Alternative RNA Splicing

 

Alternative RNA Splicing

 

The human genome sequencing project (Venter et al., 2001) estimated the number of human genes to be between 30,000-40,000, which is much less than the previous estimates based on analysis of expressed sequence tags (ESTs, 100,000 to 150,000 genes). This increased diversity at the mRNA level can, in part, be accounted for by alternative RNA splicing. Understanding this diversity will be critical for future drug discovery and diagnostics efforts.

RNA splicing is an essential, precisely regulated post-transcriptional process t hat occurs prior to mRNA translation. A gene is first transcribed into a pre-messenger RNA (pre-mRNA), which is a copy of the genomic DNA containing intronic regions destined to be removed during pre-mRNA processing (RNA splicing), as well as exonic sequences that are retained within the mature mRNA. During RNA splicing, exons can either be retained in the mature message or targeted for removal in different combinations to create a diverse array of mRNAs from a single pre-mRNA, a process referred to as alternative RNA splicing (Lopez, 1998).

Click on the figure to see the animation



Alternative splice events that affect the protein coding region of the mRNA will give rise to proteins which differ in their sequence and therefore in their activities. Alternative splicing within the non-coding regions of the RNA can result in changes in regulatory elements such as translation enhancers or RNA stability domains, which may have a dramatic effect on the level of protein expression (see the figure below). It is therefore important that the regulation of RNA splicing is at a comparable level to that observed for RNA transcription or translation. This is the case as RNA splicing occurs within a tightly regulated, multi-component molecular, machines called spliceosomes, which is under the control of intra- and extra-cellular signalling pathways.

 

The accuracy of RNA splicing is also monitored by RNA proof reading mechanisms that are able to target incorrectly spliced mRNA for destruction or can correct the error.
Genome-wide methods have recently been developed for the identification of alternative splice transcripts to support drug discovery and diagnostics efforts. These methods should allow isoform specific drug discovery and improved drug efficacy, together with the isolation of diagnostic signatures with increased specificity.

Bibliography
  • Venter, J.C. et al. (2001) The sequence of the human genome. Science 291, 1304-1351.
  • Lopez, A.J. (1998) Alternative splicing of pre-mRNA: developmental consequences and mechanisms of regulation. Annu Rev Genet 32, 279-305

reprint from www.exonhit.com

内容概要:本文探讨了在MATLAB/SimuLink环境中进行三相STATCOM(静态同步补偿器)无功补偿的技术方法及其仿真过程。首先介绍了STATCOM作为无功功率补偿装置的工作原理,即通过调节交流电压的幅值和相位来实现对无功功率的有效管理。接着详细描述了在MATLAB/SimuLink平台下构建三相STATCOM仿真模型的具体步骤,包括创建新模型、添加电源和负载、搭建主电路、加入控制模块以及完成整个电路的连接。然后阐述了如何通过对STATCOM输出电压和电流的精确调控达到无功补偿的目的,并展示了具体的仿真结果分析方法,如读取仿真数据、提取关键参数、绘制无功功率变化曲线等。最后指出,这种技术可以显著提升电力系统的稳定性与电能质量,展望了STATCOM在未来的发展潜力。 适合人群:电气工程专业学生、从事电力系统相关工作的技术人员、希望深入了解无功补偿技术的研究人员。 使用场景及目标:适用于想要掌握MATLAB/SimuLink软件操作技能的人群,特别是那些专注于电力电子领域的从业者;旨在帮助他们学会建立复杂的电力系统仿真模型,以便更好地理解STATCOM的工作机制,进而优化实际项目中的无功补偿方案。 其他说明:文中提供的实例代码可以帮助读者直观地了解如何从零开始构建一个完整的三相STATCOM仿真环境,并通过图形化的方式展示无功补偿的效果,便于进一步的学习与研究。
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