Blood-Informative Transcripts Define Nine Common Axes of Peripheral Blood Gene Expression

We describe a novel approach to capturing the covariance structure of peripheral blood gene expression that relies on the identification of highly conserved Axes of variation. Starting with a comparison of microarray transcriptome profiles for a new dataset of 189 healthy adult participants in the Emory-Georgia Tech Center for Health Discovery and Well-Being (CHDWB) cohort, with a previously published study of 208 adult Moroccans, we identify nine Axes each with between 99 and 1,028 strongly co-regulated transcripts in common. Each axis is enriched for gene ontology categories related to sub-classes of blood and immune function, including T-cell and B-cell physiology and innate, adaptive, and anti-viral responses. Conservation of the Axes is demonstrated in each of five additional population-based gene expression profiling studies, one of which is robustly associated with Body Mass Index in the CHDWB as well as Finnish and Australian cohorts. Furthermore, ten tightly co-regulated genes can be used to define each Axis as “Blood Informative Transcripts” (BITs), generating scores that define an individual with respect to the represented immune activity and blood physiology. We show that environmental factors, including lifestyle differences in Morocco and infection leading to active or latent tuberculosis, significantly impact specific axes, but that there is also significant heritability for the Axis scores. In the context of personalized medicine, reanalysis of the longitudinal profile of one individual during and after infection with two respiratory viruses demonstrates that specific axes also characterize clinical incidents. This mode of analysis suggests the view that, rather than unique subsets of genes marking each class of disease, differential expression reflects movement along the major normal Axes in response to environmental and genetic stimuli.

本研究提出一种全新方法，用于捕获外周血基因表达的协方差结构，该方法依托对高度保守的变异轴（Axes of variation）的识别。本研究首先对两项转录组微阵列谱数据集开展比较分析：其一为新生成的包含189名健康成年受试者的埃默里-佐治亚理工健康发现与福祉中心（CHDWB）队列数据集，其二为已发表的包含208名摩洛哥成年受试者的研究数据集，最终识别出9条变异轴，每条变异轴均包含99至1028个共享的强共调控转录本。每条变异轴均富集有与血液及免疫功能亚类相关的基因本体（Gene Ontology, GO）注释类别，涵盖T细胞、B细胞生理功能以及固有免疫、适应性免疫与抗病毒应答等过程。该变异轴的保守性在另外5项基于人群的基因表达谱研究中均得到验证，其中一项研究中的变异轴与CHDWB队列、芬兰队列及澳大利亚队列的体重指数（Body Mass Index, BMI）均存在显著关联。此外，可通过10个紧密共调控的基因将每条变异轴定义为"血液信息转录本（Blood Informative Transcripts, BITs）"，通过计算得分可量化个体对应的免疫活性与血液生理状态。研究表明，环境因素（包括摩洛哥人群的生活方式差异以及引发活动性或潜伏性结核的感染）会对特定变异轴产生显著影响，同时变异轴得分也存在显著的遗传力。在个性化医疗场景下，对1名受试者感染两种呼吸道病毒期间及感染后的纵向基因表达谱进行重新分析后发现，特定变异轴同样可用于表征临床事件。此种分析模式表明，与"以独特基因子集标记各类疾病"的传统观点不同，基因差异表达实则反映了机体在环境与遗传刺激下，沿主要正常变异轴发生的动态变化。