Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9

迈向 CRISPR-Cas9 之外的稳健多重基因组工程

• 批准号: 10287896
• 负责人: Le Cong
• 金额: $ 39.36万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10287896
• 关键词: Alleles; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Award; CRISPR screen; CRISPR/Cas technology; Cell Line; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Complex; Computer Models; Computer Simulation; DNA; Disease; Disease model; Engineering; Enzymes; Epigenetic Process; Funding; Genes; Genetic; Genetic Engineering; Genetic Recombination; Genome; Genome engineering; Human; Knowledge; Medicine; Metagenomics; Methods; Mining; Modality; Modeling; National Human Genome Research Institute; Nerve Degeneration; Neurons; Neurosciences; Parents; Phenotype; Proteins; RNA; Research; Resource Sharing; Resources; Screening procedure; Single Nucleotide Polymorphism; Technology; Testing; Validation; Variant; Work; base; causal variant; cohort; data analysis pipeline; experimental study; genetic variant; genome editing; genome wide association study; human disease; human stem cells; in vivo; innovation; machine learning algorithm; novel; risk variant; tool

Supplement Application Abstract Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9 Recent genome-wide association studies on Alzheimer’s Diseases (AD) and related dementias have provided a rich resource of AD risk genes and variants. While this bounty of information is poised to transform neurodegeneration research, we need tools to identify and validate their functions. Genome engineering tools such as CRISPR-Cas9 are valuable for such validation, allowing precise editing of AD-related genomic variants. However, current genetic engineering approaches are limited in efficiency, scalability, and have unwanted editing errors that could confound validation experiments. Moreover, we need tools with robust activities in challenging neuroscience models, beyond editing a few cell lines. Hence, building on our existing NHGRI-funded work, we will use innovative genome technologies for studying AD and related dementias, in collaboration with experts at the Stanford Alzheimer's Disease Research Center (ADRC). Firstly, we will use computational simulation with experimentation to develop precision tools to edit human risk variants in AD models. We will leverage and further develop our novel CRISPR enzymes and RNA-to-DNA editing tools that we recently established based on work from the parent award (JACS. 2019). Secondly, we are developing error- free gene-editors via mining metagenomic recombination enzymes. These error-free gene-editors are capable of engineering up to multi-kilobase sequences in human stem cells and neurons (Wang et al., under review). We will use this accurate gene-editing methods to engineer large AD risk alleles in neurodegeneration models, and, working with expert collaborators, demonstrate in vivo editing. Thirdly, we are developing Turbo-seq, a single-cell perturb-seq platform leveraging machine-learning algorithms and our multi-target CRISPR screen tool for AD studies (Hughes et al., submitted). We will apply Turbo-seq to simultaneously engineer single and multiple AD-associated variants in relevant disease models, with an initial focus on APOE alleles and related protective (or causal) variants. We will determine the functional consequences when genetically engineering these AD variants compared with healthy controls, integrating single-cell profiling of RNAs and proteins. Our multi-target, scalable CRISPR tools will significantly accelerate functional study of neurodegeneration variants when considering the large number of candidates, existing and from our collaborators’ work with the Stanford Extreme Phenotypes in AD (StEP AD) cohort, and help identify potential interactions between risk alleles. Overall, our plan is to build a gene-editing and single-cell toolkit, with an accompanying data- analysis pipeline for neurodegeneration research, thereby expanding the parent award’s tool-building and resource-sharing efforts into this new focus with the supplement.

补充申请摘要 超越CRISPR-Cas9的强大多重基因组工程 最近对阿尔茨海默病（AD）和相关痴呆症的全基因组关联研究表明， 提供了丰富的AD风险基因和变体资源。虽然这些丰富的信息准备 为了改变神经退行性疾病的研究，我们需要工具来识别和验证它们的功能。基因组 CRISPR-Cas9等工程工具对于这种验证是有价值的，可以精确编辑 AD相关基因组变异。然而，目前的基因工程方法局限于 效率、可伸缩性，并且具有可能混淆验证实验的不必要的编辑错误。 此外，我们需要在挑战神经科学模型方面具有强大活动的工具，而不仅仅是编辑一个 几个细胞系。因此，基于我们现有的NHGRI资助的工作，我们将使用创新的基因组 与斯坦福大学的专家合作， 阿尔茨海默病研究中心（ADRC）。首先，我们将使用计算模拟， 实验开发精确的工具来编辑AD模型中的人类风险变体。我们将利用 并进一步开发我们最近开发的新型CRISPR酶和RNA到DNA编辑工具， 根据父母奖（JACS）的工作建立。2019年）。其次，我们正在发展错误- 通过挖掘宏基因组重组酶的免费基因编辑器。这些没有错误的基因编辑器是 能够在人干细胞和神经元中工程化多个腺苷酸酶序列（Wang等， 审查中）。我们将使用这种精确的基因编辑方法来设计大型AD风险等位基因， 神经变性模型，并与专家合作者合作，展示体内编辑。 第三，我们正在开发Turbo-seq，这是一个利用机器学习的单细胞扰动seq平台 算法和我们用于AD研究的多靶CRISPR筛选工具（Hughes等人，提交）。我们将 应用Turbo-seq在相关领域同时设计单个和多个AD相关变体 疾病模型，最初的重点是APOE等位基因和相关的保护性（或因果性）变体。我们 将确定当基因工程这些AD变体时的功能后果， 与健康对照，整合RNA和蛋白质的单细胞谱。我们的多目标、可扩展 CRISPR工具将显著加速神经变性变体的功能研究， 考虑到大量的候选人，现有的和我们的合作者的工作与斯坦福大学 AD中的极端表型（StEP AD）队列，并帮助确定风险之间的潜在相互作用 等位基因总的来说，我们的计划是建立一个基因编辑和单细胞工具包，并附带数据- 神经变性研究的分析管道，从而扩大了父母奖的工具建设 和资源共享的努力，成为这一新的重点与补充。