Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, United States.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States.
Front Immunol. 2019 Nov 7;10:2616. doi: 10.3389/fimmu.2019.02616. eCollection 2019.
Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.
流感是一种传染性疾病，影响着全球成千上万的人。幼儿、老年人、免疫功能低下者和孕妇感染流感病毒的风险更高。我们的研究旨在强调流感疾病与流感疫苗接种之间差异表达的基因，包括因年龄和性别而产生的可变性。为了实现我们的目标，我们使用公开的微阵列表达数据进行了荟萃分析。我们的纳入标准包括流感患者、接受流感疫苗接种的患者和健康对照者。我们整理了 18 个微阵列数据集，共 3481 个样本（1277 个对照、297 个流感感染、1907 个流感疫苗接种）。我们使用 R 中的可用软件包对原始微阵列表达数据进行了预处理，适用于 Affymetrix 和 Illumina 微阵列平台。在下游分析之前，我们使用数据的 Box-Cox 幂变换来识别差异表达的基因。统计分析基于带有所有研究因素的线性混合效应模型和连续似然比检验（LRT），以识别差异表达的基因。我们通过疾病对 LRT 结果进行过滤（Bonferroni 调整 < 0.05），并使用双尾 10%分位数截止值来识别具有生物学意义的基因。此外，我们通过筛选疾病与年龄、疾病与性别之间具有统计学意义（Bonferroni 调整 < 0.05）相互作用的基因，来评估年龄和性别对疾病基因的影响。当我们通过疾病因素筛选 LRT 结果时，我们确定了 4889 个具有统计学意义的基因，基因富集分析（基因本体论和途径）包括先天免疫反应、病毒过程、对病毒的防御反应、造血细胞谱系和 NF-kappa B 信号通路。我们的分位数过滤基因列表由与流感感染和疫苗接种相关的 978 个基因组成。我们还确定了 907 个和 48 个基因，它们与疾病-年龄和疾病-性别相互作用具有统计学意义（Bonferroni 调整 < 0.05），分别。我们的荟萃分析方法强调了流感感染和疫苗接种的关键基因特征及其相关途径。我们还能够识别具有年龄和性别影响的基因。这为改进当前疫苗提供了潜力，并为考虑流感的普遍疫苗接种时，探索在所有年龄段表达相同的基因提供了可能性。
