Dysbindin Regulates the Transcriptional Level of Myristoylated Alanine-Rich Protein Kinase C Substrate via the Interaction with NF-YB in Mice Brain

BACKGROUND: An accumulating body of evidence suggests that Dtnbp1 (Dysbindin) is a key susceptibility gene for schizophrenia. Using the yeast-two-hybrid screening system, we examined the candidate proteins interacting with Dysbindin and revealed one of these candidates to be the transcription factor NF-YB. METHODS: We employed an immunoprecipitation (IP) assay to demonstrate the Dysbindin-NF-YB interaction. DNA chips were used to screen for altered expression of genes in cells in which Dysbindin or NF-YB was down regulated, while Chromatin IP and Reporter assays were used to confirm the involvement of these genes in transcription of Myristoylated alanine-rich protein kinase C substrate (MARCKS). The sdy mutant mice with a deletion in Dysbindin, which exhibit behavioral abnormalities, and wild-type DBA2J mice were used to investigate MARCKS expression. RESULTS: We revealed an interaction between Dysbindin and NF-YB. DNA chips showed that MARCKS expression was increased in both Dysbindin knockdown cells and NF-YB knockdown cells, and Chromatin IP revealed interaction of these proteins at the MARCKS promoter region. Reporter assay results suggested functional involvement of the interaction between Dysbindin and NF-YB in MARCKS transcription levels, via the CCAAT motif which is a NF-YB binding sequence. MARCKS expression was increased in sdy mutant mice when compared to wild-type mice. CONCLUSIONS: These findings suggest that abnormal expression of MARCKS via dysfunction of Dysbindin might cause impairment of neural transmission and abnormal synaptogenesis. Our results should provide new insights into the mechanisms of neuronal development and the pathogenesis of schizophrenia

The bifurcation angle is associated with the progression of saccular aneurysms

The role of the bifurcation angle in progression of saccular intracranial aneurysms (sIAs) has been undetermined. We, therefore, assessed the association of bifurcation angles with aneurysm progression using a bifurcation-type aneurysm model in rats and anterior communicating artery aneurysms in a multicenter case-control study. Aneurysm progression was defined as growth by ≥ 1 mm or rupture during observation, and controls as progression-free for 30 days in rats and ≥ 36 months in humans. In the rat model, baseline bifurcation angles were significantly wider in progressive aneurysms than in stable ones. In the case-control study, 27 and 65 patients were enrolled in the progression and control groups. Inter-observer agreement for the presence or absence of the growth was excellent (κ coefficient, 0.82; 95% CI, 0.61-1.0). Multivariate logistic regression analysis showed that wider baseline bifurcation angles were significantly associated with subsequent progressions. The odds ratio for the progression of the second (145°-179°) or third (180°-274°) tertiles compared to the first tertile (46°-143°) were 5.5 (95% CI, 1.3-35). Besides, the bifurcation angle was positively correlated with the size of aneurysms (Spearman's rho, 0.39; P = 0.00014). The present study suggests the usefulness of the bifurcation angle for predicting the progression of sIAs

Change in hippocampal theta oscillation associated with multiple lever presses in a bimanual two-lever choice task for robot control in rats.

Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in BMI, we investigated hippocampal theta oscillations during a two-lever choice task. During the task body-restrained rats were trained with a food reward to move an e-puck robot towards them by pressing the correct lever, ipsilateral to the robot several times, using the ipsilateral forelimb. The robot carried food and moved along a semicircle track set in front of the rat. We demonstrated that the power of hippocampal theta oscillations gradually increased during a 6-s preparatory period before the start of multiple lever pressing, irrespective of whether the correct lever choice or forelimb side were used. In addition, there was a significant difference in the theta power after the first choice, between correct and incorrect trials. During the correct trials the theta power was highest during the first lever-releasing period, whereas in the incorrect trials it occurred during the second correct lever-pressing period. We also analyzed the hippocampal theta oscillations at the termination of multiple lever pressing during the correct trials. Irrespective of whether the correct forelimb side was used, the power of hippocampal theta oscillations gradually decreased with the termination of multiple lever pressing. The frequency of theta oscillation also demonstrated an increase and decrease, before and after multiple lever pressing, respectively. There was a transient increase in frequency after the first lever press during the incorrect trials, while no such increase was observed during the correct trials. These results suggested that hippocampal theta oscillations reflect some aspects of preparatory and cognitive neural activities during the robot controlling task, which could be used for BMI

Effect of Compressive Stress in Tumor Microenvironment on Malignant Tumor Spheroid Invasion Process

In this study, we proposed an in vitro tumor model to simulate the mechanical microenvironment and investigate the effect of compressive stress on the invasion process of malignant tumors. It has been pointed out that the biomechanical environment, as well as the biochemical environment, could affect the transformation of cancer cell migration, invasion, and metastasis. We hypothesized that the solid stress caused by the exclusion of surrounding tissue could transform tumor cells from noninvasive to invasive phenotypes. Colorectal cell spheroids were embedded and cultured in agarose gels of varying concentrations to simulate the earliest stages of tumor formation and invasion. The spheroids embedded in gels at higher concentrations showed peculiar growth after 72 h of culture, and the external compressive loading imposed on them caused peculiar growth even in the gels at lower concentrations. In conclusion, the mechanical microenvironment caused the transformation of tumor cell phenotypes, promoting the growth and invasion of tumor cell spheroids

Optimal Design for Hetero-Associative Memory: Hippocampal CA1 Phase Response Curve and Spike-Timing-Dependent Plasticity

<div><p>Recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such reversed and spread-out spike patterns. In line with Lengyel’s speculation (Lengyel et al., 2005), we firstly derive optimally designed spike-timing-dependent plasticity (STDP) rules that are matched to neural interactions formalized in terms of phase response curves (PRCs) for performing the hetero-associative memory function. By maximizing object functions formulated in terms of mutual information for evaluating memory retrieval performance, we search for STDP window functions that are optimal for retrieval of normal and doubly spread-out patterns under the constraint that the PRCs are those of CA1 pyramidal neurons. The system, which can retrieve normal and doubly spread-out patterns, can also retrieve reversed patterns with the same quality. Finally, we demonstrate that purposely designed STDP window functions qualitatively conform to typical ones found in CA1 pyramidal neurons.</p></div

Comparison of purposely designed STDP window functions (Figs. 4A′–D′) and those reported for the hippocampal CA1 region.

<p>We computed the Fourier series of symmetric and asymmetric STDP window functions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g002" target="_blank">Fig. 2C</a> and compared the first two frequency components of the STDP window functions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g002" target="_blank">Fig. 2C</a> with those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g004" target="_blank">Figs. 4A′–D′</a>. (A) Symmetric and asymmetric STDP window functions composed of only the fundamental and second frequency components of the ones in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g002" target="_blank">Fig. 2C</a>. <i>Left</i>: Symmetric plasticity rule <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone.0077395-Wittenberg1" target="_blank">[16]</a>. <i>Right</i>: Asymmetric plasticity rule <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone.0077395-Bi1" target="_blank">[17]</a>. (B) Rates of fundamental and second frequency components of STDP window functions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g005" target="_blank">Fig. 5A</a> and the purposely designed ones in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g004" target="_blank">Figs. 4A′–D′</a>. We compared the amplitudes between the two Fourier coefficients of each STDP window function, i.e., and . Symmetric: <i>left</i> panel of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g005" target="_blank">Fig. 5A </a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone.0077395-Wittenberg1" target="_blank">[16]</a>. Asymmetric: <i>right</i> panel of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g005" target="_blank">Fig. 5A </a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone.0077395-Bi1" target="_blank">[17]</a>.</p

Examples of STDP window functions optimally matched to PRCs of five hippocampal CA1 pyramidal neurons shown in Fig. 2B.

<p>(A–D) By maximizing the objective function defined in Eq. (25), we searched for STDP window functions that are optimal for retrieving normal patterns. (A′–D′) By maximizing the objective function defined in Eq. (24), we searched for ones that are optimal for both retrieving normal and doubly spread-out patterns. In all cases, , . We obtained connected sets of optimal STDP window functions, as described in the main article. Each of the four panels in the upper and lower rows plots examples of optimal STDP window functions with different phases. The numbers assigned to each line correspond to the cell indexes in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077395#pone-0077395-g002" target="_blank">Fig. 2B</a>. All sets of optimal STDP window functions except for cell #1 have the same form. (A, A′) STDP window functions when , which corresponds to the symmetric STDP rule. (B, B′) STDP window functions when (B) and (B′), which correspond to the asymmetric STDP rule. (C, C′) STDP window functions when , which corresponds to the inverted symmetric STDP rule. (D, D′) STDP window functions when (D) and (D′), which correspond to the inverted asymmetric STDP rule.</p