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降解组测序与剪切位点分析研究进展

2014-09-02崔东亚杨美玲

江苏农业科学 2014年7期
关键词:研究进展

崔东亚 杨美玲

摘要:动植物细胞内都存在大量的microRNA(miRNA),miRNA的功能之一是通过互补指导内切酶切割与之互补的mRNA,对该mRNA进行转录后调控。但miRNA与靶基因之间并不是完全互补,所以通过序列计算方式预测的靶基因中假阳性也是很高的。单独验证预测的靶基因不能确定是否正确且费时费力,高通量测序与计算预测结合可以很好地验证和发现miRNA的靶基因。介绍了寻找miRNA靶基因的降解组测序方法,包括降解组测序的方法原理、试验流程、miRNA靶基因寻找等主要环节。经研究发现,降解组测序已经成为寻找miRNA靶基因的常用方法。

关键词:降解组;miRNA;靶基因;剪切位点;研究进展

中图分类号: Q754 文献标志码: A 文章编号:1002-1302(2014)07-0056-04

收稿日期:2013-10-26

作者简介:崔东亚(1978—),男,河北满城人,硕士,讲师,主要从事分子生物教学与研究。E-mail:dongyacui@163.com。microRNAs(miRNAs)是一类具有特殊功能的小RNA,约为22个核苷酸,具有抑制蛋白质编码基因的功能[1],一些茎环结构的RNA在核酸酶的作用下将茎环结构的RNA剪切加工成miRNA。成熟的miRNA与argonaute(AGO)蛋白结合形成沉默复合体,miRNA与靶基因互补,AGO蛋白则催化剪切靶基因,抑制靶基因翻译蛋白质[2]。

在动物体内RNA剪切活性来自argonaute2 (AGO2),在5′剪切片段留下3′末端羟基(3′—OH),在3′剪切片段留下5′末端单磷酸(5′—p)[3-4],但是在动物体内已证实的miRNA的靶基因还很少[5-6]。尽管如此,人工构建的siRNA(small interfering RNAs)同样可以依据miRNA类似的机制,将靶基因沉默,目前已经成为研究靶基因功能的常用试验技术。

动物的miRNA通常只有部分序列是可以与靶基因完美互补配对的,这段完美互补配对的序列通常是miRNA的第2个至第7个碱基,miRNA中这段与靶基因完全互补的序列为“种子序列”[7-8]。miRNA的3′非种子端序列在一定程度上有增强miRNA对靶基因识别的作用[8]。研究发现,一些非miRNA靶基因也含有1个或多个miRNA结合区域,为了避免非miRNA靶基因也被miRNA剪切,对非miRNA靶基因的保护机制也发生了进化[9-10],但是当导入1个siRNA/miRNA或敲除1个內源miRNA仍然可以引起很多基因表达变化,这些表达变化的基因中包含miRNA剪切的靶基因[11-13]。

植物中miRNA靶基因研究相对比较多,因为miRNA与靶基因互补的现象非常多,且miRNA切割位点比较固定,通常位于mRNA-miRNA互补区域,从miRNA 5′端算起,切割位点位于第10个和第11个核苷酸之间[14-15]。miRNA调控靶基因的方式也很多,1个miRNA可以调控多个靶基因,同时多个miRNA也可以调控同一靶基因。总之,在研究miRNA功能过程中寻找和鉴定miRNA的靶基因非常有必要。

新一代测序可以一次性测定百万级的序列,也可以根据不同需要测定特定类型的RNA,是研究转录组表达和表达调控革命性的技术革新[11]。近几年,新一代测序技术被广泛应用,如寻找新的miRNA[12]。由于植物中miRNA降解靶基因的机制相对比较清晰,所以新一代测序在分析mRNA降解片段中应用较多。借助于高通量测序分析miRNA靶基因的植物有拟南芥、玉米、棉花、草莓、番茄、葡萄、黄瓜、杨树、红豆杉、蝴蝶兰和线虫等众多物种[13-23]。

如何获得miRNA及其降解位点是研究miRNA功能的前提。miRNA通过互补方式识别靶基因,并将靶基因切割。所以,传统的研究方法是通过计算机预测和后续的试验验证[5],在验证过程中须要获得mRNA的5′末端序列;常规方法是通过5′-RACE技术[24-25],该技术首先利用poly(A)分离得到mRNA,去掉mRNA的帽子结构并加1个5′接头序列,或者直接在mRNA 5′末端加入接头。根据接头序列和oligo(dT)或特定序列,PCR扩增可以获得mRNA全部或部分序列,通过Sanger测序可以获得mRNA转录起始位点序列。通过 5′-RACE 技术即可以找到mRNA转录起始序列[26-27],也可以用于mRNA的切割序列分离[24]。通常通过miRNA预测靶基因,然后用5′-RACE对预测的mRNA开展剪切位点研究,即寻找或验证某个特定的mRNA的5′末端序列。

RACE方法是分析RNA末端的常用方法[24],新一代测序可以测定百万条序列[28]。将二者结合,能一次性测得所有RNA的末端序列,辅以生物软件分析末端序列,可以同时验证所有预测的靶基因[29]。这样不仅可以大大提高工作效率,而且还可以从降解片段入手寻找新的miRNA[35-36]。

1MmeⅠ内切酶和降解组建库流程

1.1MmeⅠ是降解组测序重要的酶

降解组分析中的关键是如何找到miRNA-AGO复合物切割的mRNA片段,获得mRNA切割位点序列,无非是获得切割位点处的序列。3′切割片段是mRNA切割产物之一,其5′末端是单磷酸结构,3′末端具有poly(A)结构。测定这样的序列,可以发现所有被剪切的mRNA序列。但这样的序列太长,超出了新一代测序技术的要求。通过化学方法将序列打断,又会将切割位点序列淹没在众多序列之中,造成背景噪音过大。

MmeⅠ从嗜甲基菌(Methylophilus methylotrophus)中分离得到,属于Ⅱ型限制性内切酶。在非回文DNA序列上,MmeⅠ识别5′-TCCRAC-3′序列 (R 表示 G或 A),在识别位点下游20 bp处将双链DNA切割,并在识别序列留下2个碱基的黏性末端。MmeⅠ的识别序列为5′-TCCRAC(N)20-3′[30]。MmeⅠ可以单一地将DNA的切割序列截取下来,单一测定切割位点序列。所以,MmeⅠ是降解组测序过程中的关键酶。

1.2降解组测序技术路线

降解组分析主要分成RNA建库测序与数据分析2个部分。其中,RNA建库是RNA测序前的RNA处理过程。RNA建库很关键,测序目的不同,RNA建库方法也不同[31-32]。根据目的RNA特点,设计特征的RNA建库路线,富集获得感兴趣的RNA,即富集目的RNA过程中同时也在去除污染RNA。降解组测序RNA处理过程有别于其他类型的RNA处理,降解组测序RNA处理过程的实质是获得被miRNA切割的 mRNA 产物[33-34]。通常mRNA 5′ 末端具有帽子结构,3′ 端具有poly(A)结构,mRNA被剪切后,形成2种剪切产物,即5′ 剪切片段和3′ 剪切片段。降解组测序就是要提取3′ 剪切片段,3′ 剪切片段的5′ 具单磷酸结构,3′ 末端具有poly(A)结构[34]。根据3′ 剪切片段特定设计RNA建库流程(图1)。

1.2.1降解组测序基本设计思路第一,根据poly(A)尾巴可将所有带有poly(A)尾巴的RNA分离得到,带有polyA尾巴的RNA主要有完整的mRNA和3′ 剪切片段2种。第二,在3′ 剪切片段5′ 末端加入RNA接头。完整的mRNA 5′ 末端为帽子结构,3′ 剪切片段的5′ 末端为单磷酸。只有单磷酸结构的RNA可以与5′ 接头相连接[35]。5′ 接头序列带有MmeⅠ识别位点,如拟南芥降解组分中5′ RNA接头:5′-GUUCAGAGUUCUACAGUCCGAC-3′,粗体标记为MmeⅠ内切酶识别序列[33]。第三,反转录RNA为DNA。即根据接头序列和oligo(dT)将3′ 剪切片段反转录为双链DNA序列。第四,酶切获得3′ 剪切片段的5′ 末端序列(即切割位点序列)。因为在接头序列中预先引入MmeⅠ识别位点,带有接头的序列会被MmeⅠ切割生成40 bp左右的序列(含接头序列)。第五,在MmeⅠ酶切位点加入DNA接头。MmeⅠ酶切后,分离酶切序列,在MmeⅠ切割位点处连接双链DNA接头,如拟南芥双链DNA接头为5′-TCGTATGCCGTCTTCTGCTTG-3′和其互补链3′-NNAGCATACGGCAGAAGACGAAC-5′[33]。第六,纯化带有双链DNA接头的序列,按照新一代测序流程进行测序。

1.2.2降解组测序设计原理通过mRNA的结构特征、miRNA调控靶基因特征以及结合新一代测序技术来实现。将测序数据回帖转录组,深入比对分析,如果在mRNA序列的某个位点发现1个回帖比对峰值,该峰值就是候选的 miRNA 剪切位点,于是从试验中找到了miRNA的作用靶基因。

1.3降解组数据分析基本流程

高通量建库和测序可以交由测序公司负责处理,因为建库、测序所需要的试剂和设备非常昂贵,有公司负责建库和测序相对比较便宜,而且现在的降解组测序技术已经非常成熟,国内也涌现出多家测序公司,甚至一些大型的科研单位自己就可以直接开展测序服务。但降解组数据分析过程则要求科研人员要熟悉miRNA调控切割靶基因的基本过程,尤其是具备一定的数据处理分析能力。

通过降解组分析可以很轻松地发现miRNA的降解片段,而且也可以根据降解片段发现新的miRNA。降解组数据分析基本分为以下几个过程(图2)。

1.3.1获得clean 数据所谓clean数据是指去除测序接头序列和低质量序列。如果是由公司测序,一般可以直接从测序公司获得clean数据,将clean数据直接用于下一步分析。但NCBI上下载的数据中有时也有测序的原始数据,利用这

些数据分析之前则须要进行数据预处理,基本过程包括:去除测序接头序列、去除简单重复序列、去除带有不确定碱基的序列、测序质量分析等。结合软件操作可以轻松实现数据处理,如FASTQ/A Clipper、cutadapt等。

1.3.2序列比对从miRNA数据库中找到miRNA的序列,与clean数据分别与参考基因匹配,分析每种序列和miRNA的匹配位点。编写脚本程序,找到miRNA匹配位点附近的降解片段。因为降解片段来自mRNA降解,miRNA与mRNA互补,根据这个特征找到降解片段剪切位置周围是否有互补miRNA存在。如果存在miRNA,计算miRNA周围降解片段数量。序列匹配软件很多,常用的有BOWTIE[36]、SOAP2[37]、BWA[38]等。

1.3.3获得靶基因如果miRNA互补区域内存在一个明显的降解片段峰值,则说明该基因很有可能是与之互补的 miRNA 靶基因,通过与周围噪音相比,确定该基因为miRNA的疑似靶基因。

1.3.4图片展示将找到的候选靶基因、miRNA和降解片段位置和数量,通过图展示出来,如常用t-plot展示,t-plot展示可采用R语言绘制;也可以用一般作图软件实现如 Excel (图3)。

2结论与讨论

miRNA是一类非常重要的调控小RNA,通过剪切mRNA来调控靶基因的功能,达到调节生理代谢或发育的目的。miRNA及其调控序列的发现,有助于深入了解miRNA的功能。然而,miRNA调控网络极其复杂,miRNA仅仅通过种子的区域序列就可以达到调控靶基因的功能,所有mRNA可同时受到多种小RNA的调控,或同一种小RNA在同一mRNA上有多个的靶位点,这会给降解组分析带来一定的困难。同时,近年来涌现出来其他类型的小RNA,如22G-RNA、26G-RNA、piRNA等小RNA[39]。这些小RNA是否直接参与mRNA的调控还不清楚,但果蝇piRNA可以通过与转座子RNA序列互补,指导内切酶将转座子RNA切割,阻止转座子转座。但在线虫中,piRNA并不直接参与靶基因的沉默,而是诱导次级小RNA 22G-RNA生成,这些次级22G-RNA调控互补序列沉默,22G-RNA是否直接参与靶基因的切割还不清楚。但有研究发现,一些次级siRNA也可以切割靶基因[40]。小RNA如何调控靶基因一直是研究的热点之一,其中小RNA参与靶基因的切割是研究的一个方向。现在有多个网站提供miRNA靶基因的预测服务[5,41-43],但预测的结果需要试验验证。理论上降解组测序几乎可以一次性验证所有预测的靶基因,可以将预测的靶基因从试验角度加以证实。降解组测序实质是测定具有一定特征的RNA 5′ 末端序列,这类RNA特征是5′ 末端具有单磷酸且3′末端具有 poly(A)[34]。这类RNA可以是来自miRNA指导的mRNA切割产物,也可以是来自mRNA的分解产物[44]。当miRNA的靶基因表达量不高或mRNA分解产物过高时,非特异性的 mRNA 降解片段容易检测到,背景噪音过高,会对miRNA靶基因分析带来困难。但测序技术也在不断提高,相应分析方法也在升级,高通量测序与数据分析技术已经成为生物试验中的常规试验检测分析方法。

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[43]Bu D C,Yu K T,Sun S L,et al. Noncode v3.0:integrative annotation of long noncoding RNAs[J]. Nucleic Acids Research,2012,40:D210-D215.

[44]Deana A C,Belasco J G. The bacterial enzyme RppH triggers messenger RNA degradation by 5′ pyrophosphate removal[J]. Nature,2008,451(7176):355-358.

[31]Malone C,Brennecke J,Czech B,et al. Preparation of small RNA libraries for high-throughput sequencing[J]. Cold Spring Harbor Protocols,2012(10):1067-1077.

[32]Christodoulou D C,Gorham J M,Herman D S,et al. Construction of normalized RNA-seq libraries for next-generation sequencing using the crab duplex-specific nuclease[M]. New York:John Wiley & Sons Inc,2011:12-16.

[33]German M A,Pillay M,Jeong D H,et al. Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends[J]. Nature Biotechnology,2008,26(8):941-946.

[34]German M A,Luo S J,Schroth G,et al. Construction of parallel analysis of RNA ends(PARE) libraries for the study of cleaved miRNA targets and the RNA degradome[J]. Nature Protocols,2009,4(3):356-362.

[35]Zhuang F L,Fuchs R T,Sun Z Y,et al. Structural bias in T4 RNA ligase-mediated 3′-adapter ligation[J]. Nucleic Acids Research,2012,40(7):e54.

[36]Langmead B,Trapnell C,Pop M,et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome[J]. Genome Biology,2009,10(3):R25.

[37]Li R Q,Yu C,Li Y R,et al. SOAP2:an improved ultrafast tool for short read alignment[J]. Bioinformatics,2009,25(15):1966-1967.

[38]Li Heng,Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform[J]. Bioinformatics,2009,25(14):1754-1760.

[39]Bagijn M P,Goldstein L D,Sapetschnig A,et al. Function,targets,and evolution of Caenorhabditis elegans piRNAs[J]. Science,2012,337(694):574-578.

[40]Vaucheret H. MicroRNA-dependent trans-acting siRNA production[J]. Sciences STKE,2005(30):pe43.

[41]Hammell M,Long D,Zhang L,et al. MirWIP:microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts[J]. Nature Methods,2008,5(9):813-819.

[42]Betel D,Koppal A,Agius P,et al. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites[J]. Genome Biology,2010,11(8):R90.

[43]Bu D C,Yu K T,Sun S L,et al. Noncode v3.0:integrative annotation of long noncoding RNAs[J]. Nucleic Acids Research,2012,40:D210-D215.

[44]Deana A C,Belasco J G. The bacterial enzyme RppH triggers messenger RNA degradation by 5′ pyrophosphate removal[J]. Nature,2008,451(7176):355-358.

[31]Malone C,Brennecke J,Czech B,et al. Preparation of small RNA libraries for high-throughput sequencing[J]. Cold Spring Harbor Protocols,2012(10):1067-1077.

[32]Christodoulou D C,Gorham J M,Herman D S,et al. Construction of normalized RNA-seq libraries for next-generation sequencing using the crab duplex-specific nuclease[M]. New York:John Wiley & Sons Inc,2011:12-16.

[33]German M A,Pillay M,Jeong D H,et al. Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends[J]. Nature Biotechnology,2008,26(8):941-946.

[34]German M A,Luo S J,Schroth G,et al. Construction of parallel analysis of RNA ends(PARE) libraries for the study of cleaved miRNA targets and the RNA degradome[J]. Nature Protocols,2009,4(3):356-362.

[35]Zhuang F L,Fuchs R T,Sun Z Y,et al. Structural bias in T4 RNA ligase-mediated 3′-adapter ligation[J]. Nucleic Acids Research,2012,40(7):e54.

[36]Langmead B,Trapnell C,Pop M,et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome[J]. Genome Biology,2009,10(3):R25.

[37]Li R Q,Yu C,Li Y R,et al. SOAP2:an improved ultrafast tool for short read alignment[J]. Bioinformatics,2009,25(15):1966-1967.

[38]Li Heng,Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform[J]. Bioinformatics,2009,25(14):1754-1760.

[39]Bagijn M P,Goldstein L D,Sapetschnig A,et al. Function,targets,and evolution of Caenorhabditis elegans piRNAs[J]. Science,2012,337(694):574-578.

[40]Vaucheret H. MicroRNA-dependent trans-acting siRNA production[J]. Sciences STKE,2005(30):pe43.

[41]Hammell M,Long D,Zhang L,et al. MirWIP:microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts[J]. Nature Methods,2008,5(9):813-819.

[42]Betel D,Koppal A,Agius P,et al. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites[J]. Genome Biology,2010,11(8):R90.

[43]Bu D C,Yu K T,Sun S L,et al. Noncode v3.0:integrative annotation of long noncoding RNAs[J]. Nucleic Acids Research,2012,40:D210-D215.

[44]Deana A C,Belasco J G. The bacterial enzyme RppH triggers messenger RNA degradation by 5′ pyrophosphate removal[J]. Nature,2008,451(7176):355-358.

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