Diabetes Data and Hypothesis Hub (D2H2)

糖尿病数据和假设中心 (D2H2)

• 批准号: 10655363
• 负责人: Alan D Attie
• 金额: $ 106.34万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655363
• 关键词: Address; Adipose tissue; Adopted; Animal Model; Bioinformatics; Biological Assay; Cell Line; Cell model; Cells; Communities; Computer Analysis; Data; Data Set; Diabetes Mellitus; Diabetic mouse; Generations; Genes; Goals; Investments; Islets of Langerhans; Knowledge; Laboratories; Link; Liver; Manuals; Measures; Metabolic Diseases; Metadata; Methods; National Institute of Diabetes and Digestive and Kidney Diseases; Non-Insulin-Dependent Diabetes Mellitus; Pathway interactions; Pharmaceutical Preparations; Phenotype; Process; Proteins; Proteome; Publishing; Research; Research Personnel; Resources; Sea; Skeletal Muscle; Source; Surveys; Technology; Testing; Tissues; Validation; Work; bioinformatics tool; complex data; computational pipelines; computer program; computerized tools; cost; data integration; data repository; digital; epigenome; experimental study; gene function; insulin secretion; large datasets; lipidome; metabolome; microbiome; model organism; novel; opportunity cost; pre-clinical; search engine; skills; small molecule; therapeutic development; tool; transcriptome; user-friendly; web portal; web-based tool

The advent of omics technologies has made it easy to survey the transcriptome, proteome, metabolome, lipidome, epigenome, and microbiome, enabling the generation of large datasets at modest cost. However, these datasets remain largely under-interrogated and thus represent a major loss of return on investment. This is mainly because most wet laboratory investigators are unaware of these data, and lack the bioinformatics skills needed to efficiently download, clean, and analyze these data. To bridge this gap, our project will establish the Diabetes Data and Hypothesis Hub (D2H2), a platform that will facilitate data-driven hypothesis generation for the diabetes and related metabolic disorder research community. D2H2 will contain: 1) thousands of high-quality assembled and curated data sets relevant to diabetes, 2) a user-friendly web-based portal to efficiently interrogate these data and link them with a suite of bioinformatics tools, and 3) a hypothesis generation interface to enable the diabetes research community to identify promising actionable clues that will emerge from the data that will be processed and integrated with the D2H2 platform. In addition, we will experimentally test tens of promising compounds, brought forward by the computational analyses, in a cell-based assays that will measure insulin secretion. Leading compounds from such a screen will be tested in animal models of diabetes to demonstrate the proof-of-concept of starting with data-driven hypotheses to facilitate early-stage therapeutics development.

组学技术的出现使得调查转录组、蛋白质组、代谢组、脂质组、表观基因组和微生物组变得容易，从而能够以适度的成本生成大型数据集。然而，这些数据集在很大程度上仍然没有得到充分调查，因此是投资回报的重大损失。这主要是因为大多数湿实验室研究人员不知道这些数据，并且缺乏有效下载，清理和分析这些数据所需的生物信息学技能。为了弥合这一差距，我们的项目将建立糖尿病数据和假设中心（D2H2），这是一个平台，将促进糖尿病和相关代谢紊乱研究社区的数据驱动假设生成。D2H2将包含：1）数千个与糖尿病相关的高质量组装和策划数据集，2）用户友好的基于Web的门户网站，可有效地查询这些数据并将其与一套生物信息学工具链接，以及3）假设生成界面，使糖尿病研究社区能够识别将从数据中出现的有希望的可操作线索，这些数据将被处理并与D2H2平台集成。此外，我们将在基于细胞的测定中实验性地测试数十种有前途的化合物，这些化合物是通过计算分析提出的，这些测定将测量胰岛素分泌。来自这种筛选的主要化合物将在糖尿病动物模型中进行测试，以证明从数据驱动的假设开始的概念验证，以促进早期治疗开发。