Deciphering the hierarchical modularity of the mammalian cell through network integration and complex genetic perturbation strategies

通过网络整合和复杂的遗传扰动策略破译哺乳动物细胞的层次模块化

• 批准号: 10551527
• 负责人: Glen Traver Hart
• 金额: $ 43.74万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551527
• 关键词: Abdominal colic; Biological Assay; Biological Process; Biology; Buffers; CRISPR/Cas technology; Cell Line; Cells; Cellular biology; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer software; Dependence; Development; Disease; Disease model; Future; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genotype; Human; Hybrids; Internet; Knock-out; Libraries; Mammalian Cell; Maps; Measures; Mediating; Mus; Mutation; Myeloid Leukemia; Normal Cell; Phenotype; Proliferating; Research; Research Personnel; Role; Saturated Fatty Acids; Screening for cancer; Shapes; Side; Suppressor Genes; Surveys; Technology; Tissues; Tumor Suppressor Proteins; Work; computer studies; computerized tools; endonuclease; experimental study; fitness; functional genomics; genome-wide; improved; insight; multimodality; next generation; novel; paralogous gene

Research in the Hart Lab has focused on the central concepts of modular cell biology, as put forward by Hartwell et al, 1999: how normal cells rely on an interconnected web of biological processes for survival and proliferation, and how mutation rewires this web of dependencies into disease states. We have developed experimental and computational tools for CRISPR- mediated perturbation studies that provide an understanding of the hierarchical organization of the mammalian cell and reveal context-specific genetic vulnerabilities. Our work can be reasonably divided into research on first-order effects of gene perturbation, accurately measuring gene essentiality and differential essentiality, and second- order effects including digenic interaction, functional buffering, and network approaches. We developed the TKOv3 CRISPR/Cas9 genome-scale library for knockout screens in human and mouse cells (Hart et al, 2017), as well as the BAGEL (Kim & Hart, 2021) and DrugZ (Colic et al, 2019) software packages for analysis of fitness and chemogenetic interaction screens. Our integrative analysis of hundreds of cell-line screens from the Cancer Dependency Map initiative yielded one of the first coessentiality maps describing functional linkages between human genes (Kim et al, 2019); the first systematic survey of proliferation suppressor genes, discovering a novel putative tumor suppressor role for saturated fatty acid synthesis in myeloid leukemia (Lenoir et al, 2021); and one of the first integrated computational and experimental studies confirming that functional buffering by, e.g., paralogs is systematically missed by monogenic CRISPR/Cas9 knockout screens (Dede et al, 2020). These latter two works used the Cas12a CRISPR endonuclease and its endogenous multiplexing capability to build efficient assays for genetic interaction between targeted gene pairs. Our future work will deepen our understanding of how both first-order and second-order effects shape modular biology, and improve our ability to decipher the natural complexity of the cell by extending this work into higher-order effects from targeted polygenic perturbations. First- generation network approaches integrate functional linkage across all contexts; future computational work will decipher lineage-specific interactions to define more precise cellular networks for functional genomics and tissue-specific disease modeling. On the experimental side, we will continue to push the state of the art in genetic perturbation technology, developing a highly multiplexed and multimodal perturbation platform that can go beyond digenic interactions and provide deeper insight into the complexity of the mammalian cell.

哈特实验室的研究集中在模块化细胞生物学的核心概念上，如 由Hartwell等人于1999年提出：正常细胞如何依赖于相互连接的生物网络 生存和繁殖的过程，以及突变如何将这种依赖网络重新连接到 疾病状态。我们已经为CRISPR开发了实验和计算工具- 介导性扰动研究，提供了对分层组织的理解 并揭示了特定背景下的遗传脆弱性。 我们的工作可以合理地分为对基因一级效应的研究 微扰，准确测量基因重要性和差异重要性，以及第二- 秩序效应包括基因相互作用、功能缓冲和网络方法。我们 开发了用于人类和人类基因敲除筛选的TKOv3 CRISPR/Cas9基因组规模文库 小鼠细胞(Hart等人，2017)以及百吉饼(Kim&Hart，2021)和Drugz(Colic等人， 2019年)用于分析适合性和化学发生相互作用屏幕的软件包。我们的 对癌症依赖图倡议的数百个细胞系筛查的综合分析 得到了第一批描述人类基因之间功能联系的相关性图谱之一 (Kim等人，2019年)；第一次对增殖抑制基因的系统调查，发现了 髓系白血病饱和脂肪酸合成中新的肿瘤抑制作用 (Lenoir等人，2021年)；以及最早的综合计算和实验研究之一 确认单基因系统地错过了例如Paralog的功能缓冲 CRISPR/Cas9基因敲除筛选(Dede等人，2020年)。后两件作品使用的是Cas12a CRISPR内切酶及其内源多重能力用于建立高效的检测方法 靶基因对之间的遗传相互作用。 我们未来的工作将加深我们对一阶和二阶如何 效应塑造了模块化生物学，并提高了我们破译自然复杂性的能力 通过将这项工作扩展到来自目标多基因扰动的更高阶效应。首先- 发电网络方法集成了所有环境中的功能链接；未来 计算工作将破译特定血统的相互作用，以定义更精确的细胞 用于功能基因组学和特定组织疾病建模的网络。关于试验性的 另一方面，我们将继续推动最先进的遗传扰动技术，发展 一个高度多路和多模式的微扰平台，可以超越DIGNAN 并提供了对哺乳动物细胞复杂性的更深层次的了解。

项目成果

Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens.

• DOI: 10.1186/s13059-020-02173-2
• 发表时间: 2020-10-15
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Dede M;McLaughlin M;Kim E;Hart T
• 通讯作者: Hart T

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform.

使用 in4mer CRISPR/Cas12a 多重敲除平台进行高效基因敲除和遗传相互作用筛选。

• DOI: 10.1038/s41467-024-47795-3
• 发表时间: 2024
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: EsmaeiliAnvar,Nazanin;Lin,Chenchu;Ma,Xingdi;Wilson,LoriL;Steger,Ryan;Sangree,AnnabelK;Colic,Medina;Wang,SidneyH;Doench,JohnG;Hart,Traver
• 通讯作者: Hart,Traver

Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier.

• DOI: 10.1186/s13073-020-00809-3
• 发表时间: 2021-01-06
• 期刊: Genome medicine
• 影响因子: 12.3
• 作者: Kim E;Hart T
• 通讯作者: Hart T

Common computational tools for analyzing CRISPR screens.

• DOI: 10.1042/etls20210222
• 发表时间: 2021-12-21
• 期刊: Emerging topics in life sciences
• 影响因子: 3.8
• 作者: Colic M;Hart T
• 通讯作者: Hart T

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens.

• DOI: 10.1186/s12859-022-05078-y
• 发表时间: 2022-11-28
• 期刊: BMC bioinformatics
• 影响因子: 3