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內容簡介

內源小分子RNA廣泛存在於各種生物中，包括人類、小鼠、果蠅、蠕蟲、真菌和細菌等。microRNA作為一種細胞調控關鍵因子能夠修飾基因的錶達。在高等真核生物中，microRNA甚至能調控約50%基因的錶達。
本書匯集瞭眾多科技工作者的前沿性工作，內容包括從細菌到人類等生物組織中microRNA調控途徑的多樣性。除瞭闡述調控小分子RNA的生物閤成機製及其加工過程，作者還探討瞭這些途徑的功能在寄主體內的重要性。
本書圍繞小分子RNA這一新發現的調控分子，針對其參與調控的廣度與創新性進行瞭闡述。小分子RNA已經成為研究基因功能的強有力工具，並帶來瞭一係列的重大發現，必將對增進基因功能與疾病治療的理解帶來革命性的改變。

目錄

前言
緻謝
編者簡介
撰稿人
第1章 MicroMining:通過計算方式發現未知的microRNA Adam Grundhoff
第2章 動物microRNA基因預測 Ola Snφve,Pal S*trom
第3章 研究microRNA存在與功能的一係列資源 Praveen Sethupathy,Molly Megraw, Artemis G. Hatzigeorgiou
第4章 大腸杆菌Hfq結閤小RNA對mRNA穩定性及翻譯的調控 Hiroji Aiba
第5章 動物細胞巾microRNA調控基因錶達的機製 Yang Yu,Timothy W. Nilsen
第6章 秀麗隱杆綫蟲microRNA Mona J. Nolde,Frank J. Slack
第7章 秀麗隱杆綫蟲小RNA的分離及鑒定 Chisato Ushida, Yusuke Hokii
第8章 MicroRNA與果蠅發育 Utpal Bhadra,Sunit KumarSingh,Singh,S. N. C. V. L. Pushpavalli,Praveensingh B. Hajeri,Manika Pal-Bhadra
第9章 斑馬魚RNA乾擾與microRNA Alex S. Flynt,Elizabeth J. Thatcher,James G. Patton
第10章 植物microRNA的産生和功能 Zoltan Havelda
第11章 擬南芥內源小RNA途徑 Manu Agarwal,Julien Curaba,Xuemei Chen
第12章 如何評價microRNA錶達——技術指導 Mirco Castoldi,Vladimir Benes,Martina U. Muckenthaler
第13章 MicroRNA基因錶達定量的方法 Lori A. Neely
第14章 MicroRNA介導的可變剪切調控 Rajesh K. Gaur
第15章 RNA聚閤酶Ⅱ介導的內含子microRNA錶達係統研究進展 Shi-Lung Lin,Shao-Yao Ying
第16章 基於microRNA的RNA聚閤酶Ⅱ錶達載體在動物細胞RNA乾擾中的應用 Anne B. Vojtek,Kwan-Ho Chung,Paresh D. Patel,David L. Turner
第17章 轉基因RNA乾擾技術——一種用於哺乳動物反嚮遺傳學研究的快速低成本方法 Linghua Qiu,Zuoshang Xu
第18章 AIDS交響麯——基於microRNA的治療方法 Yoichi R. Fujii
第19章 MicroRNA與癌癥——連點成綫 Sumedha D. Jayasena
第20章 哺乳動物巾小RNA介導的轉錄水平基因沉默 Daniel H. Kim, John J. Rossi
第21章 由RNA介導的轉錄水平基因沉默控製的基因錶達調控 Kevin V. Morris
索引

精彩書摘

1 MicroMining
Computational Approaches
to microRNA Discovery
Adam Grundhoff
Overview....................................................................................
............................1
1.1 Introduction.......................................................................................................2
1.2 When Is a Small RNA an miRNA?...................................................................2
1.3 Advantages and Disadvantages of Experimental versus Computational
miRNA Identification........................................................................................3
1.4 Computational Prediction of miRNAs..............................................................5
1.4.1 Getting Started: Upstream Filtering......................................................7
1.4.2 Following Through: Structure Prediction and Scoring....................... 12
1.4.3 Wrapping It Up: Experimental Validation........................................... 14
1.5 Viral miRNAs................................................................................................. 15
1.6 Conclusions...................................................................................................... 16
References................................................................................................................. 16
Overview
The recent past has seen the rapid identification of thousands of microRNAs
(miRNAs) encoded by various metazoan organisms as well as some viruses, and it
is very likely that many more still await discovery. Most of the hitherto-known miRNAs
have been identified via the cloning and sequencing of small RNAs. While very
powerful, this approach is not without its limitations: especially those miRNAs that
are of low abundance, or which are only expressed in certain cell types or only during
brief periods of organismal development, or are easily missed in cloning-based
screens. Thus, alternative means of miRNA discovery are needed.
Given that the signal that marks the miRNA precursor for the cellular processing
machinery appears to be a relatively simple one (i.e., a hairpin structure), and
considering the rapidly increasing availability of large-scale genomic sequencing
data for many organisms, computational methods appear ideally suited for the comprehensive
identification of hitherto-unknown miRNAs. This chapter discusses the
general principles of computational miRNA identification methods, examines their
advantages and disadvantages as compared to the cloning method, and takes a look
at the various miRNA prediction algorithms that have been developed recently.
1.1 I ntroduction
miRNAs are small (～22 nt) RNA molecules that are able to regulate the expression of
fully or partially complementary mRNA transcripts. As described in greater detail
elsewhere in this book, they are initially transcribed as part of hairpin structures
within 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 to
the cytoplasm and further processed by the Dicer complex to produce the mature,
single-stranded miRNA molecule. Recent evidence suggests that plants and animals
encode a multitude of miRNAs, many of which are evolutionarily conserved. As of
this writing, it is still true that the majority of known miRNAs have been identified
experimentally, that is, by cloning of small RNAs. However, this method has certain
limitations, and alternative means for the prediction of novel miRNAs are therefore
increasingly sought.
The observation that pre-miRNAs form characteristic stem-loops has spurred the
development of a number of computational approaches designed to identify novel
miRNA candidates based on the prediction and analysis of secondary structures.
Given the already complete or near-complete sequencing of whole genomes from
many species, such approaches hold great promise for identifying the full complement
of miRNAs encoded by a given organism. However, because the precise set of
structural features that differentiate a pre-miRNA stem-loop from the large number
of hairpins in the genome is not known, additional filters have to be employed to
reduce the number of false-positive predictions, and experimental confirmation of
the remaining candidates is required. In this chapter, I will compare the benefits
and disadvantages of computational miRNA prediction methods in comparison to
the cloning method, review principles of the existing miRNA prediction algorithms,
discuss the general challenges and pitfalls of in silico miRNA identification, and
provide 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: the
identification 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 experimental
or computational, it is important to clearly define what an miRNA is. In a biological
sense, such a definition is quite straightforward: an miRNA is simply a small,
single-stranded regulatory RNA molecule that is generated from its precursor molecules
via successive processing by Drosha and Dicer. It is much more difficult,
however, to define practicable criteria that are readily testable on an experimental
or computational basis and that can unequivocally identify a candidate sequence as
a genuine miRNA. Following the realization that miRNAs represent abundant molecules
expressed in a wide variety of organisms, a consortium of researchers agreed
on a set of criteria that have to be fulfilled before a candidate can be called a bona
fide 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 in
living cells and, furthermore, does not stem from random degradation (Expression
criteria), and (2) that the maturation of the candidate involves processing by Drosha
and Dicer (Biogenesis criteria). The expression criteria are preferentially satisfied by
detection 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 considered
sufficient evidence, especially if the library contains high copy numbers of
the particular candidate sequence.
To satisfy the biogenesis criteria, the guidelines by Ambros et al.1 call for experimental
proof of Dicer processing by demonstrating that increased levels of the precursor
accumulate in cells with decreased Dicer expression. In contrast, experimental
proof of Drosha processing is generally not required; instead, it is sufficient to show
that the putative precursor transcript has the capacity to adopt a secondary structure
that is like 小分子RNA介導的基因錶達調控（導讀版） 下載 mobi epub pdf txt 電子書

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評分☆☆☆☆☆

內容很豐富，對實驗很有指導作用

評分☆☆☆☆☆

本書圍繞小分子RNA這一新發現的調控分子，針對其參與調控的廣度與創新性進行瞭闡述。小分子RNA已經成為研究基因功能的強有力工具，並帶來瞭一係列的重大發現，必將對增進基因功能與疾病治療的理解帶來革命性的改變。

評分☆☆☆☆☆

內容很豐富，對實驗很有指導作用

評分☆☆☆☆☆

本書圍繞小分子RNA這一新發現的調控分子，針對其參與調控的廣度與創新性進行瞭闡述。小分子RNA已經成為研究基因功能的強有力工具，並帶來瞭一係列的重大發現，必將對增進基因功能與疾病治療的理解帶來革命性的改變。

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