Evolving multigenic extensions to lifespan

进化多基因延长寿命

基本信息

批准号：
8895557
负责人：
Hang Lu
金额：
$ 21.55万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2017-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8895557
关键词：
Adverse drug effect Aging Animal Model Animals Architecture Automation Biological Biological Models Caenorhabditis elegans Code Collaborations Complex Complex Genetic Trait Coupled DNA Sequence Alteration Data Development Disease Drug Targeting Engineering Generations Genes Genetic Genetic Epistasis Genetic Screening Genetic Transcription Genome Genomics Goals Hereditary Disease Heritability Heterogeneity Human Image Image Analysis Individual Knowledge Life Longevity Measures MicroRNAs Microfluidic Microchips Microfluidics Molecular Mutagenesis Mutation Nematoda Noise Phenotype Physiological Population Preparation Process Quantitative Genetics Reagent Reporter Research Research Proposals Resources Risk Scheme Sorting - Cell Movement System Techniques Technology Tissues Variant Work base deep sequencing design directed evolution fluorescence imaging gene interaction genetic variant image processing imaging platform improved innovation interest juvenile animal mature animal mutant new technology next generation sequencing novel novel strategies pressure public health relevance quantitative imaging research study software systems system analysis (computer)tool trait young adult

项目摘要

DESCRIPTION: Most biological traits including common diseases have a strong but poorly understood genetic basis. There is general interest in identifying these genetic factors as they can be used to identify individuals that are at risk for a particular disease and as experimental handles to identify novel therapies. Despite an incredible outlay of resources, the majority of causative genetic variants remain unidentified due to the underlying complexity of the genetic architectures of most diseases. Fundamental study of complex genetic traits in model organisms should identify general principles and approaches that can be used to identify causative genetic variants in human traits. In this research proposal, we are developing an unprecedented system in C. elegans to generate multigenic states in model organisms by evolving fluorescent reporters of phenotypes of interest. We will develop an automated microfluidic, fluorescent imaging and computer analysis system to rapidly measure, segment, and describe the expression of a transcriptional reporter in a tissue-specific manner. We will then use this imaging/sorting system to apply selective pressure to evolve multigenic changes to expression over multiple generations. As proof of principal, we will apply this approach to a transcriptional reporter that predicts lifespan in younger animals to evolve longer-lived animals. Causative mutations can then be rapidly identified using next-generation sequencing and carefully studied in the context of known genetic and cellular networks. This work will improve our understanding of aging, and in general transform our approaches in model organisms towards the understanding of biological traits in complex genetic diseases.

描述：包括常见疾病在内的大多数生物学特征都具有强大但知之甚少的遗传基础，人们普遍对识别这些遗传因素感兴趣，因为它们可用于识别有特定疾病风险的个体，并可作为识别新疗法的实验手段。尽管花费了令人难以置信的资源，但由于大多数疾病的遗传结构的潜在复杂性，大多数致病遗传变异仍然未被识别。对模式生物中复杂遗传性状的基础研究应该确定可用于识别致病原因的一般原则和方法。遗传的在这项研究计划中，我们正在线虫中开发一种前所未有的系统，通过进化感兴趣的表型的荧光产生器来在模型生物中产生多基因状态。然后，我们将使用该成像/分选系统以组织特异性的方式测量、分段和描述转录报告基因的表达，以快速进化多代表达的多基因变化。将这种方法应用到然后可以使用下一代测序快速识别致病突变，并在已知的遗传和细胞网络的背景下进行仔细研究。这项工作将提高我们对衰老的理解。总体而言，将我们在模式生物中的方法转变为了解复杂遗传疾病的生物学特征。