Hypergraph-based connectivity measures for signaling pathway topologies
Hypergraph-based connectivity measures for signaling pathway topologies
复制标题
DOI：
10.1371/journal.pcbi.1007384
复制
发表时间：
2019-10-01
影响因子：
4.3
通讯作者：
Ritz, Anna
中科院分区：
文献类型：
--
作者：
Franzese, Nicholas;Groce, Adam;Ritz, Anna
信号通路描述了细胞如何通过分子相互作用响应外部信号。随着我们对这些信号反应的深入了解，了解分子如何影响下游反应以及途径如何相互影响是很重要的。随着信号通路数据库中信息量的不断增长，我们有机会分析通路结构的特性。我们提出了一个关于信号通路的直观问题：两个分子何时在通路中“连接”？根据我们对反应如何连接分子的假设，这个答案会有很大的不同。在这里，检查四种方法建模的信号通路的结构拓扑结构，并提出方法来量化是否两个分子是“连接”在一个通路数据库。我们发现，现有的方法要么过于宽松（分子与许多其他分子连接），要么过于限制（分子与少数其他分子连接），我们提出了一种新的措施，提供了这两个极端之间的连续性。然后，我们扩展我们的问题，问当整个信号通路是“下游”的另一个途径，并显示两个案例研究从Reactome通路数据库，揭示通路的影响。最后，我们表明，严格的概念的连接可以捕捉蛋白质之间的功能关系，使用一个独立的基准数据集。我们的方法来量化连接的途径考虑了一个生物学动机的连接定义，奠定了基础，更复杂的分析，利用详细的信息，在pathway databases.Characterizing不同的外在信号的细胞反应是一个活跃的研究领域，和策划的途径数据库描述这些复杂的信号反应。在这里，我们重新审视信号通路分析中的一个基本问题：两个分子在网络中“连接”吗？这个问题是理解分子在通路中的潜在影响的第一步，答案取决于建模框架的选择。我们使用四种不同的通路表示来检查Reactome信号通路的连接性。我们发现，Reactome作为一个图是非常好的连接，作为一个复合图或二分图是中等好的连接，作为一个超图（它捕捉反应网络中的多对多关系）是不好的连接。我们提出了一种新的松弛超图的连接，迭代地增加从一个节点的连接，同时保持超图的拓扑结构。这个度量，B-松弛距离，提供了超图连通性和图连通性之间的参数化过渡。B-弛豫距离对参与网络中许多功能无关反应的小分子的存在敏感。我们还定义了一个分数，量化一个路径的下游影响另一个，这可以计算为B-松弛距离逐渐放松超图中的连接约束。在所有34对Reactome通路中计算该分数，揭示了具有统计学显著影响的通路对。我们提出了两个这样的案例研究，我们描述了具体的反应，有助于大的影响力得分。最后，我们研究了连接性度量捕获蛋白质之间功能关系的能力，并使用STRING数据库中的证据通道作为基准数据集。蛋白质在Reactome中是B连接的STRING相互作用的得分在统计学上显著高于在二分图表示中连接的相互作用。我们的方法奠定了基础，为其他广义的图论概念的超图，以促进信号通路分析。
Author summary Signaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules "connected" in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are "connected" in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is "downstream" of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.