Functional characterization of <i>OsLT9</i> in regulating rice leaf thickness

Jian Wang; Dagang Chen; Haifei Hu; Yamei Ma; Tifeng Yang; Jie Guo; Ke Chen; Chanjuan Ye; Juan Liu; Xinqiao Zhou; Chuanguang Liu; Junliang Zhao

doi:10.1016/j.jgg.2025.07.010

Functional characterization of OsLT9 in regulating rice leaf thickness

doi: 10.1016/j.jgg.2025.07.010

Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Rice Science and Technology, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Rice Engineering Laboratory, Guangzhou, Guangdong 510640, China

基金项目:

This study was supported by National Natural Science Foundation of China (32301845), GuangDong Basic and Applied Basic Research Foundation (2022A1515012339), National Key R&

D Program of China (2024YFD1200800),Seed industry revitalization project of special fund for rural revitalization strategy in Guangdong Province (2024-NPY-00-001), Modern Seed Industry Innovation Capacity Enhancement Program of Guangdong Academy of Agricultural Sciences, Elite Rice Plan of GDRRI (2023YG01), Guangdong Key Laboratory of Rice Science and Technology (2023B1212060042).

详细信息

通讯作者:
Xinqiao Zhou,E-mail:zhouxq@gdaas.cn

Chuanguang Liu,E-mail:liuchuanguang@gdaas.cn

Junliang Zhao,E-mail:zhao_junliang@gdaas.cn

计量
- 文章访问数: 10
- HTML全文浏览量: 5
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-03
- 录用日期: 2025-07-24
- 修回日期: 2025-07-23
- 网络出版日期: 2025-08-01

Functional characterization of OsLT9 in regulating rice leaf thickness

Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Rice Science and Technology, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Rice Engineering Laboratory, Guangzhou, Guangdong 510640, China

Funds:

This study was supported by National Natural Science Foundation of China (32301845), GuangDong Basic and Applied Basic Research Foundation (2022A1515012339), National Key R&

摘要

摘要: Leaf thickness in rice critically influences photosynthetic efficiency and yield, yet its genetic basis remains poorly understood, with few functional genes previously characterized. In this study, we employ a pangenome-wide association study (Pan-GWAS) on 302 diverse rice accessions from southern China, identifying 49 quantitative trait loci (QTLs) associated with leaf thickness. The most significant locus, qLT9, is fine-mapped to a 79 kb region on chromosome 9. Transcriptomic and genomic sequence analyses identify LOC_Os09g33480, which encodes a protein belonging to Multiple Organellar RNA Editing Factor (MORF) family, as the key candidate gene. Overexpression and complementation transgenic experiments confirm LOC_Os09g33480 (OsLT9) as the functional gene underlying qLT9, demonstrating a 24-bp Indel in its promoter correlates with the expression levels and leaf thickness. Notably, OsLT9 overexpression lines show not only thicker leaf, but also significantly enhanced photosynthetic efficiency and grain yield, establishing a link between leaf thickness modulation and yield enhancement. Population genomic analyses indicate strong selection for OsLT9 during domestication and breeding, with modern cultivars favoring thick leaf haplotype of OsLT9. This study establishes OsLT9 as a key regulator controlling leaf thickness in rice, and provides a valuable genetic resource for molecular breeding of high-yielding rice through optimization of plant architecture.
- Rice /
- Leaf thickness /
- GWAS /
- Functional gene /
- OsLT9
Abstract: Leaf thickness in rice critically influences photosynthetic efficiency and yield, yet its genetic basis remains poorly understood, with few functional genes previously characterized. In this study, we employ a pangenome-wide association study (Pan-GWAS) on 302 diverse rice accessions from southern China, identifying 49 quantitative trait loci (QTLs) associated with leaf thickness. The most significant locus, qLT9, is fine-mapped to a 79 kb region on chromosome 9. Transcriptomic and genomic sequence analyses identify LOC_Os09g33480, which encodes a protein belonging to Multiple Organellar RNA Editing Factor (MORF) family, as the key candidate gene. Overexpression and complementation transgenic experiments confirm LOC_Os09g33480 (OsLT9) as the functional gene underlying qLT9, demonstrating a 24-bp Indel in its promoter correlates with the expression levels and leaf thickness. Notably, OsLT9 overexpression lines show not only thicker leaf, but also significantly enhanced photosynthetic efficiency and grain yield, establishing a link between leaf thickness modulation and yield enhancement. Population genomic analyses indicate strong selection for OsLT9 during domestication and breeding, with modern cultivars favoring thick leaf haplotype of OsLT9. This study establishes OsLT9 as a key regulator controlling leaf thickness in rice, and provides a valuable genetic resource for molecular breeding of high-yielding rice through optimization of plant architecture.
- Rice /
- Leaf thickness /
- GWAS /
- Functional gene /
- OsLT9

HTML全文

参考文献(47)

Aneja, P., Sanyal, R., Ranjan, A., 2025. Leaf growth in third dimension: a perspective of leaf thickness from genetic regulation to ecophysiology. New Phytol. 245, 989-999.

Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina squence data. Bioinformatics 30, 2114-2120.

Chen, D., Zhou, X., Chen, K., Chen, P., Guo, J., Liu, C., Chen, Y., 2022. Fine-mapping and candidate gene analysis of a major locus controlling leaf thickness in rice (Oryza sativa L.). Mol. Breed. 42, 6.

Coneva, V., Chitwood, D.H., 2018. Genetic and developmental basis for increased leaf thickess in the Arabidopsis Cvi ecotype. Front. Plant Sci. 9, 322.

Coneva, V., Frank, M.H., Balaguer, M.A.D.L., Li, M., Sozzani, R., Chitwood, D.H., 2017. Genetic architecture and molecular networks underlying leaf thickness in desert-adapted tomato Solanum pennellii. Plant Physiol. 175, 376-391.

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., 2011. The variant call format and VCFtools. Bioinformatics 27, 2156-2158.

Dobin, A., Gingeras, T.R., 2015. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinf. 51, 11-14.

Garnier, E., Salager, J., Laurent, G., Sonie, L., 1999. Relationships between photosynthesis, nitrogen and leaf structure in 14 grass species and their dependence on the basis of expression. New Phytol. 143, 119-129.

Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., Toulmin, C., 2010. Food security: the challenge of feeding 9 billion people. Science 327, 812-818.

Hoshino, R., Yoshida, Y., Tsukaya, H., 2019. Multiple steps of leaf thickening during sun-leaf formation in Arabidopsis. Plant J. 100, 738-753.

Jinwen, L., Jingping, Y., Pinpin, F., Junlan, S., Dongsheng, L., Changshui, G., Wenyue, C., 2009. Responses of rice leaf thickness, SPAD readings and chlorophyll a/b ratios to different nitrogen supply rates in paddy field. Field Crops Res. 114, 426-432.

Kanbe, T., Sasaki, H., Aoki, N., Yamagishi, T., Ebitani, T., Yano, M., Ohsugi, R., 2008. Identification of QTLs for improvement of plant type in rice (Oryza sativa L.) Using Koshihikari/Kasalath chromosome segment substitution lines and backcross progeny F2 population. Plant Prod. Sci. 11, 447-456.

Kawahara, Y., de la Bastide, M., Hamilton, J.P., Kanamori, H., Mccombie, W.R., Ouyang, S., Schwartz, D.C., Tanaka, T., Wu, J., Zhou, S., et al., 2013. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6, 4.

Khush, G.S., 2013. Strategies for increasing the yield potential of cereals: case of rice as an example. Plant Breed. 132, 433-436.

Laza, M.R., Kondo, M., Ideta, O., Barlaan, E., Imbe, T., 2006. Identification of quantitative trait loci for δ13C and productivity in irrigated lowland rice. Crop sci. 46, 763-773.

Lee, T., Guo, H., Wang, X., Kim, C., Paterson, A.H., 2014. SNPhylo: a pipeline to construct a phylogenetic tree from huge SNP data. BMC genomics 15, 1-6.

Li, H., 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv.1303, 3997.

Li, P., Chang, T., Chang, S., Ouyang, X., Qu, M., Song, Q., Xiao, L., Xia, S., Deng, Q., Zhu, X.G., 2018. Systems model-guided rice yield improvements based on genes controlling source, sink, and flow. J Integr Plant Biol. 60, 1154-1180.

Liu, C., Zhou, X., Chen, D., Li, L., Li, J., Chen, Y., 2014. Natural variation of leaf thickness and its association to yield traits in indica rice. J. Integr. Agric. 13, 316-325.

Liu, W., Zheng, L., Qi, D., 2020. Variation in leaf traits at different altitudes reflects the adaptive strategy of plants to environmental changes. Ecol. Evol. 10, 8166-8175.

Livak, K.J., Schmittgen, T.D., 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2- ΔΔCT method. Methods 25, 402-408.

Mathan, J., Singh, A., Jathar, V., Ranjan, A., 2021. High photosynthesis rate in two wild rice species is driven by leaf anatomy mediating high Rubisco activity and electron transport rate. J. Exp. Bot. 72, 7119-7135.

Mckenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al., 2010. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303.

Peng, S., 2000. Single-leaf and canopy photosynthesis of rice, Studies in Plant Science, Elsevier, pp. 213–228.

Peng, S., Khush, G.S., Virk, P., Tang, Q., Zou, Y., 2008. Progress in ideotype breeding to increase rice yield potential. Field Crops Res. 108, 32-38.

Qi, J., Qian, Q., Bu, Q., Li, S., Chen, Q., Sun, J., Liang, W., Zhou, Y., Chu, C., Li, X., et al., 2008. Mutation of the rice Narrow leaf1 gene, which encodes a novel protein, affects vein patterning and polar auxin transport. Plant Physiol. 147, 1947-1959.

Qian, Q., Guo, L., Smith, S.M., Li, J., 2016. Breeding high-yield superior quality hybrid super rice by rational design. Natl. Sci. Rev. 3, 283-294.

Smith, M.R., Rao, I.M., Merchant, A., 2018. Source-sink relationships in crop plants and their influence on yield development and nutritional quality. Front. Plant Sci. 9, 420465.

Takenaka, M., Zehrmann, A., Verbitskiy, D., Kugelmann, M., Hartel, B., Brennicke, A., 2012. Multiple organellar RNA editing factor (MORF) family proteins are required for RNA editing in mitochondria and plastids of plants. Proc. Natl. Acad. Sci. U. S. A. 109, 5104-5109.

Trapnell, C., Hendrickson, D.G., Sauvageau, M., Goff, L., Rinn, J.L., Pachter, L., 2013. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46-53.

Walters, R.G., Horton, P., 1994. Acclimation of Arabidopsis thaliana to the light environment: changes in composition of the photosynthetic apparatus. Planta 195, 248-256.

Wang, J., Yang, W., Zhang, S., Hu, H., Yuan, Y., Dong, J., Chen, L., Ma, Y., Yang, T., Zhou, L., et al., 2023. A pangenome analysis pipeline provides insights into functional gene identification in rice. Genome Biol. 24, 19.

Wang, J., Zhang, X., Lin, Z., 2018. QTL mapping in a maize F2 population using Genotyping-by-Sequencing and a modified fine-mapping strategy. Plant Sci. 276, 171-180.

Wang, J., Zhang, Z., 2021. GAPIT Version 3: Boosting power and accuracy for genomic association and prediction. Genomics, Proteomics Bioinf. 19, 629-640.

Wang, Y., Wang, Y., Ren, Y., Duan, E., Zhu, X., Hao, Y., Zhu, J., Chen, R., Lei, J., Teng, X., et al., 2021. White panicle2 encoding thioredoxin z, regulates plastid RNA editing by interacting with multiple organellar RNA editing factors in rice. New Phytol. 229, 2693-2706.

Weston, E., Thorogood, K., Vinti, G., Lopez-Juez, E., 2000. Light quantity controls leaf-cell and chloroplast development in Arabidopsis thaliana wild type and blue-light-perception mutants. Planta 211, 807-815.

Wickham, H., Wickham, H., 2016. Data analysis. Springer.

Xiang, J., Zhang, G., Qian, Q., Xue, H., 2012. Semi-rolled leaf1 encodes a putative glycosylphosphatidylinositol-anchored protein and modulates rice leaf rolling by regulating the formation of bulliform cells. Plant Physiol. 159, 1488-1500.

Xiao, H., Xu, Y., Ni, C., Zhang, Q., Zhong, F., Huang, J., Liu, W., Peng, L., Zhu, Y., Hu, J., 2018. A rice dual-localized pentatricopeptide repeat protein is involved in organellar RNA editing together with OsMORFs. J. Exp. Bot. 69, 2923-2936.

Xie, J., Liao, H., Wang, X., Zhang, X., Ni, J., Li, Y., Tian, W., Sang, X., 2019. DLT/OsGRAS-32, regulating leaf width and thickness by controlling cell number in Oryza sativa. Mol. Breed. 39, 1-11.

Yu, J., Pressoir, G., Briggs, W.H., Vroh Bi, I., Yamasaki, M., Doebley, J.F., Mcmullen, M.D., Gaut, B.S., Nielsen, D.M., Holland, J.B., et al., 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203-208.

Zhang, C., Dong, S., Xu, J., He, W., Yang, T., 2019. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35, 1786-1788.

Zhang, M., Sun, H., Fei, Z., Zhan, F., Gong, X., Gao, S. 2014. Fastq_clean: an optimized pipeline to clean the Illumina sequencing data with quality control. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 44-48.

Zhao, S., Hu, J., Guo, L., Qian, Q., Xue, H., 2010. Rice leaf inclination2, a VIN3-like protein, regulates leaf angle through modulating cell division of the collar. Cell Res. 20, 935-947.

Zhao, X., Xu, J., Zhao, M., Lafitte, R., Zhu, L., Fu, B., Gao, Y., Li, Z., 2008. QTLs affecting morph-physiological traits related to drought tolerance detected in overlapping introgression lines of rice (Oryza sativa L.). Plant Sci. 174, 618-625.

Zheng, Z., Hu, H., Gao, S., Zhou, H., Luo, W., Kage, U., Liu, C., Jia, J., 2022. Leaf thickness of barley: genetic dissection, candidate genes prediction and its relationship with yield-related traits. Theor. Appl. Genet. 135, 1843-1854.

Zhu, T., Xia, C., Yu, R., Zhou, X., Xu, X., Wang, L., Zong, Z., Yang, J., Liu, Y., Ming, L., et al., 2024. Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits. Nat. Commun. 15, 6562.

施引文献

资源附件(0)

访问统计