Large-scale mapping of genetic interactions across diverse cell types

虽然基因组技术能够有效地对基因组进行测序，但仅凭对其基因组的了解仍然不可能准确地预测细胞的特征(表型)。了解基因组和表型之间的关系不仅对理解生物学，而且对生物制造和人类健康都有意义。造成这一认识差距的一个关键因素是缺乏对多个基因突变如何相互作用导致表型变化的了解。在这个项目中，研究人员将开发和验证一种计算-实验混合策略，以有效地绘制两种类型人类细胞的遗传相互作用网络。该项目将产生一种新的计算方法，用于选择哪些基因在特定细胞类型中发生突变，以最好地了解遗传相互作用，并将部署一条全基因组基因编辑管道，以量化人类细胞系中的遗传相互作用。由此产生的数据将有助于理解人类基因的基本功能，其中许多仍未确定。该项目对教育的更广泛影响将解决STEM学科中的一个关键挑战，即通过让初中生和高中生通过当地的编程女孩分会解决与研究相关的计算生物学问题，从而解决女性在科学和技术职业中代表性不足的问题。在理解基因和表型之间的关系方面取得的新进展需要考虑遗传交互作用。考虑到人类基因组中大量可能的基因组合，以及控制基因功能的复杂的细胞类型和组织特有的调控机制，发现基因组中的变异组合如何相互作用来影响细胞功能和表型是一个艰巨的挑战。在这个项目中，研究人员将建立在他们成功合作的基础上，在此期间，他们为模式真核生物酵母开发了基因相互作用图，以在人类细胞中进行大规模的基因相互作用研究。研究人员将开发一种新的计算方法，利用对HAP1(之前收集的数据)、HEK293T和hTERT RPE-1这三个人类细胞系的定量单一突变表型的初始测量，为特定细胞类型的遗传交互筛选最佳选择查询突变。基因组规模的CRISPR-Cas9基因编辑管道将用于对HEK293T和hTERT RPE-1细胞类型中选定的突变进行全基因组筛选，这些突变来自不同的细胞谱系。结果数据将被比较分析，以绘制出核心保守的和特定细胞类型的功能模块。这项研究的结果将有助于缩小知识差距，并增加我们对基因组变化如何与表型变化相对应的理解。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Although genomic technology enables efficient sequencing of genomes, it is still not possible to accurately make predictions of a cells characteristics (phenotype) from knowledge of its genome alone. Understanding the relationship between genome and phenotype has implications not just for understanding biology, but for biomanufacturing, and human health. A key contributor to this gap in knowledge is the lack of understanding of how multiple genetic mutations interact to cause changes in phenotypes. In this project the investigators will develop and validate a hybrid computational-experimental strategy to efficiently map genetic interaction networks for two types of human cells . The project will result in a new computational approach for selecting which genes to mutate in specific cell types to best understand genetic interactions, and will deploy a genome-wide gene editing pipeline to quantify genetic interactions in the human cell lines. The resulting data will be useful for understanding the basic functions of human genes, many of which still remain uncharacterized. The educational broader impacts of the project will address a key challenge in STEM disciplines, namely the underrepresentation of women in science and technology careers by involving middle and high-school students in solving computational biology problems related to the research through their local Girls Who Code chapter.New advances in the understanding of the relationship between genotype and phenotype demand consideration of genetic interactions. Discovering how combinations of variants in the genome interact to influence cell function and phenotype is a daunting challenge given the vast number of possible gene combinations in the human genome as well as complex cell type- and tissue-specific regulatory mechanisms governing gene function. In this project the investigators will build on their successful collaboration , during which they developed a genetic interaction map for the model eukaryote, yeast, to perform large-scale genetic interaction studies in human cells. Investigators will develop a new computational approach for optimal selection of query mutants for genetic interaction screens in specific cell types using initial measurements of quantitative single mutant phenotypes from three human cell lines, HAP1 (data previously collected), HEK293T and hTERT RPE-1. A genome-scale CRISPR-Cas9 gene editing pipeline will be used to perform genome-wide screens for the selected mutants in the HEK293T and hTERT RPE-1 cell types, which are from distinct cell lineages. The resulting data will be comparatively analyzed to map core conserved and cell type-specific functional modules. The results of the study will help reduce knowledge gap and increase our understanding of how genomic changes correspond to changes in phenotype.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.