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

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1007951 ◽

2020 ◽

Vol 16 (10) ◽

pp. e1007951

Author(s):

Tony J. Lam ◽

Moses Stamboulian ◽

Wontack Han ◽

Yuzhen Ye

Keyword(s):

Dominant Role ◽

Bacterial Species ◽

Community Analysis ◽

Microbial Interactions ◽

Phylogenetic Distance ◽

Metabolic Cooperation ◽

Bacterial Interactions ◽

Microbial Species ◽

Metabolic Models ◽

Genome Scale

Microbial 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 cooperative and competitive metabolic interactions between bacterial species. By nature, phylogenetically similar microbial species are more likely to share common functional profiles or biological pathways due to their genomic similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation between species 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 associated 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.

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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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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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Metabolic overlap in environmentally diverse microbial communities

10.1101/653881 ◽

2019 ◽

Author(s):

Eric R. Hester ◽

Mike S.M. Jetten ◽

Cornelia U. Welte ◽

Sebastian Lücker

Keyword(s):

Microbial Communities ◽

Future Research ◽

Aquatic Environments ◽

Competition And Cooperation ◽

Microbial Species ◽

Common Resource ◽

Animal Hosts ◽

Genome Scale ◽

Genome Information ◽

Detailed Picture

AbstractThe majority of microbial communities consist of hundreds to thousands of species, creating a massive network of organisms competing for available resources within an ecosystem. In natural microbial communities it appears that many microbial species have highly redundant metabolisms and seemingly are capable of utilizing the same substrates. This is paradoxical, as theory indicates that species requiring a common resource should outcompete one another. To better understand why microbial species can co-exist, we developed Metabolic Overlap (MO) as a new metric to survey the functional redundancy of microbial communities at the genome scale across a wide variety of ecosystems. Using metagenome-assembled genomes, we surveyed over 1200 studies across ten ecosystem types. We found the highest MO in extreme (i.e., low pH/high temperature) and aquatic environments, while the lowest MO was observed in communities associated with animal hosts, or the built/engineered environment. In addition, different metabolism subcategories were explored for their degree of metabolic overlap. For instance, overlap in nitrogen metabolism was among the lowest in Animal and Engineered ecosystems, while the most was in species from the Built environment. Together, we present a metric that utilizes whole genome information to explore overlapping niches of microbes. This provides a detailed picture of potential metabolic competition and cooperation between species present in an ecosystem, indicates the main substrate types sustaining the community and serves as a valuable tool to generate hypotheses for future research.

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The Impact of Intraspecies and Interspecies Bacterial Interactions on Disease Outcome

Pathogens ◽

10.3390/pathogens10020096 ◽

2021 ◽

Vol 10 (2) ◽

pp. 96

Author(s):

Jiwasmika Baishya ◽

Karishma Bisht ◽

Jeanette N. Rimbey ◽

Kiddist D. Yihunie ◽

Shariful Islam ◽

...

Keyword(s):

Microbial Communities ◽

Therapeutic Potential ◽

Well Being ◽

Microbial Interactions ◽

Disease Outcome ◽

Negative Effects ◽

Bacterial Interactions ◽

Microbial Species ◽

Host Health ◽

The Impact

The human microbiota is an array of microorganisms known to interact with the host and other microbes. These interactions can be competitive, as microbes must adapt to host- and microorganism-related stressors, thus producing toxic molecules, or cooperative, whereby microbes survive by maintaining homeostasis with the host and host-associated microbial communities. As a result, these microbial interactions shape host health and can potentially result in disease. In this review, we discuss these varying interactions across microbial species, their positive and negative effects, the therapeutic potential of these interactions, and their implications on our knowledge of human well-being.

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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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The impact of interactions on invasion and colonization resistance in microbial communities

10.1101/2020.06.11.146571 ◽

2020 ◽

Cited By ~ 1

Author(s):

Helen Kurkjian ◽

M. Javad Akbari ◽

Babak Momeni

Keyword(s):

Microbial Communities ◽

Human Health ◽

Microbial Interactions ◽

Community Resident ◽

Colonization Resistance ◽

Community Or ◽

Microbial Species ◽

Model Based ◽

Human Microbiota ◽

The Impact

AbstractIn human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions among resident microbes. Microbial communities differ from communities composed of other types of organisms in that many microbial interactions are mediated by chemicals that are released into or consumed from the environment. We ask what determines invasion outcomes in such microbial communities. Here, we use a model based on chemical-mediated interactions among microbial species to assess the impact of positive and negative interactions on invasion outcomes. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or dropped and whether the invader was maintained in the community or went extinct. We found that as the number of invaders increased relative to size of the resident community, resident communities were increasingly disrupted. As facilitation of the invader by the resident community increased, resistance outcomes were replaced by displacement and augmentation. By contrast, as facilitation increased among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, augmentation outcomes were replaced by displacement outcomes, while when inhibition of residents by invaders was eliminated, there was little change in the frequency of invasion outcomes. These results suggest that a better understanding of the chemical-mediated interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

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Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

10.1101/223198 ◽

2018 ◽

Cited By ~ 2

Author(s):

Daniel Machado ◽

Sergej Andrejev ◽

Melanie Tramontano ◽

Kiran Raosaheb Patil

Keyword(s):

Microbial Communities ◽

Single Species ◽

Model Organisms ◽

Universal Model ◽

Microbial Species ◽

Scale Models ◽

Metabolic Models ◽

User Friendly ◽

Genome Scale ◽

Automated Tool

AbstractGenome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool – CarveMe – for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009060 ◽

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.

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Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria

10.1101/2020.10.09.333336 ◽

2020 ◽

Cited By ~ 1

Author(s):

Samir Giri ◽

Leonardo Oña ◽

Silvio Waschina ◽

Shraddha Shitut ◽

Ghada Yousif ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Community Assembly ◽

Bacterial Species ◽

Exchange Interactions ◽

Structure And Function ◽

Phylogenetic Distance ◽

And Function ◽

Microbial Community Assembly ◽

Feeding Interactions

AbstractThe exchange of metabolites among different bacterial genotypes is key for determining the structure and function of microbial communities. However, the factors that govern the establishment of these cross-feeding interactions remain poorly understood. While kin selection theory predicts that individuals should direct benefits preferentially to close relatives, the potential benefits resulting from a metabolic exchange may be larger for more distantly related species. Here we distinguish between these two possibilities by performing pairwise cocultivation experiments between auxotrophic recipients and 25 species of potential amino acid donors. Auxotrophic recipients were able to grow in the vast majority of pairs tested (78%), suggesting that metabolic cross-feeding interactions are readily established. Strikingly, both the phylogenetic distance between donor and recipient as well as the dissimilarity of their metabolic networks was positively associated with the growth of auxotrophic recipients. Finally, this result was corroborated in an in-silico analysis of a co-growth of species from a gut microbial community. Together, these findings suggest metabolic cross-feeding interactions are more likely to establish between strains that are metabolically more dissimilar. Thus, our work identifies a new rule of microbial community assembly, which can help predict, understand, and manipulate natural and synthetic microbial systems.SignificanceMetabolic cross-feeding is critical for determining the structure and function of natural microbial communities. However, the rules that determine the establishment of these interactions remain poorly understood. Here we systematically analyze the propensity of different bacterial species to engage in unidirectional cross-feeding interactions. Our results reveal that synergistic growth was prevalent in the vast majority of cases analyzed. Moreover, both phylogenetic and metabolic dissimilarity between donors and recipients favored a successful establishment of metabolite exchange interactions. This work identifies a new rule of microbial community assembly that can help predict, understand, and manipulate microbial communities for diverse applications.

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