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

项目摘要/摘要 儿童克罗恩病(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患上晚期疾病 诊断。治疗儿童克罗恩病需要慎重考虑，因为它具有强大的抗炎作用 治疗可能会产生不良影响，根据疾病的严重程度，可能不需要治疗。因此， 对儿童克罗恩病人群进行风险分层，并预测未来的亚型，包括结构性表现 对疾病和对治疗缺乏反应，是一项迫切的未得到满足的需求。而疾病的基因组标记 尽管已经进行了详细的研究，但对儿童克罗恩病的代谢特征的探索还不太发达。研究 近年来发现了克罗恩氏病期间发生的代谢变化，包括脂质、氨基酸的变化 酸、三元酸和硫代谢。但对代谢变化的研究还不够详细，也没有 足够大的队列可以成为临床生物标志物，特别是用于描述疾病的亚型，而不是 克罗恩氏病与正常组织的对比。尽管代谢途径是有针对性的，而且有初步的发现 阻断代谢途径(即甲氧戊酸途径)可能对克罗恩的结局有利， 靶向代谢尚未成为普遍现象。在这项提案中，我们将利用 分析来自大型儿科CD队列的转录组数据并将这些数据映射到 数学代谢重建，以评估新陈代谢转变。我们假设识别出 以人群为基础的队列中独特的代谢变化将为克罗恩亚型的预测提供信息，两者都 以结构性和治疗性为基础。在目标1中，我们建议建立一个新的计算代谢网络 这将是针对回肠代谢功能的重建，回肠是克罗恩病理的主要部位。 该模型将作为理解镉代谢变化的参考，但也可以作为 其他研究小组研究小肠的新陈代谢变化。在目标2中，我们将利用来自 大型儿科CD队列，通过计算将一系列亚型的转录片段叠加到我们的新陈代谢中 网络重建以评估新陈代谢的变化。我们还将招募CD患者的预期队列。 从弗吉尼亚大学和埃默里大学收集组织，进行RNA测序，然后重复 我们的计算代谢模型，以验证我们对归档数据的分析。这些成果将进一步 经质谱学、代谢组学和脂类组学验证。最后，在目标3中，我们将描述转录本 和儿童克罗恩病患者来源的回肠有机化合物的代谢特征，以测试有机化合物是否是一种 为机械干预实验研究体内代谢变化提供了有价值的替代物。加在一起，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，这可以促进个性化药物治疗范例。

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.