Abd-Rabbo Diala, Michnick Stephen W
Département de Biochimie et Médecine Moléculaire, Université de Montréal, C.P. 6128, Succursale centre-ville, Montréal, Québec, H3C 3J7, Canada.
Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, C.P. 6128, Succursale centre-ville, Montréal, Québec, H3C 3J7, Canada.
BMC Syst Biol. 2017 Mar 16;11(1):38. doi: 10.1186/s12918-017-0418-0.
Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted.
We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package ( https://cran.r-project.org/web/packages/VertexSort/index.html ).
The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes in general.
激酶和磷酸酶（KP）形成了对细胞信号处理至关重要的复杂自我调节网络。尽管有大量关于激酶和磷酸酶及其底物之间相互作用的数据，但我们对它们所形成的有向网络的结构模型却非常有限，因此我们提出关于其结构如何决定这些网络中信息流的假设的能力也受到限制。
我们组装并研究了迄今为止已知的酿酒酵母最大的真正激酶 - 磷酸酶网络（KP - Net）。在KP - Net上应用顶点排序（VS）算法使我们能够阐明其层次结构，其中节点被分为顶层、核心层和底层，形成具有强连接核心层的领结结构。令人惊讶的是，磷酸酶倾向于归入顶层，这意味着它们比激酶受磷酸化的调节更少。将最广泛的KP生物学特性叠加在KP - Net层次结构上表明，核心层的激酶和磷酸酶：（i）接收最多的输入；（ii）形成涉及多种途径和决策的瓶颈；（iii）并且在时间和空间上都是受调节最多的激酶和磷酸酶之一。此外，顶层的激酶和磷酸酶比底层的更丰富且噪声更小。最后，我们表明VS算法依赖于节点度，而不会偏向排序后网络的生物学结果。VS算法可作为R包获取（https://cran.r - project.org/web/packages/VertexSort/index.html）。
我们提出的KP - Net模型具有领结层次结构，其中顶层似乎确保了最高保真度，核心层似乎介导信号整合和细胞状态依赖的信号解读。我们的酵母KP - Net模型既为我们目前对其组织的理解提供了功能见解，也为未来研究酵母和一般真核生物中的信息处理提供了框架。
