Identification and characterization of a glycosaminoglycan binding site on interleukin-10 via molecular simulation methods

The biological function of the pleiotropic cytokine interleukin-10 (IL-10), which has an essential role in inflammatory processes, is known to be affected by glycosaminoglycans (GAGs). GAGs are essential constituents of the extracellular matrix with an important role in modulating the biological function of many proteins. The molecular mechanisms governing the IL-10–GAG interaction, though, are unclear so far. In particular, detailed knowledge about GAG binding sites and recognition mode on IL-10 is lacking, despite of its imminent importance for understanding the functional consequences of IL-10–GAG interaction. In the present work, we report a GAG binding site on IL-10 identified by applying computational methods based on Coulomb potential calculations and specialized molecular dynamics simulations. The identified GAG binding site is constituted of several positively charged residues, which are conserved among species. Exhaustive conformational space sampling of a series of GAG ligands binding to IL-10 led to the observation of two GAG binding modes in the predicted binding site, and to the identification of IL-10 residues R104, R106, R107, and K119 as being most important for molecular GAG recognition. In silico mutation as well as single-residue energy decomposition and detailed analysis of hydrogen-bonding behavior led to the conclusion that R107 is most essential and assumes a unique role in IL-10–GAG interaction. This structural and dynamic characterization of GAG-binding to IL-10 represents an important step for further understanding the modulation of the biological function of IL-10.     

Characterization of neuropathological shape deformations

We present a framework for analyzing the shape deformation of structures within the human brain. A mathematical model is developed describing the deformation of any brain structure whose shape is affected by both gross and detailed physical processes. Using our technique, the total shape deformation is decomposed into analytic modes of variation obtained from finite element modeling, and statistical modes of variation obtained from sample data. Our method is general, and can be applied to many problems where the goal is to separate out important from unimportant shape variation across a class of objects. In this paper, we focus on the analysis of diseases that affect the shape of brain structures. Because the shape of these structures is affected not only by pathology but also by overall brain shape, disease discrimination is difficult. By modeling the brain's elastic properties, we are able to compensate for some of the nonpathological modes of shape variation. This allows us to experimentally characterize modes of variation that are indicative of disease processes. We apply our technique to magnetic resonance images of the brains of individuals with schizophrenia, Alzheimer's disease, and normal-pressure hydrocephalus, as well as to healthy volunteers. Classification results are presented     

Identification of glial-cell-line-derived neurotrophic factor-regulated proteins of striatum in mouse model of Parkinson disease

Glial-cell-line-derived neurotrophic factor (GDNF) is a potent survival factor for dopaminergic neurons, and hence serves as a therapeutic candidate for the treatment of Parkinson's disease. However, despite the potential clinical and physiological importance of GDNF, its mechanism of action is unclear. Therefore, we employed a state-of-the-art proteomic technique, DIGE, along with MS and a bioinformatics tool called Database for Annotation, Visualization and Integrated Discovery (DAVID), to profile proteome changes in the parkinsonian mouse striatum after GDNF challenge. Forty-six unique differentially expressed proteins were successfully identified, which were found either up-regulated and/or down-regulated at the two time points 4 and 72 h compared with the control. Proteins involved in cell differentiation and system development formed the largest part of the proteins regulated under GDNF. Furthermore, the aberrant expression of HSPs and mitochondria-associated proteins were noticeable. Moreover, mitochondrial stress 70 protein and heat shock cognate 71 kDa protein, whose relative levels increased significantly in GDNF-treated striatum, were further evaluated with Western blot and RT-PCR, demonstrating a good agreement with quantitative proteomic data. These data will provide some clues for understanding the mechanisms by which GDNF promotes the survival of dopaminergic neurons.     

Inferring Gene Regulatory Networks From Expression Data by Discovering Fuzzy Dependency Relationships

For one to infer the structures of a gene regulatory network (GRN), it is important to identify, for each gene in the GRN, which other genes can affect its expression and how they can affect it. For this purpose, many algorithms have been developed to generate hypotheses about the presence or absence of interactions between genes. These algorithms, however, cannot be used to determine if a gene activates or inhibits another. To obtain such information to better infer GRN structures, we propose a fuzzy data mining technique here. By transforming quantitative expression values into linguistic terms, it defines a measure of fuzzy dependency among genes. Using such a measure, the technique is able to discover interesting fuzzy dependency relationships in noisy, high dimensional time series expression data so that it can not only determine if a gene is dependent on another but also if a gene is supposed to be activated or inhibited. In addition, the technique can also predict how a gene in an unseen sample (i.e., expression data that are not in the original database) would be affected by other genes in it and this makes statistical verification of the reliability of the discovered gene interactions easier. For evaluation, the proposed technique has been tested using real expression data and experimental results show that the use of fuzzy-logic based technique in gene expression data analysis can be quite effective.     

Identification and prioritization of differentially expressed genes for time-series gene expression data

Identification of differentially expressed genes (DEGs) in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a flat gene filter based on the Ljung–Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization approach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.     

Biclustering of human cancer microarray data using co-similarity based co-clustering

Biclustering of gene expression data aims at finding localized patterns in a subspace. A bicluster (sometimes called a co-cluster), in the context of gene expression data, is a set of genes that exhibit similar expression intensity under a subset of experimental features (conditions). Most biclustering algorithms proposed in the literature aim at finding sub-matrices that exhibit some sort of coherence by selecting an initial sub-matrix and iteratively adding or subtracting rows and columns. These algorithms are generally dependent on the initial, hard selection of the gene and condition clusters respectively. In this work, we adapt a recently proposed approach for clustering textual data to find biclusters in gene expression data. Our proposed technique is based on the concept of co-similarity between genes (and between conditions) that exploits weighted higher order paths in a bipartite graph representation of the gene expression data. Therefore, we build statistical relations between genes and between conditions by comparing all genes and conditions before finally extracting biclusters from the data. We show that the proposed technique is able to find meaningful non-overlapping biclusters both on synthetically generated data as well as real cancer data. Our results indicate that the proposed technique is resistant to noise in the data and can successfully retrieve biclusters even in the presence of relatively large amount of noise. We also analyze our results with respect to the discovered genes and observe that our extracted biclusters are supported by biological evidences, such as enrichment of gene functions and biological processes.     

面向联机手写数学表达式的实时识别方法

数学表达式识别一般分为字符识别和结构分析两部分,而且大多数现有的方法是先进行字符识别然后将字符识别的结果作为结构分析的输入再进行结构分析,在这种分步识别的过程中,字符识别的错误会被继承到结构分析阶段,最终导致识别错误。关于数学表达式结构分析的问题,现有的方法大多是在假设所有的符号已经识别的基础上进行的。为了解决上述问题,提出了一种实时识别联机手写数学表达式的方法。该方法基于字符识别和结构分析的结合,动态地构建一棵数学表达式结构树来识别该数学表达式。在构建数学表达式树的过程中,采用了影响区域定位的方法,免去了其他不受影响区域的重复识别过程,因而提高了再次识别的效率,同时还弥补了现有实时识别方法不能乱序输入的缺陷。实验结果表明提出的方法可以得到比较满意的识别结果。     

Direct integration of microarrays for selecting informative genes and phenotype classification

The ability to provide thousands of gene expression values simultaneously makes microarray data very useful for phenotype classification. A major constraint in phenotype classification is that the number of genes greatly exceeds the number of samples. We overcame this constraint in two ways; we increased the number of samples by integrating independently generated microarrays that had been designed with the same biological objectives, and reduced the number of genes involved in the classification by selecting a small set of informative genes. We were able to maximally use the abundant microarray data that is being stockpiled by thousands of different research groups while improving classification accuracy. Our goal is to implement a feature (gene) selection method that can be applicable to integrated microarrays as well as to build a highly accurate classifier that permits straightforward biological interpretation. In this paper, we propose a two-stage approach. Firstly, we performed a direct integration of individual microarrays by transforming an expression value into a rank value within a sample and identified informative genes by calculating the number of swaps to reach a perfectly split sequence. Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes. By using our classifier that was derived from large, integrated microarray sample datasets, we achieved high accuracy, sensitivity, and specificity in the classification of an independent test dataset.     

基于高通量显微成像及分析技术的 DNA 重排研究

直接的重复序列广泛地存在于真核和原核细胞基因组中，并且与多种疾病（如遗传性神经肌 肉神经退行性疾病等）相关，因此定量重复序列的删除变得非常重要。结合高通量显微成像和分析技术，该文设计了基于三色荧光报告系统的方法来定量重复序列删除的发生。结果显示，在铜绿假单胞菌中，重复序列的删除频率在 recA 基因缺失突变株中明显降低，而 RadA 蛋白和 UvrD 蛋白的缺失则会提高重复序列的删除频率，并且重复序列的删除与细菌的生长率和启动子等因素无关。该研究有助于加深对直接重复序列相关问题的理解，并为直接重复序列删除定量提供了新的方法。     

Modelling the restoration of wild-type dynamic behaviour in DeltaF508-CFTR NBD1 by 8-cyclopentyl-1,3-dipropylxanthine

Cystic fibrosis (CF) is the most frequently occurring severe, genetic disease in western populations with an incidence as high as 1 in 2500. The principal biochemical defect in CF is a mutation in a membrane transport protein, namely the cystic fibrosis transmembrane conductance regulator (CFTR), which is responsible for the conductance of chloride ions across cell membranes. In 70% of cases a single mutation in CFTR, namely the deletion of amino acid 508 (called DeltaF508) is sufficient to cause severe disease. This mutation manifests as a failure of the protein to be effectively targeted to the membrane. Recently, it has been shown that small molecule drug therapy can restore the membrane-targeting of DeltaF508-CFTR, where the mutant channel functions adequately. We have created models of the first nucleotide-binding domain (NBD1) region (which houses the proposed binding site of these restorative drugs) of the wild-type and mutant forms of human CFTR. We have simulated the dynamical behaviour of these proteins in the presence of drugs that restore trafficking of the protein. Our results indicate that there are particular modes of dynamic motion that are distinguishable between wild-type and mutant CFTR. These regions of motion are localized in the regions of the DeltaF508 mutation and the drug-binding regions. The simulations of drug binding indicate that wild-type dynamic motions are restored in these regions. We conclude therefore that these drugs are able to alter the dynamic properties of DeltaF508-CFTR such that the drug-bound mutant protein more closely resembles the wild-type protein dynamic behaviour, and hence we hypothesize that it is this that allows for correct targeting to the membrane.     

Gene interaction – An evolutionary biclustering approach

DNA Microarray experiments form a powerful tool for studying gene expression patterns, in large scale. Sharing of the regulatory mechanism among genes, in an organism, is predominantly responsible for their co-expression. Biclustering aims at finding a subset of similarly expressed genes under a subset of experimental conditions. A small number of genes participate in a cellular process of interest. Again, a gene may be simultaneously involved in a number of cellular processes. In cellular environment, genes interact among themselves to produce enzymes, metabolites, proteins, etc. responsible for a particular function(s).In this study, a simple and novel correlation-based approach is proposed to extract gene interaction networks from biclusters in microarray data. Local search strategy is employed to add (remove) relevant (irrelevant) genes for finer tuning, in multi-objective biclustering framework. Preprocessing is done to preserve strongly correlated gene interaction pairs. Experimental results on time-series gene expression data from Yeast are biologically validated using benchmark databases and literature.     

一种基于显现模式的基因分类算法

基于基因表达谱的分类技术对于疾病检测具有十分重要的研究意义。利用显现模式（Emerging Patterns,EPs）的基因分类方法不仅可以识别癌症样本,同时可以挖掘出隐含的与癌症相关的具有生物意义的基因模式,从基因角度揭示癌症病理。针对提取显现模式时在小样本情况下将频率近似于概率的缺陷以及PCL（Prediction by Collective Likelihood）分类器的不足,提出一种基于显现模式的基因分类算法：在显现模式的提取中引入贝叶斯估计以提高熵的可靠度,并借鉴KNN思想,提出一种新的基于EP的分类算法EP-KNN（Emerging Patterns-K Nearest Neighbors）。最后在急性白血病数据集上进行实验,实验结果表明新的算法提高了分类正确率,说明了该方法的有效性。     

An evolutionary clustering algorithm for gene expression microarray data analysis

Clustering is concerned with the discovery of interesting groupings of records in a database. Many algorithms have been developed to tackle clustering problems in a variety of application domains. In particular, some of them have been used in bioinformatics research to uncover inherent clusters in gene expression microarray data. In this paper, we show how some popular clustering algorithms have been used for this purpose. Based on experiments using simulated and real data, we also show that the performance of these algorithms can be further improved. For more effective clustering of gene expression microarray data, which is typically characterized by a lot of noise, we propose a novel evolutionary algorithm called evolutionary clustering (EvoCluster). EvoCluster encodes an entire cluster grouping in a chromosome so that each gene in the chromosome encodes one cluster. Based on such encoding scheme, it makes use of a set of reproduction operators to facilitate the exchange of grouping information between chromosomes. The fitness function that the EvoCluster adopts is able to differentiate between how relevant a feature value is in determining a particular cluster grouping. As such, instead of just local pairwise distances, it also takes into consideration how clusters are arranged globally. Unlike many popular clustering algorithms, EvoCluster does not require the number of clusters to be decided in advance. Also, patterns hidden in each cluster can be explicitly revealed and presented for easy interpretation even by casual users. For performance evaluation, we have tested EvoCluster using both simulated and real data. Experimental results show that it can be very effective and robust even in the presence of noise and missing values. Also, when correlating the gene expression microarray data with DNA sequences, we were able to uncover significant biological binding sites (both previously known and unknown) in each cluster discovered by EvoCluster.     

Inclusion of Textual Documentation in the Analysis of Multidimensional Data Sets: Application to Gene Expression Data

Recently, biology has been confronted with large multidimensional gene expression data sets where the expression of thousands of genes is measured over dozens of conditions. The patterns in gene expression are frequently explained retrospectively by underlying biological principles. Here we present a method that uses text analysis to help find meaningful gene expression patterns that correlate with the underlying biology described in scientific literature. The main challenge is that the literature about an individual gene is not homogenous and may addresses many unrelated aspects of the gene. In the first part of the paper we present and evaluate the neighbor divergence per gene (NDPG) method that assigns a score to a given subgroup of genes indicating the likelihood that the genes share a biological property or function. To do this, it uses only a reference index that connects genes to documents, and a corpus including those documents. In the second part of the paper we present an approach, optimizing separating projections (OSP), to search for linear projections in gene expression data that separate functionally related groups of genes from the rest of the genes; the objective function in our search is the NDPG score of the positively projected genes. A successful search, therefore, should identify patterns in gene expression data that correlate with meaningful biology. We apply OSP to a published gene expression data set; it discovers many biologically relevant projections. Since the method requires only numerical measurements (in this case expression) about entities (genes) with textual documentation (literature), we conjecture that this method could be transferred easily to other domains. The method should be able to identify relevant patterns even if the documentation for each entity pertains to many disparate subjects that are unrelated to each other.     

Model gene network by semi-fixed Bayesian network

Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, and protein or RNA transport. Thus, learning gene network is a crucial problem in the post genome era. Most existing works learn gene networks by assuming one gene provokes the expression of another gene directly leading to an over-simplified model. In this paper, we show that the gene regulation is a complex problem with many hidden variables. We propose a semi-fixed model to represent the gene network as a Bayesian network with hidden variables. In addition, an effective algorithm based on semi-fixed structure learning is proposed to learn the model. Experimental results and comparison with the-state-of-the-art learning algorithms on artificial and real-life datasets confirm the effectiveness of our approach.     

Probing the stereospecificity of tyrosyl- and glutaminyl-tRNA synthetase with molecular dynamics

The stereospecificity of aminoacyl-tRNA synthetases helps exclude d-amino acids from protein synthesis and could perhaps be engineered to allow controlled d-amino acylation of tRNA. We use molecular dynamics simulations to probe the stereospecificity of the class I tyrosyl- and glutaminyl-tRNA synthetases (TyrRS, GlnRS), including wildtype enzymes and three point mutants suggested by three different protein design methods. l/d binding free energy differences are obtained by alchemically and reversibly transforming the ligand from L to D in simulations of the protein–ligand complex. The D81Q mutation in Escherichia coli TyrRS is homologous to the D81R mutant shown earlier to have inverted stereospecificity. D81Q is predicted to lead to a rotated ligand backbone and an increased, not a decreased l-Tyr preference. The E36Q mutation in Methanococcus jannaschii TyrRS has a predicted l/d binding free energy difference ΔΔG of just 0.5 ± 0.9 kcal/mol, compared to 3.1 ± 0.8 kcal/mol for the wildtype enzyme (favoring l-Tyr). The ligand ammonium position is preserved in the d-Tyr complex, while the carboxylate is shifted. Wildtype GlnRS has a similar preference for l-glutaminyl adenylate; the R260Q mutant has an increased preference, even though Arg260 makes a large contribution to the wildtype ΔΔG value.     

Performance of quantum cloning and deleting machines over coherence

Coherence, being at the heart of interference phenomena, is found to be an useful resource in quantum information theory. Here we want to understand quantum coherence under the combination of two fundamentally dual processes, viz., cloning and deleting. We found the role of quantum cloning and deletion machines with the consumption and generation of quantum coherence. We establish cloning as a cohering process and deletion as a decohering process. Fidelity of the process will be shown to have connection with coherence generation and consumption of the processes.     

Noise-robust algorithm for identifying functionally associated biclusters from gene expression data

Biclusters are subsets of genes that exhibit similar behavior over a set of conditions. A biclustering algorithm is a useful tool for uncovering groups of genes involved in the same cellular processes and groups of conditions under which these processes take place. In this paper, we propose a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies (1) gene sets that simultaneously exhibit additive, multiplicative, and combined patterns and allow high levels of noise, (2) multiple, possibly overlapped, and diverse gene sets, (3) biclusters that simultaneously exhibit negatively and positively correlated gene sets, and (4) gene sets for which the functional association is very high. We validate the level of functional association in our method by using the GO database, protein-protein interactions and KEGG pathways.