Genomic control of gene regulatory networks governing early human lineage decisions

控制早期人类谱系决策的基因调控网络的基因组控制

• 批准号: 10297375
• 负责人: Michael A Beer
• 金额: $ 133万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-19 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297375
• 关键词: 3-Dimensional; ATAC-seq; Adult; Atlases; Attention; Back; Binding Sites; Biological Assay; Biological Models; CRISPR interference; CRISPR screen; Cell Differentiation process; Cell model; Cell physiology; Cells; ChIP-seq; Chromatin Conformation Capture and Sequencing; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Computing Methodologies; DNA Computations; DNA Sequence Analysis; Data; Data Set; Development; Developmental Biology; Disease; Ectoderm; Elements; Embryo; Embryonic Development; Emerging Technologies; Endoderm; Enhancers; Epiblast; Gene Expression; Generations; Genes; Genetic; Genomics; Germ Cells; Germ Layers; Goals; Health; Hi-C; Histones; Homeostasis; Human; Human Development; Individual; Knowledge; Learning; Machine Learning; Maintenance; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measurement; Mesoderm; Modeling; Mus; Natural regeneration; Neuroectoderm; Organoids; Pathologic; Pathway Analysis; Peripheral; Phenotype; Physiological; Proteins; Proteomics; Publishing; Records; Regulator Genes; Regulatory Element; Research Personnel; Resolution; Role; Sequence Analysis; Signal Transduction; Somatic Cell; System; Systems Biology; Testing; Tissues; Variant; Work; algorithmic methodologies; base; cell type; design; functional genomics; genetic variant; genome-wide; genomic variation; human embryonic stem cell; improved; innovation; mathematical model; multimodality; network models; pluripotency; predictive modeling; screening; self-renewal; single-cell RNA sequencing; stem cell biology; stem cell differentiation; transcription factor; transcriptome sequencing

ABSTRACT Predicting the impact of genomic variation requires quantitative modeling to deconstruct the interplay between multiple individual variants and to determine their combined effects on gene regulatory networks (GRNs) that control cell state and cell function. We focus on the GRNs that control early human development as a paradigm. Arguably the most important lineage decision during mammalian development is the decision of epiblast cells to exit the pluripotent state (a state when the cells have the potential to give rise to all somatic cells and germ cells), and differentiate into one of the three primary germ layers, the endoderm, mesoderm, and ectoderm. This pluripotent state and the trilineage differentiation can be captured using cultured human embryonic stem cells (hESCs). Much attention has focused on the GRNs underlying the maintenance of the self-renewing pluripotent state, but the GRNs governing hESC trilineage differentiation remain largely unexplored. We previously conducted genome-scale CRISPR/Cas screens to discover protein-coding genes that regulate the transition of hESCs to definitive endoderm. Based on the genomic and genetic data and machine learning (gkm-SVM sequence analysis), we expanded our initial simple two transcription factor (TF) model to a multiple TF cooperative model. Here we propose an integrative approach examining the hESC transition to definitive endoderm, mesoderm and neuroectoderm germ layer identities to improve the generalizability of GRN models. We will perform quantitative genomic and proteomic measurements with high temporal and single-cell resolution. These quantitative measurements will be combined with perturbation of key GRN elements, core TFs and their target enhancers, to inform the generation of dynamic GRN models. To further improve the precision of our new GRN models, we will map cell trajectories during state transitions through lineage tracing combined with scRNA-seq. Beyond hESC guided differentiation, the physiological relevance of enhancers will be further interrogated in human and mouse organoids (gastruloids) and mouse embryos. We will then apply innovative new computational and algorithmic methods to our multimodal experimental data to generate GRN models, aiming to learn generalizable principles underlying the contribution of genomic variants to cellular and ultimately organismal phenotypes. Developing GRN models for the exit of pluripotency and the acquisition of germ layer identities involves dynamic modeling of the cell state transition, which will not only inform our understanding of early human development, but can also serve as the basis for construction of generalizable GRN models for biological transitions during embryonic development, adult tissue homeostasis and regeneration as well as inappropriate cell fate transitions that occur in pathological conditions such as cancer.

摘要 预测基因组变异的影响需要定量建模来解构相互作用 在多个个体变异之间，并确定它们对基因调控网络的综合影响， GRN控制细胞状态和细胞功能。我们专注于控制早期人类发育的GRNs 作为一个范例。可以说，哺乳动物发育过程中最重要的谱系决定是 细胞具有产生所有体细胞分化的潜能， 细胞和生殖细胞），并分化成三个初级胚层之一，内胚层，中胚层， 和外胚层这种多能性状态和三系分化可以使用培养的人巨噬细胞来捕获。 胚胎干细胞（hESC）。许多注意力都集中在维持 自我更新的多能状态，但GRNs管理hESC三系分化仍然主要是 未开发的我们之前进行了基因组规模的CRISPR/Cas筛选以发现蛋白质编码基因 调节hESC向定形内胚层的转变。基于基因组和遗传数据， 机器学习（gkm-SVM序列分析），我们扩展了我们最初简单的两个转录因子（TF） 模型到多TF合作模型。在这里，我们提出了一个综合的方法检查人胚胎干细胞 向定形内胚层、中胚层和神经外胚层的胚层身份转变，以改善 GRN模型的推广性。我们将进行定量基因组和蛋白质组学测量， 时间和单细胞分辨率。这些定量测量将与密钥的扰动相结合。 GRN元件、核心TF及其靶增强子，以告知动态GRN模型的生成。到 为了进一步提高我们新的GRN模型的精度，我们将在状态转换期间绘制细胞轨迹 通过谱系追踪结合scRNA-seq。除了hESC引导的分化之外， 增强子的相关性将在人和小鼠类器官（类胃体）和小鼠类胃体（类胃体）中进一步研究。 胚胎然后，我们将应用创新的新的计算和算法方法，我们的多模态 实验数据生成GRN模型，旨在学习 基因组变异对细胞和最终生物体表型的贡献。开发GRN模型， 多能性的退出和胚层身份的获得涉及细胞状态的动态建模 过渡，这不仅将告知我们对早期人类发展的理解，而且还可以作为 为胚胎发育过程中生物学转变构建可推广的GRN模型奠定了基础， 成年组织的稳态和再生以及发生在 病理状况，如癌症。