Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19.

IF 4.8 2区 医学 Q1 GENETICS & HEREDITY
Yuan-Yuan Li, Ming-Ming Yuan, Yuan-Yuan Li, Shan Li, Jing-Dong Wang, Yu-Fei Wang, Qian Li, Jun Li, Rong-Rong Chen, Jin-Min Peng, Bin Du
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引用次数: 0

Abstract

Background: The recently identified methylation patterns specific to cell type allows the tracing of cell death dynamics at the cellular level in health and diseases. This study used COVID-19 as a disease model to investigate the efficacy of cell-specific cell-free DNA (cfDNA) methylation markers in reflecting or predicting disease severity or outcome.

Methods: Whole genome methylation sequencing of cfDNA was performed for 20 healthy individuals, 20 cases with non-hospitalized COVID-19 and 12 cases with severe COVID-19 admitted to intensive care unit (ICU). Differentially methylated regions (DMRs) and gene ontology pathway enrichment analyses were performed to explore the locus-specific methylation difference between cohorts. The proportion of cfDNA derived from lung and immune cells to a given sample (i.e. tissue fraction) at cell-type resolution was estimated using a novel algorithm, which reflects lung injuries and immune response in COVID-19 patients and was further used to evaluate clinical severity and patient outcome.

Results: COVID‑19 patients had globally reduced cfDNA methylation level compared with healthy controls. Compared with non-hospitalized COVID-19 patients, the cfDNA methylation pattern was significantly altered in severe patients with the identification of 11,156 DMRs, which were mainly enriched in pathways related to immune response. Markedly elevated levels of cfDNA derived from lung and more specifically alveolar epithelial cells, bronchial epithelial cells, and lung endothelial cells were observed in COVID-19 patients compared with healthy controls. Compared with non-hospitalized patients or healthy controls, severe COVID-19 had significantly higher cfDNA derived from B cells, T cells and granulocytes and lower cfDNA from natural killer cells. Moreover, cfDNA derived from alveolar epithelial cells had the optimal performance to differentiate COVID-19 with different severities, lung injury levels, SOFA scores and in-hospital deaths, with the area under the receiver operating characteristic curve of 0.958, 0.941, 0.919 and 0.955, respectively.

Conclusion: Severe COVID-19 has a distinct cfDNA methylation signature compared with non-hospitalized COVID-19 and healthy controls. Cell type-specific cfDNA methylation signature enables the tracing of COVID-19 related cell deaths in lung and immune cells at cell-type resolution, which is correlated with clinical severities and outcomes, and has extensive application prospects to evaluate tissue injuries in diseases with multi-organ dysfunction.

无细胞 DNA 甲基化揭示了细胞特异性组织损伤,并与 COVID-19 的疾病严重程度和患者预后相关。
背景:最近发现的细胞类型特异性甲基化模式可在细胞水平追踪健康和疾病中的细胞死亡动态。本研究以 COVID-19 为疾病模型,探讨细胞特异性无细胞 DNA(cfDNA)甲基化标记在反映或预测疾病严重程度或预后方面的功效:方法:对20名健康人、20名未住院的COVID-19病例和12名入住重症监护室(ICU)的重症COVID-19病例的cfDNA进行全基因组甲基化测序。通过差异甲基化区域(DMRs)和基因本体通路富集分析,探讨了不同组群之间位点特异性甲基化的差异。利用一种新型算法估算了细胞类型分辨率下来自肺细胞和免疫细胞的cfDNA占给定样本的比例(即组织部分),该结果反映了COVID-19患者的肺损伤和免疫反应,并进一步用于评估临床严重程度和患者预后:结果:与健康对照组相比,COVID-19 患者的 cfDNA 甲基化水平普遍降低。与非住院的COVID-19患者相比,重症患者的cfDNA甲基化模式发生了显著改变,鉴定出11156个DMRs,这些DMRs主要富集在与免疫反应相关的通路中。与健康对照组相比,在COVID-19患者中观察到来自肺,特别是肺泡上皮细胞、支气管上皮细胞和肺内皮细胞的cfDNA水平明显升高。与非住院患者或健康对照组相比,重症 COVID-19 患者的 B 细胞、T 细胞和粒细胞的 cfDNA 明显较高,而自然杀伤细胞的 cfDNA 较低。此外,来自肺泡上皮细胞的cfDNA在区分不同严重程度、肺损伤程度、SOFA评分和院内死亡的COVID-19方面表现最佳,其接收者操作特征曲线下面积分别为0.958、0.941、0.919和0.955:结论:与非住院COVID-19患者和健康对照组相比,重症COVID-19患者具有独特的cfDNA甲基化特征。细胞类型特异性的 cfDNA 甲基化特征可在细胞类型分辨率上追踪与 COVID-19 相关的肺细胞和免疫细胞死亡,这与临床严重程度和预后相关,在评估多器官功能障碍疾病的组织损伤方面具有广泛的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
自引率
5.30%
发文量
150
期刊介绍: Clinical Epigenetics, the official journal of the Clinical Epigenetics Society, is an open access, peer-reviewed journal that encompasses all aspects of epigenetic principles and mechanisms in relation to human disease, diagnosis and therapy. Clinical trials and research in disease model organisms are particularly welcome.
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