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

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.

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

10.1101/2021.05.13.443977 ◽

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

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

Bioinformatics ◽

10.1093/bioinformatics/bty941 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2332-2334 ◽

Cited By ~ 16

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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Metabolic dependencies drive species co-occurrence in diverse microbial communities

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1421834112 ◽

2015 ◽

Vol 112 (20) ◽

pp. 6449-6454 ◽

Cited By ~ 286

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.

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

10.1101/2020.05.15.097725 ◽

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.

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

10.1101/318485 ◽

2018 ◽

Cited By ~ 2

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.

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Networks of energetic and metabolic interactions define dynamics in microbial communities

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1506034112 ◽

2015 ◽

Vol 112 (50) ◽

pp. 15450-15455 ◽

Cited By ~ 116

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.

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gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome Biology ◽

10.1186/s13059-021-02295-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Johannes Zimmermann ◽

Christoph Kaleta ◽

Silvio Waschina

Keyword(s):

Experimental Data ◽

Metabolic Pathways ◽

Scientific Literature ◽

State Of The Art ◽

Gap Filling ◽

Fermentation Products ◽

Metabolic Interactions ◽

Metabolic Models ◽

Genome Scale ◽

Carbon Source Utilisation

AbstractGenome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present (https://github.com/jotech/gapseq), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

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Metabolic Interactions Between Dynamic Bacterial Subpopulations

10.1101/208686 ◽

2017 ◽

Cited By ~ 2

Author(s):

Adam Z. Rosenthal ◽

Yutao Qi ◽

Sahand Hormoz ◽

Jin Park ◽

Sophia Hsin-Jung Li ◽

...

Keyword(s):

Bacillus Subtilis ◽

Microbial Communities ◽

Potential Function ◽

Low Ph ◽

Individual Species ◽

Bacterial Populations ◽

Metabolic Genes ◽

State Switching ◽

Metabolic Interactions ◽

Environmentally Sensitive

AbstractWithin multi-species microbial communities, individual species are known to occupy distinct metabolic niches. By contrast, it has remained largely unclear whether and how metabolic specialization occurs within clonal bacterial populations. The possibility of such metabolic specialization in clonal populations raises several questions: Does specialization occur, and if it does, which metabolic processes are involved? How is specialization coordinated? How rapidly do cells switch between states? And finally, what functions might metabolic specialization provide? One potential function of metabolic specialization could be to manage overflow metabolites such as acetate, which presents a toxic challenge due to low pH, and protective pH-neutral overflow metabolites. Here we show that exponentially dividing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations grew at distinct rates, and cells switched dynamically between states, with acetate influencing the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching.

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chapter 2 A Statistical Gap-Filling Method to Interpolate Global Monthly Surface Ocean Carbon Dioxide Data

Climate Change and the Oceanic Carbon Cycle ◽

10.1201/9781315207490-3 ◽

2017 ◽

pp. 15-62

Author(s):

STEVE D. JONES ◽

CORINNE LE QUÉRÉ ◽

CHRISTIAN RÖDENBECK ◽

ANDREW C. MANNING ◽

ARE OLSEN

Keyword(s):

Carbon Dioxide ◽

Gap Filling ◽

Surface Ocean ◽

Filling Method ◽

Ocean Carbon

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New techniques for gap-filling and partitioning of H<sub>2</sub>O and CO<sub>2</sub> eddy fluxes measured over forests in complex mountainous terrain

10.5194/bg-2017-247 ◽

2017 ◽

Cited By ~ 1

Author(s):

Minseok Kang ◽

Joon Kim ◽

Bindu Malla Thakuri ◽

Junghwa Chun ◽

Chunho Cho

Keyword(s):

Environmental Changes ◽

Water Cycle ◽

Co2 Flux ◽

Test Method ◽

Co2 Fluxes ◽

Gap Filling ◽

New Techniques ◽

Diameter Increment ◽

Filling Method ◽

Rain Events

Abstract. The continuous measurement of H2O and CO2 fluxes using the eddy covariance (EC) technique is still challenging for forests in complex terrain because of large amounts of wet canopy evaporation (EWC), which occur during and following rain events when the EC systems rarely work correctly, and the horizontal advection of CO2 generated at night. We propose new techniques for gap-filling and partitioning of the H2O and CO2 fluxes: (1) a model-stats hybrid method (MSH) and (2) a modified moving point test method (MPTm). The former enables the recovery of the missing EWC in the traditional gap-filling method and the partitioning of the evapotranspiration (ET) into transpiration and (wet canopy) evaporation. The latter determines the friction velocity (u*) threshold based on an iterative approach using moving windows for both time and u*, thereby allowing not only the nighttime CO2 flux correction and partitioning but also the assessment of the significance of the CO2 drainage. We tested and validated these new methods using the datasets from two flux towers, which are located at forests in hilly and complex terrains. The MSH reasonably recovered the missing EWC of 16 ~ 41 mm year−1 and separated it from the ET (14 ~ 23 % of the annual ET). The MPTm produced consistent carbon budgets using those from the previous research and diameter increment, while it has improved applicability. Additionally, we illustrated certain advantages of the proposed techniques, which enables us to understand better how ET responses to environmental changes and how the water cycle is connected to the carbon cycle in a forest ecosystem.

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