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

基于人群的代谢途径特征预测儿童克罗恩病结果

• 批准号: 10418965
• 负责人: Sana Syed
• 金额: $ 71.85万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418965
• 关键词: Adverse effects; Affect; Amino Acids; Anti-Inflammatory Agents; Antibiotics; Ascorbic Acid; Atlases; Autoimmune Diseases; Bacteria; Biochemical Pathway; Biological Markers; Biological Models; Biopsy; Child; Childhood; Chronic; Clinical; Coenzymes; Computing Methodologies; Crohn' s disease; Crohn' s disease of the ileum; Data; Data Set; Dependence; Development; Diagnosis; Disease; Disease Marker; Disease Outcome; Enzymes; Equilibrium; Fistula; Future; Genetic Transcription; Genomics; Goals; Growth and Development function; Human; Inflammation; Inflammatory; Intervention; Intestines; Kinetics; Length; Link; Lipids; Literature; Manuals; Maps; Mass Chromatography; Mass Spectrum Analysis; Mathematics; Metabolic; Metabolic Diseases; Metabolic Pathway; Metabolism; Methods; Modeling; Normal tissue morphology; Organoids; Outcome; Pathology; Pathway interactions; Patients; Pediatric Crohn' s disease; Phenotype; Physicians; Plasma; Population; Progressive Disease; Prospective cohort; Proteins; Proteomics; Proxy; RNA; Reaction; Resources; Risk; Sampling; Severity of illness; Site; Small Intestines; Sulfur Metabolism Pathway; Surveys; TNF gene; Testing; Tissues; Transcript; Tricarboxylic Acids; Universities; Uridine; Validation; Virginia; Work; adverse outcome; archive data; archived data; base; biobank; biomarker identification; cancer cell; clinical biomarkers; cohort; computerized tools; data archive; disease phenotype; disorder subtype; experimental study; fatty acid oxidation; genomic biomarker; human tissue; ileum; in silico; in vivo; innovation; lipidomics; mathematical model; metabolic abnormality assessment; metabolomics; mevalonate; novel; nutrition; patient population; patient stratification; patient subsets; pediatric patients; personalized medicine; population based; prevent; prospective; reconstruction; recruit; response; risk stratification; small molecule; success; transcriptome sequencing; transcriptomics; treatment response

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.

项目概要/摘要 小儿克罗恩病 (CD) 是一种慢性、进行性疾病，可能对儿童的健康产生严重影响 成长和发展。儿童更有可能在数年内患上这种疾病的晚期形式 诊断。治疗小儿克罗恩病需要仔细考虑，因为它具有强大的抗炎作用 治疗可能会产生副作用，并且根据疾病的严重程度可能没有必要。所以， 对儿科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 疾病和对治疗缺乏反应是一个迫切的未满足的需求。虽然疾病的基因组标记 尽管已经进行了详细的研究，但对儿科克罗恩病代谢特征的探索还不够深入。研究 近年来已经发现克罗恩病期间发生的代谢变化，包括脂质、氨基酸的变化 酸、三羧酸和硫代谢。但代谢变化尚未得到足够详细的研究，也没有得到足够的研究。 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 克罗恩病与正常组织。尽管代谢途径是可靶向的并且有初步发现 阻断代谢途径（即甲羟戊酸途径）可能对克罗恩病的结果有益， 以新陈代谢为目标尚未成为一种普遍现象。在本提案中，我们将利用 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的计算方法 数学代谢重建以评估代谢变化。我们假设识别 基于人群的独特代谢变化将为克罗恩病亚型的预测提供信息 以结构和治疗为基础。在目标 1 中，我们建议建立一个新颖的计算代谢网络 特定于回肠代谢功能的重建，回肠是克罗恩病的主要病理部位。 该模型将作为理解 CD 代谢变化的参考，但也可以作为以下资源： 其他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组学叠加到我们的代谢上 网络重建以评估新陈代谢的变化。我们还将招募一组前瞻性 CD 患者 来自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进行验证。最后，在目标 3 中，我们将分析转录组 和儿科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一种 对于研究体内代谢变化以进行机械干预实验来说是有价值的代理。在一起，这些 实验将为使用高通量代谢数据对儿科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。