OsWRKY49 on qAT5 positively regulates alkalinity tolerance at the germination stage in Oryza sativa L. ssp. Japonica

OsWRKY49 on qAT5 positively regulates alkalinity tolerance at the germination stage in Oryza sativa L. ssp. 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Japonica Jingnan Cui, Shuangshuang Li, Tong Zhang, Chong Li, Yu Duan, Shanbin Xu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4873013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract With the widespread use of the rice direct seeding cultivation model, improving the tolerance of rice varieties to salinity-alkalinity at the germination stage has become increasingly important. However, as previous studies have focused on the neutral salt stress, understanding of alkalinity tolerance is still in its infancy, and the genetic resource data is scarce. Here, we used a natural population composed of 295 Japonica rice varieties and a recombinant inbred population including 189 lines derived from Caidao (alkali-sensitive) and WD20342 (alkali-tolerant) to uncover the genetic structure of alkalinity tolerance during rice germination. A total of 15 lead SNPs and six QTLs related to relative germination potential (RGP) and relative germination index (RGI) were detected by genome-wide association study and linkage mapping. Of which, Chr5_28094966, a lead SNP was located in the interval of the mapped major QTL qAT5, that was significantly associated with both RGP and RGI in the two populations. According to the LD block analysis and QTL interval, a 425 kb overlapped region was obtained for screening the candidate genes. After haplotype analysis, qRT-PCR and parental sequence analysis, LOC_Os05g49100 (OsWRKY49) was initially considered as the candidate gene. Having studied the characteristics of rice lines with OsWRKY49 knockout and overexpression, we established that OsWRKY49 could be a positive regulator of alkalinity tolerance in rice at the germination stage. Subcellular localization showed that green fluorescent protein-tagged OsWRKY49 was localized in the nucleus. The application of OsWRKY49 could be useful for increasing alkalinity tolerance of rice direct seeding. Japonica rice germination stage alkalinity tolerance genome-wide association study linkage mapping OsWRKY49 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Rice ( Oryza sativa L.) is a predominant grain crop worldwide, feeding more than 50% of the global population. However, it is estimated that by 2050, half of the world’s agricultural land will be threatened by salinization (Jain et al. 2007 ). The saline-alkaline stress has a significant impact on rice harvests, reducing the yield by 30%. The saline-alkaline stress is divided into the NaCl/Na 2 SO 4 salt stress and NaHCO 3 /Na 2 CO 3 alkali stress (Wang et al. 2009 ). The damage caused by the high salt stress in plants mainly derives from high ion concentration and osmotic stress (Abbasi et al. 2016 ). The damage caused by alkaline stress to plants is owing both to the high ion concentration and lack of nutrient minerals, such as iron (Fe), zinc (Zn), and manganese (Mn), caused by high pH (Li et al. 2019 ). Alkaline environment is harmful to the growth and development of plants, and the physiological and biochemical mechanisms and regulatory networks of plants to resist alkaline stress are more complex than those for salt stress. With the gradual increase of scale and modernization of rice planting, the low-cost, high-efficiency, and simplified operation of direct seeding of rice is consistent with the development of the planting industry (Duan et al. 2022 ). In addition, rice planting in the saline–alkaline soil is an effective means to improve soil conditions, and it expands the planting area of rice. With the expansion of saline–alkaline land area and the promotion of rice direct seeding technology, cultivating rice varieties with good germination in a high saline–alkaline environment has become a popular breeding goal (Mei et al. 2022 ). Therefore, exploration of candidate genes associated with saline–alkaline tolerance during the rice germination stage in different rice varieties is urgently needed (Wang et al. 2008 ). Saline-alkaline tolerance is a typical quantitative genetic trait controlled by multiple genes, similar to many abiotic stress tolerance traits (Qi et al. 2008 ). To date, most studies have focused on the salt tolerance of rice and many salt tolerance-related quantitative trait loci (QTLs)/genes have been identified at various growth stages, mainly at the seedling stage (Jing and Zhang 2017 ). Research on salt tolerance mechanisms in rice is also intensifying (Sun et al. 2014 ). Up to now, 964 salt tolerance-related QTLs have been discovered, of which the largest number (514) were at the seedling stage and 31, 149, and 270 at the germination, vegetative growth, and reproductive growth stages, respectively (Jing and Zhang 2017 ). For example, the SKC1 gene (Ren et al. 2005 ) and Saltol QTL (Thomson et al. 2010 ) are early salt tolerance-related genetic regions identified by fine-mapping and map-based cloning of QTLs during the rice seedling stage. qRSL7 is a salt-tolerant QTL detected in the F 2:3 population constructed from the Weiguo (salt-tolerant) and IR36 (salt-sensitive) by QTL-seq. OsSAP16 was identified as a candidate salt-tolerance gene of qRSL7 (Lei et al. 2020 ). Compared to studies of salt tolerance, studies on alkalinity tolerance (NaHCO 3 or Na 2 CO 3 ) of rice are still in the preliminary stage (Leng et al. 2020 ). An F 2:3 population derived from Gaochan 106 and Changbai 9 was treated with alkali at the seedling and various growth stages after transplanting, and 6 and 13 QTLs related to the dead seedling rate and dead leaf rate, respectively, were obtained (Qi et al. 2008 ). Li et al. identified an alkali-tolerant QTL at seedling stage by genome-wide association analysis (GWAS) of the alkalinity tolerance score, concentration of Na + in the shoots and Na + /K + ratio in the shoots, and OsIRO3 was determined as the candidate gene of qSAT3 by subsequent experiments (Li et al. 2019 ). In addition, 10 major QTLs related to germination potential and relative alkaline damage rate were found in DH-1 populations under different levels of alkaline stress (Cheng et al. 2007 ). Moreover, 90 loci significantly associated with alkalinity tolerance were mapped by GWAS (Mei et al. 2022 ). Nonetheless, the mining and utilization of genes associated with alkalinity tolerance in rice have lagged behind the studies of salt stress, and most studies of the genetic basis for alkalinity tolerance are still at the QTL mapping stage. Many studies have recently combined GWAS and QTL mapping to improve the accuracy of QTL detection of complex traits (Shi et al. 2017 ; Sun et al. 2023 ; Wang et al. 2011a ; Yu et al. 2018 ). For example, a major QTL ( LP1 ) controlling panicle length in rice was identified by combining linkage mapping and association analysis, being narrowed down to a 90 kb region of chromosome 9 by the study of the NIL-F 2 population (Liu et al. 2016 ). The Chr6_7539486 region obtained from GWAS and qPL6 identified by linkage mapping were co-located to a 218 kb region. Two candidate genes were preliminarily identified using haplotype analysis, real-time quantitative PCR, and sequence analysis (Zheng et al. 2022 ). A major QTL ( qAT11 ) was detected using the same method at the bud stage using three usual phenotypic traits, including alkaline stress root length, control root length, and relative root length as indicators. Three candidate genes, Os11g0582700 , Os11g0583100 , and Os11g0584100 , that were possibly involved in the regulation of alkalinity tolerance in rice were obtained (Li et al. 2020 ). These studies indicated the feasibility of combining GWAS and linkage mapping for studying complex quantitative traits of alkalinity tolerance in rice. In this study, GWAS and linkage mapping were used to identify the QTLs for alkalinity tolerance during rice germination. We revealed that the candidate OsWRKY49 was a positive regulator of alkalinity tolerance in rice at the germination stage. The results of this study provide a theoretical basis for the cultivation of rice varieties suitable for direct seeding and japonica rice varieties planted in saline-alkali soil, and are of great significance for the study of rice alkali tolerance mechanism and molecular assisted breeding. Materials and methods Plant materials The experimental cohort for the GWAS was a natural population consisting of 295 temperate Japonica rice varieties from northeast area of China and other countries, including Russia, Japan, South Korea, and North Korea. The screening process of Japonica rice varieties in this population was detailed in previous studies (Li et al. 2019 ). The 189 recombinant inbred lines (RILs) that comprised the linkage mapping population were a cross between the Caidao (CD, alkali-sensitive) and WD20342 (WD, alkali-tolerant) cultivars. The materials were planted in Harbin Heilongjiang, China (44°04′-46°40′N, 125°42′-130°10′E) in April to September in 2020 according to local cultivation practices. Identification of alkalinity tolerance at the germination stage and determination of phenotypic data Fifty representative materials were selected from 295 rice varieties for the preliminary screening of alkali concentrations to induce alkalinity stress. Seeds were subjected to dormancy breaking and sterilization before the experiment. Seeds dried for 48 h in an incubator at 55°C were soaked in a 1% NaClO solution for 20 minutes. After rinsing the NaClO solution with sterile water, One hundred full and consistent seeds were selected and placed in a petri dish and 35 mL of NaHCO 3 solution at different concentrations (0-500 mM) was added. The germination temperature was 28°C. All tests were repeated three times. The number of germinated seeds was recorded every 24 h, starting from the time of placing the seeds in the solution. The seed was considered to have germinated once the radicle broke through the seed coat ( Fig. 1 A ) (Duan et al. 2022 ). According to the preliminary test results, alkalinity stress caused by a 200 mM solution of NaHCO 3 was considered appropriate, so that concentration was selected for the stress test on 295 Japonica rice materials. The test materials were subjected to the alkalinity stress or kept in control (distilled water) conditions according to the same method that was used in the preliminary test. All tests were repeated three times. The daily germination number was recorded until all tested varieties did not germinate for three consecutive days, and the data were statistically analyzed. The following indices were calculated for alkalinity tolerance evaluation: relative germination potential (RGP), defined as RGP = N1/N2 × 100%, where N1 is the number of treated seeds that germinated on day 7, and N2 is the number of control seeds that germinated on day 5—the days differed because the seeds in the control group ended up germinating on day 5; and relative germination index (RGI) was defined as ∑(Gt/Dt), where Gt = N3/N4 × 100%, N3 is the number of treated seeds that germinated on each day, and N4 is the number of control seeds that germinated on each day, and Dt is the number of germination days. GWAS of alkalinity tolerance Using the 788,369 SNPs obtained in our previous study, GWAS was performed on RGP and RGI using the mixed linear model implemented in TASSEL 5.0 software ( https://tassel.bitbucket.io/ ), considering population structure (Q) and kinship (K) (Bradbury et al. 2007 ). Phenotypic traits were considered to be significantly associated with the corresponding SNP if P <5.46 × 10 − 6 (Li et al. 2020 ). The GWAS result file was imported into R Studio 4.2.2, and the 'qqman' package was used to create Manhattan and Q-Q plots. The calculation method of LD decay distance of lead SNP is based on the study of Duan et al (Duan et al. 2022 ). LDBlockshow software (Dong et al. 2021 ) was used to draw the heat map of 1 Mb linkage region division near the lead SNP, and different colors corresponded to the degree of linkage (white, r 2 = 0; yellow, 0 < r 2 < 1; and red, r 2 = 1). QTL mapping The genetic linkage map constructed using 189 RILs contained 978 bin markers ( Fig. S1 ). QTL localization was performed using the inclusive composite interval mapping (ICIM) method with QTL IciMapping 4.2 software. The LOD score threshold was 2.5. Selection of key QTLs and haplotype analysis of candidate genes Key QTL intervals were required to meet the following characteristics: (1) in GWAS analysis results, there had to be more than two significantly associated SNP loci in key QTL intervals; (2) key QTL intervals needed to be significantly associated with both RGP and RGI; and (3) there was a co-location interval in GWAS and linkage mapping. Non-synonymous mutant SNP and promoter region SNP were used for haplotype analysis (Li et al. 2019 ). Haplotype analysis of all genes in the candidate interval was performed using DnaSP software ( www.ub.edu/dnasp/ ). Gene expression under alkaline stress treatment The seeds of CD and WD cultivars were treated with 200 mM NaHCO 3 at the germination stage. Total RNA was extracted after grinding at 0, 1.5, 3, 6, 12, and 24 h after treatment. The transcription level of candidate genes at each time point of alkaline treatment was analyzed by qRT-PCR using LightCycler96 (Roche). Primers for qRT-PCR were designed by Primer Premier 5 (Table S5 ). Sequence alignment OsWRKY49 , a candidate gene on chromosome 5, was selected based on the haplotype analysis and gene expression results. The reference sequence of OsWRKY49 was searched using Phytozome ( https://phytozome-next.jgi.doe.gov/ ), and primers for the promoter region (2 kb before the ATG region) and gene coding sequence (CDS) region were designed with Primer Premier 5. Then, OsWRKY49 gene and promoter region was cloned and sequenced in CD and WD. DNAMAN and SNAPGENES were used for sequence alignment. oswrky49 and OsWRKY49-OE The homozygous T 1 generation mutant seeds with the ZH11 background were obtained from BIOGLE GeneTech company ( http://www.biogle.cn/ ) using the CRISPR/Cas9 method in March 2023. The homozygous T 2 generation seeds were obtained in October 2023 and used to determine alkalinity tolerance. To obtain the transformation lines overexpressing OsWRKY49 , full-length OsWRKY49 cDNA was amplified by PCR and cloned into the pEGOEPubi vector with the Ubi promoter, which was then transformed into rice variety CD by the Agrobacterium -mediated method. The homozygous T 2 generation seeds were used to determine alkalinity tolerance. Subcellular localization of OsWRKY49 The fusion expression vector encoding OsWRKY49 and green fluorescent protein (GFP) was constructed and transformed into E. coli . The fusion expression vector plasmid of 35S pro :OsWRKY49-GFP was transferred into rice protoplasts, and 35S pro -GFP was used as a control. After incubation at room temperature in the dark overnight, the localization of green fluorescent protein GFP in cells was observed by laser confocal microscopy. Results Identification of alkalinity tolerance during rice germination To select the optimal concentration of NaHCO 3 for the experiment on alkalinity tolerance at the germination stage, RGI values were determined for 50 japonica rice varieties that were randomly selected from the natural population under five alkali treatments (100, 200, 300, 400, and 500 mM NaHCO 3 ). According to the screening results rice seeds did not germinate in 400 mM and 500 mM NaHCO 3 , phenotypic variation was the most abundant under 200 mM NaHCO 3, which could be used to distinguish the difference in alkalinity tolerance among different varieties. ( Table S1 ) Therefore, 200 mM NaHCO 3 was chosen as the final test concentration. We identified the mean, standard deviation, and range of RGP and RGI values of the natural and RIL populations. ( Table S2 ). RGP and RGI values differed significantly among the 295 japonica rice varieties, with range varied from 19.59–100% and from 17.66–86.03%. The means of RGP and RGI were 73.64% and 62.99% in 295 japonica rice varieties. The two parents of the RIL population, CD and WD, showed significant phenotypic differences during alkaline stress (Fig. 1 B and Table S3 ). The RGP and RGI values of CD were lower than those of WD, indicating that CD was more sensitive to alkaline stress than CD at the germination stage. The mean RGP of the RIL population varied from 5.90–98.20%, and the mean RGI ranged from 10.82–100% ( Table S3 ). The phenotypic values of RGP and RGI in the natural and RIL populations followed an approximately normal distribution, consistent with the characteristics of quantitative traits controlled by multiple genes ( Fig. 2 ) . GWAS for alkalinity tolerance at the germination stage in natural population The GWAS was performed using 788,369 SNPs obtained by our laboratory in 2019 (Li et al. 2019 ). The GWAS results are shown in two Manhattan plots and Q-Q plots (Fig. 3 ). A total of 15 lead SNPs were significantly associated with RGP and RGI. which were located on chromosomes 1, 4, 5, 6, 7, 11, and 12, with r 2 values ranging from 9.79–14.58% ( Table 1 ). In addition, we found six lead SNPs (Chr1_25070524, Chr4_10018796, Chr6_24024277, Chr7_24617661, Chr5_28094995 and Chr11_5733808) that were simultaneously associated with RGP and RGI. Linkage mapping for alkalinity tolerance at the germination stage in RIL population A total of six QTLs related to RGP and RGI were detected on chromosomes 2, 4, and 5 ( Fig. S1 ), with LOD values from 2.51 to 4.91 and proportions of phenotypic variance explained from 6.26–13.18% (Table 2). Of which, qRGP5 and qRGI5-2 were detected in the same interval C5_27979716-C5_28574587 and explained 11.45% and 13.18% of the phenotypic variation, respectively. So qRGP5 and qRGI5-2 were a major and common QTL affecting alkalinity tolerance during rice germination, and renamed as qAT5 . Interestingly, Chr5_28094966, a lead SNP significantly associated with both RGP and RGI in the GWAS analysis, was located in QTL qAT5. According to the LD block analysis of the lead SNP, a 425 kb overlapped region was obtained in GWAS and linkage mapping ( Fig. 4 A-C ) . Haplotype analysis of candidate genes A total of 71 genes were identified in the 425 kb candidate region, including 50 genes with annotated functions, 5 genes encoding retrotransposons, and 16 genes encoding proteins with unknown functions (Fig. 4 D and Table S4 ). To identify the key candidate genes significantly associated with the traits studied in this experiment, we performed haplotype analysis of non-synonymous SNPs in the exons and the SNPs in promoter regions. Finally, four genes ( LOC_Os05g48870 , LOC_Os05g49100 , LOC_Os05g49290 , and LOC_Os05g49450 ) showed significant differences in alkalinity tolerance-related traits among different haplotypes ( Fig. 5 ) . Of these four genes, LOC_Os05g49290 was divided into two haplotypes by an SNP in the 3′-untranslated region, and the other genes were each divided into two haplotypes by non-synonymous SNPs in exons. Haplotype analysis revealed that significant differences in RGP and RGI were observed between two haplotypes in LOC_Os05g49100 . In the other three genes, only RGI values were significantly different between different haplotypes. Gene expression and sequence analysis We treated CD and WD with 200 mM NaHCO 3 and sampled at 0, 1.5, 3, 6, 12, and 24 h, respectively. The transcription levels of the four candidate genes were analyzed using qRT-PCR (Table S5 ) . The results showed that LOC_Os05g49100 expression level was significantly up-regulated at 1.5-3 h after alkaline treatment. Furthermore, upon alkaline stress, the LOC_Os05g49100 expression level was significantly different between CD and WD cultivars ( Fig. 6 B ) . Expression levels of other genes were not significantly affected by alkaline stress ( Fig. 6 A, C and D) . We further sequenced the full length of LOC_Os05g49100 (including the promoter region) in CD and WD (Fig. 7 A and Table S6 ). Compared with the CD sequence, WD contained five base non-synonymous mutations and a 3 bp deletion in the third exon, as well as four base mutations and multiple base insertions, deletions in the promoter region, which contained multiple cis -acting elements (Fig. 7 B). The above results indicate that LOC_Os05g49100 is a functional gene related to alkalinity tolerance during rice germination. LOC_Os05g49100 ( OsWRKY49 ) belongs to the WRKY transcription factor family. In previous studies, the relationship between rice alkalinity tolerance and this gene was not reported. OsWRKY49 positively regulates rice alkalinity tolerance at the germination stage To validate the function of OsWRKY49 , two CRISPR knockout lines ( oswrky49-1 and oswrky49-2 ) were generated ( Fig. 8 B ) . In addition, two overexpression lines ( OsWRKY49-OE1 and OsWRKY49-OE2 ) were generated ( Fig. 8 C ) . In control conditions, the germination state of the WT, oswrky49-1 , oswrky49-2 , OsWRKY49-OE1 and OsWRKY49-OE2 lines was essentially the same, indicating that OsWRKY49 has little effect on rice germination under normal conditions ( Fig. 8 A ) . The oswrky49 and OsWRKY49-OE lines had significantly lower and higher RGP and RGI values than those in the WT line under 200 mM NaHCO 3 , respectively ( Fig. 8 A and D) . This finding suggested that OsWRKY49 played a positive role for rice alkalinity tolerance at the germination stage. Subcellular localization of OsWRKY49 Rice protoplasts expressing OsWRKY49 fused with GFP, showed green fluorescence in the nucleus, whereas GFP expression was not observed in the cytoplasm or other cell parts. In the control of GFP only, green fluorescence was distributed in both the nucleus and cytoplasm. These results showed that OsWRKY49 is localized in the nucleus ( Fig. 9 ) . Discussion The majority of previous saline–alkaline stress experiments on rice aimed at identifying salt-tolerant genes under salt stress, whereas only few studies focused on the stress caused by exposure to alkaline salts (Li et al. 2020 ). Furthermore, most of these studies concentrated on the seedling stage. With the development of direct-seeding rice, analysis of the alkalinity tolerance at the germination stage is particularly important. It should be noted that experimental conditions and rice test treatment period varied in previous studies, and that concentrations of alkaline salts also differed. Therefore, screening for the appropriate concentrations before the formal treatment can improve the accuracy of experimental results. In the studies of alkalinity tolerance at the seedling and bud stages, the concentration of the alkaline agent was generally within 100 mM (Li et al. 2017 ; Li et al. 2020 ). In addition, 0.15% Na 2 CO 3 was used as the alkaline stress treatment condition in the study of alkalinity tolerance at the germination stage (Mei et al. 2022 ). In present study, we randomly selected 50 japonica rice varieties for screening the appropriate concentration of the experiments with 0, 100, 200, 300, 400, 500 mM NaHCO 3 (Table S1 ) . We used RGI as an indicator, and the result showed that 200 mM NaHCO 3 best revealed the differences in alkali tolerance among varieties. Therefore, treatment with 200 mM NaHCO 3 was finally selected for the further experiment. The saline–alkaline tolerance of rice is a complex quantitative trait, and plants respond to the saline-alkaline stress in many ways. The study of salt tolerance was significantly deeper than the study of alkali tolerance, and only a few alkali tolerant QTLs were identified. ALT1 has a negative regulatory effect during alkaline stress. Mutant plants had stronger defense against oxidative damage and were more resistant to alkali than WT plants (Guo et al. 2014 ). In other studies, seven alkali-tolerant QTLs were identified using the F 2:3 population (Qi et al. 2009), and 19 alkali-tolerant QTLs were found using the DH-1 population (Cheng et al. 2010 ). Many SNPs, QTLs or known genes associated with saline-alkaline tolerance mapped in previous studies were located within or near the QTL interval in this study. For example, Mei et al. ( 2022 ) reported five lead SNPs (rs1_6533274, rs3_9919855, rs7_24927574, rs11_20724735, and rs12_7424176) associated with germination rate under alkali stress (GRS), mean germination time under alkali stress (MGTS), ratio of root length under alkali stress to normal condition (RRL) and germination index under alkali stress (GIS) under 0.15% Na 2 CO 3 treatment by GWAS in 428 accessions (125 Geng accessions and 278 Xian accessions), which were close to the lead SNPs detected in our study (Chr1_6203164, Chr3_10769329, Chr7_24617661, Chr11_20550506, and Chr12_6908181). In addition, the two QTLs, qIR-2 and qIR-4 , controlling rice seed germination under salt stress in previous study were close to or in the same intervals as QTLs qRGP2-1 and qRGP4-1 mapped in this study (Wang et al. 2011b ). These results illustrate the high accuracy of the two populations used in this study. We identified a 425 kb candidate region by the genome-wide association analysis and linkage analysis and identified OsWRKY49 as a candidate gene by the haplotype analysis, expression analysis, and sequence difference analysis. In our experiments, only the OsWRKY49 had extremely significant differences in RGP and RGI values between two haplotypes. The expression of OsWRKY49 was induced by alkali treatment in CD and WD, and the expression level in WD was significantly higher than that in CD. Moreover, OsWRKY49 had sequence differences in the promoter and CDS regions between CD and WD. Subsequently, we constructed knockout mutant plants and overexpression plants to determine the function of OsWRKY49 . The results showed that OsWRKY49 was a important gene related to alkalinity tolerance during rice germination. LOC_Os05g49100 ( OsWRKY49 ) belongs to the WRKY gene family that encodes transcription factors (TFs) identified in various plant species, including Arabidopsis , wheat, rice, sorghum, soybean, barley, and maize (Qi et al. 2019 ). These TFs mainly respond to abiotic and biotic stresses (Jimmy and Babu 2019 ). Several WRKY TFs in rice are related to stress tolerance and affect tolerance to saline-alkaline, drought, and temperature types of stress through salicylic acid (SA) and jasmonic acid (JA) (Qiu et al. 2004 ; Qiu et al. 2007 ). They affect the growth and development of rice at various stages, including germination, root growth (Jing et al. 2009 ), and flowering (Ashwini et al. 2016 ; Song et al. 2010 ). Ten genes encoding WRKY TFs ( OsWRKY03 , OsWRKY07, OsWRKY08 , OsWRKY11 , OsWRKY16 , OsWRKY23 , OsWRKY29 , OsWRKY36 , OsWRKY72 , and OsWRKY78 ) most similar to OsWRKY49 were obtained by BLAST alignment, and four of these genes were related to the saline-alkaline stress (Table S7 ) . OsWRKY78 was induced by salt stress and there were multiple cis -acting elements related to abiotic stress in the promoter. In addition, inhibition of OsWRKY78 expression significantly improved the salt tolerance of rice seedlings (Guo et al. 2019 ). The OsWRKY07 expression level shows high sensitivity to mannitol, NaCl, and abscisic acid (Kanwal et al. 2022 ). The expression of OsWRKY72 in different rice varieties increases significantly when plants are subjected to salt stress (Li et al. 2015 ). OsWRKY08 overexpression significantly improved osmotic stress tolerance through the abscisic acid signaling pathway (Song et al. 2009 ). OsWRKY08 expression can be induced by salt, cold, and drought stress. Previous studies have shown that most TFs are localized in the nucleus and play a role in transcriptional regulation. In this study, subcellular localization studies showed that OsWRKY49 was a TF expressed in the nucleus. We also found many cis -acting elements in the promoter region of OsWRKY49 , for example binding sites of TF families such as WRKY, DOF, and MYB. Future experiments should further elucidate the function of OsWRKY49 . In addition, screening downstream genes by RNA-seq will also be the focus of future research. Conclusion In this study, a 425 kb region was mapped on chromosome 5 using genome-wide association analysis and linkage mapping analysis. LOC_Os05g49100 ( OsWRKY49 ) was proposed as candidate gene for alkalinity tolerance at the germination stage through haplotype analysis, gene expression and sequence analyses, and its function was preliminarily verified by constructing oswrky49 and OsWRKY49-OE lines lacking or overexpressing this gene. This study provides valuable information for breeding alkalinity-tolerant rice varieties. Declarations Authors’ contributions JN C, SS L, T Z, DT Z and HL Z conceived and designed the research. C L, YX D and SB X participated in data analysis. JG W, HL L, LM Y, W X, Y J and QY B performed material development, sample preparation and data analysis. JN C wrote the manuscript. DT Z and HL Z corrected the manuscript. The final manuscript was read and approved by all authors. Competing interests The authors declare that they have no competing interests. Funding This research was funded by Key Research and Development Program of Heilongjiang Province (grant No. 2022ZX02B04), and the National Natural Science Foundation of China (grant No. U20A2025). Data availability Data from this study can be provided by the corresponding authors, upon request. References Abbasi H, Jamil M, Haq A, Ali S, Ahmad R, Malik Z, Parveen (2016) Salt stress manifestation on plants, mechanism of salt tolerance and potassium role in alleviating it: a review. Zemdirbyste-Agriculture 103:229–238 Ashwini N, Sajeevan RS, Udayakumar M, Nataraja KN (2016) Identification and Characterization of OsWRKY72 Variant in Indica Genotypes. 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Euphytica 218 Supplementary Files FigureS1.png TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 06 Sep, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers invited by journal 12 Aug, 2024 Editor assigned by journal 08 Aug, 2024 First submitted to journal 07 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4873013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339145444,"identity":"e9319ea4-7b8d-44b5-ae47-3adc016eec9f","order_by":0,"name":"Jingnan 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University","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-08-07 08:14:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4873013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4873013/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-024-04772-0","type":"published","date":"2024-12-27T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64195546,"identity":"6fd16926-4599-47a0-99ba-3431b26c0847","added_by":"auto","created_at":"2024-09-09 20:40:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8661124,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification method of alkalinity tolerance at the germination stage. \u003cstrong\u003e(A) \u003c/strong\u003eGermination standard of the rice germination rate. The first and second rows show germinated and ungerminated seeds, respectively. \u003cstrong\u003e(B)\u003c/strong\u003e The germination condition of CD and WD on the seventh day of alkali treatment.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/4a5c7b6ac8a72c87260baeb2.png"},{"id":64195034,"identity":"fcc424cd-2287-4452-8c37-41e9afc51d11","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":601204,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic variation in the relative germination potential (RGP) and relative germination index (RGI) in 295 \u003cem\u003eJaponica\u003c/em\u003e rice varieties. \u003cstrong\u003e(A and B)\u003c/strong\u003e RGP and RGI of natural populations. \u003cstrong\u003e(C and D)\u003c/strong\u003e RGP and RGI of recombinant inbred lines.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/d25cd97fcf960347a0d37b3a.png"},{"id":64195033,"identity":"921090a3-0ccc-4d98-bfae-75c2fdd75d33","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1360627,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and quantile–quantile (Q–Q) plots of the genome-wide association study of RGP and RGI. \u003cstrong\u003e(A) \u003c/strong\u003eManhattan plot for RGP. \u003cstrong\u003e(B)\u003c/strong\u003e Q–Q plot for RGP.\u003cstrong\u003e (C) \u003c/strong\u003eManhattan plot for RGI. \u003cstrong\u003e(D)\u003c/strong\u003e Q–Q plot for RGI.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/8f596aa7829877162ec86167.png"},{"id":64195030,"identity":"7355637c-4136-46a4-a8c8-339a0e25c97a","added_by":"auto","created_at":"2024-09-09 20:16:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1293673,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of candidate region by GWAS and linkage mapping. \u003cstrong\u003e(A)\u003c/strong\u003eA QTL related to alkalinity tolerance at the germination stage on chromosome 5 was identified in 184 RILs and mapped to the interval between the markers C5_27979716 and C5_28574587 by linkage mapping.\u003cstrong\u003e (B)\u003c/strong\u003e a 425-kb region that was overlapping in GWAS and linkage mapping.\u003cstrong\u003e (C) \u003c/strong\u003eThe local Manhattan plot (top) and LD heatmap (bottom) surround the lead SNP (Chr5_28094966). \u003cstrong\u003e(D)\u003c/strong\u003e The 425-kb overlap region contained 71 genes.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/ccaa55e207735d2dbf1c9eb3.png"},{"id":64195031,"identity":"6456e904-5a0f-4fac-92be-796b76b64bda","added_by":"auto","created_at":"2024-09-09 20:16:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":942695,"visible":true,"origin":"","legend":"\u003cp\u003eStructures and haplotypes of candidate genes.\u003cstrong\u003e (A) \u003c/strong\u003eStructure and haplotype analysis of \u003cem\u003eLOC_Os05g48870\u003c/em\u003e.\u003cstrong\u003e(B) \u003c/strong\u003eStructure and haplotype analysis of \u003cem\u003eLOC_Os05g49100\u003c/em\u003e. \u003cstrong\u003e(C)\u003c/strong\u003e Structure and haplotype analysis of \u003cem\u003eLOC_Os05g49290\u003c/em\u003e.\u003cstrong\u003e(D) \u003c/strong\u003eStructure and haplotype analysis of \u003cem\u003eLOC_Os05g49450\u003c/em\u003e (The * and ** suggest significance of ANOVA at P \u0026lt; 0.05 and P \u0026lt; 0.01, respectively).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/cc22355ac34a109a510cc0dd.png"},{"id":64195036,"identity":"cecc1788-e111-446c-b358-b36df9cb53de","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":341605,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of the four genes under normal growth conditions and alkali stress. \u003cstrong\u003e(A–D)\u003c/strong\u003e represent the gene expression of \u003cem\u003eLOC_Os05g48870\u003c/em\u003e, \u003cem\u003eLOC_Os05g49100\u003c/em\u003e, \u003cem\u003eLOC_Os05g49290\u003c/em\u003e, \u003cem\u003eLOC_Os05g49450\u003c/em\u003e under normal growth conditions and alkali stress. (**P \u0026lt; 0.01, ***P \u0026lt; 0.001, Students’t-test).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/157138522451ff30bd8cbcf9.png"},{"id":64195388,"identity":"6d9c97f5-698f-48a8-a469-c1428bc2cb4e","added_by":"auto","created_at":"2024-09-09 20:32:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1393798,"visible":true,"origin":"","legend":"\u003cp\u003eSequence difference of \u003cem\u003eLOC_Os05g49100\u003c/em\u003e between CD and WD. Ref is the reference sequence of the Nipponbare genome.\u003cstrong\u003e (A) \u003c/strong\u003eSequence differences of \u003cem\u003eLOC_Os05g49100\u003c/em\u003e between CD and WD. \u003cstrong\u003e(B)\u003c/strong\u003e Differences of \u003cem\u003ecis\u003c/em\u003e-acting elements in the \u003cem\u003eLOC_Os05g49100\u003c/em\u003e promoter region between CD and WD.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/59dc44af7f65cf6e48bdb402.png"},{"id":64195386,"identity":"f3826a7e-1b5c-48c2-a8ec-22c4b6c22e0f","added_by":"auto","created_at":"2024-09-09 20:32:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5171516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Growth of the wild-type (WT) line as well as lines with knockout lines (\u003cem\u003eoswrky49-1 \u003c/em\u003eand \u003cem\u003eoswrky49-2\u003c/em\u003e) or overexpression lines (\u003cem\u003eOsWRKY49-OE1 \u003c/em\u003eand\u003cem\u003e OsWRKY49-OE2\u003c/em\u003e) of the \u003cem\u003eOsWRKY49 \u003c/em\u003etreated with 0 or 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e.\u003cstrong\u003e (B)\u003c/strong\u003e \u003cem\u003eOsWRKY49\u003c/em\u003e DNA sequence in WT and \u003cem\u003eoswrky49 \u003c/em\u003elines. \u003cstrong\u003e(C) \u003c/strong\u003eThe expression of \u003cem\u003eOsWRKY49\u003c/em\u003e in WT and overexpression lines. \u003cstrong\u003e(D)\u003c/strong\u003e Significant phenotypic differences between\u003cem\u003e \u003c/em\u003eWT,\u003cem\u003e oswrky49-1\u003c/em\u003e, \u003cem\u003eoswrky49-2\u003c/em\u003e,\u003cem\u003e OsWRKY49-OE1\u003c/em\u003e and\u003cem\u003e OsWRKY49-OE2\u003c/em\u003e. (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, Student’s \u003cem\u003et\u003c/em\u003e-test).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/192fe13178dfec7b6f879801.png"},{"id":64195047,"identity":"78af4e73-0b0f-4707-808d-8927f951c5a9","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4265229,"visible":true,"origin":"","legend":"\u003cp\u003eSubcellular localization of \u003cem\u003eOsWRKY49\u003c/em\u003e in rice protoplasts.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/4b71eed06165a9d896f7a4a8.png"},{"id":72640552,"identity":"071453ab-661e-474e-8035-904e36e5cfbc","added_by":"auto","created_at":"2024-12-30 16:06:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23985464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/1c04b9a6-b453-4719-8a63-c60fdf37751b.pdf"},{"id":64195201,"identity":"de4d1804-8222-42dd-82d7-19e453c92bcf","added_by":"auto","created_at":"2024-09-09 20:24:31","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":1885710,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/ac0cf25a5904af751b2d9151.png"},{"id":64195044,"identity":"c113d1bb-ea15-4fef-b490-0951bdd0ff26","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":9874,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/129e4c8983f146f0b1b34a1a.xlsx"},{"id":64195203,"identity":"e0413273-cc99-4a7a-856a-801894f7c0fa","added_by":"auto","created_at":"2024-09-09 20:24:31","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":9671,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/a79147b907a7d8da6c5a03e4.xlsx"},{"id":64195035,"identity":"f7af3fbf-2b54-4fbd-bb85-4bf906253c9e","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":10403,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/92cbb91e3b8e42cbd762ce06.xlsx"},{"id":64195038,"identity":"0a97cbf3-8bd1-4085-bb6e-447e63e46e3a","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":13485,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/c48f4f13c9e140c11d3fa677.xlsx"},{"id":64195199,"identity":"89687678-6dbc-422d-b485-9dd83c018b42","added_by":"auto","created_at":"2024-09-09 20:24:31","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":12225,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/2983db9f318705ef0bc48aa8.xlsx"},{"id":64195046,"identity":"2ec6c6eb-50f5-4de1-9523-63618b9e954a","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"xlsx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":10010,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/5c57e417b9ec2b2d2fec1832.xlsx"},{"id":64195042,"identity":"b172be21-75f7-4ec4-bb4d-731ead2972bb","added_by":"auto","created_at":"2024-09-09 20:16:31","extension":"xlsx","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":9909,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4873013/v1/d9a1bc394c9fefe90ff4a6bc.xlsx"}],"financialInterests":"","formattedTitle":"OsWRKY49 on qAT5 positively regulates alkalinity tolerance at the germination stage in Oryza sativa L. ssp. Japonica","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) is a predominant grain crop worldwide, feeding more than 50% of the global population. However, it is estimated that by 2050, half of the world\u0026rsquo;s agricultural land will be threatened by salinization (Jain et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The saline-alkaline stress has a significant impact on rice harvests, reducing the yield by 30%. The saline-alkaline stress is divided into the NaCl/Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e salt stress and NaHCO\u003csub\u003e3\u003c/sub\u003e/Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e alkali stress (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The damage caused by the high salt stress in plants mainly derives from high ion concentration and osmotic stress (Abbasi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The damage caused by alkaline stress to plants is owing both to the high ion concentration and lack of nutrient minerals, such as iron (Fe), zinc (Zn), and manganese (Mn), caused by high pH (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alkaline environment is harmful to the growth and development of plants, and the physiological and biochemical mechanisms and regulatory networks of plants to resist alkaline stress are more complex than those for salt stress.\u003c/p\u003e \u003cp\u003eWith the gradual increase of scale and modernization of rice planting, the low-cost, high-efficiency, and simplified operation of direct seeding of rice is consistent with the development of the planting industry (Duan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, rice planting in the saline\u0026ndash;alkaline soil is an effective means to improve soil conditions, and it expands the planting area of rice. With the expansion of saline\u0026ndash;alkaline land area and the promotion of rice direct seeding technology, cultivating rice varieties with good germination in a high saline\u0026ndash;alkaline environment has become a popular breeding goal (Mei et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, exploration of candidate genes associated with saline\u0026ndash;alkaline tolerance during the rice germination stage in different rice varieties is urgently needed (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSaline-alkaline tolerance is a typical quantitative genetic trait controlled by multiple genes, similar to many abiotic stress tolerance traits (Qi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To date, most studies have focused on the salt tolerance of rice and many salt tolerance-related quantitative trait loci (QTLs)/genes have been identified at various growth stages, mainly at the seedling stage (Jing and Zhang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research on salt tolerance mechanisms in rice is also intensifying (Sun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Up to now, 964 salt tolerance-related QTLs have been discovered, of which the largest number (514) were at the seedling stage and 31, 149, and 270 at the germination, vegetative growth, and reproductive growth stages, respectively (Jing and Zhang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, the \u003cem\u003eSKC1\u003c/em\u003e gene (Ren et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and \u003cem\u003eSaltol\u003c/em\u003e QTL (Thomson et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) are early salt tolerance-related genetic regions identified by fine-mapping and map-based cloning of QTLs during the rice seedling stage. \u003cem\u003eqRSL7\u003c/em\u003e is a salt-tolerant QTL detected in the F\u003csub\u003e2:3\u003c/sub\u003e population constructed from the Weiguo (salt-tolerant) and IR36 (salt-sensitive) by QTL-seq.\u0026nbsp;\u003cem\u003eOsSAP16\u003c/em\u003e was identified as a candidate salt-tolerance gene of \u003cem\u003eqRSL7\u003c/em\u003e (Lei et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Compared to studies of salt tolerance, studies on alkalinity tolerance (NaHCO\u003csub\u003e3\u003c/sub\u003e or Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e) of rice are still in the preliminary stage (Leng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). An F\u003csub\u003e2:3\u003c/sub\u003e population derived from Gaochan 106 and Changbai 9 was treated with alkali at the seedling and various growth stages after transplanting, and 6 and 13 QTLs related to the dead seedling rate and dead leaf rate, respectively, were obtained (Qi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Li et al. identified an alkali-tolerant QTL at seedling stage by genome-wide association analysis (GWAS) of the alkalinity tolerance score, concentration of Na\u003csup\u003e+\u003c/sup\u003e in the shoots and Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e ratio in the shoots, and \u003cem\u003eOsIRO3\u003c/em\u003e was determined as the candidate gene of \u003cem\u003eqSAT3\u003c/em\u003e by subsequent experiments (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, 10 major QTLs related to germination potential and relative alkaline damage rate were found in DH-1 populations under different levels of alkaline stress (Cheng et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moreover, 90 loci significantly associated with alkalinity tolerance were mapped by GWAS (Mei et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nonetheless, the mining and utilization of genes associated with alkalinity tolerance in rice have lagged behind the studies of salt stress, and most studies of the genetic basis for alkalinity tolerance are still at the QTL mapping stage.\u003c/p\u003e \u003cp\u003eMany studies have recently combined GWAS and QTL mapping to improve the accuracy of QTL detection of complex traits (Shi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, a major QTL (\u003cem\u003eLP1\u003c/em\u003e) controlling panicle length in rice was identified by combining linkage mapping and association analysis, being narrowed down to a 90 kb region of chromosome 9 by the study of the NIL-F\u003csub\u003e2\u003c/sub\u003e population (Liu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Chr6_7539486 region obtained from GWAS and \u003cem\u003eqPL6\u003c/em\u003e identified by linkage mapping were co-located to a 218 kb region. Two candidate genes were preliminarily identified using haplotype analysis, real-time quantitative PCR, and sequence analysis (Zheng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A major QTL (\u003cem\u003eqAT11\u003c/em\u003e) was detected using the same method at the bud stage using three usual phenotypic traits, including alkaline stress root length, control root length, and relative root length as indicators. Three candidate genes, \u003cem\u003eOs11g0582700\u003c/em\u003e, \u003cem\u003eOs11g0583100\u003c/em\u003e, and \u003cem\u003eOs11g0584100\u003c/em\u003e, that were possibly involved in the regulation of alkalinity tolerance in rice were obtained (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies indicated the feasibility of combining GWAS and linkage mapping for studying complex quantitative traits of alkalinity tolerance in rice.\u003c/p\u003e \u003cp\u003eIn this study, GWAS and linkage mapping were used to identify the QTLs for alkalinity tolerance during rice germination. We revealed that the candidate \u003cem\u003eOsWRKY49\u003c/em\u003e was a positive regulator of alkalinity tolerance in rice at the germination stage. The results of this study provide a theoretical basis for the cultivation of rice varieties suitable for direct seeding and \u003cem\u003ejaponica\u003c/em\u003e rice varieties planted in saline-alkali soil, and are of great significance for the study of rice alkali tolerance mechanism and molecular assisted breeding.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eThe experimental cohort for the GWAS was a natural population consisting of 295 temperate \u003cem\u003eJaponica\u003c/em\u003e rice varieties from northeast area of China and other countries, including Russia, Japan, South Korea, and North Korea. The screening process of \u003cem\u003eJaponica\u003c/em\u003e rice varieties in this population was detailed in previous studies (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 189 recombinant inbred lines (RILs) that comprised the linkage mapping population were a cross between the Caidao (CD, alkali-sensitive) and WD20342 (WD, alkali-tolerant) cultivars. The materials were planted in Harbin Heilongjiang, China (44\u0026deg;04\u0026prime;-46\u0026deg;40\u0026prime;N, 125\u0026deg;42\u0026prime;-130\u0026deg;10\u0026prime;E) in April to September in 2020 according to local cultivation practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of alkalinity tolerance at the germination stage and determination of phenotypic data\u003c/h2\u003e \u003cp\u003eFifty representative materials were selected from 295 rice varieties for the preliminary screening of alkali concentrations to induce alkalinity stress. Seeds were subjected to dormancy breaking and sterilization before the experiment. Seeds dried for 48 h in an incubator at 55\u0026deg;C were soaked in a 1% NaClO solution for 20 minutes. After rinsing the NaClO solution with sterile water, One hundred full and consistent seeds were selected and placed in a petri dish and 35 mL of NaHCO\u003csub\u003e3\u003c/sub\u003e solution at different concentrations (0-500 mM) was added. The germination temperature was 28\u0026deg;C. All tests were repeated three times. The number of germinated seeds was recorded every 24 h, starting from the time of placing the seeds in the solution. The seed was considered to have germinated once the radicle broke through the seed coat \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e (Duan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the preliminary test results, alkalinity stress caused by a 200 mM solution of NaHCO\u003csub\u003e3\u003c/sub\u003e was considered appropriate, so that concentration was selected for the stress test on 295 \u003cem\u003eJaponica\u003c/em\u003e rice materials. The test materials were subjected to the alkalinity stress or kept in control (distilled water) conditions according to the same method that was used in the preliminary test. All tests were repeated three times. The daily germination number was recorded until all tested varieties did not germinate for three consecutive days, and the data were statistically analyzed. The following indices were calculated for alkalinity tolerance evaluation: relative germination potential (RGP), defined as RGP\u0026thinsp;=\u0026thinsp;N1/N2 \u0026times; 100%, where N1 is the number of treated seeds that germinated on day 7, and N2 is the number of control seeds that germinated on day 5\u0026mdash;the days differed because the seeds in the control group ended up germinating on day 5; and relative germination index (RGI) was defined as \u0026sum;(Gt/Dt), where Gt\u0026thinsp;=\u0026thinsp;N3/N4 \u0026times; 100%, N3 is the number of treated seeds that germinated on each day, and N4 is the number of control seeds that germinated on each day, and Dt is the number of germination days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGWAS of alkalinity tolerance\u003c/h2\u003e \u003cp\u003eUsing the 788,369 SNPs obtained in our previous study, GWAS was performed on RGP and RGI using the mixed linear model implemented in TASSEL 5.0 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tassel.bitbucket.io/\u003c/span\u003e\u003cspan address=\"https://tassel.bitbucket.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), considering population structure (Q) and kinship (K) (Bradbury et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Phenotypic traits were considered to be significantly associated with the corresponding SNP if \u003cem\u003eP\u003c/em\u003e\u0026lt;5.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The GWAS result file was imported into R Studio 4.2.2, and the 'qqman' package was used to create Manhattan and Q-Q plots.\u003c/p\u003e \u003cp\u003eThe calculation method of LD decay distance of lead SNP is based on the study of Duan et al (Duan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). LDBlockshow software (Dong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was used to draw the heat map of 1 Mb linkage region division near the lead SNP, and different colors corresponded to the degree of linkage (white, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0; yellow, 0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1; and red, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping\u003c/h2\u003e \u003cp\u003eThe genetic linkage map constructed using 189 RILs contained 978 bin markers (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). QTL localization was performed using the inclusive composite interval mapping (ICIM) method with QTL IciMapping 4.2 software. The LOD score threshold was 2.5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSelection of key QTLs and haplotype analysis of candidate genes\u003c/h2\u003e \u003cp\u003eKey QTL intervals were required to meet the following characteristics: (1) in GWAS analysis results, there had to be more than two significantly associated SNP loci in key QTL intervals; (2) key QTL intervals needed to be significantly associated with both RGP and RGI; and (3) there was a co-location interval in GWAS and linkage mapping.\u003c/p\u003e \u003cp\u003eNon-synonymous mutant SNP and promoter region SNP were used for haplotype analysis (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Haplotype analysis of all genes in the candidate interval was performed using DnaSP software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://tassel.bitbucket.io/\" target=\"_blank\"\u003ewww.ub.edu/dnasp/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ub.edu/dnasp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene expression under alkaline stress treatment\u003c/h2\u003e \u003cp\u003eThe seeds of CD and WD cultivars were treated with 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e at the germination stage. Total RNA was extracted after grinding at 0, 1.5, 3, 6, 12, and 24 h after treatment. The transcription level of candidate genes at each time point of alkaline treatment was analyzed by qRT-PCR using LightCycler96 (Roche). Primers for qRT-PCR were designed by Primer Premier 5 (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSequence alignment\u003c/h2\u003e \u003cp\u003e \u003cem\u003eOsWRKY49\u003c/em\u003e, a candidate gene on chromosome 5, was selected based on the haplotype analysis and gene expression results. The reference sequence of \u003cem\u003eOsWRKY49\u003c/em\u003e was searched using Phytozome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and primers for the promoter region (2 kb before the ATG region) and gene coding sequence (CDS) region were designed with Primer Premier 5. Then, \u003cem\u003eOsWRKY49\u003c/em\u003e gene and promoter region was cloned and sequenced in CD and WD. DNAMAN and SNAPGENES were used for sequence alignment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eoswrky49\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eOsWRKY49-OE\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe homozygous T\u003csub\u003e1\u003c/sub\u003e generation mutant seeds with the ZH11 background were obtained from BIOGLE GeneTech company (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biogle.cn/\u003c/span\u003e\u003cspan address=\"http://www.biogle.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the CRISPR/Cas9 method in March 2023. The homozygous T\u003csub\u003e2\u003c/sub\u003e generation seeds were obtained in October 2023 and used to determine alkalinity tolerance. To obtain the transformation lines overexpressing \u003cem\u003eOsWRKY49\u003c/em\u003e, full-length \u003cem\u003eOsWRKY49\u003c/em\u003e cDNA was amplified by PCR and cloned into the pEGOEPubi vector with the Ubi promoter, which was then transformed into rice variety CD by the \u003cem\u003eAgrobacterium\u003c/em\u003e-mediated method. The homozygous T\u003csub\u003e2\u003c/sub\u003e generation seeds were used to determine alkalinity tolerance.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eSubcellular localization of OsWRKY49\u003c/h2\u003e \u003cp\u003eThe fusion expression vector encoding OsWRKY49 and green fluorescent protein (GFP) was constructed and transformed into \u003cem\u003eE. coli\u003c/em\u003e. The fusion expression vector plasmid of 35S\u003csub\u003epro\u003c/sub\u003e:OsWRKY49-GFP was transferred into rice protoplasts, and 35S\u003csub\u003epro\u003c/sub\u003e-GFP was used as a control. After incubation at room temperature in the dark overnight, the localization of green fluorescent protein GFP in cells was observed by laser confocal microscopy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of alkalinity tolerance during rice germination\u003c/h2\u003e \u003cp\u003eTo select the optimal concentration of NaHCO\u003csub\u003e3\u003c/sub\u003e for the experiment on alkalinity tolerance at the germination stage, RGI values were determined for 50 \u003cem\u003ejaponica\u003c/em\u003e rice varieties that were randomly selected from the natural population under five alkali treatments (100, 200, 300, 400, and 500 mM NaHCO\u003csub\u003e3\u003c/sub\u003e). According to the screening results rice seeds did not germinate in 400 mM and 500 mM NaHCO\u003csub\u003e3\u003c/sub\u003e, phenotypic variation was the most abundant under 200 mM NaHCO\u003csub\u003e3,\u003c/sub\u003e which could be used to distinguish the difference in alkalinity tolerance among different varieties. (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) Therefore, 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e was chosen as the final test concentration. We identified the mean, standard deviation, and range of RGP and RGI values of the natural and RIL populations. (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). RGP and RGI values differed significantly among the 295 \u003cem\u003ejaponica\u003c/em\u003e rice varieties, with range varied from 19.59\u0026ndash;100% and from 17.66\u0026ndash;86.03%. The means of RGP and RGI were 73.64% and 62.99% in 295 \u003cem\u003ejaponica\u003c/em\u003e rice varieties. The two parents of the RIL population, CD and WD, showed significant phenotypic differences during alkaline stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cb\u003eand Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). The RGP and RGI values of CD were lower than those of WD, indicating that CD was more sensitive to alkaline stress than CD at the germination stage. The mean RGP of the RIL population varied from 5.90\u0026ndash;98.20%, and the mean RGI ranged from 10.82\u0026ndash;100% (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). The phenotypic values of RGP and RGI in the natural and RIL populations followed an approximately normal distribution, consistent with the characteristics of quantitative traits controlled by multiple genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGWAS for alkalinity tolerance at the germination stage in natural population\u003c/h2\u003e \u003cp\u003eThe GWAS was performed using 788,369 SNPs obtained by our laboratory in 2019 (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GWAS results are shown in two Manhattan plots and Q-Q plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A total of 15 lead SNPs were significantly associated with RGP and RGI. which were located on chromosomes 1, 4, 5, 6, 7, 11, and 12, with \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values ranging from 9.79\u0026ndash;14.58% (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). In addition, we found six lead SNPs (Chr1_25070524, Chr4_10018796, Chr6_24024277, Chr7_24617661, Chr5_28094995 and Chr11_5733808) that were simultaneously associated with RGP and RGI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLinkage mapping for alkalinity tolerance at the germination stage in RIL population\u003c/h2\u003e \u003cp\u003eA total of six QTLs related to RGP and RGI were detected on chromosomes 2, 4, and 5 (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), with LOD values from 2.51 to 4.91 and proportions of phenotypic variance explained from 6.26\u0026ndash;13.18% (Table\u0026nbsp;2). Of which, \u003cem\u003eqRGP5\u003c/em\u003e and \u003cem\u003eqRGI5-2\u003c/em\u003e were detected in the same interval C5_27979716-C5_28574587 and explained 11.45% and 13.18% of the phenotypic variation, respectively. So \u003cem\u003eqRGP5\u003c/em\u003e and \u003cem\u003eqRGI5-2\u003c/em\u003e were a major and common QTL affecting alkalinity tolerance during rice germination, and renamed as \u003cem\u003eqAT5\u003c/em\u003e. Interestingly, Chr5_28094966, a lead SNP significantly associated with both RGP and RGI in the GWAS analysis, was located in QTL \u003cem\u003eqAT5.\u003c/em\u003e According to the LD block analysis of the lead SNP, a 425 kb overlapped region was obtained in GWAS and linkage mapping \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHaplotype analysis of candidate genes\u003c/h2\u003e \u003cp\u003eA total of 71 genes were identified in the 425 kb candidate region, including 50 genes with annotated functions, 5 genes encoding retrotransposons, and 16 genes encoding proteins with unknown functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD \u003cb\u003eand Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). To identify the key candidate genes significantly associated with the traits studied in this experiment, we performed haplotype analysis of non-synonymous SNPs in the exons and the SNPs in promoter regions. Finally, four genes (\u003cem\u003eLOC_Os05g48870\u003c/em\u003e, \u003cem\u003eLOC_Os05g49100\u003c/em\u003e, \u003cem\u003eLOC_Os05g49290\u003c/em\u003e, and \u003cem\u003eLOC_Os05g49450\u003c/em\u003e) showed significant differences in alkalinity tolerance-related traits among different haplotypes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Of these four genes, \u003cem\u003eLOC_Os05g49290\u003c/em\u003e was divided into two haplotypes by an SNP in the 3\u0026prime;-untranslated region, and the other genes were each divided into two haplotypes by non-synonymous SNPs in exons. Haplotype analysis revealed that significant differences in RGP and RGI were observed between two haplotypes in \u003cem\u003eLOC_Os05g49100\u003c/em\u003e. In the other three genes, only RGI values were significantly different between different haplotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene expression and sequence analysis\u003c/h2\u003e \u003cp\u003eWe treated CD and WD with 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e and sampled at 0, 1.5, 3, 6, 12, and 24 h, respectively. The transcription levels of the four candidate genes were analyzed using qRT-PCR \u003cb\u003e(Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e)\u003c/b\u003e. The results showed that \u003cem\u003eLOC_Os05g49100\u003c/em\u003e expression level was significantly up-regulated at 1.5-3 h after alkaline treatment. Furthermore, upon alkaline stress, the \u003cem\u003eLOC_Os05g49100\u003c/em\u003e expression level was significantly different between CD and WD cultivars \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Expression levels of other genes were not significantly affected by alkaline stress \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C \u003cb\u003eand D)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further sequenced the full length of \u003cem\u003eLOC_Os05g49100\u003c/em\u003e (including the promoter region) in CD and WD (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e). Compared with the CD sequence, WD contained five base non-synonymous mutations and a 3 bp deletion in the third exon, as well as four base mutations and multiple base insertions, deletions in the promoter region, which contained multiple \u003cem\u003ecis\u003c/em\u003e-acting elements (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The above results indicate that \u003cem\u003eLOC_Os05g49100\u003c/em\u003e is a functional gene related to alkalinity tolerance during rice germination. \u003cem\u003eLOC_Os05g49100\u003c/em\u003e (\u003cem\u003eOsWRKY49\u003c/em\u003e) belongs to the WRKY transcription factor family. In previous studies, the relationship between rice alkalinity tolerance and this gene was not reported.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOsWRKY49\u003c/b\u003e \u003cb\u003epositively regulates rice alkalinity tolerance at the germination stage\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate the function of \u003cem\u003eOsWRKY49\u003c/em\u003e, two CRISPR knockout lines (\u003cem\u003eoswrky49-1\u003c/em\u003e and \u003cem\u003eoswrky49-2\u003c/em\u003e) were generated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. In addition, two overexpression lines (\u003cem\u003eOsWRKY49-OE1\u003c/em\u003e and \u003cem\u003eOsWRKY49-OE2\u003c/em\u003e) were generated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In control conditions, the germination state of the WT, \u003cem\u003eoswrky49-1\u003c/em\u003e, \u003cem\u003eoswrky49-2\u003c/em\u003e, \u003cem\u003eOsWRKY49-OE1\u003c/em\u003e and \u003cem\u003eOsWRKY49-OE2\u003c/em\u003e lines was essentially the same, indicating that \u003cem\u003eOsWRKY49\u003c/em\u003e has little effect on rice germination under normal conditions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The \u003cem\u003eoswrky49\u003c/em\u003e and \u003cem\u003eOsWRKY49-OE\u003c/em\u003e lines had significantly lower and higher RGP and RGI values than those in the WT line under 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA \u003cb\u003eand D)\u003c/b\u003e. This finding suggested that \u003cem\u003eOsWRKY49\u003c/em\u003e played a positive role for rice alkalinity tolerance at the germination stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubcellular localization of OsWRKY49\u003c/h2\u003e \u003cp\u003eRice protoplasts expressing OsWRKY49 fused with GFP, showed green fluorescence in the nucleus, whereas GFP expression was not observed in the cytoplasm or other cell parts. In the control of GFP only, green fluorescence was distributed in both the nucleus and cytoplasm. These results showed that OsWRKY49 is localized in the nucleus \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe majority of previous saline\u0026ndash;alkaline stress experiments on rice aimed at identifying salt-tolerant genes under salt stress, whereas only few studies focused on the stress caused by exposure to alkaline salts (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, most of these studies concentrated on the seedling stage. With the development of direct-seeding rice, analysis of the alkalinity tolerance at the germination stage is particularly important. It should be noted that experimental conditions and rice test treatment period varied in previous studies, and that concentrations of alkaline salts also differed. Therefore, screening for the appropriate concentrations before the formal treatment can improve the accuracy of experimental results. In the studies of alkalinity tolerance at the seedling and bud stages, the concentration of the alkaline agent was generally within 100 mM (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, 0.15% Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e was used as the alkaline stress treatment condition in the study of alkalinity tolerance at the germination stage (Mei et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In present study, we randomly selected 50 \u003cem\u003ejaponica\u003c/em\u003e rice varieties for screening the appropriate concentration of the experiments with 0, 100, 200, 300, 400, 500 mM NaHCO\u003csub\u003e3\u003c/sub\u003e\u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. We used RGI as an indicator, and the result showed that 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e best revealed the differences in alkali tolerance among varieties. Therefore, treatment with 200 mM NaHCO\u003csub\u003e3\u003c/sub\u003e was finally selected for the further experiment.\u003c/p\u003e \u003cp\u003eThe saline\u0026ndash;alkaline tolerance of rice is a complex quantitative trait, and plants respond to the saline-alkaline stress in many ways. The study of salt tolerance was significantly deeper than the study of alkali tolerance, and only a few alkali tolerant QTLs were identified. \u003cem\u003eALT1\u003c/em\u003e has a negative regulatory effect during alkaline stress. Mutant plants had stronger defense against oxidative damage and were more resistant to alkali than WT plants (Guo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In other studies, seven alkali-tolerant QTLs were identified using the F\u003csub\u003e2:3\u003c/sub\u003e population (Qi et al. 2009), and 19 alkali-tolerant QTLs were found using the DH-1 population (Cheng et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Many SNPs, QTLs or known genes associated with saline-alkaline tolerance mapped in previous studies were located within or near the QTL interval in this study. For example, Mei et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported five lead SNPs (rs1_6533274, rs3_9919855, rs7_24927574, rs11_20724735, and rs12_7424176) associated with germination rate under alkali stress (GRS), mean germination time under alkali stress (MGTS), ratio of root length under alkali stress to normal condition (RRL) and germination index under alkali stress (GIS) under 0.15% Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e treatment by GWAS in 428 accessions (125 Geng accessions and 278 Xian accessions), which were close to the lead SNPs detected in our study (Chr1_6203164, Chr3_10769329, Chr7_24617661, Chr11_20550506, and Chr12_6908181). In addition, the two QTLs, \u003cem\u003eqIR-2\u003c/em\u003e and \u003cem\u003eqIR-4\u003c/em\u003e, controlling rice seed germination under salt stress in previous study were close to or in the same intervals as QTLs \u003cem\u003eqRGP2-1\u003c/em\u003e and \u003cem\u003eqRGP4-1\u003c/em\u003e mapped in this study (Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). These results illustrate the high accuracy of the two populations used in this study.\u003c/p\u003e \u003cp\u003eWe identified a 425 kb candidate region by the genome-wide association analysis and linkage analysis and identified \u003cem\u003eOsWRKY49\u003c/em\u003e as a candidate gene by the haplotype analysis, expression analysis, and sequence difference analysis. In our experiments, only the \u003cem\u003eOsWRKY49\u003c/em\u003e had extremely significant differences in RGP and RGI values between two haplotypes. The expression of \u003cem\u003eOsWRKY49\u003c/em\u003e was induced by alkali treatment in CD and WD, and the expression level in WD was significantly higher than that in CD. Moreover, \u003cem\u003eOsWRKY49\u003c/em\u003e had sequence differences in the promoter and CDS regions between CD and WD. Subsequently, we constructed knockout mutant plants and overexpression plants to determine the function of \u003cem\u003eOsWRKY49\u003c/em\u003e. The results showed that \u003cem\u003eOsWRKY49\u003c/em\u003e was a important gene related to alkalinity tolerance during rice germination.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLOC_Os05g49100\u003c/em\u003e (\u003cem\u003eOsWRKY49\u003c/em\u003e) belongs to the WRKY gene family that encodes transcription factors (TFs) identified in various plant species, including \u003cem\u003eArabidopsis\u003c/em\u003e, wheat, rice, sorghum, soybean, barley, and maize (Qi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These TFs mainly respond to abiotic and biotic stresses (Jimmy and Babu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several WRKY TFs in rice are related to stress tolerance and affect tolerance to saline-alkaline, drought, and temperature types of stress through salicylic acid (SA) and jasmonic acid (JA) (Qiu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Qiu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They affect the growth and development of rice at various stages, including germination, root growth (Jing et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and flowering (Ashwini et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Ten genes encoding WRKY TFs (\u003cem\u003eOsWRKY03\u003c/em\u003e, \u003cem\u003eOsWRKY07, OsWRKY08\u003c/em\u003e, \u003cem\u003eOsWRKY11\u003c/em\u003e, \u003cem\u003eOsWRKY16\u003c/em\u003e, \u003cem\u003eOsWRKY23\u003c/em\u003e, \u003cem\u003eOsWRKY29\u003c/em\u003e, \u003cem\u003eOsWRKY36\u003c/em\u003e, \u003cem\u003eOsWRKY72\u003c/em\u003e, and \u003cem\u003eOsWRKY78\u003c/em\u003e) most similar to \u003cem\u003eOsWRKY49\u003c/em\u003e were obtained by BLAST alignment, and four of these genes were related to the saline-alkaline stress \u003cb\u003e(Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e)\u003c/b\u003e. \u003cem\u003eOsWRKY78\u003c/em\u003e was induced by salt stress and there were multiple \u003cem\u003ecis\u003c/em\u003e-acting elements related to abiotic stress in the promoter. In addition, inhibition of \u003cem\u003eOsWRKY78\u003c/em\u003e expression significantly improved the salt tolerance of rice seedlings (Guo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The \u003cem\u003eOsWRKY07\u003c/em\u003e expression level shows high sensitivity to mannitol, NaCl, and abscisic acid (Kanwal et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The expression of \u003cem\u003eOsWRKY72\u003c/em\u003e in different rice varieties increases significantly when plants are subjected to salt stress (Li et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eOsWRKY08\u003c/em\u003e overexpression significantly improved osmotic stress tolerance through the abscisic acid signaling pathway (Song et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). \u003cem\u003eOsWRKY08\u003c/em\u003e expression can be induced by salt, cold, and drought stress.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that most TFs are localized in the nucleus and play a role in transcriptional regulation. In this study, subcellular localization studies showed that OsWRKY49 was a TF expressed in the nucleus. We also found many \u003cem\u003ecis\u003c/em\u003e-acting elements in the promoter region of \u003cem\u003eOsWRKY49\u003c/em\u003e, for example binding sites of TF families such as WRKY, DOF, and MYB. Future experiments should further elucidate the function of \u003cem\u003eOsWRKY49\u003c/em\u003e. In addition, screening downstream genes by RNA-seq will also be the focus of future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, a 425 kb region was mapped on chromosome 5 using genome-wide association analysis and linkage mapping analysis. \u003cem\u003eLOC_Os05g49100\u003c/em\u003e (\u003cem\u003eOsWRKY49\u003c/em\u003e) was proposed as candidate gene for alkalinity tolerance at the germination stage through haplotype analysis, gene expression and sequence analyses, and its function was preliminarily verified by constructing \u003cem\u003eoswrky49\u003c/em\u003e and \u003cem\u003eOsWRKY49-OE\u003c/em\u003e lines lacking or overexpressing this gene. This study provides valuable information for breeding alkalinity-tolerant rice varieties.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eJN C, SS L, T Z, DT Z and HL Z conceived and designed the research. C L, YX D and SB X participated in data analysis. JG W, HL L, LM Y, W X, Y J and QY B performed material development, sample preparation and data analysis. JN C wrote the manuscript. DT Z and HL Z corrected the manuscript. The final manuscript was read and approved by all authors.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Key Research and Development Program of Heilongjiang Province (grant No. 2022ZX02B04), and the National Natural Science Foundation of China (grant No. U20A2025).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData from this study can be provided by the corresponding authors, upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi H, Jamil M, Haq A, Ali S, Ahmad R, Malik Z, Parveen (2016) Salt stress manifestation on plants, mechanism of salt tolerance and potassium role in alleviating it: a review. 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Euphytica 218\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Japonica rice, germination stage, alkalinity tolerance, genome-wide association study, linkage mapping, OsWRKY49","lastPublishedDoi":"10.21203/rs.3.rs-4873013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4873013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the widespread use of the rice direct seeding cultivation model, improving the tolerance of rice varieties to salinity-alkalinity at the germination stage has become increasingly important. However, as previous studies have focused on the neutral salt stress, understanding of alkalinity tolerance is still in its infancy, and the genetic resource data is scarce. Here, we used a natural population composed of 295 Japonica rice varieties and a recombinant inbred population including 189 lines derived from Caidao (alkali-sensitive) and WD20342 (alkali-tolerant) to uncover the genetic structure of alkalinity tolerance during rice germination. A total of 15 lead SNPs and six QTLs related to relative germination potential (RGP) and relative germination index (RGI) were detected by genome-wide association study and linkage mapping. Of which, Chr5_28094966, a lead SNP was located in the interval of the mapped major QTL qAT5, that was significantly associated with both RGP and RGI in the two populations. According to the LD block analysis and QTL interval, a 425 kb overlapped region was obtained for screening the candidate genes. After haplotype analysis, qRT-PCR and parental sequence analysis, LOC_Os05g49100 (OsWRKY49) was initially considered as the candidate gene. Having studied the characteristics of rice lines with OsWRKY49 knockout and overexpression, we established that OsWRKY49 could be a positive regulator of alkalinity tolerance in rice at the germination stage. Subcellular localization showed that green fluorescent protein-tagged OsWRKY49 was localized in the nucleus. The application of OsWRKY49 could be useful for increasing alkalinity tolerance of rice direct seeding.","manuscriptTitle":"OsWRKY49 on qAT5 positively regulates alkalinity tolerance at the germination stage in Oryza sativa L. ssp. Japonica","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 20:16:26","doi":"10.21203/rs.3.rs-4873013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-09-06T11:03:37+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-08-12T14:03:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-12T11:54:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T07:26:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-08-07T04:13:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9ae91110-98cd-4129-b284-122edef842eb","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:00:17+00:00","versionOfRecord":{"articleIdentity":"rs-4873013","link":"https://doi.org/10.1007/s00122-024-04772-0","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2024-12-27 15:57:15","publishedOnDateReadable":"December 27th, 2024"},"versionCreatedAt":"2024-09-09 20:16:26","video":"","vorDoi":"10.1007/s00122-024-04772-0","vorDoiUrl":"https://doi.org/10.1007/s00122-024-04772-0","workflowStages":[]},"version":"v1","identity":"rs-4873013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4873013","identity":"rs-4873013","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}