Ivanic Joseph, Yu Xueping, Wallqvist Anders, Reifman Jaques
Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, Maryland, United States of America.
PLoS One. 2009 Jun 5;4(6):e5815. doi: 10.1371/journal.pone.0005815.
Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Escherichia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.
实验性蛋白质-蛋白质相互作用（PPI）网络正越来越多地以各种方式被用于生物学发现。因此，通过识别和分类所检测到的各种确定性（特异性）和概率性（非特异性）相互作用来辨别其潜在本质至关重要。为此，我们分析了使用一系列高通量实验技术确定的PPI网络，目的是系统地量化由于蛋白质细胞丰度变化而产生的任何偏差。我们证实，使用亲和纯化方法为酵母和大肠杆菌确定的PPI网络纳入了蛋白质度数（即相互作用数量）与细胞丰度之间的相关性。观察到的相关性虽小但具有统计学意义，且在未处理（原始）和处理（高可信度）数据集中均出现。相比之下，酵母双杂交系统产生的网络不存在这种关系。虽然之前是基于mRNA丰度进行评论，但我们基于蛋白质丰度进行的更广泛分析证实了由这两种技术确定的PPI网络之间存在系统性差异。我们还证明，中心性-致死性规则（即度数较高的蛋白质更可能是必需的）可能具有误导性，因为蛋白质丰度测量表明必需蛋白质比非必需蛋白质更普遍。事实上，我们通常发现，当存在度数/丰度相关性时，非必需蛋白质和必需蛋白质的度数分布也不同。相反，当不存在度数/丰度相关性时，非必需蛋白质和必需蛋白质的度数分布没有差异。然而，我们表明，通过必需蛋白质之间相互作用的富集，必需性在此处研究的所有酵母PPI网络中都表现为一种生物学特性。这些发现为用于检测PPI的各种高通量技术的潜在本质提供了有价值的见解，并应能为高质量PPI数据集的推断和分析带来更有效的策略。
