小分子RNA介导的基因表达调控（导读版） 下载 mobi epub pdf 电子书 2024

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内容简介

内源小分子RNA广泛存在于各种生物中，包括人类、小鼠、果蝇、蠕虫、真菌和细菌等。microRNA作为一种细胞调控关键因子能够修饰基因的表达。在高等真核生物中，microRNA甚至能调控约50%基因的表达。
本书汇集了众多科技工作者的前沿性工作，内容包括从细菌到人类等生物组织中microRNA调控途径的多样性。除了阐述调控小分子RNA的生物合成机制及其加工过程，作者还探讨了这些途径的功能在寄主体内的重要性。
本书围绕小分子RNA这一新发现的调控分子，针对其参与调控的广度与创新性进行了阐述。小分子RNA已经成为研究基因功能的强有力工具，并带来了一系列的重大发现，必将对增进基因功能与疾病治疗的理解带来革命性的改变。

目录

前言
致谢
编者简介
撰稿人
第1章 MicroMining:通过计算方式发现未知的microRNA Adam Grundhoff
第2章 动物microRNA基因预测 Ola Snφve,Pal S*trom
第3章 研究microRNA存在与功能的一系列资源 Praveen Sethupathy,Molly Megraw, Artemis G. Hatzigeorgiou
第4章 大肠杆菌Hfq结合小RNA对mRNA稳定性及翻译的调控 Hiroji Aiba
第5章 动物细胞巾microRNA调控基因表达的机制 Yang Yu,Timothy W. Nilsen
第6章 秀丽隐杆线虫microRNA Mona J. Nolde,Frank J. Slack
第7章 秀丽隐杆线虫小RNA的分离及鉴定 Chisato Ushida, Yusuke Hokii
第8章 MicroRNA与果蝇发育 Utpal Bhadra,Sunit KumarSingh,Singh,S. N. C. V. L. Pushpavalli,Praveensingh B. Hajeri,Manika Pal-Bhadra
第9章 斑马鱼RNA干扰与microRNA Alex S. Flynt,Elizabeth J. Thatcher,James G. Patton
第10章 植物microRNA的产生和功能 Zoltan Havelda
第11章 拟南芥内源小RNA途径 Manu Agarwal,Julien Curaba,Xuemei Chen
第12章 如何评价microRNA表达——技术指导 Mirco Castoldi,Vladimir Benes,Martina U. Muckenthaler
第13章 MicroRNA基因表达定量的方法 Lori A. Neely
第14章 MicroRNA介导的可变剪切调控 Rajesh K. Gaur
第15章 RNA聚合酶Ⅱ介导的内含子microRNA表达系统研究进展 Shi-Lung Lin,Shao-Yao Ying
第16章 基于microRNA的RNA聚合酶Ⅱ表达载体在动物细胞RNA干扰中的应用 Anne B. Vojtek,Kwan-Ho Chung,Paresh D. Patel,David L. Turner
第17章 转基因RNA干扰技术——一种用于哺乳动物反向遗传学研究的快速低成本方法 Linghua Qiu,Zuoshang Xu
第18章 AIDS交响曲——基于microRNA的治疗方法 Yoichi R. Fujii
第19章 MicroRNA与癌症——连点成线 Sumedha D. Jayasena
第20章 哺乳动物巾小RNA介导的转录水平基因沉默 Daniel H. Kim, John J. Rossi
第21章 由RNA介导的转录水平基因沉默控制的基因表达调控 Kevin V. Morris
索引

精彩书摘

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介导的基因表达调控（导读版） 下载 mobi epub pdf txt 电子书 格式

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有点过时了，只综述了2006年以前的，目录是中文，内容是英文的，图解比较清晰。本书汇集了众多科技工作者的前沿性工作，内容包括从细菌到人类等生物组织中microRNA调控途径的多样性。除了阐述调控小分子RNA的生物合成机制及其加工过程，作者还探讨了这些途径的功能在寄主体内的重要性。

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