A biophysically comprehensive model of urothelial afferent neurons: implications for sensory signalling in urinary bladder.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2024-02-01 Epub Date: 2024-02-12 DOI:10.1007/s10827-024-00865-3
Satchithananthi Aruljothi, Rohit Manchanda
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引用次数: 0

Abstract

The urothelium is the innermost layer of the bladder wall; it plays a pivotal role in bladder sensory transduction by responding to chemical and mechanical stimuli. The urothelium also acts as a physical barrier between urine and the outer layers of the bladder wall. There is intricate sensory communication between the layers of the bladder wall and the neurons that supply the bladder, which eventually translates into the regulation of mechanical activity. In response to natural stimuli, urothelial cells release substances such as ATP, nitric oxide (NO), substance P, acetylcholine (ACh), and adenosine. These act on adjacent urothelial cells, myofibroblasts, and urothelial afferent neurons (UAN), controlling the contractile activity of the bladder. There is rising evidence on the importance of urothelial sensory signalling, yet a comprehensive understanding of the functioning of the urothelium-afferent neurons and the factors that govern it remains elusive to date. Until now, the biophysical studies done on UAN have been unable to provide adequate information on the ion channel composition of the neuron, which is paramount to understanding the electrical functioning of the UAN and, by extension, afferent signalling. To this end, we have attempted to model UAN to decipher the ionic mechanisms underlying the excitability of the UAN. In contrast to previous models, our model was built and validated using morphological and biophysical properties consistent with experimental findings for the UAN. The model included all the channels thus far known to be expressed in UAN, including; voltage-gated sodium and potassium channels, N, L, T, P/Q, R-type calcium channels, large-conductance calcium-dependent potassium (BK) channels, small conductance calcium-dependent (SK) channels, Hyperpolarisation activated cation (HCN) channels, transient receptor potential melastatin (TRPM8), transient receptor potential vanilloid (TRPV1) channel, calcium-activated chloride(CaCC) channels, and internal calcium dynamics. Our UAN model a) was constrained as far as possible by experimental data from the literature for the channels and the spiking activity, b) was validated by reproducing the experimental responses to current-clamp and voltage-clamp protocols c) was used as a base for modelling the non-urothelial afferent neurons (NUAN). Using our models, we also gained insights into the variations in ion channels between UAN and NUAN neurons.

Abstract Image

尿道传入神经元的生物物理综合模型:对膀胱感觉信号的影响。
尿路上皮是膀胱壁的最内层;它通过对化学和机械刺激做出反应,在膀胱感觉传导中发挥着关键作用。尿道膜还是尿液和膀胱壁外层之间的物理屏障。膀胱壁各层与供应膀胱的神经元之间存在着错综复杂的感觉交流,最终转化为对机械活动的调节。在自然刺激下,尿道细胞会释放出 ATP、一氧化氮(NO)、P 物质、乙酰胆碱(ACh)和腺苷等物质。这些物质作用于邻近的尿路上皮细胞、肌成纤维细胞和尿路上皮传入神经元(UAN),从而控制膀胱的收缩活动。越来越多的证据表明了尿路神经传感信号的重要性,但迄今为止,人们仍无法全面了解尿路神经传入神经元的功能及其支配因素。迄今为止,对 UAN 所做的生物物理研究还无法提供有关神经元离子通道组成的充分信息,而这对于理解 UAN 的电功能以及由此延伸的传入信号至关重要。为此,我们尝试建立 UAN 模型,以破译 UAN 兴奋性的离子机制。与以往的模型不同,我们的模型是利用与 UAN 实验结果一致的形态学和生物物理学特性建立和验证的。该模型包括迄今已知在 UAN 中表达的所有通道,包括电压门控钠和钾通道、N、L、T、P/Q、R 型钙通道、大电导钙依赖性钾(BK)通道、小电导钙依赖性(SK)通道、超极化激活阳离子(HCN)通道、瞬时受体电位美司他汀(TRPM8)、瞬时受体电位香草素(TRPV1)通道、钙激活氯化物(CaCC)通道以及内部钙动力学。我们的 UAN 模型 a) 尽可能以文献中有关通道和尖峰活动的实验数据为约束;b) 通过重现电流钳和电压钳方案的实验反应进行验证;c) 用作非泌尿道传入神经元(NUAN)建模的基础。利用我们的模型,我们还深入了解了 UAN 和 NUAN 神经元之间离子通道的变化。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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