MOLECULAR AND CELLULAR MECHANISMS OF A NEURONAL NETWORK FOR OLFACTORY LEARNING

描述（由申请人提供）：学习是神经系统的基本功能，允许动物以适应性值调节行为。虽然越来越多的知识的分子基础的学习提供了深入了解学习的机制，我们的理解不能解释大多数学习范式的行为变化。该领域的一个主要挑战是将底层神经网络的功能与行为联系起来，并解决神经回路的属性如何编码学习。我们使用遗传模式生物C。Elegans来回答这个问题。在过去的资助期间，我们已经建立了一种厌恶性嗅觉学习的形式，通过这种学习，线虫学会避免致病细菌的气味。这种形式的学习类似于加西亚效应，其中动物学会避免与胃部不适相关的食物的气味或味道。使用这种学习范式，我们已经表征了底层神经元网络的结构和功能。特别是，我们表明，一个由多巴胺能神经元ADF和下游的中间神经元RIA，以及运动神经元专门调节学习的嗅觉偏好的多巴胺能神经回路。ADF中的5-羟色胺信号调节对病原菌的厌恶学习。ADF对细菌气味的反应是增加细胞内钙信号，而C. Elegans的CaMKII同源物，α-43，在ADF中起调节学习的作用。突触后神经元RIA是厌恶性学习所必需的。RIA显示与头部运动相关的区室化轴突活动。与此同时，RIA轴突隔室也显示出由嗅觉刺激引起的同步活动。有趣的是，我们发现，厌恶训练调节活动模式， ADF和RIA的方式是一致的训练诱导的嗅觉偏好的行为变化。因此，我们假设这些学习相关的ADF和RIA神经元的功能属性的变化编码学习。我们建议测试这一假设的特点的调节机制和功能的学习相关的ADF和RIA。我们将首先通过测试ADF中的学习相关性调节RIA中的学习相关性的可能性来定义这两个学习相关性之间的相互作用。我们还将通过研究我们已经确定的几个调节学习的遗传因素的影响来描述这些学习相关物的调节。我们还将定义调节RIA活性的ADF的神经传递。其次，我们将描述ADF和RIA中学习相关物的功能。我们将使用分子和光遗传学来操纵这些神经元的特性，以（1）消除训练诱导的活动模式变化;（2）在回路的关键神经元中“建立”与遗传方法相关的学习，然后测试对嗅觉学习的影响。这些研究将揭示经验如何调节神经网络的功能，并导致经验依赖的行为变化。

DESCRIPTION (provided by applicant): Learning is an essential function of the nervous system that allows animals to modulate behavior with adaptive values. While increasing amount of knowledge on the molecular underpinnings of learning provide insights into the mechanisms underlying learning, our understanding cannot explain behavioral changes in most learning paradigms. One major challenge of the field is to link the function of the underlying neuronal network with behavior and to address how the property of neural circuitry encodes learning. We use the genetic model organism C. elegans to address this question. In the past funding period, we have established a form of aversive olfactory learning whereby the nematode learns to avoid the smell of pathogenic bacteria that make it ill. This form of learning is analogous to Garcia effect, in which animals learn to avoid the smell or taste of a food that is associated with stomach distress. Using this learning paradigm, we have characterized the structure and function of the underlying neuronal network. Particularly, we show that a serotonergic neural circuit composed of the serotonergic neuron ADF and the downstream interneuron RIA, as well as motor neurons specifically regulate learned olfactory preference. The serotonin signal in ADF regulates the aversive learning on pathogenic bacteria. ADF responds to bacterial odors with increased intracellular calcium signals and the C. elegans homolog of CaMKII, UNC-43, acts in ADF to regulate learning. The postsynaptic neuron RIA is critically required for the aversive learning. RIA displays compartmentalized axonal activity that is correlated with head movement. Meanwhile, RIA axonal compartments also display synchronous activity that is evoked by olfactory stimuli. Interestingly, we show that the aversive training modulates the activity pattern of ADF and RIA in a way that is consistent with training-induced behavioral changes in olfactory preference. Thus, we hypothesize that these learning-correlated changes in the functional attributes of ADF and RIA neurons encode learning. We propose to test this hypothesis by characterizing the regulatory mechanisms and function of the learning correlates in ADF and RIA. We will first define the interaction between these two learning correlates by testing the possibility that the learning correlate in ADF regulates the learning correlate in RIA. We will als characterize the regulation of these learning correlates by examining the effect of several genetic factors that we have identified to mediate learning. We will also define the neurotransmission of ADF that regulates RIA activity. Second, we will characterize the function of the learning correlates in ADF and RIA. We will use molecular and optogenetics to manipulate the property of these neurons to (1) eliminate the training-induced changes in their activity patterns; and (2) "build" the learning correlates with genetic methods in the key neurons of the circuit, and then test the resulting effects on olfactory learning. These studies will revea how experience modulates the function of a neural network and leads to experience-dependent behavioral changes.