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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

基本信息

批准号：
10540331
负责人：
NAFTALI KAMINSKI
金额：
$ 12.56万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10540331
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 Lung Lung Transplantation Lung diseases Machine Learning Macrophage 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 Visualization biomarker discovery cell type cigarette smoking cohort differential expression disorder subtype drug discovery gene regulatory network human disease improved never smoker new therapeutic target novel novel marker novel therapeutic intervention single-cell RNA sequencing smoking cessation therapeutic target transcription factor transcriptome sequencing transcriptomics treatment strategy

项目摘要

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.

本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