A Systems Biology Approach Using Fecal Microbiota and Metabolomics to Identify Novel Subtypes in Irritable Bowel Syndrome

利用粪便微生物群和代谢组学识别肠易激综合征新亚型的系统生物学方法

• 批准号: 10848525
• 负责人: Allen A Lee
• 金额: $ 14.58万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-19 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10848525
• 关键词: 16S ribosomal RNA sequencing; Address; Affect; Algorithms; Antibiotics; Area; Bile Acids; Bioinformatics; Biological; Biological Assay; Biostatistical Methods; Butyrates; Caring; Characteristics; Clinical; Clinical Trials; Communities; Complex; Computer Models; Culture-independent methods; Data; Diarrhea; Diet; Disaccharides; Disease; Enrollment; Foundations; Frustration; Future; Gastroenterologist; Gastrointestinal Motility; Goals; Health Personnel; Irritable Bowel Syndrome; K-Series Research Career Programs; Laboratories; Learning Skill; Longitudinal Studies; Machine Learning; Mediating; Mentors; Methods; Modeling; Monosaccharides; Multiomic Data; Oligosaccharides; Patients; Phase; Phenotype; Population; Precision Health; Predictive Analytics; Randomized; Regimen; Research; Research Activity; Research Personnel; Research Proposals; Ribosomal RNA; Risk Factors; Sampling; Sequence Analysis; Structure; Subgroup; Symptoms; Systems Biology; Techniques; Therapeutic; Time; Training; United States National Institutes of Health; Validation; Virulence Factors; Volatile Fatty Acids; career; career development; clinical phenotype; cohort; combinatorial; common treatment; dietary; experience; fecal metabolome; fecal microbiome; fecal microbiota; gut microbiome; gut microbiota; host-microbe interactions; improved; improved outcome; longitudinal analysis; longitudinal dataset; machine learning algorithm; metabolomics; microbial; microbial community; microbiome; novel; optimal treatments; patient response; polyol; potential biomarker; precision medicine; predicting response; predictive modeling; resilience; response; rifaximin; skills; stem; treatment comparison; treatment response; treatment strategy; trial comparing

PROJECT SUMMARY/ABSTRACT Candidate: Dr. Allen Lee, MD is a gastroenterologist with advanced training in gastrointestinal motility whose research focuses on identifying abnormalities in host-microbial interactions to improve care in irritable bowel syndrome (IBS). Dr. Lee’s long-term career goals are to identify novel subgroups of IBS patients which inform biological responses to therapies and guide management. The proposed K23 mentored career development award includes a 3-year plan for training, didactics, and research activities that will provide Dr. Lee with the necessary skills and experience to become a successful independent investigator. Career Development: Dr. Lee will develop skills in the following four areas: 1) culture-independent approaches to study host-microbial interactions in IBS; 2) advanced biostatistical methods to study longitudinal datasets, including mixed models or generalized additive models; 3) laboratory-based translational techniques; 4) predictive analytics, such as machine learning algorithms. These training goals will directly contribute to Dr. Lee’s long-term career goals and prepare him to submit a successful R01 application. Research Context: Common treatments in diarrhea-predominant IBS (IBS-D) are effective in ≤50% patients. Additionally, there are currently no methods to identify patients more likely to respond to different treatments. The current paradigm for managing IBS patients revolves around identifying and treating the predominant symptom complexes. However, this is a hugely inefficient model and leads to frustration for both patients and health care providers. This proposal seeks to determine whether a systems biology approach, which incorporates microbiome and metabolomics data along with detailed clinical phenotype information, may identify novel subgroups that inform treatment decisions in IBS-D. Research Plan: This career development award leverages an on-going clinical trial comparing the effects of a nonabsorbable antibiotic rifaximin with a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) in IBS-D patients to identify differences in microbial community structure and function in responders vs. non-responders to therapy. Specific Aim 1 will identify fecal microbial features by 16S rRNA sequence analysis characteristic of treatment response to rifaximin or a low FODMAP diet. Specific Aim 2 will determine how treatments with rifaximin or low FODMAP diet affect the fecal metabolome, including short chain fatty acids and bile acids, in IBS-D patients. Specific Aim 3 will develop predictive models to identify subsets of IBS-D patients responsive to treatment. We will also identify risk factors for non-response to either rifaximin or low FODMAP diet. Results from this proposal will inform two future R01 proposals to validate these findings as well as to understand the mechanisms by which host-microbial interactions may mediate response to different therapies in IBS.

项目总结/摘要 候选人：医学博士艾伦李是一个胃肠病学家与先进的培训，在胃肠动力， 研究重点是确定宿主-微生物相互作用的异常，以改善肠易激综合征的护理 IBS综合征。李博士的长期职业目标是确定新的IBS患者亚组， 对治疗的生物反应和指导管理。拟议的K23指导职业发展 该奖项包括一个为期3年的培训，教学和研究活动计划，将为李博士提供 必要的技能和经验，成为一个成功的独立调查员。 职业发展：李博士将在以下四个方面发展技能：1）文化独立 研究IBS中宿主-微生物相互作用的方法; 2）先进的生物统计学方法， 数据集，包括混合模型或广义加性模型; 3）基于实验室的转化技术; 4)预测分析，如机器学习算法。这些培训目标将直接有助于博士。 李的长期职业目标，并准备他提交一个成功的R 01申请。 研究背景：常见的治疗以肠易激综合征为主的IBS（IBS-D）在≤50%的患者中有效。 此外，目前还没有方法来识别更有可能对不同治疗做出反应的患者。 目前管理IBS患者的模式围绕着识别和治疗主要的 症状综合征然而，这是一种效率极低的模式，并导致患者和 卫生保健提供者。这项建议旨在确定是否有一个系统生物学方法， 将微生物组和代谢组学数据沿着详细的临床表型信息， 确定IBS-D治疗决策的新亚组。 研究计划：这项职业发展奖利用了一项正在进行的临床试验，比较了 不可吸收的抗生素利福昔明与低可发酵寡糖，二糖， 单糖和多元醇（FODMAP），以确定IBS-D患者中微生物群落的差异 结构和功能的差异。具体目标1将识别粪便微生物 通过16 S rRNA序列分析表征对利福昔明的治疗应答或低FODMAP 饮食.具体目标2将确定利福昔明或低FODMAP饮食治疗如何影响粪便 代谢组，包括短链脂肪酸和胆汁酸。第3章会发展 预测模型，以识别对治疗有反应的IBS-D患者的子集。我们还将确定风险因素 对利福昔明或低FODMAP饮食无反应。该提案的结果将为未来的两个R 01提供信息 建议验证这些发现，以及了解宿主微生物的机制， 相互作用可能介导IBS对不同疗法的反应。