Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.
Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.
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DOI：
10.1371/journal.pcbi.1002860
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发表时间：
2013
影响因子：
4.3
通讯作者：
Andrade-Navarro MA
中科院分区：
文献类型：
--
作者：
Schaefer MH;Lopes TJ;Mah N;Shoemaker JE;Matsuoka Y;Fontaine JF;Louis-Jeune C;Eisfeld AJ;Neumann G;Perez-Iratxeta C;Kawaoka Y;Kitano H;Andrade-Navarro MA
蛋白质的相互作用调节信号传导、催化、基因表达和许多其他细胞功能。因此，表征整个人类相互作用组是当前蛋白质组学研究的关键工作。蛋白质-蛋白质相互作用（PPI）的动态性质使这一挑战变得复杂，这取决于细胞环境：两种相互作用的蛋白质必须在同一细胞中表达并定位在同一细胞器中才能相遇。此外，相互作用是信号通路微妙控制的基础，例如通过蛋白质伴侣的翻译后修饰 - 因此，许多疾病是由这些机制的扰动引起的。尽管 PPI 具有高度的细胞状态特异性，但许多相互作用是在人工条件下测量的（例如，在酵母双杂交检测中用人类基因转染酵母细胞），或者即使在生理环境中检测到，常见的 PPI 数据库中也缺少此信息。为了克服这些问题，我们开发了一种方法，将上下文信息分配给从相互作用蛋白质的各种属性推断出的 PPI：基因表达、功能和疾病注释以及推断的途径。我们证明上下文一致性与 PPI 的实验可靠性相关，这使我们能够生成高置信度的组织和功能特异性子网络。我们说明了这些上下文过滤网络如何丰富真实的通路和疾病蛋白，以证明上下文过滤器能够突出显示与各种生物学问题相关的有意义的相互作用。我们使用这种方法来研究流感病毒使用的肺部特异性途径，指出 IRAK1、BHLHE40 和 TOLLIP 作为流感病毒致病性的潜在调节因子，并研究在阿尔茨海默病中发挥作用的信号传导途径，确定涉及 Tau 蛋白磷酸化改变的途径。最后，我们通过 Web 前端提供带注释的人类 PPI 网络，该前端允许以多种方式构建特定于上下文的网络。蛋白质-蛋白质相互作用（PPI）几乎参与所有生物过程。然而，PPI 图谱不是静态的，而是相互作用的蛋白质对取决于细胞类型、参与蛋白质的亚细胞定位和修饰以及许多其他因素。因此，了解 PPI 发生的具体条件非常重要。不幸的是，实验方法通常无法提供这些信息，或者更糟糕的是，无法在生物系统中未发现的人工条件下测量 PPI。我们开发了一种方法，可以从相互作用的蛋白质的特性中推断出缺失的信息，例如在哪些细胞类型中发现蛋白质、它们履行哪些功能以及它们是否已知在疾病中发挥作用。我们证明，我们可以推断其发生条件的 PPI 具有更高的实验可靠性。此外，我们的推论与已知的途径和疾病蛋白非常吻合。由于疾病通常影响特定的细胞类型，因此我们研究肺组织中流感蛋白和神经组织中阿尔茨海默病蛋白的 PPI 网络。在这两种情况下，我们都可以强调有趣的相互作用，可能在疾病进展中发挥作用。
Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways. Protein-protein-interactions (PPIs) participate in virtually all biological processes. However, the PPI map is not static but the pairs of proteins that interact depends on the type of cell, the subcellular localization and modifications of the participating proteins, among many other factors. Therefore, it is important to understand the specific conditions under which a PPI happens. Unfortunately, experimental methods often do not provide this information or, even worse, measure PPIs under artificial conditions not found in biological systems. We developed a method to infer this missing information from properties of the interacting proteins, such as in which cell types the proteins are found, which functions they fulfill and whether they are known to play a role in disease. We show that PPIs for which we can infer conditions under which they happen have a higher experimental reliability. Also, our inference agrees well with known pathways and disease proteins. Since diseases usually affect specific cell types, we study PPI networks of influenza proteins in lung tissues and of Alzheimer's disease proteins in neural tissues. In both cases, we can highlight interesting interactions potentially playing a role in disease progression.
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影响因子：
4.3
作者：
Agarwal S;Deane CM;Porter MA;Jones NS
通讯作者：
Jones NS
影响因子：
6.7
作者：
Geeraedts F;Goutagny N;Hornung V;Severa M;de Haan A;Pool J;Wilschut J;Fitzgerald KA;Huckriede A
通讯作者：
Huckriede A
影响因子：
14.9
作者：
Dimmer EC;Huntley RP;Alam-Faruque Y;Sawford T;O'Donovan C;Martin MJ;Bely B;Browne P;Mun Chan W;Eberhardt R;Gardner M;Laiho K;Legge D;Magrane M;Pichler K;Poggioli D;Sehra H;Auchincloss A;Axelsen K;Blatter MC;Boutet E;Braconi-Quintaje S;Breuza L;Bridge A;Coudert E;Estreicher A;Famiglietti L;Ferro-Rojas S;Feuermann M;Gos A;Gruaz-Gumowski N;Hinz U;Hulo C;James J;Jimenez S;Jungo F;Keller G;Lemercier P;Lieberherr D;Masson P;Moinat M;Pedruzzi I;Poux S;Rivoire C;Roechert B;Schneider M;Stutz A;Sundaram S;Tognolli M;Bougueleret L;Argoud-Puy G;Cusin I;Duek-Roggli P;Xenarios I;Apweiler R
通讯作者：
Apweiler R
影响因子：
8
作者：
Ivanov, S. V.;Salnikow, K.;Lerman, M. I.
通讯作者：
Lerman, M. I.
影响因子：
56.9
作者：
de Lichtenberg, U;Jensen, LJ;Bork, P
通讯作者：
Bork, P