基于蛋白质网络功能模块的蛋白质功能预测

在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.     

蛋白质网络聚类算法分析平台的设计与实现

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义。针对目前蛋白质网络聚类算法缺乏有效分析软件的事实,本文设计并实现了一个新的蛋白质网络聚类算法分析平台ClusterE。该平台实现了查全率、查准率、敏感性、特异性、功能富集分析等聚类评估方法,并且集成了FAG-EC、Dpclus、Monet、IPC-MCE、IPCA等聚类算法,不仅可以对蛋白质网络聚类分析结果进行可视化,并且可以在不同聚类分析指标下对多个聚类算法进行可视化比较与分析。该平台具有良好的扩展性,其中聚类算法以及聚类评估方法都是以插件形式集成到系统中。     

衰老机体中血管内皮细胞的损伤调控机制

衰老表现在机体多方面的变化,从各种组织器官的功能障碍,到细胞内结构变化。近年来,关于衰老与内皮细胞损伤的研究越来越受到关注。衰老机体中内皮细胞通过多种途径导致自身结构功能发生一系列改变,最终使其损伤。本文综述了衰老机体中,内皮细胞功能发生的变化,着重讨论衰老机体中诱导内皮细胞损伤的多种通路的作用机制,同时论述了作用于各通路的相关调节因子,为延缓衰老机体内皮损伤提供可能的策略。     

基于结构的单绕蛋白聚类图构建与分析

蛋白质分子进化规律研究是分子进化研究的重点,对揭示生命起源与进化机制有重要意义。本文对已知空间结构及物种信息的单绕蛋白,利用结构比对信息,构建了不同层次单绕样本系统聚类图。分析发现:功能相似蛋白存在明显聚集现象,同一超家族样本基本聚在一个大支中,同一家族样本集中在所属超家族下的小支中,功能约束下单绕样本聚类图与物种进化图有较好对应关系。结果表明:单绕蛋白的结构演化反映了蛋白质功能的约束,特定功能单绕样本的结构差异具有种属特异性,结构演化包含了物种进化信息。     

基于功率谱的流感病毒蛋白质序列结构分析

基于经典HP模型,本文采用离散傅里叶变换获取蛋白质特征,利用分层聚类方法进行蛋白质序列的结构分析。其目的是将自动信号频谱分析技术与层次聚类方法相结合,并应用到蛋白质序列结构分析中。通过流感病毒HA和NA蛋白质序列的实验结果表明:应用该方法可得到非常好的分类结果。这些研究为基于大数据的蛋白质序列的自动信息提取和结构分析提供基础。     

北京东灵山森林植物多样性的网络结构特征

一个群落可以看作是由物种相互连接的复杂系统,刻画其网络结构有助于深入揭示系统性质以及结构与功能之间的关系。在生态学中,复杂网络理论已被成功应用于食物网与互利网络的结构研究,但尚未检验其刻画生物多样性格局的能力。采用复杂网络理论研究了北京东灵山森林乔木层、灌木层、草本层植物关联关系的网络结构特征及其差异。结果表明,植物物种的共同出现是非随机的,并表现出一定的小世界模式;乔木层、灌木层、草本层植物共同出现的网络在结构特征上存在明显差异,草本层网络比乔木层和灌木层网络更松散且平均路径更长,灌木层和草本层网络的聚类系数高于乔木层且存在度的幂律分布。皆表明复杂网络理论具备反映不同层植物多样性格局差异的能力。     

基因芯片表达在乳腺癌转移过程中的聚类分析

利用基因芯片可以得到不同基因在不同生命过程中的表达,因此在医学诊断与病变分析中受到重视,并开始大量应用．经测定发现,不同基因在病变过程的不同阶段中的表达是不相同的,由此可以得到在病变过程的不同基因的表达特征．在本文中,我们给出了乳腺癌在转移过程中的基因表达特征的聚类分析法分析,并改进了k-means聚类算法,使之具有自动搜索聚类数的功能,并且有助于改善k-means算法的聚类结果陷入局部最小值的状况．通过对平均聚类误差指标的比较,kr—means要优于k-means算法．本文所得到的结果可供乳腺癌诊断与病变分析参考,同时可以应用于小型基因检测芯片的制备,也可以用于构建基因网络调控图．     

基于聚类系数的DNA序列性质的复杂网络研究

利用一种新的基于图论理论的DNA序列(片段)分析的方法,即通过复杂网络研究生物体的拓扑结构,主要通过测量聚类系数(集团系数)构建网络的拓扑结构。依据DNA序列的前缀、后缀关联性质构造了所选取DNA序列(片段)的相关网络,发现该网络分布满足幂率特征,有较大的聚类系数。结果表明构建得到的网络同时满足小世界网络和无尺度网络的特征,证明DNA序列不全是随机的序列,而是有随机扰动的确定结构的序列。     

琥珀酸对酵母细胞转录组的影响

寻找抗衰老活性小分子并研究其作用机制是衰老药物学研究的重点和热点。本文报道了一种新的抗衰老活性小分子琥珀酸,发现琥珀酸可以显著延缓芽殖酵母细胞的衰老并增强细胞的压力抗性。随后,利用DNA Microarray技术及生物信息学手段较系统分析了琥珀酸处理对基因表达谱、基因本体聚类及相关信号通路的影响。结果显示,琥珀酸处理对细胞转录组产生了显著影响,共导致3 485个基因的差异表达(P 0.05),其中1 335个基因显著上调,2 150个基因显著下调。进一步对基因本体聚类及信号通路分析显示,线粒体及核糖体生物合成相关的分子功能、细胞组分、生物学过程和信号通路可能是琥珀酸作用的主要靶点,其他可能的作用靶点还包括蛋白酶体、细胞内吞、过氧化物酶体代谢及细胞自噬等。本研究为进一步阐明琥珀酸介导的寿命及压力调控机制提供了理论参考和研究线索。     

磁休克治疗对重度抑郁症患者脑功能网络的影响

目的 磁休克治疗(MST)是一种新兴的神经调节干预技术,在重度抑郁症(MDD)治疗中得到广泛应用,然而其抗抑郁机制尚不清楚。探索MST对不同疗效MDD患者脑功能网络的调控作用,对MST治疗的抗抑郁作用机制研究具有重要意义。方法 本文对18例MDD患者MST作用前后的静息态脑电进行记录,基于皮尔逊相关方法构建脑功能网络;应用复杂网络理论对比分析脑功能网络拓扑结构的改变;根据HDRS-17评分变化率进一步区分治疗有效组和无效组,对网络特征进行二次比较。结果 MST后,MDD患者的脑功能网络平均节点度、平均聚类系数和平均全局效率值均比治疗前显著升高,平均路径长度值显著降低,小世界属性显著增大;相比于治疗无效组,治疗有效组的脑功能网络特征参数变化量更大。结论 MST显著改变了MDD患者脑功能网络拓扑结构,对患者的脑功能网络具有一定的调制作用,这些结果为MST治疗的抗抑郁机制研究提供了实验支持和理论依据。     

Differential gene expression in embryogenic and non-embryogenic clusters from cell suspension cultures ofCoffea arabica

Somatic embryogenesis (SE) is a very useful system for studying the differentiation process in plants and involves gene regulation at several levels. During SE induction in Coffea arabica cv. Catura Rojo two types of cell clusters, embryogenic (EC) and non-embryogenic (NEC), were observed. The goal of this work was to compare the most relevant characteristics between EC and NEC for a better understanding of the mechanism driving SE. Morphohistological observations indicated a correlation between the morphological features of clusters and their embryogenic competence. On the other hand, no variation at the DNA level, studied by AFLP, were found to explain the disparity in embryogenic competence of clusters, but gene expression, observed by RNA differential display, and SDS-PAGE showed differences that can explain that disparity. Our results lead us to propose that differential gene expression can modulate the embryogenic capacity of coffee cells and that the number of genes turned off in somatic cells to allow for the change from a somatic to an embryogenic state, is higher than those genes that are turned on.     

Climate impacts at multiple scales: evidence for differential population responses in juvenile Chinook salmon

1. We explored differential population responses to climate in 18 populations of threatened spring-summer Chinook salmon Onchorynchus tshawytscha in the Salmon River basin, Idaho. 2. Using data from a long-term mark-release-recapture study of juvenile survival, we found that fall stream flow is the best predictor of average survival across all populations. 3. To determine whether all populations responded similarly to climate, we used a cluster analysis to group populations that had similar annual fluctuations in survival. The populations grouped into four clusters, and different environmental factors were important for different clusters. 4. Survival in two of the clusters was negatively correlated with summer temperature, and survival in the other two clusters was positively correlated with minimum fall stream flow, which in turn depends on snow pack from the previous winter. 5. Using classification and regression tree analysis, we identified stream width and stream temperature as key habitat factors that shape the responses of individual populations to climate. 6. Climate change will likely have different impacts on different populations within this metapopulation, and recognizing this diversity is important for accurately assessing risks.     

What you are makes you eat different things-interrelations of diet,status, and sex in the early medieval population of Kirchheim unter Teck,FGR

Trace element data of the adult and immature individuals (n=151) from the early medieval graveyard at Kichheim unter Teck were subjected to cluster analysis. Distinct dietary groups resulted for both age categories that show differential access to animal protein. The assumption that these clusters represent social groups could generally be confirmed by comparison with archaeological data on status-indicating features. The distribution of individuals within the clusters was significantly different for the sexes, thus pointing to the detection of differential nutritional habits and behaviour categories by bone chemical analysis.     

Gene microarray analysis using angular distribution decomposition.

Clustering techniques have been widely used in the analysis of microarray data to group genes with similar expression profiles. The similarity of expression profiles and hence the results of clustering greatly depend on how the data has been transformed. We present a method that uses the relative expression changes between pairs of conditions and an angular transformation to define the similarity of gene expression patterns. The pairwise comparisons of experimental conditions can be chosen to reflect the purpose of clustering allowing control the definition of similarity between genes. A variational Bayes mixture modeling approach is then used to find clusters within the transformed data. The purpose of microarray data analysis is often to locate groups genes showing particular patterns of expression change and within these groups to locate specific target genes that may warrant further experimental investigation. We show that the angular transformation maps data to a representation from which information, in terms of relative regulation changes, can be automatically mined. This information can be then be used to understand the "features" of expression change important to different clusters allowing potentially interesting clusters to be easily located. Finally, we show how the genes within a cluster can be visualized in terms of their expression pattern and intensity change, allowing potential target genes to be highlighted within the clusters of interest.     

Adding confidence to gene expression clustering

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.     

Hh-induced Smoothened conformational switch is mediated by differential phosphorylation at its C-terminal tail in a dose- and position-dependent manner

The activation of Smoothened (Smo) requires phosphorylation at three clusters of Serine residues in Drosophila Hedgehog (Hh) signaling. However, the mechanism by which phosphorylation promotes Smo conformational change and subsequently activates Smo in response to Hh gradient remains unclear. Here, we show that the conformational states of Smo are determined by not only the amount but also the position of the negative charges provided by phosphorylation. By using a Smo phospho-specific antibody, we demonstrate that Smo is differentially phosphorylated at three clusters of serine residues in response to levels of Hh activity. Mutating the first cluster, compared to mutating the other clusters, impairs Smo activity more severely, whereas mutating the last cluster prohibits C-terminus dimerization. In addition, phosphorylation of the membrane proximal cluster promotes phosphorylation of the distal cluster. We propose a zipper-lock model in which the gradual phosphorylation at these clusters induces a gradual conformational change in the Smo cytoplasmic tail, which promotes the interaction between Smo and Costal2 (Cos2). Moreover, we show that Hh regulates both PKA and CK1 phosphorylation of Smo. Thus, the differential phosphorylation of Smo mediates the thresholds of Hh activity.     

Reproducible Peak Clusters on Differential Mouse Mortality Curves and Their Relation to the Gompertz Model

It is shown that differentiation of mouse mortality curves (number of animals that died at a certain age plotted versus their lifespan) results in the appearance of eight clearly distinguished clusters of peaks corresponding to increased mortality rates. Smoothing of the original mortality curves and subsequent transformation of the differential mortality curves according to the Gompertz model makes the peaks and the corresponding clusters less pronounced and drives the logarithm of the force mortality curve toward a straight line. The positions of the clusters on the lifespan axis (expressed in days) were calculated as weighted means by dividing the sum of the products of multiplication of the peak heights and their position on the lifespan axis by the sum of the peak heights within a cluster. To prove that the peaks and their clusters are not random, we have demonstrated that the positions of the clusters on the lifespan axis do not depend on the extent of mortality curve smoothing or the group of mice analyzed.     

The statistical neuroanatomy of frontal networks in the macaque

We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework.     

A biologically inspired validity measure for comparison of clustering methods over metabolic data sets

In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.