A Modular Cytokine Analysis Method Reveals Novel Associations With Clinical Phenotypes and Identifies Sets of Co-signaling Cytokines Across Influenza Natural Infection Cohorts and Healthy Controls
A Modular Cytokine Analysis Method Reveals Novel Associations With Clinical Phenotypes and Identifies Sets of Co-signaling Cytokines Across Influenza Natural Infection Cohorts and Healthy Controls
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DOI：
10.3389/fimmu.2019.01338
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发表时间：
2019-06-18
影响因子：
7.3
通讯作者：
Hertz, Tomer
中科院分区：
文献类型：
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作者：
Cohen, Liel;Fiore-Gartland, Andrew;Hertz, Tomer
细胞因子和趋化因子是免疫系统的关键信号分子。最近的技术进步使得能够测量生物样品中的多重细胞因子谱。然后，这些概况可用于识别各种临床表型的潜在生物标志物。然而，单独测试每种细胞因子的此类关联忽略了细胞因子分泌中高度依赖于背景的协变，并且由于多重假设检验而降低了检测关联的统计功效。在这里，我们提出了 CytoMod——一种用于分析细胞因子谱的新型数据驱动方法，该方法使用无监督聚类和回归来识别共同信号细胞因子的假定功能模块。每个模块代表共同信号细胞因子的生物特征。我们将这种方法应用于三个独立的自然感染流感受试者的临床队列，其中收集了细胞因子谱和临床表型。我们发现，在三分之二的队列中，细胞因子模块与临床表型显着相关，并且在许多情况下，这些关联比其中单个细胞因子的关联更强。通过比较数据集中的细胞因子模块，我们确定了细胞因子“核心”——在三个队列中聚集在一起的共表达细胞因子的特定子集。细胞因子核心也与临床表型相关。有趣的是，大多数这些核心也在一组健康对照中共同表达，这表明细胞因子共同信号传导的模式可能在一定程度上具有普遍性。无论测量技术如何，CytoMod 都可以轻松应用于任何细胞因子概况数据集，提高检测与临床表型关联的统计能力，并可能有助于揭示健康和感染中细胞因子复杂的协同信号网络。
Cytokines and chemokines are key signaling molecules of the immune system. Recent technological advances enable measurement of multiplexed cytokine profiles in biological samples. These profiles can then be used to identify potential biomarkers of a variety of clinical phenotypes. However, testing for such associations for each cytokine separately ignores the highly context-dependent covariation in cytokine secretion and decreases statistical power to detect associations due to multiple hypothesis testing. Here we present CytoMod- a novel data-driven approach for analysis of cytokine profiles that uses unsupervised clustering and regression to identify putative functional modules of co-signaling cytokines. Each module represents a biosignature of co-signaling cytokines. We applied this approach to three independent clinical cohorts of subjects naturally infected with influenza in which cytokine profiles and clinical phenotypes were collected. We found that in two out of three cohorts, cytokine modules were significantly associated with clinical phenotypes, and in many cases these associations were stronger than the associations of the individual cytokines within them. By comparing cytokine modules across datasets, we identified cytokine "cores" -specific subsets of co-expressed cytokines that clustered together across the three cohorts. Cytokine cores were also associated with clinical phenotypes. Interestingly, most of these cores were also co-expressed in a cohort of healthy controls, suggesting that in part, patterns of cytokine co-signaling may be generalizable. CytoMod can be readily applied to any cytokine profile dataset regardless of measurement technology, increases the statistical power to detect associations with clinical phenotypes and may help shed light on the complex co-signaling networks of cytokines in both health and infection.