Table_1_Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.XLSX

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body’s immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2–4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1–4, and days 7–10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

时至今日，新型冠状病毒肺炎（COVID-19）仍是全球范围内亟待解决的重大公共卫生问题。针对严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）的疫苗接种已被多国采纳为有效的应对策略。机体针对病毒感染所产生的免疫应答强度，与疫苗接种次数及接种间隔时长密切相关。本研究旨在筛选出可在不同疫苗接种场景下，调控新型冠状病毒肺炎免疫应答的特异性基因。
本研究设计了基于机器学习的分析方法，对161名受试者的血液转录组进行分析。根据疫苗接种剂量与接种时机，将受试者分为6组：I-D0、I-D2-4、I-D7组（分别对应接种ChAdOx1疫苗首剂后的第0天、第2~4天及第7天），以及II-D0、II-D1-4、II-D7-10组（分别对应接种BNT162b2疫苗第二剂后的第0天、第1~4天及第7~10天）。每个样本的特征由26364个基因的表达水平表征。受试者首剂疫苗均为ChAdOx1，第二剂则以BNT162b2为主（仅4名受试者接种了第二剂ChAdOx1）。本研究以组别作为分类标签，基因表达水平作为分类特征，采用多种机器学习算法对该分类任务进行分析。具体而言，首先通过5种特征排序算法（Lasso、LightGBM、MCFS、mRMR及PFI）对每个基因特征的重要性进行评估，得到5组特征列表。随后将这5组特征列表结合增量特征选择方法与4种分类算法，以提取核心基因、挖掘分类规则并构建最优分类器。上述筛选得到的核心基因分别为NRF2、RPRD1B、NEU3、SMC5及TPX2，既往研究已证实这些基因与免疫应答相关。本研究同时总结了可表征不同疫苗接种场景的表达规则，可为阐明疫苗诱导的抗病毒免疫分子机制提供参考。