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Title: Taxonomic and Functional Diversity of a Quercus pyrenaica Willd. Rhizospheric Microbiome in the Mediterranean Mountains

Abstract

Altitude significantly affects vegetation growth and distribution, including the developmental stages of a forest. We used shotgun Illumina sequencing to analyze microbial community composition and functional potential in melojo-oak ( Quercus pyrenaica Willd.) rhizospheric soil for three different development stages along an altitudinal gradient: (a) a low altitude, non-optimal site for forest maintenance; (b) an intermediate altitude, optimal site for a forest; and (c) a high altitude, expansion site with isolated trees but without a real forest canopy. We observed that, at each altitude, the same microbial taxa appear both in the taxonomic analysis of the whole metagenome and in the functional analysis of the methane, sulfur and nitrogen metabolisms. Although there were no major differences at the functional level, there were significant differences in the abundance of each taxon at the phylogenetic level between the rhizospheres of the forest (low and intermediate altitudes) and the expansion site. Proteobacteria and Actinobacteria were the most differentially abundant phyla in forest soils compared to the expansion site rhizosphere. Moreover, Verrucomicrobia, Bacteroidetes and Nitrospirae phyla were more highly represented in the non-forest rhizosphere. Our study suggests that rhizospheric microbial communities of the same tree species may be affected by development stage and forestmore » canopy cover via changes in soil pH and the C/N ratio.« less

Authors:
 [1];  [1];  [1];  [1];  [2];  [1]
  1. Consejo Superior de Investigaciones Cientificas, Granada (Spain)
  2. DOE Joint Genome Institute, Walnut Creek, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1408477
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Forests
Additional Journal Information:
Journal Volume: 8; Journal Issue: 10; Journal ID: ISSN 1999-4907
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
metagenomics; Mediterranean forests; melojo-oak; microbial functional diversity; biogeochemical cycles; rhizosphere metabolism

Citation Formats

Cobo-Díaz, Jose F., Fernández-González, Antonio J., Villadas, Pablo J., Toro, Nicolás, Tringe, Susannah G., and Fernández-López, Manuel. Taxonomic and Functional Diversity of a Quercus pyrenaica Willd. Rhizospheric Microbiome in the Mediterranean Mountains. United States: N. p., 2017. Web. doi:10.3390/f8100390.
Cobo-Díaz, Jose F., Fernández-González, Antonio J., Villadas, Pablo J., Toro, Nicolás, Tringe, Susannah G., & Fernández-López, Manuel. Taxonomic and Functional Diversity of a Quercus pyrenaica Willd. Rhizospheric Microbiome in the Mediterranean Mountains. United States. doi:10.3390/f8100390.
Cobo-Díaz, Jose F., Fernández-González, Antonio J., Villadas, Pablo J., Toro, Nicolás, Tringe, Susannah G., and Fernández-López, Manuel. 2017. "Taxonomic and Functional Diversity of a Quercus pyrenaica Willd. Rhizospheric Microbiome in the Mediterranean Mountains". United States. doi:10.3390/f8100390. https://www.osti.gov/servlets/purl/1408477.
@article{osti_1408477,
title = {Taxonomic and Functional Diversity of a Quercus pyrenaica Willd. Rhizospheric Microbiome in the Mediterranean Mountains},
author = {Cobo-Díaz, Jose F. and Fernández-González, Antonio J. and Villadas, Pablo J. and Toro, Nicolás and Tringe, Susannah G. and Fernández-López, Manuel},
abstractNote = {Altitude significantly affects vegetation growth and distribution, including the developmental stages of a forest. We used shotgun Illumina sequencing to analyze microbial community composition and functional potential in melojo-oak (Quercus pyrenaica Willd.) rhizospheric soil for three different development stages along an altitudinal gradient: (a) a low altitude, non-optimal site for forest maintenance; (b) an intermediate altitude, optimal site for a forest; and (c) a high altitude, expansion site with isolated trees but without a real forest canopy. We observed that, at each altitude, the same microbial taxa appear both in the taxonomic analysis of the whole metagenome and in the functional analysis of the methane, sulfur and nitrogen metabolisms. Although there were no major differences at the functional level, there were significant differences in the abundance of each taxon at the phylogenetic level between the rhizospheres of the forest (low and intermediate altitudes) and the expansion site. Proteobacteria and Actinobacteria were the most differentially abundant phyla in forest soils compared to the expansion site rhizosphere. Moreover, Verrucomicrobia, Bacteroidetes and Nitrospirae phyla were more highly represented in the non-forest rhizosphere. Our study suggests that rhizospheric microbial communities of the same tree species may be affected by development stage and forest canopy cover via changes in soil pH and the C/N ratio.},
doi = {10.3390/f8100390},
journal = {Forests},
number = 10,
volume = 8,
place = {United States},
year = 2017,
month =
}

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  • ABSTRACT Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community.more » Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCEStudies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.« less