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: 10630157
• 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/10630157
• 关键词: 3-Dimensional; ATAC-seq; Adult; Atlases; Attention; Back; Binding Sites; Biological Assay; Biological Models; CRISPR interference; CRISPR screen; Cell Fate Control; 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; Regulatory Element; Research Personnel; Resolution; Role; Sequence Analysis; Signal Transduction; Somatic Cell; System; Systems Biology; Testing; Tissues; Variant; Work; algorithmic methodologies; cell type; design; functional genomics; gene regulatory network; 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; temporal measurement; 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.

抽象的 预测基因组变异的影响需要定量建模以解构相互作用 在多个单个变体之间，并确定它们对基因调节网络的综合影响 （GRN）控制细胞状态和细胞功能。我们专注于控制早期人类发展的GRN 作为范式。可以说，哺乳动物发展过程中最重要的血统决定是 培养细胞以退出多能状态（当细胞有可能产生所有体细胞的状态 细胞和生殖细胞），并分化为三个主要的生殖层之一，内胚层中胚层， 和外胚层。可以使用培养的人来捕获这种多能状态和三利差分区分 胚胎干细胞（HESC）。非常关注的关注于维护的基础的GRN 自我更新多能状态，但管理hESC三利差分的grn在很大程度上仍然存在 未探索。我们以前进行了基因组尺度CRISPR/CAS筛选以发现蛋白质编码基因 调节hESC向确定的内胚层的过渡。基于基因组和遗传数据以及 机器学习（GKM-SVM序列分析），我们扩展了初始简单的两个转录因子（TF） 模型为多个TF合作模型。在这里，我们提出了一种检查hESC的综合方法 过渡到确定的内胚层，中胚层和神经外胚层生殖层身份，以改善 GRN模型的概括性。我们将执行具有较高的定量基因组和蛋白质组学测量 时间和单细胞分辨率。这些定量测量将与钥匙的扰动结合在一起 GRN元素，核心TFS及其目标增强剂，以告知动态GRN模型的产生。到 进一步提高了我们新的GRN模型的精度，我们将在状态过渡期间绘制细胞轨迹 通过谱系跟踪与scrna-seq结合。 hESC超越了差异化，生理学 增强子的相关性将在人类和小鼠类器官（胃底子）和小鼠中进一步询问 胚胎。然后，我们将在我们的多模式中应用创新的新计算和算法方法 实验数据生成GRN模型，旨在学习基础的可概括原则 基因组变体对细胞和最终有机表型的贡献。开发GRN模型 多能性的退出和菌属身份的获取涉及细胞状态的动态建模 过渡，这不仅会告知我们对早期人类发展的理解，还可以作为 在胚胎发育过程中建造可泛化的GRN模型的基础， 成人组织稳态和再生以及不适当的细胞命运转变 病理状况，例如癌症。