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.

在过去的几年里，我们的主要活动之一是开发一个全面的模型，用于从秒到分钟的时间尺度上的膜电位和钙的振荡。 这些导致胰岛素分泌的相应振荡。 该模型的基本假设是，较快的振荡（数十秒）源于钙离子通道的反馈，可能是钙激活钾（K（Ca））通道和ATP依赖性钾（K（ATP））通道，而较慢的振荡（5分钟）源于代谢振荡。 代谢振荡通过K（ATP）通道转换成电振荡。 值得注意的是，后者是胰岛素刺激药物的一线靶点，例如用于治疗2型糖尿病的磺酰脲类（甲苯磺丁脲，格列本脲）。 因此，该模型由电振荡器（EO）和代谢（糖酵解）振荡器（G）组成，并且被称为双振荡器模型（DOM）。 目前，我们正在以多种方式测试这一模式。 去年，我们报道了代谢振荡，测定NAD（P）H的测量，经常坚持在稳定的钙，表明钙振荡是不需要代谢振荡。 然而，这两者通常是串联的，钙波动以及平均钙水平确实影响代谢波动。 我们现在已经通过测量K（ATP）通道电导证实了这些发现，并正在准备一篇关于这个问题的论文。 我们写了一篇关于生理学中动力系统方法的评论（参考文献1），以增强其他人最近发表的两篇论文对生理学界的益处，这两篇论文提出了一种新的、更全面的（快速）β细胞电活动模型。虽然我们已经使我们的模型尽可能简单的phenemona解决，新的模型包括一个更广泛的机制。 这就提出了如何评估不同机制的相对重要性以及细胞如何使用冗余的问题。 新模型和其他类似模型的复杂性也对理解模型如何工作以及其功能和局限性提出了挑战。 评论用最少的数学描述了分叉图如何仍然可以有效地应用。 这些图是参数状态的一个层次的映射，其中发现了模型的各种行为，包括稳态，尖峰和爆发。它们还提供了一种方法来剖析动力学，通过利用不同的过程（这里，尖峰和爆发）在不同的时间尺度上运行（< 1秒vs. 10 - 60秒），并且可以被认为是半独立的。 这将集体行为简化为更简单的子系统的行为，并大大提高了分析的能力。 进化也可能利用这种时间尺度的分离，因为它使细胞功能模块化-单个子系统可以改变，而对其他子系统的影响有限。 该评论可作为本报告所述工作的有益指导。 该评论中的一幅图被选为该杂志7月号的封面艺术。 一个特别有趣的应用分离的时间尺度模型中的爆裂在β细胞是重置现象。 最早的β细胞模型（Chay-Keizer，1983）的一个见解是，尖峰发生的平台是由双稳态建立的。 也就是说，如果慢变钙是固定的，则细胞可以处于低电压（_60 mV）稳态或高电压（_20 mV）尖峰状态。 因此，短暂的电刺激应该能够将细胞从一种状态切换到另一种状态。 此外，模型预测，扰动传递的低压（无声）阶段越晚，感应的高压（活跃）阶段就越短。 实验已经证实，沉默活跃的相变可以诱导预期的，但诱导阶段的持续时间似乎并不取决于什么时候施加微扰。 在参考文献#2中，我们与Bertram小组合作表明，最近的β细胞模型（具有两个控制活跃期和沉默期持续时间的缓慢变量）可以解释这一迄今为止令人困惑的实验观察结果。 参考文献#4解决了β细胞和密切相关但不同的垂体细胞的爆发模型中的双稳态、可重置性和时间尺度分离的问题。 在我们关于神经元和内分泌细胞的数学建模的报告中详细讨论了这一点。 Max Pietropaolo（美国）密歇根州）博士后研究员Anmar Khadra和我开始了1型糖尿病（T1 D）实验室的新工作，其特征是β细胞的自身免疫破坏。 Pietropaolo对使用胰岛自身抗体作为T1 D进展风险的生物标志物长期感兴趣。 虽然进展速率的差异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β细胞，T细胞和B细胞之间相互作用的数学模型来确定潜在的机制。 我们确定了两个控制进展至T1 D的时间的关键参数，即T细胞对β细胞的亲和力及其杀伤效率。 该模型还能够阐明亲合力成熟的现象，其中T细胞亲合力随着时间的推移而增加，加速疾病进程。 参见参考文献3。