Department of Bioengineering, Stanford University, Stanford, California, United States of America.
Chinese Undergraduate Visiting Research Program, Stanford University, Stanford, California, United States of America.
PLoS Comput Biol. 2022 Apr 11;18(4):e1009497. doi: 10.1371/journal.pcbi.1009497. eCollection 2022 Apr.
The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response-usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING's high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an "Inflammation Score" that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.
许多炎症性疾病的发病机制是一个涉及代谢功能障碍和免疫反应的协调过程，通常由细胞因子和相关炎症分子的产生来调节。在这项工作中，我们试图了解参与发病机制的基因如何与免疫系统进行通信，这些基因通常与免疫系统没有明显的关联。我们使用特征学习方法，将 STRING 数据库中的人类蛋白质-蛋白质相互作用（PPI）网络嵌入到 14707 个人类基因中，该方法可以捕获高可信度的边缘。我们发现，从 STRING 的高可信度边缘中提取的特征所衍生的关联分数可以用于预测基因之间的新联系，从而能够构建出 14707 个人类基因之间所有可能对的完整预测关联图谱。特别是，我们分析了 126 种细胞因子的关联模式，发现细胞因子与人类基因相互作用的六种模式与它们的功能分类一致。为了定义细胞因子在疾病中的特定作用，我们从 DisGeNET 中收集了 11944 种疾病的基因集。我们通过计算疾病相关基因集与 126 种细胞因子之间的归一化平均关联分数，来预测细胞因子与疾病的特定关联，从而为每种疾病创建了独特的炎症基因（已知和预测）图谱。我们通过将预测的细胞因子关联与 171 种疾病的已知关联进行比较来验证我们的预测。在 95 种疾病中，预测的细胞因子图谱与已知图谱相关（p 值<0.0003）。我们通过计算炎症评分来进一步描述每种疾病的特征，该评分概括了不同的免疫反应模式。最后，通过分析疾病特异性发病机制基因、激素、受体和细胞因子之间形成的子网络，我们确定了负责发病机制和炎症反应之间相互作用的关键基因。这些基因和不同免疫紊乱中使用的相应细胞因子为药物发现提供了独特的靶点。
