Bioinformatics Core

生物信息学核心

• 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
• 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

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.

项目总结/摘要（生物信息学核心） 艾滋病毒治疗战略的制定、评估和实施将主要取决于我们的 了解“反弹能力”的艾滋病毒水库，以及我们确定何时这个水库是 减少到病毒在短期内不太可能复发的程度，证明停止抗逆转录病毒治疗是合理的 治疗（ART）。越来越多的证据表明，ART期间活跃的HIV库可能有助于 ART中断后病毒复发，HIV表达的性质在体内变化很大 关于时间、解剖区室和靶细胞谱系。在这P01，几个尖端，高- 三维技术将应用于广泛的临床样本，以表征不同的，活跃的， 艾滋病病毒库亚群及其免疫和炎症影响的前所未有的细节。这些 技术包括：HIV转录谱分析、完整前病毒DNA检测（IPDA）、完整病毒RNA检测 （IVRA），单原病毒和HIV转录物测序，单细胞RNA-seq（scRNA-seq）， 飞行（CyTOF）和nanoString GeoMX数字空间分析（DSP）生成高分辨率转录组学 和组织中的蛋白质组数据。 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 生物信息学和生物统计学分析，以确保从我们的 实验生物信息学核心，由经验丰富的研究人员组成，具有分析方面的特定专业知识 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， 传播所有三个P01项目产生的数据，为我们的 调查小组和向相关公共储存库提交数据，以最大限度地提高数据的可访问性。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项目中生成的高维数据 以确定不同的活性HIV储库亚群的分子和免疫学特征。在目标3中，我们 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），以揭示ART后病毒反弹的预测因子和HIV储库特征 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， 推动艾滋病治愈议程。