cleo Hanchen Ho ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Microbial Communities ◽  
Individual Species ◽  
Metabolic Reconstruction ◽  
Interspecies Interactions ◽  
Gap Filling ◽  
Bifidobacterium Adolescentis ◽  
Filling Method ◽  
Metabolic Interactions ◽  
Enzyme Functions ◽  
Genome Scale

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

scholarly journals METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

2020 ◽  
Author(s):  
Zhichao Zhou ◽  
Patricia Q Tran ◽  
Adam M Breister ◽  
Yang Liu ◽  
Kristopher Kieft ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Single Cell ◽  
Metabolic Networks ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Microbial Genomes ◽  
Biogeochemical Models ◽  
Genome Scale

Abstract Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

scholarly journals Networks of energetic and metabolic interactions define dynamics in microbial communities

2015 ◽  
Vol 112 (50) ◽  
pp. 15450-15455 ◽  
Author(s):  
Mallory Embree ◽  
Joanne K. Liu ◽  
Mahmoud M. Al-Bassam ◽  
Karsten Zengler
Keyword(s):  
Microbial Communities ◽  
Community Diversity ◽  
Metagenomic Data ◽  
Interspecies Interactions ◽  
Environmental Perturbations ◽  
Technological Advances ◽  
Metabolic Interactions ◽  
High Species Richness ◽  
Species Specific ◽  
And Control

Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation. By investigating methanogenic populations, we identified the multidimensional interspecies interactions that define composition and dynamics within syntrophic communities that play a key role in the global carbon cycle. Species-specific genomes were extracted from metagenomic data using differential coverage binning. We used metabolic modeling leveraging metatranscriptomic information to reveal and quantify a complex intertwined system of syntrophic relationships. Our results show that amino acid auxotrophies create additional interdependencies that define community composition and control carbon and energy flux through the system while simultaneously contributing to overall community robustness. Strategic use of antimicrobials further reinforces this intricate interspecies network. Collectively, our study reveals the multidimensional interactions in syntrophic communities that promote high species richness and bolster community stability during environmental perturbations.

scholarly journals Proxi: a Python package for proximity network inference from metagenomic data

2018 ◽  
Author(s):  
Yasser EL-Manzalawy
Keyword(s):  
Network Inference ◽  
Nearest Neighbor ◽  
Microbial Interactions ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Proximity Graphs ◽  
Link Type ◽  
Technological Advances ◽  
Python Package

AbstractSummary: Recent technological advances in high-throughput metagenomic sequencing have provided unique opportunities for studying the diversity and dynamics of microbial communities under different health or environmental conditions. Graph-based representation of metagenomic data is a promising direction not only for analyzing microbial interactions but also for a broad range of machine learning tasks including feature selection, classification, clustering, anomaly detection, and dimensionality reduction. We present Proxi, an open source Python package for learning different types of proximity graphs from metagenomic data. Currently, three types of proximity graphs are supported: k-nearest neighbor (k-NN) graphs; radius-nearest neighbor (r-NN) graphs; and perturbed k-nearest neighbor (pk-NN) graphs.Availability: Proxi Python source code is freely available at https://bitbucket.org/idsrlab/proxi/.Contact:[email protected] information: Tutorials and online documentation are available at https://proxi.readthedocs.io

scholarly journals A gap-filling algorithm for prediction of metabolic interactions in microbial communities

2021 ◽  
Author(s):  
Dafni Giannari ◽  
Cleo HC Ho ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Microbial Communities ◽  
Individual Species ◽  
Metabolic Reconstruction ◽  
Interspecies Interactions ◽  
Gap Filling ◽  
Bifidobacterium Adolescentis ◽  
Filling Method ◽  
Metabolic Interactions ◽  
Enzyme Functions ◽  
Genome Scale

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

scholarly journals METABOLIC: High-throughput profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks

2019 ◽  
Author(s):  
Zhichao Zhou ◽  
Patricia Q. Tran ◽  
Adam M. Breister ◽  
Yang Liu ◽  
Kristopher Kieft ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Single Cell ◽  
Metabolic Networks ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Microbial Genomes ◽  
Biogeochemical Models ◽  
Genome Scale

ABSTRACTBackgroundAdvances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling.ResultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ∼3 hours with 40 CPU threads to process ∼100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ∼45 mins, while it takes ∼5 hours to complete hmmsearch for ∼3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.ConclusionMETABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

scholarly journals HgtSIM: a simulator for horizontal gene transfer (HGT) in microbial communities

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4015 ◽  
Author(s):  
Weizhi Song ◽  
Kerrin Steensen ◽  
Torsten Thomas
Keyword(s):  
Microbial Community ◽  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Microbial Communities ◽  
Metagenomic Data ◽  
Community Members ◽  
Data Simulation ◽  
Simulation Tools ◽  
Link Type

The development and application of metagenomic approaches have provided an opportunity to study and define horizontal gene transfer (HGT) on the level of microbial communities. However, no current metagenomic data simulation tools offers the option to introduce defined HGT within a microbial community. Here, we present HgtSIM, a pipeline to simulate HGT event among microbial community members with user-defined mutation levels. It was developed for testing and benchmarking pipelines for recovering HGTs from complex microbial datasets. HgtSIM is implemented in Python3 and is freely available at: https://github.com/songweizhi/HgtSIM.

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