神经粘附分子CHL1在神经系统中的研究进展

粘附分子通过介导细胞间相互作用发挥其在发育、再生和突触修饰等方面的重要作用.神经细胞粘附分子CHL1(close homologue of L1)是近年发现的粘附分子,属于粘附分子免疫球蛋白超家族,集中表达于神经系统,通过亲异性作用(heterophilic interaction)介导细胞与细胞、细胞与胞外基质的相互作用,进而参与神经系统的发育、轴突的生长、迁移及导向等过程.     

神经细胞粘附分子

神经细胞粘附分子（ＮＣＡＭ）是一种糖蛋白,能介导细胞与细胞及细胞与细胞外基质间相互作用,它在细胞的识别及转移、肿瘤的浸润与生长、神经再生、跨膜信号的传导、学习和记忆等方面均起着一定的作用,本文对神经细胞粘附分子的结构、表达和功能加以概述,以增加对其的了解。     

L1CAM胞内区相互作用蛋白的筛选与鉴定

L1细胞粘附分子(L1cell adhesion molecular,L1CAM)属于神经细胞粘附分子,是属于免疫球蛋白超家族的Ⅰ型跨膜糖蛋白.L1主要在神经系统中表达,参与神经系统发育,学习记忆等重要过程作用.L1的胞内区可能参与信号转导,对于L1的功能非常重要,为探讨L1胞内区信号转导的分子机制,以L1胞内区为诱饵运用酵母双杂交技术在人脑cDNA文库中筛选其结合蛋白,挑选阳性克隆,进行DNA序列分析和同源检索,阳性克隆编码几个不同的蛋白质,其中一个候选蛋白为PAX6转录因子.为进一步验证L1胞内区和PAX6的相互作用,克隆其基因到表达质粒共转染COS-7细胞,免疫共沉淀证实了L1胞内区和PAX6的相互作用,提示L1胞内区可能参与转录调节,为深入探讨其功能提供了重要线索.     

神经细胞粘附分子与记忆

记忆的形成阶段包含着神经元突触的可塑性变化过程.近年来的研究表明,神经细胞粘附分子可同时增进突触的可塑性和维持突触结构的稳定性.许多研究证实神经细胞粘附分子对与学习和记忆相关的过程起着一定的调节作用.     

神经细胞粘附分子结构特征和生理功能

神经细胞粘附分子是一类调节细胞与细胞、细胞与细胞外基质间粘附作用的膜表面糖蛋白,主要有NCAM-180、NCAM-140、NCAM-120三种形式,多与PSA结合在一起。在神经系统中,NCAM的表达具有时间和空间特异性,最主要的作用为调节神经系统的可塑性,这种作用可能是通过PSA-NCAM对AMPA的调节作用,主要是通过调节蛋白激酶的表达和细胞内Ca^2 浓度来实现的。     

neurexin家族在突触发生和突触传递中作用的研究进展

neurexin家族属于神经细胞表面蛋白,参与细胞识别和细胞黏附,可能介导细胞信号转导。最近研究表明,neurexins在突触发生和突触传递等过程中发挥重要作用,并可能影响学习记忆功能。这些研究进展对于进一步揭示neurexins在神经突触可塑性及其在学习记忆过程中的可能作用具有重要意义。本文主要对neurexin家族的研究概况、NRXN1在突触发生和突触传递中的功能及其在学习记忆功能中的可能作用进行简要综述。     

细胞间粘附分子1的研究进展

细胞间粘附分子1(ICAM-1),又名CD54,是一种重要的细胞表面粘附分子,属免疫球蛋白超家族．它可与鼻病毒以及整合素家族成员结合,参与炎症,普通感冒,变态反应及移植排斥反应．文章就其细胞分布、表达调节、结构功能、基因工程以及临床应用进行了综述．     

Vimentin在肿瘤转移中的作用及药物研究进展

波形蛋白(vimentin)是存在于间充质细胞中的一种中间丝蛋白,近些年研究显示vimentin与肿瘤发生、转移密切相关。波形蛋白调节细胞骨架蛋白、细胞粘附分子等蛋白间的相互作用,参与肿瘤细胞和肿瘤相关内皮细胞、巨噬细胞的粘附、迁移、侵袭和细胞信号转导。其高度动态的聚合解聚间的平衡和其复杂的磷酸化形式可能是vimentin参与肿瘤转移过程及细胞-细胞间相互作用的调节机制。Vimentin在肿瘤中的功能提示,其可能是抗肿瘤转移治疗药物研究的新靶点。     

β-连环蛋白在记忆巩固过程中起重要作用

一般认为,突触的结构在记忆的巩固过程中发生变化。β-连环蛋白（β-catenin）已被证实与神经元突触的调节和重塑有关。β-连环蛋白可与钙粘蛋白（cadherin）结合形成复合物参与突触发育及其连接性和活性的调节。此外,β-连环蛋白在Wnt信号传导通路中发挥着重要作用。这条信号通路在海马切片标本中被证实参与突触可塑性的调节。由此推测,β-连环蛋白可能是影响突触可塑性的核心蛋白,并参与调节学习和记忆等重要活动。但之前尚无实验证明它在动物学习和记忆中潜在的重要作用。     

CHL1基因在肿瘤中的研究进展

CHL1(close homologue of L1)基因是神经细胞粘附分子(cell adhesion molecule,CAM)L1基因家族中的一员,既往的研究认为,CHL1基因作为神经识别分子,主要参与调节神经前体细胞的增殖和神经元亚型的特异性分化。近期的研究发现,CHL1基因参与了对细胞生长和迁移的调节,影响了多种人类肿瘤的发生和发展过程。本文拟对CHL1基因在肿瘤中的最新研究进展进行综述。     

Codes, operations, measurements and neural networks

Numerous neural codes and primary neural operations (logical and arithmetical ones, mappings, transformations) were listed [e.g. Perkel, D., Bullock, T.H., 1968. Neurosci. Res. Program Bull 6, 221-348] during the past decades. None of them is ubiquitous or universal. In reality neural operations take place in continuous time and working with unreliable elements, but they still can be simulated with synchronized discrete time scales and chaotic models. Here, a possible neural mechanism, called 'measure like' code is introduced and examined. The neurons are regarded as measuring devices, dealing with 'measures', more or less in mathematical sense. The subadditivity--eminent property of measures--may be implemented with neuronal refractoriness and such synapses operate like particle counters with dead time. This hypothetical code is neither ubiquitous, nor universal, e.g. temporal summation (multiplication) causes just the opposite phenomenon, the supra-additivity also with respect to the number of spikes (anti-measures). This is a cause of more difficult neural implementation of OR gate, than that of the AND. Possibilities for transitional mechanisms (e.g. between traditional logical gates, etc.) are stressed here. Parameter tuning might change either code or operation.     

Robustness, evolvability, and optimality of evolutionary neural networks

In a typical optimization problem, the main goal is to search for the appropriate values of the variables that provide the optimal solution of the given function. In artificial neural networks (ANN), this translates to the minimization of the error surface during training such that misclassification is minimized during generalization. However, since optimal training performance does not necessarily imply optimal generalization due to the possibility of overfitting or underfitting, we developed SEPA (Structure Evolution and Parameter Adaptation) which addressed these issues by simultaneously evolving ANN structure and weights. Since SEPA primarily relies on the perturbation function to bring variation in its population, this follow-up study aims to find out SEPAs evolvability, optimality, and robustness in other perturbation functions. Our findings indicate that SEPAs optimal generalization performances are stable and robust from the effect of the different perturbation functions. This is due to the feedback loop between its architecture evolution and weight adaptation such that any shortcoming of the former is compensated by the latter, and vice versa. Our results strongly suggest that proper ANN design requires simultaneous adaptation of ANN structure and weights to avoid one-sided or bias convergence to either the weight or architecture space.     

Estrogens,apoptosis and cells of neural origin

In mammals, estrogens have a multiplicity of effects in all known neural cells. We review here some of the mechanisms enabling estrogens to differentiate their influence on neural targets. In view of the potential interest in the use of estrogens in the therapy of several pathologies of the nervous system, we have proposed the use of a reductionist approach for the systematic understanding of estrogen activities in each specific type of target cell. We have therefore generated a model system in which to study the activation of one of the known estrogen receptors: estrogen receptor alpha. This system allowed us to identify a number of novel genes, the expression of which may be influenced following the activation of this receptor subtype by estradiol. We here report on preliminary data obtained by the study of one of these target genes, nip2, which encodes a proapoptotic protein product. We hypothesize that Nip2 might be an important molecular determinant for estrogen anti-apoptotic activity in cells of neural origin.     

Origins,functions, and potential of adult neural stem cells

In recent years, the existence of neural stem cells (NSCs) in the adult mammalian brain has been confirmed. The generation of new neurons from these cells is regulated by growth factors, hormones, and environmental cues; however, the function of newly generated neurons in the adult brain remains elusive. Two recent articles emphasize the impact of motor activity and learning on in situ hippocampal neurogenesis,^(1,2) suggesting a close link to hippocampal function. Adult NSCs can be isolated and expanded in vitro. It was presumed that the origins of the NSCs were within subependyma of the lateral ventricle; however, new evidence suggests that the “real” stem cells may reside in the ependymal lining.⁽³⁾ In a related study, these same cells were transplanted into irradiated mice and were able to integrate into the bone marrow and produce various blood cell types,⁽⁴⁾ challenging the limits of neural cell fate determination. BioEssays 21:625–630, 1999. © 1999 John Wiley & Sons, Inc.     

Learning, memory, and the role of neural network architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.     

Xerl, a novel CNS-specific secretory protein, establishes the boundary between neural plate and neural crest.

A novel gene, Xerl, has been found as a CNS-specific gene encoding a secretory protein. In order to clarify a function of Xerl, we first examined Xerl-expressing areas during early development. Comparison with XlSox-2-positive neural plate and ADAM13-positive neural crest showed that Xerl expression was limited within the neural plate area. Microinjection of Xerl mRNA into 2- or 4-cell stage embryos indicated that Xerl overexpression caused the regional expansion of XlSox-2- and NCAM-positive neural plate, which was concomitant with the outer shift of ADAM13-positive region. The Xerl injection resulted in incomplete neural closure because of the local overproduction of the neuroepithelium. In contrast, loss of function analysis of Xerl indicated that Xerl inhibition caused the ectopic differentiation of neural crest cells. In the conjugation experiment using chordin-injected animal caps, Xerl promoted chordin-induced XlSox-2 expression, whereas Xerl inhibition caused ADAM13expression even in the injection with a high dose of chordin. Animal cap assays also showed that Xerl expression was induced by chordin. In the functional analysis using truncated forms of Xerl, Xerl deltaL (lacking LNS domain) worked as a dominant negative form that induced the overproduction of neural crest cells. These results suggest that Xerl is involved in the boundary formation of the neural plate through exclusion of neural crest cell differentiation.     

Waves, bumps, and patterns in neural field theories

Neural field models of firing rate activity have had a major impact in helping to develop an understanding of the dynamics seen in brain slice preparations. These models typically take the form of integro-differential equations. Their non-local nature has led to the development of a set of analytical and numerical tools for the study of waves, bumps and patterns, based around natural extensions of those used for local differential equation models. In this paper we present a review of such techniques and show how recent advances have opened the way for future studies of neural fields in both one and two dimensions that can incorporate realistic forms of axo-dendritic interactions and the slow intrinsic currents that underlie bursting behaviour in single neurons.     

Metastable states, phase transitions, and persistent neural activity

A new mathematical model to describe the spiking rate of a neural population is derived, which considers both the mean and the variance of the activity. Bifurcation analysis identifies a critical interval of parameter values in which the standard bistability regime coexists with an additional third attractor corresponding to the metastable state of bounded mean activity and high variance. To understand the structure of spatio-temporal activity in the metastable state, we study a simple discrete-time model of binary elements with random noise locally coupled on the grid, which produces rich dynamics including metastability. A critical value of the noise amplitude is identified; in the vicinity of this value the system is flexible and can easily generate transitions between UP and DOWN metastable states, either autonomously or in response to a control process. These metastable states and phase transitions provide a proper basis for the modelling of persistent neural activity reported in many experimental studies.     

Human altruism: economic, neural, and evolutionary perspectives

Human cooperation represents a spectacular outlier in the animal world. Unlike other creatures, humans frequently cooperate with genetically unrelated strangers, often in large groups, with people they will never meet again, and when reputation gains are small or absent. Experimental evidence and evolutionary models suggest that strong reciprocity, the behavioral propensity for altruistic punishment and altruistic rewarding, is of key importance for human cooperation. Here, we review both evidence documenting altruistic punishment and altruistic cooperation and recent brain imaging studies that combine the powerful tools of behavioral game theory with neuroimaging techniques. These studies show that mutual cooperation and the punishment of defectors activate reward related neural circuits, suggesting that evolution has endowed humans with proximate mechanisms that render altruistic behavior psychologically rewarding.