School of Life Sciences, Shanghai University, 200444 Shanghai, China.
College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China.
Front Biosci (Landmark Ed). 2022 Jun 27;27(7):204. doi: 10.31083/j.fbl2707204.
COVID-19 displays an increased mortality rate and higher risk of severe symptoms with increasing age, which is thought to be a result of the compromised immunity of elderly patients. However, the underlying mechanisms of aging-associated immunodeficiency against (SARS-CoV-2) remains unclear. Epigenetic modifications show considerable changes with age, causing altered gene regulations and cell functions during the aging process. The DNA methylation patterns among patients with coronavirus 2019 disease (COVID-19) who had different ages were compared to explore the effect of aging-associated methylation modifications in SARS-CoV-2 infection.
Patients with COVID-19 were divided into three groups according to age. Boruta was used on the DNA methylation profiles of the patients to remove irrelevant features and retain essential signature sites to identify substantial aging-associated DNA methylation changes in COVID-19. Next, these features were ranked using the minimum redundancy maximum relevance (mRMR) method, and the feature list generated by mRMR was processed into the incremental feature selection method with decision tree (DT), random forest, -nearest neighbor, and support vector machine to obtain the key methylation sites, optimal classifier, and decision rules.
Several key methylation sites that showed distinct patterns among the patients with COVID-19 who had different ages were identified, and these methylation modifications may play crucial roles in regulating immune cell functions. An optimal classifier was built based on selected methylation signatures, which can be useful to predict the aging-associated disease risk of COVID-19.
Existing works and our predictions suggest that the methylation modifications of genes, such as , , , , , and , are closely associated with age in patients with COVID-19, and the 39 decision rules extracted with the optimal DT classifier provides quantitative context to the methylation modifications in elderly patients with COVID-19. Our findings contribute to the understanding of the epigenetic regulations of aging-associated COVID-19 symptoms and provide the potential methylation targets for intervention strategies in elderly patients.
COVID-19 显示出随着年龄的增长，死亡率增加和出现严重症状的风险更高，这被认为是老年患者免疫功能受损的结果。然而，与衰老相关的针对 (SARS-CoV-2) 的免疫缺陷的潜在机制尚不清楚。表观遗传修饰随着年龄的增长而发生相当大的变化，导致衰老过程中基因调控和细胞功能的改变。比较了患有 2019 年冠状病毒病（COVID-19）的患者之间的 DNA 甲基化模式，以探讨与衰老相关的甲基化修饰在 SARS-CoV-2 感染中的作用。
根据年龄将 COVID-19 患者分为三组。使用 Boruta 对患者的 DNA 甲基化谱进行处理，以去除不相关的特征并保留重要的特征位，以鉴定 COVID-19 中与衰老相关的重要 DNA 甲基化变化。然后，使用最小冗余最大相关性 (mRMR) 方法对这些特征进行排序，并用 mRMR 生成的特征列表处理成决策树 (DT)、随机森林、最近邻和支持向量机的增量特征选择方法，以获得关键的甲基化位点、最优分类器和决策规则。
确定了在不同年龄的 COVID-19 患者中表现出不同模式的几个关键甲基化位点，这些甲基化修饰可能在调节免疫细胞功能方面发挥关键作用。基于选定的甲基化特征构建了最优分类器，可用于预测 COVID-19 相关的衰老疾病风险。
现有研究和我们的预测表明，COVID-19 患者中基因的甲基化修饰，如 、 、 、 、 、 等与年龄密切相关，最优 DT 分类器提取的 39 条决策规则为 COVID-19 老年患者的甲基化修饰提供了定量背景。我们的研究结果有助于了解与衰老相关的 COVID-19 症状的表观遗传调控，并为老年患者的干预策略提供潜在的甲基化靶标。
