Immunology Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN 55905.
Department of Immunology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905.
J Immunol. 2018 Mar 1;200(5):1917-1928. doi: 10.4049/jimmunol.1701099. Epub 2018 Jan 19.
Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4 cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4 cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness.
人类的免疫具有显著的个体间异质性,这导致了人类群体对免疫干扰的反应各不相同。群体研究表明,除了个体间的异质性外,系统免疫特征在个体内部也具有纵向稳定性,这些特征可能可靠地决定个体对免疫干扰的反应方式。我们假设,在群体水平上分析这些特征之间的关系,可能会发现与免疫刺激反应结果相对应的基线免疫表型。为了验证这一点,我们在相隔 1 个月的两个时间点,分别测量了健康个体外周血 CD4 细胞中的全基因组表达,在基线时和 CD3/CD28 刺激后。通过确定在个体内的两个时间点之间表达水平稳定且在每个状态下个体间存在差异的基因,定义了系统 CD4 细胞基线和刺激后分子免疫反应特征(MIRS)。使用基线和刺激 MIRS 基因集,对至少三个个体的所有可能表型分组进行迭代差异基因表达分析,揭示了共享的基线和反应表型分组,表明基线 MIRS 包含免疫反应性的决定因素。此外,在独立的健康队列的基线数据中,还鉴定出了大量共享表型定义的决定因素集。合并队列并重复分析,确定了超过 6000 个基线免疫表型组,这表明 MIRS 概念可能在许多免疫干扰情况下都很有用。这些发现表明,复杂基因表达可变性的模式可以用于定义免疫表型并发现免疫反应性的决定因素。