无义介导的mRNA降解(NMD)

无义介导的mRNA降解(nonsense-mediated mRNA decay,NMD）作为真核细胞中重要RNA监控机制,识别并降解开放阅读框中含有提前终止密码子（premature termination codon,PTC）的mRNA,以避免因截短的蛋白产物积累对细胞造成毒害. NMD还调控正常生理基因的表达,暗示其在真核细胞中扮演重要角色. NMD途径的关键是PTC的识别.本文通过3种模型来分别阐述发现于哺乳动物、酵母等不同有机体的识别机制.通常由NMD因子UPF1（up-frameshift）等被招募至含PTC的mRNA上,借助这些因子组装形成“功能复合体”并激活降解.但目前对于PTC识别后的过程仍认识有限,本文通过综述NMD途径的分子机制以更好地理解其生物学意义.     

mRNA 的无义介导降解

无义介导的mRNA衰变（NMD）选择性地迅速降解含有提前终止密码子的mRNA,保护机体免于截短蛋白质产生的有害效应,是真核细胞广泛存在的保守的mRNA监视机制,有重要生物学意义。关于其基本机制目前提出了一些模型。     

真核生物mRNA降解途径

mRNA降解在真核生物的基因表达调控中发挥重要作用.目前,已经鉴定了多种参与mRNA降解 的酶和复合物,并发现细胞质处理小体可能是降解mRNA的主要位点.本文着重总结了正常和 异常mRNA降解的主要途径以及各途径相关因子和酶的功能,并讨论了细胞质处理小体在mR NA降解过程中的作用.最后对该领域今后的研究重点和方向作了探讨.     

真核细胞中无义介导的mRNA降解

无义介导的mRNA降解(NMD)作为一种有效的细胞监控机制,主要监测细胞转录产物的提前终止密码子(PTC),并使得含有PTC的mRNA被迅速降解,从而防止其被翻译成为缺陷性的蛋白质.尽管NMD具有一定的保守性,但在酵母、哺乳动物以及后来的果蝇细胞中都发现有所不同.目前对于NMD的研究已进入了结构领域并发现它与端粒调控和RNAi等机制相互关联.     

蓝氏贾第虫无义mRNA的降解

无义介导的mRNA降解途径（nonsense-mediated mRNA decay,NMD）作为细胞内的一种重要的mRNA质量监控机制,可以降解含有提前终止密码子（premature termination codon,PTC）的异常转录本,从而避免截短蛋白质对细胞的毒害,但其详细的分子机制有待进一步阐释。蓝氏贾第虫（Giardia lamblia)作为一种寄生性单细胞原生动物,进化地位特殊,对其NMD途径的研究有利于阐明基因表达调控的分子和进化机制。本研究通过酵母双杂交及体外pull-down实验分析了贾第虫NMD途径因子上游移码蛋白1(Giardia lamblia up-frameshift 1,GlUPF1)、贾第虫RNA结合蛋白（Giardia lamblia HRP1, GlHRP1）、贾第虫核糖核酸外切酶（Giardia lamblia Ski7p,GlSki7p、Giardia lamblia XRN1,GlXRN1）之间的相互作用关系。结果表明,GlUPF1全长与GlHRP1、GlXRN1(1~500 aa)、GlSki7p间均可发生相互作用。而且GlUPF1的CH结构域和C端结构域分别与GlHRP1、GlXRN1（1~500 aa）、GlSki7p相互作用。说明GlUPF1在贾第虫NMD途径中作为招募平台,在无义mRNA识别和降解过程中发挥重要作用。为此,结合本实验室之前的研究结果,我们提出原生动物贾第虫的NMD途径：在提前终止密码子处SURF(SMG1-UPF1-eRF1-eRF3)复合物形成后,GlUPF1被磷脂酰肌醇3-激酶(suppressor with morphogenetic effect on genitalia 1,SMG1)磷酸化修饰, NMD途径激活,随后GlUPF1与HRP1相互作用,将转录本标记为NMD底物;GlUPF1进而招募下游贾第虫5′-3′核糖核酸降解酶GlXRN1、贾第虫3′-5′ 核糖核酸降解因子GlSki7p,最终降解靶标mRNA。     

NMD作用的顺式调控元件

真核生物利用无义介导的mRNA降解(nonsense-mediated mRNA decay,NMD),对含有提前终止密码子(premature termination codons,PTC)的异常转录产物进行快速清除,防止毒害性截短蛋白(truncatedproteins)的产生,是真核生物重要的mRNA监视机制。NMD作用的启动与多种顺式调控元件有关,它们包括：提前终止密码子的标识;PTC下游特定位置的序列元件,在酵母细胞称为DSE(downstream sequence element,DSE),在哺乳动物细胞主要为内含子剪接依赖性序列元件(exon-exon junction,EEJ);稳定作用元件(stabilizer elements,STE)对NMD作用的阻抑调节;以及其他与NMD作用相关的序列,如poly(A)延长、5’-UTR的uORF(upstream open reading frame,uORF)和程序化核糖体移码(programmed-1 ribosomal frameshift,-1PRF)信号序列等。NMD途径中的这些顺式调控元件可能是分子遗传调控的关键靶点。     

Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI

Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.