Current Science

Open Access Open Access  Restricted Access Subscription Access

Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice


Affiliations
1 Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
 

Salinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall's tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified. Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.

Keywords

Correlation Coefficient, Gene Regulatory Networks, Rice, Salinity.
User
Notifications
Font Size

  • Das, S., Pandey, P., Rai, A. and Mohapatra, C., A computational system biology approach to construct gene regulatory networks for salinity response in rice (Oryza sativa). Indian J. Agric. Sci., 2015, 85(12), 1546–1552.

  • Wu, R. and Garg, A., Engineering rice plants with trehaloseproducing genes improves tolerance to drought, salt and low temperature. ISBN News Report, 2003; http://www.isb.vt.edu

  • Kole, C. et al., Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects. Front. Plant Sci., 2015, 6, 563.

  • Schaffter, T., Marbach, D. and Floreano1, D., GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 2011, 27, 2263–2270.

  • Bansal, M., Belcastro, V., Ambesi-Impiombato, A. and di Bernardo, D., How to infer gene networks from expression profiles. Mol. Syst. Biol., 2007, 3, 78.

  • Karlebach, G. and Shamir, R., Modelling and analysis of gene regulatory networks. Nature Rev. Mol. Cell Biol., 2008, 9, 770–780.

  • Akutsu, T., Miyano, S. and Kuhara, S., Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics, 2000, 16, 727–734.

  • Ching, W., Fung, E., Ng, N. and Akutsu, T., On construction of stochastic genetic networks based on gene expression sequences. Int. J. Neural Syst., 2005, 15, 297–310.

  • Hirose, O., Nariai, N., Tamada, Y., Bannai, H., Imoto, S. and Miyano, S., Estimating gene networks from expression data and binding location data via Boolean networks. Lecture Notes Comput. Sci., 2000, 3480, 349–356.

  • Zhang, B. and Horvath, S., A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 2005, 4, 17.

  • Raza, K. and Jaiswal, R., Reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles. Int. J. Bioinformat. Biosci., 2013, 3(2), 25–34; doi:10.5121/ijbb.2013.3103.

  • Imoto, S., Goto, T. and Miyano, S., Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pac. Symp. Biocomput., 2002, 7, 175–186.

  • Perrin, B. E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J. and D’Alche-Buc, F., Gene networks inference using dynamic Bayesian networks. Bioinformatics, 2003, 19(2), 138–148.

  • Cotsaftis, O., Plett, D., Johnson, A. A. and Walia, H., Rootspecific transcript profiling of contrasting rice genotypes in response to salinity stress. Mol. Plant, 2011, 4(1), 25–41.

  • Gautier, L., Cope, L., Bolstad, B. M. and Irizarry, R. A., Affy – analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 2004, 20, 307–315.

  • Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U. and Speed, T. P., Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4, 249–264.

  • Shannon, P. T., Grimes, M., Kutlu, B., Bot, J. J. and Galas, D. J., RCytoscape: tools for exploratory network analysis. BMC Bioinformat., 2013, 14, 217–226.

  • Huang, X. et al., A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes Dev., 2009, 23, 1805–1817; doi:10.1101/gad.1812409.

  • Sun, J., Jiang, H., Xu, Y., Li, H., Wu, X., Xie, Q. and Li, C., The CCCH-type zinc finger proteins AtSZF1 and AtSZF2 regulate salt stress responses in Arabidopsis. Plant Cell Physiol., 2007, 48(8), 1148–1158.

  • https://www.arabidopsis.org/servlets/TairObject?id=46633&type=array_ele_pre_exp

  • Seki, M., Narusaka, M., Ishida, J., Nanjo, T. and Fujita, M., Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J., 2002, 31(3), 279–292; doi:10.1046/j.1365-313x.2002.01359.

  • Wang, J., Chen, L., Wang, Y., Zhang, J., Liang, Y. and Xu, D., A computational systems biology study for understanding salt tolerance mechanism in rice. PLoS ONE, 2013, 8(6), e64929; doi:10.1371/journal.pone.0064929

  • Kobayashi, S., Satone, H., Tan, E., Kurokochi, H., Asakawa S., Liu, S. and Takano, T., Transcriptional responses of a bicarbonatetolerant monocot, Puccinellia tenuiflora, and a related bicarbonatesensitive species, Poa annua, to NaHCO3 stress. Int. J. Mol. Sci., 2015, 16(1), 496–509; doi:10.3390/ijms16010496.

  • Sun, X. et al., A heavy metal-associated protein (AcHMA1) from the halophyte, Atriplex canescens (Pursh) nutt., confers tolerance to iron and other abiotic stresses when expressed in Saccharomyces cerevisiae. Int. J. Mol. Sci., 2014, 15(8), 14891–14906; doi: 10.3390/ijms150814891.

  • Huang, D. W., Sherman, B. T. and Lempicki, R. A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protoc., 2008, 4, 44–57.

  • Bahieldin, A. et al., RNA-Seq analysis of the wild barley (H. spontaneum) leaf transcriptome under salt stress. C. R. Biologies, 2015; http://dx.doi.org/10.1016/j.crvi.2015.03.010.

  • Zsigmond, L. et al., Arabidopsis PPR40 connects abiotic stress responses to mitochondrial electron transport. Plant Physiol., 2008, 146(4), 1721–1737; doi:10.1104/pp.107.111260.

  • Miller, G., Shulaev, V. and Mittler, R., Reactive oxygen signaling and abiotic stress. Physiol. Plant., 2008, 133(3), 481–489; doi:10.1111/j.1399-3054.2008.01090.x

  • Liu, J. and Zhu, J., A calcium sensor homolog required for plant salt tolerance. Science, 1998, 280(5371), 1943–1945; doi:10.1126/science.280.5371.1943.

  • Walia, H., Wilson, C., Ismail, A. M. and Close, T. J., Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress. BMC Genom., 2009, 10, 398.

  • Walia, H. et al., Comparative transcriptional profiling of two contrasting rice genotypes under salinity stress during the vegetative growth stage. Plant Physiol., 2005, 139(2), 822–835.

  • Wang, J. et al., Transcriptomic profiling of the salt-stress response in the halophyte Halogeton glomeratus. BMC Genom., 2015, 16, 169; doi:10.1186/s12864-015-1373-z

  • Jin-Long, G., Li-Ping, X., Jing-Ping, F., Ya-Chun, S., Hua-Ying, F., You-Xiong, Q. and Jing-Sheng, X., A novel dirigent protein gene with highly stem-specific expression from sugarcane, response to drought, salt and oxidative stresses. Plant Cell Rep., 2011, 31(10), 1801–1812.

  • Ma, S., Gong, Q. and Bohnert, H. J., Dissecting salt stress pathways. J. Exp. Bot., 2006, 57(5), 1097–1107.

  • Min, J. H., Chung, J. S., Lee, K. H. and Kim, C. S., The CONSTANS‐like 4 transcription factor, AtCOL4, positively regulates abiotic stress tolerance through an abscisic acid‐dependent manner in Arabidopsis. J. Integr. Plant Biol., 2014, 30, 123–134.

  • Kosova, K., Vitamvas, P., Urban, M. O., Klima, M., Roy, A. and Prasil, T. M., Biological networks underlying abiotic stress tolerance in temperate crops – a proteomic perspective. Int. J. Mol. Sci., 2015, 16, 20913–20942; doi:10.3390/ijms160920913.

  • Pais, S. M., Téllez-Iñón, M. T. and Capiati, D. A., Serine/ threonine protein phosphatases type 2A and their roles in stress signalling. Plant Signal Behav., 2009, 4(11), 1013–1015.

  • Ferreira, C. et al., A member of the sugar transporter family, stl1p is the glycerol/h+ symporter in Saccharomyces cerevisiae. Mol. Biol. Cell, 2005, 16(4), 2068–2076; doi:10.1091/mbc.E04-10-0884.

  • Sairam, R. K. and Tyagi, A., Physiology and molecular biology of salinity stress tolerance in plants. Curr. Sci., 2004, 86(3), 407–421.


Abstract Views: 327

PDF Views: 117




  • Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice

Abstract Views: 327  |  PDF Views: 117

Authors

Samarendra Das
Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Prabina Kumar Meher
Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Upendra Kumar Pradhan
Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
Amrit Kumar Paul
Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

Abstract


Salinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall's tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified. Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.

Keywords


Correlation Coefficient, Gene Regulatory Networks, Rice, Salinity.

References





DOI: https://doi.org/10.18520/cs%2Fv112%2Fi06%2F1257-1262