scholarly journals Considering the Lives of Microbes in Microbial Communities

mSystems ◽  
2018 ◽  
Vol 3 (2) ◽  
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.

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 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.

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

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.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

scholarly journals Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development

2017 ◽  
Vol 35 (6) ◽  
pp. 551-560 ◽  
Author(s):  
Abbas H Rizvi ◽  
Pablo G Camara ◽  
Elena K Kandror ◽  
Thomas J Roberts ◽  
Ira Schieren ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Rna Seq

Single cell sequencing: An engineer and business person's perspective on how we got here

Lab on a Chip ◽  
2017 ◽  
Vol 17 (20) ◽  
pp. 3349-3350
Author(s):  
Mark Gilligan
Keyword(s):  
Single Cell ◽  
Single Cell Sequencing ◽  
Sequencing Technologies

Microfluidics entrepreneur Mark Gilligan provides a perspective on the development of single-cell sequencing technologies.

scholarly journals Sequencing and Computational Approaches to Identification and Characterization of Microbial Organisms

2013 ◽  
Vol 5 ◽  
pp. BECB.S10886 ◽  
Author(s):  
Brijesh Singh Yadav ◽  
Venkateswarlu Ronda ◽  
Dinesh P. Vashista ◽  
Bhaskar Sharma
Keyword(s):  
Sequence Data ◽  
Microbial Interactions ◽  
Microbial Pathogens ◽  
Nucleotide Sequence Data ◽  
Computational Approaches ◽  
Microbial Detection ◽  
Sequencing Technologies ◽  
Sequencing Platforms ◽  
Identification And Characterization

The recent advances in sequencing technologies and computational approaches are propelling scientists ever closer towards complete understanding of human-microbial interactions. The powerful sequencing platforms are rapidly producing huge amounts of nucleotide sequence data which are compiled into huge databases. This sequence data can be retrieved, assembled, and analyzed for identification of microbial pathogens and diagnosis of diseases. In this article, we present a commentary on how the metagenomics incorporated with microarray and new sequencing techniques are helping microbial detection and characterization.

scholarly journals EpicPCR 2.0: Technical and Methodological Improvement of a Cutting-Edge Single-Cell Genomic Approach

2021 ◽  
Vol 9 (8) ◽  
pp. 1649
Author(s):  
Véronica L. Roman ◽  
Christophe Merlin ◽  
Marko P. J. Virta ◽  
Xavier Bellanger
Keyword(s):  
Microbial Communities ◽  
Single Cell ◽  
Operational Taxonomic Unit ◽  
Rrna Gene ◽  
Environmental Matrices ◽  
Taxonomic Assignment ◽  
Integrative And Conjugative Elements ◽  
Functional Sequence ◽  
Methodological Improvement ◽  
New Gene

EpicPCR (Emulsion, Paired Isolation and Concatenation PCR) is a recent single-cell genomic method based on a fusion-PCR allowing us to link a functional sequence of interest to a 16S rRNA gene fragment and use the mass sequencing of the resulting amplicons for taxonomic assignment of the functional sequence-carrying bacteria. Although it is interesting because it presents the highest efficiency for assigning a bacterial host to a marker, epicPCR remains a complex multistage procedure with technical difficulties that may easily impair the approach depth and quality. Here, we described how to adapt epicPCR to new gene targets and environmental matrices while identifying the natural host range of SXT/R391 integrative and conjugative elements in water microbial communities from the Meurthe River (France). We notably show that adding a supplementary PCR step allowed us to increase the amplicon yield and thus the number of reads obtained after sequencing. A comparison of operational taxonomic unit (OTU) identification approaches when using biological and technical replicates demonstrated that, although OTUs can be validated when obtained from three out of three technical replicates, up to now, results obtained from two or three biological replicates give a similar and even a better confidence level in OTU identification, while allowing us to detect poorly represented SXT/R391 hosts in microbial communities.

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