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Nature:选择性剪接的定性研究

来源:Nature 2008-11-28 11:04

专题:Nature报道

当人类基因组被解码时,科学家发现基因数量比他们预计的要少,这重新激发了他们对选择性剪接(从一个基因产生一个以上蛋白的一种机制)的兴趣。

Licatalosi等人研究出一种对活组织中的RNA–蛋白结合相互作用进行定性的不带偏见的、涉及整个基因组的方法,并且通过将该方法应用于哺乳动物大脑而显示了其潜力。他们对神经选择性剪接调控因子Nova的结合点进行了定性,并且获得了意想不到的发现:该调控因子也许在调控选择性多腺苷酸化中还有另一种功能。在另一项研究中,Wang等人利用对mRNAs的深度测序来研究不同人组织和癌症中的选择性剪接。通过将序列数据与剪接点进行比对,他们发现,选择性剪接在人类多外显子基因中实质上是普遍现象。他们还发现,选择性剪接与mRNA多腺苷酸化在机制上有联系。(生物谷Bioon.com)

生物谷推荐原始出处:

Nature 456, 464-469 (27 November 2008) | doi:10.1038/nature07488

HITS-CLIP yields genome-wide insights into brain alternative RNA processing

Donny D. Licatalosi1, Aldo Mele1, John J. Fak1, Jernej Ule3, Melis Kayikci3, Sung Wook Chi1, Tyson A. Clark4, Anthony C. Schweitzer4, John E. Blume4, Xuning Wang2, Jennifer C. Darnell1 & Robert B. Darnell1

1 Laboratory of Molecular Neuro-Oncology and Howard Hughes Medical Institute,
2 Biocomputing, Information Technology, The Rockefeller University, 1230 York Avenue, New York, New York 10021, USA
3 MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
4 Expression Research, Affymetrix, Inc., Santa Clara, California 95051, USA

Protein–RNA interactions have critical roles in all aspects of gene expression. However, applying biochemical methods to understand such interactions in living tissues has been challenging. Here we develop a genome-wide means of mapping protein–RNA binding sites in vivo, by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). HITS-CLIP analysis of the neuron-specific splicing factor Nova revealed extremely reproducible RNA-binding maps in multiple mouse brains. These maps provide genome-wide in vivo biochemical footprints confirming the previous prediction that the position of Nova binding determines the outcome of alternative splicing; moreover, they are sufficiently powerful to predict Nova action de novo. HITS-CLIP revealed a large number of Nova–RNA interactions in 3' untranslated regions, leading to the discovery that Nova regulates alternative polyadenylation in the brain. HITS-CLIP, therefore, provides a robust, unbiased means to identify functional protein–RNA interactions in vivo.

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