Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex Disease

识别复杂疾病的致病基因-细胞类型对的综合方法

• 批准号: 10675476
• 负责人: Kuan-lin Huang
• 金额: $ 42.31万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675476
• 关键词: Address; Affect; Algorithms; Automobile Driving; Biological; Cell Separation; Cells; Chromosome Mapping; Complex; DNA sequencing; Data; Development; Disease; Etiology; Foundations; Genes; Genomics; Investigation; Masks; Methods; Patients; Population; Proteome; Research; Signal Transduction; Tissues; Translating; causal variant; cell type; cohort; design; diagnostic biomarker; diagnostic strategy; disease phenotype; empowerment; epigenome; epigenomics; gene discovery; genetic variant; genome wide association study; human disease; innovation; large scale data; molecular scale; multiple omics; novel diagnostics; programs; transcriptome; transcriptome sequencing; transcriptomics; treatment strategy

Project Summary Identifying causal genes and cell types underlying disease etiologies are essential for designing targeted diagnostic and treatment strategies. Genome-wide association study (GWAS), DNA-sequencing, and RNA- sequencing studies have identified potentially causal genes in multiple human diseases. While these methods provide disease-associated “gene lists”, they suffer from major shortcomings given the lack of cell-type information. First, each tissue is composed of multiple cell types with diverse contributions to disease phenotypes, and thus studies using bulk-tissue data alone result in the ambiguity of the causal cell populations. Secondly, causal gene signals from rare cell types may be masked in bulk tissues. Finally, understanding which genes are perturbed in which cell types is required for designing downstream functional studies. To identify the gene-cell pairs driving human disease, systematic approaches to integrate patient-cohort data with cell-type-specific data are urgently needed. My research program aims to identify causal genes and cell types driving human diseases using multi-omics approaches. Our central hypothesis is that dysregulated genes mapped to specific cell types drive disease etiologies. Previously, we developed algorithms that integrate large-scale data of common and rare genomic variants, epigenomes, transcriptomes, and proteomes to identify causal genes in tissue affecting specific cell types, providing strong biological and technical foundations for the project. Further, the proposed approaches are empowered by rapidly-expanding cell-specific epigenomic and transcriptomic data using sorted cell populations or single-cell profiling. In the next 5-year period, we will specifically develop algorithms that integrate genomic findings from patient cohorts with cell-specific transcriptomic data, addressing two major questions: (1) What are the gene-cell type pairs contributing to disease etiologies? (2) How are expressions of disease-associated genes regulated at a single-cell level? The proposed project will strongly impact the field by discovering gene-cell pairs associated with a wide range of diseases for downstream investigation. The development will afford new methods to integrate purified and single-cell transcriptome data to expand on findings from large-scale patient genomic cohorts. In the long term, the successfully identified gene-cell pairs can be translated into diagnostic markers or treatment targets of human disease.

项目概要 识别疾病病因学的致病基因和细胞类型对于设计靶向药物至关重要 诊断和治疗策略。全基因组关联研究 (GWAS)、DNA 测序和 RNA 测序 测序研究已经确定了多种人类疾病的潜在致病基因。虽然这些方法 提供与疾病相关的“基因列表”，但由于缺乏细胞类型，它们存在重大缺陷 信息。首先，每个组织都由多种细胞类型组成，对疾病有不同的贡献 表型，因此仅使用大量组织数据的研究会导致因果细胞群的模糊性。 其次，来自稀有细胞类型的因果基因信号可能在大量组织中被掩盖。终于明白了 设计下游功能研究需要哪些细胞类型需要哪些基因受到干扰。到 识别驱动人类疾病的基因细胞对，将患者队列数据与 迫切需要细胞类型特异性数据。我的研究计划旨在识别致病基因和细胞类型 使用多组学方法驱动人类疾病。我们的中心假设是失调的基因 映射到特定细胞类型驱动疾病病因。之前，我们开发了集成的算法 常见和罕见基因组变异、表观基因组、转录组和蛋白质组的大规模数据来识别 组织中影响特定细胞类型的因果基因，为 项目。此外，所提出的方法得到了快速扩展的细胞特异性表观基因组和 使用分选的细胞群或单细胞分析的转录组数据。未来5年，我们将 专门开发将患者群体的基因组发现与细胞特异性相结合的算法 转录组数据，解决两个主要问题：（1）基因-细胞类型对有助于什么 疾病病因？ (2)单细胞中疾病相关基因的表达是如何调控的 等级？拟议的项目将通过发现与广泛相关的基因细胞对对该领域产生重大影响。 下游调查的一系列疾病。这一发展将为整合纯化的新方法提供新的方法 和单细胞转录组数据，以扩展大规模患者基因组队列的发现。在漫长的 术语，成功鉴定的基因-细胞对可以转化为诊断标记或治疗靶标 的人类疾病。

项目成果

Analysis of sex-specific risk factors and clinical outcomes in COVID-19.

• DOI: 10.1038/s43856-021-00006-2
• 发表时间: 2021
• 期刊: COMMUNICATIONS MEDICINE
• 作者: Jun, Tomi;Nirenberg, Sharon;Weinberger, Tziopora;Sharma, Navya;Pujadas, Elisabet;Cordon-Cardo, Carlos;Kovatch, Patricia;Huang, Kuan-lin
• 通讯作者: Huang, Kuan-lin

Proteomic Analyses Identify Therapeutic Targets in Hepatocellular Carcinoma.

• DOI: 10.3389/fonc.2022.814120
• 发表时间: 2022
• 期刊: Frontiers in oncology
• 影响因子: 4.7

Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population.

建模在部分接种人群中SARS-COV-2 DELTA变体的传播。

• DOI: 10.3390/v14010158
• 发表时间: 2022-01-16
• 期刊: Viruses
• 作者: Avila-Ponce de León U;Avila-Vales E;Huang K
• 通讯作者: Huang K

Genomic Determinants of Homologous Recombination Deficiency across Human Cancers.

• DOI: 10.3390/cancers13184572
• 发表时间: 2021-09-12
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Qing T;Wang X;Jun T;Ding L;Pusztai L;Huang KL
• 通讯作者: Huang KL

Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

COVID-19 结果的风险和免疫决定因素的多种族调查。

• DOI: 10.21203/rs.3.rs-1055587/v1
• 发表时间: 2022
• 期刊: Research square
• 作者: Jun,Tomi;Mathew,Divij;Sharma,Navya;Nirenberg,Sharon;Huang,Hsin-Hui;Kovatch,Patricia;Wherry,EJohn;Huang,Kuan-Lin
• 通讯作者: Huang,Kuan-Lin