Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency

利用遗传变异剖析重编程为多能性的基因调控网络

• 批准号: 10473738
• 负责人: Chongyuan Luo
• 金额: $ 135.13万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473738
• 关键词: ATAC-seq; Affect; Alleles; Architecture; Binding; Biological; Biological Assay; CRISPR interference; Candidate Disease Gene; Cell Differentiation process; Cell Line; Cell Nucleus; Cells; Cellular Morphology; ChIP-seq; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Models; Consensus; Cytosine; DNA Methylation; Data; Data Set; Disease model; Drug Screening; Due Process; Enhancers; Event; Fibroblasts; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Engineering; Genetic Transcription; Genetic Variation; Genome; Genomics; Goals; Heterogeneity; Human; Human Genetics; Human Genome; In Situ; Individual; Joints; Knowledge; Maintenance; Maps; Measurement; Mediating; Methods; Methylation; Modeling; Molecular; Molecular Conformation; Mouse Strains; Multiomic Data; Nature; Patients; Phenotype; Population; Population Genetics; Process; Quantitative Trait Loci; RNA; RNA methylation; Regenerative Medicine; Regulation; Regulator Genes; Regulatory Element; Resolution; Resources; Role; Series; Site; Somatic Cell; Specificity; Statistical Methods; System; Testing; Therapeutic; Time; Untranslated RNA; Variant; Work; base; c-myc Genes; causal variant; cell type; embryonic stem cell; epigenome; epigenomics; experimental study; gene interaction; gene regulatory network; genetic variant; genome sequencing; genomic data; histone modification; induced pluripotent stem cell; innovation; insight; molecular phenotype; multiple omics; novel; pluripotency; predictive modeling; programs; repaired; single-cell RNA sequencing; stem cells; transcription factor; transcriptional reprogramming; transcriptome; transcriptomics; whole genome

PROJECT SUMMARY The reprogramming of somatic human cells to induced pluripotent stem cells (iPSCs) by only four transcription factors (TFs) Oct4, Sox2, Klf4, and cMyc (OSKM) is one of the most striking remodelings of gene regulatory networks. The remarkable ability of OSKM to reprogram diverse somatic cell types into iPSCs that are functionally indistinguishable from embryonic stem cells indicates that OSKM leverages a fundamental mechanism for network remodeling that may be generally applicable to all cell fate transitions. Previous studies of reprogramming have identified the crucial role of cooperative TF binding in repressing somatic programs and activating pluripotent ones. However, associating TF binding dynamics and epigenomic remodeling with key bifurcation events during reprogramming is confounded by the highly heterogeneous nature of the reprogramming process and the lack of knowledge regarding how the transition from somatic to pluripotent regulatory programs occurs in individual cells. In this project, we aim to model the regulatory network underlying the cell fate change of reprogramming using three types of single-cell multi-omic profiles generated from critical time points during reprogramming. We will interrogate the network leveraging natural perturbation of reprogramming and pluripotency by genetic variants. Genetic variation is well known to modulate the regulatory network of pluripotency and contributes to the variability of cellular phenotypes and differentiation capacity of iPSC lines. We will generate population-scale single-cell joint profiling of RNA and DNA methylation (snmCT- seq), joint profiling of RNA and chromatin accessibility (scRNA + ATAC-seq) and single-nucleus joint profiling of chromatin conformation and DNA methylation (sn-m3C-seq), allowing the cell-type-specific determination of transcriptome, chromatin accessibility and methylation states at regulatory elements, as well as enhancer-gene looping to connect non-coding variants to their regulatory target. To integrate OSKM binding with the single-cell transcriptomic and epigenomic dynamics, we will determine the allele-specific binding of TFs and histone modifications using a pooled-alleles ChIP-seq strategy. We will use Dynamic Regulatory Events Miner (DREM) to construct predictive models by integrating transcription factor-gene interaction information with time- and pseudotime-series genomics data. To determine the genetic regulation of the reprogramming network, we will apply the novel statistical method FastGxE to distinguish cell-type-specific from the shared genetic component of gene expression regulation, to enhance the sensitivity for identifying cell-type-specific quantitative trait loci (QTLs). To test the regulatory network, we will experimentally determine the function of network hub genes and non-coding variants using high-throughput CRISPR interference and precise variant replacement experiments. Our proposed project integrates diverse approaches including single-cell multi-omics, computational modeling, and genetic engineering, and will likely provide new insights into the mechanism by which TFs remodel regulatory networks of cell type identity and serve as a model for similar analyses in other systems.

项目总结