The gut microbiome, host genetics, and risk for inflammatory bowel disease

肠道微生物组、宿主遗传学和炎症性肠病的风险

• 批准号: 8978408
• 负责人: Kelly Ann Shaw
• 金额: $ 4.31万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8978408
• 关键词: Abdominal Pain; Accounting; Age of Onset; Autoimmune Responses; Bacteria; Bacterial Genome; Biological Markers; Biopsy; Blood Tests; Canada; Characteristics; Childhood; Chronic; Clinical; Colectomy; Collection; Colonoscopy; Colorectal Cancer; Control Groups; Crohn' s disease; Data; Diagnosis; Diarrhea; Disease; Distal part of ileum; Environmental Exposure; Environmental Risk Factor; Exposure to; Eye; Feces; Fever; Gene Family; Genes; Genetic; Genetic Risk; Genetic Variation; Genome; Genotype; Goals; Healthcare Systems; Heart; Human Genetics; Immune response; Immune system; Individual; Inflammation; Inflammatory Bowel Diseases; Intestines; Joints; Knowledge; Left; Location; Logistic Regressions; Lung; Meta-Analysis; Microbe; Modeling; Monitor; Operative Surgical Procedures; Pancreas; Pathogenesis; Patients; Pharmacologic Substance; Population; Quality of life; Risk; Risk Assessment; Risk Factors; Role; Sampling; Stratification; Symptoms; Testing; Time; Ulcerative Colitis; base; bone; cancer risk; case control; cohort; disorder control; disorder risk; early onset; experience; gastrointestinal; genetic risk factor; improved; indexing; insight; metagenome; microbial; microbiome; mortality; public health relevance; rRNA Genes; symptom management; tool; young adult; Abdominal Pain; Accounting; Age of Onset; Autoimmune Responses; Bacteria; Bacterial Genome; Biological Markers; Biopsy; Blood Tests; Canada; Characteristics; Childhood; Chronic; Clinical; Colectomy; Collection; Colonoscopy; Colorectal Cancer; Control Groups; Crohn' s disease; Data; Diagnosis; Diarrhea; Disease; Distal part of ileum; Environmental Exposure; Environmental Risk Factor; Exposure to; Eye; Feces; Fever; Gene Family; Genes; Genetic; Genetic Risk; Genetic Variation; Genome; Genotype; Goals; Healthcare Systems; Heart; Human Genetics; Immune response; Immune system; Individual; Inflammation; Inflammatory Bowel Diseases; Intestines; Joints; Knowledge; Left; Location; Logistic Regressions; Lung; Meta-Analysis; Microbe; Modeling; Monitor; Operative Surgical Procedures; Pancreas; Pathogenesis; Patients; Pharmacologic Substance; Population; Quality of life; Risk; Risk Assessment; Risk Factors; Role; Sampling; Stratification; Symptoms; Testing; Time; Ulcerative Colitis; base; bone; cancer risk; case control; cohort; disorder control; disorder risk; early onset; experience; gastrointestinal; genetic risk factor; improved; indexing; insight; metagenome; microbial; microbiome; mortality; public health relevance; rRNA Genes; symptom management; tool; young adult

 DESCRIPTION (provided by applicant): Inflammatory bowel disease (IBD) is a serious, incurable gastrointestinal illness characterized by chronic inflammation which profoundly impacts quality of life for patients. Symptoms can include abdominal pain or discomfort, fever, diarrhea, and bloody stools. Up to 25% of patients experience other symptoms involving the heart, lungs, eyes, pancreas, bones, or joints. Around 1.5 million people have IBD in the U.S. and Canada, where rates are among the highest worldwide, and an estimated 17,000-93,000 new diagnoses are made each year. Importantly, peak age of onset is young adulthood, and 7-20% of diagnoses are pediatric. Because individuals require blood tests to monitor disease, surveillance colonoscopy, pharmaceutical management of symptoms, and often surgery, IBD has a large impact on the healthcare system. Recent meta-analysis of genetic data revealed 163 loci significantly associated with IBD that explain less than 15% of variance in disease risk. Intriguingly, associated loci are enriched for genes involved in host response to intestinal microbes. Previous studies have shown IBD patients' gut bacterial populations, or microbiomes, have altered configurations compared to control individuals, including overall reduction in diversity as well as altered abundance of specific bacteria. At this time, despite information that both genetic and microbiome risk factors exist for IBD, no study has taken both into account simultaneously. The validity of imputing bacterial genomes to look at microbial activity in the gut has also not been studied in IBD. We propose to investigate these issues using genotype and microbiome data from the RISK cohort, the largest collection of early-onset, treatment-naïve Crohn's cases and non-IBD controls. Our first aim is to test the hypothesis that using genetic and microbiome data in combination is a more precise estimator of IBD risk than either genetic or microbiome profile alone. Gut microbiome dysbiosis scores will be calculated from 16S rRNA gene profiling of terminal ileum biopsy and fecal samples as described by Gevers et al. Polygenic risk scores will be calculated using significant loci and effect sizes from the recent Jostins et al. meta-analysis of IBD. We will then use logistic regression to test for association o case/control status with polygenic risk score, dysbiosis index, and both polygenic risk score and dysbiosis index. Our second aim is to test the hypothesis that metagenomes imputed from fecal samples more precisely estimate IBD risk compared to taxonomic information. We will impute the metagenomes of the same cohort discussed in Aim 1 using established tool PICRUSt. We can then include these gene family abundances in our model to test whether differences in the imputed metagenome demonstrate better association with case/control status compared to dysbiosis index, polygenic risk score, or the two in combination. The proposed study will be the first to evaluate IBD risk using both genetic and microbiome data in combination. Our long-term goal is to advance understanding of the mechanisms of IBD pathogenesis and help improve diagnosis and treatment. More broadly, this study will contribute important preliminary insights into how the host genome and microbiome interact.

 描述（由适用提供）：炎症性肠病（IBD）是一种严重的，无法治愈的胃肠道疾病，其特征是慢性炎症，深刻影响患者的生活质量。症状可能包括腹痛或不适，发烧，腹泻和血腥的粪便。多达25％的患者经历了其他症状，涉及心脏，肺，眼睛，胰腺，骨骼或关节。在美国和加拿大，大约有150万人拥有IBD，在全球范围内最高，估计每年进行17,000-93,000个新诊断。重要的是，发病年龄是年轻的成年，而7-20％的诊断为儿科。由于个人需要血液检查以监测疾病，监测结肠镜检查，症状的药物管理以及经常进行手术，因此IBD对医疗保健系统产生了很大的影响。最近对遗传数据的荟萃分析显示，163个基因座与IBD显着相关，这些基因座可解释疾病风险差异的15％。有趣的是，相关的局部富含与宿主对肠道微生物反应的基因富集。先前的研究表明，与对照个体相比，IBD患者的肠道细菌群体或微生物组的构型改变了，包括多样性的总体降低以及特定细菌的抽象改变。这次，目的地信息 IBD都存在遗传和微生物组风险因素，没有任何研究很容易考虑到这两者。归纳细菌基因组观察肠道中微生物活性的有效性 也没有在IBD中研究。我们建议使用来自风险队列的基因型和微生物组数据研究这些问题，这是最大的早期发病，未经治疗的克罗恩病例和非IBD对照。我们的第一个目的是检验以下假设：组合使用遗传和微生物组数据是IBD风险的更精确的估计量，而不是遗传或微生物组概况。如Gevers等人所述，将从末端回肠活检和粪便样本的16S rRNA基因分析计算肠道微生物组的分数。多基因风险评分将使用最近的Jostins等人的局部和效应大小来计算。 IBD的荟萃分析。然后，我们将使用logistic回归来测试与多基因风险评分，失调指数以及多基因风险评分和失调指数相关的病例/控制状态。我们的第二个目的是检验以下假设：与分类信息相比，从粪便样品中估算的岩元组更精确地估计了IBD风险。我们将使用已建立的工具PICRUST在AIM 1中讨论的同一队列的宏基因组。然后，我们可以在模型中包括这些基因家族丰度，以测试与营养不良指数，多基因风险评分或两个组合相比，估算的元基因组中的差异与病例/对照状态的差异是否更好。拟议的研究将是第一个使用遗传和微生物组数据组合评估IBD风险的研究。我们的长期目标是提高对IBD发病机理机制的理解，并有助于改善诊断和治疗。从更广泛的角度来看，这项研究将对宿主基因组和微生物组如何相互作用的重要初步见解。