In Silico Insights into the Symbiotic Nitrogen Fixation in Sinorhizobium meliloti via Metabolic Reconstruction

Abstract

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.ResultsThrough 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.ConclusionsAs 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.

Modelling nutritional mutualisms: challenges and opportunities for data integration

“…A series of studies on Rhizobium etli (Resendis-Antonio et al 2007 found that an objective function of maximizing nitrogen fixation and production of known bacterial compounds (per input of carbon from the plant) yielded results consistent with observed fixation rates. However, Zhao et al (2012) found that an objective function of strong coupling between nitrogen fixation and carbonnitrogen exchange better agreed with published literature (e.g. proteomic and biochemical analyses) on which metabolic pathways have been reported as active or inactive in Sinorhizobium meliloti.…”

“…However, Zhao et al . () found that an objective function of strong coupling between nitrogen fixation and carbon‐nitrogen exchange better agreed with published literature (e.g. proteomic and biochemical analyses) on which metabolic pathways have been reported as active or inactive in Sinorhizobium meliloti .…”

“…Consequently, either the objective function needs to be specific for the type of nodule the rhizobia inhabits, or the objective function needs to be designed to not depend on the rhizobia growing through maturity. Cell scale models that use FBA commonly seek to identify an appropriate objective function to constrain the metabolic patterns for the system (Resendis-Antonio et al 2011;Zhao et al 2012). This can be used to focus the modelling results on processes of interest.…”

“…The completeness of these networks is tested by their ability to computationally use known substrates to produce known products (including cell components). Predictions about which genes are essential for the mutualism can be tested by comparing model predictions to phenotypes of mutants that lack a particular reaction (Zhao et al 2012). Flux balance analysis (FBA; Box 1) of cell scale models can be used to test 'objective functions' that embody hypotheses about metabolic strategies by comparing the model predictions to growth measurements or other empirical data.…”

Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast, Yarrowia lipolytica

“…FBA [35], [36] is a constraint-based modeling approach to determine the flux distribution in genome-scale metabolic networks based on linear optimization of an objective function (typically the rate of biomass production). Accordingly, it can be used as a tool to predict the in silico cell growth rate or production rates of biotechnologically important metabolites such as ATP.…”