Immune Programs and Related T Cell Mechanisms of Pulmonary Complications After COVID-19 Illness

COVID-19 疾病后肺部并发症的免疫程序和相关 T 细胞机制

• 批准号: 10886167
• 负责人: Judith A Woodfolk
• 金额: $ 41万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10886167
• 关键词: Acute; Address; Airway Disease; Antigen Presentation; Antiviral Response; B-Lymphocytes; Blood; CD8-Positive T-Lymphocytes; COVID-19; COVID-19 complications; COVID-19 patient; CXCL13 gene; Cell secretion; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Chronic; Chronic Lung Injury; Clinical; Coculture Techniques; Complex; Data; Dependence; Disease; Disease Progression; Dyspnea; Elements; Epitopes; Equilibrium; Evolution; Fibrosis; Flow Cytometry; Gene Expression; Genetic Transcription; Homing; Immune; Immune system; In Vitro; Inflammation; Inflammation Mediators; Inflammatory; Interferon Type II; Knowledge; Lung; Lung diseases; Machine Learning; Maps; Measures; Mediator; Methods; Molecular; Monitor; Nature; Oligonucleotides; Outcome; Pathogenicity; Pathway interactions; Patients; Phenotype; Plasma; Population; Positioning Attribute; Process; Proteins; Pulmonary Fibrosis; Pulmonary Inflammation; Pulmonary Pathology; Recovery; Resolution; Rhinovirus infection; Risk; SARS-CoV-2 antigen; Sampling; Severities; Sorting; Specificity; Specimen; Stains; Statistical Methods; T-Lymphocyte; T-bet protein; Testing; Tissues; Virus; Work; airway inflammation; cell type; chemokine; cohort; combinatorial; coronavirus disease; cytokine; design; disease phenotype; exhaust; exhaustion; experience; human model; in vitro testing; in vivo; innovation; lung imaging; lung injury; machine learning method; migration; molecular dynamics; molecular targeted therapies; new therapeutic target; novel; potential biomarker; programs; protein complex; response; senescence; severe COVID-19; single-cell RNA sequencing; synergism; tool; transcription factor; transcriptome

SUMMARY Patients who survive severe COVID-19 illness are at risk of developing pulmonary complications. While this may resolve, some patients experience progression of their disease, resulting in severe lung damage. Although T cells are critical to anti-viral responses in the lungs, sustained T-cell alterations in the blood after COVID-19 illness implicates them in persistent disease. There is a major knowledge gap regarding if, and how, circulating T cells contribute to lung pathology. Our understanding is hampered by the variable clinical nature of pulmonary disease, as well as the challenges to identifying pathogenic T cells in the blood and defining relationships to disease course. The current project overcomes these barriers by applying innovative single-cell methods and powerful machine learning tools that are exquisitely tailored to detect disease-relevant cell populations in the blood, and to define the cellular and molecular dynamics that constitute immune programs governing pulmonary complications of COVID-19. The study leverages a unique and highly characterized cohort of COVID-19 patients defined by their severity of acute illness and developing fibrosis. By garnering data on hundreds of cellular and molecular features in a large sample of patients, we are now poised to significantly advance the field. Preliminary findings reveal pulmonary phenotypes that discriminate severe airway disease, and perturbations in discrete CD4+ and CD8+ T-cell populations, including IFN-γ-producing virus-specific cells, related to these clinical entities. A shared feature of T cells identified is their expression of T-bet, a transcription factor also expressed by tissue-homing B cells that persist after COVID-19 illness. Further, our data support an interplay between these T cells and B cells. Accordingly, we will test the overarching hypothesis that sustained perturbations in novel CD4+ and CD8+ T cells expressing T-bet, including virus-specific cells, mark pro-fibrotic pulmonary phenotypes. These cells create a persistent feedforward circuit of IFN-γ-dependent inflammation through coordinated actions with B cells. First, immune programs of pulmonary disease will be defined and their trajectories mapped in relation to progression and recovery on the basis of concerted T cells, other immune cells and inflammatory mediators operating in vivo over 2 years. This will involve resolving protein signatures and gene expression profiles of T cells at unprecedented depth to determine their functions and evolution (Aim 1). Next, the contributions of epitope-specific T cells to divergent recovery paths will be distinguished in order to address how progression and resolution is regulated by virus epitopes (Aim 2). To this end, a combinatorial tetramer method will monitor the proportions and functions of up to 6 epitope specificities within each subject. Finally, we will confirm the ability for pathogenic T cells from subjects with developing fibrosis, to synergize with T-bet+ B cells by promoting IFN-γ-dependent inflammation in vitro, through a process involving a novel potential biomarker of disease progression, CXCL13 (Aim 3). Assembling “elements of the T-cell puzzle” will identify mechanisms of disease that reveal new therapeutic targets for halting or reversing lung inflammation after COVID-19 illness.

概括 患有严重 COVID-19 疾病的患者有发生肺部并发症的风险。 尽管T已解决，但一些患者的疾病进展，导致严重的肺部损伤。 细胞对于肺部的抗病毒反应以及 COVID-19 后血液中 T 细胞的持续变化至关重要 疾病使他们与持续性疾病有关，对于是否以及如何传播存在重大知识差距。 T 细胞对肺部病理学的贡献因肺部临床性质的多变而受到阻碍。 疾病，以及识别血液中致病性 T 细胞并定义其关系的挑战 当前的项目通过应用创新的单细胞方法克服了这些障碍。 强大的机器学习工具，经过精心定制，可检测疾病相关细胞群 血液，并定义构成控制肺部的免疫程序的细胞和分子动力学 该研究利用了独特且高度特征化的 COVID-19 患者队列。 通过收集数百个细胞和纤维化的数据来定义其急性疾病的严重程度和发展的纤维化。 基于大量患者样本的分子特征，我们现在准备显着推进该领域的发展。 研究结果揭示了区分严重气道疾病的肺部表型以及离散的扰动 CD4+和CD8+T细胞群，包括产生IFN-γ的病毒特异性细胞，与这些临床相关 所识别的 T 细胞的一个共同特征是它们表达 T-bet，这是一种也表达的转录因子。 此外，我们的数据支持这些细胞之间的相互作用。 因此，我们将测试新奇中持续扰动的总体假设。 表达 T-bet 的 CD4+ 和 CD8+ T 细胞（包括病毒特异性细胞）标志着促纤维化的肺部表型。 这些细胞通过协调作用创建了 IFN-γ 依赖性炎症的持续前馈回路 首先，将定义肺部疾病的免疫程序并绘制其轨迹。 基于协同 T 细胞、其他免疫细胞和炎症的进展和恢复的关系 介质在体内运行超过 2 年，这将涉及解决蛋白质特征和基因表达。 以前所未有的深度对 T 细胞进行分析，以确定其功能和进化（目标 1）。 将区分表位特异性 T 细胞对不同恢复路径的贡献，以便解决如何 为此，采用组合四聚体方法 将监测每个受试者内多达 6 个表位特异性的比例和功能。 确认来自患有纤维化的受试者的致病性 T 细胞与 T-bet+ B 细胞协同作用的能力 通过在体外促进 IFN-γ 依赖性炎症，通过涉及一种新的潜在生物标志物的过程 疾病进展，CXCL13（目标 3）组装“T 细胞难题的要素”将确定疾病进展的机制。 揭示了在 COVID-19 患病后停止或逆转肺部炎症的新治疗靶点。