Bioinformatics Core

生物信息学核心

• 批准号: 10404414
• 负责人: Yi-Hsiang Hsu
• 金额: $ 9.07万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10404414
• 关键词: Adult; Affect; Anabolic Agents; Antibodies; Architecture; Bioinformatics; Biological Process; Biology; Biopsy; Bone Density; Bone Marrow Aspiration; Bone structure; Cells; Center for Translational Science Activities; Clinical; Clinical Research; Cloud Computing; Computer Analysis; Data; Data Analyses; Data Analytics; Data Collection; Data Set; Development; Environment; FDA approved; Female; Femur; Forteo; Foundations; Fracture; Future; Gene Expression; Gene Expression Profile; Genes; Genetic Variation; Genetic study; Goals; Human; Human Genetics; Human Genome; International; Knockout Mice; Late Effects; Modeling; Molecular; Molecular Mechanisms of Action; Molecular Profiling; Morphology; Mouse Strains; Mus; National Heart, Lung, and Blood Institute; Osteoblasts; Osteocalcin; Osteocytes; Osteogenesis; Osteoporosis; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Physiologic pulse; Pilot Projects; Postmenopausal Osteoporosis; Postmenopause; Process; Quality Control; Reporting; Research Personnel; Research Project Grants; Resources; Sampling; Skeleton; Techniques; Time; Trans-Omics for Precision Medicine; Woman; biobank; bioinformatics pipeline; bone; bone cell; cell type; clinical phenotype; computational platform; data management; data quality; design; fracture risk; genetic association; genetic variant; genome sequencing; human data; inhibitor; innovation; long bone; novel; osteoporosis with pathological fracture; parathyroid hormone (1-34); phenotypic data; pre-clinical; preclinical study; precursor cell; prevent; progenitor; response; side effect; single-cell RNA sequencing; skeletal; stem cell biology; tibia; time use; transcriptome; transcriptome sequencing; transcriptomics; treatment effect; treatment strategy; whole genome

Project Summary – Bioinformatics Core The objective of this Bioinformatics Core is to support statistical and bioinformatics analyses to study the effects of osteoporosis anabolic therapies (romosozumab, a sclerostin inhibitor, Scl- Ab) on osteoblast progenitors as well as osteocytes in clinical and pre-clinical studies with anabolic therapies. A short anabolic treatment window from Scl-Ab has been reported; in contrast to teriparatide (PTH) that has a longer anabolic window. This Bioinformatics Core will serve as the critical foundation for the research projects by analyzing cross-species data from single cell RNA-seq (scRNA-seq) and bulk RNA-seq using innovative techniques and approaches. The capabilities of the Bioinformatics Core will be enhanced by being able to leverage existing datasets of whole genome sequencing in tens thousands of human samples; and osteocyte gene expression signatures and deeply skeletal phenotyping from a unique mouse knockout dataset from our collaborators. To support Project #1, Project #2 as well as pilot projects, the Bioinformatics Core will provide statistical design, computational analysis, data quality control and management support as well as accessing to other external human genetics and bioinformatics data and resource. Specifically, in Aim 1, the Bioinformatics Core seeks to build a synergistic data management, data analytical and bioinformatics pipelines over cloud computing environment which allows interaction within investigators through data collection, data analyses, information sharing and data interpretation. In Aim 2, the Core will provide statistical and bioinformatics analyses for scRNA-seq and bulk RNA-seq generated from clinical and pre-clinical studies to identify osteoblast precursor cell clusters over time with either Scl-Ab or PTH treatments; osteoblast lineage tracing; osteoblast progenitors and osteocytes gene expression patterns; and co-expression pattern over time. In addition, cross-species integrating analysis will be conducted to identify homologous and divergent cell types between human and mice. In Aim 3, we will prioritize genes regulating bone formation and Identify molecular signatures affecting treatment effect via human gene expression (in bone biopsies) and genetic variation associated with bone phenotypes (BMD, HR-PQCT derived bone structure, fracture) in large-scale whole genome sequenced studies. In Aim 4, To further characterize skeletal function and phenotypes of the genes identified from project# and project#2 transcriptome analyses, we will utilize the existing KO mice deep phenotyping data; and generate a new KO mice strain. Ultimately, these merged analyses will generate powerfully informed, novel hypotheses regarding bone stem cell biology and responses to bone anabolic agents.

项目摘要-生物信息学核心 该生物信息学核心的目标是支持统计和生物信息学分析 研究骨质疏松症合成代谢疗法(罗莫佐单抗，一种硬化素抑制剂，scl-1)的效果。 AB)在临床和临床前研究中对成骨祖细胞和骨细胞的影响 合成代谢疗法。据报道，scl-Ab的合成代谢治疗窗口较短； 与Teriparatide(PTH)相比，Teriparatide的合成代谢窗口更长。这个生物信息学核心将 作为研究项目的关键基础，分析来自 使用创新技术和方法的单细胞RNA-seq(scRNA-seq)和散装rna-seq。 生物信息学核心的能力将通过利用现有的 数以万计的人类样本的全基因组测序数据集；以及骨细胞 一种独特的小鼠基因敲除的基因表达特征和深层骨骼表型 来自我们合作者的数据集。为支持项目1、项目2以及试验项目， 生物信息学核心将提供统计设计、计算分析、数据质量控制 和管理支持，以及获得其他外部人类遗传学和 生物信息学数据和资源。具体地说，在目标1中，生物信息学核心寻求建立一个 云上的协同数据管理、数据分析和生物信息学管道 允许调查人员通过数据收集进行交互的计算环境， 数据分析、信息共享和数据解释。在目标2中，核心将提供 临床来源的scRNA-seq和Bulk RNA-seq的统计和生物信息学分析 和临床前研究，以确定成骨细胞前体细胞群随时间的变化 或甲状旁腺激素治疗；成骨细胞谱系追踪；成骨祖细胞和骨细胞基因 表达模式；以及随着时间的推移共表达模式。此外，跨物种融合 将进行分析，以确定人类和人类之间的同源和不同细胞类型 老鼠。在目标3中，我们将优先考虑调控骨形成的基因，并确定分子 影响治疗效果的信号通过人类基因表达(在骨活检中)和基因 与骨表型(BMD、HR-pQCT衍生骨结构、骨折)相关的变异 大规模的全基因组测序研究。在目标4中，为了进一步描述骨骼功能 和项目#和项目2转录组分析中确定的基因的表型，我们 将利用现有的KO小鼠深度表型数据；并产生新的KO小鼠品系。 最终，这些合并的分析将产生见多识广的新奇假设 关于骨干细胞生物学和对骨合成代谢药物的反应。