Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, Cincinnati, Ohio, United States of America.
PLoS One. 2012;7(7):e41202. doi: 10.1371/journal.pone.0041202. Epub 2012 Jul 24.
Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.
铜绿假单胞菌（PA）是一种普遍存在的机会性病原体，能够在囊性纤维化和慢性阻塞性肺疾病患者中引起高度问题和慢性感染。随着多药耐药性 PA 的患病率增加，传统的“一个基因，一种药物，一种疾病”模式正在失去效力。另一方面，网络药理学可能有希望发现新的药物靶点来治疗各种 PA 感染。然而，鉴于迫切需要发现新的药物靶点，尚未构建具有高精度和高覆盖率的 PA 蛋白质-蛋白质相互作用（PPI）网络。在这项研究中，我们通过基于机器学习的方法整合了 PA 蛋白质/基因的各种基因组特征，预测了一个全基因组规模的 PA PPI 网络。预测了共涵盖 4181 种蛋白质的 54107 种相互作用。进一步组装和分析了一个包含预测的高可信度相互作用、参考集和验证相互作用的高可信度网络，其中包含 3343 种蛋白质和 19416 种潜在相互作用。本研究中预测的互作网络是 PA 中第一个具有显著覆盖率和高精度的大规模 PPI 网络。后续分析，包括基于现有小规模 PPI 数据的验证以及与其他模式生物的网络结构比较，表明了预测 PPI 网络的有效性。根据其在高可信度网络中的重要性和拓扑重要性，识别和优先考虑了潜在的药物靶点。进一步提取和分析了人类与 PA 之间的宿主-病原体蛋白质相互作用。此外，还对反西格玛因子 MucA、负周质海藻酸盐调节剂 MucB 和转录调节因子 RhlR 的蛋白质相互作用进行了案例研究。可访问预测 PPI 数据集的 Web 服务器可在 http://research.cchmc.org/PPIdatabase/ 上获得。
