Integrating single-cell based transcriptomic signatures for identifying therapeutic targets of COPD

整合基于单细胞的转录组特征来识别 COPD 的治疗靶点

• 批准号: 10360807
• 负责人: NAFTALI KAMINSKI
• 金额: $ 12.56万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360807
• 关键词: AGTR2 gene; Biological; Cells; Chronic Obstructive Pulmonary Disease; Clinical; Complex; Data; Databases; Disease; Drug Targeting; Endothelial Cells; Gene Expression; Gene Expression Profiling; Genes; Genetic Transcription; Genomics; Genotype; Goals; Heritability; Individual; Knowledge; Lead; Lung; Lung Transplantation; Lung diseases; Machine Learning; Maps; Mediating; Methods; Molecular; Network-based; Noise; Pathogenesis; Pathologic; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Population; Proxy; Public Health; Research; Resolution; Resources; Role; Smoker; Smoking; Structure; Structure of parenchyma of lung; Technology; Therapeutic Effect; Thromboplastin; Tissues; base; biomarker discovery; cell type; cigarette smoking; cohort; differential expression; disorder subtype; drug discovery; gene regulatory network; human disease; improved; learning network; macrophage; never smoker; new therapeutic target; novel; novel marker; single cell analysis; single-cell RNA sequencing; smoking cessation; therapeutic target; transcription factor; transcriptome sequencing; transcriptomics; treatment strategy

The main goal of this proposal is to apply novel machine learning and network-based methods to facilitate the discovery of biomarkers in diseases and therapeutic targets of drugs. While single-cell RNA sequencing (scRNAseq) technology has enabled gene expression profiling at single cell resolution, and has helped detect perturbation in thousands of genes across many different cell types and potential novel disease mechanisms, identifying viable therapeutic targets out of thousands of candidates is still a challenging problem. Therefore, it is essential to identify a high-priority and streamlined set of potential drug targets whose role in the disease could be experimentally validated in the lab with finite resources. This proposal is motivated by a recent discovery by our group: We identified both cell-type-specific and disease associated changes by analyzing single-cell transcriptomics profiles of COPD and healthy lung tissues. One limitation is that these results come from a subset of most severe COPD patients and may not be generalized to all COPD subtypes. The scRNA study also has limitation for predicting gene regulatory network (GRN), due to inflated noise level and sparsity of the data. Therefore, we propose to leverage information from bulk transcriptomic data from large cohorts, such as the Genotype-Tissue Expression (GTEx) and The Lung Genomics Research Consortium (LGRC). We believe that using GRN from bulk-level, large cohort, RNAseq data as the baseline GRN for COPD lungs will lead to more robust identification of disease-associated cell types and pathways, and the results will be more generalizable to all COPD subtypes. In Aim 1 we will identify a list of transcription factors (TFs) that that are most active in COPD and most likely to regulate the cell-type-specific transcriptomic signatures identified in our scRNA study. This will be achieved by applying novel machine learning and network methods to integrate the GRN from GTEx and LGRC cohorts and scRNA data. In Aim 2 we will apply this method to study the effects of cigarette smoking (CS). This aim is motivated by our scRNA-seq study which identified distinct gene expression perturbations in two AT2 subpopulations. As our COPD subjects were advanced patients who had stopped smoking in anticipation of their lung transplant, these results may reflect persistent pathologic changes that continue after smoking cessation. Therefore, we will apply the same approach as in Aim 1 to identify cell- type specific transcriptomic signatures of cigarette smoking in COPD human lung tissues and TFs that are likely to mediate these signatures. In Aim 3 we will identify a list of potential drugs suitable for targeting the TF modules based on the results from Aims 1 and 2. We will map the TF modules based on Aims 1 and 2 to DrugBank, a Drug Target Discovery database, and we will identify the drugs that are most likely to effectively target these modules based on network proximity to TFs within the module. We believe these drugs and drug targets may give us the best chance to validate novel TF/drug targets that may lead to new treatment strategy for COPD patients.

该提案的主要目标是应用新颖的机器学习和基于网络的方法来促进 发现疾病的生物标志物和药物的治疗靶点。而单细胞 RNA 测序 (scRNAseq) 技术实现了单细胞分辨率的基因表达谱分析，并帮助检测 许多不同细胞类型和潜在的新疾病机制中数千个基因的扰动， 从数千种候选药物中找出可行的治疗靶点仍然是一个具有挑战性的问题。因此，它 对于确定一组高度优先且精简的潜在药物靶标至关重要，这些靶标在疾病中的作用 可以在资源有限的实验室中进行实验验证。该提案的提出是受到最近的一项 我们小组的发现：我们通过分析确定了细胞类型特异性和疾病相关的变化 COPD 和健康肺组织的单细胞转录组学特征。一个限制是这些结果来自 来自最严重 COPD 患者的子集，可能无法推广到所有 COPD 亚型。单链RNA 由于噪声水平过高和稀疏性，该研究在预测基因调控网络（GRN）方面也存在局限性 的数据。因此，我们建议利用来自大型队列的大量转录组数据的信息， 例如基因型组织表达 (GTEx) 和肺基因组学研究联盟 (LGRC)。我们 相信使用来自批量水平、大型队列、RNAseq 数据的 GRN 作为 COPD 肺部的基线 GRN 将 导致对疾病相关细胞类型和途径的更可靠的识别，结果将更加可靠 可推广至所有 COPD 亚型。在目标 1 中，我们将确定一系列转录因子 (TF)，它们是 在 COPD 中最活跃，并且最有可能调节我们在我们的研究中发现的细胞类型特异性转录组特征 单链RNA研究。这将通过应用新颖的机器学习和网络方法来集成 来自 GTEx 和 LGRC 队列的 GRN 以及 scRNA 数据。在目标 2 中，我们将应用这种方法来研究 吸烟（CS）。这一目标是由我们的 scRNA-seq 研究推动的，该研究鉴定了不同的基因 两个 AT2 亚群中的表达扰动。由于我们的慢性阻塞性肺病受试者是晚期患者，他们患有 因预期肺移植而停止吸烟，这些结果可能反映了持续的病理变化 戒烟后仍会持续。因此，我们将应用与目标 1 相同的方法来识别细胞- COPD 人肺组织和 TF 中吸烟的类型特异性转录组特征 可能会调解这些签名。在目标 3 中，我们将确定适合靶向 TF 的潜在药物清单 基于目标 1 和 2 的结果的模块。我们将基于目标 1 和 2 的 TF 模块映射到 DrugBank，一个药物靶标发现数据库，我们将识别最有可能有效的药物 根据模块内 TF 的网络邻近度来定位这些模块。我们相信这些药物和药物 目标可能为我们提供验证新的 TF/药物目标的最佳机会，从而可能导致新的治疗策略 对于慢性阻塞性肺病患者。