Sequence-based Machine Learning for Inference of Dynamic Cell State Gene Network Models

基于序列的机器学习用于动态细胞状态基因网络模型的推理

• 批准号: 10665735
• 负责人: Michael A Beer
• 金额: $ 46.39万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665735
• 关键词: ATAC-seq; Affect; Algorithms; Base Sequence; Binding; Binding Sites; Biological Assay; Biological Models; CRISPR interference; Cell model; Cells; ChIP-seq; Chromatin; Chromatin Loop; Clustered Regularly Interspaced Short Palindromic Repeats; Computing Methodologies; Data; Data Set; Development; Disease; Elements; Embryo; Endoderm; Enhancers; Equation; Event; Gene Expression; Genes; Genetic; Genetic Variation; Genomics; Human Development; Individual; Kinetics; Learning; Location; Machine Learning; Maps; Measurement; Methods; Modeling; Phenotype; Property; Quantitative Trait Loci; Regulatory Element; Reporter; Reporting; Resolution; SOX17 gene; Stimulus; Structure; System; Testing; Time; Tissues; Training; Variant; Work; XCL1 gene; cofactor; combinatorial; design; embryonic stem cell; epigenomics; gene network; gene regulatory network; genome wide association study; genomic locus; genomic variation; human disease; improved; in vivo; innovation; knock-down; machine learning model; model building; network models; novel; promoter; response; simulation; single-cell RNA sequencing; stem cell differentiation; temporal measurement; time use; transcription factor; transcriptome sequencing

Most disease associated GWAS variants have relatively modest effects on expression in reporter or CRISPR perturbation assays. In addition, enhancer disruption in vivo often has surprisingly weak phenotypic consequences. We hypothesize that a critical missing element is our lack of quantitative models of how multiple TFs interact at an enhancer, and how multiple enhancers interact at a locus to respond to perturbations in a nonlinear way through altered gene network activity. Predicting the impact of genomic variation thus requires quantitative modeling of how one variant's impact depends on other variants through their combined effect on altered cellular regulatory state. The central aim of this proposal is to develop computational methods to infer quantitative models of these combinatorial interactions by training on temporally-resolved measurements of gene activity, enhancer activity, and core cell fate-regulating transcription factor (TF) activity across cell state transitions in early human development. Our preliminary studies show that while promoter knockdown has robust effects on target gene expression, individual enhancer knockdown is often weaker and affects temporal transition dynamics, but not the final steady state. We show that gene network models based on sequence-based machine learning are consistent with these observations. We propose improvements to our sequence based models to develop kinetic rate equation and stochastic simulation gene network models to predict the variable and often temporal effects of enhancer perturbation. We will generate high time resolution ATAC, H3K27ac, and scRNA-seq data to train these models, and validate the gene network predictions of network response with CRISPRi in a native genomic context. We will first focus on our embryonic- stem-cell to definitive-endoderm (ESC-DE) system, and we will then develop methods to generalize application of these focused models to larger ENCODE regulatory datasets. Our work will enable a quantitative understanding of how the altered activity of regulatory elements affects the stability and dynamics of the gene regulatory networks within which the element operates, and how they play a role in controlling developmentally important and disease relevant cell state transitions.

大多数疾病相关的GWAS变体对记者或 CRISPR扰动测定。此外，体内增强子的破坏通常会出人意料地较弱 表型后果。我们假设关键的缺失因素是我们缺乏定量 多个TF如何在增强器上相互作用的模型，以及多个增强器在一个轨迹上相互作用的模型 通过改变基因网络活性以非线性方式响应扰动。预测影响 因此，基因组变异需要定量建模，使一个变体的影响如何取决于另一个变体 通过其对细胞调节状态改变的综合作用的变体。这个的核心目的 建议是开发计算方法来推断这些组合的定量模型 通过对时间分辨的基因活性，增强剂活性和 核心细胞命运调节转录因子（TF）在人类早期的细胞状态转变中的活性 发展。我们的初步研究表明，尽管启动子敲低对目标具有强大的影响 基因表达，单个增强子敲低通常较弱，会影响时间过渡 动态，但不是最终的稳定状态。我们显示基于基于序列的基因网络模型 机器学习与这些观察结果一致。我们建议对我们的顺序进行改进 基于开发动力学速率方程和随机模拟基因网络模型的模型 增强子扰动的变量且通常是时间影响。我们将产生高时间分辨率 ATAC，H3K27AC和SCRNA-SEQ数据训练这些模型，并验证基因网络预测 在本地基因组环境中与CRISPRI的网络响应。我们将首先关注我们的胚胎 - 词干到确定的doderm（ESC-DE）系统，然后我们将开发一种概括的方法 将这些重点模型应用于较大的编码监管数据集。我们的工作将使 对调节元件的改变活动如何影响稳定性和的定量理解 基因调节网络的动力学在其中运行的基因调节网络，以及它们如何在 控制发育上重要和疾病相关的细胞状态转变。