An Alternative Pathobiology underlying Severe Asthma

工程摘要 此应用程序是一个指导病人为导向的研究职业发展奖题为“一个 严重哮喘潜在的替代病理学。”我是一名医学助理教授， 匹兹堡大学寻求在台式鼠类研究技术和生物信息学方面的额外培训 和“组学分析，从台式过渡到转化哮喘研究。我研究的重点是 1型（T1）炎症在哮喘中的作用，以及这种途径如何导致疾病的严重程度。 虽然在理解和治疗传统描述的2型（T2）方面取得了很大进展， 使用新型生物疗法治疗哮喘炎症，近50%的哮喘患者缺乏这一证据 T2通路，甚至一些患有T2炎症的患者对T2疗法反应不佳。更好地了解 哮喘中的其他炎症途径以及它们如何导致疾病对于识别新的 治疗这些患者。我的初步数据表明，T1炎症的存在， 通过增加IFN-γ的表达，趋化因子CXCL 10和CCL 5以及表达T1的组织驻留细胞可以被激活。 记忆性T细胞（T1 TRM），约30%的哮喘患者。这些患者往往有更严重的哮喘， 急性加重和全身性皮质类固醇的使用增加。然而，依赖支气管镜检查样本 限制了我们研究这一途径随时间推移的临床效果的能力。此外，我们对 T1炎症对哮喘气道的影响，以及T1 TRM细胞在维持/驱动这一过程中的作用。 表型未知。这项研究旨在提高我们对哮喘T1炎症的理解， 方法：（1）利用我们的T1显性小鼠重度哮喘模型，评估TRM的建立和再激活， T1高哮喘。(2)为了评估马拉韦罗（一种CCR 5抑制剂）在影响T1特异性变化方面的重要性， 气道上皮和肥大细胞的患病率。(3)多中心严重哮喘研究计划提供 大量哮喘受试者的配对痰液样本的临床数据，使我们能够测量T1 炎症标志物，评估其与临床结果的相关性，并确定未来的生物标志物，以更好地 识别T1高哮喘患者用于未来试验。这个项目将提供一个独特的见解小说和穷人 了解哮喘的途径，有可能为高度流行的哮喘产生令人兴奋的新治疗选择。 严重的疾病。这项研究也将为我提供机会，获得生物信息学的技能， 大型组学数据集分析，这将促进我作为一个翻译医生科学家在严重的发展 哮喘的治疗，同时提高我的鼠模型技能这项工作将在纽约大学进行。 匹兹堡在肺，过敏和重症监护部门，其中有一个强大的发展记录， 医生科学家和拥有高度发达的基础设施转化研究。我也 组建了一支在哮喘领域获得国际认可的高水平多学科导师团队 生物信息学和机器学习。

Project Abstract This application is for a Mentored Patient-Oriented Research Career Development Award entitled “An Alternative Pathobiology underlying Severe Asthma.” I am an Assistant Professor of Medicine at the University of Pittsburgh seeking additional training in benchtop murine research techniques and bioinformatics and ‘omics analysis to transition from benchtop to translational asthma research. The focus of my research is on the role of Type-1 (T1) inflammation in asthma and how this pathway may contribute to disease severity. While great strides have been made in understanding and treating traditionally described Type-2 (T2) inflammation in asthma with novel biologic therapies, nearly 50% of asthma patients lack evidence of this pathway, and even some with T2 inflammation fail to respond well to T2 therapies. A better understanding of other inflammatory pathways in asthma and how they contribute to disease is essential to identifying novel therapeutics for these patients. My preliminary data have demonstrated the presence of T1 inflammation marked by increased expression of IFN-γ, the chemokines CXCL10 and CCL5 as well as T1 expressing tissue resident memory T-cells (T1 TRM) in ~30% of asthma patients. These patients tend to have more severe asthma, greater exacerbations and higher use of systemic corticosteroids. However, the reliance on bronchoscopy for samples has limited our ability to study the clinical effects of this pathway over time. Furthermore, our understanding of the effects of T1 inflammation on the airways in asthma and the role of T1 TRM cells in maintaining/driving this phenotype are unknown. This study aims to improve our understanding of asthmatic T1 inflammation in three ways: (1) Utilize our T1 dominant murine severe asthma model to assess TRM establishment and reactivation in T1High asthma. (2) To assess the importance of maraviroc (a CCR5 inhibitor) in effecting T1 specific changes on the airway epithelium and mast cell prevalence. (3) The multicenter Severe Asthma Research Program provides clinical data in a large number of asthma subjects with paired sputum samples that will allow us to measure T1 inflammatory markers, assess their correlation with clinical outcomes and identify future biomarkers to better identify T1High asthmatics for future trials. This project will provide unique insight into a novel and poorly understood pathway in asthma with the potential to yield exciting new treatment options for a highly prevalent and severe disease. This study will also provide the opportunity for me to acquire skills in bioinformatics and large ‘omics dataset analysis that will foster my development as a translational physician scientist in severe asthma in addition to advancing my murine model skillset. The work will be carried out at the University of Pittsburgh in the division of Pulmonary, Allergy and Critical Care which has a strong track record of developing physician scientists and boasts a highly developed infrastructure for translational research. I have also assembled a highly accomplished multi-disciplinary mentorship team that is internationally recognized in asthma research, bioinformatics and machine learning.