Bioinformatics Core

生物信息学核心

基本信息

批准号：
10459931
负责人：
Satish Kumar Pillai
金额：
$ 12.74万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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：
2022-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10459931
关键词：
Anatomy Bioinformatics Biological Biological Assay Cell Lineage Clinical Complex Cytometry DNA Data Data Set Development Drug or chemical Tissue Distribution Ensure Evaluation Genbank Gene Expression Genetic Sequence Databases Genetic Transcription HIV Immunologics Inflammatory Interruption Knowledge Discovery Link Measures Methods Molecular Nature Pathogenesis Performance Play Procedures Proteomics Proviruses Recrudescences Research Personnel Resolution Role Sampling Seasons Sequence Read Archive Technology Time Tissues Transcript United States National Institutes of Health Validation Viral antiretroviral therapy clinically relevant data management digital experience experimental study high dimensionality in vivo insight learning strategy machine learning algorithm machine learning method multidimensional data nano-string potential biomarker predictive signature public repository relational database repository single-cell RNA sequencing theories transcriptomics viral RNA viral rebound virus genetics

项目摘要

PROJECT SUMMARY/ABSTRACT (Bioinformatics Core) The development, evaluation and implementation of HIV cure strategies will depend critically on our understanding of the “rebound-competent” HIV reservoir, and our capacity to determine when this reservoir is reduced to the point that viral recrudescence is unlikely in the near-term, justifying cessation of antiretroviral therapy (ART). Increasing evidence suggests that the active HIV reservoir during ART likely contributes to viral recrudescence following ART interruption, and the nature of HIV expression varies dramatically in vivo with respect to time, anatomic compartment, and target cell lineage. In this P01, several cutting-edge, high- dimensional technologies will be applied to a broad range of clinical samples to characterize diverse, active HIV reservoir subsets and their immunological and inflammatory impact in unprecedented detail. These technologies include: HIV transcription profiling, the intact proviral DNA assay (IPDA), intact viral RNA assay (IVRA), single provirus and HIV transcript sequencing, single-cell RNA-seq (scRNA-seq), Cytometry by time of flight (CyTOF), and nanoString GeoMX digital spatial profiling (DSP) generating high-resolution transcriptomic and proteomic data from tissues. Our massive and complex datasets will require extensive and sophisticated data management, integration, and bioinformatic and biostatical analyses to ensure that clinically relevant biological insights are distilled from our experiments. The Bioinformatics Core, staffed by seasoned investigators with specific expertise in the analysis of high-dimensional data to study HIV pathogenesis and persistence, will perform these critical functions within our P01 project. We will pursue three Specific Aims in the Bioinformatics Core: In Aim 1, we will compile, curate, and disseminate data generated by all three P01 projects, building a searchable, relational database for our investigative team and submitting data to relevant public repositories to maximize data accessibility. In Aim 2, we will apply ensemble machine learning methods to high-dimensional data generated in all three P01 projects to identify molecular and immunologic signatures of diverse active HIV reservoir subsets. In Aim 3, we will apply advanced knowledge discovery methods to data generated from analytical treatment interruption (ATI) samples (Project #3) to reveal predictors and HIV reservoir signatures of viral rebound following ART cessation. The Bioinformatics Core will play a central role in ensuring successful implementation of our P01 objectives to advance the HIV cure agenda.

项目摘要/摘要（生物信息学核心）艾滋病毒治疗策略的制定、评估和实施将主要取决于我们的对“有反弹能力”的 HIV 病毒库的了解，以及我们确定该病毒库何时出现的能力减少到短期内不太可能出现病毒复发的程度，证明停止抗逆转录病毒治疗是合理的越来越多的证据表明，ART 期间活跃的 HIV 病毒库可能有助于 ART 中断后病毒复发，体内 HIV 表达的性质发生显着变化关于时间、解剖区室和靶细胞谱系，在这个 P01 中，有几个尖端的、高水平的。维度技术将应用于广泛的临床样本，以表征多样化的、活跃的 HIV 储存库亚群及其免疫和炎症影响的细节前所未有。技术包括：HIV 转录分析、完整原病毒 DNA (IPDA)、完整病毒 RNA 分析 (IVRA)、单一原病毒和 HIV 转录本测序、单细胞 RNA-seq (scRNA-seq)、按时间进行的细胞计数 Flight (CyTOF) 和 nanoString GeoMX 数字空间分析 (DSP) 生成高分辨率转录组和来自组织的蛋白质组数据。我们庞大而复杂的数据集将需要广泛而复杂的数据管理、集成和生物信息和生物静态分析，以确保从我们的研究中提取临床相关的生物学见解生物信息学核心，由经验丰富的研究人员组成，具有特定的分析专业知识。用于研究艾滋病毒发病机制和持久性的高维数据，将在我们的P01项目。我们将在生物信息学核心中追求三个具体目标：在目标 1 中，我们将编译、策划和传播所有三个 P01 项目生成的数据，为我们的项目构建一个可搜索的关系数据库调查团队并向相关公共存储库提交数据，以最大限度地提高数据可访问性在目标 2 中，我们将把集成机器学习方法应用于所有三个 P01 项目生成的高维数据在目标 3 中，我们将识别不同活性 HIV 病毒库子集的分子和免疫学特征。将先进的知识发现方法应用于分析治疗中断 (ATI) 生成的数据样本（项目#3）揭示 ART 后病毒反弹的预测因子和 HIV 病毒库特征停止。生物信息学核心将在确保成功实施我们的 P01 目标方面发挥核心作用推进艾滋病毒治疗议程。