Dataset Information

Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism.

ABSTRACT: Microbiome studies of the lower airways based on bacterial 16S rRNA gene sequencing assess microbial community structure but can only infer functional characteristics. Microbial products, such as short-chain fatty acids (SCFAs), in the lower airways have significant impact on the host's immune tone. Thus, functional approaches to the analyses of the microbiome are necessary. Here we used upper and lower airway samples from a research bronchoscopy smoker cohort. In addition, we validated our results in an experimental mouse model. We extended our microbiota characterisation beyond 16S rRNA gene sequencing with the use of whole-genome shotgun (WGS) and RNA metatranscriptome sequencing. SCFAs were also measured in lower airway samples and correlated with each of the sequencing datasets. In the mouse model, 16S rRNA gene and RNA metatranscriptome sequencing were performed. Functional evaluations of the lower airway microbiota using inferred metagenome, WGS and metatranscriptome data were dissimilar. Comparison with measured levels of SCFAs shows that the inferred metagenome from the 16S rRNA gene sequencing data was poorly correlated, while better correlations were noted when SCFA levels were compared with WGS and metatranscriptome data. Modelling lower airway aspiration with oral commensals in a mouse model showed that the metatranscriptome most efficiently captures transient active microbial metabolism, which was overestimated by 16S rRNA gene sequencing. Functional characterisation of the lower airway microbiota through metatranscriptome data identifies metabolically active organisms capable of producing metabolites with immunomodulatory capacity, such as SCFAs.

SUBMITTER: Sulaiman I

PROVIDER: S-EPMC8643072 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism.

Sulaiman Imran I Wu Benjamin G BG Li Yonghua Y Tsay Jun-Chieh JC Sauthoff Maya M Scott Adrienne S AS Ji Kun K Koralov Sergei B SB Weiden Michael M Clemente Jose C JC Jones Drew D Huang Yvonne J YJ Stringer Kathleen A KA Zhang Lingdi L Geber Adam A Banakis Stephanie S Tipton Laura L Ghedin Elodie E Segal Leopoldo N LN

The European respiratory journal 20210729 1

<h4>Background</h4>Microbiome studies of the lower airways based on bacterial 16S rRNA gene sequencing assess microbial community structure but can only infer functional characteristics. Microbial products, such as short-chain fatty acids (SCFAs), in the lower airways have significant impact on the host's immune tone. Thus, functional approaches to the analyses of the microbiome are necessary.<h4>Methods</h4>Here we used upper and lower airway samples from a research bronchoscopy smoker cohort. ...[more]

PMID: 33446604

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Project description:BackgroundAdvances 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 microbial contributions to biogeochemical cycling.ResultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies 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, 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 microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or 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, community-scale microbial functional networks using a newly defined metric "MW-score" (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h 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 the 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 under GPLv3 at https://github.com/AnantharamanLab/METABOLIC . Video abstract.

Project description:The plant rhizosphere interfaces an array of microbiomes related to plant growth and development. Cultivar-specific soil microbial communities with respect to their taxonomic structure and specific function have not been investigated explicitly in improving the adaptation of lentil cultivars under rice-fallow ecology. The present study was carried out to decipher the rhizosphere microbiome assembly of two lentil cultivars under rice-fallow ecology for discerning the diversity of microbial communities and for predicting the function of microbiome genes related to nitrogen (N) and phosphorus (P) cycling processes deploying high-throughput whole (meta) genome sequencing. The metagenome profile of two cultivars detected variable microbiome composition with discrete metabolic activity. Cyanobacteria, Bacteroidetes, Proteobacteria, Gemmatimonadetes, and Thaumarchaeota were abundant phyla in the "Farmer-2" rhizosphere, whereas Actinobacteria, Acidobacteria, Firmicutes, Planctomycetes, Chloroflexi, and some incompletely described procaryotes of the "Candidatus" category were found to be robustly enriched the rhizosphere of "Moitree". Functional prediction profiles of the microbial metagenomes between two cultivars revealed mostly house keeping genes with general metabolism. Additionally, the rhizosphere of "Moitree" had a high abundance of genes related to denitrification processes. Significant difference was observed regarding P cycling genes between the cultivars. "Moitree" with a profuse root system exhibited better N fixation and translocation ability due to a good "foraging strategy" for improving acquisition of native P under the nutrient depleted rice-fallow ecology. However, "Farmer-2" revealed a better "mining strategy" for enhancing P solubilization and further transportation to sinks. This study warrants comprehensive research for explaining the role of microbiome diversity and cultivar-microbe interactions towards stimulating microbiome-derived soil reactions regarding nutrient availability under rice-fallow ecology.

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Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism.

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Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism.

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