In this study, the drought responses of two genotypes, IRAT109 and Zhenshan 97 (ZS97), representing upland and paddy rice, respectively, were systematically compared at the morphological, physiological and transcriptional levels. IRAT109 has better performance in traits related to drought avoidance, such as leaf rolling, root volumes, the ratio of leaf water loss and relative conductivity. At the transcriptional level, more genes were induced by drought in IRAT109 at the early drought stage, but more genes had dynamic expression patterns in ZS97 at different drought degrees. Under drought conditions, more genes related to reproductive development and establishment of localization were repressed in IRAT109, but more genes involved in degradation of cellular components were induced in ZS97. By checking the expression patterns of 36 drought-responsive genes (located in 14 quantitative trail loci [QTL] intervals) in ZS97, IRAT109 and near isogenic lines (NILs) of the QTL intervals, we found that more than half of these genes had their expression patterns or expression levels changed in the NILs when compared to that in ZS97 or IRAT109. Our results may provide valuable information for dissecting the genetic bases of traits related to drought resistance, as well as for narrowing the candidate genes for the traits.
References
[1]
Zhang, Q. Strategies for developing Green Super Rice. Proc. Natl. Acad. Sci. USA 2007, 104, 16402–16409.
[2]
Boonjung, H.; Fukai, S. Effects of soil water deficit at different growth stages on rice growth and yield under upland conditions. 2. Phenology, biomass production and yield. Field Crops Res 1996, 48, 47–55.
[3]
Pantuwan, G.; Fukai, S.; Cooper, M.; Rajatasereekul, S.; O’Toole, J.C. Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands. 2. Selection of drought-resistant genotypes. Field Crops Res 2002, 73, 169–180.
[4]
Nguyen, H.N.; Babu, R.C.; Blum, A. Breeding for drought resistance in rice: physiology and molecular genetics consideration. Crop Sci 1997, 17, 1426–1434.
[5]
Luo, L.J. Breeding for water-saving and drought-resistance rice (WDR) in China. J. Exp. Bot 2010, 61, 3509–3517.
[6]
Yue, B.; Xiong, L.; Xue, W.; Xing, Y.; Luo, L.; Xu, C. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil. Theor. Appl. Genet 2005, 111, 1127–1136.
[7]
Seki, M.; Narusaka, M.; Abe, H.; Kasuga, M.; Yamaguchi-Shinozaki, K.; Carninci, P.; Hayashizaki, Y.; Shinozaki, K. Monitoring the expression pattern of 1300 Arabidopsis genes under drought and cold stresses by using a full-length cDNA microarray. Plant Cell 2001, 13, 61–72.
[8]
Rabbani, M.A.; Maruyama, K.; Abe, H.; Khan, M.A.; Katsura, K.; Ito, Y.; Yoshiwara, K.; Seki, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiol 2003, 133, 1755–1767.
[9]
Zhang, W.; Ruan, J.; Ho, T.H.; You, Y.; Yu, T.; Quatrano, R.S. Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana. Bioinformatics 2005, 21, 3074–3081.
[10]
Ueda, A.; Kathiresan, A.; Bennett, J.; Takabe, T. Comparative transcriptome analyses of barley and rice under salt stress. Theor. Appl. Genet 2006, 112, 1286–1294.
[11]
Walia, H.; Wilson, C.; Ismail, A.M.; Close, T.J.; Cui, X. Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress. BMC Genomics 2009, 10, 398.
[12]
Wang, H.; Zhang, H.; Gao, F.; Li, J.; Li, Z. Comparison of gene expression between upland and lowland rice cultivars under water stress using cDNA microarray. Theor. Appl. Genet 2007, 115, 1109–1126.
[13]
Degenkolbe, T.; Do, P.T.; Zuther, E.; Repsilber, D.; Walther, D.; Hincha, D.K.; Kohl, K.I. Expression profiling of rice cultivars differing in their tolerance to long-term drought stress. Plant Mol. Biol 2009, 69, 133–153.
[14]
Lenka, S.K.; Katiyar, A.; Chinnusamy, V.; Bansal, K.C. Comparative analysis of drought-responsive transcriptome in Indica rice genotypes with contrasting drought tolerance. Plant Biotechnol. J. 2010. voume number?, 1–13.
[15]
Wang, D.; Pan, Y.; Zhao, X.; Zhu, L.; Fu, B.; Li, Z. Genome-wide temporal-spatial gene expression profiling of drought responsiveness in rice. BMC Genomics 2011, 12, 149–163.
[16]
Zou, G.H.; Mei, H.W.; Liu, H.Y.; Liu, G.L.; Hu, S.P.; Yu, X.Q.; Li, M.S.; Wu, J.H.; Luo, L.J. Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers. Theor. Appl. Genet 2005, 112, 106–113.
[17]
Yue, B.; Xue, W.; Xiong, L.; Yu, X.; Luo, L.; Cui, K.; Jin, D.; Xing, Y.; Zhang, Q. Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 2006, 172, 1213–1228.
[18]
Ding, X.; Li, X.; Xiong, L. Evaluation of near-isogenic lines for drought resistance QTL and fine mapping of a locus affecting flag leaf width, spikelet number, and root volume in rice. Theor. Appl. Genet 2011, 123, 815–826.
[19]
Xiang, Y.; Tang, N.; Du, H.; Ye, H.; Xiong, L. Characterization of OsbZIP23 as a key player of the basic leucine zipper transcription factor family for conferring abscisic acid sensitivity and salinity and drought tolerance in rice. Plant Physiol 2008, 148, 1938–1952.
[20]
Xiao, B.Z.; Chen, X.; Xiang, C.B.; Tang, N.; Zhang, Q.F.; Xiong, L.Z. Evaluation of seven function-known candidate genes for their effects on improving drought resistance of transgenic rice under field conditions. Mol. Plant 2009, 2, 73–83.
[21]
Ding, X.; Hou, X.; Xie, K.; Xiong, L. Genome-wide identification of BURP domain-containing genes in rice reveals a gene family with diverse structures and responses to abiotic stresses. Planta 2009, 230, 149–163.
[22]
Xiao, B.; Huang, Y.; Tang, N.; Xiong, L. Over-expression of a LEA gene in rice improves drought resistance under the field conditions. Theor. Appl. Genet 2007, 115, 35–46.
[23]
Wang, L.; Xie, W.; Chen, Y.; Tang, W.; Yang, J.; Ye, R.; Liu, L.; Lin, Y.; Xu, C.; Xiao, J.; et al. A dynamic gene expression atlas covering the entire life cycle of rice. Plant J. 2010, 61, 752–766.
[24]
Jain, M.; Nijhawan, A.; Tyagi, A.K.; Khurana, J.P. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem. Biophys. Res. Commun 2006, 345, 646–651.
[25]
Jain, M.; Nijhawan, A.; Arora, R.; Agarwal, P.; Ray, S.; Sharma, P.; Kapoor, S.; Tyagi, A.K.; Khurana, J.P. F-box proteins in rice: genome-wide analysis, classification, temporal and spatial gene expression during panicle and seed development, and regulation by light and abiotic stress. Plant Physiol 2007, 143, 1467–1483.
Zhou, J.; Wang, X.; Jiao, Y.; Qin, Y.; Liu, X.; He, K.; Chen, C.; Ma, L.; Wang, J.; Xiong, L.; et al. Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle. Plant Mol. Biol 2007, 63, 591–608.
[28]
Hazen, S.P.; Pathan, M.S.; Sanchez, A.; Baxter, I.; Dunn, M.; Estes, B.; Chang, H.S.; Zhu, T.; Kreps, J.A.; Nguyen, H.T. Expression profiling of rice segregating for drought tolerance QTLs using a rice genome array. Funct. Integr. Genomics 2005, 5, 104–116.
[29]
Robin, S.; Pathan, M.S.; Courtois, B.; Lafitte, R.; Carandang, S.; Lanceras, S.; Amante, M.; Nguyen, H.T.; Li, Z. Mapping osmotic adjustment in an advanced backcross inbred population of rice. Theor. Appl. Genet 2003, 107, 1288–1296.
[30]
Xue, W.; Xing, Y.; Weng, X.; Zhao, Y.; Tang, W.; Wang, L.; Zhou, H.; Yu, S.; Xu, C.; Li, X.; et al. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat. Genet 2008, 40, 761–767.
[31]
Schubert, V. SMC proteins and their multiple functions in higher plants. Cytogenet. Genome Res 2009, 124, 202–214.
[32]
Affymetrix standard protocols, Available online: http://www.affymetrix.com/products/arrays/specific/rice.affx , accessed on 17 February 2013.
[33]
R platform, Available online: http://www.R-project.org , accessed on 17 February 2013.
[34]
Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high-density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193.
Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. affy: analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20, 307–315.
[37]
Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5, R80.
[38]
Breitling, R.; Armengaud, P.; Amtmann, A.; Herzyk, P. Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 2004, 573, 83–92.
[39]
Alexa, A.; Rahnenfuhrer, J.; Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 2006, 22, 1600–1607.
[40]
Wang, J.; Yu, H.; Xie, W.; Xing, Y.; Yu, S.; Xu, C.; Li, X.; Xiao, J.; Zhang, Q. A global analysis of QTLs for expression variations in rice shoots at the early seedling stage. Plant J 2010, 63, 1063–1074.
[41]
Hartweck, L.M.; Olszewski, N.E. Rice GIBBERELLIN INSENSITIVE DWARF1 is a gibberellin receptor that illuminates and raises questions about GA signaling. Plant Cell 2006, 18, 278–282.
