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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.

摘要不同的人对不同的营养素和饮食限制的反应方式截然不同。有些人体重容易增加，但其他人无论吃什么都保持苗条。此外，代谢性疾病可能会有所不同无论是罕见的单基因孟德尔疾病还是常见的多因素代谢性疾病，如肥胖和2型糖尿病。在很大程度上，这种可变性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在人群中既罕见又常见的代谢性疾病。如果有人能做到这一点，那将是非常有价值的找出影响个体对饮食的反应的罕见和常见的变异不同类型的代谢性疾病。罕见的变异通常通过连锁作图和整体定位来鉴定。使用带有受影响个体的家系进行基因组测序。相比之下，常见的变体通常是通过使用大量患有和不患有疾病的人群进行的全基因组关联研究来确定。我们将为线虫的大量遗传个体开发个性化的代谢网络模型线虫，既代表健康的新陈代谢状态，又模仿人类新陈代谢的先天错误。使用我们的实验系统和方法，我们将能够推导出罕见和常见的预测受营养影响的各种代谢性状的变化。我们将广泛验证这样的预测。使用CRISPR/Cas9介导的基因组编辑。