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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.

总结