Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

The phosphatase and tensin homolog deleted on chromosome ten (PTEN) gene encodes a tumor suppressor phosphatase that has recently been found to be frequently mutated in patients with endometriosis, endometrial cancer and ovarian cancer. Here, we present the first computational analysis of 13 somatic missense PTEN mutations associated with these phenotypes. We found that a majority of the mutations are associated in conserved positions within the active site and are clustered within the signature motif, which contain residues that play a crucial role in loop conformation and are essential for catalysis. In silico analyses were utilized to identify the putative effects of these mutations. In addition, coarse‐grained models of both wild‐type (WT) PTEN and mutants were constructed using elastic network models to explore the interplay of the structural and global dynamic effects that the mutations have on the relationship between genotype and phenotype. The effects of the mutations reveal that the local structure and interactions affect polarity, protein structure stability, electrostatic surface potential, and global dynamics of the protein. Our results offer new insight into the role in which PTEN missense mutations contribute to the molecular mechanism and genotypic‐phenotypic correlation of endometriosis, endometrial cancer, and ovarian cancer. Proteins 2016; 84:1625–1643. © 2016 Wiley Periodicals, Inc.     

不同鸟类生境网络复合与优化——以苏锡常地区白鹭、鸳鸯、雉鸡为例

通过复合优化不同焦点物种的生境网络表征多数物种生境网络实现物种多样性保护,具有重要理论价值和实践意义。以苏锡常地区为研究区域,以白鹭、鸳鸯和雉鸡3种鸟类为焦点物种,通过采用生境斑块约束条件模型与最小成本路径法相结合的方法识别出生境网络;从满足集合覆盖问题视角出发,通过采用网络构成要素复合的方法进行网络复合,其中廊道与廊道的复合通过采用评价生态系统服务价值的方法实现;通过采用观测数据为生境斑块和迁移廊道增补依据的方法进行复合生境网络优化。结果表明:生境网络经过复合,廊道总面积、总长度及生态系统服务价值在分别减少了16%、68%和10%的情况下,保持了斑块间的连接度、兼顾到经济效益和生态效益最大化、覆盖了占总数86%以上的75处观测点;复合生境网络经过优化,覆盖了2010年及以后所有84处观测点,廊道总面积、总长度及生态系统服务价值分别增加了19%、21%和27%;说明生境网络复合优化定量分析方法和思路具有可行性,为网络复合研究提供了借鉴。     

使用线性微分方程构建初步的乳腺癌转移相关基因网络模型

随着基因芯片的技术的推广,越来越多的表达数据需要被处理和分析．利用这些表达数据提取基因调控矩阵从而构建基因网络是一个重要的问题．通过线性微分方程模型可以初步构建基因网络,了解网络结构,提取最显著的信息．然而由于分子生物学的条件限制或者数据来源的限制,导致实验数据不充分,使方程组无解．本文使用三次样条方法,对26例临床、病理资料完备的具有淋巴结转移的乳腺癌基因表达数据进行插值处理,使表达数据满秩,从而使用最小二乘法解出加权矩阵,构建初步的表达基因调控网络．通过对构建的基因网络的初步分析表明:乳腺癌转移的形成是由多基因异常引起多条传导通路异常,致使细胞恶性转化的结果,这与生物学上公认的看法是相一致的．因此,利用此线性模型方法对基因表达谱进行分析兵有一定可行性,在认识乳腺癌转移机制,乳腺癌诊断和治疗方面具有一定的理论和应用价值．     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task

Since the discovery of steady-state visually evoked potential (SSVEP), it has been used in many fields. Numerous studies suggest that there exist three SSVEP neural networks in different frequency bands. An obvious phenomenon has been observed, that the amplitude and phase of SSVEP can be modulated by a cognitive task. Previous works have studied this modulation on separately activated SSVEP neural networks by a cognitive task. If two or more SSVEP neural networks are activated simultaneously in the process of a cognitive task, is the modulation on different SSVEP neural networks the same? In this study, two different SSVEP neural networks were activated simultaneously by two different frequency flickers, with a working memory task irrelevant to the flickers being conducted at the same time. The modulated SSVEP waves were compared with each other and to those only under one flicker in previous studies. The comparison results show that the cognitive task can modulate different SSVEP neural networks with a similar style.     

Human microRNA similarity in breast cancer

MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

Hemagglutinin of Influenza Virus Partitions into the Nonraft Domain of Model Membranes

The HA of influenza virus is a paradigm for a transmembrane protein thought to be associated with membrane-rafts, liquid-ordered like nanodomains of the plasma membrane enriched in cholesterol, glycosphingolipids, and saturated phospholipids. Due to their submicron size in cells, rafts can not be visualized directly and raft-association of HA was hitherto analyzed by indirect methods. In this study, we have used GUVs and GPMVs, showing liquid disordered and liquid ordered domains, to directly visualize partition of HA by fluorescence microscopy. We show that HA is exclusively (GUVs) or predominantly (GPMVs) present in the liquid disordered domain, regardless of whether authentic HA or domains containing its raft targeting signals were reconstituted into model membranes. The preferential partition of HA into ld domains and the difference between lo partition in GUV and GPMV are discussed with respect to differences in packaging of lipids in membranes of model systems and living cells suggesting that physical properties of lipid domains in biological membranes are tightly regulated by protein-lipid interactions.