scholarly journals Quantifying biosynthetic network robustness across the human oral microbiome

2018 ◽  
Author(s):  
David B. Bernstein ◽  
Floyd E. Dewhirst ◽  
Daniel Segrè
Keyword(s):  
Microbial Communities ◽  
Metabolic Networks ◽  
Oral Microbiome ◽  
Network Robustness ◽  
Species Comparison ◽  
Metabolic Products ◽  
Metabolic Interactions

AbstractMetabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-culturedSaccharibacteria(TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.

scholarly journals Enumerating all possible biosynthetic pathways from metabolic networks

2017 ◽  
Author(s):  
Aarthi Ravikrishnan ◽  
Meghana Nasre ◽  
Karthik Raman
Keyword(s):  
Microbial Communities ◽  
Metabolic Networks ◽  
Amino Acid Biosynthesis ◽  
Breadth First Search ◽  
Acid Biosynthesis ◽  
Complex Molecules ◽  
Graph Theoretic ◽  
Metabolic Interactions ◽  
Target Molecules ◽  
Precursor Molecules

AbstractExhaustive identification of all alternate possible pathways that exist within metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities.We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular length between a given set of source and target molecules. Our algorithm employs aguidedbreadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from predicting amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying larger graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein.A Python-based implementation of MetQuest is available athttps://github.com/RamanLab/MetQuest

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.

In Situ Gene Expression in Mixed-Culture Biofilms: Evidence of Metabolic Interactions between Community Members

1998 ◽  
Vol 64 (2) ◽  
pp. 721-732 ◽  
Author(s):  
Søren Møller ◽  
Claus Sternberg ◽  
Jens Bo Andersen ◽  
Bjarke Bak Christensen ◽  
Juan Luis Ramos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microbial Communities ◽  
Pure Culture ◽  
Fluorescent Protein ◽  
Confocal Laser ◽  
Specific Gene ◽  
Community Members ◽  
Metabolic Interactions ◽  
Green Fluorescent

ABSTRACT Microbial communities growing in laboratory-based flow chambers were investigated in order to study compartmentalization of specific gene expression. Among the community members studied, the focus was in particular on Pseudomonas putida and a strain of anAcinetobacter sp., and the genes studied are involved in the biodegradation of toluene and related aromatic compounds. The upper-pathway promoter (Pu) and themeta-pathway promoter (Pm) from the TOL plasmid were fused independently to the gene coding for the green fluorescent protein (GFP), and expression from these promoters was studied inP. putida, which was a dominant community member. Biofilms were cultured in flow chambers, which in combination with scanning confocal laser microscopy allowed direct monitoring of promoter activity with single-cell spatial resolution. Expression from thePu promoter was homogeneously induced by benzyl alcohol in both community and pure-culture biofilms, while the Pmpromoter was induced in the mixed community but not in a pure-culture biofilm. By sequentially adding community members, induction ofPm was shown to be a consequence of direct metabolic interactions between an Acinetobacter species and P. putida. Furthermore, in fixed biofilm samples organism identity was determined and gene expression was visualized at the same time by combining GFP expression with in situ hybridization with fluorescence-labeled 16S rRNA targeting probes. This combination of techniques is a powerful approach for investigating structure-function relationships in microbial communities.

scholarly journals Need for Laboratory Ecosystems To Unravel the Structures and Functions of Soil Microbial Communities Mediated by Chemistry

mBio ◽  
2018 ◽  
Vol 9 (4) ◽  
Author(s):  
Kateryna Zhalnina ◽  
Karsten Zengler ◽  
Dianne Newman ◽  
Trent R. Northen
Keyword(s):  
Microbial Communities ◽  
Metabolic Networks ◽  
Soil Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Consortia ◽  
Cellular Biology ◽  
Model Organisms ◽  
Soil Microbial ◽  
Integrative Framework ◽  
Genome Annotations

ABSTRACTThe chemistry underpinning microbial interactions provides an integrative framework for linking the activities of individual microbes, microbial communities, plants, and their environments. Currently, we know very little about the functions of genes and metabolites within these communities because genome annotations and functions are derived from the minority of microbes that have been propagated in the laboratory. Yet the diversity, complexity, inaccessibility, and irreproducibility of native microbial consortia limit our ability to interpret chemical signaling and map metabolic networks. In this perspective, we contend that standardized laboratory ecosystems are needed to dissect the chemistry of soil microbiomes. We argue that dissemination and application of standardized laboratory ecosystems will be transformative for the field, much like how model organisms have played critical roles in advancing biochemistry and molecular and cellular biology. Community consensus on fabricated ecosystems (“EcoFABs”) along with protocols and data standards will integrate efforts and enable rapid improvements in our understanding of the biochemical ecology of microbial communities.

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 Syntrophic H2 production enhances the performance of primarily acetate-supplemented reactors treating sulphate contaminated solutions

2021 ◽  
Author(s):  
Tomas Hessler ◽  
Susan T.L. Harrison ◽  
Jillian F. Banfield ◽  
Robert J. Huddy
Keyword(s):  
Microbial Communities ◽  
Packed Bed ◽  
H2 Production ◽  
Flow Path ◽  
Packed Bed Bioreactor ◽  
Sulfate Removal ◽  
Metabolic Interactions ◽  
Fermentative Hydrogen ◽  
Biological Sulfate Reduction ◽  
The Impact

Biological sulfate reduction (BSR) represents a promising bioremediation strategy, yet the impact of metabolic interactions on performance has been largely unexplored. Here, genome-resolved metagenomics was used to characterise 17 microbial communities associated with reactors operated with defined sulfate-contaminated solutions. Pairs of reactors were supplemented with lactate or with acetate plus a small amount of fermentable substrate. At least thirty draft quality genomes, representing all the abundant bacteria, were recovered from each metagenome. All of the 22 SRB genomes encode genes for H2 consumption. And of the total 163 genomes recovered, 130 encode 321 NiFe and FeFe hydrogenases. The lactate-supplemented packed-bed bioreactor was particularly interesting as it resulted in stratified microbial communities that were distinct in their predominant metabolisms. Pathways for fermentation of lactate and hydrogen production were enriched towards the inlet whereas increased autotrophy and acetate-oxidizing SRB were evident towards the end of the flow path. We hypothesized that high sulfate removal towards the end of the flow path, despite acetate being an electron donor that typically sustains low SRB growth rates, was stimulated by H2 consumption. This hypothesis was supported by sustained performance of the predominantly acetate-supplemented stirred-tank reactor, which was dominated by diverse fermentative, hydrogen-evolving bacteria and low-abundance SRB capable of acetate and hydrogen consumption. We conclude that the performance of BSR reactors supplemented with inexpensive acetate can be improved by the addition of a low concentration of fermentable material due to stimulation of syntrophic relationships among hydrogen-producing non-SRB and dual hydrogen- and acetate-utilising SRB.

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.

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