Analysis of Microbial Functions in the Rhizosphere Using a Metabolic-Network Based Framework for Metagenomics Interpretation

Abstract

Advances in metagenomics enable high resolution description of complex bacterial communities in their natural environments. Consequently, conceptual approaches for community level functional analysis are in high need. Here, we introduce a framework for a metagenomics-based analysis of community functions. Environment-specific gene catalogs, derived from metagenomes, are processed into metabolic-network representation. By applying established ecological conventions, network-edges (metabolic functions) are assigned with taxonomic annotations according to the dominance level of specific groups. Once a function-taxonomy link is established, prediction of the impact of dominant taxa on the overall community performances is assessed by simulating removal or addition of edges (taxa associated functions). This approach is demonstrated on metagenomic data describing the microbial communities from the root environment of two crop plants – wheat and cucumber. Predictions for environment-dependent effects revealed differences between treatments (root vs. soil), corresponding to documented observations. Metabolism of specific plant exudates (e.g., organic acids, flavonoids) was linked with distinct taxonomic groups in simulated root, but not soil, environments. These dependencies point to the impact of these metabolite families as determinants of community structure. Simulations of the activity of pairwise combinations of taxonomic groups (order level) predicted the possible production of complementary metabolites. Complementation profiles allow formulating a possible metabolic role for observed co-occurrence patterns. For example, production of tryptophan-associated metabolites through complementary interactions is unique to the tryptophan-deficient cucumber root environment. Our approach enables formulation of testable predictions for species contribution to community activity and exploration of the functional outcome of structural shifts in complex bacterial communities. Understanding community-level metabolism is an essential step toward the manipulation and optimization of microbial function. Here, we introduce an analysis framework addressing three key challenges of such data: producing quantified links between taxonomy and function; contextualizing discrete functions into communal networks; and simulating environmental impact on community performances. New technologies will soon provide a high-coverage description of biotic and a-biotic aspects of complex microbial communities such as these found in gut and soil. This framework was designed to allow the integration of high-throughput metabolomic and metagenomic data toward tackling the intricate associations between community structure, community function, and metabolic inputs.

Continuously Monocropped Jerusalem Artichoke Changed Soil Bacterial Community Composition and Ammonia-Oxidizing and Denitrifying Bacteria Abundances

“…However, we only predicted bacteria function from a taxonomy assignment in this study ( Langille et al, 2013 ). Further researches should focus on getting direct evidence of changes in soil microbial functions in our continuously monocropped JA system through approaches such as metagenomic or metatranscriptomic sequencing ( Choi et al, 2017 ; Ofaim et al, 2017 ).…”

The Chemistry of Plant–Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

“…For example, a co-culture metabolomics approach has been proposed ( Allwood et al, 2010 ) to assess the intracellular metabolomes (metabolic fingerprints) of both host and pathogen and their extruded (extracellular) metabolites (metabolic footprints). However, in order to fully evaluate the changes occurring in the host plant due to these tritrophic interactions under conditions relevant to disease and resistance, there is a need for combining the information provided by different techniques, including metagenomics and metametabolomics ( Heinken and Thiele, 2015 ; Jorge et al, 2016 ; Ofaim et al, 2017 ). This novel approach to metabolomics analyses of host–pathogen interactions will facilitate a greater understanding of both their independent metabolism and the metabolic cross-talk which represents the interactome.…”

Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions

“…New sequencing technologies allow now revealing the dynamics of community shifts and together with modeling approaches lay foundations for the educated design of community function [17]. Progress in sequencing technologies promotes the description of the bio-diversity and metabolic activity of microorganisms in ecological niches [18][19][20][21]. Parallel advancement of computational tools such as Genome-scale metabolic models (GSMM) and respective simulation algorithms such as Flux Balance Analysis (FBA) further enable in silico analysis of microbial interactions [19,20,22,23].…”