Implicit Learning in Osteocyte Network under Mechanical Loading

描述（由申请人提供）：本申请涉及广泛的挑战领域（15）“转化科学”和具体的挑战主题“15- ar -104骨和神经系统”。骨适应需要骨细胞原位检测机械信号，并将骨细胞网络中的信号整合为骨细胞系统中适当的活动。据推测，骨细胞网络模拟了一个简单的神经系统，可以通过内隐学习的基本形式，如习惯化和敏化，获得对体内生物力学刺激的短期记忆。然而，这些行为都没有在骨细胞网络中得到实验证实，并且对骨细胞作为神经网络的行为知之甚少。骨细胞网络被认为是骨重塑过程中主要的机械传感器，是骨记忆功能最有能力的候选者。在这项研究中，我们假设“对机械刺激的基本形式的可塑性变化的能力是骨细胞网络固有的和基本的特性，骨细胞网络中的短期记忆可以通过三种基本形式的内隐学习来实现：习惯化、敏化和经典条件反射”。根据我们的体外骨细胞网络模型和骨细胞钙信号研究的经验，本研究的目标是：(1)通过记录和分析骨细胞网络内[Ca2+]i波的实时传播，研究骨细胞网络对流体剪切刺激的习惯化、敏化和经典条件反射行为，并确定骨细胞网络内隐学习过程中的良性和有害机械刺激；(2)研究CaMK通路、间隙连接细胞间通讯和P2Y2受体在骨细胞网络对机械刺激的习惯化、敏化和经典调节中的作用，方法是使用药物抑制剂或基于sirna的敲除这3种蛋白中的一种。如果能够克服与骨细胞网络中记忆存储相关的重大挑战，我们将有机会开创骨力学转导研究和基本生理系统行为研究的新范式。这些知识将极大地有利于寻找治疗骨质疏松症的策略和新药，骨质疏松症影响着数百万人，造成了超过100亿美元的经济负担。这个RC1应用程序提出了一个新的假设，即内隐记忆存在于骨细胞网络在机械负荷下。在体外骨细胞网络中，我们将分析细胞内钙波对各种流体刺激模式的实时响应，以研究内隐学习的三种基本形式，即习惯化、敏化和经典条件反射。机械转导机制对了解骨质疏松症的病因和预防骨质疏松具有重要意义。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (15) "Translational Science" and specific Challenge Topic "15-AR-104 Bone and the Nervous System". Bone adaptation requires osteocytes to detect mechanical signals in situ and integrate the signals in the osteocytic network into appropriate activities in the bone cell system. It is conjectured that a bone cell network mimics a simple neuronal system and can acquire short-term memory to in vivo biomechanical stimuli through the elementary forms of implicit learning, e.g., habituation and sensitization. However, none of these behaviors have been confirmed experimentally in bone cell networks, and little is known about how bone cells behave as a neuronal network. The osteocytic network, recognized as the major mechanical sensor in the bone remodeling process, is the most capable candidate to accommodate the memory function in bone. In this study, we hypothesize that "the ability for elementary forms of plastic change in response to mechanical stimulation is an inherent and fundamental property of the osteocytic network, and the short-term memory in osteocytic networks can be achieved by three elementary forms of implicit learning: habituation, sensitization, and classical conditioning". In the light of our novel in vitro osteocytic network model and experience in calcium signaling research of bone cells, the goals of this study are to: (1) investigate the habituation, sensitization, and classical conditioning behaviors of osteocytic networks to fluid shear stimuli by recording and analyzing the real-time [Ca2+]i wave propagation inside the cell network and to determine the benign and noxious mechanical stimuli in the implicit learning process of osteocytic networks; and (2) investigate the roles of CaMK pathway, gap junction intercellular communication, and P2Y2 receptor in the habituation, sensitization, and classical conditioning of osteocytic networks to mechanical stimuli by using pharmacological inhibitors or siRNA-based knockdown of one of the 3 proteins. If the significant challenges associated with the memory storage in osteocytic network can be overcome, we will have an opportunity to initiate a new paradigm in research of bone mechanotransduction and behavior studies of fundamental physiological systems. The knowledge will profoundly benefit the hunt for strategies and new drugs for the treatment of osteoporosis, which affects millions of people with more than a $10 billion financial burden. This RC1 application proposes to test a novel hypothesis that the implicit memory exists in osteocyte networks under mechanical loading. The real-time intracellular calcium waves in response to various patterns of fluid flow stimulation will be analyzed to study the three elementary forms of implicit learning, i.e., habituation, sensitization, and classical conditioning, in in vitro osteocytic networks. The mechanotransduction mechanisms have a great importance in understanding the etiology of osteoporosis and preventing bone loss in space.