Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network

MOTIVATION: Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments. RESULTS: In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

一种挖掘关键基因的新方法及其应用(英文)

近来,人们发现从疾病相关基因中寻找关键基因对疾病的诊断和治疗很重要。癌相关基因的网络是根据正常和患病的胶质瘤组织的基因表达谱建立。根据建立的基因网络和CIPHER方法,不同阈值下的正常和患病的胶质瘤表型网络被建立。根据已知的疾病和表型间的关联,另一组正常和患病的胶质瘤表型网络被建立。将两种方法建立的相应的表型网络进行比较,匹配度最大时对应的阈值及基因和表型网络被确定。在此基础上,通过打分方法得到了7个关键基因:DMBT1,ERBB2,NF2,PDGFB,AR,ARAF和TP53。文献查询发现其中5个基因与胶质瘤的形成和发展密切相关。剩下两个基因中的ARAF也间接地参与胶质瘤形成。因此,这两个基因可能在胶质瘤的形成中起重要作用。这一预测仍需要实验验证。     

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.     

Genome-wide combination profiling of copy number and methylation offers an approach for deciphering misregulation and development in cancer cells

Copy number changes and DNA methylation alterations are crucial to gene regulation in mammals. Recently, a number of microarray studies have been based on copy number and DNA methylation alterations in order to find clinical biomarkers of carcinogenesis. In this study, we attempted to combine profiles of copy number and methylation patterns in four human cancer cell lines using BAC microarray-based approaches and we detected several clinically important genes which showed genetic and epigenetic relationships. Within the clones analyzed, many contained cancer-related genes involved in cell cycle regulation, cell division, signal transduction, tumor necrosis, cell differentiation, and cell proliferation. One clone included the FHIT gene, a well-known tumor suppressor gene involved in various human cancers. Our combined profiling techniques may provide a method by which to find new clinicopathologic cancer biomarkers, and support the idea that systematic characterization of the genetic and epigenetic events in cancers may rapidly become a reality.     

MSClique: Multiple Structure Discovery through the Maximum Weighted Clique Problem

We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.     

A statistical method for identifying differential gene-gene co-expression patterns

MOTIVATION: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states. RESULTS: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.     

Protein threading with profiles and distance constraints using clique based algorithms

With the advent of experimental technologies like chemical cross-linking, it has become possible to obtain distances between specific residues of a newly sequenced protein. These types of experiments usually are less time consuming than X-ray crystallography or NMR. Consequently, it is highly desired to develop a method that incorporates this distance information to improve the performance of protein threading methods. However, protein threading with profiles in which constraints on distances between residues are given is known to be NP-hard. By using the notion of a maximum edge-weight clique finding algorithm, we introduce a more efficient method called FTHREAD for profile threading with distance constraints that is 18 times faster than its predecessor CLIQUETHREAD. Moreover, we also present a novel practical algorithm NTHREAD for profile threading with Non-strict constraints. The overall performance of FTHREAD on a data set shows that although our algorithm uses a simple threading function, our algorithm performs equally well as some of the existing methods. Particularly, when there are some unsatisfied constraints, NTHREAD (Non-strict constraints threading algorithm) performs better than threading with FTHREAD (Strict constraints threading algorithm). We have also analyzed the effects of using a number of distance constraints. This algorithm helps the enhancement of alignment quality between the query sequence and template structure, once the corresponding template structure is determined for the target sequence.     

Self-organizing fuzzy graphs for structure-based comparison of protein pockets

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.     

Similarities and differences of gene expression in yeast stress conditions

MOTIVATION AND METHODS: All living organisms and the survival of all cells critically depend on their ability to sense and quickly adapt to changes in the environment and to other stress conditions. We study stress response mechanisms in Saccharomyces cerevisiae by identifying genes that, according to very stringent criteria, have persistent co-expression under a variety of stress conditions. This is enabled through a fast clique search method applied to the intersection of several co-expression graphs calculated over the data of Gasch et al. This method exploits the topological characteristics of these graphs. RESULTS: We observe cliques in the intersection graphs that are much larger than expected under a null model of changing gene identities for different stress conditions but maintaining the co-expression topology within each one. Persistent cliques are analyzed to identify enriched function as well as enriched regulation by a small number of TFs. These TFs, therefore, characterize a universal and persistent reaction to stress response. We further demonstrate that the vertices (genes) of many cliques in the intersection graphs are co-localized in the yeast genome, to a degree far beyond the random expectation. Co-localization can hypothetically contribute to a quick co-ordinated response. We propose the use of persistent cliques in further study of properties of co-regulation.     

Extraction of phylogenetic network modules from the metabolic network

Background  

In bio-systems, genes, proteins and compounds are related to each other, thus forming complex networks. Although each organism has its individual network, some organisms contain common sub-networks based on function. Given a certain sub-network, the distribution of organisms common to it represents the diversity of its function.     

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among > 235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.     

Conservative and compensatory evolution in oxidative phosphorylation complexes of angiosperms with highly divergent rates of mitochondrial genome evolution

Interactions between nuclear and mitochondrial gene products are critical for eukaryotic cell function. Nuclear genes encoding mitochondrial‐targeted proteins (N‐mt genes) experience elevated rates of evolution, which has often been interpreted as evidence of nuclear compensation in response to elevated mitochondrial mutation rates. However, N‐mt genes may be under relaxed functional constraints, which could also explain observed increases in their evolutionary rate. To disentangle these hypotheses, we examined patterns of sequence and structural evolution in nuclear‐ and mitochondrial‐encoded oxidative phosphorylation proteins from species in the angiosperm genus Silene with vastly different mitochondrial mutation rates. We found correlated increases in N‐mt gene evolution in species with fast‐evolving mitochondrial DNA. Structural modeling revealed an overrepresentation of N‐mt substitutions at positions that directly contact mutated residues in mitochondrial‐encoded proteins, despite overall patterns of conservative structural evolution. These findings support the hypothesis that selection for compensatory changes in response to mitochondrial mutations contributes to the elevated rate of evolution in N‐mt genes. We discuss these results in light of theories implicating mitochondrial mutation rates and mitonuclear coevolution as drivers of speciation and suggest comparative and experimental approaches that could take advantage of heterogeneity in rates of mtDNA evolution across eukaryotes to evaluate such theories.     

Dynamic Epistasis under Varying Environmental Perturbations

Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes. We explored this issue by applying flux balance analysis to simulate epistatic landscapes under various environmental perturbations. Specifically, we looked at gene-gene epistatic interactions, where the mutations were assumed to occur in different genes. We predicted that epistasis tends to become more positive from glucose-abundant to nutrient-limiting conditions, indicating that selection might be less effective in removing deleterious mutations in the latter. We also observed a stable core of epistatic interactions in all tested conditions, as well as many epistatic interactions unique to each condition. Interestingly, genes in the stable epistatic interaction network are directly linked to most other genes whereas genes with condition-specific epistasis form a scale-free network. Furthermore, genes with stable epistasis tend to have similar evolutionary rates, whereas this co-evolving relationship does not hold for genes with condition-specific epistasis. Our findings provide a novel genome-wide picture about epistatic dynamics under environmental perturbations.     

A Fisher scoring algorithm for the weighted regression method of QTL mapping

An improved weighted least square (LS) method for quantitative trait loci (QTL) mapping using the estimating equation (EE) algorithm was developed recently. The method is more efficient than both the LS and the weighted LS methods and slightly less efficient than the mixture model maximum likelihood (ML) method. The iteration process of the EE algorithm is implicit. We developed a Fisher-scoring algorithm for the weighted LS method. The iteration process is explicit and easy to program. In addition, the method automatically provides an approximate variance-covariance matrix for the estimated QTL parameters as a by-product of the iteration process. As a consequence, a W-test statistic can be used for testing the significance of QTL. To compare the Fisher scoring algorithm with the expectation maximization (EM)-based ML method, we also developed a slightly simplified method to compute the variance-covariance matrix of the estimated parameters under the EM algorithm.