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

摘要 疾病预防控制中心建议艾滋病毒感染者（PWH）接种SARS-CoV-2疫苗。然而，在这方面， 只有最少的科学数据支持这一重要的公共卫生原则， 抗逆转录病毒治疗（ART）中的免疫功能障碍和慢性合并症以及持续的HIV储库 PWH。我们建议在一个特征明确的男性队列中进行一项正式的、全面的、纵向的研究， 和女性PWH与HIV未感染对照（HUC）的比较对于评估免疫原性至关重要 威尔斯亲王医院的新型冠状病毒疫苗MACS-WIHS联合队列研究（MWCCS）是分析 慢性HIV感染对COVID-19辉瑞和Moderna RNA疫苗的相互影响 对SARS-CoV-2和HIV储库的免疫反应。在目标1中，我们建议进行一项 对100例男性PWH患者和100例正常对照者的抗体中和功能和B淋巴细胞反应进行了深入分析 女性接种PWH，25名男性和25名女性接种HUC，来自MWCCS。在目标2中，我们 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的参与者在接种疫苗前后对SARS-CoV-2疫苗的反应。在目标3中， 我们将评估COVID-19疫苗对持续存在的HIV宿主的影响（数量和组成， 免疫参数）。Aim 4将机器学习方法应用于 在目标1-3中量化的一整套特征，以预测COVID-19疫苗反应结果， 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， 重要的是，在评估科学上前所未有的疫苗预防方面， 在艾滋病毒感染的背景下，COVID-19大流行的阶段。

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.