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Genomic control of gene regulatory networks governing early human lineagedecisions

控制早期人类谱系决定的基因调控网络的基因组控制

基本信息

批准号：
10840531
负责人：
Michael A Beer
金额：
$ 9.75万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
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/10840531
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 parent grant pluripotency predictive modeling screening self-renewal single-cell RNA sequencing stem cell biology stem cell differentiation temporal measurement transcription factor transcriptome sequencing

项目摘要

ABSTRACT (unchanged from the parent grant) 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（与父母补助金相同）