skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.

Abstract

Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizalmore » symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.« less

Authors:
; ; ; ; ;  [1]
  1. Biosciences Division
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1019243
Report Number(s):
ANL/BIO/JA-69352
Journal ID: 1752-0509; TRN: US201114%%713
DOE Contract Number:  
DE-AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
BMC Syst. Biol.
Additional Journal Information:
Journal Volume: 5; Journal Issue: May 13, 2011
Country of Publication:
United States
Language:
ENGLISH
Subject:
59 BASIC BIOLOGICAL SCIENCES; 54 ENVIRONMENTAL SCIENCES; ALLANTOIN; BIOLOGICAL PATHWAYS; CARBOHYDRATES; CARBON; ECOSYSTEMS; ENZYMES; FRUCTOSE; FUNGI; GENES; GLUCOSE; GLYCINE; MEMBRANES; METABOLITES; NITROGEN; NUTRIENTS; ORGANIC MATTER; RNA; SACCHARIDES; SOILS; SYMBIOSIS; TERRESTRIAL ECOSYSTEMS

Citation Formats

Larsen, P E, Sreedasyam, A, Trivedi, G, Podila, G K, Cseke, L J, Collart, F R, On Assignment, Scientific Staffing), and Univ. of Alabama at Huntsville). Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.. United States: N. p., 2011. Web. doi:10.1186/1752-0509-5-70.
Larsen, P E, Sreedasyam, A, Trivedi, G, Podila, G K, Cseke, L J, Collart, F R, On Assignment, Scientific Staffing), & Univ. of Alabama at Huntsville). Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.. United States. doi:10.1186/1752-0509-5-70.
Larsen, P E, Sreedasyam, A, Trivedi, G, Podila, G K, Cseke, L J, Collart, F R, On Assignment, Scientific Staffing), and Univ. of Alabama at Huntsville). Fri . "Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.". United States. doi:10.1186/1752-0509-5-70.
@article{osti_1019243,
title = {Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.},
author = {Larsen, P E and Sreedasyam, A and Trivedi, G and Podila, G K and Cseke, L J and Collart, F R and On Assignment, Scientific Staffing) and Univ. of Alabama at Huntsville)},
abstractNote = {Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.},
doi = {10.1186/1752-0509-5-70},
journal = {BMC Syst. Biol.},
number = May 13, 2011,
volume = 5,
place = {United States},
year = {2011},
month = {5}
}