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A Phylodynamic Artificial Intelligence framework to predict evolution of SARS-CoV-2 variants of concern in Immunocompromised persons with HIV (PhAI-CoV)

用于预测免疫功能低下的 HIV 感染者 (PhAI-CoV) 中关注的 SARS-CoV-2 变异体进化的系统动力学人工智能框架

基本信息

批准号：
10664035
负责人：
MARIA LUISA ALCAIDE
金额：
$ 74.42万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-12 至 2027-06-30
项目状态：
未结题

项目摘要

Summary The United States (US) is the most affected country worldwide by the ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. The availability of effective vaccines had initially slowed down new infections, reducing incidence of severe coronavirus disease 2019 (COVID-19) cases, hospitalization burden, and deaths. Unfortunately, vaccine hesitancy, and the emergence of new, highly transmissible variants of concern (VOCs), such as the Delta variant that has rapidly become the dominant one in the US among both non-vaccinated and vaccine breakthrough cases, have caused a new dramatic epidemic surge in July-August 2021, and are likely to be an ongoing problem hindering epidemic eradication efforts. Although data on increased mortality and worse clinical outcome in people with HIV (PWH) with COVID-19 is somewhat equivocal, recent surveys indicate that PWH has a higher likelihood of severe disease or death than patients without immune dysfunction. Moreover, while most people effectively clear SARS-CoV-2 in 2-4 weeks, several reports of infection in immunosuppressed individuals have shown intra-host emergence of multi-mutational variants, some at sites linked to immune evasion, especially in case of persistent infection. The overarching goal of the proposed project is to investigate SARS-CoV-2 genomes intra-host evolution in the context of HIV infection by developing a phylodynamic and artificial Intelligence framework to assess the emergence and likelihood of SARS-CoV-2 VOC (PhAI-CoV) in immunocompromised PWH. The hypothesis is that SARS-CoV-2 infection in PWH can result in enhanced evolution of viral variants that can efficiently be tracked by phylodynamic analysis and predicted to be VOCs by artificial intelligence algorithms. To test such a hypothesis, we developed three specific aims that will investigate three complementary, albeit independent, issues. We will use a well-characterized cohort of PWH and rigorously collected longitudinal data and samples from patients with SARS-CoV-2 co-infection in Miami, Florida, one of the cities with the highest HIV and SARS-CoV-2 infection burden in the US. In Specific Aim 1 we will recruit and retain n=120 PWH with acute SARS-CoV-2 infection, as well as n=120 matching controls with acute SARS-CoV2 infection but without HIV, and study how COVID-19 disease severity differs by HIV status, depending on SARS-CoV-2 vaccination history and infecting variant. In Specific Aim 2, we will Investigate intra- host SARS-CoV-2 evolution throughout the duration of infection to assess the likelihood of SARS-CoV-2 infection in PWH to result in sustained intra-host evolution leading to the emergence of novel viral variants. In specific Aim 3, we will develop an artificial intelligence algorithm that can predict the likelihood of new variants to be VOCs. Understanding the evolutionary scenarios of SARS-CoV-2 variants emergence within HIV infection and evaluating the probability for increased strain infectivity and/or pathogenicity will provide a crucial tool for planning and implementing public health measures before transmission occurs in the general population.

摘要美国是全球受持续严重急性呼吸系统综合症影响最严重的国家 SARS冠状病毒2(SARS-CoV-2)大流行有效疫苗的可获得性最初已经放缓。降低新增感染、降低重症冠状病毒病发病率2019年(新冠肺炎)病例住院负担和死亡。不幸的是，疫苗的犹豫不决，以及新的、高度可传播的变种的出现令人担忧(VOC)，如Delta变体，它已迅速成为美国这两种类型中的主导未接种疫苗和疫苗突破病例在7-8月造成了新的戏剧性疫情激增 2021年，并可能成为阻碍消灭流行病努力的持续问题。尽管数据有所增加新冠肺炎携带者的死亡率和更差的临床结果有些模棱两可，最近调查表明，与没有免疫力的患者相比，PWH患严重疾病或死亡的可能性更高。功能障碍。此外，虽然大多数人在2-4周内有效地清除了SARS-CoV-2，但有几份感染报告在免疫抑制的个体中，已经显示出宿主内出现多个突变变体，其中一些位于与免疫逃避有关，特别是在持续感染的情况下。拟议项目的总体目标是研究HIV感染背景下SARS-CoV-2基因组在宿主内的进化用于评估SARS-CoV-2 VOC出现和可能性的系统动力学和人工智能框架 (Phai-CoV)在免疫低下的PWH中。假设威尔斯亲王医院感染SARS-CoV-2可导致增强病毒变异的进化，可以通过系统动力学分析有效地跟踪并预测 VOCs由人工智能算法实现。为了检验这一假设，我们制定了三个具体目标调查三个相辅相成但又相互独立的问题。我们将使用一群特征良好的威斯康星医院并严格收集了迈阿密SARS-CoV-2混合感染患者的纵向数据和样本，佛罗里达州，美国艾滋病毒和SARS-CoV-2感染负担最高的城市之一。在具体目标1中，我们将招募和保留120名患有急性SARS-CoV-2感染的威斯康星医院，以及n=120名与急性SARS-CoV2感染但没有艾滋病毒，并研究艾滋病毒状态对新冠肺炎疾病严重程度的影响，取决于SARS-CoV-2疫苗接种史和感染变种。在具体目标2中，我们将调查内部宿主SARS-CoV-2在感染期间的演变以评估感染SARS-CoV-2的可能性在PWH中导致持续的宿主内进化，从而出现新的病毒变体。具体而言目标3，我们将开发一种人工智能算法，它可以预测新变种的可能性挥发性有机化合物。了解SARS-CoV-2变异株在HIV感染和感染中出现的进化情景评估菌株传染性和/或致病性增加的可能性将提供一个重要的工具在普通人群中发生传播之前规划和实施公共卫生措施。