Host methylation predicts SARS-CoV-2 infection and clinical outcome.
Host methylation predicts SARS-CoV-2 infection and clinical outcome.
复制标题
DOI：
10.1038/s43856-021-00042-y
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Barnes KC
中科院分区：
文献类型：
--
作者：
Konigsberg IR;Barnes B;Campbell M;Davidson E;Zhen Y;Pallisard O;Boorgula MP;Cox C;Nandy D;Seal S;Crooks K;Sticca E;Harrison GF;Hopkinson A;Vest A;Arnold CG;Kahn MG;Kao DP;Peterson BR;Wicks SJ;Ghosh D;Horvath S;Zhou W;Mathias RA;Norman PJ;Porecha R;Yang IV;Gignoux CR;Monte AA;Taye A;Barnes KC
自SARS-CoV-2大流行开始以来，大多数临床检测都集中在RT-PCR上。冠状病毒感染后的宿主表观基因组操作表明，DNA甲基化特征可能将SARS-CoV-2感染患者与未感染个体区分开来，并有助于预测COVID-19疾病的严重程度，即使是在最初的表现中。我们定制了Illumina的Infinium MethylationEPIC阵列，以增强免疫反应检测，并对164名COVID-19患者和296名患者对照的外周血样本进行了纵向疾病严重程度测量。表观全基因组关联分析显示，病例与对照状态存在13，033个全基因组显著甲基化位点。干扰素信号转导和病毒应答相关基因和途径在差异甲基化位点中显著富集。我们观察到高度显着的关联基因先前报道的遗传关联研究（如IRF 7，OAS 1）。使用机器学习技术，使用稀疏回归构建的模型产生了高度预测性的结果：病例与对照状态的交叉验证最佳拟合AUC为93.6%，住院、ICU入院和进展至死亡的最佳拟合AUC分别为79.1%、80.8%和84.4%。总之，外周血中由与感染状态、疾病严重程度和临床恶化相关的关键免疫相关途径驱动的强COVID-19特异性表观遗传特征为病毒感染患者的诊断和预后提供了有用的见解。病毒感染以多种方式影响身体，包括通过表观基因组的变化，表观基因组是对影响基因活性的个体基因集合的化学修饰的总和。在这里，我们分析了来自患有和没有COVID-19的人的血液样本中的表观基因组，以确定我们是否可以找到与SARS-CoV-2感染一致的变化。使用统计和机器学习技术的组合，我们确定了SARS-CoV-2感染的标志物以及COVID-19疾病的严重程度和进展。这些疾病进展的信号从第一次走进医院时的初始抽血开始就存在。总之，这些方法证明了测量表观基因组用于监测SARS-CoV-2状态和严重程度的潜力。Konigsberg等人分析了SARS-CoV-2病例和对照组血液样本中的DNA甲基化。作者使用机器学习对感染与非感染个体进行分类，并预测与疾病严重程度相关的临床结果。
Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR. Host epigenome manipulation post coronavirus infection suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. We customized Illumina’s Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity. Konigsberg et al. profile DNA methylation in blood samples from SARS-CoV-2 cases and controls. The authors use machine learning to classify infected vs. non-infected individuals and predict clinical outcomes related to disease severity.