A computational system biology approach to construct gene regulatory networks for salinity response in rice (Oryza sativa)

SAMARENDRA DAS; PRIYANKA PANDEY; ANIL RAI; CHINMAYEE MOHAPATRA

doi:10.56093/ijas.v85i12.54293

Authors

SAMARENDRA DAS Scientist, Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012
PRIYANKA PANDEY Assistant Professor, National Institute of Biomedical Genomics, Kalyani, West Bengal 741 251
ANIL RAI Principal Scientist and Head (CABin), Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012
CHINMAYEE MOHAPATRA PhD Scholar, Institue of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh 221 005

https://doi.org/10.56093/ijas.v85i12.54293

Keywords:

Gene regulatory network, Multiple linear regression, Singular value decomposition, Target gene, Transcription factor

Abstract

Salinity is one of the most common abiotic stress which limits agricultural crop production. Salinity stress tolerance in rice (Oryza sativa L.) is an important trait controlled by various genes. The mechanism of salinity stress response in rice is quite complex. Modelling and construction of genetic regulatory networks is an important tool and can be used for understanding this underlying mechanism. This paper considers the problem of modeling and construction of Gene Regulatory Networks using Multiple Linear Regression and Singular Value Decomposition approach coupled with a number of computational tools. The gene networks constructed by using this approach satisfied the scale free property of biological networks and such networks can be used to extract valuable information on the transcription factors, which are salt responsive. The gene ontology enrichment analysis of selected nodes is performed. The developed model can also be used for predicting the gene responses under stress condition and the result shows that the model fits well for the given gene expression data in rice. In this paper, we have identified ten target genes and a series of potential transcription factors for each target gene in rice which are highly salt responsive.

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References

Akbar M and Ponnamperuma F N. 1980. Saline soil of South and Southeast Asia as potential rice lands, Rice Research Strategies for the Future. IRRI, Manila, Philippines, pp 265– 81.

Akutsu T, Miyano S and Kuhara S. 2000. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16: 727–34. DOI: https://doi.org/10.1093/bioinformatics/16.8.727

Albert R. 2005. Scale-free networks in cell biology. Journal of Cell Science 118: 4 947–57. DOI: https://doi.org/10.1242/jcs.02714

Allakhverdiev S I, Sakamoto A, Nishiyama Y, Inaba M and Murata N. 2000b. Ionic and osmotic effects of NaCl-induced inactivation of photosystems I and II in Synechococcus sp. Plant Physiology 123: 1047–56. DOI: https://doi.org/10.1104/pp.123.3.1047

Bansal M, Belcastro V, Ambesi-Impiombato A and di Bernardo D. 2007. How to infer gene networks from expression profiles. Molecular Systems Biology 3: 78. DOI: https://doi.org/10.1038/msb4100158

Barabasi A L and Albert R. 1999. Emergence of scaling in random networks. Science 286: 509–12. DOI: https://doi.org/10.1126/science.286.5439.509

Boros E, Ibaraki T and Makino K. 1998. Error-free and best-fit extensions of partially defined Boolean functions. Information and Computation 140: 254–83. DOI: https://doi.org/10.1006/inco.1997.2687

Bray E, Bailey S J and Weretilnyk E. 2000. Responses to abioticstresses. (In) Biochemistry and Molecular Biology of Plants, pp 1158–1203. American Society of Plant Biologists.

Chen B S, Yang S K, Lan C Y and Chuang Y J 2008. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining. BMC Medical Genomics 1: 46. DOI: https://doi.org/10.1186/1755-8794-1-46

Ching W, Fung E, Ng N and Akutsu T. 2005. On construction of stochastic genetic networks based on gene expression sequences. International Journal of Neural Systems 15: 297– 310. DOI: https://doi.org/10.1142/S0129065705000256

Cotsaftis O, Plett D, Johnson A A and Walia H. 2011. Rootspecific transcript profiling of contrasting rice genotypes in response to salinity stress. Mol. Plant 4(1): 25–41. DOI: https://doi.org/10.1093/mp/ssq056

de Jong H. 2002. Modeling and simulation of genetic regulatory systems: A literature review. Journal of Computational Biology 9: 69–103. DOI: https://doi.org/10.1089/10665270252833208

Gautier L, Cope L, Bolstad B M and Irizarry R A. 2004. Affy— analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20: 307–15. DOI: https://doi.org/10.1093/bioinformatics/btg405

Gentleman R C, Carey V J, Bates D M, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y and Gentry J. 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5: R80. DOI: https://doi.org/10.1186/gb-2004-5-10-r80

Hastie T J. 1992. Generalized additive models. (In) Statistical Models. Chambers J M and Hastie T J (Eds). Wadsworth & Brooks/Cole.

Haury A C, Mordelet F,Vera-Licona P and Vert J P. 2012. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Systems Biology 6: 145. DOI: https://doi.org/10.1186/1752-0509-6-145

Hirose O, Nariai N, Tamada Y, Bannai H, Imoto S and Miyano S. 2006. Estimating gene networks from expression data and binding location data via Boolean networks. Lecture Note in Computer Scineces 3480: 349–56. DOI: https://doi.org/10.1007/11424857_38

Huang D W, Sherman B T and Lempicki R A. 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4: 44 – 57. DOI: https://doi.org/10.1038/nprot.2008.211

Huang T, Liu L and Xie L. 2010. Using GeneReg to construct time delay gene regulatory networks. BMC Research Notes 3: 142–52. DOI: https://doi.org/10.1186/1756-0500-3-142

Ideker T E, Thorsson V and Karp R M. 2000. Discovery of regulatory interactions through perturbation: Inference and experimental design. Proceedings of Pacific Symposium Biocomputing 5: 302–13.

Ihaka R and Gentleman R. 1996. R: A language for data analysis and graphics. Journal of Computation Graphics and Statistics 5: 299–314. DOI: https://doi.org/10.1080/10618600.1996.10474713

Irizarry R A, Hobbs B, Collin F, Beazer-Barclay Y D, Antonellis K J, Scherf U and Speed T P. 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–64. DOI: https://doi.org/10.1093/biostatistics/4.2.249

Jain M, Tyagi A K and Khurana, J P. 2008. Genome-wide identification, classification, evolutionary expansion and expression of homeobox genes in rice. FEBS Journal 275: 2 845–61. DOI: https://doi.org/10.1111/j.1742-4658.2008.06424.x

Joseph E A and Mohanan K V. 2013. A study on the effect of salinity stress on the growth and yield of some native rice cultivars of Kerala state of India. Agriculture, Forestry and Fisheries 2(3): 141–50. DOI: https://doi.org/10.11648/j.aff.20130203.14

Liu C W, Hsu Y K, Cheng Y H, Yen H C, Wu Y P, Wang C S and Lai C C. 2012. Proteomic analysis of salt-responsive ubiquitinrelated proteins in rice roots. Rapid Commun. Mass Spectrom 26(15): 1 649–60. DOI: https://doi.org/10.1002/rcm.6271

Narciso J and Hossain M. 2002. World Rice Statistics. IRRI, Manila Philippines.

Noda K, Shinohara A, Takeda M, Matsumoto S, Miyano S and Kuhara S. 1998. Finding genetic network from experiments by weighted network model. Genome Informatics 9: 141–50.

Pandit A, Rai V, Bal S and Sinha S. 2010. Combining QTL mapping and transcriptome profiling of bulked RILs for identification of functional polymorphism for salt tolerance genes in rice (Oryza sativa L.). Molecular Genetics and Genomics 284(2):121–36. DOI: https://doi.org/10.1007/s00438-010-0551-6

Priya P and Jain M. 2013. RiceSRTFDB: A database of rice TFcontaining comprehensive expression, cis-regulatory element and mutant information to facilitate gene function analysis. Database, Article ID bat027, doi:10.1093/database/bat027. DOI: https://doi.org/10.1093/database/bat027

Rahimi A and Biglarifard A. 2011. Influence of NaCl salinity and different substracts on plant growth, mineral nutrient assimilation and fruit yield of strawberry. Not Bot Horti Agrobo 39(2): 219–26. DOI: https://doi.org/10.15835/nbha3925632

Rajendran N, Smith C and Mazhawidza W. 2009. Molecular and phylogenetic analysis of pyridoxal phosphate-dependent acyltransferase of exiguobacterium acetylicum. Z. Naturforsch 64: 891–8. DOI: https://doi.org/10.1515/znc-2009-11-1222

Shannon P T, Grimes M, Kutlu B, Bot J Jand Galas D J. 2013. RCytoscape: tools for exploratory network analysis. BMC Bioinformatics, 9(14): 217. DOI: https://doi.org/10.1186/1471-2105-14-217

Shmulevich I, Saarinen A, Yli-Harja O and Astola J. 2002. Inference of genetic regulatory networks under the best-fit extension paradigm. (In) Computational and Statistical Approaches To Genomics. Zhang W and Shmulevich I (Eds). Kluwer, Boston, MA.

Stumpf M P H, Wiuf C and May R M. 2005. Subnets of scalefree networks are not scale-free: Sampling properties of networks. Proceedings of National Academy of Science USA 102 (12): 4 221–4. DOI: https://doi.org/10.1073/pnas.0501179102

Tanji K K. 1990. Nature and extent of agricultural salinity. Agricultural Salinity Assessment and Management 71: 1–17. DOI: https://doi.org/10.1061/9780784411698.ch01

Walia H, Wilson C, Ismail A M and Close T J. 2009. Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress. BMC Genomics 10: 398. DOI: https://doi.org/10.1186/1471-2164-10-398

Walia H, Wilson C, Condamine P, Liu X, Ismail A M Zeng L, Wanamaker S I, Mandal J, Xu J, Cui X and Close T J 2005. Comparative transcriptional profiling of two contrasting rice genotypes under salinity stress during the vegetative growth stage. Plant Physiology. 139(2): 822–35. DOI: https://doi.org/10.1104/pp.105.065961

Wang M, Verdier J, Benedito V A, Tang Y, Murray J D, Ge Y, Becker J D, Carvalho H, Rogers C, Udvardi M and He J. 2013. LegumeGRN: A gene regulatory network prediction server for functional and comparative studies. Plos One 8(6): e64929. doi:10.1371/journal.pone.0064929. DOI: https://doi.org/10.1371/journal.pone.0067434

Wang X, Bojing D, Liu M Sun N and Qi X. 2013. Arabidopsis transcription factor wrky33 is involved in drought by directly regulating the expression of cesa8. American Journal of Plant Sciences 4: 21–7. DOI: https://doi.org/10.4236/ajps.2013.46A004

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

Wu W S, Li W H and Chen B S. 2006. Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle. BMC bioinformatic, 7: 421. DOI: https://doi.org/10.1186/1471-2105-7-421

Yeung K and Ruzzo W. 2001. An empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17: 763–74. DOI: https://doi.org/10.1093/bioinformatics/17.9.763

Zhang S Q, Ching W K, Tsing N K, Leung H Y and Guo D. 2010. A new multiple regression approach for the construction of genetic regulatory networks. Artificial Intelligence in Medicine 48:153–60. DOI: https://doi.org/10.1016/j.artmed.2009.11.001

Zhang B and Horvath S. 2005. A general framework for weighted gene co-expression network analysis. Statistics, Applied Genetics Molecular Biology 4: 17–42. DOI: https://doi.org/10.2202/1544-6115.1128