Population-Based Characterization of Metabolic Pathways to Predict Pediatric Crohn's Disease Outcomes

项目概要/摘要 小儿克罗恩病（CD）是一种慢性进行性疾病，可对儿童的 增长和发展。儿童更有可能在几年内发展成这种疾病的晚期形式。 诊断.治疗小儿克罗恩病的情况下，需要仔细考虑作为强大的抗炎 治疗可能具有副作用，并且根据疾病的严重程度可能不是必需的。因此，我们认为， 对儿童克罗恩病人群进行风险分层，并预测未来的亚型，包括结构表现 对疾病和对治疗缺乏反应的需求是一个迫切的未满足的需求。虽然疾病的基因标记 虽然已经进行了详细的研究，但对小儿克罗恩病的代谢特征的探索还不太发达。研究 近年来，已经确定了克罗恩病期间发生的代谢变化，包括脂质、氨基和蛋白质的变化， 酸、三羧酸和硫代谢。但是，代谢变化还没有得到足够详细的研究， 足够大的队列成为临床生物标志物，特别是用于描绘疾病的亚型，而不是 克罗恩病与正常组织。尽管代谢途径是有针对性的， 阻断代谢途径（即，甲羟戊酸途径），可以有益于克罗恩病的结果， 靶向代谢尚未成为普遍现象。在本提案中，我们将利用 计算方法来分析来自大型儿科CD队列的转录组学数据，并将该数据映射到 数学代谢重建来评估代谢变化。我们假设， 基于人群的队列中独特的代谢变化将为克罗恩病亚型的预测提供信息， 结构和治疗为基础。在目标1中，我们提出建立一个新的计算代谢网络 重建，这将是特定的回肠，克罗恩病的主要部位的代谢功能。 该模型将作为理解CD代谢变化的参考，但也可以作为 其他研究小肠代谢变化的小组。在目标2中，我们将利用来自 大型儿科CD队列，以计算覆盖转录组学从一系列亚型到我们的代谢 网络重建以评估代谢变化。我们还将招募一组前瞻性CD患者 从弗吉尼亚大学和埃默里大学，收集组织，进行RNA测序，并重复 我们的计算代谢模型来验证我们对存档数据的分析。这些结果将进一步 通过质谱代谢组学和脂质组学验证。最后，在目标3中，我们将分析转录组学 以及小儿克罗恩病患者来源的回肠类器官的代谢组学特征，以测试类器官是否是 有价值的代理研究体内代谢变化的机械干预实验。所有这些 实验将为使用高通量代谢数据对儿童克罗恩病进行风险分层铺平道路。 患者亚型，这可以促进个性化的药物治疗模式。

PROJECT SUMMARY/ABSTRACT Pediatric Crohn's Disease (CD) is a chronic, progressive disease which can have a severe impact on a child's growth, and development. Children are more likely to develop advanced forms of the disease within years of diagnosis. Treating pediatric Crohn's cases requires careful consideration as powerful anti-inflammatory treatments may have adverse effects and may not be necessary depending on severity of disease. Therefore, risk stratifying pediatric Crohn's populations, and predicting future subtypes, including structural manifestations of disease and a lack of response to treatments, is an urgent unmet need. While genomic markers of disease have been studied at length, exploration of the metabolic signature of pediatric Crohn's is less developed. Studies in recent years have identified metabolic changes which occur during Crohn's, including changes in lipid, amino acid, tricarboxylic acid and sulfur metabolism. But metabolic shifts have not been studied in enough detail or in large enough cohorts to become clinical biomarkers, especially for delineating subtypes of disease rather than Crohn's versus normal tissue. And although metabolic pathways are targetable and there are preliminary findings that blocking metabolic pathways (i.e., the mevalonate pathway), can be beneficial for Crohn's outcome, targeting metabolism has not become a widespread phenomenon. In this proposal, we will leverage computational methods to analyze transcriptomics data from large pediatric CD cohorts and map this data onto mathematical metabolic reconstructions to assess metabolic shifts. We hypothesize that identification of unique metabolic shifts in population-based cohorts will inform prediction of Crohn's subtypes, both structural and treatment-based. In Aim 1, we propose to build a novel computational metabolic network reconstruction that will be specific to the metabolic functioning of the ileum, a primary site of Crohn's pathology. This model will serve as a reference for understanding CD metabolic shifts but can also serve as a resource for other groups studying metabolism shifts in the small bowel. In Aim 2, we will leverage existing data from the large pediatric CD cohort, to computationally overlay transcriptomics from a range of subtypes onto our metabolic network reconstruction to assess shifts in metabolism. We will also recruit a prospective cohort of CD patients from both the University of Virginia and Emory University, collect tissue, perform RNA sequencing, and repeat our computational metabolic modeling to validate our analysis of archived data. These results will be further validated by mass spectrometry metabolomics and lipidomics. Finally, in Aim 3, we will profile the transcriptomic and metabolomic signatures of pediatric Crohn's-patient derived ileal organoids, to test if organoids are a valuable proxy for studying metabolic shifts in vivo for mechanistic intervention experiments. Together, these experiments will pave the way towards using high-throughput metabolic data to risk stratify pediatric Crohn's patient subtypes, which can facilitate personalized medicine treatment paradigms.