Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (12): 2423-2439.doi: 10.3724/SP.J.1006.2021.02084
• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles Next Articles
LIU Ya-Wen1(), ZHANG Hong-Yan1,2,*(), CAO Dan2, LI Lan-Zhi2
[1] | Gong Z Z, Xiong L M, Shi H Z, Yang S H, Herrera-Estrella L R, Xu G H, Chao D Y, Li J R, Wang P Y, Qin F, Li J G, Ding Y L, Shi Y T, Wang Y, Yang Y Q, Guo Y, Zhu J K. Plant abiotic stress response and nutrient use efficiency. Sci China (Life Sci Edn), 2020, 63:635-674. |
[2] | Hossain M R, Bassel G W, Pritchard J, Sharma G P, Ford-Lloyd B V. Trait specific expression profiling of salt stress responsive genes in diverse rice genotypes as determined by modified significance analysis of microarrays. Front Plant Sci, 2016, 7:567. |
[3] |
Zhou Y, Yang P, Cui F L, Zhang F T, Luo X D, Xie J K. Transcriptome analysis of salt stress responsiveness in the seedlings of Dongxiang wild rice (Oryza rufipogon Griff.). PLoS One, 2016, 11(1):e0146242.
doi: 10.1371/journal.pone.0146242 |
[4] |
Song T, Das D, Yang F, Chen M X, Tian Y, Cheng C L, Sun C, Xu W F, Zhang J H. Genome-wide transcriptome analysis of roots in two rice varieties in response to alternate wetting and drying irrigation. Crop J, 2020, 8:586-601.
doi: 10.1016/j.cj.2020.01.007 |
[5] | Kaur S, Iquebal M A, Jaiswal S, Tandon G, Sundaram R M, Gautam R K, Suresh K P, Rai A, Kumar D. A meta-analysis of potential candidate genes associated with salinity stress tolerance in rice. Agric Gene, 2016, 1:126-134. |
[6] | Serin E A R, Nijveen H, Hilhorst H W M, Ligterink W. Learning from co-expression networks: possibilities and challenges. Front Plant Sci, 2016, 7:444. |
[7] |
Zhou G Y, Soufan O, Ewald J, Hancock R E W, Basu N, Xia J G. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res, 2019, 47(W1):W234-W241.
doi: 10.1093/nar/gkz240 |
[8] | 王凯莉, 张礼, 刘学军. 融合多平台表达数据的转录组差异表达分析. 计算机学报, 2018, 41:1195-1210. |
Wang K L, Zhang L, Liu X J. Differential expression analysis based on integrating transcriptome expression data from multiple platforms. Chin J Comp, 2018, 41:1195-1210 (in Chinese with English abstract). | |
[9] |
Cheng Y F, Li L, Qin Z S, Li X, Qi F. Identification of castration-resistant prostate cancer-related hub genes using weighted gene co-expression network analysis. J Cell Mol Med, 2020, 24:1-12.
doi: 10.1111/jcmm.v24.1 |
[10] | 李旭凯, 李任建, 张宝俊. 利用WGCNA鉴定非生物胁迫相关基因共表达网络. 作物学报, 2019, 45:1349-1364. |
Li X K, Li R J, Zhang B J. Identification of rice stress-related gene co-expression modules by WGCNA. Acta Agron Sin, 2019, 45:1349-1364 (in Chinese with English abstract). | |
[11] |
Zhu M D, Xie H J, Wei X J, Dossa K, Yu Y Y, Hui S J, Tang G H, Zeng X S, Yu Y H, Hu P S, Wang J L. WGCNA analysis of salt-responsive core transcriptome identifies novel hub genes in rice. Genes, 2019, 10:719.
doi: 10.3390/genes10090719 |
[12] |
Lv Y M, Xu L, Dossa K, Zhou K, Zhu M D, Xie H J, Tang S J, Yu Y Y, Guo X Y, Zhou B. Identification of putative drought-responsive genes in rice using gene co-expression analysis. Bioinformation, 2019, 15:480-488.
doi: 10.6026/bioinformation |
[13] |
Hopper D W, Ghan R, Schlauch K A, Cramer G R. Transcriptomic network analyses of leaf dehydration responses identify highly connected ABA and ethylene signaling hubs in three grapevine species differing in drought tolerance. BMC Plant Biol, 2016, 16:118.
doi: 10.1186/s12870-016-0804-6 |
[14] | 秦天元, 孙超, 毕真真, 梁文君, 李鹏程, 张俊莲, 白江平. 基于WGCNA的马铃薯根系抗旱相关共表达模块鉴定和核心基因发掘. 作物学报, 2020, 46:1033-1051. |
Qin T Y, Sun C, Bi Z Z, Liang W J, Li P C, Zhang J L, Bai J P. Identification of drought-related co-expression modules and hub genes in potato roots based on WGCNA. Acta Agron Sin, 2020, 46:1033-1051 (in Chinese with English abstract). | |
[15] |
Reshef D N, Reshef Y A, Finucane H K, Grossman S R, McVean G, Turnbaugh P J, Lander E S, Mitzenmacher M, Sabeti P C. Detecting novel associations in large data sets. Science, 2011, 334:1518-1524.
doi: 10.1126/science.1205438 pmid: 22174245 |
[16] | Britt C L, Weisburd D. Statistical Power. Handbook of Quantitative Criminology. New York: Springer, 2010. pp 313-32. |
[17] |
Durinck S, Spellman P T, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc, 2009, 4:1184-1191.
doi: 10.1038/nprot.2009.97 |
[18] |
Steffen D, Yves M, Arek K, Sean D, Bart D M, Alvis B, Wolfgang H. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics, 2005, 21:3439-3440.
pmid: 16082012 |
[19] | Kroll K W, Mokaram N E, Pelletier A R, Frankhouser D E, Westphal M S, Stump P A, Stump C L, Bundschuh R, Blachly J S, Yan P. Quality control for RNA-Seq (QuaCRS): an integrated quality control pipeline. Cancer Inform, 2014, 13(S3):7-14. |
[20] |
Chen S F, Zhou Y Q, Chen Y R, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 2018, 34:i884-i890.
doi: 10.1093/bioinformatics/bty560 |
[21] |
Liao Y, Smyth G K, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 2014, 30:923-930.
doi: 10.1093/bioinformatics/btt656 |
[22] | Love M I, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genom Biol, 2014, 15:31-46. |
[23] |
Smita S, Katiyar A, Pandey D M, Chinnusamy V, Archak S, Bansal K C. Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice. Bioinformation, 2013, 9:72-78.
doi: 10.6026/bioinformation |
[24] |
Shaik R, Ramakrishna W. Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiol, 2014, 164:481-495.
doi: 10.1104/pp.113.225862 pmid: 24235132 |
[25] |
Tian T, Liu Y, Yan H Y, You Q, Yi X, Du Z, Xu W Y, Su Z. AgriGO v2.0: a GO analysis toolkit for the agricultural community. Nucleic Acids Res, 2017, 45(W1):W122-W129.
doi: 10.1093/nar/gkx382 |
[26] |
Szklarczyk D, Gable A L, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva N T, Morris J H, Bork P, Jensen L J, Mering C V. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1):D607-D613.
doi: 10.1093/nar/gky1131 |
[27] |
Shannon P, Markiel A, Ozier O, Baliga N S, Wang J T, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genom Res, 2003, 13:2498-2504.
doi: 10.1101/gr.1239303 |
[28] | 王胜昌, 涂海甫, 胡丹, 吴奈, 岑祥, 熊立仲. 水稻抗非生物逆境功能基因的发掘. 生命科学, 2016, 28:1216-1229. |
Wang S C, Tu H F, Hu D, Wu N, Cen X, Xiong L Z. The exploitation of rice functional genes for abiotic stress. Chin Bull Life Sci, 2016, 28:1216-1229 (in Chinese with English abstract). | |
[29] | Zahedi S M, Karimi M, Venditti A. Plants adapted to arid areas: specialized metabolites. Nat Prod Res, 2019: 1-18. |
[30] |
Murad M A, Khan A L, Muneer S. Silicon in horticultural crops: cross-talk, signaling, and tolerance mechanism under salinity stress. Plants, 2020, 9:460.
doi: 10.3390/plants9040460 |
[31] |
Sripinyowanich S, Klomsakul P, Boonburapong B, Bangyeekhun T, Asami T, Gu Y H, Buaboocha T, Chadchawan S. Exogenous ABA induces salt tolerance in indica rice (Oryza sativa L.): the role of OsP5CS1 and OsP5CR gene expression during salt stress. Environ Exp Bot, 2013, 86:94-105.
doi: 10.1016/j.envexpbot.2010.01.009 |
[32] | Ngan K G, Pati P K, Richaud F, Parizot B, Bidzinski P, Mai C D, Bès M, Bourrié I, Meynard D, Beeckman T, Selvaraj M G, Manabu I, Genga A M, Brugidou C, Do V N, Guiderdoni E, Morel J B, Gantet P. OsMADS26 negatively regulates resistance to pathogens and drought tolerance in rice. Plant Physiol, 2015, 169:2935-2949. |
[33] |
Zong W, Tang N, Yang J, Peng L, Ma S Q, Xu Y, Li G L, Xiong L Z. Feedback regulation of ABA signaling and biosynthesis by a bZIP transcription factor targets drought-resistance-related genes. Plant Physiol, 2016, 171:2810-2825.
doi: 10.1104/pp.16.00469 pmid: 27325665 |
[34] |
Ouyang S Q, Liu Y F, Liu P, Lei G, He S J, Ma B, Zhang W K, Zhang J S, Chen S Y. Receptor-like kinase OsSIK1 improves drought and salt stress tolerance in rice (Oryza sativa) plants. Plant J, 2010, 62:316-329.
doi: 10.1111/j.1365-313X.2010.04146.x |
[35] |
Cheng Y W, Qi Y C, Zhu Q, Chen X, Wang N, Zhao X, Chen H Y, Cui X J, Xu L L, Zhang W. New changes in the plasma-membrane-associated proteome of rice roots under salt stress. Proteomics, 2009, 9:3100-3114.
doi: 10.1002/pmic.200800340 |
[36] |
Fang H M, Meng Q L, Xu J W, Tang H J, Tang S Y, Zhang H S, Huang J. Knock-down of stress inducible OsSRFP1 encoding an E3 ubiquitin ligase with transcriptional activation activity confers abiotic stress tolerance through enhancing antioxidant protection in rice. Plant Mol Biol, 2015, 87:441-458.
doi: 10.1007/s11103-015-0294-1 |
[37] |
Ouyang J, Cai Z Y, Xia K F, Wang Y Q, Duan J, Zhang M Y. Identification and analysis of eight peptide transporter homologs in rice. Plant Sci, 2010, 179:374-382.
doi: 10.1016/j.plantsci.2010.06.013 |
[38] | Kothari K S, Dansana P K, Jitender G, Tyagi A K. Rice stress associated protein 1 (OsSAP1) interacts with aminotransferase (OsAMTR1) and pathogenesis-related 1a protein (OsSCP) and regulates abiotic stress responses. Front Plant Sci, 2016, 7:1057. |
[39] |
Wei Y D, Xu H B, Diao L R, Zhu Y H, Xie H G, Cai Q H, Wu F X, Wang Z H, Zhang J F, Xie H A. Protein repair L-isoaspartyl methyl transferase 1 ( PIMT1) in rice improves seed longevity by preserving embryo vigor and viability. Plant Mol Biol, 2015, 89:475-492.
doi: 10.1007/s11103-015-0383-1 |
[40] |
He S, Tan L L, Hu Z L, Chen G P, Wang G X, Hu T Z. Molecular characterization and functional analysis by heterologous expression in E. coli under diverse abiotic stresses for OsLEA5, the atypical hydrophobic LEA protein from Oryza sativa L. Mol Genet Genom, 2012, 287:39-54.
doi: 10.1007/s00438-011-0660-x |
[1] | TIAN Tian, CHEN Li-Juan, HE Hua-Qin. Identification of rice blast resistance candidate genes based on integrating Meta-QTL and RNA-seq analysis [J]. Acta Agronomica Sinica, 2022, 48(6): 1372-1388. |
[2] | ZHENG Chong-Ke, ZHOU Guan-Hua, NIU Shu-Lin, HE Ya-Nan, SUN wei, XIE Xian-Zhi. Phenotypic characterization and gene mapping of an early senescence leaf H5(esl-H5) mutant in rice (Oryza sativa L.) [J]. Acta Agronomica Sinica, 2022, 48(6): 1389-1400. |
[3] | ZHOU Wen-Qi, QIANG Xiao-Xia, WANG Sen, JIANG Jing-Wen, WEI Wan-Rong. Mechanism of drought and salt tolerance of OsLPL2/PIR gene in rice [J]. Acta Agronomica Sinica, 2022, 48(6): 1401-1415. |
[4] | ZHENG Xiao-Long, ZHOU Jing-Qing, BAI Yang, SHAO Ya-Fang, ZHANG Lin-Ping, HU Pei-Song, WEI Xiang-Jin. Difference and molecular mechanism of soluble sugar metabolism and quality of different rice panicle in japonica rice [J]. Acta Agronomica Sinica, 2022, 48(6): 1425-1436. |
[5] | YAN Jia-Qian, GU Yi-Biao, XUE Zhang-Yi, ZHOU Tian-Yang, GE Qian-Qian, ZHANG Hao, LIU Li-Jun, WANG Zhi-Qin, GU Jun-Fei, YANG Jian-Chang, ZHOU Zhen-Ling, XU Da-Yong. Different responses of rice cultivars to salt stress and the underlying mechanisms [J]. Acta Agronomica Sinica, 2022, 48(6): 1463-1475. |
[6] | YANG Jian-Chang, LI Chao-Qing, JIANG Yi. Contents and compositions of amino acids in rice grains and their regulation: a review [J]. Acta Agronomica Sinica, 2022, 48(5): 1037-1050. |
[7] | DENG Zhao, JIANG Nan, FU Chen-Jian, YAN Tian-Zhe, FU Xing-Xue, HU Xiao-Chun, QIN Peng, LIU Shan-Shan, WANG Kai, YANG Yuan-Zhu. Analysis of blast resistance genes in Longliangyou and Jingliangyou hybrid rice varieties [J]. Acta Agronomica Sinica, 2022, 48(5): 1071-1080. |
[8] | YANG De-Wei, WANG Xun, ZHENG Xing-Xing, XIANG Xin-Quan, CUI Hai-Tao, LI Sheng-Ping, TANG Ding-Zhong. Functional studies of rice blast resistance related gene OsSAMS1 [J]. Acta Agronomica Sinica, 2022, 48(5): 1119-1128. |
[9] | ZHU Zheng, WANG Tian-Xing-Zi, CHEN Yue, LIU Yu-Qing, YAN Gao-Wei, XU Shan, MA Jin-Jiao, DOU Shi-Juan, LI Li-Yun, LIU Guo-Zhen. Rice transcription factor WRKY68 plays a positive role in Xa21-mediated resistance to Xanthomonas oryzae pv. oryzae [J]. Acta Agronomica Sinica, 2022, 48(5): 1129-1140. |
[10] | WANG Xiao-Lei, LI Wei-Xing, OU-YANG Lin-Juan, XU Jie, CHEN Xiao-Rong, BIAN Jian-Min, HU Li-Fang, PENG Xiao-Song, HE Xiao-Peng, FU Jun-Ru, ZHOU Da-Hu, HE Hao-Hua, SUN Xiao-Tang, ZHU Chang-Lan. QTL mapping for plant architecture in rice based on chromosome segment substitution lines [J]. Acta Agronomica Sinica, 2022, 48(5): 1141-1151. |
[11] | WANG Xia, YIN Xiao-Yu, Yu Xiao-Ming, LIU Xiao-Dan. Effects of drought hardening on contemporary expression of drought stress memory genes and DNA methylation in promoter of B73 inbred progeny [J]. Acta Agronomica Sinica, 2022, 48(5): 1191-1198. |
[12] | LEI Xin-Hui, WAN Chen-Xi, TAO Jin-Cai, LENG Jia-Jun, WU Yi-Xin, WANG Jia-Le, WANG Peng-Ke, YANG Qing-Hua, FENG Bai-Li, GAO Jin-Feng. Effects of soaking seeds with MT and EBR on germination and seedling growth in buckwheat under salt stress [J]. Acta Agronomica Sinica, 2022, 48(5): 1210-1221. |
[13] | WANG Ze, ZHOU Qin-Yang, LIU Cong, MU Yue, GUO Wei, DING Yan-Feng, NINOMIYA Seishi. Estimation and evaluation of paddy rice canopy characteristics based on images from UAV and ground camera [J]. Acta Agronomica Sinica, 2022, 48(5): 1248-1261. |
[14] | KE Jian, CHEN Ting-Ting, WU Zhou, ZHU Tie-Zhong, SUN Jie, HE Hai-Bing, YOU Cui-Cui, ZHU De-Quan, WU Li-Quan. Suitable varieties and high-yielding population characteristics of late season rice in the northern margin area of double-cropping rice along the Yangtze River [J]. Acta Agronomica Sinica, 2022, 48(4): 1005-1016. |
[15] | CHEN Yue, SUN Ming-Zhe, JIA Bo-Wei, LENG Yue, SUN Xiao-Li. Research progress regarding the function and mechanism of rice AP2/ERF transcription factor in stress response [J]. Acta Agronomica Sinica, 2022, 48(4): 781-790. |
|