Stimulus secretion coupling in pancreatic beta-cells

胰腺β细胞的刺激分泌耦合

• 批准号: 8349645
• 负责人: Arthur Sherman
• 金额: $ 24.06万
• 依托单位: NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8349645
• 关键词: Accounting; Action Potentials; Address; Appearance; Area; Arts; Autoantibodies; Avidity; B-Lymphocytes; Behavior; Beta Cell; Biological Assay; Biological Markers; Calcium; Calcium Oscillations; Cell model; Cell physiology; Cells; Chemicals; Collaborations; Communities; Coupled; Coupling; Cyclic AMP; Defect; Development; Disease; Endocrine; Equation; Evolution; Feedback; Glyburide; Human; Immune; Individual; Insulin; Insulin-Dependent Diabetes Mellitus; Ion Channel; Islets of Langerhans; Journals; Lead; Maps; Mathematics; Measurement; Membrane Potentials; Metabolic; Metabolism; Methods; Michigan; Modeling; Neurons; Non-Insulin-Dependent Diabetes Mellitus; Organ; Pancreas; Paper; Pharmaceutical Preparations; Phase; Phase Transition; Physiologic pulse; Physiological; Physiology; Pituitary Gland; Plasma; Postdoctoral Fellow; Potassium; Process; Protein Kinase C; Published Comment; Reading; Relative (related person); Reporting; Risk; Rodent; Signal Pathway; Stimulus; Structure of beta Cell of islet; Sulfonylurea Compounds; System; T-Lymphocyte; Testing; Time; Tolbutamide; Work; Writing; active control; cell fixing; glucose metabolism; insight; insulin secretion; interest; islet; killings; mathematical model; millisecond; research study; stem; tool; voltage

One of our main activities over the last few years has been the development of a comprehensive model for oscillations of membrane potential and calcium on time scales ranging from seconds to minutes. These lead to corresponding oscillations of insulin secretion. The basic hypothesis of the model is that the faster (tens of seconds) oscillations stem from feedback of calcium onto ion channels, likely calcium-activated potassium (K(Ca)) channels and ATP-dependent potassium (K(ATP)) channels, whereas the slower (five minutes) oscillations stem from oscillations in metabolism. The metabolic oscillations are transduced into electrical oscillations via the K(ATP) channels. The latter, notably, are a first-line target of insulin-stimulating drugs, such as the sulfonylureas (tolbutamide, glyburide) used in the treatment of Type 2 Diabetes. The model thus consists of an electrical oscillator (EO) and a metabolic (glycolytic) oscillator (G)) and is referred to as the Dual Oscillator Model (DOM). We are currently testing this model in several ways. Last year we reported that metabolic oscillations, assayed by NAD(P)H measurements, often persist in steady calcium, indicating that calcium oscillations are not required for metabolic oscillations. The two, however, are generally found in tandem, and the calcium oscillations, as well as mean calcium level, do influence the metabolic oscillations. We have now confirmed these findings with measurements of K(ATP) channel conductance and are preparing a paper on the subject. We have written a commentary (Ref. # 1)about dynamical systems methods in physiology in order to enhance the benefit for the physiology community of two recent papers by others presenting a new, more comprehensive model for (fast) beta-cell electrical activity. Whereas we have made our models as simple as possible for the phenemona addressed, the new model includes a much wider set of mechanisms. This raises issues of how to assess the relative importance of the different mechanisms and of how cells use redundancy. The complexity of the new model and others like it also poses a challenge for understanding how the model works and what its capabilities and limitations are. The commentary describes with a minimum of mathematics how bifurcation diagrams can still be applied effectively. Such diagrams are at one level maps of the parameter regimes in which the various behaviors of the model, including steady states, spiking and bursting, are found. They also provide a way to dissect the dynamics by exploiting the fact that different processes (here, spiking and bursting) operate on different time scales (< 1 sec vs. 10 - 60 sec) and can be considered as semi-independent. This reduces the collective behavior into the behavior of simpler sub-systems and greatly increases the power of analysis. Evolution may exploit such timescale separation as well, as it serves to make cell function modular - the individual subsystems can be altered with limited effect on the others. The review can be profitably read as a didactic guide to the work described in this report. A figure from the commentary was selected as the cover art for the journal's July issue. A particularly interesting application of the separation of timescales in models for bursting in beta cells is the phenomenon of resetting. An insight from the earliest beta-cell model (Chay-Keizer, 1983) is that the plateau from which spiking occurs is established by bi-stability. That is, if the slow variable calcium is fixed, the cell can sit at either a low-voltage (-60 mV) steady state or a high-voltage (-20 mV) spiking state. Consequently, brief electrical stimuli should be able to switch the cell from one state to the other. Moreover, the models predicted that the later in the low-voltage (silent) phase in which the perturbation is delivered, the shorter would be the induced high-voltage (active) phase. Experiments have confirmed that silent-active phase transitions can be induced as expected, but the duration of the induced phase does not seem to depend on when the perturbation is applied. In Ref. # 2 we show in collaboration with the Bertram group that more recent beta-cell models, with two slow variables controlling the active and silent phase durations can account for this heretofore puzzling experimental observation. Ref. # 4 addresses the issues of bistability, resettability and separation of timescales in models of bursting for both beta cells and closely related but different pituitary cells. It is discussed in detail in our report on Mathematical Modeling of Neurons and Endocrine Cells. In collaboration with Max Pietropaolo (U. Michigan) post-doctoral fellow Anmar Khadra and I began a new line of work for the lab on Type 1 Diabetes (T1D), characterized by auto-immune destruction of beta cells. Pietropaolo has a long-standing interest in use of islet autoantibodies as biomarkers of risk for progression to T1D. While differences in rate of progression have been correlated with the appearance of different autoantibodies or the number of autoantibody types, we sought to determine the underlying mechanism by developing a mathematical model for the interactions among beta cells, T cells and B cells. We identified two key parameters controlling the time to progression to T1D, the avidity of the T cells for beta cells and their killing efficiency. The model was also able to illuminate the phenomenon of avidity maturation, in which T-cell avidity increases over time, accelerating the disease process. See Ref. # 3.

在过去几年中，我们的主要活动之一是开发了一个综合模型，用于振荡膜电位和钙的时间尺度范围从秒到几分钟。 这些导致胰岛素分泌的相应振荡。 该模型的基本假设是，钙的振荡速度较快（数十个秒）源于钙在离子通道上的反馈，可能是钙激活的钾（K（CA））通道和ATP依赖性钾（K（ATP））通道，而较慢（五分钟）的振动源于振动效果。 代谢振荡通过K（ATP）通道转导为电振荡。 尤其是后者是胰岛素刺激药物的一线靶标，例如用于治疗2型糖尿病的磺酰氟烷（Tolbutamide，Glyburide）。 因此，该模型由电振荡器（EO）和代谢（糖酵解）振荡器（G））组成，并称为双振荡器模型（DOM）。 我们目前正在以几种方式测试该模型。 去年，我们报告说，NAD（P）H测量值测定的代谢振荡通常持续存在稳定的钙，表明代谢振荡并不需要钙振荡。 然而，这两者通常在串联中发现，钙振荡以及平均钙水平确实会影响代谢振荡。 现在，我们已经通过测量K（ATP）通道电导率确认了这些发现，并正在准备有关该受试者的论文。 我们写了一篇评论（参考文献＃1），介绍了生理学中的动力学系统方法，以增强其他人最近提出了一个新的，更全面的（快速）β细胞电活动模型的生理学界的好处。尽管我们使我们的模型在所解决的现象中尽可能简单，但新模型包含了更广泛的机制。 这提出了如何评估不同机制的相对重要性以及细胞使用冗余的相对重要性的问题。 新模型和其他类似模型的复杂性也为了解该模型的工作方式以及其功能和局限性是一个挑战。 评论用最少的数学描述了如何有效地应用分叉图。 此类图是参数制度的一个级别图，其中模型的各种行为，包括稳态，尖峰和爆发。 他们还通过利用不同的过程（在此，尖峰和爆发）在不同的时间尺度上运行（<1 sec vs. 10-60 sec）的事实，提供了一种剖析动力学的方法，并且可以被视为半独立性。 这将集体行为减少到更简单的子系统的行为中，并大大增加了分析的力量。 进化也可以利用这种时间尺度的分离，因为它可以使细胞功能模块化 - 可以改变单个子系统，对其他子系统的影响有限。 该评论可以作为本报告中描述的工作的教学指南有利可图。 评论中的一个数字被选为日报七月号的封面艺术。 在β细胞中爆发的模型中分离时间尺度的一个特别有趣的应用是重置的现象。 最早的β细胞模型的见解（Chay-Keizer，1983）是，出现尖峰的高原是由双稳定性建立的。 也就是说，如果缓慢的可变钙是固定的，则该单元可以位于低压（-60 mV）稳态或高压（-20 mV）尖峰状态的位置。 因此，简短的电刺激应能够将细胞从一个状态切换到另一个状态。 此外，这些模型预测，在传递扰动的低压（静音）相位，较短的是诱导的高压（活动）相。 实验已经确认可以按预期诱导无声活性相变，但是诱导相的持续时间似乎并不取决于何时应用扰动。 在参考＃2我们与Bertram集团合作显示，最近有两个缓慢的变量控制着活跃和无声的相位持续时间，可以解释这种令人困惑的实验性观察。 参考。 ＃4解决了Beta细胞爆发模型和密切相关但不同的垂体细胞中爆发模型中的双重性，可重复性和分离的问题。 在我们关于神经元和内分泌细胞数学建模的报告中详细讨论了它。 与马克斯·皮特罗帕洛（Max Pietropaolo）（美国密歇根州）合作，博士后同胞安玛·哈德拉（Anmar Khadra）和我开始为实验室的1型糖尿病（T1D）进行新的工作，其特征是β细胞的自动免疫破坏。 Pietropaolo长期以来对使用胰岛自身抗体的使用兴趣是有可能进展为T1D风险的生物标志物。 尽管进展速率的差异已与不同自身抗体的出现或自身抗体类型的数量相关，但我们试图通过开发用于β细胞，T细胞和B细胞之间相互作用的数学模型来确定基本机制。 我们确定了两个关键参数，这些参数控制了进展到T1D的时间，T1D是β细胞的T细胞的亲和力及其杀伤效率。 该模型还能够阐明亲和成熟的现象，其中T细胞的流动随着时间的推移而增加，从而加速了疾病过程。 参见参考。 ＃3。