Diabetes Data and Hypothesis Hub (D2H2)

糖尿病数据和假设中心 (D2H2)

基本信息

批准号：
10414588
负责人：
Alan D Attie
金额：
$ 108.07万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
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：
2022-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10414588
关键词：
Address Adipose tissue Adopted Animal Model Bioinformatics Biological Assay Cell Line Cell model Cells Communities Complex Computer Analysis Data Data Set Diabetes Mellitus Diabetic mouse Gene Proteins Generations Genes Goals Investments Islets of Langerhans Knowledge Laboratories Lead 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 Proteome Publishing Research Research Personnel Resources Sea Skeletal Muscle Source Surveys Technology Testing Tissues Validation Work base bioinformatics tool computational pipelines computer program computerized tools cost data repository digital epigenome experimental study gene function insulin secretion large datasets lipidome metabolome microbiome novel opportunity cost pre-clinical protein metabolite search engine skills small molecule therapeutic development tool transcriptome user-friendly web portal web-based tool

项目摘要

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转录组、蛋白质组、代谢组、脂质组、表观基因组和微生物组的调查变得容易，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研究，因此代表了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。为了弥补这一差距，我们的项目将建立糖尿病数据和假设中心（D2H2），该平台将促进糖尿病和相关代谢紊乱研究界以数据驱动的假设生成。 D2H2 将包含：1) 数千个与糖尿病相关的高质量组装和整理数据集，2) 一个用户友好的基于网络的门户，可有效查询这些数据并将其与一套生物信息学工具链接起来，3) 一个假设生成接口，使糖尿病研究界能够识别从将与 D2H2 平台处理和集成的数据中出现的有希望的可操作线索。此外，我们将在基于细胞的测定中对计算分析提出的数十种有前途的化合物进行实验测试，以测量胰岛素分泌。此类筛选中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以促进早期治疗开发的概念验证。