Abstract
The adaptation to aerobic environments enables upland rice to produce a sustainable grain yield under suboptimal conditions. In order to understand the molecular mechanisms involved in providing the adaptation of upland rice to aerobic environments, we previously developed introgression lines (ILs) with the irrigated rice variety Minghui63 (MH63) as the recipient parent and the upland rice variety Luyin46 (LY46) as the donor parent for subsequent identification of the relevant genes. In this study, IL-U135 was analyzed in detail because of its adaptation to aerobic conditions. Transcriptome profiling revealed 20 and 8 differentially expressed genes (DEGs) between IL-U135 and MH63 under anaerobic and aerobic conditions, respectively. In contrast, 306 and 188 DEGs were identified between LY46 and MH63 under anaerobic and aerobic conditions, respectively. Gene ontology (GO) analysis indicated that the adaptation of upland rice to aerobic environments is intimately associated with CLV3/ESR-related (CLE) signal transduction pathways, CLAVATA1 kinase activity, and salicylic acid related metabolism biosynthetic pathways. The IL-U135, LY46 and MH63 genomes were resequenced to map the genes responsible for adaptations to aerobic conditions. One the basis of an integrated analysis of the transcriptomic and genomic profiles, we propose that genes encoding an NBS-LRR protein (LOC_Os11g10570) and an ATP-binding protein (LOC_Os11g31480) may contribute to the adaptation of upland rice to aerobic environments. Our results may provide new insights into the molecular mechanism underlying the adaptation of rice to aerobic conditions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- MH 63:
-
Minghui63
- LY 46:
-
Luyin46
- DEGs:
-
Differentially expressed genes
- QTL:
-
Quantitative trait loci
- RNA-Seq:
-
RNA sequencing
- SA:
-
Salicylic acid
- SLs:
-
Segments derived from LY46
- GO:
-
Gene ontology
- BP:
-
Biological process
- CC:
-
Cellular component
- MF:
-
Molecular function
- JA:
-
Jasmonic acid
- IAA:
-
Auxin
- EA:
-
Ethylene
- GA:
-
Gibberellic acid
- CTK:
-
Cytokinin
- BR:
-
Brassinosteroid
- ABA:
-
Abscisic acid
- SNPs:
-
Single nucleotide polymorphisms
- InDels:
-
Insertion/deletions
- RT-qPCR:
-
Quantitative reverse transcription pcr
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- CDS:
-
Coding regions
- UTR:
-
Untranslated region
References
Alexa A, Rahnenfuhrer J (2006) Enrichment analysis for gene ontology. R Package Version
Ali F, Bano A, Fazal A (2017) Recent methods of drought stress tolerance in plants. Plant Growth Regul 82:363–375. https://doi.org/10.1007/s10725-017-0267-2
Anders S, Huber W (2012) Differential expression of RNA-seq data at the gene level—the DESeq package. European Molecular Biology Laboratory, Heidelberg
Anders S, Pyl PT, Huber W (2015) HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169. https://doi.org/10.1093/bioinformatics/btu638
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170
Chang T (1976) The origin, evolution, cultivation, dissemination, and diversification of Asian and African rices. Euphytica 25:425–441. https://doi.org/10.1007/BF00041576
Chini A, Grant JJ, Seki M, Shinozaki K, Loake GJ (2004) Drought tolerance established by enhanced expression of the CC-NBS-LRR gene, ADR1, requires salicylic acid, EDS1 and ABI1. Plant J 38:810–822. https://doi.org/10.1111/j.1365-313X.2004.02086.x
Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6:80–92. https://doi.org/10.4161/fly.19695
Courtois B, Shen L, Petalcorin W, Carandang S, Mauleon R, Li Z (2003) Locating QTLs controlling constitutive root traits in the rice population IAC 165 x Co39. Euphytica 134:335–345. https://doi.org/10.1023/B:EUPH.0000004987.88718.d6
Davis RF, Earl HJ, Timper P (2014) Effect of simultaneous water deficit stress and meloidogyne incognita infection on cotton yield and fiber quality. J Nematol 46:108–118
Dievart A, Perin C, Hirsch J, Bettembourg M, Lanau N, Artus F, Bureau C, Noel N, Droc G, Peyramard M, Pereira S, Courtois B, Morel JB, Guiderdoni E (2016) The phenome analysis of mutant alleles in Leucine-Rich Repeat Receptor-Like Kinase genes in rice reveals new potential targets for stress tolerant cereals. Plant Sci 242:240–249. https://doi.org/10.1016/j.plantsci.2015.06.019
Feng Q, Zhao KJ (2011) The influence of RNAi targeting of OsGA20ox2 gene on plant height in rice. Plant Mol Biol Rep 29:952–960. https://doi.org/10.1007/s11105-011-0309-2
Fu J, Wu H, Ma S, Xiang D, Liu R, Xiong L (2017) OsJAZ1 attenuates drought resistance by regulating JA and ABA signaling in rice. Front Plant Sci 8:2108. https://doi.org/10.3389/fpls.2017.02108
Habibi G (2012) Exogenous salicylic acid alleviates oxidative damage of barley plants under drought stress. Acta Bio Szeged 56:57–63
Hironori N, Kazumitsu O, Mitsuhiro O, Yuki H, Yoshio S (2005) Genealogy of the "Green Revolution" gene in rice. Genes Genet Syst 80:351–356. https://doi.org/10.1266/ggs.80.351
Islam A, Zhang Y, Anis G, Rani MH, Anley W, Shen X, Cao L, Cheng S, Wu W (2020) Mapping and validation of a major quantitative trait locus qRN5a associated with increasing root number under low potassium in rice. Plant Growth Regul. https://doi.org/10.1007/s10725-020-00574-8
Joshi R, Rohit MSC, Shukla A, Pant RC (2009) Aerobic rice: water use sustainability. Oryza: Int J Rice 46:1–5
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484. https://doi.org/10.1093/nar/gkm882
Kato Y, Katsura K (2014) Rice adaptation to aerobic soils: physiological considerations and implications for agronomy. Plant Prod Sci 17:1–12. https://doi.org/10.1626/pps.17.1
Kawahara Y, de la Bastide M, Hamilton JP, Kanamori H, McCombie WR, Ouyang S, Schwartz DC, Tanaka T, Wu J, Zhou S, Childs KL, Davidson RM, Lin H, Quesada-Ocampo L, Vaillancourt B, Sakai H, Lee SS, Kim J, Numa H, Itoh T, Buell CR, Matsumoto T (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:4. https://doi.org/10.1186/1939-8433-6-4
Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360. https://doi.org/10.1038/nmeth.3317
Kuroha T, Nagai K, Gamuyao R, Wang DR, Furuta T, Nakamori M, Kitaoka T, Adachi K, Minami A, Mori Y, Mashiguchi K, Seto Y, Yamaguchi S, Kojima M, Sakakibara H, Wu J, Ebana K, Mitsuda N, Ohme-Takagi M, Yanagisawa S, Yamasaki M, Yokoyama R, Nishitani K, Mochizuki T, Tamiya G, McCouch SR, Ashikari M (2018) Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Science 361:181–186. https://doi.org/10.1126/science.aat1577
Lafitte R (2003) Managing water for controlled drought in breeding plots. In: Fischer KS, Lafitte R, Fukai S, Atlin G, Hardy B (eds) Breeding rice for drought-prone environments. Breeding to improve yield under adverse environments: direct selection for grain yield, 1st edn. Los Baños, International Rice Research Institute, pp 23–26
Lafitte HR, Courtois B, Arraudeau M (2002) Genetic improvement of rice in aerobic systems: progress from yield to genes. Field Crops Res 75:171–190. https://doi.org/10.1016/s0378-4290(02)00025-4
Lalitha S (2000) Primer Premier 5. Biotechnol Softw Internet J 1:270–272. https://doi.org/10.1089/152791600459894
Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26:589–595. https://doi.org/10.1093/bioinformatics/btp324
Li Z, Mu P, Li C, Zhang H, Li Z, Gao Y, Wang X (2005) QTL mapping of root traits in a doubled haploid population from a cross between upland and lowland japonica rice in three environments. Theor Appl Genet 110:1244–1252. https://doi.org/10.1007/s00122-005-1958-z
Li C, Shen H, Wang T, Wang X (2015) ABA regulates subcellular redistribution of OsABI-LIKE2, a negative regulator in ABA signaling, to control root architecture and drought resistance in Oryza sativa. Plant Cell Physiol 56:2396–2408. https://doi.org/10.1093/pcp/pcv154
Lilley JM, Ludlow MM, Mccouch SR, O'Toole JC (1996) Locating QTL for osmotic adjustment and dehydration tolerance in rice. J Exp Bot 47:1427–1436. https://doi.org/10.1093/jxb/47.9.1427
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25:402–408. https://doi.org/10.1006/meth.2001.1262
Lyu J, Li B, He W, Zhang S, Gou Z, Zhang J, Meng L, Li X, Tao D, Huang W, Hu F, Wang W (2014) A genomic perspective on the important genetic mechanisms of upland adaptation of rice. BMC Plant Biol. https://doi.org/10.1186/1471-2229-14-160
Maier F, Zwicker S, Huckelhoven A, Meissner M, Funk J, Pfitzner AJ, Pfitzner UM (2011) Nonexpressor of pathogenesis-related proteins1 (NPR1) and some NPR1-related proteins are sensitive to salicylic acid. Mol Plant Pathol 12:73–91. https://doi.org/10.1111/j.1364-3703.2010.00653.x
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. https://doi.org/10.1101/gr.107524.110
Nieri B, Canino S, Versace R, Alpi A (1998) Purification and characterization of an endoprotease from alfalfa senescent leaves. Phytochemistry 49:643–649. https://doi.org/10.1016/s0031-9422(98)00280-5
Ouk M, Basnayake J, Tsubo M, Fukai S, Fischer KS, Cooper M, Nesbitt H (2006) Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice. Field Crops Res 99:48–58. https://doi.org/10.1016/j.fcr.2006.03.003
Patel DP, Das A, Munda GC, Ghosh PK, Bordoloi JS, Kumar M (2010) Evaluation of yield and physiological attributes of high-yielding rice varieties under aerobic and flood-irrigated management practices in mid-hills ecosystem. Agric Water Manag 97:1269–1276. https://doi.org/10.1016/j.agwat.2010.02.018
Phule AS, Barbadikar KM, Maganti SM, Seguttuvel P, Subrahmanyam D, Babu M, Kumar PA (2019) RNA-seq reveals the involvement of key genes for aerobic adaptation in rice. Sci Rep 9:5235. https://doi.org/10.1038/s41598-019-41703-2
Pieterse CM, van Wees SC, Hoffland E, van Pelt JA, van Loon LC (1996) Systemic resistance in Arabidopsis induced by biocontrol bacteria is independent of salicylic acid accumulation and pathogenesis-related gene expression. Plant Cell 8:1225–1237. https://doi.org/10.1105/tpc.8.8.1225
Price AH, Steele KA, Moore BJ, Barraclough PP, Clark LJ (1999) A combined RFLP and AFLP map of upland rice (Oryza sativa) used to identify QTL for root-penetration ability. Theor Appl Genet 100:49–56. https://doi.org/10.1007/s001220050007
Robin S, Pathan MS, Courtois B, Lafitte R, Carandang S, Lanceras S, Amante M, Nguyen HT, Li Z (2003) Mapping osmotic adjustment in an advanced back-cross inbred population of rice. Theor Appl Genet 107:1288–1296. https://doi.org/10.1007/s00122-003-1360-7
Sandhu N, Jain S, Battan KR, Jain RK (2012) Aerobic rice genotypes displayed greater adaptation to water-limited cultivation and tolerance to polyethyleneglycol-6000 induced stress. Physiol Mol Biol Plants 18:33–43. https://doi.org/10.1007/s12298-011-0094-2
Sandhu N, Jain S, Kumar A, Mehla BS, Jain R (2013) Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation. BMC Genet 14:104. https://doi.org/10.1186/1471-2156-14-104
Sandhu N, Singh A, Dixit S, Sta Cruz MT, Maturan PC, Jain RK, Kumar A (2014) Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet 15:63. https://doi.org/10.1186/1471-2156-15-63
Sandhu N, Torres RO, Sta Cruz MT, Maturan PC, Jain R, Kumar A, Henry A (2015) Traits and QTLs for development of dry direct-seeded rainfed rice varieties. J Exp Bot 66:225–244. https://doi.org/10.1093/jxb/eru413
Shankar R, Bhattacharjee A, Jain M (2016) Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses. Sci Rep 6:23719. https://doi.org/10.1038/srep23719
Shen L, Courtois B, Mcnally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for rootdepth through marker-aided selection. Theor Appl Genet 103:75–83. https://doi.org/10.1007/s001220100538
Silveira RD, Abreu FR, Mamidi S, McClean PE, Vianello RP, Lanna AC, Carneiro NP, Brondani C (2015) Expression of drought tolerance genes in tropical upland rice cultivars (Oryza sativa). Genet Mol Res 14:8181–8200. https://doi.org/10.4238/2015.July.27.6
Song T, Xu F, Yuan W, Zhang Y, Liu T, Chen M, Hu Q, Tian Y, Xu W, Zhang J (2018) Comparison on physiological adaptation and phosphorus use efficiency of upland rice and lowland rice under alternate wetting and drying irrigation. Plant Growth Regul 86:195–210. https://doi.org/10.1007/s10725-018-0421-5
Steele KA, Price AH, Shashidhar HE, Witcombe JR (2006) Marker-assisted selection to introgress rice QTLs controlling root traits into an Indian upland rice variety. Theor Appl Genet 112:208–221. https://doi.org/10.1007/s00122-005-0110-4
Swarbrick PJ, Huang K, Liu G, Slate J, Press MC, Scholes JD (2008) Global patterns of gene expression in rice cultivars undergoing a susceptible or resistant interaction with the parasitic plant Striga hermonthica. New Phytol 179:515–529. https://doi.org/10.1111/j.1469-8137.2008.02484.x
Torun H (2019) Time-course analysis of salicylic acid effects on ROS regulation and antioxidant defense in roots of hulled and hulless barley under combined stress of drought, heat and salinity. Physiol Plant 165:169–182. https://doi.org/10.1111/ppl.12798
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515. https://doi.org/10.1038/nbt.1621
Usadel B, Nagel A, Thimm O, Redestig H, Blaesing OE, Palacios-Rojas N, Selbig J, Hannemann J, Piques MC, Steinhauser D, Scheible WR, Gibon Y, Morcuende R, Weicht D, Meyer S, Stitt M (2005) Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol 138:1195–1204. https://doi.org/10.1104/pp.105.060459
Venuprasad R, Lafitte H, Atlin G (2007) Response to direct selection for grain yield under drought stress in rice. Crop Sci. https://doi.org/10.2135/cropsci2006.03.0181
Venuprasad R, Dalid CO, Valle MD, Zhao D, Espiritu M, Cruz MTS, Amante M, Kumar A, Atlin GN (2009) Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor Appl Genet 120:177–190. https://doi.org/10.1007/s00122-009-1168-1
Venuprasad R, Bool ME, Quiatchon L, Sta Cruz MT, Amante M, Atlin GN (2012) A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol Breed 30:535–547. https://doi.org/10.1007/s11032-011-9642-2
Vikram P, Swamy BPM, Dixit S, Ahmed HU, Teresa Sta Cruz M, Singh AK, Kumar A (2011) qDTY1.1, a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet 12:89. https://doi.org/10.1186/1471-2156-12-89
Wittenbach VA, Ackerson RC, Giaquinta RT, Herbert RR (1980) Changes in photosynthesis, ribulose bisphosphate carboxylase, proteolytic activity, and ultrastructure of soybean leaves during senescence. Crop Sci 20:225–231. https://doi.org/10.2135/cropsci1980.0011183X002000020019x
Yang Y, Qi M, Mei C (2004) Endogenous salicylic acid protects rice plants from oxidative damage caused by aging as well as biotic and abiotic stress. Plant J 40:909–919. https://doi.org/10.1111/j.1365-313X.2004.02267.x
Yu Y, Yang D, Zhou S, Gu J, Wang F, Dong J, Huang R (2017) The ethylene response factor OsERF109 negatively affects ethylene biosynthesis and drought tolerance in rice. Protoplasma 254:401–408. https://doi.org/10.1007/s00709-016-0960-4
Yue B, Xue W, Xiong L, Yu X, Luo L, Cui K, Jin D, Xing Y, Zhang Q (2006) Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 172:1213–1228. https://doi.org/10.1534/genetics.105.045062
Zhang J, Zheng HG, Aarti A, Pantuwan G, Nguyen TT, Tripathy JN, Sarial AK, Robin S, Babu RC, Nguyen BD (2001) Locating genomic regions associated with components of drought resistance in rice: comparative mapping within and across species. Theor Appl Genet 103:19–29. https://doi.org/10.1007/s001220000534
Zhao Y, Chan Z, Gao J, Xing L, Cao M, Yu C, Hu Y, You J, Shi H, Zhu Y, Gong Y, Mu Z, Wang H, Deng X, Wang P, Bressan RA, Zhu JK (2016) ABA receptor PYL9 promotes drought resistance and leaf senescence. Proc Natl Acad Sci USA 113:1949–1954. https://doi.org/10.1073/pnas.1522840113
Zhao Y, Zhang H, Xu J, Jiang C, Yin Z, Xiong H, Xie J, Wang X, Zhu X, Li Y, Zhao W, Rashid MAR, Li J, Wang W, Fu B, Ye G, Guo Y, Hu Z, Li Z, Li Z (2018) Loci and natural alleles underlying robust roots and adaptive domestication of upland ecotype rice in aerobic conditions. PLoS Genet 14:e1007521. https://doi.org/10.1371/journal.pgen.1007521
Zheng HG, Babu RC, Pathan MS, Ali L, Huang N, Courtois B, Nguyen HT (2000) Quantitative trait loci for root-penetration ability and root thickness in rice: comparison of genetic backgrounds. Genome 43:53. https://doi.org/10.1139/g99-065
Zheng XY, Zhou M, Yoo H, Pruneda-Paz JL, Spivey NW, Kay SA, Dong X (2015) Spatial and temporal regulation of biosynthesis of the plant immune signal salicylic acid. Proc Natl Acad Sci USA 112:9166–9173. https://doi.org/10.1073/pnas.1511182112
Acknowledgements
We thank Public Technology Service Center, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences for technical support. We are grateful for the critical comments from Dr. Qingjun Xie (South China Agricultural University) for this paper.
Funding
This work was supported by grants from the National Natural Science Foundation of China (31360330) to P.X, “One-Three-Five” Strategic Planning of Chinese Academy of Sciences (2017XTBG-T02) and Strategic Leading Science & Technology Programme (XDA08020203).
Author information
Authors and Affiliations
Contributions
DQY designed the experiments, JW and PX provided the materials, JY and PX performed the experiments and analyzed the RNA-Seq and wrote the manuscript, FW gave a support for experiment. All authors have discussed the results and contributed to the drafting of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
10725_2020_606_MOESM1_ESM.tif
Supplementary file1 (TIF 586 kb) Supplementary Figure. 1 Chromosome structure of IL-U135. Red rectangle indicates the introgression fragments from LuYin 46 into Ming Hui 63. The details of introgression fragments position were in the Supplementary Table S4.
10725_2020_606_MOESM2_ESM.tif
Supplementary file2 (TIF 820 kb) Supplementary Figure. 2 Validation of RNA-seq by quantitative reverse transcription PCR (RT-qPCR) assays. The correlation coefficient (R2) between the two datasets is 0.977. a. The RT-qPCR results with the RNA-seq for 10 randomly selected introgressed genes. b. The RT-qPCR results with the RNA-seq for two introgressed genes LOC_Os11g31480 and LOC_Os11g10570.
10725_2020_606_MOESM4_ESM.xls
Supplementary file4 (XLS 25 kb) Supplementary Table S2: Mapping results for RNA-seq reads of Minghui63, Luyin46, and IL-U135 under anaerobic and aerobic conditions.
10725_2020_606_MOESM5_ESM.xlsx
Supplementary file5 (XLSX 11 kb) Supplementary Table S3: Mapping results of the genome resequencing reads of Minghui63, Luyin46, and IL-U135.
10725_2020_606_MOESM6_ESM.xlsx
Supplementary file6 (XLSX 13 kb) Supplementary Table S4: The schematic diagram of introgression fragments’ position. Red indicates the details of introgression fragments’ position from LuYin 46 into Ming Hui 63.
10725_2020_606_MOESM7_ESM.xlsx
Supplementary file7 (XLSX 12583 kb) Supplementary Table S5: The details of SNPs for Introgressed region from LY 46 to MH 63.
10725_2020_606_MOESM8_ESM.xlsx
Supplementary file8 (XLSX 3294 kb) Supplementary Table S6: The details of InDels for Introgressed region from LY 46 to MH 63.
10725_2020_606_MOESM9_ESM.xlsx
Supplementary file9 (XLSX 207 kb) Supplementary Table S7: Details of SNPs of introgression and differential expressed genes between MH63 and LY46, MH63 and IL-U135.
10725_2020_606_MOESM10_ESM.xlsx
Supplementary file10 (XLSX 43 kb) Supplementary Table S8: Details of InDels of introgression and differential expressed genes between MH63 and LY46, MH63 and IL-U135.
Rights and permissions
About this article
Cite this article
Yang, J., Wang, F., Tao, D. et al. Characterization of genes responsive to aerobic conditions by transcriptomic and genomic analyses of upland rice. Plant Growth Regul 91, 289–303 (2020). https://doi.org/10.1007/s10725-020-00606-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10725-020-00606-3