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

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.

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

2018 ◽  
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.

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

2018 ◽  
Vol 46 (15) ◽  
pp. 7542-7553 ◽  
Author(s):  
Daniel Machado ◽  
Sergej Andrejev ◽  
Melanie Tramontano ◽  
Kiran Raosaheb Patil
Keyword(s):  
Microbial Species ◽  
Metabolic Models ◽  
Genome Scale

scholarly journals Microbial Species Coexistence Depends on the Host Environment

mBio ◽  
2020 ◽  
Vol 11 (4) ◽  
Author(s):  
Peter Deines ◽  
Katrin Hammerschmidt ◽  
Thomas C. G. Bosch
Keyword(s):  
Community Composition ◽  
Carrying Capacity ◽  
Species Coexistence ◽  
Bacterial Species ◽  
Microbial Interactions ◽  
Native Community ◽  
Host Environment ◽  
Microbial Species ◽  
Relative Abundances

ABSTRACT Organisms and their resident microbial communities form a complex and mostly stable ecosystem. It is known that the specific composition and abundance of certain bacterial species affect host health and fitness, but the processes that lead to these microbial patterns are unknown. We investigate this by deconstructing the simple microbiome of the freshwater polyp Hydra. We contrast the performance of its two main bacterial associates, Curvibacter and Duganella, on germfree hosts with two in vitro environments over time. We show that interactions within the microbiome but also the host environment lead to the observed species frequencies and abundances. More specifically, we find that both microbial species can only stably coexist in the host environment, whereas Duganella outcompetes Curvibacter in both in vitro environments irrespective of initial starting frequencies. While Duganella seems to benefit through secretions of Curvibacter, its competitive effect on Curvibacter depends upon direct contact. The competition might potentially be mitigated through the spatial distribution of the two microbial species on the host, which would explain why both species stably coexist on the host. Interestingly, the relative abundances of both species on the host do not match the relative abundances reported previously nor the overall microbiome carrying capacity as reported in this study. Both observations indicate that rare microbial community members might be relevant for achieving the native community composition and carrying capacity. Our study highlights that for dissecting microbial interactions the specific environmental conditions need to be replicated, a goal difficult to achieve with in vitro systems. IMPORTANCE This work studies microbial interactions within the microbiome of the simple cnidarian Hydra and investigates whether microbial species coexistence and community stability depend on the host environment. We find that the outcome of the interaction between the two most dominant bacterial species in Hydra’s microbiome differs depending on the environment and results in a stable coexistence only in the host context. The interactive ecology between the host and the two most dominant microbes, but also the less abundant members of the microbiome, is critically important for achieving the native community composition. This indicates that the metaorganism environment needs to be taken into account when studying microbial interactions.

scholarly journals High-dimensional microbiome interactions shape host fitness

2017 ◽  
Author(s):  
Alison L. Gould ◽  
Vivian Zhang ◽  
Lisa Lamberti ◽  
Eric W. Jones ◽  
Benjamin Obadia ◽  
...  
Keyword(s):  
Bacterial Species ◽  
Gut Bacteria ◽  
Microbial Interactions ◽  
Individual Species ◽  
Community Interactions ◽  
Host Fitness ◽  
Bacterial Interactions ◽  
Fitness Traits ◽  
Host Physiology ◽  
The Impact

AbstractGut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host in turn shapes the gut microbiome. Microbiomes co-evolve with their hosts and have been implicated in host speciation. However, it is unclear to what extent individual species versus community interactions within the microbiome are linked to host fitness. Here we combinatorially dissect the natural microbiome of Drosophila melanogaster and reveal that interactions between bacteria shape host fitness through life history tradeoffs. We find that the same microbial interactions that shape host fitness also shape microbiome abundances, suggesting a potential evolutionary mechanism by which microbiome communities (rather than just individual species) may be intertwined in co-selection with their hosts. Empirically, we made germ-free flies colonized with each possible combination of the five core species of fly gut bacteria. We measured the resulting bacterial community abundances and fly fitness traits including development, reproduction, and lifespan. The fly gut promoted bacterial diversity, which in turn accelerated development, reproduction, and aging: flies that reproduced more died sooner. From these measurements we calculated the impact of bacterial interactions on fly fitness by adapting the mathematics of genetic epistasis to the microbiome. Host physiology phenotypes were highly dependent on interactions between bacterial species. Higher-order interactions (involving 3, 4, and 5 species) were widely prevalent and impacted both host physiology and the maintenance of gut diversity. The parallel impacts of bacterial interactions on the microbiome and on host fitness suggest that microbiome interactions may be key drivers of evolution.SignificanceAll animals have associated microbial communities called microbiomes that can influence the physiology and fitness of their host. It is unclear to what extent individual microbial species versus ecology of the microbiome influences fitness of the host. Here we mapped all the possible interactions between individual species of bacteria with each other and with the host’s physiology. Our approach revealed that the same bacterial interactions that shape microbiome abundances also shape host fitness traits. This relationship provides a feedback that may favor the emergence of co-evolving microbiome-host units.

scholarly journals Microbial species coexistence depends on the host environment

2019 ◽  
Author(s):  
Peter Deines ◽  
Katrin Hammerschmidt ◽  
Thomas CG Bosch
Keyword(s):  
Community Composition ◽  
Carrying Capacity ◽  
Species Coexistence ◽  
Bacterial Species ◽  
Microbial Interactions ◽  
Native Community ◽  
Host Environment ◽  
Microbial Species ◽  
Initial Starting

AbstractOrganisms and their resident microbial communities form a complex and mostly stable ecosystem. It is known that the specific composition and abundance of certain bacterial species affect host health and fitness, but the processes that lead to these microbial patterns are unknown. We investigate this by deconstructing the simple microbiome of the freshwater polyp Hydra. We contrast the performance of its two main bacterial associates, Curvibacter and Duganella, on germ free hosts with two in vitro environments over time. We show that interactions within the microbiome but also the host environment lead to the observed species frequencies and abundances. More specifically, we find that both microbial species can only stably coexist in the host environment, whereas Duganella outcompetes Curvibacter in both in vitro environments irrespective of initial starting frequencies. While Duganella seems to benefit through secretions of Curvibacter, its competitive effect on Curvibacter depends upon direct contact. The competition might potentially be mitigated through the spatial structure of the two microbial species on the host, which would explain why both species stably coexist on the host. Interestingly, the fractions of both species on the host do not match the fractions reported previously nor the overall microbiome carrying capacity as reported in this study. Both observations indicate that the rare microbial community members might be relevant for achieving the native community composition and carrying capacity. Our study highlights that for dissecting microbial interactions the specific environmental conditions need to be replicated, a goal difficult to achieve with in vitro systems.ImportanceThis work studies microbial interactions within the microbiome of the simple cnidarian, Hydra, and investigates whether microbial species coexistence and community stability depends on the host environment. We find that the outcome of the interaction between the two most dominant bacterial species in Hydra’s microbiome differs depending on the environment and only results in a stable coexistence in the host context. The interactive ecology between the host, the two most dominant microbes, but also the less abundant members of the microbiome, are critically important for achieving the native community composition. This indicates that the metaorganism environment needs to be taken into account when studying microbial interactions.

scholarly journals The Impact of Intraspecies and Interspecies Bacterial Interactions on Disease Outcome

Pathogens ◽  
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.

scholarly journals Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jack Jansma ◽  
Sahar El Aidy
Keyword(s):  
Human Health ◽  
Flux Balance Analysis ◽  
Bacterial Species ◽  
Flux Balance ◽  
Interaction Field ◽  
In Silico Simulation ◽  
Balance Analysis ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Genome Scale

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

scholarly journals Impact of Substratum Surface on Microbial Community Structure and Treatment Performance in Biological Aerated Filters

2013 ◽  
Vol 80 (1) ◽  
pp. 177-183 ◽  
Author(s):  
Lavane Kim ◽  
Eulyn Pagaling ◽  
Yi Y. Zuo ◽  
Tao Yan
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Bacterial Species ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Rrna Gene ◽  
Content Type ◽  
Property Change ◽  
And Control ◽  
Start Ups

ABSTRACTThe impact of substratum surface property change on biofilm community structure was investigated using laboratory biological aerated filter (BAF) reactors and molecular microbial community analysis. Two substratum surfaces that differed in surface properties were created via surface coating and used to develop biofilms in test (modified surface) and control (original surface) BAF reactors. Microbial community analysis by 16S rRNA gene-based PCR-denaturing gradient gel electrophoresis (DGGE) showed that the surface property change consistently resulted in distinct profiles of microbial populations during replicate reactor start-ups. Pyrosequencing of the bar-coded 16S rRNA gene amplicons surveyed more than 90% of the microbial diversity in the microbial communities and identified 72 unique bacterial species within 19 bacterial orders. Among the 19 orders of bacteria detected,BurkholderialesandRhodocyclalesof theBetaproteobacteriaclass were numerically dominant and accounted for 90.5 to 97.4% of the sequence reads, and their relative abundances in the test and control BAF reactors were different in consistent patterns during the two reactor start-ups. Three of the five dominant bacterial species also showed consistent relative abundance changes between the test and control BAF reactors. The different biofilm microbial communities led to different treatment efficiencies, with consistently higher total organic carbon (TOC) removal in the test reactor than in the control reactor. Further understanding of how surface properties affect biofilm microbial communities and functional performance would enable the rational design of new generations of substrata for the improvement of biofilm-based biological treatment processes.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan
Keyword(s):  
Arabidopsis Thaliana ◽  
Flux Balance Analysis ◽  
Critical Assessment ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Living Organisms ◽  
Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

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