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Analysis of microbial functions in the rhizosphere using a metabolic-network based framework for metagenomics interpretation
Year:
2017
Source of publication :
Frontiers in Microbiology
Authors :
Freilich, Shiri
;
.
Minz, Dror
;
.
Ofaim, Shany
;
.
Ofek, Maya
;
.
Sela, Noa
;
.
Volume :
8
Co-Authors:
Ofaim, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel, Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Ofek-Lalzar, M., Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Beit Dagan, Israel
Sela, N., Department of Plant Pathology and Weed Research, Agricultural Research Organization, The Volcani Center, Beit Dagan, Israel
Jinag, J., Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
Kashi, Y., Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Minz, D., Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Beit Dagan, Israel
Freilich, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
Facilitators :
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0
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Total pages:
1
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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. ©2017 Wang, Du, Yu, Deng and He.
Note:
Related Files :
Actinomycetales
bioinformatics
microbial ecology
molecular genetics
Rhizobiales
rhizosphere
species dominance
Show More
Related Content
More details
DOI :
10.3389/fmicb.2017.01606
Article number:
1606
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
24205
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:05
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Scientific Publication
Analysis of microbial functions in the rhizosphere using a metabolic-network based framework for metagenomics interpretation
8
Ofaim, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel, Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Ofek-Lalzar, M., Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Beit Dagan, Israel
Sela, N., Department of Plant Pathology and Weed Research, Agricultural Research Organization, The Volcani Center, Beit Dagan, Israel
Jinag, J., Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Nanjing, China
Kashi, Y., Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Minz, D., Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Beit Dagan, Israel
Freilich, S., Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel
Analysis of microbial functions in the rhizosphere using a metabolic-network based framework for metagenomics interpretation
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. ©2017 Wang, Du, Yu, Deng and He.
Scientific Publication
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