Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.
Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.
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DOI：
10.3389/fimmu.2017.00445
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发表时间：
2017
影响因子：
7.3
通讯作者：
Poland GA
中科院分区：
文献类型：
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作者：
Zimmermann MT;Kennedy RB;Grill DE;Oberg AL;Goergen KM;Ovsyannikova IG;Haralambieva IH;Poland GA
对流感疫苗的体液免疫反应是在多系统水平上发生的。由于当涉及多个基因及其跨多个细胞类型的调节组件时，强大的免疫反应需要协调，我们使用多种高通量技术检查了流感疫苗接种队列。在这项研究中，我们试图更彻底地了解免疫细胞组成和基因表达如何相互关联，并在流感疫苗接种后导致个体间差异。我们首先假设，流感疫苗接种后观察到的许多差异表达(DE)基因是由参与者外周血单个核细胞(PBMC)组成的变化引起的，这些变化是通过流式细胞仪进行评估的。我们证明了我们研究中的DE基因与PBMC组成的变化有关。我们从128个其他公开可用的基于PBMC的疫苗研究中收集了DE基因，并发现平均57%与我们研究中的特定细胞亚集水平(用于控制错误发现的排列)相关，这表明我们已经确定的关联可能是基于PBMC的转录组学的一般特征。其次，我们假设，通过考虑PBMC组成、基因表达和基因调控之间的相互作用，可以产生更稳健的疫苗反应模型。我们使用机器学习来生成B细胞ELISPOT应答结果和血凝抑制(HAI)抗体效价的预测模型。顶部HAI和B-CELL ELISPOT模型的接收器工作曲线下面积(AUC)分别为0.64和0.79，线性模型决定系数分别为0.08和0.28。对于B细胞ELISPOT结果，CpG甲基化具有最大的预测能力，突出了潜在的新的调节特征，对免疫反应至关重要。仅使用外周血单核细胞成分的B细胞ELISOT模型的性能较低(AuC = 0.67)，但强调了众所周知的机制。我们的分析表明，三个数据集(细胞组成、mRNA-Seq和DNA甲基化)中的每一个都可能为体液免疫反应结果的预测提供不同的信息。我们认为，这些发现对于解释当前基于组学的研究很重要，并为更彻底地了解流感疫苗接种的个体间免疫反应奠定了基础。
The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindividual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed (DE) genes observed after influenza vaccination result from changes in the composition of participants’ peripheral blood mononuclear cells (PBMCs), which were assessed using flow cytometry. We demonstrated that DE genes in our study are correlated with changes in PBMC composition. We gathered DE genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we have identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve (AUC) of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC = 0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three data sets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of interindividual immune responses to influenza vaccination.