Large-scale mapping of genetic interactions across diverse cell types

尽管基因组技术能够实现高效的基因组测序，但仅从基因组的知识来准确预测细胞特征（表型）仍然是不可能的。了解基因组和表型之间的关系不仅对理解生物学有意义，而且对生物制造和人类健康也有意义。造成这种知识差距的一个关键因素是缺乏对多种基因突变如何相互作用导致表型变化的理解。在这个项目中，研究人员将开发并验证一种混合计算-实验策略，以有效地绘制两种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，用于选择在特定细胞类型中突变哪些基因，以最好地了解遗传相互作用，并将部署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，其中许多基因仍未被描述。该项目对教育的广泛影响将解决STEM学科的一个关键挑战，即女性在科学和技术职业中的代表性不足，通过让初中和高中学生通过当地的“编程女孩”分会解决与研究相关的计算生物学问题。基因型和表型之间关系的新进展需要考虑遗传相互作用。考虑到人类基因组中存在大量可能的基因组合，以及复杂的细胞类型和组织特异性调控机制，发现基因组中变体的组合如何相互作用以影响细胞功能和表型是一项艰巨的挑战。在这个项目中，研究人员将以他们成功的合作为基础，在此期间，他们为真核生物模型酵母开发了一个基因相互作用图谱，以便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，通过对三种人类细胞系HAP1（先前收集的数据）、HEK293T和hTERT RPE-1的定量单突变表型的初步测量，对特定细胞类型的遗传相互作用筛选的查询突变进行最佳选择。基因组规模的CRISPR-Cas9基因编辑管道将用于对HEK293T和hTERT RPE-1细胞类型中选择的突变体进行全基因组筛选，这些突变体来自不同的细胞系。结果数据将进行比较分析，以绘制核心保守和细胞类型特异性功能模块。研究结果将有助于缩小知识差距，增加我们对基因组变化如何对应表型变化的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.