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.

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.