Discrimination of Alternative Spliced Isoforms by Real-Time PCR Using Locked Nucleic Acid (
科技英语翻译1
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► 2)通顺易懂 ► 译文的语言符合译语语法结构及表达习惯,容易为读者所理解和接受。
► A. When a person sees, smells, hears or touches something, then he is perceiving.
2. Cramped(狭窄的) conditions means that passengers’ legs cannot move around freely.
空间狭窄,旅客的两腿就不能自由活动。
3. All bodies are known to possess weight and occupy space.
忠实、通顺(普遍观点)
► 科技英语文章特点:(well-knit structure;tight logic;various styles)结构严谨,逻辑严密,文体多样
1. 科技翻译的标准:准确规范,通顺易懂,简洁明晰 1)准确规范
所谓准确,就是忠实地,不折不扣地传达原文的全部信息内容。 所谓规范,就是译文要符合所涉及的科学技或某个专业领域的专业语言表
实验结果等,而不是介绍这是这些结果,理论或现象是由谁发 明或发现的。
► In this section, a process description and a simplified process flowsheet are given for each DR process to illustrate the types of equipment used and to describe the flow of materials through the plant. The discussion does not mention all the variations of the flowsheet which may exist or the current status of particular plants. In the majority of the DR processes described in this section, natural gas is reformed in a catalyst bed with steam or gaseous reduction products from the reduction reactor. Partial oxidation processes which gasify liquid hydrocarbons, heavy residuals and coal are also discussed. The reformer and partial oxidation gasifier are interchangeable for several of the DR processes.
分裂可行性问题的算法与应用
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分裂可行性问题的算法与应用分裂可行性问题的算法与应用摘要:分裂可行性问题(Feasibility Splitting Problem,FSP)是一种重要的组合优化问题,其应用涉及到诸多的领域。
目前,研究人员已经提出了多种求解FSP的算法,并取得了较好的效果。
本文首先概述了FSP的定义和特点,然后综述了近年来FSP的求解算法和应用领域,并对其进行了比较和分析。
最后,本文提出了FSP未来研究的方向和挑战,并对其应用前景进行了展望。
关键词:分裂可行性问题;组合优化;求解算法;应用领域;研究前景1、引言分裂可行性问题是一种重要的组合优化问题,其广泛应用于生产调度、物流配送、信道分配等领域。
FSP的目标是将一个复杂问题分解成若干互不干扰的子问题,使得每个子问题都容易求解,并且它们的解可以组成原问题的一个可行解,从而大大降低了问题求解的难度。
近年来,随着计算机技术和算法研究的不断进步,FSP的求解算法也日趋成熟。
2、FSP的定义和特点FSP是一种基于分裂思想的组合优化问题,其定义如下:对于一个给定的可行域S和一个优化目标f(x),FSP的目标是将S分成若干个不相交的子集S1、S2、...、Sm,使得xi∈Si (i=1,2,...,m) 且S1∪S2∪...∪Sm=S,同时最小化∑i∈m f(xi)。
FSP的特点如下:(1)FSP是一个NP-难度问题,没有多项式时间的精确求解算法。
(2)FSP可以通过分解可行解,将原问题转化为若干个具有相同或者相似结构的子问题,使得每个子问题能够在独立的条件下被求解。
3、FSP的求解算法目前,研究人员已经提出了多种不同的求解FSP的算法,主要包括基于粒子群优化的算法、基于差分进化算法的算法、基于遗传算法的算法、基于模拟退火算法的算法等。
下面将对这些算法进行简要介绍。
(1)基于粒子群优化的算法:该算法基于粒子群优化思想,通过模拟鸟群捕食活动的过程来搜索问题的最优解。
该算法具有收敛速度快、搜索能力强的优点。
特异性免疫应答的特点及其机制
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Vk
Germline
Jk
Ck
Rearranged 1° transcript
SplicedmRNA
Ig重链V区基因重排
Cm
VH 1-123
DH1-27
JH 1-9
Ig重链C区基因重排
Cm Primary transcript RNA AAAAA
Each domain of the H chain is encoded by a separate exon
的特异性免疫无反应状态
其本质是对抗原特异应答的T与B细胞,
在抗原刺激下,不能被激活产生特异免疫 效应细胞,从而不能执行正免疫应答效应 的现象。
免疫耐受与免疫抑制的比较
免疫耐受 产生原因 细胞系消失或不活化 Ts细胞的抑制作用 可先天或后天获得, 特别是免疫功能未成 熟或减弱时容易形成 高 长期,一时性或终生 免疫抑制 免疫活性细胞发育缺 损或增殖分化障碍 先天缺损或人为产生, 如X-射线,免疫抑制 药物,抗淋巴细胞血 清的作用 无 一时性
中枢性记忆性T细胞 具有较强TCR信号和较低的共刺激信号活 化阈,产生细胞因子的量多
效应性记忆性T细胞
具有直接胞毒活性,可产生大量的细胞 因子和穿孔素
记忆性细胞的产生
记忆性T细胞的产生
中枢性记忆性T细胞,来源于刚刚活化的初 始T细胞,因IL-2水平较低或存在高水平 IL-15而分化为记忆性T细胞
可传递记忆-指围产期个体抵抗性记忆-个体通过感染或接种而再次
接触相同抗原,可产生较初次应答更快、
更强的应答
免疫记忆细胞的生物学特征
记忆性T、B细胞 是指对特异性抗原有记忆能力、寿命较 长的淋巴细胞。当再次遇到相同抗原后,
MapSplice Accurate mapping of RNA-seq reads for splice junction discovery
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MapSplice:Accurate mapping of RNA-seq reads for splice junction discoveryKai Wang 1,Darshan Singh 2,Zheng Zeng 1,Stephen J.Coleman 3,Yan Huang 1,Gleb L.Savich 4,Xiaping He 4,Piotr Mieczkowski 4,Sara A.Grimm 4,Charles M.Perou 4,James N.MacLeod 3,Derek Y.Chiang 4,Jan F.Prins 2and Jinze Liu 1,*1Department of Computer Science,University of Kentucky,Lexington,KY 40506,2Department of Computer Science,University of North Carolina,Chapel Hill,NC 27599-3175,3Gluck Equine Research Center,Department of Veterinary Science,University of Kentucky,Lexington,KY 40546-0099and 4Department of Genetics and UNC Lineberger Comprehensive Cancer Center,University of North Carolina,Chapel Hill,NC 27599-7295,USAReceived April 25,2010;Revised June 21,2010;Accepted June 28,2010ABSTRACTThe accurate mapping of reads that span splice junctions is a critical component of all analytic tech-niques that work with RNA-seq data.We introduce a second generation splice detection algorithm,MapSplice,whose focus is high sensitivity and spe-cificity in the detection of splices as well as CPU and memory efficiency.MapSplice can be applied to both short (<75bp)and long reads (!75bp).MapSplice is not dependent on splice site features or intron length,consequently it can detect novel canonical as well as non-canonical splices.MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy.We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data.Experimental studies also support the accuracy of the algorithm.Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alterna-tive splicing on a global level as well as the differ-ences between molecular subtypes of breast cancer.These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions.Software download URL:y .edu/p/bioinfo/MapSplice.INTRODUCTIONAlternative splicing is a fundamental mechanism that generates transcript diversity.Particular combinations of cis -acting sequences,trans -acting splicing regulators and histone modifications contribute to differential exon usage among diverse cell types (1,2).Through shuffling of exons,splice sites and untranslated regions can drastically alter the cellular function of proteins (3,4).Notably,SNPs have been linked to changes in transcript isoform proportions among different individ-uals (5).In some cases,rare mutations that alter splicing patterns have been linked to disease (6–9).Thus,tran-scriptome profiling should comprise a comprehensive survey of alternative splicing.Microarrays were the first technology to enable global assessment of alternative splicing (10–13).Oligo-nucleotides designed to span two adjacent exons can be used to measure the abundance of splice junctions.However,these splice junction probes only interrogate a predefined set of transcript isoforms.Due to the large number of hypothetical exon–exon combinations,micro-arrays are not efficient at discovering novel transcript isoforms.Deep transcriptome sequencing provides sufficient read counts to measure relative proportions of transcript isoforms,as well as to discover new isoforms (1,14–17).Several high-throughput technologies currently sample short sequence tags,typically <200bp in size.The accurate mapping of sequence tags that span splice junctions is the foundation for transcript isoform recon-struction (18,19).One approach relies on existing tran-script annotations to create a database of potential splice junction sequences.Similar to the above limitation with microarrays,the construction of predefined align-ment databases limits the set of possible splice junctions interrogated.Recently methods have been developed to find novel splice junctions from short sequence tags.The pioneering QPALMA algorithm adopted a machine learning*To whom correspondence should be addressed.Tel:+18592573101;Fax:+18593231971;Email:liuj@Published online 28August 2010Nucleic Acids Research,2010,Vol.38,No.18e178doi:10.1093/nar/gkq622ßThe Author(s)2010.Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (/licenses/by-nc/2.5),which permits unrestricted non-commercial use,distribution,and reproduction in any medium,provided the original work is properly cited.algorithm to predict splice junctions from a training set of positive controls(20).The TopHat algorithm con-structs candidate splice junctions by pairing candidate exons and evaluating the alignment of reads to such candidates(21).SpliceMap is another method that uses splice siteflanking bases in locating potential splice sites(22).We introduce the MapSplice algorithm to detect splice junctions without any dependence on splice site features. This enables MapSplice to discover non-canonical junc-tions and other novel splicing events,in additional to the more common canonical junctions.MapSplice can be generally applied to both short and long RNA-seq reads.In addition,MapSplice leverages the quality and diversity of read alignments that include a given splice to increase specificity in junction discovery.As a result, MapSplice demonstrates high specificity and sensitivity. Performance results are established using synthetic data sets and validated experimentally.We have used MapSplice to investigate significant dif-ferences in alternative splicing between a set of basal and luminal breast cancer tissues.Experimental validation of 20exon skipping events by quantitative RT–PCR(qRT–PCR)correctly identified isoform proportions that are highly correlated(Pearson’s correlation=0.86)with their estimates based on splice junctions.Splice junctions also recapitulated the difference between molecular subtypes of breast cancer.On a global level,the propor-tion of splice junctions in various categories of alterna-tive splicing was concordant with a previous RNA-seq study.MATERIALS AND METHODSThe goal of MapSplice is tofind the exon splice junctions present in the sampled mRNA transcriptome,and to determine the most likely alignment of each mRNA sequence tag to a reference genome.Each tag corresponds to a number of consecutive nucleotides read from an mRNA transcript,where the length of the tag is determined by the protocol and the sequencing technol-ogy.For example,the Illumina Genome Analyzer IIx gen-erates over20M tags of size up to100bp per sequencing lane.MapSplice operates in two phases to achieve its goal.In the‘tag alignment’phase,candidate alignments of the mRNA tags to the reference genome G are determined. Tags with a contiguous alignment fall within an exon and can be mapped directly to G,but tags that include one or more splice junctions require a gapped alignment,with each gap corresponding to an intron spliced out during transcription.Since multiple possible alignments may be found,the result of this phase is,in general,a set of can-didate alignments for each tag.In the‘splice inference phase,’splice junctions that appear in the alignments of one or more tags are analyzed to determine a splice significance score based on the quality and diversity of alignments that include the splice.The purpose of this phase is to reject spurious splices and to provide a basis for choosing the most likely alignment for each tag based on a combination of align-ment quality and splice significance.An overview of the algorithm can be found in Figure1.The two phases are described in the following two sections.Tag alignmentLetÂbe the set of tags and let m be the tag length.A tag T2Âhas an‘exonic alignment’if it can be aligned in its entirety to a consecutive sequence of nucleotides in G. T has a‘spliced alignment’if its alignment to G requires one or more gaps.MapSplice identifies candidate tag alignments in three steps.First,tags are partitioned into consecutive short segments and an exonic alignment to G is attempted for each segment.In the second step,segments that do not have an exonic alignment are considered for spliced align-ment using a splice junction search technique that starts from neighboring segments already aligned.In thefinal step,segment alignments for a tag are merged tofind can-didate overall alignments for each tag.The details of the steps follow next.Step1:partition tags into segments.Tags inÂof length m are partitioned into n consecutive segments of length k where k m=2.Typically k is20–25for tags of length50or greater.As k is decreased,the chance that a segment contains one or more splice junctions decreases correspondingly,but the chance for multiple spurious alignments of the segment increases.The segments making up a tag T are labeled t1,t2,...,t n where the number of segments n¼mÄÅ:Step2:exonic alignment of segments.Exonic alignment of segments can be performed using fast approximate aligners such as Bowtie(23),and BWA(24),or aligners with more general error tolerance models such as SOAP2 (25),BFAST(26)and MAQ(27).For each segment t i of tag T,let n i be the number of possible exonic alignments of t i to the genome,determined using one of the algorithms mentioned above with an error tolerance of k mismatches.When n i¼1the segment has a unique exonic alignment.When n i>1the segment has multiple alignments,each of which is considered in the subsequent steps.Step3:spliced alignment of segments.If n i¼0,segment t i does not have an exonic alignment.One possible reason is that it may have a gapped alignment crossing a splice junction.In general,if the minimum exon length is at least2k,then for every pair of consecutive segments in T at least one segment should have an exonic alignment. Therefore,the alignment of segment t i is localized to the alignments of its neighbors.The following two techniques are used tofind a spliced alignment for segments,and are illustrated in Figure2.If t iÀ1and t i+1both have exonic alignments,then we perform a‘double-anchored’spliced alignment of t i for all combinations of exonic alignments of t iÀ1and t i+1.If only one neighboring segment t j has an exonic alignment,e178Nucleic Acids Research,2010,Vol.38,No.18P AGE2OF14Figure 1.An overview of the MapSplice pipeline.The algorithm contains two phases:tag alignment (Step 1–Step 4)and splice inference (Step 5–Step 6).In the ‘tag alignment’phase,candidate alignments of the mRNA tags to the reference genome G are determined.In the ‘splice inference’phase,splice junctions that appear in one or more tag alignments are analyzed to determine a splice significance score based on the quality and diversity of alignments that include the splice.Ambiguous candidate alignments are resolved by selecting the alignment with the overall highest quality match and highest confidence splice junctions.P AGE 3OF 14Nucleic Acids Research,2010,Vol.38,No.18e178then we perform a ‘single-anchored’alignment starting from the n j possible alignments of t j .(a)Double-anchored spliced alignment:the splicedalignment of t i to the genomic interval between anchors t i À1and t i +1need only consider the k +1possible positions of the splice junction x within t i and minimize alignment mismatch.Formally,the ‘Hamming distance’D H ðS ,T Þbetween two equal length sequences S and T is defined as the number of corresponding positions with mismatching bases.We define the spliced alignment between the segment t ½1:k and the genomic interval G ½i :j asspliced-align ðt ½1:k ,G ½i :j Þ¼min arg 1 x <k D H ðt ½1:x ,G ½i :i +x À1 Þ+D H ðt ½x +1:k ,G ½j Àðk Àx Þ+1:j Þwhich yields the optimal position x of the splice junction in t that gives the best spliced alignment to the given genomic interval.The splice junction x defines the intron as G ½i +x À1:j Àðk Àx Þ+1 :To find the spliced alignment for t i between two aligned segments,let g i À1and g i +1be the leftmost genomic coordinates in the alignment oft i À1and t i +1,respectively,and compute spliced–align ðt ½1:k ,G ½g i À1+k :g i +1À1 Þ:In case the alignment cost for the splice junction exceeds the error tolerance threshold k ,the align-ment for t i fails.If there exists more than one splice junction position with minimum cost for t i ,multiple alignments are recorded for t i :(b)Single-anchored spliced alignment:in the case of asingle anchor t i À1upstream of the unaligned t i ,we conduct a search for s i ,the h -base suffix of t i in the genomic region downstream from t i À1:Similarly,in the case of a single anchor t i +1downstream from t i ,the search is for the h -base prefix p i of t i in the region upstream from t i +1:In either case this search is limited in range by a parameter D ,the maximum intron size for single anchor search,typically set to 50000bp.All single anchored alignments can be resolved with a single traversal of (the expressed portion of)the genome using a sliding window of size D .An h -mer index is maintained during this traversal,mapping occurrences of an h -mer p i to the downstream anchor t i +1within distance D and occurrences of an h -mer s i to the upstream anchor t i À1within distance D .As the window moves,new entries are added as anchors come within range,and old entries are deleted as anchors fall out of range.When the h -mer at the current coordinate c in the genome scan is mapped to a downstream segment t i +1,spliced-align ðt ½1:k ,G ½c :g i +1À1 Þgives the best spliced alignment which is recorded if it is within the segment error threshold "k .Similarly,when the h -mer is mapped to an upstream segment t i À1,we record spliced-align ðt ½1:k ,G ½g i À1+k :c +h Þif it is within the segment error threshold k .(c)Spliced alignment in the presence of small exons:ifan exon shorter than 2k is included in a transcript,it is possible that two adjacent segments t i and t i +1both include a splice junction so that neither can be aligned continuously within an exonic region.If the exon is shorter than k ,even a single segment might include more than one splice junction.The following approach allows us to detect exons with size less than 2k .Assume S is a sequence of one or two missed segments between two anchors ½a ,b that cannot be successfully aligned in the previous steps,potentially due to short exons.We divide S into a sequential set of h -mers and index S with these h -mers.By extend-ing the sequential scan of the genome used in single-anchored spliced alignment,h -mers on the ref-erence genome in ½a ,b can all be searched simultan-eously.When a match exists,two double-anchored spliced alignments will be performed:one is between a and the 50-site of the h -mer alignment;and the other is between the 30-site of h -mer align-ment and b .According to the pigeon-hole principle,if the exon is no shorter than 2h ,one of the h -mers in the un-aligned segments will fall within an exon and thus trigger the subsequent spliced alignments.Therefore,this method is guaranteed to detect small exons longer than 2h and possibly detect shorter exons.The typical h -mer size is 6–8bps.When exons are shorter than 2h ,the chances of finding a spliced alignment decrease.Reducing h will lead to an increasing number of spurious matches that will be difficult to filterout.GenomemRNA tag Tt 1t 2t 3t 4kkhj 1j 23n o x e 2n o x e 1n o x e Figure 2.A portion of an mRNA transcript sampled by tag T consists of the 30end of exon 1,all of exon 2and the 50end of exon 3.T is split into segments t 1,...,t n each of length k to identify the alignment of T to the genome.Provided no exon has a length less than 2k nucleotides,at least one of every two consecutive segments must have an exonic alignment.In this example with n ¼4,segments t 1and t 3have exonic alignment.Segment t 2has spliced alignment;the splice junction j 1can be easily discovered using the double-anchor search method starting from t 1and t 3.The spliced alignment for t 4is discovered by searching downstream in the genome for an occurrence of the suffix h -mer of t 4.When such an occurrence is found,the double-anchor search method is used to evaluate a possible splice junction j 2between t 3and the h -mer occurrence.e178Nucleic Acids Research,2010,Vol.38,No.18P AGE 4OF 14Step 4:merging segment alignments.The assembly of a complete tag alignment from individual alignments of its segments is straightforward if each segment is aligned uniquely and connects to its neighboring segments without a gap.However,a given segment t i may be aligned at multiple locations.In this case,the possible combinations of alignments must be searched to find the best overall alignment for the tag.Let i be the set of alignments for segment t i and when0<n i ,let j i be the j -th alignment of t i ,where 1 i n and 1 j n i .In principle there exist Qn i ¼1n i different combinations of alignments,but most can be ruled out by a simple coherence test based on contiguity of consecu-tive segment alignments.Two adjacent segments t i and t i +1with exonic alignment that are not contiguous on the genome are checked for a splice junction between the two segments using the double-anchored spliced alignment method.This proced-ure also corrects inaccurate splice points due to the error tolerance in the alignment of t i and t i +1.For each assembly of segments that yields a candidate alignment for T ,we compute its ‘mismatch score’,a modified Hamming distance between T and its alignment to the genome G T .The mismatch score takes into account base call qualities when available.A poor quality base call can improve the score when associated with a mismatched base,but can also decrease the score when associated with a matched base (28).The base call quality for a given base x in the overall alignment of a tag T can be converted to a probability p that x was called incorrectly,and so the expected mismatch s ðx ,y Þin the alignment of base x to base y in the genome is given bys ðx ,y Þ¼ð1Àp Þ=f x ,x ¼yp =ð1Àf x Þ,x ¼y &ð1Þwhere f x is the probability of base x in the background dis-tribution of nucleotides.We assume a uniform distribu-tion for nucleotides hence f x ¼1=4for all x .Thus,given a proposed alignment of T ¼b 1...b m and G T ¼g i 1...g i m and p i the probability that nucleotide b i was called incor-rectly,the expected mismatch is E ½mismatch ðT ,G T Þ ¼Æj ¼1,m s ðb j ,g i j Þ:The candidate alignment is retained if E ½mismatch ðT ,G T Þ k ,otherwise it is discarded.Note that while each segment was aligned allowing "k mismatches,the overall tag alignment only allows a total of k expected mismatches.We define the quality of the alignment to be k ÀE ½mismatch ðT ,G T Þ :Splice junction inferenceSplice junction alignments introduce a multiplicity of ways in which a tag may be split into pieces,each of which may be separately aligned to the genome.For a given tag,at most one of these is the true alignment.Splice inference leverages the extensive sampling of splice junctions by tags to compute a junction quality that can be used to distin-guish true splice junctions from spurious splice junctions and to determine the best alignment among the remaining candidate alignments of a tag.Step 5:splice junction quality.For a given splice J ¼ðJ d ,J a Þ,where J d is the last coordinate of the donor exon and J a is the first coordinate of the acceptor exon,we consider the set A ðJ Þof tags that include a splice junction for J in a candidate alignment.We define two statistical measures on A ðJ Þ:the ‘anchor significance’s ðA ðJ ÞÞ,determined by the alignment in A ðJ Þthat maximizes sig-nificance as a result of long anchors on each side of the splice junction,and the ‘entropy’h ðA ðJ ÞÞ,measured by the diversity of splice junction positions in A ðJ Þ:(i)Anchor significance of a splice junction:a tag that includes a splice junction has some number of con-tiguous bases aligned on either side of the splice site.An alignment with a short anchor on one side has low confidence,since we expect it to be easy to find other occurrences of the sequence of nucleotides found in the short anchor each of which might equally well be the correct target.We define the anchor significance of a splice J in a tag T 2A ðJ Þas follows.Let T p be the maximal contiguous sequence of bases in T with exonic alignment ending at coordinate J d in the genome,and let T q be the maximal contiguous sequence of bases in T with exonic alignment starting from coordinate J a :These are the two anchors,each of which has at least one alignment (the one in T ).The expected number of alignments in the genome for an anchor T a is therefore given byE ðT a Þ¼1+D 4j T a jif T a found by single anchor search 1+N4j T a j otherwise &Here,we model the genome as a sequence of inde-pendent random variables with uniform distributionover A,C,T,G ,so that the chance that a length n sequence aligns at a given coordinate is simply 4Àn .For double-anchor alignments,the search space is effectively the entire genome of length N .For single anchor alignments,we only consider occurrences within distance D .Since we assume that only one of the potential alignments is correct and the rest are spurious,the chance of a spurious alignment of anchor T a is 0<1ÀE ðT a ÞÀ1<1:Thus the log-transformed significance of anchor T a is s ðT a Þ¼Àlog 2ð1ÀE ðT a ÞÀ1Þ:The anchor alignment of junction J in T is only as significant as the anchor with least confidence,hence is s T ðJ Þ¼min ðs ðT p Þ,s ðT q ÞÞ:The anchor significance of junction J over all occur-rences in T 2A ðJ Þis the occurrence with greatest anchor significance:s ðA ðJ ÞÞ¼max T 2A ðJ Þs T ðJ Þ(ii)Entropy:in principle,the RNA-seq protocolsamples each transcript uniformly,so that the position of a true splice junction J within A ðJ Þis expected to be uniformly distributed on 1::m ,provided the sampling is sufficiently deep and theP AGE 5OF 14Nucleic Acids Research,2010,Vol.38,No.18e178splice junction is not too close to the end of the transcript.To measure the uniformity of the sampling,we apply Shannon maximum entropy to the distribution of splice junction positions in AðJÞfor splice J.Let p i with1i<m be the frequency of occurrence of a splice junction J at position i within AðJÞ:The Shannon entropy can be measured ashðAðJÞÞ¼ÀX1i<mp i log2p iThe higher the Shannon entropy,the closer the dis-tribution is to uniform,and therefore the higher the chance that the junction is part of some transcript that was sampled uniformly.(iii)Combined Metric:the combined metric pðJÞis the posterior probability that junction J is a true junction determined using Bayesian regression.The observed data of pðJÞare the entropy and anchor significance of J within AðJÞand the average quality of read alignments that include J.pðJÞ¼ ÁsðAðJÞÞ+ ÁhðAðJÞÞ+ ÁqðAðJÞÞ+"We apply linear regression to obtain the best config-uration of , and that achieves the maximum sensitivity and specificity in junction classification.Step6:best alignment for tags.For each tag T,we select the candidate alignment T G that achieves the highest score when combining alignment quality from Step4and junction quality from Step5.Synthetic data generation for validationTo evaluate the sensitivity and specificity of MapSplice, we generated synthetic data sets of tags derived from transcripts cataloged in the Alternative Splicing and Transcript Diversity(ASTD)database(29).This database collects full-length transcripts illustrating alternative splicing events in genes from human,mouse and rat.A synthetic‘transcriptome’is generated by randomly selecting genes and expression levels according to an empirical distribution of tags per gene observed in Ref.(1).Within a gene,transcripts are selected at random following various submodels that determine expression level of individual transcripts relative to the overall gene. The synthetic transcriptome characterized in this fashion is then sampled to yield two synthetic RNA-seq data sets. The noise-free data set samples the transcripts exactly and the resultant tags align to the reference genome exactly to model single-nucleotide variations in the data base tran-scripts.The noisy data set introduces mutations into base calls following empirical Illumina base call quality profiles.The resulting data sets mimic the observed distri-bution of errors in tags in Ref.(30).Experimental validation by qRT–PCRTotal RNA isolated from MCF-7and SUM-102cells was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit with RNase inhibitor (Applied Biosystems,Foster City,CA,USA)as per manu-facturer’s instructions.Relative expression levels of the transcripts of interest were determined by qRT–PCR on the Applied Biosystems7300Real Time PCR System with premade or custom TaqMan Gene Expression Assays (Applied Biosystems,Foster City,CA,USA)containing primersflanking the splice sites of interest and FAM/ MGB-labeled oligonucleotide probes.PCR reactions were carried out as per manufacturer’s instructions. cDNA equivalent to100ng of total RNA was amplified with1m l of TaqMan assay in Gene Expression master mix in a total volume of20m l.Each assay was performed in triplicate.Thermal Cycling conditions were as follows: 50 C for2min,95 C for10min,40cycles of95 C for 15s and60 C for1min.C t values were determined in the manufacturer’s software,data was further analyzed in Excel utilizing comparative C t method.For compari-sons of relative expression levels between the two cell lines, C t values for the transcripts of interest werefirst normalized to those of HPRT1.RESULTSJunction inferenceWe constructed a synthetic noise-free RNA-seq data set with20M100bp tags sampling46311distinct transcripts from the ASTD.The tags were aligned to the reference genome(hg18)using the MapSplice algorithm steps1–4 with k¼25,h¼8and k¼1:To establish the training data set containing both true and false junctions,no re-strictions on splice siteflanking sequences or maximum intron size were enforced.We randomly selected10K true junctions and10K false junctions as a training set and analyzed the three different junction classification metrics utilized in Step5of MapSplice:alignment quality entropy and anchor signifi-cance,as well the combined metric obtained by linear regression of thefirst three metrics.Five-fold cross-validation was applied to avoid sample bias in training.The ROC curve that illustrates the sensitivity and specificity of each metric is shown in Figure3.The combined metric(solid green curve)offers better classifi-cation results than individual metrics simply because indi-vidual metrics only capture one property of a junction. At the best point,the combined metric achieves a true-positive rate of96.3%and false-positive rate of8%. We also compared the results with one of the most commonly used metrics:junction coverage(the number of tags aligned to a junction).In many studies,a junction is considered to be true if at least three tags are aligned to the junction.However,as shown in Figure3, coverage(solid red curve)is the least reliable metric and yields the worst performance in terms of junction classification.The specific parameters , and in the combined metric obtained from this synthetic data set were used for all data sets processed by MapSplice in this article. Slightly improved sensitivity can be obtained by using par-ameters obtained by logistic regression that are specialized for data sets with specific tag lengths.Experiments on thee178Nucleic Acids Research,2010,Vol.38,No.18P AGE6OF14。
五种lsat逻辑推理错误解析
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五种lsat逻辑推理错误解析我们都知道逻辑错误,一般指思维过程中违反形式逻辑规律的要求和逻辑规则而产生的错误。
如“偷换概念”、“偷换论题”、“自相矛盾”等。
但是你们知道五种lsat逻辑推理错误吗?我们一定明白:逻辑推理:主要是指遵循逻辑规律来分析推理的思路,把不同排列顺序的意识进行相关性的推导就是逻辑推理。
在同一思维过程中,同一个概念或同一个思想对象,必须保持前后一致性,亦即保持确定性,这是逻辑推理的一条重要思维规律。
以下五种典型的逻辑错误,在LSAT逻辑部分最为常见:1. % vs.absolute no.2. representative sample3. necessity vs. sufficency4. correlation vs. causation5. alternative possibility这几种错误是对偶的关系:比如,当看到出现百分比/比例/单位含量等时,首先要想的是绝对数量上的变化,反之亦然,当看到出现绝对数量时,考虑比例上的变化;又如,当人举一个例子,想推而广之,得出一种带有普遍性的结论时(与类比不同,类比是from case to case),就要考虑他举的这个例子具有代表性与否,如果是出现在逻辑错误类型的试题中时,问题往往就出在这里;必要条件与充分条件不用多讲,是到底谁推出谁的问题,充分条件时能合理地推出结论,而必要条件时就不行,题目往往是把两者加以混淆;一般关系与因果关系题,常见的是A after B, therefore B casues A. 其实,两者的关系也许是A cause B,也许是C causes both A and B,或者AB根本没什么关系。
涉及其他可能性的题目,太多了。
其实也与因果有关。
当结论说A causes B,或者隐含了A 就是B的原因时,其假设就是没有其他原因同时起作用。
如果有可能是其他原因在发生作用,说A导致了B就不能保证了。
这种错误往往是忽视了其他某个可能的其他原因。
A BIOINFORMATICS PIPELINE FOR THE EXPERIMENTAL VALIDATION OF TUMOR SPECIFIC ALTERNATIVE SPL
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A BIOINFORMATICS PIPELINE FOR THEEXPERIMENTAL VALIDATION OF TUMOR SPECIFICALTERNATIVE SPLICING VARIANTSNatanja Kirschbaum-Slager, Raphael Bessa Parmigiani, Helena Brentani, Anamaria A.Camargo, Sandro J. de Souza.Ludwig Institute for cancer research, Sao Paulo, BrazilABSTRACTAlternative splicing is a major source of genetic diversity(1) and it has been shown to be involved in the development of many diseases(2).Several genes have been shown to spe-cifically express certain isomers in tumors(3).The identific-ation of such tumor specific variants is of great therapeutic and diagnostic value.In our laboratory a transcriptome relational database was de-veloped in which ESTs and full lengths were aligned to the human genome sequence(unigene release153,dbEST re-lease July2002and genome built29).The database shows all genes and their isoforms that can possibly be processed by exon skipping, a specific form of alternative splicing.A pipeline was created in order to screen this database for tumor expressed exons and putative tumor candidates are be-ing validated experimentally.The expression pattern of iso-forms of21000genes containing at least one full length mRNA sequence was screened for tumor specific splicing variants.We found4308clusters with at least two alternat-ive splicing variants in brain,2524in lung,1563in breast and1435clusters in prostate.While screening for variants containing an exon that was expressed in one tissue in tumor and not in any non tumorous tissue,only5candidates were found.However,when analyzing within one tissue for vari-ants that were expressed in tumor tissue but not in the nor-mal counterpart,we found675candidates.Variants where considered candidates for experimental validation when they were confirmed by at least two ESTs from different libraries from the same tumorous ing this strategy we found 19candidates for brain tumor,12for lung,13for prostate and28for breast tumors.Our first experimental data show that3out of5brain candidates might indeed be preferen-tially expressed in two types of brain tumor tissues as com-pared to normal brain tissue.Of these genes,2appeared to be generally overexpressed in tumor and one showed tumor preferred expression of one variant only.Future experiments will show the exact expression pattern of all tumor expressed variants in the four tissues and suitable candidates will be in-vestigated functionally.REFERENCES[1] Modrek, B. and Lee, C. A genomic view of alternative splicing. Nature Genetics 2002, 30:13-18.[2]Krawczak, M., Reiss, J., Cooper, D.N. The mutationalspectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences. Hum. Genet. 1992, 90: 41-54.[3] Caballero OL, de Souza SJ, Brentani RR, Simpson AJ. Alternative spliced transcripts as cancer markers. DisMarkers. 2001;17(2):67-75.。
Inside the Human Genome-chapter-3[1]
![Inside the Human Genome-chapter-3[1]](https://img.taocdn.com/s3/m/aa527cfc910ef12d2af9e7d7.png)
PRINTED FROM OXFORD SCHOLARSHIP ONLINE (). (c) Copyright Oxford University Press, 2003 - 2010. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see /oso/public/privacy_policy.html). Subscriber: Guangdong Medical College; date: 28 October 2010
Inside the Human Genome, A Case for Non-Intelligent Design Avise, John C., Dis tinguis hed Profes s or of Ecology and Evolutionary Biology, Univers ity of California, Irvine Print publication date: 2010, Publis hed to Oxford Scholars hip Online: May 2010 Print ISBN-13: 978-0-19-539343-9, doi:10.1093/acprof:os o/9780195393439.001.0001
Split Genes
Geneticists had long supposed that each protein-specifying gene in humans and other animals was a straightforward linear sequence of nucleotides coding for a particular polypeptide. But in 1977, research teams led by Richard Roberts and Phillip Sharp independently announced a startling discovery: protein-coding genes routinely include noncoding sequences interspersed with the coding segments. The intragenic spacers between coding sequences became known as introns and the coding regions henceforth were termed exons. Thus, each standard gene in a eukaryotic organism (any creature whose cells contain nuclei) consists of alternating exons and introns. This unanticipated discovery of “split genes” earned Roberts and Sharp a Nobel Prize in 1993.
Multiscale modeling of alternative splicing regulation
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Multiscale Modeling of Alternative SplicingRegulationDamien Eveillard1,Delphine Ropers2,Hidde de Jong3,Christiane Branlant2,and Alexander Bockmayr11LORIA,Universit´e Henri Poincar´eBP239,54506Vandœuvre-l`e s-Nancy,FranceDamien.Eveillard@loria.fr2Laboratoire de Maturation des ARN et Enzymologie Mol´e culaireUMR7567CNRS-UHP,BP239,54506Vandoeuvre-l`e s-Nancy,France3INRIA Rhˆo ne-Alpes,Helix Project655avenue de l’Europe,Montbonnot,38334Saint Ismier,FranceAbstract.Alternative splicing is a key process in post-transcriptionalregulation,by which several kinds of mature RNA can be obtained fromthe same premessenger ing a constraint programming approach,we model the alternative splicing regulation at different scales(single sitevs.multiple sites),thus exploiting different types of available experimen-tal data.1IntroductionAlternative splicing is a biological process occurring in post-transcriptional reg-ulation ofRNA.Through the elimination ofselected introns,alternative splicing allows generating several kinds ofmRNA f rom the same premessenger RNA.The combinatorial effect ofsplicing contributes to biological diversity,especially in the case ofthe human immunodeficiency virus(HI V-1).Recent biological studies show the impact that SR proteins have on the dynamics ofpost-transcriptional regulation via the control ofthe splicing process[8].SR proteins can be divided into two functional classes:they may either activate or inhibit splicing.Due to the complexity ofalternative splicing regulation,the knowledge that can be gained from experiments is limited.Each experiment focus on one splicing site. In afirst approach,we model SR regulation in this restricted ing differential equations,we develop a qualitative model for the A3splicing site in H V-1.The qualitative behavior depends on the values ofthe reaction kinetic parameters.Experimental results available to us validate thisfirst approach in the equilibrium phase.Our second approach aims at validation on a higher scale. The ultimate goal is to obtain a model that can be validated qualitatively both on the scale ofa single splicing site and on the scale ofthe whole HI V-1,in order to represent the global effect ofalternative splicing in the HI V-1cycle.Part of this work was done within the ARC INRIA“Process Calculi and Biology of Molecular Networks”,http://contraintes.inria.fr/cpbioC.Priami(Ed.):CMSB2003,LNCS2602,pp.75–87,2003.c Springer-Verlag Berlin Heidelberg200376Damien Eveillard et al.Our models are developed in a constraint programming framework[2,3]. Constraint programming seems well-suited for modeling biological systems be-cause it allows one to handle partial or incomplete information on the system state.Each constraint gives one piece ofinf ormation on the system.The overall knowledge is accumulated in the constraint store.The constraint engine avail-able in constraint programming systems operates on the constraint store.It may add new information to the store or check whether some property is entailed by the information already available.While a constraint model may be refined whenever additional biological knowledge becomes available,it allows one to make useful inferences even from partial and incomplete information.Therefore,constraint programming seems to be a natural computational approach to face the current situation in systems biology as it is described by B.Palsson[16]:“Because biological information is incomplete,it is necessary to take into account the fact that cells are subject to certain constraints that limit their possible behaviors.By imposing these con-straints in a model,one can then determine what is possible and what is not,and determine how a cell is likely to behave,but never predict its behavior precisely.”The organisation ofthe paper is as f ollows:we start in Sect.2with a de-scription ofthe biological process ofalternative splicing regulation.Based on a number ofbiological hypotheses,we develop a continuous model ofthe regu-lation at one splicing site.This model includes competition and compensation ofdifferent proteins on two binding sites,ESE and ESS2.The single-site model is validated in a qualitative way by extracting from the model a splice efficiency function,which can be measured in experiments.In Sect.3,wefirst simulate the single-site continuous model in the hybrid concurrent constraint programming language Hybrid cc[9,10].Then we derive a more global model involving two generic splicing sites,which may be generalized to multiple sites.This means that we model at two different scales,using the splice efficiency as an abstrac-tion ofthe local model ofone site in the more global context ofdifferent sites. The two-site model uses the constraint solving and default reasoning facilities of Hybrid cc.This allows us to make predictions on the global behavior even in absence ofdetailed local inf ormation on some ofthe splicing sites.2The Biological Problemof Alternative Splicing Regulation2.1Biological ProcessEukaryotic and virus gene expression is based on production ofRNA contain-ing intronic sequences.The process ofsplicing allows f or intron elimination and junction ofexonic sequences[14],see Fig.1.Splicing is a major biological pro-cess in the HIV-1life cycle:the viral RNA either remains unchanged to serve as genomic RNA for new virions,or it is alternatively spliced to allow for the production ofvirion proteins[26].The viral genome,initially RNA,is integratedMultiscale Modeling of Alternative Splicing Regulation77Fig.1.Representation ofthe splicing processinto the host genome.In the HIV-1case,splicing regulation is a complex phe-nomenon involving4donors sites(SD)and8acceptors sites(SA),which may yield40proteins[17].This combinatorial complexity is achieved by regulating the selection ofthe acceptor site[15,17].Protein factors control the regulation via binding sites.We focus in our study on the acceptor site A3,where splicing can be repressed by hnRNP A/B via the ESS2binding site[4,6],see Fig.2.Recent experimental studies carried out in our group[18]show that an ESE sequence can activate splicing at the A3site when SC35and ASF/SF2proteins bind to it.More specif-ically,the ratio ofhnRNP A/B and SR proteins determines the splice efficiency at the A3site.2.2Biological HypothesesWe model the regulation by SR proteins in the restricted context ofthe A3 splicing site(see Fig.2)under the following hypotheses:–We study only one splicing site.Thus,we consider regulation at the scale corresponding to our experimental results.These yield the splice efficiency as the ratio ofthe mature RNA over premessenger RNA.SC35Fig.2.Regulatory elements ofthe A3splicing site78Damien Eveillard et al.–We suppose that the splicing process involves two steps,relating three func-tional classes ofRNA:immature,intermediate,and mature RNA,see Fig.1.Intermediate RNA corresponds to immature RNA activated by proteins.Mature RNA corresponds to mature RNA and introns in lariat.–The protein concentration in experiments is saturated.Therefore,we as-sume that it is constant,despite the binding ofproteins to the RNA during regulation.–SR proteins have two functions.They regulate the splicing process,and they initialize the splicing machinery.–Regulation is controlled by the ESE and ESS2binding sites,which are inde-pendent.Thus,only indirect interaction is possible between ESE and ESS2.–The SR proteins ASF/SF2and SC35may activate thefirst splicing reaction by binding to the site ESE.We assume that these two proteins compensate each other.–The hnRNP proteins may inhibit thefirst splicing reaction by binding to the site ESS2.On the other hand,ifthe SC35proteins bind to ESS2,this activates thefirst splicing reaction.Therefore we have a competition between hnRNP and SC35.–Due to a lack ofexperimental results,we assume Michaelis-Menten kinetics for all proteins.Our goal is a qualitative model validated at equilibrium.Thus,we do not consider transient states.Our biological hypotheses are summarized in Fig.3 RASFFig.3.Schematic representation ofthe splicing site regulation2.3Formal ModelThe biological hypotheses can be translated into a mathematical formalism, which leads to a system ofordinary differential equations based on the Michaelis-Menten relation[22].The single-site model will later be integrated into a largerMultiscale Modeling of Alternative Splicing Regulation79 multi-site model,see Sect.3.We describe the splicing process by three ordi-nary differential equations(ODEs)corresponding to the three functional classes ofRNA.Two terms represent thefirst splicing reaction.Thefirst term repre-sents the cooperation between ASF/SF2and SC35in the regulation ofESE. Since we assume compensation,only the sum ofactivator proteins is important. We represent this interaction by a Michaelis-Menten function depending on the quantity ofimmature RNA and controlled by the sum ofthe proteins ASF/SF2 and SC35[13]:ϕESE(ASF+SC)k ESE+ASF+SCrnaThe symbols used are given in Tab.1.The second term captures the antago-nistic function of hnRNP and SC35proteins on the site ESS2.In this case,the Michaelis-Menten function represents the inhibitive competition between two proteins:hnRNP and SC35[27].t depends on the quantity ofintermediary RNA:ϕR×Rk R(1+SCk SC+R ) rnaIThe second splicing reaction is modeled by a simplefirst order kinetic with con-stant parametersκandκ .Different RNAs decrease proportional to the same degradation factorλ.We formalize the biological process by the system of dif-ferential equations:d(rna) dt =ϕR×Rk R(1+SCk SC+R)rnaI−ϕESE(ASF+SC)k ESE+ASF+SCrna−λ·rnad(rnaI)dt =ϕESE(ASF+SC)k ESE+ASF+SCrna−ϕR·Rk R(1+SCk SC+R)rnaI−κ·rnaI +κ ·rnaM−λ·rnaId(rnaM)dt=κ·rnaI−κ ·rnaM−λ·rnaM2.4Validation of the Regulatory SystemThe formal model of regulation at a single-site can be directly simulated in the constraint programming language Hybrid cc,see Sect.3.1.However,before do-ing this,it shouldfirst be validated with respect to existing biological knowledge.A mathematical analysis ofthe ODE system in Sect.2.3shows that the partial derivatives in the Jacobian matrix have two characteristic properties:–the partial derivatives on the diagonal elements are negative.–the partial derivatives on the extra diagonal elements are positive.80Damien Eveillard et al.Table 1.Symbols and units for the biological variables and parametersSymbol Variables and Parametersunit rna Immature RNA µM rnaI Intermediary RNA µM rnaM Mature RNA µM ASF Protein ASF/SF2µM SC Protein SC35µM R Protein hnRNP µM ϕESE Maximal affinity for the enhancer s −1ϕR Maximal affinity of hnRNP s −1k ESE Half saturation coefficient for the enhancer µM k SC Half saturation coefficient for SC35µM k R Half saturation coefficient for hnRNP µM κReaction rate s −1κReaction rates −1λDegradation coefficient s −1In our model,the RNA concentrations do not reach an equilibrium,but continue to decrease until total degradation ofRNA.However,the above two properties imply that the splice efficiency defined byefficiency (t)=rnaM (t )rna (t )reaches an equilibrium [1].From the condition d efficiency /dt =0,we may derive the following formula for the splice efficiency in the equilibrium phase:efficiency =κ·ϕESE (ASF +SC )(k R ·k SC +k R ·SC +R ·k SC )κ (k ESE +ASF +SC )·ϕR ·R ·k SCIt is reasonable to assume that this equilibrium is reached after a short tran-sient phase,so that it can be measured by experiments.According to our formula,the splice efficiency is–an increasing function of the activators SC and ASF .–a decreasing function of the inhibitor R .Experimental results show that–rnaM/rna increases with an increase ofactivator proteins.–rnaM/rna decreases with an increase ofinhibitor proteins.These results ofour model correlate with available experimental data.In summary,the model may be considered as qualitatively validated under the hypotheses described in Sect.2.2.We next consider simulation in the concurrent constraint language Hybrid cc .Multiscale Modeling of Alternative Splicing Regulation81 3Multiscale Modeling and Simulation with Hybrid cc3.1Hybrid Concurrent Constraint Programmingn constraint programming,the user specifies constraints on the behavior ofthesystem that is being studied.Each constraint expresses some partial information on the system state.The constraint solver may check constraints for consistencyor infer new constraints from the given ones.In concurrent constraint program-ming(cc),different computation processes may run concurrently.Interaction is possible via the constraint store.The store contains all the constraints cur-rently known about the system.A process may tell the store a new constraint, or ask the store whether some constraint is entailed by the information currently available,in which case further action is taken[19].One major difficulty in theoriginal cc framework is that cc programs can detect only the presence ofinf or-mation,not its absence.To overcome this problem,[20]proposed to add to the cc paradigm a sequence ofphases ofexecution.At each phase,a cc program is executed.At the end,absence ofinf ormation is detected,and used in the next phase.This results in a synchronous reactive programming language,Timed cc.But,the question remains how to detect negative information instantaneously. Default cc extends cc by a negative ask combinator if c else A,which im-poses the constraints of A unless the rest ofthe system imposes the constraint c. Logically,this can be seen as a default.Introducing phases as in Timed cc leads to Timed Default cc[21].Only one additional construct is needed:hence A, which starts a copy of A in each phase after the current one.binators of Hybrid ccAgents Propositionsc c holds nowif c then A if c holds now,then A holds nowif c else A if c will not hold now,then A holds nownew X in A there is an instance A[T/X]that holds nowA,B both A and B hold nowhence A A holds at every instant after nowalways A same as(A,hence A)when(c)A same as(if c then A,hence(if c then A))unless(c)A else B same as(if c then B,if c else A)Hybrid cc[9,10]is an extension of Default cc over continuous time.First continuous constraint systems are allowed,i.e.,constraints may involve differen-tial equations that express initial value problems.Second,the hence operator is interpreted over continuous time.It imposes the constraints of A at every real time instant after the current one.The evolution of a system in Hybrid cc is piecewise continuous,with a sequence ofalternating point and interval phases.82Damien Eveillard et al.All discrete changes take place in a point phase,where a simple Default cc program is executed.In a continuous phase,computation proceeds only through the evolution oftime.The interval phase,whose duration is determined in the previous point phase,is exited as soon as the status ofa conditional changes[10]. Tab.2summarizes the basic combinators of Hybrid cc.Hybrid cc is well-suited for modeling dynamic biological systems,as argued in[2,3].3.2Single-Site Model:Local ModelingThe single-site model from Sect.2.3with experimental values can be expressed directly in Hybrid cc.interval t,rna,rnaI,rnaM;t=0;rna=0.06;rnaI=0;rnaM=0;always{t’=200;rna’=(Pr*R*rnaI)/(kr*(1+(SC/ksc))+R)-(Pese*(ASF+SC)*rna)/(kese+ASF+SC)-delta*rna;rnaI’=(Pese*(ASF+SC)*rna)/(kese+ASF+SC)-(Pr*R*rnaI)/(kr*(1+(SC/ksc))+R)-k*rnaI+kk*rnaM-delta*rnaI; rnaM’=k*rnaI-kk*rnaM-delta*rnaM;}sample(rna,rnaI,rnaM);During the simulation,we obtain the predicted equilibrium for the splice effi-ciency,see Fig.4.Under our hypotheses,which include protein competition and compensation,the model correctly simulates the alternative splicing activity. This supports the hypotheses made in the model such as the role ofthe ESE and ESS2binding sites.3.3Two Site Model:Global ModelingA realistic model ofalternative splicing has to reflect the combinatorial com-plexity discussed in Sect.2.1.Assuming that regulation is modular[12],the single-site model may be seen as one module inside a larger framework.The qualitative validation given in Sect.2.4justifies the introduction ofthe single-site model into a larger scale model involving several splicing sites.To illustrate this,we consider the generic example oftwo splice acceptor sites(SA)associated with one donor site(SD),see Fig.5.The behavior at one splicing site can be captured by a single function,the splice efficiency,which depends on the protein concentrations.This function is used in a larger-scale global model that describes the choice between two acceptor sites A3and A4.In the HIV-1case,the A3site is the default splicing site.Only ifthe splice efficiency eff1gets smaller than eff2,regulation switches to the other state.Recent work[5]shows the linearity ofthe splicing kinetics.Thus,on the larger scale,we may consider splicing as a linear process described by two ordinaryMultiscale Modeling of Alternative Splicing Regulation83Fig.4.splicing reactionfor new scaleFig.5.Single-site model inside a general splicing regulation modeldifferential equations involving some kinetic constant.Thefirst system represents the default behavior characterized by the constant Ka4.Following local changes on a single-site,the model may exhibit a different behavior,characterized by the kinetic constant Ka3.Thus,in this example,only the kinetic constants change, while the overall structure ofthe ODE system remains the same.This default behavior can be naturally expressed in Hybrid cc using the combinator unless(c)A.84Damien Eveillard et al.always{if(eff1<=eff2){rna’=-Ka3*rna;rnam1’=0;rnam2’=Ka3*rna;};unless((eff1<=eff2)){rna’=-Ka4*rna;rnam1’=Ka4*rna;rnam2’=0;};}Note that unless(c)A is not equivalent to if¬c then A.The second alterna-tive will be chosen ifthe solver cannot verif y that(eff1<=eff2).This may have two reasons:–(eff1<=eff2)is false,i.e.(eff1>eff2),or–(eff1<=eff2)is unknown(default behavior).Simulation in Hybrid cc yields the behavior illustrated in Fig.6.We observe the switch from thefirst system of ODEs to the second when eff2passes the upper threshold for eff1.4Conclusion and Further ResearchOur approach combines mathematical and computational methods.Mathemati-cal analysis allows us to validate the single-site model in a qualitative way.This is possible using the experimental data obtained in our group.The validationMultiscale Modeling of Alternative Splicing Regulation 85shows the consistency ofour biological hypotheses.Based on this,we can ex-tract the splice efficiency as a suitable abstraction ofthe local behavior at one site inside a more global model involving different sites.For the experimental biologist,the single-site model may serve as a computational tool to evaluate his knowledge on a fine-grained biological process.On the computational side,the constraint solving and default reasoning ca-pabilities of Hybrid cc allow us to exploit as much as possible the incomplete knowledge that is typical for ongoing biological research.Indeed,default be-havior may compensate the lack ofexperimental data.Thus,using constraint programming,we can delimit with our model the possible splicing behavior.Similar to mathematical analysis,constraint reasoning therefore may provide a powerful qualitative validation.The combination ofmathematical analysis and computational methods is also the key to the multiscale modeling developed in this paper.It leads to the qualitative validation represented by the extraction ofthe splice efficiency function.The splice efficiency characterizes the modularity of the regulation.Thus,the smaller-scale behavior is represented in the larger-scale model,based on the single-site splice efficiency.The extraction ofa suitable criterion on the smaller scale is crucial to understanding an experimental process from a systems biology perspective.Furthermore,constraints can be used to handle the problem ofmissing data in a multiscale model.Different scales usually correspond to biological experiments yielding different types ofresults.Despite the variety of possible experiments,these must be integrated into a global model in order to better understand the biological process.Multiscale modeling requires a close interaction between biological and com-putational approaches,as illustrated by Fig.7.ComputationalBiological problem cycleexperiment cycle Modelvalidation & simulationin vitro experimentsConstraint programming Refinement in biological & computional approachBiological experiment validation tools inferred biological propertiesFig.7.Representation ofideal interactions f or modeling biology86Damien Eveillard et al.I n the context ofalternative splicing regulation,we are currently workingon new experimental data for the quantitative validation of our models.On the computational side,we have integrated our model into a general HIV-1 model[11].Preliminary results show that the modification ofa splice constant may induce different behaviors in the HIV-1life cycle ing the extended model,we may validate several biological hypotheses on the global effect of alternative splicing in the full HIV-1life cycle.References[1]Bernard,O.,and Gouz´e J.-L.:Transient behavior of biological models as a toolof qualitative validation-Application to the Droop model and to a N-P-Z model.J.Biol.Syst,4(3)(1996)303–31480[2]Bockmayr,A.,Courtois,A.:Modeling biological systems in hybrid concurrentconstraint programming(Abstract).In2nd Int.Conf.Systems Biology,ICSB’01, Pasadena,CA(2001)76,82[3]Bockmayr,A.,Courtois,A.:Using hybrid concurrent constraint programmingto model dynamic biological systems.In18th International Conference on Logic Programming,ICLP’02,Copenhagen Springer,LNCS2401(2002)76,82[4]Caputi,M.A.,Mayeda,M.A.,Krainer,A.R.,and Zahler,A.M.:hnRNP A/Bproteins are required for inhibition of HIV-1pre-mRNA splicing.EMBO18(14) (1999)4060–777[5]Dautry,F.,Weill,D.:Kinetic analysis of mRNA metabolism.In InterdisciplinarySchool on imaging,modelling and manipulating transcriptional regulatory net-works,Ambleteuse(2002)20/9282[6]Del Gatto-Konczak,F.,Olive,M.,Gesnel,M.C.,and Breathnach,R.:hnRNPA1recruited to an exon in vivo can function as an exon splicing silencer.Mol.Cell.Biol.19(1)(1999)251–6077[7]Graveley,B.R.,Hertel,K.J.,Maniatis,T.:A systematic analysis of the factorsthat determine the strength of pre-mRNA splicing enhancers.EMBO17(22) (1998)6747–6756[8]Graveley,B.R.:Sorting out the complexity of SR protein functions.RNA6(2000)1197–121175[9]Gupta,V.,Jagadeesan,R.,Saraswat V.:Computing with continuous change.Science of computer programming30(1-2)(1998)3–4976,81[10]Gupta,V.,Jagadeesan,R.,Saraswat V.,Bobrow,D.G.:Programming in hybridconstraint languages.In Hybrid Systems II,226–251.Springer,LNCS999(1995) 76,81,82[11]Hammond,B.J.:Quantitative Study of the Control of HIV-1Gene Expression.J.Theor.Biol.163(1993)199–22186[12]Hartwell,L.H.,Hopfield,J.J.,Leibler,S.,Murray,A.W.:From molecular tomodular cell biology.Nature402(1999)C47–C5282[13]Heinrich,R.,Schuster,S.:The regulation of cellular systems.Int’l ThomsonPublishing,New York(1996)79[14]Moore,M.J.,Query,C.C.,Sharp,P.A.:Splicing of precursors to mRNA by thespliceosome.In The RNA World,Cold Spring Harbor Laboratory Press(1993) 303–35776Multiscale Modeling of Alternative Splicing Regulation87 [15]O’Reilly,M.,McNally,M.T.,Beemon,K.L.:Two strong5’splice sites and com-peting,suboptimal3’splice sites involved in alternative splicing of human im-munodeficiency virus type1RNA.Virology213(2)(1995)373–8577[16]Palsson,B.:The challenges of in silico biology.Nature Biotechnology18(2000)1147–115076[17]Purcell,D.F.,Martin,M.A.:Alternative splicing of human immunodeficiencyvirus type1mRNA modulates viral protein expression,replication,and infectivity.J.Virol.67(11)(1993)6365–637877[18]Ropers, D.,Ayadi,L.,Jacquenet,S.,M´e reau, A.,Thomas, D.,Mougin, A.,Bilodeau,P.,Stoltzfus,C.M.,Gattoni,R.,St´e venin,J.,Branlant,C.:A complex regulation of the central A3to A5acceptor sites in HIV-1RNA.In Eukaryotic mRNA Processing(2001)77[19]Saraswat,V.A.:Concurrent constraint programming.ACM Doctoral DissertationAwards.MIT Press(1993)81[20]Saraswat,V.A.,Jagadeesan,R.,Gupta,V.:Foundations of timed concurrentconstraint programming.In9th Symp.Logic in Computer Science,LICS’94, Paris IEEE(1994)71–8081[21]Saraswat,V.A.,Jagadeesan,R.,Gupta,V.:Timed default concurrent constraintprogramming.Journal of Symbolic Computation22(5/6)(1996)475–52081 [22]Segel,L.A.:Modelling dynamic phenomena in molecular and cellular biology.Cambridge University Press(1984)78[23]Si,Z.H.,Amendt,B.A.,Stoltzfus,C.M.:Splicing efficiency of human immun-odeficiency virus type1tat RNA is determined by both a suboptimal3’splice site and a10nucleotide exon splicing silencer element located within tat exon2.N.A.R.25(4)(1997)861–867[24]Si,Z.H.,Rauch,D.,Stoltzfus,C.M.:The exon splicing silencer in human im-munodeficiency virus type1Tat exon3is bipartite and acts early in spliceosome assembly.Mol.Cell.Biol18(9)(1998)5404–13[25]Staffa,A.,Cochrane,A.:The tat/rev intron of human immunodeficiency virustype1is inefficiently spliced because of suboptimal signals in the3’splice site.J.Virol.68(5)(1994)3071–9[26]Tang,H.,Kuhen,K.L.,Wong-Staal,F.:Lentivirus replication and regulation.Annu.Rev.Genet33(1999)133–17076[27]Voit,E.:Computational Analysis of Biochemical Systems.Cambridge UniversityPress(2000)79。
RNA的选择性剪切
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RNA的选择性剪切
三、选择生剪接的功能和生物学意义 1. 选择性剪接是在RNA水平调控基因表达的 机制之一。 2. 选择性剪接使生物表型具有多样性与复杂 性。
在很多情况下,有内含子的基因首先转录成 RNA 前体,之后内含子被去除,外显子被拼接 到一起。成熟的mRNA只含有外显子序列。 我 们把内含子的删除过程称为RNA的剪接
路漫漫其修远兮, 吾将上下而求索
通过对mRNA序列与结构基因核苷酸序列的比较,我们可以确定外显子 与内含子的接合点。发现,在一个内含子的两端并不存在广泛的相似性或 互补性。然而,接点还是具有很好的保守性,尽管只是短短的一致序列。
RNA的选择性剪切
二 单个基因选择性剪接的方式
1. 内含子的保留; 2. 可变外显子的保留或切除; 3. 3’和5’剪接位点的转移(shift)导致外 显子的增长或缩短。
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RNA的选择性剪切
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心理学专业英文词汇【D】
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disability 失能disability 无能disability glare 失能眩光disadvantage 不利disadvantaged 社会地位低下的disadvantaged children 生活条件差的儿童disadvantaged children in schooldisaffect 使疏远disagreeableness 不愉快disallow 拒绝承认disappearance of conditional reflex 条件反射的消失disappearing difference 渐退差别disappearing differences method 差别渐减法disappointing 失望disappointment 失望disapprobation 非难disarming 消除敌意disarranged sentence testDisarranged Sentence Test 词句重组测验disassimilation 异化作用disassociation 分裂disaster 灾难disaster psychology 灾害心理学disavow 不承认disbelief 不相信disbelieve 不相信discard 抛弃discarded variable 抛弃变量抛 变项discern 辨出discerning 有辨别力的discernment 辨出discharge 放射discharge 释放discharge of affect 情感释放情感释放dischronation 时间觉障碍时间觉障碍disciplinary measure 纪律措施纪律措施disciplinary method 维纪方法维纪方法disciplinary problem 纪律问题纪律问题discipline 纪律discipline 学科disclaim 放弃disclosure of self 内心表露discombobulate 扰乱discombobulating theory 困乱论困乱论discomfort 不舒适discomfort glare 不舒适眩光discomfort glare rating 不舒适眩光等级评定discomfort index 不舒适指数discomposure 不安disconfirmed expectancy 预期落空disconjunctive concept 选言概念disconnection syndrome 分离综合症disconnection theory 分离说disconsolate 忧郁的discontinuity 间断性discontinuity condition 不连续条件discontinuity hypothesis 非连续假说discontinuity theory 非连续论非连续论discontinuity theory of learning 非连续学习论discontinuous variable 离散型随机变量discontinuous volume 非连续容量discord 不和谐discordance 不一致性discounting principle 折扣原则discounting rule of attribution 归因折扣原则discouragement 沮丧discouraging 令人沮丧的令人沮丧的discourse 话语discourse analysis 话语分析话语分析discourteous 不礼貌的discourtesy 粗鲁行为discover 发现discovery learning 启发式学习discovery method 发现法discovery method of teaching 启发式教学法discovery procedure 发现程序发现程序discovery teaching method 启发式教学法discredit 怀疑discrepancy 不符合discrepancy 差距discrepancy score 差异分数discrete 不连续的discrete measure 间断量数discrete probability distribution 间断机率分布discrete stimulus 间断刺激discrete trial 个别试验discrete variable 分散变量discrete variable 间断变量discretization 离散化discrimeter 辨别计discriminability 辨别力discriminability index 辨别力指标discriminable 可辨别的discriminal dispersion 辨别离散discriminal process 辨别过程辨别过程discriminance 辨别法discriminant 判别式discriminant analysisdiscriminant functiondiscriminant functiondiscriminant validity 区分效度区分效度discriminate 区别discriminate against 歧视discriminated operant 辨别操作discriminating 辨别的discriminating power 鉴别力discrimination 辨别discrimination 歧视discrimination box 辨别箱discrimination examination 甄别考试discrimination factor 区别因素区别因子discrimination index 辨别指数辨别指数discrimination learning 辨别学习discrimination method 辨别法discrimination reaction 辨别反应discrimination time 辨别时间辨别时间discrimination training 辨别训练discriminational 歧视的discriminative cues 辨别线索辨别 索discriminative power 辨别力discriminative reaction time 辨别反应时discriminative stimulus 辨别刺激discriminator 辨别者discriminatory 差别对待discriminatory analysis 识别分析discuss 讨论discussion 讨论discussion leader 议事领袖discussion method 讨论法discussion with pictures 看图谈话disdain 轻视disease 疾病disembosom 倾吐disengagement theory of aging 老年减少参与论disequilibration 不平衡disequilibrium 失衡disesteem 轻视disesthesia 感觉迟钝disexuality 双重性特征disfunction 机能失常disgnosia 认知障碍disguise 假装disguised test 蒙蔽测验disgust 厌恶dishabituation 习惯化解除习惯化解除disharmonious 不和谐的disharmony 不和谐dishearten 失去勇气dishonesty 不诚实disillusion 醒悟disillusionment 醒悟disincentive 不利诱因disinhibition 去抑效应disinhibition 抑制解除disinhibition theory 抑制解除说disintegrate typedisintegrated personality 分裂性人格disintegrated type 分裂型disintegration 分裂disintegration 解体disintegration of consciousness 意识解体disintegrative method of learning 分解学习法disjunction 析取disjunction type 析取型disjunctive concept 析取概念选言概念disjunctive set 不连接的组集disk file 磁盘存储器dislike 厌恶dismal 忧郁的dismay 惊愕disobey 不服从disorder 失调disorder 障碍disorder behavior 失常行为disorder of affective tone 情调变态disorder of body schema 身体图式障碍disorder of memory 记忆障碍记忆障碍disorder of sleep wake scheduledisorder of thought stream 思流障碍disorganization 错乱disorganized schizophrenia 错乱型精神分裂症disorientation 定向障碍disorientation 迷向disowning projection 否认投射否认投射disparage 轻视disparate 不同的disparate point 不相称点disparate retinal point 网膜不相称点disparate sensation 不同感官感觉disparity 差异dispassionate 平心静气的平心静气的dispatch 调度dispersed learning 分散学习分散学习dispersion 分散度dispersion 离中趋势dispersion of liability for guilt 罪责扩散dispersive 分散的dispersive process 分散过程分散过程dispersivity 分散性dispirited 没有精神的displace 移置displaced aggression 移置攻击移置攻击displaced aggression 转向攻击转向攻击displaced speech 移位言语displacement 移置作用displacement 转移displacement activitydisplacement behaviordisplacement of affectdisplacement of goal 目标换置目标换置displacement of role 角色代替角色代替displacement of visual movement 视动移位displacement threshold 运动距离阈display control compatibilitydisplay 显示display luminance 显示器亮度display model 显示模型display panel 显示仪表板显示仪表板display unit 显示器displays 显示器disposition 气质disposition 性情dispositional 素质上的dispositional attribution 素质归因disproportion 不相称disquietude 不安disregard 不理disrelish 不喜爱disrespect 无礼disruption 分裂disruptive behavior 破坏行为破坏行为disruptive behavior disorder 破坏行为疾病disruptive camouflage 破坏性伪装dissatisfaction 不满足dissatisfactory 令人不满的令人不满的dissatisfied 不满的dissatisfied factors 非满足因素dissected sentence testdissemble 掩饰dissent 异议disservice 危害dissimilarity coefficient 异化系数dissimilation 异化dissimulation 虚饰dissociated anesthesia 分离性感觉缺失dissociated learning 孤立学习孤立学习dissociated personalitydissociation 分裂dissociation 离解dissociation anesthesia 分离性感觉缺失dissociation hysteria 分离性癔症dissociation theory 分离说dissociative 分裂的dissociative amnesia 离解失忆症dissociative disorder 解离性障碍dissociative histeria 解离性歇斯底里dissociative neurosis 解离型神经官能症dissociative pattern 解离模式解离模式dissociative reaction 解离反应解离反应dissociative type 解离型dissolute 无节制的dissolution 退化dissonance 不协调dissonance arousaldissonance reduction 失谐消减dissonance theory 失谐论dissonant informationdissuasion 劝阻dissymmetry 不相称distal 末梢的distal 远的distal color 远色distal receptor 远距感受器远距感受器distal response 远反应distal stimulus 远刺激distal variable 远变量distance 距离distance assimilation 非邻近同化distance constancy 距离常性距离常性distance cues 距离线索distance depth cue 距离深度线索distance estimation 距离估计距离估计distance judgment 距离判断距离判断distance method 距离法distance of visual cognition 视认距离distance paradox 距离的矛盾distance perception 距离知觉距离知觉distance receptor 远距感受器distance sense 远距感觉distance vision 远距视觉distance zone 亲疏空间distant control 遥控distaste 厌恶distinction 差别distinctive competencies 独特资能distinctive feature 辨别属性辨别属性distinctive information 独特性信息distinctiveness 特殊性distinguish 区别distort 曲解distorted room 视物变形小屋distorted view 偏见distorted vision 斜视distortion 变相distortion 曲解distortion 失真distortion of reality 现实曲解现实曲解distortion of stimulus condition 刺激条件的改变distracter 误选项distractibility 分心作用distractibility 注意力分散注意力分散distractible 分心的distraction 分心distraction 扰乱distraction in communication 沟通的误解distraction item 扰乱项目distraction task 扰乱任务distraction technique 分心技术distractor 扰乱项目distractor 误选项distractor technique 扰乱项目法distress 苦恼distress call 苦难喊叫distressful 令人苦恼的distribute 分配distributed learning 分配学习分配学习distributed practice 分配练习分配练习distributed repetition 分配复习distributed thinking 分配思维分配思考distribution freedistribution free testsdistribution 分布distribution curve 分配曲线distribution method 分布法distribution of attention 注意分配distribution of illumination 照明分布distribution of practice 练习分布distribution on curvedistributional analysisdistributive 分配的distributive education 职业教育distributive justice 分配公平分配公正distributive lattice 分配点阵分配点阵distributively 分配上地distributivitydistributor 分配者distributor 经销商distributor 配置器distrust 不信任disturb 困扰disturbance 障碍disturbance of association 联想障碍disturbance of consciousness 意识障碍disturbance of light sense 光觉障碍disturbance of memorydisturbance of visual field 视野障碍disturbed children 失调儿童失调儿童disuse 失用disuse principle 失用原则disvolution 退化dither 发抖diurnal 昼夜的divergence 分散性divergence tendency 离中趋势离中趋势divergent association 发散联想divergent lines illusion 分散线条错觉divergent phenomenadivergent production 发散性生产divergent question 扩散性问题divergent thinking 扩散性思考diversiondiversion 导离diversion hypothesis 导离假设导离假设diversity 差异性diversity 多种性divertive play 娱乐性游戏娱乐性 戏divided attention 分散注意divided attention method 分散注意法divided brain 分脑divided self 分裂自我divination 预言divination 占卜divine cure 心灵治疗diving accident 潜水事故division 分配Division 1. General Psychology 普通心理学Division 10. Psychology and the Arts 心理学和艺术Division 12. Clinical Psychology 临床心理学Division 13. Consulting Psychology 顾问心理学Division 14. Society for Industrial and Organizational Psychology 工业和组织心理学Division 15. Educational Psychology 教育心理学Division 16. School Psychology 学校心理学Division 17. Counseling Psychology 咨询心理学Division 18. Psychologists in Public Service 公共服务心理学者Division 19. Military Psychology 军事心理学Division 2. Society for the Teaching of Psychology 心理学教学Division 20. Adult Development and Aging 成人发展和老年Division 21. Applied Experimental and Engineering Psychologists 应用实验和工程心理学Division 22. Rehabilitation Psychology 康复心理学Division 23. Society for Consumer Psychology 消费心理学Division 24. Theoretical and Philosophical Psychology 理论和哲学心理学Division 25. Experimental Analysis of Behavior 行为的实验分析Division 26. History of Psychology 心理学史Division 27. Society for Community Research and Action 社区研究与活动Division 28. Psychopharmacology and Substance Abuse 心理药理学及药物滥用 心理药理学及药物滥用Division 29. Psychotherapy 心理治疗Division 3. Experimental Psychology 实验心理学Division 30. Psychological Hypnosis 心理催眠Division 31. State Psychology Association Affairs 州心理学会事务Division 32. Humanistic Psychology 人本心理学Division 33. Mental Retardation and Developmental Disabilities 智力落后发育性伤残Division 34. Population and Environmental Psychology 人口和环境心理学Division 35. Psychology of Women 妇女心理学Division 36. Psychology of Religion 宗教心理学Division 38. Health Psychology 健康心理学Division 39. Psychoanalysis 心理分析Division 40. Clinical Neuropsychology 临床神经心理学Division 41. American Psychology Law Society 法律心理学Division 42. Psychologists in Independent Practice 独立开业者Division 43. Family Psychology 家庭心理学Division 44. Society for the Psychology Study of Gay Issues 同性恋心理学研究 同性恋心理学研究Division 45. Society for the Psychological Study of Ethnic Minority Issues 少数民族心理学研究Division 46. Media Psychology 媒介心理学Division 47. Exercise and Sport Psychology 运动心理学Division 48. Peace Psychology 和平心理学Division 49. Group Psychology and Group Psychotherapy 团体心理学及团体心理治疗Division 50. Addictions 癖心理学Division 51. Society for the Psychological Study of Men and Masculinity 男性心理学研究Division 52. International Psychology 国际心理学Division 6. Behavior Neuroscience and Comparative Psychology 行为神经科学和比较心理学Division 7. Developmental Psychology 发展心理学Division 8. Society of Personality and Social Psychology 个性和社会心理学Division 9. Society for Psychological Study of Social Issues 社会问题心理学研究division of function 机能分工机能分工division of labor 劳动分工divisional system 事业部制divorce 离婚divorcee 离了婚的人dizygotic twins 异卵孪生子 卵孪生子dizziness 眩晕dizzy 头晕目眩DL 差别阈限DNA 合成DNA 合成酶DNA 模板DNA 脱氧核糖核酸DNA evolution DNA 进化DNA ligaseDNA 合成酶DNA replicationDNA 复制DNA synthesisDNA 合成DNA templateDNA 模板DNase 脱氧核糖核酸酶do well 进展情况良好进展情况良好docility 可塑性doctrinaire 空谈理论者doctrinairism 教条主义doctrinairism 空谈理论doctrine 学说doctrine of acquirable knowledge 学知说doctrine of autogenesis 自然发生说doctrine of correspondence 符合说doctrine of descent 世代延续说doctrine of evil human nature 性恶论doctrine of formal discipline 形式训练说doctrine of good human nature 性善论doctrine of interest 兴趣主义兴趣主义doctrine of maximum satisfaction 最大满足学说doctrine of no good and no evil human nature 性无善无恶论doctrine of prescience 先知论doctrine of signatures 信号说doctrine of specific nerve energy 神经特殊能量说doctrine of the bipolarity of affections 情二端说doctrine of the same origin of mind and body 一本论doctrine of three classes of human nature 性三品论doctrine of two origin of mind and body 二本论doctrinism 教义至上论DOF 直接观察式dogma 教条dogmatism 教条主义Dogmatism Scale 教条主义量表dol 多尔doldrums 忧郁dolichocephalic 长头的doll s eye responsedoll play 洋娃娃游戏Dollard Millerdolorimeter 测痛仪dolour 悲哀doltish 愚笨的domain referenced testingdomain 范围domain of variables 变量范围变数范畴domain sampling 范畴内抽样domal sampling 按户抽样domestic 驯养的domestic problem 家庭问题domestic relation 家庭关系domestication 驯养dominance submissiondominance subordinate relationship dominance 优势dominance 支配dominance aggression 支配攻击dominance behavior 支配行为支配行为dominance hierarchy 统治阶层dominance hierarchy 支配等级dominance hierarchy in animals 动物统治等级dominant 显性的dominant 优势的dominant activity 主导活动dominant age class 优势年龄组dominant character 显性dominant cue 显性线索dominant culture 主文化dominant eye 优势眼dominant foot 优势足dominant gene 显性基因dominant hand 优势手dominant hemispheredominant inheritance 显性遗传dominant personality 支配性人格dominant response 主要反应主要反应dominant role 主要角色dominant trait 显性特质dominant trait 支配性dominant type 支配型dominant value orientation 显著价值取向dominant wave length 主波长dominantive motives in learning 主导性学习动机domination 支配dominator 优势神经元Domoor s signDonder s lawdoodah 激动door indoor indopa 多巴dopa oxidase 多巴氧化酶dopamine 多巴胺dopamine hypothesis of schizophrenia 精神分裂症的多巴胺假说dopamine receptor 多巴胺受体dopaminergic 多巴胺能的多巴胺能的dopase 多巴氧化酶dopey 麻醉状态的Doppler effect 多普勒声光效应Doppler shift 多普勒转移多普勒转移dormancy callus 固定胼胝体dormant state 静态dormifacient 促睡眠的dormitive 安眠的dorsal 背侧的dorsal chord 脊索dorsal column 脊柱dorsal column nucleus 脊柱核dorsal column tract 脊柱束dorsal horn 背角dorsal root 背根dorsal root ganglion 背根神经节dorsal spinocerebellar tract 脊髓小脑束dosage 剂量doso visual systemdossier 病历表册DOT 职业分类典dotage 老年记忆衰退dote 昏愦dotted substance 点状物质dotting test 打点测验double aspect theorydouble blinddouble blind crossoverdouble blind experimentdouble blind interactiondouble blind proceduredouble blind researchdouble blind techniquedouble entry tabledouble frequency tabledouble loop learningdouble point aesthesiometerdouble 双重的double alternate method 双交替法double alternation problem 双次交替问题double approach avoidance conflictdouble bind child 双重约束儿童double bind interaction 双困互动double bind theory 双重约束理论double blind 双盲double blind experiment 双盲实验double blind procedure 双盲程序double characters of performer 演员的双重性格double consciousness 双重意识double depletion hypothesis 双重减液说double dissociation 双重分离双重分离double hermeneutics 双重释义双重诠释double image 双像double inheritance 双重遗传性double innervation 双重神经支配double interpretation illusion 双重译释错觉double pain 双重痛觉double personality 双重人格双重人格double reciprocal innervation 双重相反神经支配double representation 双重代表double sampling 双重抽样double self 双重自我double standard 双重标准double technique 双重技术double thinking 双重思维double vision 复视doubt 怀疑doubter 持怀疑态度者持怀疑态度者douce 文雅的doughtydower 天赋Down s syndromeDowney Will Temperament Test 道内意志 气质测验downward looking horizontal situation d downward communication 下行沟通downward mobility 趋下流动趋下流动DPADQ 发展商数DQ 衰退商数drama psychology 戏剧心理学drama therapy 戏剧治疗法戏剧治疗法dramatic 戏剧的dramatic conflicts 戏剧冲突dramatic entanglement 戏剧纠纷dramatic expression 戏剧表情戏剧表情dramatic feeling 戏剧感情dramatic play 表演游戏dramatic properties 戏剧性dramatic rhythm 戏剧节奏dramatic situation 戏剧性情境dramatic therapy 表演疗法dramatical 戏剧的dramaticism 戏剧性dramatization 戏剧化draw oneself up 控制自己drawing 绘画drawing apparatus 绘画器Drawing Completion Test 绘画完成测验绘画完成测验drawing scale 绘画量表Draw a Horse Test 画马测验Draw a House Test 绘屋测验Draw a Man Test 画人测验Draw a Person Test 画人测验Draw a Tree Test 绘树测验dread 恐怖dreadful 可怕的dream series methoddream statedream 梦dream analysis 析梦dream association 忆梦联想dream censorship 梦的监察dream consciousness 梦的意识dream content 梦境dream content 梦之内容dream deprivation 梦境剥夺梦境剥夺dream determinant 梦之成因梦之成因dream ego 梦中自我dream instigator 梦之缘起dream interpretation 梦的解释梦之解析dream interpretation 释梦dream recall 梦境回忆dream symbol 梦的象征dream symbolism 析梦象征主义dream wish fulfillmentdream wish 梦愿dream work 梦工作dream world 梦想世界dreamboat 理想人物dreamer 梦想家dreamful 多梦的dreaming sleepdreamland 梦境dreamless 无梦的dreamless sleep 无梦睡眠dreamlife 醉生dreamlike 梦幻的dreamscape 梦幻景象dreamy 恍惚的dreamy state 梦样状态dressing apraxia 衣着运用不能drift 演变drill 训练drinker 嗜酒者drinking 饮drinking drive behavior 酒后驾驶行为drive inducing operationdrive oriented behaviordrive reduction hypothesisdrive reduction theorydrive 内驱力drive arousal 驱力激发状态drive arousal stimulus 驱力激发刺激drive conversion 驱力转向drive descending theory 内驱力降低说drive displacement 驱力移置驱力移置drive primary 原始驱欲drive psychology 内驱力心理学drive reduction 驱力减低drive reduction hypothesis 驱力减低假说drive secondary 次级驱欲drive simulator 驾驶模拟器驾驶模拟器drive specificity 驱力定向drive state 驱力状态drive stimulus 驱力刺激driver 驾驶员driver characteristics 驾驶员特性driver education 驾驶员教育driver judgment 驾驶员判断driver license testing 司机执照考试driving behavior 驾驶行为driving environment 驾驶环境驾驶环境driving force 驱动力DRL 延迟反应学习dromitropic 影响传导的dromitropic action 影响传导作用dromogram 血流速度描记图dromomania 漫游狂dromophobia 恐奔跑症droop 衰颓drop outdrop quizzesdrop recorder 记滴器dropouts 退学drowse 打瞌睡drowsiness 困倦drowsy wave 欲睡波drug dependent insomniadrug rehabilitationdrug takingdrug 药物drug abuse 药物滥用drug addiction 药物成瘾drug dependence 药物依赖drug education 反吸毒教育反吸毒教育drug habituation 服药习惯化drug holiday 药物假期drug induced hallucination 药致幻觉drug influence 药物效应drug intoxication 药物中毒drug overdose 服药过量drug problem 吸毒问题drug psychosis 药物中毒性精神病drug synergism 药物合并效果drug therapy 药物疗法drug tolerance 耐药性drug withdrawal 停药drugs 毒品drumhead 鼓膜drunken 酒醉的dry bulb temperancedry climate 乾燥气候DS 驱力刺激驱力刺激DS 去同步睡眠DTPS 弥散性丘脑皮层投射系统dual code hypothesisdual leadership therapydual memory theorydual sex therapydual 双的dual aspectism 双相论dual career couples 双职工dual coding hypothesis 双重编码说dual coding model of reading 阅读的双通道模式dual coding theory 双重编码论dual descent 双重遗传dual function 双重作用dual impression 双重印象dual loyalty 二重忠心dual memory hypothesis 二重记忆说dual personality 双重人格dual phenomenon 双重现象dual psychology 二重心理学dual role 双重角色dualism 二元论dualistic theory of human nature 性二元论duality 两重性duality of aesthetic feeling 美感的两重性duality of structure 结构二元性duality of the innermost being 内心两重性Dubini s chorea diseasedubious 犹豫的dubitation 迟疑ductus semicircular 半规管dudgeon 愤恨due 适当的dulcet 令人舒服的dull 迟钝的dull children 迟钝儿童dull pain 钝痛dullness 迟钝dumbness 哑dummy 模拟人dummy 哑巴dummy head 模拟头dump 信息转储Duncan s new multipleDuncan multiple range testDunfermline 登弗姆林营养指标Dunlap chronoscope 丹拉普计时器Dunn multiple comparisons 邓恩多重比较法Dunnett s procedureDunnett s tD testdupe 欺骗duplex 双的duplex theory 二元学说duplex theory in vision 视觉双功能说duplicate test 替换测验duplication 复份duplicity theory 双重作用说duplicity theory of vision 双视觉说duration 持续时间duration 久暂duration of diapause 滞育时间滞育时间duration of impulse 脉冲宽度脉 宽度duration of sunshine 日照时间日照时间duration of vision 视觉持续时间duration sampling 时限抽样duress 强迫Duret s lesionDurrell analysis of reading difficulty 杜雷尔阅读困难分析Durrell Listening Reading Series 杜雷尔听读系列dusky 忧郁的dust 灰尘duteous 忠实的duty 责任DV 因变量Dvorine Color Vision Test 德氏颜色视觉测验dwarf 侏儒dwarfism 侏儒症dyad 双方dyadic conflict 双方冲突dyadic interaction 双方相互作用dyadic interorganizational relationship 双方组织间关系dynameter 肌力计dynamic 动力的dynamic action of food 食物的动力作用dynamic analysis 动力分析dynamic balance 动态平衡dynamic behavior 能动行为dynamic crossroads 动力抉择点dynamic data 动态资料dynamic display 动态显示器dynamic effect 动态影响dynamic equilibriumdynamic graphic display 动态图示显示器dynamic lattice 动力方格dynamic logic 动力逻辑dynamic material 活动教材dynamic model 动态模型dynamic network 动态网络dynamic organization 动态组织dynamic perception 动力知觉动力知觉dynamic programming 动态规划dynamic psychiatry 动力精神病学dynamic psychology 动力心理学dynamic resignation 主动型退避dynamic roadway displays 动态路面显示器dynamic selection 动态选择dynamic situation principle 动态情境原则dynamic sociology 动态社会学dynamic state 动态dynamic stereotype 动力定型动力定型dynamic strength 动态性力量dynamic teaching program 活力教学计划dynamic theory 动力论dynamic trait 动力特性dynamic type 动型dynamic visual acuity 动态视敏度dynamic visual field 动视野dynamics 动力学dynamics of vocational adjustment 职业适应动力学dynamism 精神动学dynamite 精力充沛者dynamo 精力充沛者dynamogenesis 动力发生dynamograph 肌力描记器肌力描记器dynamometer 握力计dynamometry 肌力测定法肌力测定法dynamopathic 机能性的dynamoscope 动力测验器动力测验器dynamoscopy 动力测验法动力测验法dysacousis 听力减退dysadaptation 眼调节障碍眼调节障碍dysadrenia 肾上腺机能障碍dysanagnosia 诵读障碍dysantigraphia 抄写障碍dysaphia 触觉障碍dysarthria 发音困难dysarthria literalis 口吃dysarthrosis 构语障碍dysaudia 听力障碍dysautonomia 自主运动不能dysbasia 步行困难dysbulia 丧志症dyscalculia 计算失能dyschiasia 定位觉障碍dyschiria 左右感觉障碍左右感觉障碍dyschromasia 色觉障碍dyschromatopsia 色觉障碍dyschronism 定时障碍dyscinesia 运动障碍dyscoimesis 睡眠困难dyscorticism 肾上腺皮质机能障碍dysdiadochokinesia 轮替运动障碍dysecoia 听力减退dysembryoplasia 胚胎发育不良dysendocriniadysendocrinism 内分泌障碍dysequilibrium 平衡失调dyserethesia 感受性不良dyserethism 应激性不良dysergasia 脑控制不良dysergia 传出性运动失调dysesthesia 感觉迟钝dysfunction 机能障碍dysfunctional behaviordysfunctional conflictdysfunctional expectancies 功能不良的预期dysfunctional self evaluation dysgenesis 发育不全dysgenic 非优生的dysgenic 种族退化的。
discretization method
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discretization method
离散化方法是将连续型数据在给定精度的情况下转换为离散型数
据的过程。
离散化方法主要应用于数据挖掘、机器学习以及特征工程
等领域中。
离散化方法的主要目的是简化数据计算、降低数据存储需
求和提高算法性能。
离散化方法通常有两种:等宽离散化和等频离散化。
等宽离散化
是指将数据按照一定步长进行分段,每一段的取值范围相同。
例如,
将年龄数据按照10岁一个步长进行分段,年龄分为0-9岁、10-19岁、20-29岁等等。
等频离散化是指将数据按照一定比例进行分段,使每段中的数据占据总数据的比例相同。
例如,假设将100个数分成10个等份,则每个份中有10个数,相应地按照数据的大小进行排序即可。
离散化方法的主要优点是简单易用,并且可减少计算复杂度和存
储空间。
但是,离散化方法常常可能会导致数据信息的丢失,对数据
的精度和准确性造成影响。
因此,在使用离散化方法时,我们需要考
虑数据的特点和实际需求,适时地运用离散化方法,从而获得更好的
数据处理效果。
总的来说,离散化方法是一种在数据处理和分析中非常常用的方法。
离散化方法能够降低数据处理的难度,从而提高算法的性能和数
据分析的效能。
对于想要进行数据挖掘、机器学习等领域中的数据处
理和分析工作的从业者来说,掌握离散化方法是非常必要的技能。
Alternativesplicin1--2选择性剪切
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Alternative splicing 选择性剪接Alternative splicing produces two protein isoforms.选择性剪接产生两种蛋白亚型。
Alternative splicing is a regulated process during gene expression that results in a single gene coding for multiple proteins. In this process, particular exons of a gene may be included within, or excluded from, the final, processed messenger RNA produced from that gene.[1]Consequently the proteins translated from alternatively spliced mRNAs will contain differences in their amino acid sequence and, often, in their biological functions (see Figure). Notably, alternative splicing allows the human genome to direct the synthesis of many more proteins than would be expected from its 20,000 protein-coding genes. Alternative splicing is sometimes termed differential splicing, but it does not increase gene expression.选择性剪接是基因表达,其导致单基因编码多种蛋白质中一个调节的过程。
diffusion变分推断
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diffusion变分推断
Diffusion变分推断是一种统计建模方法,用于推断隐藏变量在网络中的传播过程。
它基于一种变分推断算法,结合网络中各节点的观测值和隐藏变量值,估计隐藏变量在网络中的传播路径和传播强度。
在Diffusion变分推断中,隐藏变量表示信息传播的源节点和传播强度,观测值表示在网络中观测到的传播行为或结果。
通过最大化隐藏变量和观测值的后验概率分布的下界,可以估计隐藏变量的值。
Diffusion变分推断通常使用迭代的方法,通过不断更新隐藏变量和观测值的分布来逐步逼近后验概率分布。
这种方法一般需要对网络进行建模,定义节点之间的传播关系和传播强度,以及观测结果的概率分布。
然后,通过迭代更新隐藏变量和观测值的分布,估计隐藏变量的值。
Diffusion变分推断在社交网络分析、信息传播分析、传染病传播模型等领域有广泛应用。
它可以帮助我们理解和预测信息在网络中的传播路径和传播强度,从而对社会和生物系统的动态行为进行建模和预测。
MBg-11 RNA Splicing
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THE CHEMISTRY OF RNA SPLICING
Sequences within the RNA Determine Where Splicing Occurs How are the introns and exons distinguished from each other? How are introns removed? How are exons joined with high precision? The borders between introns and exons are marked by specific nucleotide sequences within the pre-mRNAs. These sequences delineate where splicing will occur. Thus the exon-intron boundary—that is, the boundary at the 5' end of the intron—is marked by a sequence called the 5' splice site. The intron-exon boundary at the 3' end of the intron is marked by the 3' splice site.
根据intron 拼接点的序列特征,可分为tRNA intron, group I intron, group II intron, mRNA intron 四大类.
•tRNA splicing •Group I intron •Group II intron •mRNA splicing
pre-mRNA intron
调节剪切基因
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Alternative splicing is a crucial mechanism for gene regu l ation and for generating proteomic diversity. Recent estimates indicate that the expression of nearly 95% of human multi-exon genes involves alternative splicing 1,2. In metazoans, alternative splicing plays an important part in generating different protein products that func-tion in diverse cellular processes, including cell growth, differentiation and death.Splicing is carried out by the spliceosome, a massive structure in which five small nuclear ribonucleoprotein particles (snRNPs) and a large number of auxiliary pro-teins cooperate to accurately recognize the splice sites and catalyse the two steps of the splicing reaction 1,2 (BOX 1). Spliceosome assembly (BOX 1) begins with the recognition of the 5′ splice site by the snRNP U1 and the binding of splicing factor 1 (SF1) to the branch point 3 and of the U2 auxiliary factor (U2AF) heterodimer to the polypyrimi-dine tract and 3′ terminal AG 4,5. This assembly is ATP independent and results in the formation of the E com-plex, which is converted into the ATP-dependent, pre-spliceosomal A complex after the replacement of SF1 by the U2 snRNP at the branch point. Further recruitment of the U4/U6–U5 tri-snRNP complex leads to the forma-tion of the B complex, which is converted into to the cata-lytically active C complex after extensive conformational changes and remodelling.The decision as to which exon is removed and which exon is included involves RNA sequence elements and protein regulators. Depending on the position and func-tion of the cis-regulatory elements, they are divided into four categories: exonic splicing enhancers (ESEs), exonic splicing silencers (ESSs), intronic splicing enhancers (ISEs) and intronic splicing silencers (ISSs). ESEs are usually bound by members of the SR (Ser–Arg) protein family 6–8 (BOX 2). ISSs and ESSs are commonly bound by hetero g eneous nuclear RNPs (hnRNPs; TABLE 1), which have one or more RNA-binding domains and protein–protein interaction domains 9,10. ISEs are not as well characterized as the other three types of element, although recently several proteins, such as hnRNP F , hnRNP H , neuro-oncological ventral antigen 1 (NOV A1), NOV A2, FOX1 and FOX2 (also known as RBM9), have been shown to bind ISEs and to stimulate splicing 11–14.Choices of alternative splicing have long been thought to be made at the stages of splice site recognition and early spliceosome assembly, and indeed this is frequently the case 1. However, several recent studies have shown that the decision can be made at different stages of spliceo-some assembly, and even during conformational changes between the two transesterification steps 15–17. In addi-tion, there has been accumulating evidence showing thecoupling of RNA transcription to splicing regulation 18–22.Alternative splicing contributes to genomic diversity and tissue specificity 23. Comprehending tissue-specific alternative splicing requires an understanding of the regulatory network of protein–protein, protein–RNA and RNA–RNA interactions that are involved in this pro-cess 1,24. Tissue-specific alternative splicing is thought to be controlled by differentially expressed splicing regulators 1,25 and/or ubiquitously expressed splicing factors of different concentrations and/or activity 26,27. In addition, tremen-dous progress has been made using high-throughputDepartment of Biological Sciences, Columbia University, New Y ork, New Y ork 10027, USA.Correspondence to J.L.M. e-mail:jlm2@ doi:10.1038/nrm2777Published online 23 September 2009 Corrected online 12 October 2009Small nuclearribonucleoprotein particle(snRNP). A protein, including U1, U2, U4, U5 and U6, which contains U-rich small nuclear RNAs (snRNAs) and both small nuclear ribonucleoprotein (snRNP)-specific and common proteins, and is a corecomponent of the spliceosome.Branch pointA nucleotide, usually an adenosine, within a variably conserved branch point sequence upstream of the 3′ splice site, the 2′ hydroxyl group of which attacks the 5′ splice site in the first step of splicing.Mechanisms of alternative splicing regulation: insights from molecular and genomics approachesMo Chen and James L. ManleyAbstract | Alternative splicing of mRNA precursors provides an important means of genetic control and is a crucial step in the expression of most genes. Alternative splicing markedly affects human development, and its misregulation underlies many human diseases.Although the mechanisms of alternative splicing have been studied extensively, until the past few years we had not begun to realize fully the diversity and complexity of alternative splicing regulation by an intricate protein–RNA network. Great progress has been made by studying individual transcripts and through genome-wide approaches, which togetherprovide a better picture of the mechanistic regulation of alternative pre-mRNA splicing.o s t-t r a n s c r i P t i o n a l c o n t r o lREVIEWSSR (Ser–Arg) protein family A family of nuclear factors that have many important roles in splicing mRNA precursorsin metazoan organisms, functioning in both constitutive and alternative splicing.methods, which has not only revealed many more newalternative splicing events28–30, but has also acceleratedthe process of understanding its regulation in differenttissues by examining the expression levels of protein regu-lators and helping to define cis-regulatory elements31,32.In this Review, we discuss mechanisms of alternativesplicing control from different perspectives. At whichstage of spliceo s ome assembly is the decision of whetherto include an alternative exon made? what protein factorsare involved? How does RNA polymerase II (RNAP II)function in alternative splicing regulation? Are the kine-tics of spliceosome assembly important for this process?which mechanisms control tissue-specific alternativesplicing? we address these questions and conclude bysummarizing the diversity of the mechanisms of alter-native splicing regulation and speculate on the futuredirections of alternative splicing research.splice site recognition and selectionMost human genes contain multiple exons, and the aver-age length of exons (50–250 bp) is much shorter thanthat of intervening sequences (frequently thousands ofbps). Early stages of spliceosome assembly occur aroundthe exons owing to the large size of introns33. This type ofexon-centred splice site recognition is referred to as ‘exondefinition’ (REf. 34)(BOX 1). Exon definition must even-tually be converted to intron definition, which occursby cross-intron interactions between the U1 and U2snRNPs35,36. The best-studied mechanisms of alternativesplicing regulation involve controlling splice site recogni-tion by facilitating or interfering with the binding of theU1 or U2 snRNP to the splice sites.Facilitating splice site recognition. SR proteins haveimportant roles in facilitating splice site recognition. Forexample, they recruit the U1 snRNP to the 5′ splice site andthe U2AF complex and U2 snRNP to the 3′ splice site bybinding to an ESE and directly interacting with proteintargets37–40(fIG. 1a). These interactions are mediated bytheir RS (Arg–Ser repeat-containing) domains41,42, which haveto be properly phosphorylated and dephosphorylated1,43,44.SR proteins also cooperate with other positive regulatoryfactors to form larger splicing enhancing complexes byinteracting with other RS domain-containing proteins,such as transformer 2 (TRA2) and the SR-related nuclearmatrix proteins SRm160 (also known as SRRM1) andSRm300 (also known as SRRM2)45–47(fIG. 1a). Bindingand recruiting can also be achieved by intronic bind-ing proteins: for example, T cell-restricted intracellularantigen 1 (TIA1) binds a U-rich sequence downstream ofweak 5′ splice sites to recruit the U1 snRNP48,49; and Src-associated in mitosis 68 kDa protein (SAM68; also knownas KHDRBS1) binds and recruits U2AF to the 3′splicesite of exon V5 of the transmembrane glycoprotein CD44pre-mRNA50.The SR protein SRp38 (also known as TASR51, NSSR52,FUSIP1 and SRrp40 (REf. 53)) has been characterized asa general splicing repressor that is activated by dephos-phorylation54,55 (see below). However, recent studiesindicate that it also functions as a sequence-dependentsplicing activator when phosphorylated39. It was foundthat in vitro SRp38 activates the formation and splicingof the A complex by facilitating the recruitment of theU1 and U2 snRNPs to the pre-mRNA and stabilizingthe 5′ splice site and branch site recognition (fIG. 2a).Notably, SRp38, unlike other SR proteins, cannot comple-ment cytoplasmic S100 extracts (which contain all factorsrequired for splicing except SR proteins) to activatesplicing56. Although an A-like complex is formed in thepresence of S100 and SRp38, it is stalled or inactive andrequires a specific (and currently unknown) coactivatorto proceed 39 (fIG. 2a). In vivo , SRp38 was shown to favour inclusion of the Flip exon of the GRIA2 (glutamate receptor, ionotropic, AMPA 2; also known as GluRB ) pre-mRNA 52, whereas the mutually exclusive Flop exon is included when SRp38 is absent 39. Interestingly, both exons contain SRp38-binding sites, and it was proposed that the intracellular concentrations of SRp38 as well as differential binding to the exons (that is, stronger binding to the Flip exon) influence the decision to include either the Flip or Flop exon 39.Inhibiting splice site recognition. Inhibition of splice site recognition can be achieved in many ways. First, when splicing silencers are located close to splice sites or to splic-ing enhancers, inhibition can occur by sterically blocking the access of snRNPs or of positive regulatory factors. For example, polypyrimidine-tract binding protein (PTB ; also known as PTB1 and hnRNP I), binds the polypyrimidine tract and blocks the binding of U2AF to regulated exons 57–59. In addition, hnRNP A1 binds ISSs that are located upstream of exon 3 in HIV Tat pre-mRNA and prevents binding of the U2 snRNP 60. Finally, tissue-specific splicing factors FOX1 and FOX2 inhibit the formation of the E ′ complex by binding to an intronic sequence to prevent SF1 from binding to the branch site of CAlCA (calcitonin-relatedpolypeptide-α) pre-mRNA 61(fIG. 1b).Splicing inhibitors also sterically block the binding of activators to enhancers. Hu/ELAV family proteins inhibit U1 snRNP binding by competing with the binding of TIA1 to an AU-rich sequence downstream of the 5′ splice site of exon 23a of neurofibromatosis type 1 pre-mRNA 62. FOX1 and FOX2 also inhibit E complex formation by binding to an exonic sequence in the CAlCA pre-mRNA close to the ESE that TRA2 and SRp55 bind to, prevent-ing the recruitment of U2AF by the activators 61 (fIG. 1b). Finally, hnRNP A1 binds an ESS upstream of the TRA2-dependent ESE in exon 7 in the SMN2 (survival of motor neuron 2) pre-mRNA, possibly inhibiting the formation or stabilization of the U2 snRNP complex 63,64 (fIG.1c).Some silencers can be over 100–200 bp away from enhancers, and a simple ‘bind and block’ model thus cannot explain their inhibitory effect. One explanation for the activity of such splicing inhibitors is that they function by masking splice site recognition through multimerization along the RNA 58. Another model pro-poses that the alternative exon might be ‘looped out’ in a process involving protein–protein interactions between RNA-binding proteins bound at sites spanning the alter-native exon 58,65–67, and that this loop formation may steri-cally interfere with further spliceosome assembly, even though splice site recog n ition may not be inhibited 67. For example, hnRNP A1 binds to elements upstream and downstream of exon 7B in its own pre-mRNA to pro-mote skipping of exon 7B 68. hnRNP A1 has also been shown to bind to exonic and intronic silencers in SMN2 exon 7 and intron 7, and it is proposed that an inter-action between hnRNP A1 mole c ules is required to fully suppress the inclusion of SMN2 exon 7 (REfS 64,69,70). In addition, PTB has been shown to bind sites flanking the SRC N1 exon, and mutating one of the PTB sites affects the binding of PTB to the other site 58,71.REVIEWSNATURE REVIEwS | Molecular cell BiologyVOlUME 10 | NOVEMBER 2009 | 743Heterologous nuclear RNP (hnRNP). A pre-mRNA- or mRNA-binding protein that associates with transcripts during or after transcription and influences their function and fate. Some hnRNPs shuttle in and out of nuclei, whereas others are constitutively nuclear.Alternative exonAn exon that is included in mature mRNA in certain cellular contexts but excluded in others.RS (Arg–Ser repeat- containing) domainA protein domain that is variable in length and enriched in Arg–Ser dipeptides and seems to be involved in protein–protein andprotein–RNA interactions.Hu/ELAV family proteinA protein belonging to a family of nervous system-specific RNA-binding proteins that specifically bind to AU-rich sequences.CLIPA method that combines cross-linking and immunopre-cipitation to identify in vivo targets of RNA-binding binatorial effects of activators and inhibitors.Splicing of individual pre-mRNAs is frequently con-trolled by combinatorial or competitive effects of bothactiv a tors and inhibitors. The final decision of whetheran alternative exon is included is determined by theconcentration or activity of each type of regulator,often by SR proteins and hnRNPs72–74. For example, theSR protein 9G8 (also known as SFRS7) and hnRNP Fand hnRNP H regulate the splicing of α-tropomyosinexon 2 by competing for binding to the same element75;hnRNP A1 and the SR proteins ASF/SF2 (also known asSFRS1) and SC35 (also known as SFRS2) have antago-nistic functions in splicing of β-tropomyosin exon 6B76;and CElF (CUGBP- and ETR3-like factor)-family pro-teins ETR3 (also known as CElF2) and CUGBP1 acti-vate the splicing of exon 5 of TNTT2 (troponin T type 2;also known as cTNT) by displacing PTB77. A recentstudy showed that at least some Drosophila melanogasterSR proteins and hnRNPs do not have as many commontargets as had been thought78. Using small interferingRNAs (siRNAs) to deplete individual splicing factorsfollowed by splicing-sensitive microarray analysis, theauthors compared genes regulated in opposite direc-tions by hnRNPs and two SR proteins (dASF andBC52). Surprisingly, less than 5% of the genes overlap.However, a systematic analysis of more SR and hnRNPswill be necessary to decide whether antagon i zing effectsbetween these proteins is a main mode of alternativesplicing regulation.Position-dependent splicing regulation. The nature ofthe activity of cis-acting elements and their cognatebinding proteins in some cases depends on their posi-tion relative to regulated exons. Several proteins, suchas NOVA1, NOVA2, FOX1, FOX2, hnRNP l, hnRNPl-like, hnRNP F and hnRNP H, have been shown to actas either repressors or activators depending on the loca-tion of their binding site11–14,79–83. For example, NOVA1binds to an ISE in GABRG2 (GABA A receptor, γ2) pre-mRNA and promotes inclusion of exon 9 (REf. 84), butit binds to the ESS in the alternative exon 4 of its ownpre-mRNA and prevents exon 4 from being included81.Similarly to NOVA1, hnRNP l can activate or repressupstream alternative exons, and this probably dependson the location of its binding site relative to the regu-lated 5′ splice site12. hnRNP H promotes the formationof ATP-dependent spliceosomal complexes when itbinds to G-rich sequences (G runs) downstream of the5′ splice site85, but it inhibits splicing when the G-richsequences are located in exons86. By using informationfrom mRNA transcripts that are know to be targeted byNOVA1 and NOV A2 and by searching for YCAY clus-ters (which are NOV A1 and NOV A2 binding sequences)around regulated exons, one group11 drew an ‘mRNAmap’ that includes the location of NOVA1 and NOV A2binding sites and the consequence of each binding event.This map provides insight into the mechanisms under-lying the effects of NOVA1 and NOVA2 on splicing.For example, binding of NOVA1 and NOV A2 to an ESSinhibits the formation of the pre-spliceosomal E com-plex by altering its composition before the binding ofhnRNPs and inhibits the binding of U1 snRNP11. Bycontrast, NOVA1 and NOV A2 binding to an ISE down-stream of the alternative exon promotes the formationof spliceosomal complexes A, B and C11. A new tech-nique that combines CLIP and high-throughput sequenc-ing, known as HITS-ClIP80 (ClIP-seq)13,87,88, not onlyverified the reproducibility of this mRNA map, butBCL-2-like 2; CALCA, calcitonin-related polypeptide-α; GRIN3B, glutamate receptor, ionotropic, NMDA 3B; FGFR2, fibroblastgrowth factor receptor 2; hnRNP, heterogeneous nuclear ribonucleoprotein particle; hnRNP LL, hnRNP L-like; IKBKAP, inhibitorof κ-light polypeptide gene enhancer in B cells, kinase complex-associated protein; NA, not applicable; ND, not determined;NOS, nitric oxide synthase; SMN2, survival motor neuron protein 2; PLP, proteolipid protein; PTB, polypyrimidine-tract bindingprotein; RRM, RNA recognition motif; TNTT2, troponin T type 2; TPM1, α-tropomyosin.744 | NOVEMBER 2009 | VOlUME 10 /reviews/molcellbioNature Reviews | Molecular Cell BiologyExon 1Exon 4caExon 1Exon 4Inclusion of exon 7 in SMN1also provided genome-wide information on the target genes of NOVA1, NOV A2 and FOX2 as well as possiblymechanisms by which these proteins are regulated 80.why does the position of splicing regulatory ele-ments determine the action of cognate splicing factors? It is possible that enhancers are positioned so that when the splicing factors bind to them, the splice sites of thealternative exon are better presented to the splicing machinery by changing the local mRNA structure. By contrast, silencing elements function in the opposite manner, by competing with components of the splicing machinery or by changing the structure of the mRNA to impede splice site recognition.Roles for RNA in alternative splicing regulation. The selection of the splice site can also be influenced by sec-ondary structures in the pre-mRNA. Perhaps the most striking example of this is the complex alter n ative splicing that is observed in D. melanogaster Dscam pre-mRNA. The exon 6 cluster of Dscam consists of 48 mutually exclusive exons. Pairing between a conserved sequence located downstream of constitutive exon 5 (the docking site) and another conserved sequence, a variant of which is located upstream of each exon 6 variant (the selector sequence), allows the inclusion of only one exon 6 vari-ant 89; the other variants are excluded by the binding of hrp36, a D. melanogaster hnRNP A homologue, to the selector sequence 90.Secondary structures can affect alternative splicing by masking splice sites 91 or by binding sites for splicing fac-tors 92,93. For example, a stem and loop secondary structure was shown to sequester alternative exon 6B of the chicken β-tropomyosin pre-mRNA, leading to its exclusion 94. The IDX exon of RAS can form a secondary structure with an ISS (RASISS1), preventing the binding of hnRNP H to RASISS1. Unwinding this secondary structure by the RNA helicase P68 exposes the binding site, partially explain-ing the exclusion of IDX 93. Riboswitches, which control gene expression in prokaryotes 95, also have the potential to modulate alternative splicing. Splicing of the NMT1 (N -tetradecanoyltransferase 1) pre-mRNA in Neurospora crassa was found to respond to the co e nzyme thiamine pyrophosphate through a riboswitch-like structure 96. It remains to be seen, however, whether this intriguing mechanism operates in higher eukaryotes. In mammals, small nucleolar RNAs (snoRNAs) have also been impli-cated in alternative splicing regulation 97. For example, the snoRNA HBII 52 regulates the alternative splicing of HTR2C pre-mRNA by binding to a silencing element in exon Vb to promote its inclusion 98.regulation by U1 and U2 snrnP pairingAfter the 5′ and 3′ splice sites are recognized and exons are defined, exon definition must be converted to intron definition, which involves cross-intron inter-action between U1 and U2 snRNPs, to form a functional spliceosome. Precisely when this occurs and when the commitment to splice site pairing happens have been intensively investigated over the past few years. Several studies have shown that the commitment to splicing of at least some alternative exons occurs during splice site pairing in the A complex 35,36. For example, ATP hydro-lysis is required for splice site pairing, which locks splice sites into a splicing pattern after U2 snRNP binding to the branch site 36. Additional studies, discussed below, also provided evidence that binding of U1 and U2 snRNPs to splice sites to define an exon does not necessarily commit the exon to splicing 15,16,99.Figure 1 | Mechanisms of alternative splicing by splice site selection. Schematic depicting mechanisms of splicing activation. a | SR (Ser–Arg) proteins bind to exonic splicing enhancers (ESEs) to stimulate the binding of U2AF to the upstream 3′ splice site (ss) or the binding of the U1 small nuclear ribonucleoprotein (snRNP) to the downstream 5′ ss. SR proteins function with other splicing co-activators, such as transformer 2 (TRA2) and the SR-related nuclear matrix proteins SRm160–SRm300. T cell-restricted intracellular antigen 1 (TIA1) binds to U-rich sequences (intronic splicing enhancers (ISEs)) immediately downstream of 5′ splice sites to facilitate U1 binding. CELF (CUGBP- and ETR3-like factor) proteins, such as ETR3, bind to similar sequences as polypyrimi-dine-tract binding protein (PTB), thereby activating splicing by competing with PTB. b | Fox1 and Fox2 inhibit the inclusion of CALCA (calcitonin-related polypeptide-α) exon 4 by blocking the binding of splicing factor 1 (SF1) to the branch point (top panel) and of TRA2 and SRp55 to ESEs (bottom panel), thereby inhibiting spliceosome assembly at two stages, the E ′ and E complexes. The arrow indicates that SRp55 and TRA2 promote binding of the U2AF complex. c | Single-nucleotide differences in SMN2 (survival of motor neuron 2) compared with SMN1 create binding sites for heterogeneous nuclear ribonucleoprotein particle A1 (hnRNP A1) (or hnRNP A2) in exon 7 and in the downstream intron in SMN2 pre-mRNA. hnRNP A1 (or hnRNP A2) may then inhibit the formation or stabilization of the U2 snRNP complex, eitherdirectly or indirectly by blocking the activity of the downstream TRA2-dependent ESE. Note that it has also been suggested that the base change in exon 7 destroys an ASF/SF2-dependent ESE 163,164 (but see also REf . 69).REVIEWSNATURE REVIEwS | Molecular cell BiologyVOlUME 10 | NOVEMBER 2009 | 745Nature Reviews | Molecular Cell Biology GRIA2Catalytically active spliceosomeFlip –/–Heat shock or M phase of abcFigure 2 | Phosphorylation switches the general splicing repressor Srp38 into a sequence-specific activator.a | Phosphorylated SRp38 (Ser–Arg protein 38) activates splicing by recruiting the U1 and U2 small nuclearribonucleoprotein particles (snRNPs) to splice sites (ss). SRp38 binds SRp38-dependent exonic splicing enhancers (ESEs) in target transcripts and facilitates the association of U1 and U2 snRNPs with the pre-mRNA to stabilize 5′ ss and branch site recognition by interacting with U1 and U2 snRNPs, respectively. However, the spliceosomal A complex formed is stalled in S100 extract, in which an SRp38-specific cofactor from NF40-60 is absent, which is required to proceed through the splicing pathway. b | SRp38 enhances the inclusion of the Flip exon of GRIA2 (glutamate receptor, ionotropic, AMPA 2) pre-mRNA relative to the mutually exclusive Flop exon. Both exons containSRp38-binding sites (indicated by black bars under exon 14 (Flop) and exon 15 (Flip)), but the site in Flip is stronger (indicated by the thicker bar), and Flip inclusion is therefore favoured in the presence of SRp38. c | Proteinphosphatase 1 (PP1) dephosphorylates SRp38 on heat shock. Under normal conditions, phosphorylated SRp38 isassociated with 14-3-3 proteins, which help to protect SRp38 from dephosphorylation, and PP1 activity is inhibited by PP1-associated proteins, including nuclear inhibitor of PP1 (NIPP1). During heat shock, PP1 dissociates from NIPP1 and directly binds to and dephosphorylates SRp38, which has dissociated from 14-3-3 proteins. Part a of the figure modified, with permission, from Nature Struct. Mol. Biol. REf .39 © (2008) Macmillan Publishers Ltd. All rights reserved. Part c of the figure modified, with permission, from REf .151 © (2007) Elsevier.REVIEWSRegulation by protein factors. A new splicing inhibition mechanism was demonstrated recently by a study show-ing that binding of hnRNP l to an ESS can inhibit the pairing of U1 and U2 snRNPs15. An ATP-dependent spli-ceosome-like complex, known as A-like exon-definition complex (AEC), was found to form across alternative exon 4 of the CD45 pre-mRNA, even when its inclusion was inhibited. The AEC contains U1 and U2 snRNPs and displays the same gel mobility as the A complex, but progression into the B complex is inhibited. They pro-posed a model in which an A-like complex forms across exons when hnRNP l is not present, after which the U4/U6–U5 tri-snRNP complex is recruited to the intron-defined A complex to form the B complex. However, when hnRNP l is present, an hnRNP l-containing AEC prevents the U1 or U2 snRNPs bound to the splice sites of exon 4 from cross-intron pairing with the adjacent U2 or U1, resulting in exon 4 skipping. There are two possible ways in which the binding of hnRNP l might interfere with snRNP pairing. One is that binding of hnRNP l physically shields the interaction between the snRNPs. Another possibility is that hnRNP l induces a change in the conformation of the pre-mRNA that pre-vents cross-intron pairing of the snRNP-bound alternative exon. It is intriguing that the AEC, at least superficially, resembles the stalled A complex formed by SRp38 in S100 extract (see above). Although the importance of this is unknown, the following example suggests that such complexes may be more widespread than realized. PTB is another inhibitory splicing factor that has been shown to function, in some cases, by blocking the transition from exon definition to intron definition16,65 (fIG. 3a). One group16 studied the mechanism of PTB inhibition by comparing the active and inactive spliceo-somal complexes from neuronal wERI-1 cell nuclear extracts, in which the SRC N1 exon is included, and from Hela cell nuclear extracts, in which the SRC N1 exon is excluded. PTB is highly expressed by Hela cells, whereas a less repressive brain paralogue, nPTB (also known as PTB2 and brPTB), is expressed by wERI-1 cells100–103 (see below).Similarly to the example provided by hnRNP l, ATP-dependent exon definition complexes form in nuclear extracts from both cell lines. The protein compositions of the exon definition E and A complexes (EDE and EDA, respectively) that formed on constitu-tive exon 4 on nuclear extracts from both cell lines were similar, and PTB was found only in complexes formed in Hela nuclear extracts. By contrast, the EDE and EDA complexes that formed on the substrate containing both exon N1 and exon 4 in wERI-1 nuclear extracts and in Hela nuclear extracts differed in their properties and protein compositions. The wERI-1 EDE can move on to functional A, B and C complexes following ATP addi-tion, whereas the EDE formed in Hela nuclear extracts can only localize to a ‘dead end’ A complex. Several proteins only exist in the functional A complex formed in wERI-1 nuclear extracts, such as the PRP19 com-plex104,105 and SRm160–SRm300 complex46,106, which are might be important for 3′ and 5′ splice site bridging and exon N1 inclusion, and are excluded from the spliceo-some by PTB. This study provides a new mechanism for how different protein compositions in different tissues can help to determine the alternative splicing pattern through a silencing factor and its interactions with other splicing factors to prevent intron definition. This study also reveals for the first time that the protein composi-tion of different exon and intron definition complexes can vary, and thus begins to decipher a new mechanism for alternative splicing regulation and tissue specificity. RBM5(RNA-binding protein 5; also known as lUCA15 and H37) is a putative tumour suppressor pro-tein107,108 that promotes the exclusion of exon 6 of CD95 (also known as FAS) pre-mRNA109. RBM5 was found to interact with sequences in exon 6 but to not affect the association of U1 and U2 snRNPs to the adjacent splice sites. Instead, RBM5 inhibited the incorporation of the U4/U6–U5 tri-snRNP complex on the introns flanking exon 6, thereby blocking the maturation of pre-spliceosome s. RBM5 also promoted the pairing of U1 and U2 at the distal splice sites, contributing further to the exclusion of exon 6 (REf. 109) (fIG. 3b).In addition to alternative splicing regulation by the inhibition of intron definition, it is also possible that alternative splicing is stimulated by the activation of intron definition. In vitro experiments with substrates containing expanded introns have shown that the pres-ence of binding sites for hnRNPs near intron boundaries can facilitate splicing82. This presumably involves cross-intron interactions between hnRNPs that help to bring together the ends of the introns, indicating a possible positive regulatory role for hnRNPs in splicing.Cis-acting elements that affect U1 and U2 snRNP pairing by modulating 5′ splice site competition have recently been identified. In one study an in vitro screen was carried out for splicing silencers (ESSs and ISSs) that alter the selection of the 5′ splice site by choosing a distal, weak 5′ splice site over a proximal, strong 5′ splice site99. The isolated silencers did not affect whether U1 snRNP bound to the 5′ splice site, but instead somehow altered the conformation of the proximal U1 snRNP–5′ splice site complex so that it lost its advantage to compete with the distal U1 snRNP–5′ splice site complex for the pair-ing with the U2 snRNP–3′ splice site complex. This study suggests a new mechanism by which silencers can subtly affect splice site selection that does not involve sequester-ing splice sites, but instead entails changing the conform-ation of the snRNP–pre-mRNA complex. Moreover, the silencers do not affect the rate-limiting step of splicing but instead affect how well U1 and U2 snRNPs pair, which in turn can influence splice site choice.transcription-coupled alternative splicingTwo models have been proposed to explain the role of RNAP II in the regulation of alternative splicing110. The first model is known as the recruitment model, in which RNAP II and transcription factors interact, directly or indi-rectly, with splicing factors22,111,112, thereby increasing or decreasing the efficiency of splicing. One study has shown that the structure of the promoter can affect the splicing pattern of the FN1 (fibronectin 1) pre-mRNA by facili-tating the differential recruitment of ASF/SF2 (REf. 113). It is conceivable that this reflects the ability of differentREVIEWS。
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Abstract—Determination of quantitative expression levelsof alternatively spliced isoforms provides an important approach to the understanding of the functional significanceof each isoform. Real-time PCR using exon junction overlapping primers has been shown to allow specific detection of each isoform. However, this design often suffers from severe cross amplification of sequences with high homology at the exon junctions. We used human GFRα2b asa model to evaluate the specificity of primers substituted with locked nucleic acids (LNAs). We demonstrate here that single LNA substitutions at different positions of 3’ terminus could improve the discrimination of the primers against GFRα2a template, a highly homologous isoform. While LNA substitutions of GFRα2b primer at the residues possessing different sequences as GFRα2a has limited improvement in specificity, two consecutive LNA substitutions preceding the different sequences has dramatically improved the discrimination by greater than 100,000-fold compared to the non-substituted primer. Thus, LNA when substituted at certain residues can allow the discrimination of highly homologous sequences.Index Terms—Alternatively spliced isoforms, real-time PCR, locked nucleic acid (LNA)I. I NTRODUCTIONLTERNATIVE splicing is a widespread processused in higher eukaryotes to regulate gene expression and functional diversification of proteins. In a recent genome-wide microarray analyses, it is estimated that greater than 74% of the human multi-exon genes are alternatively spliced [1]. Compared with its wild-type counterpart, a splice variant can exhibit different enzymatic or signaling activities, novel subcellular localization and altered protein stability [2]. In order to understand the physiological and pathological contributions of alternative spliced isoforms, it is essentialto determine the quantitative expression levels of each isoform.Various methods have been used to quantify alternatively spliced isoforms, including Northern blotting, ribonuclease protection assay, semi-quantitative RT-PCR and competitive RT-PCR. However, utilizations of these assays are restrained due to their intrinsic limitations of efficiency, sensitivity and specificity. Until recently, isoform-specific real-time PCR has been successfully applied for quantifying spliced variants with outstanding performance [3]-[6]. By designing primers or probes targeting the unique exon junctions of spliced isoforms, real-time PCR assay can be applied to discriminate these isoforms. Previously, we have successfully designed primers targeting exon junctions of mouse GFRα2 isoforms to quantify the expression levels of these isoforms in mouse tissues [3]. However, if there is high sequence similarity between the unique exon junctions of the isoforms (e. g. human GFRα2a and 2b), such design will suffer from significant cross amplification.In the present study, we developed an isoform-specific real-time PCR assay using exon-overlapping primers incorporated with locked nucleic acids (LNAs). LNAs are ribonucleotides containing a methylene bridge that connects the 2´-oxygen of ribose with the 4´-carbon (Fig.1). This bridge results in a locked 3´-endo conformation that reduces the conformational flexibility of the ribose. The incorporation of LNA into a DNA primer increases the melting temperature (T m) and improves the hybridization affinity for complementary sequences [7]. Using human GFRα2b isoform as a model, we demonstrate that LNA substituted primers allow the specific amplification of the desired sequences over highly homologous sequences by at least 100,000-fold.Discrimination of Alternative Spliced Isoforms by Real-Time PCR Using Locked Nucleic Acid(LNA) Substituted PrimerGuoqiang WAN1, 3, Heng-Phon TOO1,2,*1 Department of Biochemistry, National University of Singapore, Lower Kent Ridge Road, Singapore1192602 MEBCS, Singapore-MIT Alliance, Singapore3 Division of Biomedical Sciences, Johns Hopkins in Singapore, Biopolis, Singapore 138669* Corresponding author. Fax: +65 779 1453. E-mail address: bchtoohp@.sg (H.-P. Too)A Fig. 1. Structural representations of DNA, RNA and LNA molecules.The specificity of each LNA substituted primer is highly dependent on the position and the number of LNA substitutions. We found that two consecutive LNA substitutions 5’ preceding the sequence difference have maximal effect on specificity of the primer.II. M ATERIALS AND METHODSA. Templates and PrimersOpen reading frame of human GFR α2 isoforms were cloned into p-GEMT vector (Promega). Plasmids linearized with XmnI (Promega) were used as PCR templates. DNA primers were designed using DNAMAN software. Primers with specific LNA substitutions have identical sequence as the DNA counterparts. All the primers are listed in Table 1.Table 1.List of primers used in this study. tgcctcttcttctttctaggtGAGB+5-L123LNA substitutions are denoted as capital letters.tgcctcttcttctttctaGgtgaG B+5-L16tgcctcttcttctttctAGgtgag B+5-L67tgcctcttcttctttctagGTgag B+5-L45tgcctcttcttctttcTaggtgag B+5-L8tgcctcttcttctttctAggtgag B+5-L7tgcctcttcttctttctaGgtgag B+5-L6tgcctcttcttctttctagGtgag B+5-L5tgcctcttcttctttctaggTgag B+5-L4tgcctcttcttctttctaggtGag B+5-L3tgcctcttcttctttctaggtgAg B+5-L2tgcctcttcttctttctaggtgaG B+5-L1tgcctcttcttctttctaggtgag B+5-L0tgcctcttcttctttctagggaca C+5-L0tgcctcttcttctttctagacgag A+5-L0gcagatggagatgtaggaggag P553R SequenceLabelB. Real-Time PCRSequence-independent real-time PCR using SYBR Green I was performed on iCycler iQ (Biorad, Hercules, USA). Fifty microliter PCR reaction for amplifying GFR α2 isoforms was performed in 1X XtensaMix-SG TM (BioWORKS) containing 2.5 mM MgCl 2, 100 nM of each primer and 0.5 U DyNAzyme II (Finnzymes, Finland). The PCR condition consists of initial denaturation at 95°C for 3 min followed by 40 cycles of 95°C for 1 min, 60°C for 30 s and 72°C for 1 min. Real-time detections were carried out at annealing stages. Melting curves were generated after amplification. The threshold cycles (Ct) were calculated using the Optical interface iQ5 software.III. R ESULTSA. Specificity of Exon Overlapping DNA Primers for GFR α2 IsoformsThe human GFR α2 gene is known to produce three known isoforms by alternative splicing of pre-mRNA. GFR α2a consists of all the exons from 1 to 9, whereas GFR α2b and GFR α2c do not have exon 2 and exon 2-3, respectively (Fig. 2). Due to the absence of unique exons in GFR α2b and GFR α2c isoforms, specific detection of these transcripts can only achieved using real-time PCR primers targeting the unique exon junctions.Firstly, we adopted the previous design strategy for mouse GFR α2 isoforms, using primers with 5 base pairs (bp) crossing the unique exon junction of each isoform [3]. The Ct values for specific and non-specific amplification of the primers were listed in Table 2. Although this strategy was successfully applied to discriminate GFR α2c from GFR α2a and GFR α2b by 100,000-fold (Table 2 and Fig. 3C), discrimination between GFR α2a and GFR α2b was extremely poor (Table 2). Specific DNA primer for GFR α2b (B+5-L0) cross amplified GFR α2a isoform with discrimination level lower than 5-fold (Fig. 3B). Likewise, A+5-L0 also mis-amplified GFR α2b isoform significantly (Fig 3A). Therefore, the design strategy of previous study could not be successfully applied to discriminate human GFR α2a and GFR α2b isoforms.Table 2.specific and non-specific amplification of exon overlapping primers for GFR α2 isoforms 30.311.229.026.9C+5-L027.323.410.212.0B+5-L033.622.812.811.6A+5-L0water 2c 2b 2a Threshold Cycle (Ct)Primers15-9324GFR α2aGFR α2b145-9GFR α2cFig. 2. Exon organizations of human GFR α2 isoforms. Isoform-specific forward primers were designed to overlap the unique exon junction of each isoform (dashed arrows). A common reverse primer was used to amplify all the isoforms (solid arrow).Sequence alignment of GFR α2a and GFR α2b isoforms reveal exon junctions 1/2 and 1/3 to have significant sequence similarity (Fig. 4). As a result, primer B+5-L0 can mis-prime to GFR α2a template at position I, further extension of this primer towards exon 3 may render it to mis-prime at position II (Fig. 4).Because GFR α2a possess the unique exon 2, specific primer can be designed within this exon. Conversely, the design for GFR α2b is limited to exon junction 1/3. In order to specifically quantify GFR α2b isoform withconstrained primer design, modifications of this DNA primer are required to maximize its specificity.B. Improved Specificity of the Primers with LNA SubstitutionsPrevious studies have shown that LNA substitutions can increase the hybridization temperature and increase the specificity to complementary template [8], [9]. Therefore, we designed a set of primers for GFR α2b with identical sequence (B+5) but different LNA substitutions (Table 1). Briefly, single LNA was placed at first to eighth residue from 3’ of the primer. Sequence differences between GFR α2a and GFR α2b resides at fourth and fifth residue. Primers with multiple LNA substitutions were also synthesized. The specificity of these primers is measured together with the DNA primer control (Table 3). In contrast to the other reports [8], [10], 3’ terminal LNA substitution (L1) did not affect the PCR efficiency as seen with similar Ct values between DNA and LNA primers (2b). However, PCR was inhibited using primers L16 and L123. About 100-fold increase in specificity was achieved by single LNA substitution at first (L1) and sixth (L6) residue. LNA substitutions at the fourth and fifth residues (L45) did not improve the specificity significantly. Consecutive LNA substitutions at sixth and seventh residues (L67) dramatically improved the discrimination by 19 cycles, which corresponds to 100,000-fold (Fig. 5).* No amplification was observed using primers with L16***L123Table 3.Specificity of B+5 primers with LNA substitutions*31.217.213.616.418.912.313.514.514.918.512.0Ct (2a)∆Ct Ct (2b)Primerand L123 LNA substitutions.**L1618.712.5L674.812.4L45 2.211.4L8 4.711.7L7 6.512.4L60.911.4L5 2.111.4L4 2.811.7L3 3.511.4L2 6.911.7L1 1.810.2L0In conclusion, despite the extremely poor specificity of B+5 DNA primer, LNA substitutions in this primer can improve its specificity dramatically. Discrimination of highly homologous alternative spliced isoforms can be achieved by appropriate substitutions of LNA residues in the exon overlapping primers.GFR α2aGFR α2b1Fig. 4. Challenge of designing specific primers for GFR α2b isoform.Fig. 3. Amplification of GFR α2 isoforms using primers A+5-L0 (A), B+5-L0 (B) and C+5-L0 (C).A2a 2b 2cwaterB2b 2a 2c waterC2c 2a water2bIV. D ISCUSSIONAlternative splicing event is believed to contribute tothe expression and functional diversification of a limited set of genes of a genome. In order to understand the physiological contributions of the isoforms, quantitative expression of these isoforms becomes essential. Because of the mechanism of splicing, shorter isoforms usually lack unique exons as targets for specific quantification. Sequence similarity at unique exon junctions poses more difficulty in primer design for real-time PCR quantification.In this study, we presented the challenge in designing specific primers for alternatively spliced isoforms and demonstrated the application of LNA substituted primers to achieve desired assay specificity. Compared to the DNA primers which showed extremely poor discrimination of homologous isoforms, single LNA substitution can improve the specificity depending on the position of substitution. Although 3’ terminal LNA substitution has been shown to severely inhibit PCR, we did not observe such effect. Conversely, terminal LNA substitution has significantly improved the discrimination. Such contradiction could be due to sequence variation at 3’ terminal or differences in the DNA polymerases used as discussed previously [8]. Multiple LNA substitutions led to identification of highly specific LNA/DNA chimeric primer that could achieve 100,000-fold specificity against highly homologous template. It is interesting that this primer contains two consecutive LNA residues 3’ preceding the sequence differences of the isoforms. However, substitution of the residues with sequence differences with LNAs did not improve specificity significantly. It has been observed that the DNA sugar 3’ of the LNA is driven to a C3’-endo conformation as shown by NMR structures of DNA/LNA chimeric oligos complementary to RNA [11]. Therefore, the substituted LNA residues may induce conformational changes in the adjacent DNA ribose moiety, thus improving the affinity of these DNA residues to perfect match sequences. The predictive rules governing LNA substituted primers for high specificity amplification of DNA template remains to be elusive.LNA/DNA chimeric probes designed for SNP genetyping analysis has been widely reported [12], [13]. This is the first report demonstrating the application ofLNA/DNA chimeric primers in the discrimination of alternatively spliced isoforms. In summary, LNA substituted primers can exhibit outstanding specificitycompared to their DNA primer counterparts. We have identified a highly specific LNA substituted primer for GFR α2b isoform. We suggest that LNA molecules couldbe applied similarly to other highly homologousalternative spliced isoforms where limited DNA primer designs are available.R EFERENCES[1] J. M. Johnson, J. Castle, P. Garrett-Engele, Z. Kan, P. M. Loerch,C. D. Armour, R. Santos, E. E. Schadt, R. Stoughton, and D. D. Shoemaker, "Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays," Science , vol. 302, pp. 2141-4, 2003.[2] S. Stamm, "Signals and their transduction pathways regulatingalternative splicing: a new dimension of the human genome," Hum Mol Genet , vol. 11, pp. 2409-16, 2002.[3] Y. W. Wong, G. M. Sia, and H. P. Too, "Quantification of mouseglial cell-line derived neurotrophic factor family receptor alpha 2 alternatively spliced isoforms by real time detection PCR using SYBR Green I," Neurosci Lett , vol. 320, pp. 141-5, 2002.[4] L. F. Yoong, Z. N. Peng, G. Wan, and H. P. 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Nikiforov, "Single nucleotidepolymorphism genotyping using short, fluorescently labeled locked nucleic acid (LNA) probes and fluorescence polarization detection," Nucleic Acids Res , vol. 30, pp. e91, 2002.[13] L. A. Ugozzoli, D. Latorra, R. Puckett, K. Arar, and K. Hamby,"Real-time genotyping with oligonucleotide probes containing locked nucleic acids," Anal Biochem , vol. 324, pp. 143-52, 2004.Fig. 5. Specific amplification of GFR α2b using primer B+5-L67. 2a 2b water。