Comprehensive Proteomic Classifier for the Molecular Characterization of Pulmonary Sarcoidosis

用于肺结节病分子特征的综合蛋白质组学分类器

• 批准号: 10297189
• 负责人: Maneesh Bhargava
• 金额: $ 69.95万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297189
• 关键词: Address; Adrenal Cortex Hormones; Aryl Hydrocarbon Receptor; Bioinformatics; Biological; Biological Markers; Biological Process; Biopsy; Blood; Bronchoalveolar Lavage; Bronchoalveolar Lavage Fluid; Caring; Cell physiology; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Cicatrix; Classification; Clathrin; Clinic; Clinical; Clinical Data; Cross-Sectional Studies; Data; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Marker; Disease Progression; Disease remission; Enrollment; Event; Exclusion; FRAP1 gene; Foundations; Future; Gene Expression; Gene Proteins; Genes; Genetic Transcription; Genomics; Goals; Granulomatous; Health; Heterogeneity; IL8 gene; Immune; Immune response; Immune system; Immunology; Immunosuppressive Agents; Impairment; Interstitial Lung Diseases; Link; Liquid substance; Lung; Lung diseases; Mass Spectrum Analysis; Mediating; Minnesota; Modeling; Molecular; Morbidity - disease rate; Outcome; Pathogenicity; Pathway interactions; Patient Outcomes Assessments; Patients; Performance; Phagosomes; Phenotype; Phosphorylation; Phosphotransferases; Population; Process; Prognosis; Progressive Disease; Protein-Serine-Threonine Kinases; Proteins; Proteome; Proteomics; Publishing; Pulmonary Sarcoidosis; Receptor Signaling; Reporting; Resolution; Sarcoidosis; Severities; Signal Transduction; Smoking Status; Source; Systemic disease; Testing; Transcript; Translating; Universities; Validation; Work; base; bioinformatics tool; biological systems; biomedical referral center; case control; clinical application; clinical care; clinically relevant; cohort; community setting; computerized tools; diagnostic biomarker; differential expression; disabling disease; follower of religion Jewish; improved; improved outcome; insight; integrin-linked kinase; mortality; novel; phosphoproteomics; predictive marker; predictive modeling; prognostic; prospective; rapid diagnosis; recruit; rho; side effect; single-cell RNA sequencing; statistics; targeted treatment; therapeutic target; tool; transcriptome sequencing; unnecessary treatment

PROJECT SUMMARY The primary goal of this study is to construct predictive models (classifiers) of pulmonary sarcoidosis and progressive (P) vs. non-progressive (NP) disease that will ultimately serve to improve outcomes of pulmonary sarcoidosis. We have assembled a unique investigative team with expertise in proteomics, immunology, genomics, sarcoidosis clinical care, as well as bioinformatics and statistics. Sarcoidosis is a diagnostically challenging immune-mediated systemic disease. It results in significant morbidity and mortality, primarily due to progressive pulmonary disease, although the factors that drive pulmonary disease and P vs. NP disease are unknown. The strategies to treat pulmonary sarcoidosis, including the triggers to initiate treatment, are non- specific; treatment usually relies on suppressing the immune system with corticosteroids and is associated with considerable side-effects. Transcriptional changes in the lung and blood have defined a signature of P disease in cross-sectional studies. Since proteins are the main effectors of cellular function and their alterations result in disruption of biologic systems and disease development, they are a logical source of biomarkers. Our preliminary data from bronchoalveolar lavage fluid and cells demonstrate significant proteome wide alterations in pulmonary sarcoidosis vs controls and P vs NP disease. We hypothesize that effective markers of disease and those distinguishing progressive from non-progressive disease will reflect biological processes active in disease and progression. Secondarily, by characterizing cellular proteins, global phosphorylation events and cell-specific RNA expression, we will define known proteins/gene/pathways such as the PI3K/Akt/mTOR and other serine-threonine kinase signaling mechanisms as well as novel pathogenic proteins/genes, such as endocytic and aryl hydrocarbon receptor signaling, which will have implications for mechanism and therapy. We will use high-resolution mass spectrometry (MS), advanced bioinformatics and computational tools in well- phenotyped sarcoidosis patients. In Aim 1, we will determine a disease-specific classifier for diagnosing sarcoidosis using a Discovery Cohort of sarcoidosis cases and diseased and healthy controls (already recruited) for the development and Validation Cohort (recruited for this study) of sarcoidosis cases and controls to verify and optimize the classifier performance. In Aim 2, we will identify a protein classifier of P vs NP disease using the same approach as in Aim 2. In Aim 3 we will use a novel single-cell RNA-sequencing approach, CITE-seq to identify transcription from specific cells, and integrate it with protein changes, including examination of global phosphorylation events to identify kinase signaling and discover cell-specific cellular proteins/genes associated with disease and progression in a subset of our Validation Cohort. At the end of this study, we will have defined diagnostic biomarkers of disease and progression that can be translated easily to the clinic. We will also gain insights into the sarcoidosis pulmonary proteins and transcripts that may serve as potential therapeutic targets and provide potential mechanistic information with future study.

项目摘要 本研究的主要目的是构建肺结节病的预测模型（分类器）， 进展性（P）与非进展性（NP）疾病，最终将有助于改善肺动脉高压的结局。 结节病我们组建了一个独特的调查团队，他们在蛋白质组学，免疫学， 基因组学、结节病临床护理以及生物信息学和统计学。结节病是一种诊断上 挑战免疫介导的系统性疾病。它导致显著的发病率和死亡率，主要是由于 进行性肺部疾病，尽管驱动肺部疾病和P与NP疾病的因素是 未知治疗肺结节病的策略，包括启动治疗的触发因素，是非 特异性;治疗通常依赖于用皮质类固醇抑制免疫系统， 相当大的副作用。肺和血液中的转录变化定义了P疾病的特征 在横断面研究中。由于蛋白质是细胞功能的主要效应物，它们的改变导致 生物系统的破坏和疾病的发展，它们是生物标志物的逻辑来源。我们的初步 来自支气管肺泡灌洗液和细胞的数据表明，在肺组织中， 结节病与对照组和P与NP疾病。我们假设疾病的有效标志物和那些 区分进行性疾病和非进行性疾病将反映生物学过程的活性， 疾病和进展。其次，通过表征细胞蛋白，全局磷酸化事件和 细胞特异性RNA表达，我们将定义已知的蛋白质/基因/途径，如PI 3 K/Akt/mTOR和 其它丝氨酸-苏氨酸激酶信号传导机制以及新的致病蛋白/基因，例如 内吞和芳香烃受体信号传导，这将对机制和治疗产生影响。我们 将使用高分辨率质谱（MS），先进的生物信息学和计算工具， 结节病患者的表型在目标1中，我们将确定用于诊断的疾病特异性分类器。 使用结节病病例以及患病和健康对照（已招募）的发现队列的结节病 用于结节病病例和对照的开发和验证队列（为本研究招募），以验证 并优化分类器性能。在目标2中，我们将使用以下方法鉴定P vs NP疾病的蛋白质分类器： 与目标2相同的方法。在目标3中，我们将使用一种新的单细胞RNA测序方法，CITE-seq 识别来自特定细胞的转录，并将其与蛋白质变化整合，包括检查全局 磷酸化事件，以鉴定激酶信号传导并发现相关的细胞特异性细胞蛋白/基因 在我们的验证队列的一个子集中的疾病和进展。在本研究结束时，我们将定义 疾病和进展的诊断生物标志物，可以很容易地转化为临床。我们也将获得 对结节病肺部蛋白质和转录物的深入了解，这些蛋白质和转录物可能作为潜在的治疗靶点 为今后的研究提供了潜在的机理信息。