Laboratorio de Biomedicina Molecular II, ENMH, Instituto Politécnico Nacional, Mexico City, Mexico.
Facultad de Ciencias, Universidad Autónoma del Estado de México. Carretera Toluca-Ixtlahuaca km 15.5 Cerrillo Piedras Blancas 50200 Toluca, Estado de México, Mexico.
Biosci Rep. 2023 Feb 27;43(2). doi: 10.1042/BSR20221911.
Protein-protein interactions (PPI) play a key role in predicting the function of a target protein and drug ability to affect an entire biological system. Prediction of PPI networks greatly contributes to determine a target protein and signal pathways related to its function. Polyadenylation of mRNA 3'-end is essential for gene expression regulation and several polyadenylation factors have been shown as valuable targets for controlling protozoan parasites that affect human health. Here, by using a computational strategy based on sequence-based prediction approaches, phylogenetic analyses, and computational prediction of PPI networks, we compared interactomes of polyadenylation factors in relevant protozoan parasites and the human host, to identify key proteins and define potential targets for pathogen control. Then, we used Entamoeba histolytica as a working model to validate our computational results. RT-qPCR assays confirmed the coordinated modulation of connected proteins in the PPI network and evidenced that silencing of the bottleneck protein EhCFIm25 affects the expression of interacting proteins. In addition, molecular dynamics simulations and docking approaches allowed to characterize the relationships between EhCFIm25 and Ehnopp34, two connected bottleneck proteins. Interestingly, the experimental identification of EhCFIm25 interactome confirmed the close relationships among proteins involved in gene expression regulation and evidenced new links with moonlight proteins in E. histolytica, suggesting a connection between RNA biology and metabolism as described in other organisms. Altogether, our results strengthened the relevance of comparative genomics and interactomics of polyadenylation factors for the prediction of new targets for the control of these human pathogens.
蛋白质-蛋白质相互作用(PPI)在预测靶蛋白的功能和药物影响整个生物系统的能力方面起着关键作用。预测 PPI 网络极大地有助于确定靶蛋白及其与功能相关的信号通路。mRNA 3'-端的多聚腺苷酸化对于基因表达调控至关重要,已经有几种多聚腺苷酸化因子被证明是控制影响人类健康的原生动物寄生虫的有价值的靶点。在这里,我们通过使用基于序列预测方法、系统发育分析和 PPI 网络计算预测的计算策略,比较了相关原生动物寄生虫和人类宿主中的多聚腺苷酸化因子的相互作用组,以鉴定关键蛋白并定义潜在的病原体控制靶点。然后,我们使用溶组织内阿米巴作为工作模型来验证我们的计算结果。RT-qPCR 分析证实了 PPI 网络中连接蛋白的协调调节,并证明了瓶颈蛋白 EhCFIm25 的沉默会影响相互作用蛋白的表达。此外,分子动力学模拟和对接方法能够表征 EhCFIm25 和 Ehnopp34 之间的关系,这两种连接的瓶颈蛋白。有趣的是,EhCFIm25 相互作用组的实验鉴定证实了参与基因表达调控的蛋白质之间的密切关系,并证实了与溶组织内阿米巴中月光蛋白的新联系,表明 RNA 生物学和代谢之间的联系与在其他生物体中描述的一样。总之,我们的结果加强了多聚腺苷酸化因子的比较基因组学和相互作用组学对于预测这些人类病原体控制的新靶点的重要性。