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

基本信息

项目摘要

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.
项目总结 新冠肺炎大流行是一个多世纪以来最严重的全球卫生危机。在高收入国家, 在过去的18个月里,严重新冠肺炎患者的预后明显改善,因为提供了 高质量的危重护理和免疫调节剂的管理,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转译研究推动的 这表明免疫-炎症反应失调是重症新冠肺炎的主要病理特征。 随着新冠肺炎在预防HIC方面的进展,疫情负担已向中低档转移。 收入国家,现在占每日新冠肺炎死亡率的40%。这一负担尤其是 在撒哈拉以南非洲地区很严重,在那里反复出现的新冠肺炎激增已经超过了脆弱的健康 该系统和病例死亡率在世界上名列前茅。尽管免疫学背景下的 新冠肺炎在南非是独一无二的,因为艾滋病毒负担很高,而且住院成年人的年龄相对较小,在 其他因素,对该地区严重新冠肺炎的免疫病理机制知之甚少。通过一个 哥伦比亚大学和乌干达病毒研究所建立了合作关系,我们有 在乌干达前瞻性地招募了400多名患有新冠肺炎的患者,涵盖了整个疾病严重程度的范围。 利用这一独特的队列,这项研究的总体目标是确定新冠肺炎的生物学机制 在乌干达使用多模式方法进行宿主免疫分析的临床严重性。我们将确定 成人新冠肺炎患者血清可溶性免疫标志物与重症危重病的关系 乌干达使用最小偏差的机器学习方法(目标1);确定生物途径和免疫细胞 使用全血核糖核酸测序数据在乌干达建立与严重危重新冠肺炎相关的资料(目标2); 并结合生物标记物和RNA测序数据来确定艾滋病毒感染对先天和 新冠肺炎中的获得性免疫反应(目标3)。直接面向美国国立卫生研究院新冠肺炎研究重点,我们的 研究结果将(I)促进对免疫病理机制的基本理解,这些免疫病理机制推动了 在特别行政区政府和其他脆弱、艾滋病毒负担高的环境中严重感染新冠肺炎,以及(Ii)将冠状病毒感染者- 19分成生物驱动的和具有临床意义的亚组,对这些亚组采取局部反应的治疗策略 可以更精确地进行调查和开发。

项目成果

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Matthew John Cummings其他文献

Matthew John Cummings的其他文献

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{{ truncateString('Matthew John Cummings', 18)}}的其他基金

Subtyping sepsis in Uganda using clinical, pathogen, and host response profiling
使用临床、病原体和宿主反应分析对乌干达脓毒症进行分型
  • 批准号:
    10448162
  • 财政年份:
    2022
  • 资助金额:
    $ 20.84万
  • 项目类别:
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
  • 财政年份:
    2022
  • 资助金额:
    $ 20.84万
  • 项目类别:
Subtyping sepsis in Uganda using clinical, pathogen, and host response profiling
使用临床、病原体和宿主反应分析对乌干达脓毒症进行分型
  • 批准号:
    10560608
  • 财政年份:
    2022
  • 资助金额:
    $ 20.84万
  • 项目类别:
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