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ABSTRACT: The anterior interpositus nucleus (AIN) is the proposed site of memory formation of eyeblink conditioning. A large part of the underlying molecular events, however, remain unknown. To elucidate the molecular mechanisms, we examined transcriptional changes in the AIN of mice trained with delay eyeblink conditioning using microarray, quantitative real-time RT-PCR, and in situ hybridization techniques. Microarray analyses suggested that transcriptionally up-regulated gene sets were largely different between early (3-d training) and late (7-d) stages. Quantitative real-time RT-PCR aided by laser microdissection indicated that the expression of representative EARLY genes (Sgk, IkBa, and Plekhf1) peaked at 1-d training in both the paired and unpaired conditioning groups, and was maintained at a higher level in the paired group than in the unpaired group after 3-d training. In situ hybridization revealed increased expression of these genes in broad cerebellar areas, including the AIN, with no hemispheric preferences. In contrast, the expression of representative LATE genes (Vamp1, Camk2d, and Prkcd) was selectively increased in the AIN of the 7-d paired group, with dominance in the ipsilateral AIN. Increased Vamp1 mRNA expression was restricted to the ipsilateral dorsolateral hump, a subregion of the AIN. These expression patterns of two distinct subsets of genes fit well with the two-stage learning theory, which proposes emotional and motor learning phases, and support the notion that AIN has a crucial role in memory formation of eyeblink conditioning.
Proceedings of the National Academy of Sciences 05/2006; 103(14):5549-54. · 9.68 Impact Factor
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ABSTRACT: A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outlier exclusion and works well even in the case of small sample size. Validation of this method with experimental data has suggested that it is superior to the conventional method.
Journal of Bioinformatics and Computational Biology 01/2005; 2(4):669-79.
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ABSTRACT: Given the vast amount of gene expression data, it is essential to develop a simple and reliable method of investigating the fine structure of gene interaction. We show how an information geometric measure achieves this.
We introduce an information geometric measure of binary random vectors and show how this measure reveals the fine structure of gene interaction. In particular, we propose an iterative procedure by using this measure (called IPIG). The procedure finds higher-order dependencies which may underlie the interaction between two genes of interest. To demonstrate the method, we investigate the interaction between the two genes of interest in the data from human acute lymphoblastic leukemia cells. The method successfully discovered biologically known findings and also selected other genes as hidden causes that constitute the interaction.
Softwares are currently not available but are possibly made available in future at http://www.mns.brain.riken.go.jp/~nakahara/DNA_pub.html where all the related information is also linked.
Bioinformatics 07/2003; 19(9):1124-31. · 5.47 Impact Factor
