Construction of disease-specific cytokine profiles by associating disease genes with immune responses.
Construction of disease-specific cytokine profiles by associating disease genes with immune responses.
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DOI：
10.1371/journal.pcbi.1009497
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发表时间：
2022-04
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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许多炎性疾病的发病机制是一个涉及代谢功能障碍和免疫应答的协调过程，通常由细胞因子和相关炎性分子的产生调节。在这项工作中，我们试图了解参与发病机制的基因如何以明显的方式与免疫系统沟通，这些基因通常与免疫系统无关。我们已经从STRING数据库中嵌入了一个人类蛋白质-蛋白质相互作用（PPI）网络，其中包含14，707个人类基因，使用特征学习捕获高置信度边缘。我们发现，我们从STRING的高置信度边缘提取的特征中获得的预测关联得分可用于预测基因之间的新连接，从而能够构建14，707个人类基因之间所有可能对的预测关联的完整图谱。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其功能分类一致。为了确定细胞因子的疾病特异性作用，我们从DisGeNET收集了11，944种疾病的基因集。我们使用这些基因集通过计算疾病相关基因集和126种细胞因子之间的归一化平均关联得分来预测疾病特异性基因与细胞因子的关联;这为每种疾病创建了独特的炎症基因谱（已知和预测）。我们通过与171种疾病的已知关联进行比较，验证了我们预测的细胞因子关联。预测的细胞因子谱与95种疾病中的已知细胞因子谱相关（p值<0.0003）。我们通过计算总结不同免疫应答模式的“炎症评分”进一步表征了每种疾病的特征。最后，通过分析疾病特异性发病基因、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疾病所使用的相应细胞因子为药物发现提供了独特的靶点。抗TNF治疗在多种炎性疾病中的成功表明存在定义高度保守的炎症机制的共享细胞因子框架。然而，测试新的细胞因子抑制剂的功效的临床试验表明与不同疾病相关的一组更复杂的相互作用的细胞因子机制。在这项工作中，我们的目标是确定疾病特异性作用的细胞因子介导的发病机制和炎症过程，专注于自身免疫性疾病。我们假设特定的临床表型是由疾病特异性细胞因子和疾病相关基因（通过遗传学、转录组学和代谢功能障碍分析鉴定）之间的相互作用引起的，即使它们也可能具有共同的细胞因子元件和保守的炎症机制。我们已经开发了新的网络方法，显示出强大的能力，以确定特征性细胞因子和遗传因素之间的差异关联，有助于发病机制。我们已经在171种研究充分的疾病上验证了我们的方法;细胞因子和疾病模块之间的预测关联与已发表的数据相关。我们的预测提供了潜在的差异的分子机制，可能是负责临床表型。
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. The success of anti-TNF treatment in multiple inflammatory diseases suggest that there is a shared cytokine framework that defines highly conserved mechanisms of inflammation. However, clinical trials testing the efficacy of new cytokine inhibitors suggest a more complex set of interacting cytokine mechanisms that are associated with different diseases. In this work, we aim to define the disease-specific role of cytokines that mediate pathogenesis and inflammatory processes, focusing on autoimmune diseases. We hypothesize that specific clinical phenotypes result from the interactions between disease-specific cytokines and disease-related genes (identified through genetics, transcriptomics, and analysis of metabolic dysfunctions), even though they also may share a common cytokine elements and conserved mechanisms of inflammation. We have developed novel network methods that show a robust ability to identify differential associations between characteristic cytokines and genetics factors contributing to pathogenesis. We have validated our methods on 171 well-studied diseases; the predicted associations between cytokines and disease modules correlate with the published data. Our predictions provide the underlying difference of molecular mechanisms that may be responsible for clinical phenotypes.
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DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
DOI：
10.1038/s41577-019-0131-x
发表时间：
2019-04
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
Altan-Bonnet G;Mukherjee R
通讯作者：
Mukherjee R
影响因子：
65.1
作者：
Sanyal, Arun J.
通讯作者：
Sanyal, Arun J.
影响因子：
9.8
作者：
Ahola-Olli, Ari V.;Wurtz, Peter;Raitakari, Olli T.
通讯作者：
Raitakari, Olli T.
疾病扰动网络中的最佳控制节点作为联合治疗的目标
影响因子：
16.6
作者：
Hu, Yuxuan;Chen, Chia-hui;Tan, Kai
通讯作者：
Tan, Kai