Blood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells
Blood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells
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DOI：
10.1164/rccm.202008-3093oc
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发表时间：
2021-07-15
影响因子：
24.7
通讯作者：
Noth, Imre
中科院分区：
文献类型：
--
作者：
Huang, Yong;Oldham, Justin M.;Noth, Imre
理由：特发性肺纤维化（IPF）的疾病活动性仍然是高度可变的，知之甚少，难以预测。目的：利用基因表达的短期纵向变化来确定预测未来植被覆盖度下降的预测因子，并描述相关途径和细胞类型。方法：74例来自COMET（相关结果与生化标志物来估计IPF的时间进展）队列的患者被分为进展（> - 10% FVC下降）和稳定。计算个体在基线至4个月之间的血液基因表达变化，并根据未来FVC状态进行回归，从而确定表达变化、样本量和统计效力。通过通路分析来预测下游效应并确定新的靶点。在COMET中构建了FVC进展预测因子，并使用独立队列进行了验证。健康对照组的外周血单核单细胞rna测序数据作为参考，从大量外周血单核rna测序数据中表征与FVC下降相关的细胞类型组成。测量结果和主要结果：纵向模型减少了稳定组和进展组的基因表达变异，与横截面模型相比，统计能力增加。在独立IPF队列中，FVC进展预测因子预测患者未来FVC下降的敏感性为78%，特异性为86%。模式识别受体通路和mTOR通路分别下调和上调。细胞反褶积使用单细胞rna测序数据确定自然杀伤细胞与进展显著相关。结论：序列转录组变化预测未来植被覆盖度下降。对参与进展信号的细胞类型的分析支持自然杀伤细胞在IPF进展中的新参与。
Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict.Objectives: To identify a predictor using short-term longitudinal changes in gene expression that forecasts future FVC decline and to characterize involved pathways and cell types.Methods: Seventy-four patients from COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-progression in IPF) cohort were dichotomized as progressors (>10% FVC decline) or stable. Blood gene-expression changes within individuals were calculated between baseline and 4 months and regressed with future FVC status, allowing determination of expression variations, sample size, and statistical power. Pathway analyses were conducted to predict downstream effects and identify new targets. An FVC predictor for progression was constructed in COMET and validated using independent cohorts. Peripheral blood mononuclear single-cell RNA-sequencing data from healthy control subjects were used as references to characterize cell type compositions from bulk peripheral blood mononuclear RNAsequencing data that were associated with FVC decline.Measurements and Main Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared with a cross-sectional model. The FVC predictor for progression anticipated patients with future FVC decline with 78% sensitivity and 86% specificity across independent IPF cohorts. Pattern recognition receptor pathways and mTOR pathways were downregulated and upregulated, respectively. Cellular deconvolution using single-cell RNA-sequencing data identified natural killer cells as significantly correlated with progression.Conclusions: Serial transcriptomic change predicts future FVC decline. An analysis of cell types involved in the progressor signature supports the novel involvement of natural killer cells in IPF progression.