The genetic landscape of a physical interaction.
The genetic landscape of a physical interaction.
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DOI：
10.7554/elife.32472
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发表时间：
2018-04-11
期刊：
影响因子：
7.7
通讯作者：
Lehner B
中科院分区：
文献类型：
--
作者：
Diss G;Lehner B
人类遗传学和进化生物学的一个关键问题是不同基因的突变如何结合联合收割机来改变表型。系统地绘制遗传相互作用图谱的努力主要是利用基因缺失。然而，大多数遗传变异由不同且难以预测影响的点突变组成。在这里，通过开发一种新的基于测序的蛋白质相互作用测定- deepPCA -我们量化了> 120，000对点突变对FOS和JUN原癌基因产物之间AP-1转录因子复合物形成的影响。遗传相互作用在顺式（在一个蛋白质内）和反式（在两个分子之间）中都是丰富的，并且包括两类-可以使用三参数全局模型预测的由热力学驱动的相互作用，以及邻近位置的残基之间的结构相互作用。这些结果揭示了物理相互作用如何产生可定量预测的遗传相互作用。蛋白质是细胞的分子主力，由称为氨基酸的小单元组成，这些小单元像链条的链环一样连接在一起。每种蛋白质都由独特的氨基酸组合组成，这是由称为基因的特定DNA序列决定的。基因的变化-突变-可以在它编码的蛋白质中产生变异，例如通过将一种氨基酸交换为另一种氨基酸。同一基因的不同突变可以以不同的方式改变蛋白质。其中一些变化是无害的，但其他变化可能会阻碍蛋白质发挥其作用。例如，蛋白质结构的微小变化可能会影响它与其他分子的结合方式。人们可能在相同的基因中有相同的突变，但经历不同的后果。例如，两个人可能携带相同的致病突变，但一个人的病情严重，另一个人的症状轻微。原因之一是其他基因的变化抵消或增强了突变的影响。这种现象被称为遗传相互作用，但人们对其了解甚少，特别是在分子水平上。在这里，Diss和Lehner开发了一种名为deepPCA的方法，用于研究实验室中蛋白质突变的后果。实验集中在两个人类基因上，这两个基因编码两种通常相互附着的蛋白质。两个突变是人工创造的，要么在每个基因中一个，要么在其中一个基因中两个。Diss和Lehner随后研究了两种突变蛋白质仍然可以相互附着的强度。通过用超过120，000对不同的突变重复这一过程，可以研究一个突变如何根据同一蛋白质或结合伴侣中其他突变的存在而产生不同的影响。总体而言，Diss和Lehner发现遗传相互作用是两种机制的结果。在第一种情况下，两种突变共同引起特定的结构变化，改变蛋白质相互结合的方式。在第二种情况下，变化仅取决于单个突变的初始热力学效应的大小，而不取决于其特定的物理和化学性质。为了预测第二种类型的遗传相互作用的后果，不需要知道这两种突变的身份或确切影响。了解和预测遗传相互作用对于开发个性化医疗非常重要，其中治疗是根据个体的遗传组成量身定制的。这些知识也将有助于研究基因是如何共同进化的。
A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes – interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions. Proteins, the molecular workhorses of the cell, are made of small units called amino acids attached together like the links of a chain. Each protein is composed of a unique combination of amino acids, which is determined by a specific sequence of DNA called a gene. A change in a gene – a mutation – can create a variation in the protein it codes for, for instance by swapping a type of amino acid for another. Different mutations in the same gene can alter a protein in different ways. Some of these changes are harmless, but other can hinder how the protein performs its role. For example, a small change in the structure of a protein could affect how it will bind to other molecules. It is possible for people to have identical mutations in the same genes, but experience different consequences. For instance, two persons could carry the same disease-inducing mutation, but one has a severe version of the condition and the other only mild symptoms. One reason is that changes in other genes cancel out or enhance the effect of a mutation. This phenomenon is known as a genetic interaction and it remains poorly understood, especially at the molecular level. Here, Diss and Lehner developed a method, called deepPCA, to study the consequences of mutations in proteins in the laboratory. The experiments focused on two human genes which code for two proteins that normally attach to each other. Two mutations were artificially created, either one in each gene, or two in one of them. Diss and Lehner then examined how strongly the two mutated proteins could still attach to each other. By repeating this process with over 120,000 different pairs of mutations, it became possible to study how one mutation can have different effects depending on the presence of other mutations in the same protein or in the binding partner. Overall, Diss and Lehner found that genetic interactions are the result of two mechanisms. In the first one, the two mutations together cause specific structural changes that modify how proteins bind to each other. In the second one, the changes solely depend on the magnitude of the initial, thermodynamic effects of individual mutations, but not on their specific physical and chemical properties. To predict the consequences of this second type of genetic interactions, knowing the identity or the exact effects of the two mutations is not necessary. Understanding and predicting genetic interactions is important to develop personalized medicine, where treatments are tailored based on the genetic make up of an individual. This knowledge will also help to study how genes have evolved together.