Bioinformatics Core: Core C

生物信息学核心：核心 C

基本信息

批准号：
10224804
负责人：
Rafick Pierre Sekaly
金额：
$ 19.32万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-10 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10224804
关键词：
AIDS/HIV problem Acquired Immunodeficiency Syndrome Algorithms Anaerobic Bacteria Animals Antibodies Antigens Archaea Autophagocytosis Bacteria Bacterial Genes Bioinformatics CD8-Positive T-Lymphocytes Cells Characteristics Clinical Cohort Studies Collection Communities Computer Models DNA DNA Sequence Data Data Analyses Descriptor Development Diet Research Disease Enteral Enterobacteriaceae Food Generations Genes Grant HIV-1 Helminths Herpesviridae Infections Human Human Genetics Immune Immune response Immune system Individual Inflammatory Inflammatory Bowel Diseases Influenza Interleukin-10 Intestines Lead Libraries Link Macaca Mammals Measures Metadata Metagenomics Methods Molecular Molecular Profiling Mus Parasites Parasitic infection Phenotype Physiological Physiology Preparation Process Productivity Regulatory T-Lymphocyte Reproducibility Research Research Personnel Ribosomal RNA Role SIV Sampling Sampling Studies Services Shotguns Source Statistical Data Interpretation T cell response Testing Time Toll-like receptors Vaccination Vaccines Variant Viral Virus bacteriome co-infection computational pipelines computerized tools digital fecal microbiome fungus gut microbiome helminth infection host-associated microbial communities human disease immunogenic immunogenicity microbiome microbiome analysis microbiome signature mycobiome novel strategies novel vaccines predictive signature programs vaccination outcome vaccine efficacy vaccine response virome

项目摘要

Abstract: The microbiome is a key contributor to many physiologic parameters and diseases in mammals, but the effects on vaccine responses are less well defined. The microbiome has co-evolved with mammals over millions of years to include an estimated 100 trillion bacteria and perhaps 10-fold more viruses. We have recently found that anaerobic bacteria have profound effects on the generation of IL10-expressing CD4 Treg cells through autophagy gene-dependent mechanisms in both mice and humans. Importantly, it has been shown that preexisting antibodies to enteric bacteria can skew vaccine responses to cross-reacting HIV-1 antigen, arguably rendering a vaccine less protective and that enteric bacterial components regulate the vaccine response to influenza in mice through activation of Toll-like receptors. Helminth infections also have profound effects on host immune response. Limited study in human suggests that helminth infection may alter bacterial microbiome61. Helminths are documented to lower the efficacy of vaccination. The role of Core C is to provide centralized computational and technologic expertise, sequencing, and algorithms to generate an in depth analysis of the microbiome in subjects within the study cohorts that will be critical to understand both difference in the microbiome between subjects with and without helminth infection and the relationship between these descriptors and vaccine responses. Integrated analysis linking variations in the microbiome to study cohort metadata including measures of immune responses and other '-omic' signatures may lead to identification of digital and molecular signatures within the microbiome and host baseline immune characteristics that may be predictive of vaccine efficacy. Should the hypothesis of the U19 grant be validated (that the microbiome correlates with changes in basal immune parameters and vaccine responses), we will identify computational models that can be used as a component of analyses of new vaccines; these will be made accessible to the broader research community.

抽象的：微生物组是哺乳动物中许多生理参数和疾病的关键因素，但是影响关于疫苗的反应的定义不太明确。微生物组已与数百万的哺乳动物共同发展包括估计有100万亿个细菌，可能多10倍的病毒。我们最近发现厌氧菌细菌对通过小鼠和人类的自噬基因依赖机制。重要的是，已经表明对肠细菌的先前抗体可能会偏向于交叉反应的HIV-1抗原的反应，可以说，可以使疫苗降低保护性，并且肠细菌成分调节疫苗通过激活Toll样受体对小鼠流感的反应。蠕虫感染也有对宿主免疫反应的深刻影响。人类的有限研究表明，蠕虫感染可能会改变细菌微生物组61。记录了蠕虫以降低疫苗接种的功效。核心C的作用是提供集中的计算和技术专业知识，测序以及算法对研究组中的受试者的微生物组进行深度分析，了解有和没有的受试者之间的微生物组的差异至关重要蠕虫感染以及这些描述子与疫苗反应之间的关系。融合的分析将微生物组中的变化联系起来研究队列元数据，包括免疫测量响应和其他“ - 摩尼克”特征可能会导致识别数字和分子特征。微生物组和宿主基线免疫特征可能可以预测疫苗功效。应该验证U19授予的假设（微生物组与基础免疫的变化相关参数和疫苗响应），我们将确定可以用作组成部分的计算模型分析新疫苗；这些将使更广泛的研究社区可以进入。