Identifying secreted protein networks affecting human pancreatic islet function in type 2 diabetes using public omic databases

使用公共组学数据库识别影响 2 型糖尿病患者胰岛功能的分泌蛋白网络

• 批准号: 10488268
• 负责人: Sushant Bhatnagar
• 金额: $ 18.56万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10488268
• 关键词: Accounting; Adult; Affect; Architecture; B-Lymphocytes; Beta Cell; Biochemical; Bioinformatics; Biological; Biological Process; Biology; Blood Glucose; Cell physiology; Cells; Complement 1q; Complex; Consumption; Coupled; D Cells; Data; Data Analyses; Data Set; Databases; Diabetes Mellitus; Disease; Endocrine; Epidemic; Failure; Future; Gene Expression Profiling; Gene set enrichment analysis; Genes; Glucagon; Goals; Hormone secretion; Hormones; Human; Impairment; Inflammation; Insulin; Insulin Antagonists; Insulin Resistance; Islet Cell; Islets of Langerhans; Knowledge; Maintenance; Measures; Mediating; Messenger RNA; Metabolic; Metabolic stress; Mission; Mus; Network-based; Non-Insulin-Dependent Diabetes Mellitus; Obese Mice; Obesity; Organ; Outcome; Pancreas; Pathway Analysis; Pathway interactions; Peripheral; Persons; Population; Predisposition; Preparation; Proteins; Public Health; Publishing; Recombinants; Role; Sampling; Signal Pathway; Signal Transduction; Somatostatin; Testing; Thinness; Time; Tissues; Transcript; United States; United States National Institutes of Health; Validation; Variant; autocrine; base; candidate validation; cell type; computer framework; cost; data mining; diabetes risk; diabetic; differential expression; gene network; genetic regulatory protein; glucose metabolism; improved; insight; insulin secretion; islet; multiple datasets; non-diabetic; novel; obese person; pancreatic islet function; paracrine; protein protein interaction; screening; sequencing platform; single-cell RNA sequencing; therapeutic target; tool; transcriptome; transcriptomics

The diabetes epidemic affects ~10% of the US adult population. An elevated blood sugar level is the hallmark of diabetes, and the coordinated secretion of endocrine hormones from critically important pancreatic islets of Langerhans is required for the proper control of whole-body glucose metabolism. Increased metabolic stress due to obesity causes each islet cell type (a, b, d) to adapt by altering their hormone secretion. However, in certain obese individuals, failure of this adaptation, disrupts the islet microenvironment, leading to elevated blood glucose levels and the onset of type 2 diabetes (T2D). The underlying mechanisms of how distinct islet cells affect each other’s functions are not known. Secreted proteins are critical intra- and inter- cell type metabolic regulators that have improved our understanding of mechanisms underlying obesity-induced T2D. Thus, the premise of this project is that secreted proteins-mediated crosstalk in islets is essential for proper functioning and adaptation of a, b, d-cells in lean, obese, and T2D states. Secreted proteins comprise ~11% of the total human transcriptome, and our preliminary data have identified ~850 differentially expressed transcripts that encode for secreted proteins in mouse islets with obesity. Yet, the function for only a handful of them has been well-characterized. Our long-term goal is to identify secreted proteins that improve islet function for the treatment of human T2D. A major roadblock towards achieving this goal is the technical limitations in identifying and costly yet time-consuming functional characterization of secreted proteins in islets using conventional biochemical approaches. In a test analysis of one data set at high stringency, 44 islet-derived secreted protein regulators were identified to affect mouse islet function in obesity. Interestingly, the functional characterization of the top candidate secreted protein led to the discovery of a novel pathway inhibiting insulin secretion from b-cells. Excitingly, validation of the use of our quantitative bioinformatics framework is a leap towards effective data mining in expediting the identification of novel secreted protein regulators of islet function associated with the disease state (s). The objective here is to identify secreted protein regulators that affect islet function in human T2D using network analysis on combined publicly available whole islet transcriptomics datasets. We propose the following aims to achieve the objective: 1) Identify candidate secreted protein regulators; 2) superclusters for functional prediction of candidate secreted proteins in islets associated with human obesity and T2D; and 3) biological validation of the candidate secreted proteins to affect islet function. The successful completion will identify novel regulators of islet function in human obesity and T2D, improving knowledge of mechanisms underlying human T2D risks, and possibly identifying therapeutic targets to improve islet function in T2D. Additionally, insights obtained by integrating multiple data sets accounting for variations in sample preparation and sequencing (platform bias), sequencing depths, and networks/correlation architecture (due to sample handling) will form the basis for elucidating the secreted protein network across distinct islet cell-types.

糖尿病流行影响约 10% 的美国成年人口，血糖水平升高是其标志。 糖尿病，以及至关重要的胰岛协调分泌内分泌激素 适当控制全身葡萄糖代谢需要朗格汉斯增加的代谢压力。 由于肥胖导致每种胰岛细胞类型（a、b、d）通过改变其激素分泌来适应。 某些肥胖个体，这种适应失败会破坏胰岛微环境，导致血糖升高 血糖水平与 2 型糖尿病 (T2D) 的发病机制不同。 分泌蛋白对细胞内和细胞间类型的代谢至关重要。 调节剂提高了我们对肥胖诱发 T2D 机制的理解。 该项目的前提是胰岛中分泌蛋白介导的串扰对于正常功能至关重要 a、b、d 细胞在瘦、肥胖和 T2D 状态下的适应分泌蛋白约占总数的 11%。 人类转录组，我们的初步数据已经鉴定出约 850 个差异表达的转录本 编码肥胖小鼠胰岛中的分泌蛋白，但其中只有少数具有功能。 我们的长期目标是确定可改善胰岛功能的分泌蛋白以进行治疗。 人类 T2D 的实现这一目标的主要障碍是识别技术的限制和成本高昂。 使用传统生物化学对胰岛分泌蛋白进行耗时的功能表征 在对一组高严格数据集进行测试分析时，发现了 44 种胰岛来源的分泌蛋白调节剂。 被确定会影响肥胖小鼠的胰岛功能。 候选分泌蛋白导致发现了抑制 B 细胞胰岛素分泌的新途径。 令人兴奋的是，对我们的定量生物信息学框架的使用进行验证是迈向有效数据的飞跃 挖掘加速识别与胰岛功能相关的新型分泌蛋白调节剂 这里的目标是确定影响人类胰岛功能的分泌蛋白调节剂。 我们建议使用对公开可用的全胰岛转录组数据集进行网络组合分析的 T2D。 以下目标是为了实现这一目标：1) 确定候选分泌蛋白调节因子；2) 超簇； 与人类肥胖和 T2D 相关的胰岛候选分泌蛋白的功能预测；3) 候选分泌蛋白影响胰岛功能的生物学验证将成功完成。 确定人类肥胖和 T2D 中胰岛功能的新调节因子，提高对机制的了解 潜在的人类 T2D 风险，并可能确定改善 T2D 胰岛功能的治疗靶点。 此外，通过整合多个数据集获得的见解说明了样品制备的变化 和测序（平台偏差）、测序深度和网络/相关架构（由于样本 处理）将成为阐明不同胰岛细胞类型的分泌蛋白网络的基础。