Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Material and Experimental Design
2.2. Photosynthetic Parameter and Grain Yield Measurement
2.3. Statistical Analysis
2.4. DNA Sequencing, Mapping and Variant Detection
2.5. Variant Annotation
2.6. Ontology Enrichment Analysis and Expression Profile of Candidate Genes
3. Results
3.1. Variation in and Correlations between Photosynthetic Performance Parameters of 30 Rice Varieties
3.2. Clustering Rice Varieties Using Differences in the Correlation between PN and Ci
3.3. Correlation between PN and Grain Yield of the Rice Groups
3.4. Whole-Genome Resequencing Analysis and Variant Discovery
3.5. Structural and Functional Annotation of Variants between LYR and HYR
3.6. Distribution of LYR- and HYR-Shared Variants Detected on Rice Chromosomes
3.7. Characteristics of LYR- and HYR-Shared Variants
3.8. GO Enrichment Analysis of Genes Containing High- and Moderate-Impact Variants
3.9. Potential Genes Containing a Large Number of High- and Moderate-Impact Polymorphisms
4. Discussion
4.1. Salt-Affected Photosynthetic Characteristics of Flowering Rice Exposed to Saline Soil
4.2. Prediction of Grain Yield by Photosynthetic Performance and Salt-Stressed Flowering Rice
4.3. Validation of Genome-Wide Sequence Variants Revealed Potential Genes
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Mittler, R. Abiotic stress, the field environment and stress combination. Trends Plant Sci. 2006, 11, 15–19. [Google Scholar] [CrossRef] [PubMed]
- Hoang, M.T.; Tran, N.T.; Nguyen, K.T.; Williams, B.; Wurm, P.; Bellairs, S.; Mundree, S. Improvement of salinity stress tolerance in rice: Challenges and opportunities. Agronomy 2016, 6, 54. [Google Scholar] [CrossRef]
- Munns, R.; Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 2008, 59, 651–681. [Google Scholar] [CrossRef] [PubMed]
- Chaves, M.M.; Flexas, J.; Pinheiro, C. Photosynthesis under drought and salt stress: Regulation mechanisms from whole plant to cell. Ann. Bot. 2009, 103, 551–560. [Google Scholar] [CrossRef] [PubMed]
- Delfine, S.; Alvino, A.; Zacchini, M.; Loreto, F. Consequences of salt stress on conductance to CO2 diffusion, rubisco characteristics and anatomy of spinach leaves. Funct. Plant Biol. 1998, 25, 395–402. [Google Scholar] [CrossRef]
- Megdiche, W.; Hessini, K.; Gharbi, F.; Jaleel, C.A.; Ksouri, R.; Abdelly, C. Photosynthesis and photosystem 2 efficiency of two salt-adapted halophytic seashore Cakile maritima ecotypes. Photosynthetica 2008, 46, 410–419. [Google Scholar] [CrossRef]
- Yeo, A.R.; Caporn, S.J.M.; Flowers, T.J. The effect of salinity upon photosynthesis in rice (Oryza sativa L.): Gas exchange by individual leaves in relation to their salt content. J. Exp. Bot. 1985, 36, 1240–1248. [Google Scholar] [CrossRef]
- Hussain, S.; Zhang, J.-H.; Zhong, C.; Zhu, L.-F.; Cao, X.-C.; Yu, S.-M.; Allen Bohr, J.; Hu, J.-J.; Jin, Q.-Y. Effects of salt stress on rice growth, development characteristics, and the regulating ways: A review. J. Integr. Agric. 2017, 16, 2357–2374. [Google Scholar] [CrossRef] [Green Version]
- Fricke, W.; Akhiyarova, G.; Veselov, D.; Kudoyarova, G. Rapid and tissue-specific changes in ABA and in growth rate in response to salinity in barley leaves. J. Exp. Bot. 2004, 55, 1115–1123. [Google Scholar] [CrossRef]
- Long, S.P.; Zhu, X.-G.; Naidu, S.L.; Ort, D.R. Can improvement in photosynthesis increase crop yields? Plant Cell Environ. 2006, 29, 315–330. [Google Scholar] [CrossRef]
- Mathan, J.; Bhattacharya, J.; Ranjan, A. Enhancing crop yield by optimizing plant developmental features. Development 2016, 143, 3283–3294. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fahad, S.; Noor, M.; Adnan, M.; Khan, M.A.; Rahman, I.U.; Alam, M.; Khan, I.A.; Ullah, H.; Mian, I.A.; Hassan, S.; et al. Chapter 28—Abiotic stress and rice grain quality. In Advances in Rice Research for Abiotic Stress Tolerance; Hasanuzzaman, M., Fujita, M., Nahar, K., Biswas, J.K., Eds.; Woodhead Publishing: Cambridge, UK, 2019; pp. 571–583. [Google Scholar]
- Lobell, D.B.; Gourdji, S.M. The influence of climate change on global crop productivity. Plant Physiol. 2012, 160, 1686–1697. [Google Scholar] [CrossRef] [PubMed]
- Reddy, I.N.B.L.; Kim, B.-K.; Yoon, I.-S.; Kim, K.-H.; Kwon, T.-R. Salt tolerance in rice: Focus on mechanisms and approaches. Rice Sci. 2017, 24, 123–144. [Google Scholar] [CrossRef]
- Yu, J.; Hu, S.; Wang, J.; Wong, G.K.-S.; Li, S.; Liu, B.; Deng, Y.; Dai, L.; Zhou, Y.; Zhang, X.; et al. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 2002, 296, 79–92. [Google Scholar] [CrossRef] [PubMed]
- Goff, S.A.; Ricke, D.; Lan, T.-H.; Presting, G.; Wang, R.; Dunn, M.; Glazebrook, J.; Sessions, A.; Oeller, P.; Varma, H.; et al. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 2002, 296, 92–100. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Xiao, J.; Chen, L.; Huang, X.; Cheng, Z.; Han, B.; Zhang, Q.; Wu, C. Rice functional genomics research: Past decade and future. Mol. Plant 2018, 11, 359–380. [Google Scholar] [CrossRef] [PubMed]
- Huang, X.; Lu, T.; Han, B. Resequencing rice genomes: An emerging new era of rice genomics. Trends Genet. 2013, 29, 225–232. [Google Scholar] [CrossRef] [PubMed]
- Varshney, R.K.; Nayak, S.N.; May, G.D.; Jackson, S.A. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 2009, 27, 522–530. [Google Scholar] [CrossRef] [Green Version]
- Nielsen, R.; Paul, J.S.; Albrechtsen, A.; Song, Y.S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 2011, 12, 443. [Google Scholar] [CrossRef] [PubMed]
- McCouch, S.R.; Zhao, K.; Wright, M.; Tung, C.-W.; Ebana, K.; Thomson, M.; Reynolds, A.; Wang, D.; DeClerck, G.; Ali, M.L.; et al. Development of genome-wide SNP assays for rice. Breed. Sci. 2010, 60, 524–535. [Google Scholar] [CrossRef] [Green Version]
- Fincher, G.; Paltridge, N.; Langridge, P. Functional genomics of abiotic stress tolerance in cereals. Brief. Funct. Genom. 2006, 4, 343–354. [Google Scholar] [Green Version]
- Mehra, P.; Pandey, B.K.; Giri, J. Genome-wide DNA polymorphisms in low phosphate tolerant and sensitive rice genotypes. Sci. Rep. 2015, 5, 13090. [Google Scholar] [CrossRef] [PubMed]
- Jain, M.; Moharana, K.C.; Shankar, R.; Kumari, R.; Garg, R. Genomewide discovery of DNA polymorphisms in rice cultivars with contrasting drought and salinity stress response and their functional relevance. Plant Biotechnol. J. 2014, 12, 253–264. [Google Scholar] [CrossRef] [PubMed]
- Singhabahu, S.; Wijesinghe, C.; Gunawardana, D.; Senarath-Yapa, M.D.; Kannangara, M.; Edirisinghe, R.; Dissanayake, H.W.V. Whole genome sequencing and analysis of Godawee, a salt tolerant indica rice variety. J. Res. Rice 2017, 5. [Google Scholar] [CrossRef]
- García Morales, S.; Trejo-Téllez, L.I.; Gómez Merino, F.C.; Caldana, C.; Espinosa-Victoria, D.; Herrera Cabrera, B.E. Growth, photosynthetic activity, and potassium and sodium concentration in rice plants under salt stress. Acta Scientiarum. Agron. 2012, 34, 317–324. [Google Scholar]
- Dionisio-Sese, M.L.; Tobita, S. Effects of salinity on sodium content and photosynthetic responses of rice seedlings differing in salt tolerance. J. Plant Physiol. 2000, 157, 54–58. [Google Scholar] [CrossRef]
- Bhaswati, G.; Nasim, A.-M.; Saikat, G. Response of rice under salinity stress: A review update. J. Res. Rice 2016, 4. [Google Scholar]
- Hussain, M.; Ahmad, S.; Hussain, S.; Lal, R.; Ul-Allah, S.; Nawaz, A. Chapter six—Rice in saline soils: Physiology, biochemistry, genetics, and management. In Advances in Agronomy; Sparks, D.L., Ed.; Academic Press: Cambridge, MA, USA, 2018; Volume 148, pp. 231–287. [Google Scholar]
- Lekklar, C.; Pongpanich, M.; Suriya-arunroj, D.; Chinpongpanich, A.; Tsai, H.; Comai, L.; Chadchawan, S.; Buaboocha, T. Genome-wide association study for salinity tolerance at the flowering stage in a panel of rice accessions from Thailand. BMC Genom. 2019, 20, 76. [Google Scholar] [CrossRef]
- Vajrabhaya, M.; Vajrabhaya, T. Somaclonal variation for salt tolerance in rice. In Biotechnology in Agriculture and Forestry; Bajaj, Y.P.S., Ed.; Springer-Verlag: Berlin/Heidelberg, Germany, 1991; pp. 368–382. [Google Scholar]
- Kawahara, Y.; Bastide, M.; Hamilton, J.P.; Kanamori, H.; McCombie, W.R.; Ouyang, S. Improvement of the Oryza sativa nipponbare reference genome using next generation sequence and optical map data. Rice 2013, 6. [Google Scholar] [CrossRef] [PubMed]
- Missirian, V.; Comai, L.; Filkov, V. Statistical mutation calling from sequenced overlapping DNA pools in tilling experiments. BMC Bioinform. 2011, 12, 287. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Durbin, R. Fast and accurate short read alignment with burrows–wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed]
- McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The genome analysis toolkit: A mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [PubMed]
- Cingolani, P.; Platts, A.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L. 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 2012, 6. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Qi, M.; Liu, J.; Zhang, Y. Carmo: A comprehensive annotation platform for functional exploration of rice multi-omics data. Plant J. 2015, 83, 359–374. [Google Scholar] [CrossRef] [PubMed]
- Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer-Verlag: New York, NY, USA, 2016. [Google Scholar]
- Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
- Cheong, W.-H.; Tan, Y.-C.; Yap, S.-J.; Ng, K.-P. Clico fs: An interactive web-based service of circos. Bioinformatics 2015, 31, 3685–3687. [Google Scholar] [CrossRef] [PubMed]
- Song, W.-Y.; Wang, G.-L.; Chen, L.-L.; Kim, H.-S.; Pi, L.-Y.; Holsten, T.; Gardner, J.; Wang, B.; Zhai, W.-X.; Zhu, L.-H.; et al. A receptor kinase-like protein encoded by the rice disease resistance gene, xa21. Science 1995, 270, 1804. [Google Scholar] [CrossRef]
- Nonomura, K.-I.; Morohoshi, A.; Nakano, M.; Eiguchi, M.; Miyao, A.; Hirochika, H.; Kurata, N. A germ cell–specific gene of the ARGONAUTE family is essential for the progression of premeiotic mitosis and meiosis during sporogenesis in rice. Plant Cell 2007, 19, 2583. [Google Scholar] [CrossRef]
- Zhang, Y.; Xiao, W.; Luo, L.; Pang, J.; Rong, W.; He, C. Downregulation of OsPK1, a cytosolic pyruvate kinase, by t-DNA insertion causes dwarfism and panicle enclosure in rice. Planta 2012, 235, 25–38. [Google Scholar] [CrossRef]
- Bouharmont, J.; Kinet, J.M.; Lutts, S. Changes in plant response to NaCl during development of rice (Oryza sativa L.) varieties differing in salinity resistance. J. Exp. Bot. 1995, 46, 1843–1852. [Google Scholar]
- Zeng, L.; Shannon, M.C.; Lesch, S.M. Timing of salinity stress affects rice growth and yield components. Agric. Water Manag. 2001, 48. [Google Scholar] [CrossRef]
- Waśkiewicz, A.; Beszterda, M.; Goliński, P. ABA: Role in plant signaling under salt stress. In Salt Stress in Plants: Signalling, Omics and Adaptations; Ahmad, P., Azooz, M.M., Prasad, M.N.V., Eds.; Springer: New York, NY, USA, 2013; pp. 175–196. [Google Scholar]
- Golldack, D.; Li, C.; Mohan, H.; Probst, N. Tolerance to drought and salt stress in plants: Unraveling the signaling networks. Front. Plant Sci. 2014, 5. [Google Scholar] [CrossRef]
- Ouyang, W.; Struik, P.C.; Yin, X.; Yang, J. Stomatal conductance, mesophyll conductance, and transpiration efficiency in relation to leaf anatomy in rice and wheat genotypes under drought. J. Exp. Bot. 2017, 68, 5191–5205. [Google Scholar] [CrossRef] [Green Version]
- Jones, H.G. Stomatal control of photosynthesis and transpiration. J. Exp. Bot. 1998, 49, 387–398. [Google Scholar] [CrossRef]
- Nilson, S.E.; Assmann, S.M. The control of transpiration. Insights from Arabidopsis. Plant Physiol. 2007, 143, 19. [Google Scholar] [CrossRef]
- Morison, J.I.L.; Lawson, T. Does lateral gas diffusion in leaves matter? PlantCell Environ. 2007, 30, 1072–1085. [Google Scholar] [CrossRef]
- Evans, J.R.; Kaldenhoff, R.; Genty, B.; Terashima, I. Resistances along the CO2 diffusion pathway inside leaves. J. Exp. Bot. 2009, 60, 2235–2248. [Google Scholar] [CrossRef]
- Flexas, J. Genetic improvement of leaf photosynthesis and intrinsic water use efficiency in C3 plants: Why so much little success? Plant Sci. 2016, 251, 155–161. [Google Scholar] [CrossRef]
- Xiong, D.; Flexas, J. Leaf economics spectrum in rice: Leaf anatomical, biochemical, and physiological trait trade-offs. J. Exp. Bot. 2018, 69, 5599–5609. [Google Scholar] [CrossRef]
- Xiao, Y.; Zhu, X.-G. Components of mesophyll resistance and their environmental responses: A theoretical modelling analysis. PlantCell Environ. 2017, 40, 2729–2742. [Google Scholar] [CrossRef]
- Warren, C.R.; Dreyer, E. Temperature response of photosynthesis and internal conductance to CO2: Results from two independent approaches. J. Exp. Bot. 2006, 57, 3057–3067. [Google Scholar] [CrossRef]
- Tomás, M.; Flexas, J.; Copolovici, L.; Galmés, J.; Hallik, L.; Medrano, H.; Ribas-Carbó, M.; Tosens, T.; Vislap, V.; Niinemets, Ü. Importance of leaf anatomy in determining mesophyll diffusion conductance to CO2 across species: Quantitative limitations and scaling up by models. J. Exp. Bot. 2013, 64, 2269–2281. [Google Scholar] [CrossRef]
- Xiong, D.; Flexas, J.; Yu, T.; Peng, S.; Huang, J. Leaf anatomy mediates coordination of leaf hydraulic conductance and mesophyll conductance to CO2 in Oryza. New Phytol. 2017, 213, 572–583. [Google Scholar] [CrossRef]
- Terashima, I.; Hanba, Y.T.; Tholen, D.; Niinemets, Ü. Leaf functional anatomy in relation to photosynthesis. Plant Physiol. 2011, 155, 108. [Google Scholar] [CrossRef]
- Flexas, J.; Ribas-CarbÓ, M.; Diaz-Espejo, A.; GalmÉS, J.; Medrano, H. Mesophyll conductance to CO2: Current knowledge and future prospects. PlantCell Environ. 2008, 31, 602–621. [Google Scholar] [CrossRef]
- Gaju, O.; DeSilva, J.; Carvalho, P.; Hawkesford, M.J.; Griffiths, S.; Greenland, A.; Foulkes, M.J. Leaf photosynthesis and associations with grain yield, biomass and nitrogen-use efficiency in landraces, synthetic-derived lines and cultivars in wheat. Field Crop. Res. 2016, 193, 1–15. [Google Scholar] [CrossRef]
- Wu, A.; Hammer, G.L.; Doherty, A.; von Caemmerer, S.; Farquhar, G.D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants 2019, 5, 380–388. [Google Scholar] [CrossRef]
- Makino, A. Photosynthesis, grain yield, and nitrogen utilization in rice and wheat. Plant Physiol. 2011, 155, 125. [Google Scholar] [CrossRef]
- Karki, S.; Rizal, G.; Quick, W.P. Improvement of photosynthesis in rice (Oryza sativa L.) by inserting the c4 pathway. Rice 2013, 6, 28. [Google Scholar] [CrossRef]
- Shen, B.-R.; Wang, L.-M.; Lin, X.-L.; Yao, Z.; Xu, H.-W.; Zhu, C.-H.; Teng, H.-Y.; Cui, L.-L.; Liu, E.E.; Zhang, J.-J.; et al. Engineering a new chloroplastic photorespiratory bypass to increase photosynthetic efficiency and productivity in rice. Mol. Plant 2019, 12, 199–214. [Google Scholar] [CrossRef]
- Furbank, R.T.; Quick, W.P.; Sirault, X.R.R. Improving photosynthesis and yield potential in cereal crops by targeted genetic manipulation: Prospects, progress and challenges. Field Crop. Res. 2015, 182, 19–29. [Google Scholar] [CrossRef] [Green Version]
- Wankhade, S.D.; Cornejo, M.J.; Mateu-Andrés, I.; Sanz, A. Morpho-physiological variations in response to NaCl stress during vegetative and reproductive development of rice. Acta Physiol. Plant 2013, 35, 323–333. [Google Scholar] [CrossRef]
- Yu, Y.; Assmann, S.M. The effect of NaCl on stomatal opening in Arabidopsis wild type and agb1 heterotrimeric G-protein mutant plants. Plant Signal. Behav. 2015, 11, e1085275. [Google Scholar] [CrossRef]
- Almeida, D.M.; Oliveira, M.M.; Saibo, N.J.M. Regulation of Na+ and K+ homeostasis in plants: Towards improved salt stress tolerance in crop plants. Genet. Mol. Biol. 2017, 40, 326–345. [Google Scholar] [CrossRef]
- Morton, B.R. Neighboring base composition and transversion/transition bias in a comparison of rice and maize chloroplast noncoding regions. Proc. Natl. Acad. Sci. USA 1995, 92, 9717–9721. [Google Scholar] [CrossRef]
- Subbaiyan, G.K.; Waters, D.L.E.; Katiyar, S.K.; Sadananda, A.R.; Vaddadi, S.; Henry, R.J. Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol. J. 2012, 10, 623–634. [Google Scholar] [CrossRef]
- Kashima, K.; Mejima, M.; Kurokawa, S.; Kuroda, M.; Kiyono, H.; Yuki, Y. Comparative whole-genome analyses of selection marker–free rice-based cholera toxin B-subunit vaccine lines and wild-type lines. BMC Genom. 2015, 16, 48. [Google Scholar] [CrossRef]
- Draisma, S.G.A.; Prud’Homme van Reine, W.F.; Stam, W.T.; Olsen, J.L. A reassessment of phylogenetic relationships within the Phaeophyceae based on RUBISCO large subunit and ribosomal DNA sequences. J. Phycol. 2001, 37, 586–603. [Google Scholar] [CrossRef]
- Chai, C.; Shankar, R.; Jain, M.; Subudhi, P.K. Genome-wide discovery of DNA polymorphisms by whole genome sequencing differentiates weedy and cultivated rice. Sci. Rep. 2018, 8, 14218. [Google Scholar] [CrossRef]
- Oh, J.-H.; Lee, Y.-J.; Byeon, E.-J.; Kang, B.-C.; Kyeoung, D.-S.; Kim, C.-K. Whole-genome resequencing and transcriptomic analysis of genes regulating anthocyanin biosynthesis in black rice plants. 3 Biotech 2018, 8, 115. [Google Scholar] [CrossRef] [Green Version]
- Nejat, N.; Mantri, N. Plant immune system: Crosstalk between responses to biotic and abiotic stresses the missing link in understanding plant defence. Curr. Issues Mol. Biol. 2017, 23, 1–15. [Google Scholar] [CrossRef]
- Głowacki, S.; Macioszek, V.K.; Kononowicz, A.K. R proteins as fundamentals of plant innate immunity. Cell Mol. Biol. Lett. 2011, 16, 1–24. [Google Scholar] [CrossRef]
- Fujita, M.; Fujita, Y.; Noutoshi, Y.; Takahashi, F.; Narusaka, Y.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Crosstalk between abiotic and biotic stress responses: A current view from the points of convergence in the stress signaling networks. Curr. Opin. Plant. Biol. 2006, 9, 436–442. [Google Scholar] [CrossRef]
- Kim, Y.; Tsuda, K.; Igarashi, D.; Hillmer, R.A.; Sakakibara, H.; Myers, C.L.; Katagiri, F. Signaling mechanisms underlying the robustness and tunability of the plant immune network. Cell Host Microbe 2014, 15, 84–94. [Google Scholar] [CrossRef]
- Ye, Y.; Ding, Y.; Jiang, Q.; Wang, F.; Sun, J.; Zhu, C. The role of receptor-like protein kinases (RLKs) in abiotic stress response in plants. Plant Cell Rep. 2017, 36, 235–242. [Google Scholar] [CrossRef]
- Park, C.-J.; Ronald, P.C. Cleavage and nuclear localization of the rice XA21 immune receptor. Nat. Commun. 2012, 3, 920. [Google Scholar] [CrossRef] [Green Version]
- Caddell, D.F.; Park, C.-J.; Thomas, N.C.; Canlas, P.E.; Ronald, P.C. Silencing of the rice gene LRR1 compromises rice Xa21 transcript accumulation and XA21-mediated immunity. Rice 2017, 10, 23. [Google Scholar] [CrossRef]
- Gao, L.-L.; Xue, H.-W. Global analysis of expression profiles of rice receptor-like kinase genes. Mol. Plant 2012, 5, 143–153. [Google Scholar] [CrossRef]
- Shiu, S.-H.; Karlowski, W.M.; Pan, R.; Tzeng, Y.-H.; Mayer, K.F.X.; Li, W.-H. Comparative analysis of the receptor-like kinase family in Arabidopsis and rice. Plant Cell 2004, 16, 1220–1234. [Google Scholar] [CrossRef]
- Komiya, R.; Ohyanagi, H.; Niihama, M.; Watanabe, T.; Nakano, M.; Kurata, N.; Nonomura, K.-I. Rice germline-specific Argonaute MEL1 protein binds to phasiRNAs generated from more than 700 lincRNAs. Plant J. 2014, 78, 385–397. [Google Scholar] [CrossRef]
- Khraiwesh, B.; Zhu, J.-K.; Zhu, J. Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim. Biophys. Acta 2012, 1819, 137–148. [Google Scholar] [CrossRef] [Green Version]
- Ku, Y.-S.; Wong, J.W.-H.; Mui, Z.; Liu, X.; Hui, J.H.-L.; Chan, T.-F.; Lam, H.-M. Small RNAs in plant responses to abiotic stresses: Regulatory roles and study methods. Int. J. Mol. Sci. 2015, 16, 24532–24554. [Google Scholar] [CrossRef]
- Kumar, V.; Khare, T.; Shriram, V.; Wani, S.H. Plant small RNAs: The essential epigenetic regulators of gene expression for salt-stress responses and tolerance. Plant Cell Rep. 2018, 37, 61–75. [Google Scholar] [CrossRef]
- Ambasht, P.K.; Kayastha, A.M. Plant pyruvate kinase. Biol. Plant. 2002, 45, 1–10. [Google Scholar] [CrossRef]
- Andre, C.; Froehlich, J.E.; Moll, M.R.; Benning, C. A heteromeric plastidic pyruvate kinase complex involved in seed oil biosynthesis in Arabidopsis. Plant Cell 2007, 19, 2006. [Google Scholar] [CrossRef]
- Baud, S.; Wuillème, S.; Dubreucq, B.; De Almeida, A.; Vuagnat, C.; Lepiniec, L.; Miquel, M.; Rochat, C. Function of plastidial pyruvate kinases in seeds of Arabidopsis thaliana. Plant J. 2007, 52, 405–419. [Google Scholar] [CrossRef]
- Zhang, Y.-H.; Chen, C.; Shi, Z.-H.; Cheng, H.-M.; Bing, J.; Ma, X.-F.; Zheng, C.-X.; Li, H.-J.; Zhang, G.-F. Identification of salinity-related genes in ENO2 mutant (eno2−) of Arabidopsis thaliana. J. Integr. Agric. 2018, 17, 94–110. [Google Scholar] [CrossRef]
- Prabhakar, V.; Löttgert, T.; Gigolashvili, T.; Bell, K.; Flügge, U.-I.; Häusler, R.E. Molecular and functional characterization of the plastid-localized phosphoenolpyruvate enolase (eno1) from Arabidopsis thaliana. FEBS Lett. 2009, 583, 983–991. [Google Scholar] [CrossRef]
No. | Variety | Normal | Salt stress | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
gs | Ci | E | gs | Ci | E | ||||||||
1 | Pokkali | 0.93 | *** | 0.50 | * | 0.85 | *** | 0.90 | *** | 0.19 | ns | 0.87 | *** |
2 | Hahng Nahk | 0.54 | * | 0.07 | ns | 0.36 | ns | 0.84 | *** | -0.07 | ns | 0.73 | ** |
3 | Daw Khao | 0.82 | *** | -0.08 | ns | 0.79 | *** | 0.96 | *** | 0.31 | ns | 0.96 | *** |
4 | Man Wua | 0.72 | ** | 0.09 | ns | 0.74 | ** | 0.87 | *** | 0.18 | ns | 0.91 | *** |
5 | Plah Sew Dam | 0.80 | *** | 0.05 | ns | 0.77 | *** | 0.95 | *** | 0.39 | ns | 0.98 | *** |
6 | E-mum | 0.93 | *** | 0.54 | * | 0.81 | *** | 0.84 | *** | 0.32 | ns | 0.89 | *** |
7 | Rahk Haeng | 0.68 | ** | -0.17 | ns | 0.82 | *** | 0.70 | ** | -0.38 | ns | 0.78 | *** |
8 | In Paeng | 0.91 | *** | 0.52 | * | 0.75 | *** | 0.81 | *** | 0.08 | ns | 0.83 | *** |
9 | Sam Ahang | 0.39 | ns | -0.32 | ns | 0.07 | ns | 0.77 | *** | 0.06 | ns | 0.57 | * |
10 | Ma Yom | 0.82 | *** | 0.39 | ns | 0.82 | *** | 0.90 | *** | 0.72 | ** | 0.94 | *** |
11 | Tah Bahn | 0.71 | ** | -0.01 | ns | 0.63 | ** | 0.86 | *** | -0.57 | * | 0.86 | *** |
12 | Mahk Yom | 0.59 | * | -0.33 | ns | 0.36 | ns | 0.84 | *** | 0.28 | ns | 0.81 | *** |
13 | Hahng Mah Nai | 0.73 | ** | 0.29 | ns | 0.61 | * | 0.84 | *** | 0.08 | ns | 0.83 | *** |
14 | Khitom Khao | 0.86 | *** | -0.36 | ns | 0.74 | ** | 0.89 | *** | -0.52 | * | 0.88 | *** |
15 | Mahk Bid | 0.61 | * | 0.06 | ns | 0.10 | ns | 0.87 | *** | 0.41 | ns | 0.78 | *** |
16 | Leuang Dong | 0.97 | *** | 0.51 | * | 0.88 | *** | 0.86 | *** | -0.51 | * | 0.74 | ** |
17 | Ruang Diaw | 0.91 | *** | 0.37 | ns | 0.84 | *** | 0.87 | *** | 0.39 | ns | 0.84 | *** |
18 | Mae Mai | 0.88 | *** | 0.37 | ns | 0.83 | *** | 0.96 | *** | -0.50 | * | 0.97 | *** |
19 | Plah Khaeng | 0.85 | *** | 0.32 | ns | 0.81 | *** | 0.93 | *** | 0.44 | ns | 0.94 | *** |
20 | Jao Khao | 0.83 | *** | -0.28 | ns | 0.74 | ** | 0.95 | *** | -0.60 | * | 0.89 | *** |
21 | Muay Hin | 0.72 | ** | 0.15 | ns | 0.76 | *** | 0.87 | *** | 0.20 | ns | 0.87 | *** |
22 | Dawk Mai | 0.89 | *** | 0.16 | ns | 0.84 | *** | 0.94 | *** | 0.40 | ns | 0.92 | *** |
23 | Ta Pow Lom | 0.91 | *** | 0.32 | ns | 0.75 | *** | 0.96 | *** | -0.46 | ns | 0.91 | *** |
24 | Di Si | 0.56 | * | -0.38 | ns | 0.57 | * | 0.85 | *** | -0.66 | ** | 0.90 | *** |
25 | Med Makham | 0.88 | *** | 0.40 | ns | 0.81 | *** | 0.82 | *** | -0.63 | ** | 0.84 | *** |
26 | Niaw Mali | 0.93 | *** | 0.45 | ns | 0.88 | *** | 0.94 | *** | 0.40 | ns | 0.95 | *** |
27 | Daw Dawk Mai | 0.63 | ** | -0.11 | ns | 0.65 | ** | 0.86 | *** | 0.44 | ns | 0.92 | *** |
28 | Nahng Nuan | 0.87 | *** | 0.49 | ns | 0.80 | *** | 0.87 | *** | -0.65 | ** | 0.79 | *** |
29 | Sew Mae Jan | 0.70 | ** | 0.14 | ns | 0.78 | *** | 0.92 | *** | 0.56 | * | 0.93 | *** |
30 | Leuang Pratew123 | 0.89 | *** | 0.41 | ns | 0.82 | *** | 0.95 | *** | -0.39 | ns | 0.95 | *** |
Variety | Total Reads | Mapped Locations | Mapping Rate (%) | Number of SNPs | Number of InDels | ||
---|---|---|---|---|---|---|---|
Total | Per 100 kb | Total | Per 100 kb | ||||
MY | 38,018,919 | 34,035,049 | 89.5 | 707,759 | 188.8 | 89,400 | 23.7 |
KK | 35,704,270 | 32,986,362 | 92.4 | 678,820 | 181.2 | 85,700 | 22.8 |
JK | 31,293,199 | 29,419,191 | 94 | 497,512 | 132.7 | 61,385 | 16.3 |
NN | 33,632,482 | 31,973,632 | 95.1 | 583,761 | 155.7 | 73,471 | 19.5 |
RAP Id | Description | Chr | Position | Ref | Alt | Sequence Ontology |
---|---|---|---|---|---|---|
OS01G0689900 | OsWAK10d - OsWAK receptor-like cytoplasmic kinase OsWAK-RLCK | 1 | 28495524 | G | T | missense |
1 | 28495527 | C | G | missense | ||
1 | 28495528 | C | A | missense | ||
1 | 28495538 | A | C | missense | ||
OS01G0781200 | rp1 | 1 | 33101161 | A | G | missense |
OS01G0810600 | protein kinase domain containing protein | 1 | 34439934 | T | G | missense & splice region |
1 | 34442278 | T | C | missense | ||
1 | 34442281 | G | T | missense | ||
1 | 34442374 | A | C | missense | ||
1 | 34442388 | G | T | missense | ||
1 | 34442401 | T | C | missense | ||
1 | 34442404 | T | C | missense | ||
1 | 34442433 | A | G | missense | ||
1 | 34442448 | T | A | missense | ||
1 | 34442454 | C | T | missense | ||
OS01G0836700 | GPR107 precursor | 1 | 35869685 | T | C | missense |
OS02G0127700 | phosphoribosyl transferase | 2 | 1439928 | AC | A | frameshift |
2 | 1439888 | AGGG | A | disruptive inframe deletion | ||
OS02G0523500 | TUDOR protein with multiple SNc domains | 2 | 19100892 | A | T | missense |
OS03G0124300 | receptor-like protein kinase | 3 | 1410653 | C | A | missense |
OS03G0262300 | AT hook motif family protein | 3 | 8596557 | A | AGGGGACGGCGAC | disruptive inframe insertion |
OS03G0347200 | ABH1 | 3 | 12984797 | G | T | missense |
OS03G0800200 | PAZ domain containing protein, OsMEL1 | 3 | 33375066 | C | T | splice acceptor |
3 | 33376480 | GAC | G | frameshift | ||
3 | 33376483 | G | GTA | frameshift | ||
3 | 33375166 | A | G | missense | ||
3 | 33375169 | C | A | missense | ||
3 | 33375170 | G | T | missense | ||
3 | 33376283 | T | G | missense | ||
3 | 33376290 | G | A | missense | ||
3 | 33376305 | C | T | missense | ||
3 | 33379671 | C | T | missense | ||
OS04G0457800 | BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 | 4 | 22872259 | A | C | missense |
4 | 22872262 | G | C | missense | ||
OS05G0466900 | protein kinase family protein | 5 | 22914110 | C | T | missense |
OS05G0548300 | MDR-like ABC transporter | 5 | 27208244 | A | T | missense |
OS05G0596600 | RecF/RecN/SMC N terminal domain containing protein | 5 | 29737585 | AAT | A | frameshift |
5 | 29737590 | T | TTA | frameshift | ||
5 | 29737576 | G | A | missense | ||
5 | 29737582 | C | T | missense | ||
5 | 29737589 | C | T | missense | ||
5 | 29737620 | T | G | missense | ||
5 | 29737621 | T | A | missense | ||
5 | 29737625 | T | C | missense | ||
5 | 29737629 | C | A | missense & splice region | ||
OS06G0116100 | CPuORF21 - conserved peptide uORF-containing transcript | 6 | 887012 | T | G | missense |
OS06G0167500 | SHR5-receptor-like kinase | 6 | 3417483 | A | G | missense |
OS06G0585982 | receptor-like protein kinase precursor | 6 | 22953022 | A | C | missense |
6 | 22953076 | G | A | missense | ||
OS07G0695400 | KIP1 | 7 | 29635510 | A | G | missense |
7 | 29636167 | A | G | missense | ||
7 | 29635792 | A | G | missense | ||
OS08G0124100 | lectin-like receptor kinase 1 | 8 | 1315037 | T | C,A | missense |
OS08G0190300 | NB-ARC domain containing protein | 8 | 5279107 | C | G | missense |
OS08G0564100 | ABC transporter, ATP-binding protein | 8 | 28252908 | TC | T | frameshift |
8 | 28252913 | GC | G | frameshift | ||
OS09G0348400 | senescence-induced receptor-like serine/threonine-protein kinase | 9 | 10945517 | G | A | missense |
9 | 10945523 | A | G | missense | ||
OS10G0151100 | OsWAK103 - OsWAK receptor-like protein kinase | 10 | 3065467 | G | C | missense |
OS10G0346600 | vacuolar-sorting receptor precursor | 10 | 10412967 | G | C | missense |
OS10G0468500 | receptor-like protein kinase precursor | 10 | 17314599 | G | C | missense |
OS11G0148500 | pyruvate kinase, OsPK1 | 11 | 2242566 | G | A | stop gained |
11 | 2242576 | AG | A | frameshift | ||
11 | 2242326 | C | A | missense | ||
11 | 2242329 | G | A | missense | ||
11 | 2242336 | G | C | missense | ||
11 | 2242338 | G | A | missense & splice region | ||
11 | 2242516 | C | A | missense & splice region | ||
11 | 2242584 | GAAC | G | conservative inframe deletion | ||
OS11G0227100 | NB-ARC domain containing protein | 11 | 6657328 | T | A | stop gained |
11 | 6657183 | A | C | missense | ||
11 | 6657186 | A | G | missense | ||
11 | 6657264 | T | C | missense | ||
11 | 6657340 | A | G | missense | ||
11 | 6657367 | G | A | missense | ||
11 | 6657405 | A | G | missense | ||
11 | 6657435 | A | T | missense | ||
11 | 6657480 | A | G | missense | ||
11 | 6657518 | C | G | missense | ||
11 | 6657528 | G | A | missense | ||
11 | 6657533 | G | T | missense | ||
11 | 6657537 | A | G | missense | ||
OS12G0102500 | senescence-induced receptor-like serine/threonine-protein kinase | 12 | 119480 | G | C | missense |
12 | 119522 | C | T | missense | ||
OS12G0197500 | SGS3 | 12 | 5038711 | T | C | missense |
OS12G0197700 | leafbladeless1 | 12 | 5049404 | C | T | missense |
12 | 5049448 | C | T | missense | ||
12 | 5049449 | A | G | missense |
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Lekklar, C.; Suriya-arunroj, D.; Pongpanich, M.; Comai, L.; Kositsup, B.; Chadchawan, S.; Buaboocha, T. Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress. Genes 2019, 10, 562. https://doi.org/10.3390/genes10080562
Lekklar C, Suriya-arunroj D, Pongpanich M, Comai L, Kositsup B, Chadchawan S, Buaboocha T. Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress. Genes. 2019; 10(8):562. https://doi.org/10.3390/genes10080562
Chicago/Turabian StyleLekklar, Chakkree, Duangjai Suriya-arunroj, Monnat Pongpanich, Luca Comai, Boonthida Kositsup, Supachitra Chadchawan, and Teerapong Buaboocha. 2019. "Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress" Genes 10, no. 8: 562. https://doi.org/10.3390/genes10080562
APA StyleLekklar, C., Suriya-arunroj, D., Pongpanich, M., Comai, L., Kositsup, B., Chadchawan, S., & Buaboocha, T. (2019). Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress. Genes, 10(8), 562. https://doi.org/10.3390/genes10080562