In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.

BACKGROUND: Sinorhizobium meliloti is a soil bacterium, known for its capability to establish symbiotic nitrogen fixation (SNF) with leguminous plants such as alfalfa. S. meliloti 1021 is the most extensively studied strain to understand the mechanism of SNF and further to study the legume-microbe i...

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Main Authors: Hansheng Zhao, Mao Li, Kechi Fang, Wenfeng Chen, Jing Wang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2012-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3272708?pdf=render
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author Hansheng Zhao
Mao Li
Kechi Fang
Wenfeng Chen
Jing Wang
author_facet Hansheng Zhao
Mao Li
Kechi Fang
Wenfeng Chen
Jing Wang
author_sort Hansheng Zhao
collection DOAJ
description BACKGROUND: Sinorhizobium meliloti is a soil bacterium, known for its capability to establish symbiotic nitrogen fixation (SNF) with leguminous plants such as alfalfa. S. meliloti 1021 is the most extensively studied strain to understand the mechanism of SNF and further to study the legume-microbe interaction. In order to provide insight into the metabolic characteristics underlying the SNF mechanism of S. meliloti 1021, there is an increasing demand to reconstruct a metabolic network for the stage of SNF in S. meliloti 1021. RESULTS: Through an iterative reconstruction process, a metabolic network during the stage of SNF in S. meliloti 1021 was presented, named as iHZ565, which accounts for 565 genes, 503 internal reactions, and 522 metabolites. Subjected to a novelly defined objective function, the in silico predicted flux distribution was highly consistent with the in vivo evidences reported previously, which proves the robustness of the model. Based on the model, refinement of genome annotation of S. meliloti 1021 was performed and 15 genes were re-annotated properly. There were 19.8% (112) of the 565 metabolic genes included in iHZ565 predicted to be essential for efficient SNF in bacteroids under the in silico microaerobic and nutrient sharing condition. CONCLUSIONS: As the first metabolic network during the stage of SNF in S. meliloti 1021, the manually curated model iHZ565 provides an overview of the major metabolic properties of the SNF bioprocess in S. meliloti 1021. The predicted SNF-required essential genes will facilitate understanding of the key functions in SNF and help identify key genes and design experiments for further validation. The model iHZ565 can be used as a knowledge-based framework for better understanding the symbiotic relationship between rhizobia and legumes, ultimately, uncovering the mechanism of nitrogen fixation in bacteroids and providing new strategies to efficiently improve biological nitrogen fixation.
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spelling doaj.art-b39a4e845ba74f00a50bef24d21087492022-12-21T23:41:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0172e3128710.1371/journal.pone.0031287In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.Hansheng ZhaoMao LiKechi FangWenfeng ChenJing WangBACKGROUND: Sinorhizobium meliloti is a soil bacterium, known for its capability to establish symbiotic nitrogen fixation (SNF) with leguminous plants such as alfalfa. S. meliloti 1021 is the most extensively studied strain to understand the mechanism of SNF and further to study the legume-microbe interaction. In order to provide insight into the metabolic characteristics underlying the SNF mechanism of S. meliloti 1021, there is an increasing demand to reconstruct a metabolic network for the stage of SNF in S. meliloti 1021. RESULTS: Through an iterative reconstruction process, a metabolic network during the stage of SNF in S. meliloti 1021 was presented, named as iHZ565, which accounts for 565 genes, 503 internal reactions, and 522 metabolites. Subjected to a novelly defined objective function, the in silico predicted flux distribution was highly consistent with the in vivo evidences reported previously, which proves the robustness of the model. Based on the model, refinement of genome annotation of S. meliloti 1021 was performed and 15 genes were re-annotated properly. There were 19.8% (112) of the 565 metabolic genes included in iHZ565 predicted to be essential for efficient SNF in bacteroids under the in silico microaerobic and nutrient sharing condition. CONCLUSIONS: As the first metabolic network during the stage of SNF in S. meliloti 1021, the manually curated model iHZ565 provides an overview of the major metabolic properties of the SNF bioprocess in S. meliloti 1021. The predicted SNF-required essential genes will facilitate understanding of the key functions in SNF and help identify key genes and design experiments for further validation. The model iHZ565 can be used as a knowledge-based framework for better understanding the symbiotic relationship between rhizobia and legumes, ultimately, uncovering the mechanism of nitrogen fixation in bacteroids and providing new strategies to efficiently improve biological nitrogen fixation.http://europepmc.org/articles/PMC3272708?pdf=render
spellingShingle Hansheng Zhao
Mao Li
Kechi Fang
Wenfeng Chen
Jing Wang
In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
PLoS ONE
title In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
title_full In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
title_fullStr In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
title_full_unstemmed In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
title_short In silico insights into the symbiotic nitrogen fixation in Sinorhizobium meliloti via metabolic reconstruction.
title_sort in silico insights into the symbiotic nitrogen fixation in sinorhizobium meliloti via metabolic reconstruction
url http://europepmc.org/articles/PMC3272708?pdf=render
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