scholarly journals Modular assembly of polysaccharide-degrading microbial communities in the ocean

2018 ◽  
Author(s):  
Tim N. Enke ◽  
Manoshi S. Datta ◽  
Julia Schwartzman ◽  
Nathan Cermak ◽  
Désirée Schmitz ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Metabolic Networks ◽  
Interspecific Interactions ◽  
Modular Assembly ◽  
Modular Logic ◽  
Colonization Dynamics ◽  
Polysaccharide Composition ◽  
Module Type ◽  
Substrate Independent ◽  
Complex Organic

AbstractMany complex biological systems such as metabolic networks can be divided into functional and organizational subunits, called modules, which provide the flexibility to assemble novel multi-functional hierarchies by a mix and match of simpler components. Here we show that polysaccharide-degrading microbial communities in the ocean can also assemble in a modular fashion. Using synthetic particles made of a variety of polysaccharides commonly found in the ocean, we showed that the particle colonization dynamics of natural bacterioplankton assemblages can be understood as the aggregation of species modules of two main types: a first module type made of narrow niche-range primary degraders, whose dynamics are controlled by particle polysaccharide composition, and a second module type containing broad niche-range, substrate-independent taxa whose dynamics are controlled by interspecific interactions, in particular cross-feeding via organic acids, amino acids and other metabolic byproducts. As a consequence of this modular logic, communities can be predicted to assemble by a sum of substrate-specific primary degrader modules, one for each complex polysaccharide in the particle, connected to a single broad-niche range consumer module. We validate this model by showing that a linear combination of the communities on single-polysaccharide particles accurately predicts community composition on mixed-polysaccharide particles. Our results suggest thus that the assembly of heterotrophic communities that degrade complex organic materials follow simple design principles that can be exploited to engineer heterotrophic microbiomes.

scholarly journals Viability-Resolved Metagenomics Reveals Antagonistic Colonization Dynamics of Staphylococcus epidermidis Strains on Preterm Infant Skin

mSphere ◽  
2021 ◽  
Author(s):  
K. Taylor Hellmann ◽  
Carly E. Tuura ◽  
James Fish ◽  
Jaimin M. Patel ◽  
D. Ashley Robinson
Keyword(s):  
Preterm Infant ◽  
Microbial Communities ◽  
Preterm Infants ◽  
Staphylococcus Epidermidis ◽  
Fine Scale ◽  
Colonization Dynamics

The skin is a habitat for microbes that commonly infect preterm infants, but the use of sequencing for fine-scale study of the microbial communities of skin that develop in these infants has been limited by technical barriers. We treated skin swabs of preterm infants with a photoreactive dye that eliminates DNA from nonviable microbes and then sequenced the remaining DNA.

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 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 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 Interspecific social interactions shape public goods production in natural microbial communities

2019 ◽  
Author(s):  
Elze Hesse ◽  
Siobhan O’Brien ◽  
Adela M. Luján ◽  
Florian Bayer ◽  
Eleanor M. van Veen ◽  
...  
Keyword(s):  
Microbial Community ◽  
Public Goods ◽  
Social Interactions ◽  
Microbial Communities ◽  
Interspecific Interactions ◽  
Similar Time ◽  
Focal Species ◽  
Ecological Selection ◽  
Social Behaviours

AbstractSome microbial public goods benefit conspecifics, as well as other species. Here, we use evolution and competition experiments to determine how exploitation of public goods by the wider microbial community shapes the production of an interspecific public good: metal-detoxifying siderophores. By simultaneously studying whole microbial communities and an embedded focal species, we show that interspecific exploitation results in both ecological selection against microbial taxa that produce relatively large amounts of siderophores, and evolution of reduced siderophore production within taxa over similar time scales. Our findings demonstrate the crucial role of interspecific interactions in shaping microbial social behaviours.One sentence summary –Interspecific exploitation shapes the evolution and ecology of public goods production

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 Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

2021 ◽  
Author(s):  
Devlin Moyer ◽  
Alan R. Pacheco ◽  
David B. Bernstein ◽  
Daniel Segrè
Keyword(s):  
Microbial Communities ◽  
Metabolic Networks ◽  
Living Systems ◽  
Pruning Algorithm ◽  
Network Pruning ◽  
Open Questions ◽  
Topological Characteristics ◽  
Constraint Based Modeling ◽  
Artificial Chemistries ◽  
Python Package

AbstractUncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of 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 Year-round dynamics of amplicon sequence variant communities differ among eukaryotes, Mimiviridae, and prokaryotes in a coastal ecosystem

2021 ◽  
Author(s):  
Florian Prodinger ◽  
Hisashi Endo ◽  
Yoshihito Takano ◽  
Yanze Li ◽  
Kento Tominaga ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Community Dynamics ◽  
Environmental Changes ◽  
Interspecific Interactions ◽  
Survival Strategies ◽  
Marker Genes ◽  
Sequence Variant ◽  
Dna Viruses ◽  
Seawater Samples ◽  
One Year

AbstractCoastal seawater is the habitat of different microbial communities. These communities are affected by seasonal environmental changes and fluctuating nutrient availability, as well as competitive and cooperative interspecific interactions. In this work, we investigated the seasonal dynamics of communities of eukaryotes, a major group of double-stranded DNA viruses infecting eukaryotes (i.e. Mimiviridae), as well as prokaryotes in the Uranouchi Inlet, Kochi, Japan. We performed metabarcoding using ribosomal RNA genes and the Mimiviridae polB gene as marker genes in 43 seawater samples collected during 20 months. Communities characterized by the compositions of amplicon sequence variants (ASVs) showed synchronic seasonal cycles for eukaryotes, Mimiviridae, and prokaryotes. However, the community dynamics showed intriguing differences in several aspects such as the recovery rate after a year. We further show that the differences in the community dynamics can be explained by differences in the recurrence/persistence levels of individual ASVs among eukaryotes, Mimiviridae, and prokaryotes. Mimiviridae ASVs were less persistent than eukaryotic ASVs, and prokaryotic ASVs were the most persistent. We argue that the differences in the specificity of interactions (i.e. virus-eukaryote vs prokaryote-eukaryote) as well as the survival strategies are at the origin of the distinct community dynamics among eukaryotes, Mimiviridae, and prokaryotes.One sentence summaryA one year observation of coastal microbial communities revealed similar but different community dynamics for eukaryotes, a group of large viruses, and prokaryotes.

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