Adderley Jack, O'Donoghue Finn, Doerig Christian, Davis Stephen
School of Health and Biomedical Sciences, RMIT University, Bundoora VIC 3083, Australia.
School of Science, RMIT University, Melbourne, VIC 3053, Australia.
Curr Res Microb Sci. 2022 Jun 28;3:100149. doi: 10.1016/j.crmicr.2022.100149. eCollection 2022.
Large datasets of phosphorylation interactions are constantly being generated, but deciphering the complex network structure hidden in these datasets remains challenging. Many phosphorylation interactions occurring in human cells have been identified and constitute the basis for the known phosphorylation interaction network. We overlayed onto this network phosphorylation datasets obtained from an antibody microarray approach aimed at determining changes in phospho-signalling of host erythrocytes, during infection with the malaria parasite . We designed a pathway analysis tool denoted MAPPINGS that uses random walks to identify chains of phosphorylation events occurring much more or much less frequently than expected. MAPPINGS highlights pathways of phosphorylation that work synergistically, providing a rapid interpretation of the most critical pathways in each dataset. MAPPINGS confirmed several signalling interactions previously shown to be modulated by infection, and revealed additional interactions which could form the basis of numerous future studies. The MAPPINGS analysis strategy described here is widely applicable to comparative phosphorylation datasets in any context, such as response of cells to infection, treatment, or comparison between differentiation stages of any cellular population.
大量的磷酸化相互作用数据集不断产生，但解读隐藏在这些数据集中的复杂网络结构仍然具有挑战性。人类细胞中发生的许多磷酸化相互作用已被识别，构成了已知磷酸化相互作用网络的基础。我们将通过抗体微阵列方法获得的磷酸化数据集叠加到该网络上，该方法旨在确定疟原虫感染期间宿主红细胞磷酸化信号的变化。我们设计了一种名为MAPPINGS的通路分析工具，它使用随机游走识别发生频率比预期高得多或低得多的磷酸化事件链。MAPPINGS突出显示协同作用的磷酸化通路，为每个数据集中最关键的通路提供快速解读。MAPPINGS证实了先前显示受感染调节的几种信号相互作用，并揭示了可能构成众多未来研究基础的其他相互作用。这里描述的MAPPINGS分析策略广泛适用于任何背景下的比较磷酸化数据集，例如细胞对感染、治疗的反应，或任何细胞群体分化阶段之间的比较。
