Network-Based Prediction and Analysis of HIV Dependency Factors

HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.HIV依赖因子（HIV Dependency Factors，HDFs）是一类对HIV复制至关重要，但在被沉默时不会对宿主细胞造成致死性影响的人类蛋白质。此前三项全基因组RNA干扰（RNA interference，RNAi）实验分别筛选得到了HDFs集合，但各集合间的重叠度极低。本研究将这三项研究的数据与人类蛋白质相互作用网络相结合，并采用名为SinkSource的直观算法及另外4种已发表的算法，预测新的HDFs。经交叉验证，本研究所用算法与其余几种方法均展现出优异的精确率与召回率。本研究预测的诸多HDFs已被证实可与HIV蛋白相互作用，且这些HDFs隶属于多个已知会被HIV操控的蛋白质复合物与生物学过程。本研究还证实，在两种艾滋病病程存在差异的非人灵长类物种中，诸多预测得到的HDF基因在应对猴免疫缺陷病毒（Simian Immunodeficiency Virus，SIV）感染的早期应答过程中，展现出显著不同的表达模式。本研究结果表明，仍有大量HDFs有待发掘，且这些HDFs有望作为预后标志物，用于评估病理转归与艾滋病发病风险。更广泛地说，若多个独立实验室针对同一生物学系统或现象开展了多项全基因组基因水平研究，本研究方法可用于在分子相互作用网络的框架下同步解析这些研究，并判断各研究间是相互支持还是存在矛盾。