Large scale nutrigenetics and genomics in a tractable metazoan model

易处理的后生动物模型中的大规模营养遗传学和基因组学

基本信息

批准号：
9423155
负责人：
Erik Christian Andersen
金额：
$ 73.93万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
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：
2017-09-20 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9423155
关键词：
Affect Amino Acids Animals Biochemical Pathway Biological Assay Biological Models Biomass CRISPR/Cas technology Caenorhabditis elegans Carbohydrates Chromosome Mapping Code Coenzymes Complex Data Diet Disease Eating Economics Enzyme Stability Enzymes Essential Amino Acids Ethics Exhibits Family Fatty Acids Gene Expression Genes Genetic Genetic Variation Genome Genomics Genotype Human Inborn Genetic Diseases Individual Individual Differences Laboratories Link Mass Spectrum Analysis Measurement Mediating Mendelian disorder Metabolic Metabolic Diseases Metabolism Modeling Molecular Profiling Nematoda Non-Insulin-Dependent Diabetes Mellitus Nutrient Nutritional Requirements Obesity Phenotype Physiological Population Propionates Reaction Recombinants Sampling Severities Severity of illness Stable Isotope Labeling System Testing Thinness Transcript Untranslated RNA Variant Vitamins Weight Gain Whole Organism base dietary restriction experimental study genome editing genome sequencing genome wide association study genome-wide ketotic hyperglycinemia metabolomics network models nutrition nutrition related genetics nutritional genomics rare variant response trait transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

SUMMARY Individuals can respond to diverse nutrients and dietary restrictions in markedly different ways. Some people easily gain weight, but others remain thin no matter what they eat. Additionally, metabolic diseases can differ dramatically among individuals in a population, for both rare single-gene Mendelian diseases and common multifactorial metabolic diseases such as obesity and type 2 diabetes. In large part, this variability suggests that individual genetic differences greatly affect the likelihood to get sick as well as the severity of the illness for both rare and common metabolic diseases across a population. It would be extremely valuable if one could identify both rare and common variants that contribute to individual responses to diet and to the acquisition of different types of metabolic diseases. Rare variants are usually identified by linkage mapping and whole- genome sequencing using families with affected individuals. By contrast, common variants are usually identified by genome-wide association studies using large populations of people with and without a disease. We will develop personalized metabolic network models for a large set of genetic individuals of the nematode C. elegans, both representing healthy metabolic state and mimicking an inborn error of human metabolism. With our experimental system and approach we will be able to derive predictions of both rare and common variation in a variety of metabolic traits influenced by nutrition. We will extensively validate such predictions using CRISPR/Cas9-mediated genome editing.

概括每个人对不同的营养素和饮食限制的反应方式明显不同。有些人很容易发胖，但有些人无论吃什么仍然很瘦。此外，代谢性疾病也可能有所不同对于罕见的单基因孟德尔疾病和常见的孟德尔疾病，群体中的个体之间的差异显着多因素代谢疾病，如肥胖和2型糖尿病。在很大程度上，这种变化表明个体遗传差异极大地影响了患病的可能性以及疾病的严重程度人群中罕见和常见的代谢疾病。如果能够的话，那将是非常有价值的识别有助于个体对饮食的反应和获得不同类型的代谢性疾病。罕见变异通常通过连锁作图和整体鉴定来识别使用有受影响个体的家庭进行基因组测序。相比之下，常见的变体通常是通过使用大量患病和未患病人群的全基因组关联研究来确定。我们将为大量线虫遗传个体开发个性化代谢网络模型秀丽隐杆线虫，既代表健康的代谢状态，又模仿人类新陈代谢的先天性错误。通过我们的实验系统和方法，我们将能够得出罕见和常见的预测受营养影响的各种代谢特征的变化。我们将广泛验证此类预测使用 CRISPR/Cas9 介导的基因组编辑。