Determinants of inception of inflammation in inflammatory bowel diseases

炎症性肠病炎症发生的决定因素

• 批准号: 10474628
• 负责人: Ashwin N Ananthakrishnan
• 金额: $ 108.57万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-24 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474628
• 关键词: Affect; Autoimmune Diseases; Bacteroides; Biological; Biological Markers; Biopsy; Blood specimen; Carnitine; Cells; Characteristics; Clinical; Colon; Complex; Consumption; Crohn' s disease; Data; Development; Diet; Direct Costs; Disease; Disease remission; Drug Kinetics; Environment; Environmental Exposure; Feces; Food; Future; Gene Expression Profile; Genes; Genetic Transcription; Health; Immune; Individual; Inflammation; Inflammatory; Inflammatory Bowel Diseases; Influentials; Intake; Interleukin-10; Intervention; Machine Learning; Maintenance; Measurement; Mediating; Metagenomics; Modeling; Molecular; Morbidity - disease rate; Mucous Membrane; Neural Network Simulation; Pathogenesis; Pathway interactions; Patient Recruitments; Patients; Pharmaceutical Preparations; Physical activity; Pilot Projects; Proteome; Proteomics; Recurrent disease; Registries; Relapse; Resistance; Resolution; Role; Sampling; Serum; Smoking; Sphingolipids; Stress; Technology; Time; Tissues; Training; Ulcerative Colitis; United States; Up-Regulation; Work; cohort; deep learning; deep learning algorithm; defined contribution; disorder prevention; dysbiosis; fecal metabolome; fecal microbiome; follow-up; gut microbiome; healthy volunteer; insight; learning strategy; machine learning model; machine learning prediction; metabolome; metabolomics; microbial; microbiome; novel therapeutics; outcome prediction; predicting response; prevent; prospective; recruit; recurrent neural network; relapse prediction; relapse risk; single-cell RNA sequencing; soluble fiber; targeted treatment; therapeutic target; therapy development; tool; transcriptome; transcriptomics; treatment optimization

Project Summary Crohn’s disease (CD) and ulcerative colitis (UC) affect over 2 million individuals in the United States and are associated with considerable morbidity. Existing treatments achieve remission in fewer than 50% of patients. Further, despite achieving endoscopic remission, up to 30% of patients with CD or UC will relapse over the subsequent three years. Pathophysiologic mechanisms leading to relapse have not been well established. The central premise of our proposal is that despite endoscopic, there exists a local pro-inflammatory microbial milieu and transcriptional profile that favors disease relapse. We hypothesize that current clinical tools do not have sufficient resolution to capture this state. Existing cohorts, by recruiting patients in a heterogeneous state of active inflammation, cannot be used to infer mechanisms of loss of remission and inception of inflammation. A targeted effort that comprehensively and longitudinally profiles a homogeneous cohort of patients in deep remission is essential to define the dynamic relationship between microbial alterations, metabolomic, transcriptional, and proteomic perturbations, and onset of inflammation. Identifying deficient components favoring relapse also allows the development of intervention to replace these deficiencies, thereby extending remission. They will also provide clues and serve as starting points for development of novel therapies. In the first aim, we will recruit 300 patients with IBD in clinical and endoscopic remission and prospectively, systematically follow them for 3 years. We will comprehensively characterize such patients through serial sampling of mucosal and fecal microbiome, serum and fecal metabolome, and proteome in addition to detailed environmental exposure assessment and measurement of drug pharmacokinetics. We will determine the dynamic predictive utility of each of these parameters in defining future relapse from a state of quiescence. In the second aim, we will define the role of pro-inflammatory changes at the cellular level by performing single cell transcriptomic analysis from colonic and ileal biopsies in patients with quiescent CD and UC recruited as above. This will provide important insights into loss of control of inflammation at the tissue level that determines future clinical activity. The final study aim will train and validate a machine-learning predictive model to define the contribution of each additional biologic layer to inception of inflammation and to identify more robust biomarkers of a state of sustained remission. Defining the molecular basis of future relapse in patients in deep remission will provide insights into the ‘pre-disease’ state, allowing for identification of immune pathways of relevance in preventing disease. Defining the fundamental mechanisms through which disease inception occurs from quiescence is critically important to inform key steps in the pathogenesis of these complex diseases, which in turn, will offer opportunities for targeted mechanism-driven interventions to aid durable maintenance of remission and health. The approaches and analyses outlined also have broad applicability to other autoimmune diseases.

项目摘要 克罗恩病(CD)和溃疡性结肠炎(UC)在美国影响着200多万人， 与相当高的发病率相关。现有的治疗方法在不到50%的患者中获得缓解。 此外，尽管实现了内窥镜下的缓解，高达30%的CD或UC患者将在 随后的三年。导致复发的病理生理机制尚未完全确定。这个 我们建议的中心前提是，尽管有内窥镜检查，但存在局部促炎微生物环境 以及有利于疾病复发的转录特征。我们假设目前的临床工具没有 有足够的分辨率来捕捉这种状态。现有队列，通过招募处于不同状态的患者 活动性炎症，不能用来推断缓解丧失和炎症开始的机制。一个 有针对性的努力，全面和纵向描述深部患者的同类队列 缓解对于确定微生物变化、新陈代谢、 转录和蛋白质组的紊乱，以及炎症的发生。确定有缺陷的组件 支持复发也允许发展干预措施来取代这些缺陷，从而扩大 减刑。它们还将提供线索，并作为开发新疗法的起点。在 第一个目标，我们将招募300名临床和内窥镜下缓解的IBD患者， 系统地跟踪他们3年。我们将通过系列节目来全面描述这类患者的特征 粘膜和粪便微生物组，血清和粪便代谢组，以及蛋白质组的样本 环境暴露评估和药物药代动力学测量。我们将确定 这些参数中的每一个在定义来自静止状态的未来复发时的动态预测效用。在……里面 第二个目标，我们将通过进行单细胞实验，在细胞水平上定义促炎变化的作用 从上述静止期CD和UC患者的结肠和回肠活检中进行转录分析。 这将为在决定未来的组织水平上失去对炎症的控制提供重要的见解 临床活动。最终的研究目标是训练和验证机器学习预测模型，以定义 每一额外的生物层对炎症的开始和识别更强大的生物标志物的贡献 处于持续缓解的状态。确定深度缓解患者未来复发的分子基础将 提供对疾病前状态的洞察，从而识别与疾病相关的免疫途径 预防疾病。明确疾病发生的基本机制 静默对于了解这些复杂疾病的发病机制中的关键步骤至关重要。 反过来，将为有针对性的机制驱动的干预提供机会，以帮助持久维持缓解 和健康。概述的方法和分析对其他自身免疫性疾病也具有广泛的适用性。