Bioinformatics Core

生物信息学核心

• 批准号: 10614011
• 负责人: Satish Kumar Pillai
• 金额: $ 11.91万
• 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/10614011
• 关键词: Acceleration; Anatomy; Binding; Bioinformatics; Biological; Biological Assay; Cell Lineage; Clinical; Cytometry; DNA; Data; Data Set; Development; Drug or chemical Tissue Distribution; Ensure; Evaluation; Genbank; Gene Expression; Genetic Sequence Databases; Genetic Transcription; HIV; Immunologics; Immunophenotyping; 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; complex data; data management; data submission; 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.

项目摘要/摘要（生物信息学核心） 艾滋病毒治疗策略的发展，评估和实施将取决于我们 了解“反弹能力”的艾滋病毒水库，以及我们确定该水库何时的能力 降低到近期病毒复发不太可能是抗逆转录病毒的合理性的程度 治疗（艺术）。越来越多的证据表明，在艺术期间活跃的艾滋病毒水库可能有助于 艺术中断后的病毒复发，HIV表达的性质各种体内急剧 相对于时间，解剖区室和靶细胞谱系。在此P01中，几个尖端，高 - 尺寸技术将应用于广泛的临床样本，以表征潜水员，活跃的 艾滋病毒水库子集及其免疫学和炎症影响，以空前的细节。这些 技术包括：HIV转录分析，完整的病毒DNA分析（IPDA），完整的病毒RNA分析 （IVRA），单一病毒和HIV转录本测序，单细胞RNA-SEQ（SCRNA-SEQ），按时间划分的细胞术 飞行（cytof）和纳米弦GEOMX数字空间分析（DSP）产生高分辨率转录组 和来自组织的蛋白质组学数据。 我们庞大而复杂的数据集将需要广泛而复杂的数据管理，集成以及 生物信息学和生物抑制分析，以确保将临床相关的生物学见解与我们的 实验。生物信息学核心，由经验丰富的调查人员组成，具有特定的专业知识 研究HIV发病机理和持久性的高维数据将在内部执行这些关键功能 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AIM 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 调查团队并将数据提交给相关的公共存储库，以最大程度地提高数据可访问性。在AIM 2中， 我们将将整体机器学习方法应用于所有三个P01项目中生成的高维数据 确定潜水活性HIV储量亚集的分子和免疫学特征。在AIM 3中，我们将 将高级知识发现方法应用于分析治疗中断产生的数据（ATI） 样本（项目＃3）揭示了艺术后病毒反弹的预测因子和艾滋病毒储量特征 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 提出艾滋病毒治愈议程。