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: 10372456
• 负责人: Sushant Bhatnagar
• 金额: $ 22.28万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372456
• 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; 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; 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。升高的血糖水平是 糖尿病以及至关重要的胰岛的内分泌激素分泌 适当控制全身葡萄糖代谢需要兰格汉斯。代谢应激增加 由于肥胖，导致每种胰岛细胞类型（a，b，d）通过改变其骑马的分泌来适应。但是，在 某些肥胖个体，这种适应的失败会破坏胰岛微环境，导致血液升高 葡萄糖水平和2型糖尿病的发作（T2D）。胰岛细胞如何的基本机制 影响彼此的功能尚不清楚。分泌的蛋白质是关键的细胞内和细胞间代谢 加强了我们对肥胖引起的T2D基础机制的理解的监管机构。那， 该项目的前提是，胰岛中的分泌蛋白介导的串扰对于正确的功能至关重要 在精益，肥胖和T2D状态中A，B，D细胞的适应。秘密蛋白质占总数的11％ 人类转录组和我们的初步数据已经确定了〜850个不同表达的成绩单 在具有肥胖症的小鼠胰岛中编码分泌的蛋白质。但是，只有少数的功能是 特殊的。我们的长期目标是确定改善治疗胰岛功能的分泌蛋白质 人类T2D。实现这一目标的主要障碍是确定和昂贵的技术限制 然而，使用常规生化的胰岛中分泌蛋白的耗时功能表征 方法。在对高严格性的一个数据集的测试分析中，44个胰岛衍生的分泌蛋白质调节剂 被确定会影响肥胖症中的小鼠胰岛功能。有趣的是，顶部的功能表征 候选分泌的蛋白质导致发现了一种新的途径，该途径抑制了B细胞的胰岛素分泌。 令人兴奋的是，验证使用我们的定量生物信息学框架是迈向有效数据的飞跃 开采加快与该胰岛功能的新型分泌蛋白调节剂相关的新型蛋白质调节剂 疾病状态。这里的目的是确定影响人类胰岛功能的分泌蛋白质调节剂 T2D使用网络分析在整个胰岛转录组学数据集上使用网络分析。我们建议 以下目的是实现目标：1）确定候选蛋白质调节剂； 2）超级流行器 与人类肥胖和T2D相关的胰岛中候选蛋白质的功能预测； 3） 候选蛋白质的生物学验证以影响胰岛功能。成功完成将 识别人类肥胖和T2D中胰岛功能的新型调节剂，从而提高了机制的知识 人类T2D的潜在风险，并可能识别治疗靶标，以改善T2D中的胰岛功能。 此外，通过整合多个数据集来考虑样本准备的变化，获得的见解 和测序（平台偏差），测序深度和网络/相关体系结构（由于样本 处理）将构成跨不同胰岛类型的秘密蛋白质网络的基础。