Members of the activity of bc1 complex (ABC1) family are protein kinases that are widely found in prokaryotes and eukaryotes. Previous studies showed that several plant ABC1 genes participated in the abiotic stress response. Here, we present the systematic identification of rice and Arabidopsis ABC1 genes and the expression analysis of rice ABC1 genes. A total of 15 and 17 ABC1 genes from the rice and Arabidopsis genomes, respectively, were identified using a bioinformatics approach. Phylogenetic analyses of these proteins suggested that the divergence of this family had occurred and their main characteristics were established before the monocot-dicot split. Indeed, species-specific expansion contributed to the evolution of this family in rice and Arabidopsis after the monocot-dicot split. Intron/exon structure analysis indicated that most of the orthologous genes had similar exon sizes, but diverse intron sizes, and the rice genes contained larger introns, moreover, intron gain was an important event accompanying the recent evolution of the rice ABC1 family. Multiple sequence alignment revealed one conserved amino acid segment and four conserved amino acids in the ABC1 domain. Online subcellular localization predicted that nine rice ABC1 proteins were localized in chloroplasts. Real-time RT-PCR established that the rice ABC1 genes were primarily expressed in leaves and the expression could be modulated by a broad range of abiotic factors such as H2O2, abscisic acid, low temperature, drought, darkness and high salinity. These results reveal that the rice ABC1 gene family plays roles in the environmental stress response and specific biological processes of rice.
镉是一种非必需的重金属元素,对动植物有严重毒害作用。几个与ABC1(activity of the bc1 complex)家族有关的基因参与植物镉胁迫的应答。本研究从玉米中克隆并鉴定了一个类ABC1基因,命名为ZmABC1-10。该基因cDNA全长2 519 bp,包含一个2 250 bp的开放阅读框,编码一个预测的叶绿体膜蛋白。启动子顺式元件扫描发现该基因含有大量的非生物胁迫、光以及植物激素应答元件。表达模式分析表明,该基因主要在叶片、茎秆等绿色组织中表达。镉处理实验表明,该基因能够被诱导并且受植物发育时期的调控。除镉之外,该基因还受多种非生物因素包括ABA、H2O2、干旱和黑暗的共同调控。此外,本研究利用基因组序列信息共鉴定出19个玉米ABC1基因。对植物界8个代表性物种中148个ABC1蛋白进行系统发育分析表明,在长期进化过程中植物ABC1蛋白已经发生了分化;物种特异性扩张是植物中该家族进化的主要动力。这些结果表明ZmAbc1-10是一个镉应答因子并且可能在植物对非生物胁迫的适应中发挥重要作用。
ABC1(Activity of bc1 complex)家族属于蛋白质激酶家族,其成员普遍存在于原核和真核生物中。已有研究表明,几个植物ABC1基因参与非生物胁迫应答。为了解ABC1基因在水稻中的结构和功能,采用生物信息学方法分别在水稻和拟南芥上鉴定出15个和17个ABC1基因,并进行了系统发育和表达分析。结果表明,该家族在单、双子叶植物分离之前就已经发生了分化,其基本特征已经形成;单、双子叶植物分离之后,该家族在水稻和拟南芥中均以物种特异的方式进行了扩增。内含子/外显子结构分析显示多数直系同源基因之间外显子大小接近,而内含子差别较大,水稻含有更多大的内含子;内含子获得是近期伴随水稻ABC1家族进化的重要事件。多序列比对显示,ABC1结构域具有1个保守的氨基酸片段和4个保守的氨基酸残基。在线亚细胞定位预测9个水稻ABC1蛋白定位在叶绿体上。实时定量RT-PCR分析表明,水稻ABC1基因主要在叶片中表达,并且受多种非生物胁迫因素包括H2O2、脱落酸、低温、干旱、黑暗和高盐的调控。说明水稻ABC1家族不仅在逆境胁迫应答中发挥重要作用,可能还与水稻特定的生理过程有关。
Chromosome segment substitution lines have been created in several experimental models,including many plant and animal species,and are useful tools for the genetic analysis and mapping of complex traits.The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL's effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study.Furthermore,we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects,examined the fully Bayesian SSVS (stochastic search variable selection) approach,tested the empirical Bayes (E-BAYES) method,and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
