METABOLIC: High-throughput profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks

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.

Download Full-text

METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

10.21203/rs.3.rs-113327/v1 ◽

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.

Download Full-text

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

Biochemical Society Transactions ◽

10.1042/bst20190172 ◽

2020 ◽

Vol 48 (2) ◽

pp. 399-409

Author(s):

Baizhen Gao ◽

Rushant Sabnis ◽

Tommaso Costantini ◽

Robert Jinkerson ◽

Qing Sun

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Environmental Remediation ◽

Real Life ◽

Design Principles ◽

Microbial Interactions ◽

Potential Applications ◽

New Gene ◽

Global Biogeochemical Cycles ◽

Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

Download Full-text

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.

Download Full-text

Agricultural Pollution Risks Influence Microbial Ecology in Honghu Lake

10.1101/244657 ◽

2018 ◽

Author(s):

Maozhen Han ◽

Melissa Dsouza ◽

Chunyu Zhou ◽

Hongjun Li ◽

Junqian Zhang ◽

...

Keyword(s):

Community Structure ◽

Microbial Community ◽

Physicochemical Properties ◽

Microbial Communities ◽

Microbial Ecology ◽

Agricultural Pollution ◽

Agricultural Activities ◽

Sediment Samples ◽

Water And Sediment ◽

Honghu Lake

AbstractBackgroundAgricultural activities, such as stock-farming, planting industry, and fish aquaculture, can influence the physicochemistry and biology of freshwater lakes. However, the extent to which these agricultural activities, especially those that result in eutrophication and antibiotic pollution, effect water and sediment-associated microbial ecology, remains unclear.MethodsWe performed a geospatial analysis of water and sediment associated microbial community structure, as well as physicochemical parameters and antibiotic pollution, across 18 sites in Honghu lake, which range from impacted to less-impacted by agricultural pollution. Furthermore, the co-occurrence network of water and sediment were built and compared accorded to the agricultural activities.ResultsPhysicochemical properties including TN, TP, NO3--N, and NO2--N were correlated with microbial compositional differences in water samples. Likewise, in sediment samples, Sed-OM and Sed-TN correlated with microbial diversity. Oxytetracycline and tetracycline concentration described the majority of the variance in taxonomic and predicted functional diversity between impacted and less-impacted sites in water and sediment samples, respectively. Finally, the structure of microbial co-associations was influenced by the eutrophication and antibiotic pollution.ConclusionThese analyses of the composition and structure of water and sediment microbial communities in anthropologically-impacted lakes are imperative for effective environmental pollution monitoring. Likewise, the exploration of the associations between environmental variables (e.g. physicochemical properties, and antibiotics) and community structure is important in the assessment of lake water quality and its ability to sustain agriculture. These results show agricultural practices can negatively influence not only the physicochemical properties, but also the biodiversity of microbial communities associated with the Honghu lake ecosystem. And these results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity; and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu lake.

Download Full-text

Considering the Lives of Microbes in Microbial Communities

mSystems ◽

10.1128/msystems.00155-17 ◽

2018 ◽

Vol 3 (2) ◽

Cited By ~ 13

Author(s):

Elizabeth A. Shank

Keyword(s):

Complex Systems ◽

Microbial Communities ◽

Single Cell ◽

Functional Capacity ◽

Cellular Differentiation ◽

Microbial Interactions ◽

Metabolite Production ◽

Sequencing Technologies ◽

Microbiome Research ◽

Visualization Systems

ABSTRACT Over the last decades, sequencing technologies have transformed our ability to investigate the composition and functional capacity of microbial communities. Even so, critical questions remain about these complex systems that cannot be addressed by the bulk, community-averaged data typically provided by sequencing methods. In this Perspective, I propose that future advances in microbiome research will emerge from considering “the lives of microbes”: we need to create methods to explicitly interrogate how microbes exist and interact in native-setting-like microenvironments. This approach includes developing approaches that expose the phenotypic heterogeneity of microbes; exploring the effects of coculture cues on cellular differentiation and metabolite production; and designing visualization systems that capture features of native microbial environments while permitting the nondestructive observation of microbial interactions over space and time with single-cell resolution.

Download Full-text

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

mBio ◽

10.1128/mbio.01175-18 ◽

2018 ◽

Vol 9 (4) ◽

Cited By ~ 13

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.

Download Full-text

Evaluation of DESS as a storage medium for microbial community analysis

10.7287/peerj.preprints.27041v1 ◽

2018 ◽

Author(s):

Kevin M Lee ◽

Madison Adams ◽

Jonathan L Klassen

Keyword(s):

Community Structure ◽

Microbial Community ◽

Microbial Communities ◽

Microbial Ecology ◽

Microbial Community Structure ◽

Community Analysis ◽

Microbial Community Analysis ◽

Ant Colonies ◽

And Storage ◽

Phosphate Buffered Saline

Microbial ecology research requires sampling strategies that accurately represent the microbial community under study. These communities must typically be transported from the collection location to the laboratory and then stored until they can be processed. However, there is a lack of consensus on how best to preserve microbial communities during transport and storage. Here, we evaluated DESS (Dimethyl sulfoxide, Ethylenediamine tetraacetic acid, Saturated Salt) solution as a broadly applicable preservative for microbial ecology experiments. We stored fungus gardens grown by the ant Trachymyrmex septentrionalis in DESS, 15% glycerol, and phosphate buffered saline (PBS) to test the ability of these preservatives to maintain the structure of fungus garden microbial communities. Variation in microbial community structure due to differences in preservative type was minimal when compared to variation between ant colonies. Additionally, DESS preserved the structure of a defined mock community more faithfully than either 15% glycerol or PBS. DESS is inexpensive, easy to transport, and effective in preserving microbial community structure. We therefore conclude that DESS is a valuable preservative for use in microbial ecology research.

Download Full-text

How sample heterogeneity can obscure the signal of microbial interactions

10.1101/520668 ◽

2019 ◽

Cited By ~ 1

Author(s):

David W. Armitage ◽

Stuart E. Jones

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Species Interactions ◽

Dynamic Models ◽

Spatial Scales ◽

Microbial Interactions ◽

Vital Rates ◽

Ecological Processes ◽

Correlation Networks ◽

Community Data

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

Download Full-text

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Frontiers in Microbiology ◽

10.3389/fmicb.2021.642422 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lauren M. Lui ◽

Erica L.-W. Majumder ◽

Heidi J. Smith ◽

Hans K. Carlson ◽

Frederick von Netzer ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Microbial Ecology ◽

High Throughput Sequencing ◽

Spatial Scales ◽

Data Types ◽

Modeling Framework ◽

Rapid Acquisition ◽

Mechanistic Understanding ◽

And Function

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Download Full-text

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.

Download Full-text