Identification of candidate genes for drought stress tolerance in rice by the integration of a genetic (QTL) map with the rice genome physical map

Identification of candidate genes for drought stress tolerance in rice by the integration of a genetic (QTL) map with the rice genome physical map 1 2 first-author 2 1 3 1 †E-mail:r.Bruskiewich@cgiar.org †E-mail:r.Bruskiewich@cgiar.org r.Bruskiewich@cgiar.org Biometrics & Bioinformatics Unit, International Rice Research Institute DAPO 7777, Metro Manila, Philippines Biometrics & Bioinformatics Unit, International Rice Research Institute DAPO 7777, Metro Manila, Philippines Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China Department of Computing Science, University of the Philippines-Diliman Quezon City, Philippines Department of Computing Science, University of the Philippines-Diliman Quezon City, Philippines Genetic improvement for drought stress tolerance in rice involves the quantitative nature of the trait, which reflects the additive effects of several genetic loci throughout the genome. Yield components and related traits under stressed and well-water conditions were assayed in mapping populations derived from crosses of Azucena×IR64 and Azucena×Bala. To find the candidate rice genes underlying Quantitative Trait Loci (QTL) in these populations, we conducted in silico analysis of a candidate region flanked by the genetic markers RM212 and RM319 on chromosome 1, proximal to the semi-dwarf (sd1) locus. A total of 175 annotated genes were identified from this region. These included 48 genes annotated by functional homology to known genes, 23 pseudogenes, 24 ab initio predicted genes supported by an alignment match to an EST (Expressed sequence tag) of unknown function, and 80 hypothetical genes predicted solely by ab initio means. Among these, 16 candidate genes could potentially be involved in drought stress response. Rice genome sequence Candidate genes Drought stress Quantitative Trait Loci (QTL) INTRODUCTION 1 Drought stress is a major constraint to rice ( Oryza sativa ) production and yield stability in rained ecosystems (Dey and Upadhyaya, 1996 1996 ). Rice must be made more drought tolerant, but this is a somewhat contradictory objective considering that rice is most commonly grown under flooded conditions. Achieving drought tolerance in rice will require a deeper understanding of the possible physiological mechanisms available for water stress tolerance and the identification of favorable alleles for introgression into rice varieties that otherwise suit specific environments. Quantitative Trait Loci (QTL) are useful starting points for identifying such alleles. QTL relating to drought tolerance have been identified in rice, barley, and grain sorghum (Price et al., 2002a 2002a ). However, positionally cloning a QTL gene locus normally requires fine scale mapping with large mapping populations across many seasons. Availability of the whole rice genome sequences (Goff et al., 2002 2002 ; Yu et al., 2002 2002 ; Sasaki et al., 2002 2002 ; Feng et al., 2002 2002 ) provides a new tool for this task, along with a means of characterizing their associated molecular functions. In this study, we exploited this new source of data by anchoring drought stress tolerance QTL maps reported by other researchers (Lafitte et al., 2002 2002 ; Price et al., 2002a 2002a ; 2002b 2002b ; 2002c 2002c ; Causse et al., 1994 1994 ; Harushima et al., 1998 1998 ) to a rice physical and sequence map using sequence based markers. We then identified genes as candidates with respect to position. Drought stress most severely impacts yield when applied during the reproductive stage of the rice plant. The sd1 is one of the most important genes in rice, whose recessive character results in a shortened culm with improved lodging resistance and a harvest index (Jennings, 1964 1964 ). The rice height-related QTL was shown to be tightly linked to restricted fragment length polymorphism (RFLP) markers RZ730 and RG810 on the long arm of chromosome 1 (Li et al., 2003 2003 ), consisting of the QTL region of drought resistance (Lafitte et al., 2002 2002 ). As further evidence for drought tolerance gene candidacy, we aligned EST sequences from an available drought stressed panicle library onto our candidate gene structures. We also assessed gene candidacy based on literature support. MATERIALS AND METHODS 1 Genetic map for IR64×Azucena and Bala× Azucena QTL mapping populations 2 Published genetic mapping data from a double haploid mapping population, IR64×Azucena (Temnykh et al., 2001 2001 ) and Bala×Azucena (Price et al., 2002b 2002b ) were used as starting points for this study. IR64×Azucena map included 93 amplified fragment length polymorphism (AFLP), 481 simple sequence repeat (SSR), 15 ‘known’ genes, 1 isozyme, a restriction fragment length polymorphism (RFLP) and 13 random amplification of polymorphic DNA (RAPD) markers adapted from oats. The Bala×Azucena population includes 7 SSR, 105 RFLP and 34 AFLP markers, as well as one marker derived from oats. Consensus map construction 2 To identify genetic map regions containing multiple overlapping QTL across populations, we computationally merged the maps of the two aforementioned mapping populations with common anchor markers using the NCGR ISYS comparative mapping tool (NCGR; http://www.ncgr.org/isys http://www.ncgr.org/isys ; Siepel et al., 2001 2001 ). Rice genome sequence map 2 Identification of candidate genes corresponding to a QTL interval requires a contiguous genome sequence map. Although chromosome 1 and chromosome 4 were essentially completed by the International Rice Genome Sequencing Project (IRGSP) during the latter part of this project, early in the project we needed to assemble our own sequence map using available public data. This assembly work focussed on rice chromosome 1. The input data for map assembly included the list of clone names, GenBank accession numbers and the public rice physical map (Chen et al., 2002 2002 ). We assembled the bacterial artificial chromosome (BAC) clone sequences into a pseudo-molecule using a suite of Perl scripts graciously provided by Dr. David Beare of the Sanger Centre, United Kingdom. Anchoring sequence-based genetic markers to the sequence map 2 We used a local implementation of the “electronic polymerase chain reaction” approach (“ePCR”; Schuler, 1997 1997 ) using Perl regular expression alignment of primer sequences with orientation and threshold distance constraints to identify putative PCR amplicons in target sequences. The primers for the analysis were obtained retrieved from either the Gramene database (Ware et al., 2002 2002 ; www.gramene.org www.gramene.org ) or the Japan Rice Genome Project (RGP) database (Sasaki, 2001 2001 ). Markers were correlated with the physical map by ePCR, run against the rice genome BAC sequences, retrieval from the Gramene database and by BLAST (Altschul et al., 1990 1990 ) alignment searches of the rice BAC sequences. Database storage and visualization of maps 2 The resulting genetic (QTL), physical and sequence maps compiled for the study were stored in the International Rice Information System (IRIS; www.iris.irri.org www.iris.irri.org ; Bruskiewich et al., 2003 2003 ) and visualized using a comparative mapping tool developed at the National Center for Genome Resources. To facilitate identification of candidate genes, we also have developed QTL2Gene ( http://ibi.zju.edu.cn/qtl2gene/qtl2gene.htm http://ibi.zju.edu.cn/qtl2gene/qtl2gene.htm ) as a flexible database using the public rice genome sequence and RFLP and SSR markers. The database provides an interface for searching the genes underlying one QTL using two flanking markers’ name. Other information related with this paper has been published on website ( http://ibi.zju.edu.cn/publish/QTL2gene/drought.htm http://ibi.zju.edu.cn/publish/QTL2gene/drought.htm ). Choice of candidate QTL region for detailed analysis 2 The choice of candidate QTL region on rice chromosome 1 was chosen based on a survey of drought QTL (Price et al., 2002c 2002c ; Lafitte et al., 2002 2002 ) and International Rice Research Institute (IRRI) fine mapping work ( 1 1 Zhang Li, personal communications). This region is flanked by RM212 and RM319, which were shown to be located on 148.7 cM (CentiMorgan) and 150.5 cM of IR64×Azucena genetic map respectively, and spans a map distance of approximately 1.8 cM across the sd1 gene locus. Gene and functional annotation 2 We downloaded gene annotation for the RM212-RM319 candidate region from the Japan Rice Genome Project (RGP) sequence data found in GenBank ( www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov ) and from the rice databases of The Institute for Genomic Research (TIGR; www.tigr.org www.tigr.org ). These annotations were assessed using the gbrowse sequence browser from the Generic Model Organism Database project ( http://www.gmod.org http://www.gmod.org ; Stein et al., 2002 2002 ; Lewis et al., 2002 2002 ). Drought panicle cDNA library 2 An IR64-based drought stressed rice panicle cDNA library was constructed by researchers at the International Rice Research Institute (Arumugam et al., 2005 2005 ). Briefly, the normalized library was constructed from pooled mRNA obtained from the rice panicles collected from control (well watered) and water stressed plants at 2 d before heading, at heading, 50% flowering and 4 d after 50% flowering. Water stress was applied by not watering for several consecutive days. Expressed sequence tags (EST) obtained by 5′ and/or 3′ end sequencing of the clones were clustered and annotated using standard in silico approaches (Altschul et al., 1990 1990 ). RESULTS 1 Alignment of the rice genetic map onto the physical map and gene identification 2 The chromosome 1 consensus map for IR64×Azucena and Bala×Azucena was anchored using sequence-based markers to the assembled rice physical map. The candidate sequence interval spanning the candidate QTL flanked by RM212 to RM319 was then identified. The assembly of seven BAC clones in this interval yielded a contiguous sequence region of 855008 base pairs within which we identified 175 predicted gene structures. Fig. 1 1 shows a synopsis of gene annotation categories. The RGP annotation included 48 (27%) genes annotated by functional homology to known genes, 23 (13%) pseudogenes, 24 (14%) ab initio predicted genes supported by an alignment match to an EST of unknown function, and 80 (46%) hypothetical genes predicted by ab initio algorithms. Mapping drought stressed panicle library EST into the region 2 To collect further supporting evidence for the function of the predicted genes in the region, EST sequences obtained from the IRRI drought stressed panicle library were aligned by BLAST (Altschul et al., 1990 1990 ) against candidate gene sequences. This analysis revealed that 30 candidate gene predictions match EST sequences in the library (Table 1 1 ). 2 Gene name 2 Gene 2 BLAST score 2 Percent identity 2 EST GenBank accession ID 2 EST 2 Annotation Start Stop Start Stop B1011A07.16 607 852 690 75.61 CA762217 2 247 Putative pectinesterase 2.1 precursor B1070A12.1 2131 2424 1380 96.6 CA765415 1 294 Putative serine/threonine kinase B1070A12.17 1305 973 1655 99.7 CA760294 352 685 Hypothetical protein P0010B10.6 1221 1024 990 100 CA760259 488 685 Hypothetical protein P0010B10.7 511 344 831 99.4 CA763140 493 660 Zinc protease P0010B10.12 1 249 1245 100 CA762979 439 687 Helicase-like transcription factor P0010B10.21 458 570 330 77.19 CB096315 212 325 Hypothetical protein P0010B10.21 458 570 321 76.32 CB096974 175 288 Hypothetical protein P0010B10.22 861 443 1663 88.54 CA759417 267 685 Secretory carrier membrane protein B1100D10.21 582 1 2868 99.31 CA762908 135 715 Acyl-CoA:1-acylglycerol-3-ph-osphate acyltransferase B1100D10.25 223 38 513 76.72 CB096516 86 271 Hypothetical protein B1100D10.33 1790 1918 304 71.76 CA764883 1 130 Serine/threonine kinase receptor precursor (EC 2.7.1.37) (S-receptor kinase) P0458E05.33 354 47 720 74.14 CA759325 282 592 Hypothetical protein P0482C06.17 483 227 638 74.81 CA760143 176 429 Hypothetical protein P0682C06.15 330 1 1533 96.06 CA766823 326 655 Putative prolyl endopeptidase P0682C06.16 360 2 1375 87.19 CA763742 143 500 Unknown protein P0699H05.4 1520 2043 2593 99.43 CA766000 2 525 Putative subtilase P0699H05.7 2136 1602 2524 97.01 CB096421 317 851 Putative subtilase P0699H05.7 1678 2136 2286 99.78 CA766686 1 459 Putative subtilase P0699H05.7 378 926 2414 93.88 CA763105 162 714 Putative subtilase P0699H05.9 120 44 162 74.07 CA766969 204 279 Hypothetical protein P0699H05.17 292 1 1436 99.32 CB096487 1 291 Hypothetical protein P0699H05.17 323 1 1606 99.69 CA766130 35 357 Hypothetical protein P0699H05.22 670 708 195 100 CA767024 2 40 Ethylene-responsive element binding factor 3 P0699H05.22 708 194 1977 87.4 CA759952 183 696 Ethylene-responsive element binding factor 3 Candidate genes placed into drought signal pathways 2 In our study, we found 16 candidate genes in the region with some literatures support (Shinozaki and Yamaguchi-Shinozaki, 1996 1996 ; Ingram and Bartels, 1996 1996 ; Yamaguchi-Shinozaki et al., 2002 2002 ) potentially involved in drought stress response (Table 2 2 ). Classes Gene name Putative functional description Protein_id Reference Receptor P0557A01.8 Kinase-like BAB89770.1 (Fujimoto et al., 2000 2000 ) P0010B10.11 Kinase-like protein receptor BAB63567.1 (Zhu, 2002 2002 ) B1070A12.1 Serine/threonine kinase BAB92578.1 (Xiong and Zhu, 2001 2001 ) B1070A12.2 Receptor kinase BAB92579.1 (Xiong et al., 2002 2002 ) B1070A12.5 Receptor kinase BAB92581.1 (Xiong et al., 2002 2002 ) B1100D10.29 Diacylglycerol kinase BAB92552.1 (Arisz et al., 2003 2003 ) Ca 2+ B1100D10.24 Cyclic nucleotide and calmodulin-regulated ion channel (Urao et al., 1994 1994 ) Signalling B1070A12.7 1,4-benzoquinone reductase BAB92583.1 (Choi et al., 2002 2002 ) P0415A04.13 Peroxidase-like protein contains EST AU075654 (E1982) (Kim et al., 2003 2003 ) P0557A01.35 Polyphenol oxidase BAB89784.1 (Zhang and Kirkham, 1994 1994 ) P0684C02.3 Polyphenol oxidase BAB89047.1 (Zhang and Kirkham, 1994 1994 ) B1070A12.14 Auxin-responsive GH3 BAB92590.1 (Kovtun et al., 2000 2000 ) Transcript P0010B10.12 Helicase-like transcription factor BAB63568.1 (Zhu, 2002 2002 ) P0699H05.22 Ethylene-responsive element binding factor 3 (Fujimoto et al., 2000 2000 ) P0415A04.24 Nuclear transport factor 2 BAB90110.1 (Seki et al., 2002 2002 ) Vacuolar P0010B10.27 Vacuolar sorting-associated protein (Gaxiola et al., 2001 2001 ) DISCUSSION 1 In this study, we anchored rice QTL maps to the rice physical map to identify drought stress tolerance candidate genes based on candidate QTL position. We chose the sd1 region of chromosome 1 for our attempt to link drought stress tolerance genotype to phenotype using bioinformatics because of strong combined genetic evidence for the existence of a large effect QTL for stress tolerance and because of the relatively complete rice genome sequences in the region. A set of candidate genes of known or inferred function were identified in this region using rice genome annotation. Published literature supports candidacy of some of these genes in drought stress response. Fig. 1 1 shows one generic drought signal pathway (Zhu, 2002 2002 ) against which the candidate genes identified in this study could potentially be assigned. This pathway has three classes of genes: (1) signal perception, such as P0557A01.8; (2) generation of second messengers, such as B1070A12.7; and (3) gene expression. Candidate genes potentially implicated in this pathway are listed on the right hand side of the Fig. 1 1 . Another possible pathway for drought signals is the Salt-Overly-Sensitive (SOS) pathway (Xiong et al., 2002 2002 ), as shown in Fig. 2 2 . Hypothetically, a myristoylated calcium-binding protein encoded by SOS3 may sense a drought stress or salt-elicited calcium signal and translate it downstream; SOS3 interacts with and activates SOS2, a serine/threonine protein kinase. SOS2 and SOS3 regulate the expression level of SOS1, a salt tolerance effecter gene encoding a plasma membrane Na + /H + antiporter. In our study, we found several genes that could act in this pathway: a vacuolar protein, a serine/threonine kinase, three kinds of putative receptor kinase genes and a putative diacylglycerol kinase gene. Endogenous abscisic acid (ABA) levels have been reported to increase as a result of water deficit in many physiological studies, and therefore ABA is thought to be involved in the signal transduction. ABA-dependent and ABA-independent signal pathways in the activation of stress-inducible genes under dehydration conditions have been reported (Shinozaki and Yamaguchi-Shinozaki, 1997 1997 ), as shown in Fig. 3 3 . We identified an ethylene-responsive element binding factor (EREBP) gene (P0699H05.22) that could be a candidate related to this pathway. EREBP-like genes are known to be induced by a variety of abiotic stresses, including drought and chilling stresses (Gilmour et al., 1998 1998 ). A few of the smaller gene clusters were composed of a single family of closely related genes underlying one QTL, and most of these are within 1–2 cM. Thus, we investigated this region for gene family and found that 4 rust resistance protein clusters on BAC clone (Accession number: B1100D10). These genes have above 89% identity to proteins as shown by ClastalW tool with default parameters. The rust resistance family in this region indicated that it may related to disease resistance. 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We thank Aixia Ren for assistance with the pathway studies. Some of the drought panicle EST sequences and annotation were obtained from the Hans Bonhert laboratory at the University of Illinois. Additional drought panicle EST sequence gene annotation was generated by S. Rudd of the Munich Information on Protein Sequences (MIPS) center. 10.1631/jzus.2005.B0382 rted partly by the Rockefeller Foundation thesis dissertation training grant and the National Hi-Tech Research and Development Program (863) of China 2004-09-15 2004-12-26 2005-04-10 2022-01-05 Copyright © 2005, Journal of Zhejiang University Science 2005 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 05 6B