项目名称: 利用代谢物谱图及全基因组SNP标记挖掘水稻耐盐候选功能基因
项目编号: No.31471429
项目类型: 面上项目
立项/批准年度: 2015
项目学科: 食品科学、农学基础与作物学
项目作者: 赵秀琴
作者单位: 中国农业科学院作物科学研究所
项目金额: 84万元
中文摘要: 盐害是限制水稻生产的主要非生物逆境之一,明确耐盐生理代谢及相关分子机制有利于解决粮食安全问题。盐胁迫对植株的主要影响之一是渗透胁迫,已有研究发现有多种小分子物质参与了渗透胁迫反应。目前有大量水稻耐盐遗传学及基因水平的研究报道,但忽视了小分子代谢物在耐盐反应中的作用。代谢物是基因表达的下游产物,是连接基因与表型的桥梁,且其遗传力相对较高。针对于此,本项目拟以IR29/Pokkali构建的重组自交系(RIL)群体为研究材料,采用代谢物谱图分析方法,主要利用气质谱联用技术(GC-MS),分析盐处理及对照条件下水稻叶及根部的代谢物特征,高效挖掘耐盐相关代谢物,并结合该群体覆盖全基因组的单核苷酸多态性(SNP)数据,分析相关代谢物的QTL位点及遗传效应;在此基础上,整合生物信息学,挖掘重要代谢物的候选功能基因。研究结果将极大丰富对水稻耐盐代谢机制的理解且有助于推动耐盐育种进程。
中文关键词: 水稻;盐胁迫;代谢物谱图;单核苷酸多态性;候选基因
英文摘要: Salt stress in one of the major environmental factors limiting rice productivity and it's beneficial to solve the problem of food security by understanding the physiological mechanism and the relative molecular basis for salt tolerance (ST). The hiperosmotic stress is one of the major influences of the salt treatments on plants and many small molecular metabolites participate the osmotic adjustments actively. To date, abundant genetic and gene level researches on ST in rice have been reported, however, the functions of metabolites on ST were ignored greatly. The metabolites are the downstream products of the genes and the bridges between genes and phenotypes,meanwhile,they have higher heritability than complex traits. Thus, at present study, the important metabolites related to the ST of rice were analyzed with recombined introgression line (RIL) population derived from the cross between IR29 and Pokkali by metabolic profiling method. The analysis were conducted under both control and salt stress conditions with gas chromatograph-mass spectrometer (GC-MS) technique. The QTL controlling ST metabolites were analyzed with the genome-wide SNP. The candidate genes related to ST metabolites were discovered by combining the information of the important QTLs with bioinformatics.The results presented in this study would improve our understanding on physiological and molecular mechanism of ST in rice and promote the breeding practice.
英文关键词: Rice;Salt stress;Metabolic profiling;SNP;Candidate genes