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. 2013 Jun 7;8(6):e64929.
doi: 10.1371/journal.pone.0064929. Print 2013.

A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice

Free PMC article

A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice

Juexin Wang et al. PLoS One. .
Free PMC article


Salinity is one of the most common abiotic stresses in agriculture production. Salt tolerance of rice (Oryza sativa) is an important trait controlled by various genes. The mechanism of rice salt tolerance, currently with limited understanding, is of great interest to molecular breeding in improving grain yield. In this study, a gene regulatory network of rice salt tolerance is constructed using a systems biology approach with a number of novel computational methods. We developed an improved volcano plot method in conjunction with a new machine-learning method for gene selection based on gene expression data and applied the method to choose genes related to salt tolerance in rice. The results were then assessed by quantitative trait loci (QTL), co-expression and regulatory binding motif analysis. The selected genes were constructed into a number of network modules based on predicted protein interactions including modules of phosphorylation activity, ubiquity activity, and several proteinase activities such as peroxidase, aspartic proteinase, glucosyltransferase, and flavonol synthase. All of these discovered modules are related to the salt tolerance mechanism of signal transduction, ion pump, abscisic acid mediation, reactive oxygen species scavenging and ion sequestration. We also predicted the three-dimensional structures of some crucial proteins related to the salt tolerance QTL for understanding the roles of these proteins in the network. Our computational study sheds some new light on the mechanism of salt tolerance and provides a systems biology pipeline for studying plant traits in general.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1
Figure 1. Improved volcano plot of GSE14403.
The horizontal axis represents MergeValue obtained by bootstraps SVM-RFE. The vertical axis shows the –log(p-value) from t-test. Black dots indicate selected probes with MergeValue threshold of 0.5 and t-test p-value threshold of 0.05. Red stars indicate selected probes mapped on QTL region. Blue dots indicate unselected probes.
Figure 2
Figure 2. Salt-tolerance protein interaction modules that are related to QTLs.
Red nodes represent the proteins located in QTL regions. Yellow nodes represent the proteins located in flanked regions with the lengths of the QTL regions.
Figure 3
Figure 3. The largest module in the salt tolerance protein interaction network.
Black nodes indicate genes covered by QTLs. Yellow nodes indicate genes covered by extended QTLs.
Figure 4
Figure 4. Detected motif in the upstream sequences of the largest module.
Figure 5
Figure 5. The whole genome mapping of selected genes and their interactions.
Blue lines in the outer circle represent all the 556 selected genes. Red regions on the chromosome are the QTLs while the grey and yellow regions are the extended QTLs with one QTL length at each side of the flanking region. Inside the circle, green links show the protein-protein interactions among 51 genes in the largest module.
Figure 6
Figure 6. Part of the Arabidopsis Plant-Pathogen Interaction pathway in KEGG (, where white boxes indicate that no genes have been assigned, green boxes have known genes in Arabidopsis, and boxes highlighted in red show the three mapped genes in the largest module.
Figure 7
Figure 7. Predicted structural model of protein Os01g0725800.

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