AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100, TracesCS6B03G0356400, and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
Xiao J, Liu B, Yao Y, Guo Z, Jia H, Kong L, Zhang A, Ma W, Ni Z, Xu S, et al. Wheat genomic study for genetic improvement of traits in China. Sci China Life Sci. 2022;65(9): 1718–1775.
Sultana N, Islam S, Juhasz A, Ma W. Wheat leaf senescence and its regulatory gene network. Crop J. 2021;9(4): 703–717.
Kamal NM, Alnor Gorafi YS, Abdelrahman M, Abdellatef E, Tsujimoto H. Stay-green trait: A prospective approach for yield potential, and drought and heat stress adaptation in globally important cereals. Int J Mol Sci. 2019;20(23): 5837.
Munaiz ED, Martínez S, Kumar A, Caicedo M, Ordás B. The senescence (stay-green)—An important trait to exploit crop residuals for bioenergy. Energies. 2020;13(4): 790.
Thomas H, Ougham H. The stay-green trait. J Exp Bot. 2014;65(14): 3889–3900.
Kumar R, Harikrishna, Barman D, Ghimire OP, Gurumurthy S, Singh PK, Chinnusamy V, Padaria JC, Arora A. Stay-green trait serves as yield stability attribute under combined heat and drought stress in wheat (Triticum aestivum L.). Plant Growth Regul. 2021;96(1): 67–78.
Kipp S, Mistele B, Schmidhalter U. Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques. Funct Plant Biol. 2014;41(3): 227.
Ren T, Fan T, Chen S, Chen Y, Ou X, Jiang Q, Peng W, Ren Z, Tan F, Luo P, et al. Identification and validation of quantitative trait loci for the functional stay green trait in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping. Theor Appl Genet. 2022;135(4): 1429–1441.
Shi S, Azam FI, Li H, Chang X, Li B, Jing R. Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes. Euphytica. 2017;213(11): 246.
Christopher M, Paccapelo V, Kelly A, Macdonald B, Hickey L, Richard C, Verbyla A, Chenu K, Borrell A, Amin A, et al. QTL identified for stay-green in a multi-reference nested association mapping population of wheat exhibit context dependent expression and parent-specific alleles. Field Crop Res. 2021;270: Article 108181.
Uauy C, Distelfeld A, Fahima T, Blechl A, Dubcovsky J. A NAC gene regulating senescence improves grain protein, zinc, and iron content in wheat. Science. 2006;314(5803): 1298–1301.
Li H, Liu H, Hao C, Li T, Liu Y, Wang X, Yang Y, Zheng J, Zhang X. The auxin response factor TaARF15-A1 negatively regulates senescence in common wheat (Triticum aestivum L.). Plant Physiol. 2023;191(2): 1254–1271.
Song P, Wang J, Guo X, Yang W, Zhao C. High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. Crop J. 2021;9(3): 633–645.
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives. Mol Plant. 2020;13(2): 187–214.
Jin X, Zarco-Tejada PJ, Schmidhalter U, Reynolds MP, Hawkesford MJ, Varshney RK, Yang T, Nie C, Li Z, Ming B, et al. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geosci Remote Sens Mag. 2021;9(1): 200–231.
Shakoor N, Lee S, Mockler TC. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol. 2017;38: 184–192.
Guo W, Carroll ME, Singh A, Swetnam TL, Merchant N, Sarkar S, Singh AK, Ganapathysubramanian B. UAS-based plant phenotyping for research and breeding applications. Plant Phenomics. 2021;2021: 9840192.
Hassan MA, Yang M, Rasheed A, Tian X, Reynolds M, Xia X, Xiao Y, He Z. Quantifying senescence in bread wheat using multispectral imaging from an unmanned aerial vehicle and QTL mapping. Plant Physiol. 2021;187(4): 2623–2636.
Liedtke JD, Hunt CH, George-Jaeggli B, Laws K, Watson J, Potgieter AB, Cruickshank A, Jordan DR. High-throughput phenotyping of dynamic canopy traits associated with stay-green in grain sorghum. Plant Phenomics. 2020;2020: 4635153.
Christopher M, Chenu K, Jennings R, Fletcher S, Butler D, Borrell A, Christopher J. QTL for stay-green traits in wheat in well-watered and water-limited environments. Field Crop Res. 2018;217: 32–44.
Gitelson AA, Kaufman YJ, Merzlyak MN. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 1996;58(3): 289–298.
Compton JT. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 1979;8(2): 127–150.
Maccioni A, Agati G, Mazzinghi P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol B Biol. 2001;61(1-2): 52–61.
Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sens Environ. 1996;55(2): 95–107.
Araus JL, Kefauver SC, Vergara-Díaz O, Gracia-Romero A, Rezzouk FZ, Segarra J, Buchaillot ML, Chang-Espino M, Vatter T, Sanchez-Bragado R, et al. Crop phenotyping in a context of global change: What to measure and how to do it. J Integr Plant Biol. 2022;64(2): 592–618.
Cao X, Liu Y, Yu R, Han D, Su B. A comparison of UAV RGB and multispectral imaging in phenotyping for stay green of wheat population. Remote Sens. 2021;13(24): 5173.
Wu J, Yu R, Wang H, Zhou C', Huang S, Jiao H, Yu S, Nie X, Wang Q, Liu S, et al. A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments. Plant Biotechnol J. 2021;19(1): 177–191.
Ma S, Wang M, Wu J, Guo W, Chen Y, Li G, Wang Y, Shi W, Xia G, Fu D, et al. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant. 2021;14(12): 1965–1968.
Thompson CN, Guo W, Sharma B, Ritchie GL. Using normalized difference red edge index to assess maturity in cotton. Crop Sci. 2019;59(5):2167–2177.
Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci. 2019;282:95–103.
Gitelson AA, Merzlyak MN. Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res. 1998;22(5):689–692.
Earl DA, vonHoldt BM. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2011;4(2):359–361.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.
Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–824.
Yi K, Yan W, Li X, Yang S, Li J, Yin Y, Yuan F, Wang H, Kang Z, Han D, et al. Identification of long intergenic noncoding RNAs in Rhizoctonia cerealis following inoculation of wheat. Microbiol Spectr. 2023;11(3):e0344922.
Chen Y, Song W, Xie X, Wang Z, Guan P, Peng H, Jiao Y, Ni Z, Sun Q, Guo W. A collinearity-incorporating homology inference strategy for connecting emerging assemblies in the Triticeae tribe as a pilot practice in the plant Pangenomic era. Mol Plant. 2020;13(12):1694–1708.
Engqvist M, Drincovich MF, Flügge UI, Maurino VG. Two D-2-hydroxy-acid dehydrogenases in Arabidopsis thaliana with catalytic capacities to participate in the last reactions of the methylglyoxal and beta-oxidation pathways. J Biol Chem. 2009;284(37):25026–25037.
Besseau S, Li J, Palva ET. WRKY54 and WRKY70 co-operate as negative regulators of leaf senescence in Arabidopsis thaliana. J Exp Bot. 2012;63(7):2667–2679.
Inoue Y, Peñuelas J, Miyata A, Mano M. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sens Environ. 2008;112(1):156–172.
Hassan M, Yang M, Rasheed A, Jin X, Xia X, Xiao Y, He Z. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sens. 2018;10(6):809.
Christopher JT, Christopher MJ, Borrell AK, Fletcher S, Chenu K. Stay-green traits to improve wheat adaptation in well-watered and water-limited environments. J Exp Bot. 2016;67(17):5159–5172.
Hao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zheng J, Liu H, Bi Z, Xu F, et al. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant. 2020;13(12):1733–1751.
Liu L, Zhou Y, Szczerba MW, Li X, Lin Y. Identification and application of a rice senescence-associated promoter. Plant Physiol. 2010;153(3):1239–1249.
Borrill P, Harrington SA, Simmonds J, Uauy C. Identification of transcription factors regulating senescence in wheat through gene regulatory network modelling. Plant Physiol. 2019;180(3):1740–1755.
Rebetzke GJ, Jimenez-Berni J, Fischer RA, Deery DM, Smith DJ. Review: High-throughput phenotyping to enhance the use of crop genetic resources. Plant Sci. 2019;282:40–48.
Ort DR, Merchant SS, Alric J, Barkan A, Blankenship RE, Bock R, Croce R, Hanson MR, Hibberd JM, Long SP, et al. Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc Natl Acad Sci USA. 2015;112(28):8529–8536.
Li Q, Yang H, Guo J, Huang Q, Zhong S, Tan F, Ren T, Li Z, Chen C, Luo P. Comparative transcriptome analysis revealed differential gene expression involved in wheat leaf senescence between stay-green and non-stay-green cultivars. Front Plant Sci. 2022;13:971927.
Chapman EA, Orford S, Lage J, Griffiths S. Capturing and selecting senescence variation in wheat. Front Plant Sci. 2021;12: Article 638738.
Miryeganeh M. Senescence: The compromised time of death that plants may call on themselves. Genes. 2021;12(2):143.
Woo HR, Kim HJ, Lim PO, Nam HG. Leaf senescence: Systems and dynamics aspects. Annu Rev Plant Biol. 2019;70(1):347–376.
Guo Y, Ren G, Zhang K, Li Z, Miao Y, Guo H. Leaf senescence: Progression, regulation, and application. Mol Hortic. 2021;1(1):5.
Li X, Liu T, Chen W, Zhong S, Zhang H, Tang Z, Chang Z, Wang L, Zhang M, Li L, et al. Wheat WCBP1 encodes a putative copper-binding protein involved in stripe rust resistance and inhibition of leaf senescence. BMC Plant Biol. 2015;15:239.
Shimoda Y, Ito H, Tanaka A. Arabidopsis STAY-GREEN, Mendel's Green cotyledon gene, encodes magnesium-dechelatase. Plant Cell. 2016;28(9):2147–2160.
Sato T, Shimoda Y, Matsuda K, Tanaka A, Ito H. Mg-dechelation of chlorophyll a by stay-green activates chlorophyll b degradation through expressing non-yellow coloring 1 in Arabidopsis thaliana. J Plant Physiol. 2018;222:94–102.
Touzy G, Lafarge S, Redondo E, Lievin V, Decoopman X, le Gouis J, Praud S. Identification of QTLs affecting post-anthesis heat stress responses in European bread wheat. Theor Appl Genet. 2022;135(3):947–964.
Otegui MS, Noh YS, Martínez DE, Vila Petroff MG, Andrew Staehelin L, Amasino RM, Guiamet JJ. Senescence-associated vacuoles with intense proteolytic activity develop in leaves of Arabidopsis and soybean. Plant J. 2005;41(6):831–844.
Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
京公网安备11010802044758号