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: 10651894
• 负责人: Matthew John Cummings
• 金额: $ 23.62万
• 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/10651894
• 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; Classification; Clinical; Clinical Data; Coagulation Process; Collaborations; Country; Critical Care; Critical Illness; Cytotoxic T-Lymphocytes; Data; Dimensions; Enrollment; Genetic Variation; Genomics; Goals; HIV; HIV Infections; Health system; Hospitalization; Immune; Immune response; Immunologic Markers; Immunologics; Immunologist; Income; Inflammatory; Inflammatory Response; Innate Immune Response; Interferons; Interleukin-6; 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; Translational Research; 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; immune modulating agents; 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大流行是世纪以来最严重的全球健康危机。在高收入国家， 在过去18个月里，由于提供了 高质量的重症监护和免疫调节剂，如皮质类固醇和 白细胞介素-6拮抗剂。这些治疗药物的有效使用是由转化研究驱动的 将失调的免疫炎症反应确定为严重COVID-19的关键病理特征。 随着高收入国家在COVID-19预防方面取得进展，大流行负担已转移到中低收入国家， 收入国家，现在占与COVID-19相关的每日死亡率的40%以上。这一负担尤其 在撒哈拉以南非洲（SSA），COVID-19的反复激增已经压倒了脆弱的健康状况， 系统和病死率都是世界上最高的。虽然免疫学背景下， 由于艾滋病毒负担高和住院成人年龄相对较轻， 其他因素，对该地区严重COVID-19的免疫病理学知之甚少。通过 哥伦比亚大学和乌干达病毒研究所建立了合作关系， 在乌干达前瞻性地招募了400多名患有COVID-19的患者，涵盖了疾病的整个严重程度。 利用这一独特的队列，本研究的总体目标是确定COVID-19的生物学机制 临床严重程度在乌干达使用多模式方法宿主免疫谱。康贝特人将以 中国成年COVID-19患者可溶性免疫生物标志物与重症危重症的关系 乌干达使用最小偏差机器学习方法（目标1）;识别生物途径和免疫细胞 使用全血RNA测序数据的乌干达严重危急COVID-19相关图谱（目标2）; 并整合生物标志物和RNA测序数据，以确定HIV感染对先天和 COVID-19中的适应性免疫反应（Aim 3）。直接针对NIH COVID-19研究重点，我们的 研究结果将（i）促进对免疫病理学机制的基本理解， 在撒哈拉以南非洲和其他脆弱的高艾滋病毒负担环境中，严重的COVID-19，以及（ii）对COVID-19患者进行分类， 19例患者分为生物学驱动和具有临床意义的亚组， 可以更精确地研究和开发。