Whole-Genome Analysis of MR219 and Its Drought-Tolerant Mutant, NMR151 to Elucidate Drought Resistance Mechanisms
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Abstract
Background: Drought is among the most severe abiotic stresses limiting global rice (Oryza sativa L.) productivity and threatening food security, particularly in high-yielding but stress-sensitive cultivars such as MR219. Understanding the genomic basis of drought tolerance is essential to support the development of resilient rice varieties. This study aimed to identify genome-wide variants and drought-responsive genes associated with induced drought tolerance in the mutant line NMR151 derived from MR219. Methods: Whole-genome resequencing was performed on the drought-tolerant mutant NMR151 and its parental line MR219. High-quality paired-end reads totaling 92.1 million (MR219) and 78.5 million (NMR151) were mapped to the O. sativa cv. Nipponbare reference genome with an alignment efficiency of 91.4% and coverages of 35.4× and 29.6×, respectively. Variant calling, functional annotation using SnpEff, and visualization through the Integrative Genomics Viewer (IGV) were employed to detect and characterize single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Results: A total of 4.27 million and 4.18 million polymorphisms were identified in MR219 and NMR151, dominated by SNPs and InDels with a transition-to-transversion ratio of 2.4. Comparative analysis revealed 3.01 million shared and 1.17 million unique SNPs in NMR151, indicating mutation-driven allelic diversification. Most variants occurred in intergenic (33.2 %), upstream (29.1 %), and downstream (24.6 %) regions, while six drought-responsive genes (OsDREB2A, OsNAC6, OsLEA3-1, OsAPX2, OsCPK21, and OsP5CS1) were uniquely mutated in NMR151. Conclusions: The drought resilience of NMR151 results primarily from regulatory fine-tuning rather than extensive coding alterations. These findings provide valuable molecular insights and genomic markers for breeding climate-resilient rice varieties through mutation and marker-assisted selection.
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References
FAO. The State of Food Security and Nutrition in the World 2022: Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable; Food and Agriculture Organization of the United Nations: Rome, 2022. https://doi.org/10.4060/cc0639en
Zhang, Q.; Lin, Z.; Li, W.; Peng, S. Global Rice Production, Trade, and Sustainability: Challenges and Prospects. Annu. Rev. Plant Biol. 2021, 72, 745–770. https://doi.org/10.1146/annurev-arplant-081720-015129
Kawahara, Y.; de la Bastide, M.; Hamilton, J. P.; Kanamori, H.; McCombie, W. R.; Ouyang, S.; et al. Improvement of the Oryza sativa Nipponbare Reference Genome Using Next-Generation Sequence and Optical Map Data. Rice 2013, 6, 4. https://doi.org/10.1186/1939-8433-6-4
Ma, X.; Han, Y.; Wu, J.; Xu, Y. Integrative Genomics of Drought Adaptation in Rice: Regulatory Networks and Translational Applications. Trends Plant Sci. 2024, 29, 547–563. https://doi.org/10.1016/j.tplants.2024.01.003
Han, Y.; Ma, X.; Zhang, D.; Li, S.; Wang, Q. Physiological and Molecular Mechanisms of Rice Responses to Drought Stress. Front. Plant Sci. 2022, 13, 875296. https://doi.org/10.3389/fpls.2022.875296
Shamsudin, N. A. A.; Swamy, B. P. M.; Ratnam, W.; Cruz, M. T. S.; Sandhu, N.; Raman, A.; Kumar, A. Marker-Assisted Pyramiding of Drought Yield QTLs into a Popular Malaysian Rice Cultivar MR219. BMC Genet. 2016, 17, 30. https://doi.org/10.1186/s12863-016-0334-0
Department of Statistics Malaysia (DOSM). Selected Agricultural Indicators, Malaysia 2023; DOSM: Putrajaya, 2023.
Bernier, J.; Kumar, A.; Venuprasad, R.; Spaner, D.; Atlin, G. N.; Lafitte, H. R. Characterization of Drought Stress Environments for Upland Rice and Screening Methodologies. Field Crops Res. 2009, 114, 350–359. https://doi.org/10.1016/j.fcr.2009.09.002
Shamsudin, N. A. A.; Swamy, B. P. M.; Ratnam, W.; Cruz, M. T. S.; Sandhu, N.; Raman, A.; Kumar, A. Marker-Assisted Pyramiding of Drought Yield QTLs into MR219. BMC Genet. 2016, 17, 30. https://doi.org/10.1186/s12863-016-0334-0
Dixit, S.; Singh, A.; Cruz, M. T. S.; Maturan, P. C.; Amante, M.; Kumar, A. Multiple Major QTL Lead to Stable Yield Performance of Rice Cultivars across Varying Drought Intensities. BMC Genet. 2014, 15, 16. https://doi.org/10.1186/1471-2156-15-16
Oladosu, Y.; Rafii, M. Y.; Abdullah, N.; Hussin, G.; Ramli, A.; Rahim, H. A. Genetic Diversity and Physiological Performance of Gamma-Irradiated Rice Mutants under Drought Stress. Pertanika J. Trop. Agric. Sci. 2019, 42, 1125–1142.
Malaysian Nuclear Agency. Gamma Irradiation Breeding for Improved Drought-Tolerant Rice (MR219 Mutants); Technical Report; Bangi, 2019.
FNCA. Report on Mutation Breeding Project in Asia: Rice Mutant Varieties and Their Impact; Forum for Nuclear Cooperation in Asia, 2020.
Huang, X.; Kurata, N.; Wei, X.; Wang, Z. X.; Wang, A.; Zhao, Q.; et al. A Map of Rice Genome Variation Reveals the Origin of Cultivated Rice. Nature 2012, 490, 497–501. https://doi.org/10.1038/nature11532
Varshney, R. K.; Bohra, A.; Yu, J.; Graner, A.; Zhang, Q.; Sorrells, M. E. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. Trends Plant Sci. 2021, 26, 631–649. https://doi.org
/10.1016/j.tplants.2021.02.002
Shankar, R.; Singh, A.; Verma, R. L. Genomic Dissection of Promoter Variants Driving Differential Drought Tolerance in Rice. Sci. Rep. 2023, 13, 5421. https://doi.org/10.1038/s41598-023-32498-1
Ahmad, F.; Kamaruddin, A. A.; Kamaruzaman, A. S.; Hasan, N.; Harun, A. R. Whole-Genome Resequencing Data of Drought-Tolerant Rice Mutant Lines Derived from MR219. NCBI SRA 2023; SRR21043964, SRR20995190.
Bolger, A. M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170
Li, H.; Durbin, R. Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform. Bioinformatics 2009, 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324
Li, H.; Handsaker, B.; Wysoker, A.; et al. The Sequence Alignment/Map Format and SAMtools. Bioinformatics 2009, 25, 2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Wang, Z.; Zhang, X.; Wu, L. Evaluation of SNP-Calling Accuracy across Variant Callers for Crop Genomes. Front. Plant Sci. 2024, 15, 1412391. https://doi.org/10.3389/fpls.2024.1412391
Lou, Q.; Xu, Y.; Kumar, A. Variant Detection and Quality Control in Plant Resequencing Studies Using GATK Pipelines. Comput. Struct. Biotechnol. J. 2024, 22, 1162–1174. https://doi.org/10.1016/j.csbj.2024.01.045
Ren, Y.; Sun, G. Functional Annotation of SNPs Using ANNOVAR in Crop Genomes. Bioinform. Comput. Biol. 2019, 17, 151–160.
Cingolani, P.; Platts, A.; Wang, L. L.; Coon, M.; Nguyen, T.; Wang, L.; et al. SnpEff: Annotating and Predicting Effects of SNPs. Fly 2012, 6, 80–92. https://doi.org/10.4161/fly.19695
Jiang, H.; Li, J.; Tang, X.; Liu, M.; Wang, Z. Genome-Wide Identification of Drought-Responsive Variants in Rice. Front. Genet. 2023, 14, 1125583. https://doi.org/10.3389/fgene.2023.1125583
Singh, R. K.; Dixit, S.; Kumar, A. Genomic Selection and Haplotype-Based Improvement for Drought Tolerance in Rice. Plant Biotechnol. J. 2024, 22, 458–472. https://doi.org/10.1111/pbi.14142
Robinson, J. T.; Thorvaldsdóttir, H.; Winckler, W.; Guttman, M.; Lander, E. S.; Getz, G.; Mesirov, J. P. Integrative Genomics Viewer. Nat. Biotechnol. 2011, 29, 24–26. https://doi.org/10.1038/nbt.1754
Lee, D. K.; Jung, H.; Jang, G.; Jeong, J. S.; Kim, Y. S.; Ha, S. H.; et al. Overexpression of the OsNAC6 Transcription Factor Enhances Drought Tolerance. Plant Sci. 2017, 256, 128–138. https://doi.org/10.1016/j.plantsci.2016.12.009
Pang, Y.; Zhang, Z.; Wen, Q.; Liu, S. Functional Roles of Antioxidant Enzymes in Drought Tolerance. Plant Physiol. Biochem. 2023, 196, 268–279. https://doi.org/10.1016/j.plaphy.2023.05.013
Jarin, A. M.; Subudhi, P. K.; Xu, W. Genetic Regulation of ROS Homeostasis under Drought Stress in Rice. Plant Mol. Biol. Rep. 2024, 42, 90–108. https://doi.org/10.1007/s11105-023-01425-3
