Immune response to COVID-19 vaccine in HIV infected men and women

摘要 美国疾病控制与预防中心建议艾滋病毒携带者接种SARS-CoV-2疫苗。然而， 只有极少的科学数据支持这一重要的公共卫生原则，这与正在进行的 免疫功能障碍和慢性合并症与抗逆转录病毒治疗中持续的艾滋病毒蓄积物(ART) 威尔斯亲王医院。我们建议在一组特征良好的男性队列中进行正式的、全面的、纵向的研究 女性PWH与HIV非感染对照(Huc)的比较是评估其免疫原性的关键 威尔斯亲王医院的新冠肺炎疫苗。MACS-WIHS联合队列研究(MWCCS)是分析的理想选择 慢性HIV感染对新冠肺炎、辉瑞和现代核糖核酸疫苗的交互影响 对SARS-CoV-2和艾滋病毒宿主的免疫反应。在目标1中，我们建议进行一项- 100例男性PWH和100例男性PWH抗体中和功能及B淋巴细胞反应的深度分析 来自MWCCS的女性接种了PWH，25名男性和25名女性接种了Huc。在目标2中，我们将 广泛表征免疫细胞表型、可溶性标志物和功能性细胞免疫 目标1参与者在接种前后对SARS-CoV-2疫苗的反应。在《目标3》中， 我们将评估新冠肺炎疫苗对持久性艾滋病毒宿主的影响(数量和成分， 免疫参数)。Aim 4将机器学习方法应用于 在AIMS 1-3中量化的一整套功能，用于预测新冠肺炎疫苗的应答结果和 确定影响疫苗反应的关键参数。我们相信这项研究是独一无二的， 对于评估科学上史无前例的疫苗预防来说，这一点很重要，而且非常及时 在艾滋病毒感染的背景下，新冠肺炎大流行处于阶段。

ABSTRACT The CDC recommends that persons living with HIV (PWH) be vaccinated against SARS-CoV-2. However, there are minimal scientific data to support this important public health principle in relation to ongoing immune dysfunction and chronic comorbidities and the persistent HIV reservoir in antiretroviral treated (ART) PWH. We propose that a formal, comprehensive, longitudinal study in a well-characterized cohort of male and female PWH compared to HIV uninfected controls (HUC) is essential to assess the immunogenicity of COVID-19 vaccines in PWH. The MACS-WIHS Combined Cohort Study (MWCCS) is ideal for analyzing the interactive impact of chronic HIV infection on COVID-19 Pfizer and Moderna RNA-based vaccine immune responses to SARS-CoV-2 and the HIV reservoir. In Aim 1, we propose to conduct an in- depth analysis of antibody neutralizing function and B lymphocyte responses in 100 male PWH and 100 female vaccinated PWH, and 25 male and 25 female vaccinated HUC, from the MWCCS. In Aim 2, we will extensively characterize immune cell phenotypes, soluble markers, and functional cellular immune responses to SARS-CoV-2 vaccination in the participants from Aim 1 before and after vaccination. In Aim 3, we will evaluate the effects of COVID-19 vaccine on the persistent HIV reservoir (amount and composition, immune parameters) before and after immunization. Aim 4 applies machine learning approaches on the entire set of features quantified in Aims 1-3 to predict COVID-19 vaccine response outcomes and determine the critical parameters that influence vaccine responses. We believe that this study is unique, important, and critically timely for assessing the scientifically unprecedented, vaccine prevention phase of the COVID-19 pandemic in the context of HIV infection.