CRCNS: Computational Model for Neural Stem Cell Divisions in the Adult Brain

描述(申请人提供)：成人大脑在一生中保持产生新神经元的能力。干细胞通过一系列事件产生新的神经元，这些事件包括对称和不对称分裂以及细胞形态的持续变化。随着年轻的神经元与其他细胞建立连接，并整合到先前存在的神经元回路中，这种级联反应达到了顶峰。持续性神经发生只在成人大脑的一些区域观察到。在其中之一的海马齿状回，新的神经元可能对学习、记忆和情绪很重要。成体神经发生是一个动态的过程，它对各种刺激做出反应，这些刺激可以增强或抑制其输出，并可能影响分化级联的任何步骤。例如，抗抑郁药物氟西汀(百忧解)增强了海马神经发生，而衰老则减少了海马神经发生。然而，促神经源性或抗神经源性因子影响分化级联的步骤及其作用机制尚不清楚。这个合作项目的主要目标是开发一个成人神经发生的计算模型。为了实现这一目标，我们将开发一种新的研究方法，将计算和实验技术相结合，对定义成人神经发生的步骤进行定量研究。Enikolopov实验室(实验合作者)开发了一种新方法，使用一组遗传编码的标记来监控祖细胞通过分化级联的进展。这种方法允许在细胞分裂和分化时确定不同类型细胞的丰度作为时间的函数。丰度只对实验开始时分裂的细胞进行评估，并与不同细胞类型的成对相关函数相对应。为了将丰度作为时间的函数转换为不同类型细胞的分裂和分化速率，我们将使用Koulakov博士团队开发的计算模型。这个计算模型也将被用来确定衰老和抗抑郁药物对分裂和分化级联参数的影响。我们将研究全基因组基因表达谱随分化级联阶段的变化。我们将监测基因表达对衰老和抗抑郁药物的依赖性，并将阐明潜在的基因调控动力学。最后，我们将从理论上研究成人神经发生的假定计算特性。该提案的具体目标(SA)包括： SA 1：建立成年海马区干细胞分裂和分化级联的计算模型。这一目标将允许从实验数据中推断出分割图和转换率，并将允许研究老化和抗抑郁药物引起的变化。 SA 2：解剖控制成年海马神经发生的转录调控网络。我们将研究与衰老和抗抑郁药物相关的基因表达的变化，并揭示潜在的调控网络机制。 SA 3：研究神经干细胞独特的计算特性。在这里，我们将计算学习速度作为表示稀疏性的函数，并将论证基于细胞的学习规则比传统的基于突触的Hebb规则更快地适应新的刺激。 智力价值：拟议的研究将在两个层面上为系统生物学做出贡献。首先，我们将阐明神经干细胞分化导致新神经元产生的机制。其次，我们将开发从细胞丰度随时间变化的数据中确定分裂和分化率的方法。这一计算框架可能成为其他生物学领域干细胞分化研究的标准。 更广泛的影响：这个项目基于理论科学、新颖的计算方法和神经生物学的前沿实验之间的协同作用。该奖项将为培养年轻的神经科学家提供一个独特的跨学科环境。我们预计，两名博士后研究员将通过这一奖项接受培训，他们专门从事理论和实验方法。对于更广泛的社会：我们的研究将有助于阐明与衰老相关的认知衰退的机制，并确定抗抑郁药物治疗的靶点。因为在美国，抑郁症的终生发病率在男性中超过12%，在女性中超过20%，我们的研究可能会对公共健康做出重大贡献。

DESCRIPTION (provided by applicant): Adult brain retains the ability to produce new neurons throughout life. New neurons are generated from stem cells through a cascade of events which include symmetric and asymmetric divisions and continuous changes of cell morphology. The cascade culminates with the young neurons establishing connections with other cells and becoming integrated into the pre-existing neuronal circuitry. Persistent neurogenesis is observed only in a number of the adult brain regions. In one of them, the hippocampal dentate gyrus, new neurons may be important for learning, memory, and mood. Adult neurogenesis is a dynamic process that responds to a wide range of stimuli which can enhance or suppress its output and may affect any step of the differentiation cascade. For instance, an antidepressant drug fluoxetine (Prozac) enhances, whereas aging decreases, hippocampal neurogenesis. However, the steps of the differentiation cascade affected by pro- or anti-neurogenic factors and the mechanisms of their action are not known. The main goal of this collaborative project is to develop a computational model of adult neurogenesis. Towards this goal, we will develop a novel research method that integrates computational and experimental techniques for a quantitative investigation of the steps that define adult neurogenesis. A new approach developed in Enikolopov lab (experimental collaborator) uses a set of genetically encoded markers to monitor the progression of progenitor cells through the differentiation cascade. This approach allows to determine the abundances of different cell types as a function of time as the cells divide and differentiate. The abundances are evaluated only for cells that were dividing in the beginning of the experiment and correspond to the pair wise correlation function for different cell types. To convert the abundances as a function of time into the rates of division and differentiation of various cell types we will use the computational model developed by the group of Dr. Koulakov. This computational model will also be used to determine the effects of aging and antidepressants on the parameters of division and differentiation cascade. We will investigate the changes occurring in the genome-wide gene expression profiles as a function of stage of the differentiation cascade. We will monitor the dependence of gene expression on both aging and antidepressants and will elucidate the underlying gene regulation dynamics. Finally, we will study theoretically the putative computational properties of the adult neurogenesis. The specific aims (SA) of this proposal include: SA 1: To develop a computational model for the stem cell division and differentiation cascade in the adult hippocampus. This aim will allow inferring the division diagram and transition rates from the experimental data and will allow to study the changes induced by aging and antidepressants. SA 2: To dissect the transcription regulation network controlling adult hippocampal neurogenesis. We will investigate changes of gene expression associated with aging and antidepressant drugs and will uncover potential regulatory network mechanisms. SA 3: To study the unique computational properties of neural stem cells. Here we will calculate the rate of learning as a function of sparseness of representation and will argue that cell-based learning rules adopt to new stimuli faster than conventional synapse based Hebb rules. Intellectual merit: The proposed research will contribute to systems biology on two levels. First, we will elucidate the mechanisms of neural stem differentiation leading to the production of new neurons. Second, we will develop methods for determining division and differentiation rates from time-dependent data of cell abundances. This computational framework may become standard in the studies of stem cell differentiation in other fields of biology. Broader impacts: This project is based on the synergy between theoretical sciences, novel computational methods, and cutting-edge experiments in neurobiology. The award will provide a unique crossdisciplinary environment for training of young neuroscientists. We expect that two postdoctoral fellows, specializing in theoretical and in experimental approaches, will receive training through this award. To broader society: Our studies will help to elucidate the mechanisms of cognitive decline associated with aging and to determine the targets of antidepressant drug therapies. Because the lifetime incidence of depression in the US is more than 12% in men and 20% in women, our studies may substantially contribute to public health.