Multimodal immune profiling to determine mechanisms of COVID-19 clinical trajectory in Uganda

多模式免疫分析以确定乌干达 COVID-19 临床轨迹的机制

• 批准号: 10508523
• 负责人: Matthew John Cummings
• 金额: $ 20.84万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-23 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10508523
• 关键词: Address; Adrenal Cortex Hormones; Adult; Africa; Africa South of the Sahara; Age; Applications Grants; Automobile Driving; B-Lymphocytes; Biological; Biological Markers; Blood specimen; CD4 Positive T Lymphocytes; CD8-Positive T-Lymphocytes; COVID-19; COVID-19 pandemic; COVID-19 patient; COVID-19 prevention; COVID-19 severity; Case Fatality Rates; Cells; Clinical; Clinical Data; Coagulation Process; Collaborations; Country; Critical Care; Critical Illness; Cytotoxic T-Lymphocytes; Data; Enrollment; Genetic Variation; Genomics; Goals; HIV; HIV Infections; Health system; Immune; Immune response; Immunologic Markers; Immunologics; Immunologist; Immunomodulators; Income; Inflammatory; Inflammatory Response; Interferons; Interleukin-6; Investigation; Lung; Machine Learning; Outcome; Pathologic; Pathway interactions; Patient-Focused Outcomes; Patients; Physicians; Prospective cohort; RNA; Recurrence; Research; Research Institute; Research Priority; Sampling; Scientist; Severities; Severity of illness; Signal Transduction; Subgroup; T-Cell Activation; Therapeutic Agents; Therapeutic Uses; Uganda; United States National Institutes of Health; Universities; Virus; Whole Blood; adaptive immune response; antagonist; biomarker signature; cell type; cohort; cytokine; endothelial dysfunction; exhaustion; genetic signature; global health; high risk; immune activation; immunopathology; immunoregulation; improved; insight; low and middle-income countries; machine learning method; mortality; multimodality; pandemic disease; prognostic; prospective; response; severe COVID-19; transcriptome; transcriptome sequencing; transcriptomics; treatment strategy; young adult

PROJECT SUMMARY The COVID-19 pandemic is the greatest global health crisis in over a century. In high-income countries (HICs), outcomes for patients with severe COVID-19 have improved markedly over the past 18 months due to provision of high-quality critical care and administration of immunomodulatory agents such as corticosteroids and interleukin-6 antagonists. Effective use of these therapeutic agents was driven by translational investigations that identified dysregulated immune-inflammatory responses as key pathological features in severe COVID-19. Following advances in COVID-19 prevention in HICs, the pandemic burden has shifted to low- and middle- income countries, which now account for >40% of daily mortality related to COVID-19. This burden is particularly severe in sub-Saharan Africa (SSA), where recurrent COVID-19 surges have overwhelmed fragile health systems and case fatality rates are among the highest in the world. Although the immunological context of COVID-19 in SSA is unique due to high HIV burden and the relatively young age of hospitalized adults, among other factors, little is known about the immunopathology of severe COVID-19 in the region. Through an established collaboration between Columbia University and Uganda Virus Research Institute, we have prospectively enrolled over 400 patients with COVID-19 in Uganda across the entire spectrum of illness severity. Leveraging this unique cohort, the overall goal of this study is to determine biological mechanisms of COVID-19 clinical severity in Uganda using a multimodal approach to host immune profiling. We will determine the relationship between soluble immune biomarkers and severe-critical illness among adults with COVID-19 in Uganda using minimally-biased machine learning methods (Aim 1); identify biological pathways and immune cell profiles associated with severe-critical COVID-19 in Uganda using whole-blood RNA sequencing data (Aim 2); and integrate biomarker and RNA-sequencing data to determine the effect of HIV-infection on innate and adaptive immune responses in COVID-19 (Aim 3). Directly addressing NIH COVID-19 research priorities, our results will (i) advance fundamental understanding of the immunopathological mechanisms driving the burden of severe COVID-19 in SSA and other vulnerable, high HIV burden settings, and (ii) classify patients with COVID- 19 into biologically-driven and clinically-meaningful subgroups for whom locally-responsive treatment strategies can be more precisely investigated and developed.

项目摘要 COVID-19大流行是一个多世纪以来最大的全球健康危机。在高收入国家（HIC）中， 由于提供，在过去的18个月中，严重COVID-19患者的结局已明显改善 高质量的重症监护和免疫调节剂，例如皮质类固醇和 白介素-6拮抗剂。这些治疗剂的有效使用是由翻译调查驱动的 这发现免疫炎症反应失调是严重Covid-19的关键病理特征。 随着Covid-19预防HIC的进步，大流行负担已转移到低和中间 收入国家，现在占与19日相关的每日死亡率的40％。这个负担特别是 撒哈拉以南非洲（SSA）的严重严重 系统和案例死亡率是世界上最高的。虽然是 SSA中的Covid-19是由于高艾滋病毒负担和相对年轻的成年人而独一无二的 其他因素，对于该地区严重的Covid-19的免疫病理学知之甚少。通过一个 在哥伦比亚大学和乌干达病毒研究所之间建立了合作，我们已经 在整个疾病严重程度中，乌干达的19例Covid-19患者预期招募了400多名患者。 利用这一独特的队列，这项研究的总体目标是确定Covid-19的生物学机制 乌干达的临床严重程度采用多模式方法来托管免疫分析。我们将确定 可溶性免疫生物标志物与covid-19的成年人中的严重危害疾病之间的关系 乌干达使用最小偏见的机器学习方法（AIM 1）；识别生物途径和免疫细胞 使用全血RNA测序数据与乌干达的严重批判性共同-19相关的轮廓（AIM 2）； 并整合生物标志物和RNA-seter-seter-seter-ster-seter-se-ster-ster-ster-ster-ster-ster-se-ster-se-ster-ster-ster-ster-seto hiv感染影响影响 COVID-19中的自适应免疫反应（AIM 3）。直接解决NIH Covid-19的研究重点，我们的 结果将（i）提出对免疫病理机制的基本理解 SSA和其他易受伤害的高HIV负担环境中的严重COVID-19，以及（ii）对患者进行covid- 19进入生物学驱动和临床上的亚组 可以更精确地研究和开发。