小分子RNA介導(dǎo)的基因表達(dá)調(diào)控

內(nèi)容概要

內(nèi)源小分子RNA廣泛存在于各種生物中，包括人類(lèi)、小鼠、果蠅、蠕蟲(chóng)、真菌和細(xì)菌等。microRNA作為一種細(xì)胞調(diào)控關(guān)鍵因子能夠修飾基因的表達(dá)。在高等真核生物中，microRNA甚至能調(diào)控約50%基因的表達(dá)。
本書(shū)匯集了眾多科技工作者的前沿性工作，內(nèi)容包括從細(xì)菌到人類(lèi)等生物組織中microRNA調(diào)控途徑的多樣性。除了闡述調(diào)控小分子RNA的生物合成機(jī)制及其加工過(guò)程，作者還探討了這些途徑的功能在寄主體內(nèi)的重要性。
本書(shū)圍繞小分子RNA這一新發(fā)現(xiàn)的調(diào)控分子，針對(duì)其參與調(diào)控的廣度與創(chuàng)新性進(jìn)行了闡述。小分子RNA已經(jīng)成為研究基因功能的強(qiáng)有力工具，并帶來(lái)了一系列的重大發(fā)現(xiàn)，必將對(duì)增進(jìn)基因功能與疾病治療的理解帶來(lái)革命性的改變。

書(shū)籍目錄

前言
致謝
編者簡(jiǎn)介
撰稿人
第1章 MicroMining:通過(guò)計(jì)算方式發(fā)現(xiàn)未知的microRNA Adam Grundhoff
第2章 動(dòng)物microRNA基因預(yù)測(cè) Ola Snφve,Pal S*trom
第3章 研究microRNA存在與功能的一系列資源 Praveen Sethupathy,Molly Megraw, Artemis
G. Hatzigeorgiou
第4章 大腸桿菌Hfq結(jié)合小RNA對(duì)mRNA穩(wěn)定性及翻譯的調(diào)控 Hiroji Aiba
第5章 動(dòng)物細(xì)胞巾microRNA調(diào)控基因表達(dá)的機(jī)制 Yang Yu,Timothy W. Nilsen
第6章 秀麗隱桿線蟲(chóng)microRNA Mona J. Nolde,Frank J. Slack
第7章 秀麗隱桿線蟲(chóng)小RNA的分離及鑒定 Chisato Ushida, Yusuke Hokii
第8章 MicroRNA與果蠅發(fā)育 Utpal Bhadra,Sunit KumarSingh,Singh,S. N. C. V.
L. Pushpavalli,Praveensingh B. Hajeri,Manika Pal-Bhadra
第9章 斑馬魚(yú)RNA干擾與microRNA Alex S. Flynt,Elizabeth J. Thatcher,James G.
Patton
第10章 植物microRNA的產(chǎn)生和功能 Zoltan Havelda
第11章 擬南芥內(nèi)源小RNA途徑 Manu Agarwal,Julien Curaba,Xuemei Chen
第12章 如何評(píng)價(jià)microRNA表達(dá)——技術(shù)指導(dǎo) Mirco Castoldi,Vladimir Benes,Martina U.
Muckenthaler
第13章 MicroRNA基因表達(dá)定量的方法 Lori A. Neely
第14章 MicroRNA介導(dǎo)的可變剪切調(diào)控 Rajesh K. Gaur
第15章 RNA聚合酶Ⅱ介導(dǎo)的內(nèi)含子microRNA表達(dá)系統(tǒng)研究進(jìn)展 Shi-Lung Lin,Shao-Yao Ying
第16章 基于microRNA的RNA聚合酶Ⅱ表達(dá)載體在動(dòng)物細(xì)胞RNA干擾中的應(yīng)用 Anne B. Vojtek,Kwan-Ho
Chung,Paresh D. Patel,David L. Turner
第17章 轉(zhuǎn)基因RNA干擾技術(shù)——一種用于哺乳動(dòng)物反向遺傳學(xué)研究的快速低成本方法 Linghua Qiu,Zuoshang
Xu
第18章 AIDS交響曲——基于microRNA的治療方法 Yoichi R. Fujii
第19章 MicroRNA與癌癥——連點(diǎn)成線 Sumedha D. Jayasena
第20章 哺乳動(dòng)物巾小RNA介導(dǎo)的轉(zhuǎn)錄水平基因沉默 Daniel H. Kim, John J. Rossi
第21章 由RNA介導(dǎo)的轉(zhuǎn)錄水平基因沉默控制的基因表達(dá)調(diào)控 Kevin V. Morris
索引

章節(jié)摘錄

1 MicroMiningComputational Approachesto microRNA DiscoveryAdam GrundhoffOverview................................................................................................................11.1 Introduction.......................................................................................................21.2 When Is a Small RNA an miRNA?...................................................................21.3 Advantages and Disadvantages of Experimental versus ComputationalmiRNA Identification........................................................................................31.4 Computational Prediction of miRNAs..............................................................51.4.1 Getting Started: Upstream Filtering......................................................71.4.2 Following Through: Structure Prediction and Scoring....................... 121.4.3 Wrapping It Up: Experimental Validation........................................... 141.5 Viral miRNAs................................................................................................. 151.6 Conclusions...................................................................................................... 16References................................................................................................................. 16OverviewThe recent past has seen the rapid identification of thousands of microRNAs(miRNAs) encoded by various metazoan organisms as well as some viruses, and itis very likely that many more still await discovery. Most of the hitherto-known miRNAshave been identified via the cloning and sequencing of small RNAs. While verypowerful, this approach is not without its limitations: especially those miRNAs thatare of low abundance, or which are only expressed in certain cell types or only duringbrief periods of organismal development, or are easily missed in cloning-basedscreens. Thus, alternative means of miRNA discovery are needed.Given that the signal that marks the miRNA precursor for the cellular processingmachinery appears to be a relatively simple one (i.e., a hairpin structure), andconsidering the rapidly increasing availability of large-scale genomic sequencingdata for many organisms, computational methods appear ideally suited for the comprehensiveidentification of hitherto-unknown miRNAs. This chapter discusses thegeneral principles of computational miRNA identification methods, examines theiradvantages and disadvantages as compared to the cloning method, and takes a lookat the various miRNA prediction algorithms that have been developed recently.1.1 I ntroductionmiRNAs are small (～22 nt) RNA molecules that are able to regulate the expression offully or partially complementary mRNA transcripts. As described in greater detailelsewhere in this book, they are initially transcribed as part of hairpin structureswithin much larger precursor transcripts (the so-called primary RNAs or pri-miRNAs).Following excision of the stem-loops by the RNase III?like enzyme Drosha,the isolated hairpins (called precursor miRNAs or pre-miRNAs) are exported tothe cytoplasm and further processed by the Dicer complex to produce the mature,single-stranded miRNA molecule. Recent evidence suggests that plants and animalsencode a multitude of miRNAs, many of which are evolutionarily conserved. As ofthis writing, it is still true that the majority of known miRNAs have been identifiedexperimentally, that is, by cloning of small RNAs. However, this method has certainlimitations, and alternative means for the prediction of novel miRNAs are thereforeincreasingly sought.The observation that pre-miRNAs form characteristic stem-loops has spurred thedevelopment of a number of computational approaches designed to identify novelmiRNA candidates based on the prediction and analysis of secondary structures.Given the already complete or near-complete sequencing of whole genomes frommany species, such approaches hold great promise for identifying the full complementof miRNAs encoded by a given organism. However, because the precise set ofstructural features that differentiate a pre-miRNA stem-loop from the large numberof hairpins in the genome is not known, additional filters have to be employed toreduce the number of false-positive predictions, and experimental confirmation ofthe remaining candidates is required. In this chapter, I will compare the benefitsand disadvantages of computational miRNA prediction methods in comparison tothe cloning method, review principles of the existing miRNA prediction algorithms,discuss the general challenges and pitfalls of in silico miRNA identification, andprovide an outlook of what might be expected from these approaches in the future.Finally, I will consider a special application of the miRNA prediction problem: theidentification of miRNAs in viral genomes.1.2 W hen is a small RNA an miRNA ?In order to devise approaches designed to identify miRNAs, be they experimentalor computational, it is important to clearly define what an miRNA is. In a biologicalsense, such a definition is quite straightforward: an miRNA is simply a small,single-stranded regulatory RNA molecule that is generated from its precursor moleculesvia successive processing by Drosha and Dicer. It is much more difficult,however, to define practicable criteria that are readily testable on an experimentalor computational basis and that can unequivocally identify a candidate sequence asa genuine miRNA. Following the realization that miRNAs represent abundant moleculesexpressed in a wide variety of organisms, a consortium of researchers agreedon a set of criteria that have to be fulfilled before a candidate can be called a bonafide miRNA.1 According to these guidelines, it is necessary to provide evidence that(1) the candidate sequence is expressed as an appropriately sized RNA molecule inliving cells and, furthermore, does not stem from random degradation (Expressioncriteria), and (2) that the maturation of the candidate involves processing by Droshaand Dicer (Biogenesis criteria). The expression criteria are preferentially satisfied bydetection of a distinct band of approximately 22 nt on a Northern blot. Alternatively,the ability to detect the molecule in a library of cloned, size-selected RNAs is consideredsufficient evidence, especially if the library contains high copy numbers ofthe particular candidate sequence.To satisfy the biogenesis criteria, the guidelines by Ambros et al.1 call for experimentalproof of Dicer processing by demonstrating that increased levels of the precursoraccumulate in cells with decreased Dicer expression. In contrast, experimentalproof of Drosha processing is generally not required; instead, it is sufficient to showthat the putative precursor transcript has the capacity to adopt a secondary structurethat is likely to be amenable to Drosha processing. Of course, given the incompleteknowledge of the rules governing recognition of target mRNAs by Drosha, it is notknown what exactly makes a given RNA structure amenable to Drosha processing,and (as will be discussed later) this complicates the computational predictionof miRNA candidates considerably. Based on the characteristics of known miRNAprecursor structures, however, it is generally agreed that the minimal requirementsare (1) the adopted structure is a hairpin that does not contain many or large internalbulges, and (2) the mature miRNA is to be found within the stem (not the loop) partof the hairpin.Evolutionary conservation serves as a third biogenesis criterion: As miRNAsare often conserved in closely related (and sometimes even in distant) species,phylogenetic conservation of the miRNA sequence itself as well as its fold-backstructure is considered strong evidence that the candidate sequence represents agenuine miRNA. An ideal miRNA candidate would meet all of the preceding criteria;however, it is generally considered sufficient to provide convincing evidencefor at least one criterion out of the two categories. Indeed, because Dicer knockoutcells are not readily available for most organisms, and effective knockdown ofDicer is technically challenging, positive experimental proof of Dicer processingis rarely shown.1.3 A dvantages and Disadvantages of Experimentalversus Computational miRNA IdentificationThe “traditional” approach to identifying miRNAs consists of cloning of small RNAmoieties. Although several protocols for the efficient cloning of such molecules havebeen devised, they all rely on the common principle of ligating linkers to size-fractionatedRNAs, followed by cDNA synthesis and typically PCR amplification. Theobtained products are then either cloned (often after concatamerization to increasethe information obtained in a single-sequence read) and sequenced, or subjecteddirectly to massive parallel sequencing approaches (“deep sequencing”). Accordingto the guidelines described earlier, these candidates are then further evaluated toensure that the putative pre-miRNA sequence adopts an appropriate hairpin structurearound the candidate. If this is the case, the candidate can generally be considered abona fide miRNA, since the recovery of the clone from a small RNA library alreadysatisfies the expression criterion (nevertheless, Northern blots are often performed toallow for proper quantification of the miRNA).The cloning approach has been extremely successful, and although increasingnumbers of miRNAs are being identified via computational means, the majorityof confirmed miRNAs currently listed in the miRNA database (miRBase, http://microrna.sanger.ac.uk) still have been identified via this method. One of the greatadvantages of the cloning protocol is that it provides the precise sequence of themature miRNA molecule. Therefore, in contrast to hybridization-based methods,even closely related miRNAs that differ in only one nucleotide position can be distinguished.Also, the currently available computational prediction tools generally onlyallow identification of miRNA precursors but do not reliably predict the location ofDrosha and Dicer cleavage sites. In contrast, cloning identifies the precise 5′ and 3′termini of the mature miRNA molecule.As it appears that nucleotides 2 to 8 of the miRNA (the so-called seed region)are especially important for target recognition, knowledge of the precise ends (andparticularly the 5′ terminus) is a distinct advantage if a computational predictionof target transcripts is to be performed. As might be expected, the frequency withwhich a given miRNA is cloned often is approximately equivalent to its abundance(although this frequency may also be affected by other factors; see the following text)and therefore provides a rough estimate of its expression levels. Thus, abundantlyexpressed miRNAs are usually readily identified. However, it can be challengingto achieve a saturated screen that also captures rare miRNAs. Furthermore, even ifsuch miRNAs are contained within the library, one can never be entirely certain thatenough clones have been sequenced to identify all of them.In addition to these constraints, the scope of a cloning screen is also limited byits source material; naturally, only miRNAs that are expressed in the cells fromwhich the RNA material was derived can be identified. Many miRNAs, however,are expressed in a tissue-dependent manner, or are only expressed at certain developmentalstages. This limitation can be partially overcome for relatively simple organisms,where the RNA can be prepared from whole animals (e.g., mixed larvae stagesand adults from worms or insects).In organisms with higher complexity such as vertebrates, however, the situation ismore difficult: RNA from different embryonic or adult tissues can be mixed, but thesensitivity of the screen will dramatically decrease with the complexity of the sourcematerial, and it is very unlikely that nonabundant miRNAs could be identified insuch screens. While these problems could be theoretically solved by massive screeningefforts, that is, performing separate screens with material prepared from everyindividual tissue at each developmental stage, the cloning approach also appearslimited in a more fundamental way. Several observations suggest that some miRNAsare more readily cloned than others owing to intrinsic properties such as sequencecomposition, the presence of certain nucleotides at their termini, or posttranscriptionalmodifications such as methylation or RNA editing.2?6Computational approaches to miRNA discovery are not subject to many of thelimitations that apply to the cloning method. Certainly, one of the biggest advantagesof computational miRNA identification is the universal scope of the analysis; as theprediction does not require experimental material, it can potentially discover all ofthe miRNAs encoded by a given organism, even those that are expressed only atvery low levels, in rare cells, or during brief periods of development. However, thisadvantage is partially annulled by the insufficient precision of the presently availablealgorithms: as the programs (to varying degrees) produce large numbers of falsepositivepredictions, experimental verification is still a necessity. Northern blotting isfrequently performed to investigate the expression of the computationally predictedcandidates, or the predicted sequences are amplified from small RNA libraries.These procedures are not particularly compatible with high-throughput screening,and since many computational methods produce large numbers of candidates, only asmall contingent of the predictions is usually subjected to experimental verification,whereas the majority remains untested. More importantly, the experimental validationmethods are subject to many of the same limitations that hamper the cloningapproach. Thus, even if an experimental verification is attempted and fails, it is oftenimpossible to decide whether the failure was due to a false-positive prediction, insufficientsensitivity of the experimental detection method, or lack of expression in thetested tissue or cell line.It is thus perhaps not surprising that the expression criterion has not been satisfiedfor most computationally predicted miRNA candidates. While some groups haveattempted to reconcile these difficulties by developing expression analysis tools thatare, for example, more sensitive or allow high-throughput screening, there is alsotremendous effort to increase the reliability of computational prediction methodssuch that experimental confirmation is becoming less important.1.4 Computational Prediction of miRNA sA plethora of computational approaches aimed at the prediction of miRNAs havebeen devised, and although nearly all of them use the evaluation of features that arethought to be characteristic for miRNAs in order to identify novel candidates, theyvary significantly in scope, complexity, and level of sophistication of the underlyingalgorithms. Some approaches strive to identify the totality of miRNAs encoded by agiven organism, whereas others aim to identify only miRNAs that represent closelyrelated ortho- or paralogs of those that are already known. Some programs investigatesome of the largest genomes, those of mammals, whereas others consider onlysome of the smallest, those of viruses.Despite these differences, most of the approaches function according to a commonscheme that might be abstracted as follows. First, a pool of input sequences(usually representing the complete genome of a given organism) is filtered in order tolimit the number of candidates that have to be evaluated by downstream algorithms.I will refer to this process as upstream filtering in the following. The filtered pool isthen subjected to a structure prediction. The obtained structures are then comparedto those of known pre-miRNAs, and a score calculation is performed, depending onthe degree of similarity. Finally, experimental validation is attempted, usually for aselection of the highest-scoring candidates.There are considerable differences in the degree to which structural features areinvestigated during the scoring step; sometimes the filter might simply ensure that thecandidate forms a hairpin structure, whereas in other cases it might investigate thecandidate’s structure down to the minutest detail. The level of sophistication, in largepart, will depend on the design of the upstream filter and the efficiency with whichthis filter preselects a set of candidates enriched for genuine miRNAs. For example,phylogenetic conservation is the most widely used upstream filter (and at least presently,it is also appears to be the most efficient). Indeed, if the sequence of a knownmature miRNA is perfectly conserved in a closely (or even distantly) related species,a relatively simple structural analysis that shows that the ability of the surroundingsequences to adopt a fold-back structure is conserved as well might suffice.In contrast, an ab initio prediction method in which the upstream filter is minimalwill require a much more detailed structural analysis during the downstream scoringstep. Thus, a highly efficient upstream filter requires a less elaborate downstreamstructure evaluation, and vice versa. The cloning method might be considered a specialcase of this scheme in which the upstream filtering is based on an experimentalprocedure; since this method produces only little background, the subsequent structuralinvestigation can be minimal.All of the available computational approaches are subject to the productionof false-positive (i.e., candidates that pass the filters but do not represent genuinemiRNAs) and false-negative predictions (i.e., bona fide miRNAs that are rejectedduring the upstream filtering or the downstream scoring step). The ratio with whichtrue-positive versus false-positive predictions are made will determine the algorithm’saccuracy, while the ratio of true-positive versus false-negative predictionswill determine its sensitivity. Such rates are frequently estimated in order to judge analgorithm’s performance.Estimating the rate of false-negative predictions is a relatively straightforwardprocess. Often, only a limited number of the contingent of known miRNAs is used toestablish the parameters of the filtering and scoring algorithms. The remaining miRNAsare then subjected to the prediction procedure, and the number of rejected versusretained miRNAs is determined. Alternatively, the full complement of miRNAsis repeatedly passed through the filters, and the method parameters are adjusteduntil an acceptable ratio between rejected and accepted miRNAs is achieved (whatexactly an acceptable ratio is will greatly vary with the overall design and scope ofthe method).The estimation of false-positive prediction rates is a more complicated matter:in order to measure such numbers with high reliability, one ideally wouldhave a set of sequences that assuredly does not contain any miRNAs at all, or aset in which all of the genuine miRNAs are known beforehand. In theory, sucha set can be created artificially from randomly generated sequences, or by shufflingnaturally occurring ones, but since biological sequences are nonrandom, sucha reference set would be hardly representative of the experimental sequence set.Alternatively, one might select genetic elements that have known functions andare thus unlikely to additionally represent miRNAs, but this would reduce thecomplexity of the reference set so drastically that the gained information would beclose to meaningless.In reality, the rate of false-positive predictions is often estimated on an experimentalbasis. For this purpose, a representative subset of the predictions (or all of

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