Comparison of pathways associated with hepatitis B- and C-infected hepatocellular carcinoma using pathway-based class discrimination method

Molecular signatures causing hepatocellular carcinoma (HCC) from chronic infection of hepatitis B virus (HBV) or hepatitis C virus (HCV) are not clearly known. Using microarray datasets composed of HCV-positive HCC or HBV-positive HCC, pathways that could discriminate tumor tissue from adjacent non-tumor liver tissue were selected by implementing nearest shrunken centroid algorithm. Cancer-related signaling pathways and lipid metabolism-related pathways were predominantly enriched in HCV-positive HCC, whereas functionally diverse pathways including immune-related pathways, cell cycle pathways, and RNA metabolism pathways were mainly enriched in HBV-positive HCC. In addition to differentially involved pathways, signaling pathways such as TGF-β, MAPK, and p53 pathways were commonly significant in both HCCs, suggesting the presence of common hepatocarcinogenesis process. The pathway clustering also verified segregation of pathways into the functional subgroups in both HCCs. This study indicates the functional distinction and similarity on the pathways implicated in the development of HCV- and/or HBV-positive HCC.     

An algorithm for efficient identification of branched metabolic pathways

This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.     

Categorization of metabolome in bacterial systems

Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/.     

雌激素受体信号通路在乳腺癌发生和治疗中的作用

雌激素受体信号通路在调控乳腺细胞增殖和凋亡等生理机能中发挥重要功能,该通路出现调控异常时可导致乳腺癌发生。雌激素受体在乳腺癌发生中的作用机制包括核受体介导的基因组信号通路和膜受体介导的非基因组信号通路以及二者的相互作用。基于雌激素受体信号通路及其关键信号分子的靶向治疗是开展乳腺癌治疗的重要策略与有效途径。对雌激素受体结构以及雌激素受体信号通路在乳腺癌发生和治疗中的作用作一综述。     

基于全局角度网络策略的复杂疾病风险通路识别

复杂疾病的发生发展与机体内生物学通路的功能紊乱有密切联系,从高通量数据出发,利用计算机辅助方法来研究疾病与通路间的关系具有重要意义.本文提出了一个新的基于网络的全局性通路识别方法.该方法利用蛋白质互作信息和通路的基因集组成信息构建复杂的蛋白质-通路网.然后,基于表达谱数据,通过随机游走算法从全局层面优化疾病风险通路.最终,通过扰动方式识别统计学显著的风险通路.将该网络运用于结肠直肠癌风险通路识别,识别出15个与结肠直肠癌发生与发展过程显著相关的通路.通过与其他通路识别方法(超几何检验,SPIA)相比较,该方法能够更有效识别出疾病相关的风险通路.     

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.     

生物信息学中途径研究进展

叙述了生物信息学中途径的研究背景;综述了近几年来相关途径及生物化学数据库及其特点;介绍了有关的途径分析方法.同时对于途径研究应用作了展望.     

A graph layout algorithm for drawing metabolic pathways

MOTIVATION: A large amount of data on metabolic pathways is available in databases. The ability to visualise the complex data dynamically would be useful for building more powerful research tools to access the databases. Metabolic pathways are typically modelled as graphs in which nodes represent chemical compounds, and edges represent chemical reactions between compounds. Thus, the problem of visualising pathways can be formulated as a graph layout problem. Currently available visual interfaces to biochemical databases either use static images or cannot cope well with more complex, non-standard pathways. RESULTS: This paper presents a new algorithm for drawing pathways which uses a combination of circular, hierarchic and force-directed graph layout algorithms to compute positions of the graph elements representing main compounds and reactions. The algorithm is particularly designed for cyclic or partially cyclic pathways or for combinations of complex pathways. It has been tested on five sample pathways with promising results.     

Immune-To-Brain Signaling: How Important are the Blood–Brain Barrier-independent Pathways?

A conceptual obstacle for understanding immune-to-brain signaling is the issue of the blood-brain barrier (BBB). In the last 30 years, several pathways have been investigated to address the question of how peripheral immune signals are transmitted into the brain. These pathways can be categorized into two types: BBB-dependent pathways and BBB-independent pathways. BBB-dependent pathways involve the BBB as a relay station or porous barrier, whereas BBB-independent pathways use neuronal routes that bypass the BBB. Recently, a complete BBB-dependent ascending pathway for immune-to-brain signaling has been described. Details of BBB-independent pathways are still under construction. In this review, I will summarize the current progress in unraveling immune-to-brain signaling pathways. In addition, I will provide a critical analysis of the literature to point to areas where our knowledge of the immunological afferent signaling to the central nervous system is still sorely lacking.     

Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait''s genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function.     

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer''s disease (AD), Parkinson''s disease (PD) and Huntington''s disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.     

Computer-aided synthesis of biochemical pathways

The synthesis of biochemical pathways satisfying stoichiometric constraints is discussed. Stoichiometric constraints arise primarily from designating compounds as required or allowed reactants, and required or allowed products of the pathways; they also arise from similar restrictions on intermediate metabolites and bioreactions participating in the pathways. An algorithm for the complete and correct solution of the problem is presented; the algorithm satisfies each constraint by recursively transforming a base-set of pathways. The algorithm is applied to the problem of lysine synthesis from glucose and ammonia. In addition to the established synthesis routes, the algorithm constructs several alternative pathways that bypass key enzymes, such as malate dehydrogenase and pyruvate dehydrogenase. Apart from the construction of pathways with desired characteristics, the systematic synthesis of pathways can also uncover fundamental constraints in a particular problem, by demonstrating that no pathways exist to meet certain sets of specifications. In the case of lysine, the algorithm shows that oxaloacetate is a necessary intermediate in all pathways leading to lysine from glucose, and that the yield of lysine over glucose cannot exceed 67% in the absence of enzymatic recovery of carbon dioxide.     

Developmental signaling in Hydra: what does it take to build a "simple" animal?

Developmental processes in multicellular animals depend on an array of signal transduction pathways. Studies of model organisms have identified a number of such pathways and dissected them in detail. However, these model organisms are all bilaterians. Investigations of the roles of signal transduction pathways in the early-diverging metazoan Hydra have revealed that a number of the well-known developmental signaling pathways were already in place in the last common ancestor of Hydra and bilaterians. In addition to these shared pathways, it appears that developmental processes in Hydra make use of pathways involving a variety of peptides. Such pathways have not yet been identified as developmental regulators in more recently diverged animals. In this review I will summarize work to date on developmental signaling pathways in Hydra and discuss the future directions in which such work will need to proceed to realize the potential that lies in this simple animal.     

骨髓间充质干细胞向肝细胞分化的相关细胞因子及信号通路的研究进展

骨髓干细胞包括造血干细胞（HSCs）和间充质干细胞（MSCs）,骨髓间充质干细胞（BMSCs）是一类具有自我更新、增殖和多向分化能力的细胞,具有不对称分裂和无限增殖的特点。在肝细胞生长因子（HGF）的作用下,BMSCs可以分化为肝细胞,参与诱导这一分化过程的相关信号通路包括NF-kB信号通路、Notch信号通路、MAPK信号通路、Wnt信号通路和STAT3信号通路。文章主要就BMSCs分化为肝细胞的相关信号通路进行了综述。     

Exploring poly-beta-hydroxy-butyrate metabolism through network-based extreme pathway analysis

The objective of this article is to obtain a more detailed insight into poly-beta-hydroxybutyrate (PHB) metabolism through network-based metabolic pathway analysis. We employ extreme pathways to perform this study, because calculating and interpreting extreme pathways is a promising way for pathway analysis and metabolic engineering. After giving an in silico model of butanoate metabolism of Bacillus thuringiensis 97-27 (btk), extreme pathways were calculated and classified. Furthermore, the type I and II extreme pathways were further classified and analyzed in detail based on their structure and functional capabilities. Besides "historical" biochemical pathways, the results also suggest that there are some novel pathways.     

人参皂苷-Rh2诱导肿瘤细胞凋亡信号转导通路研究进展

人参皂苷-Rh2诱导肿瘤细胞凋亡机制复杂,且不同机制间相互联系。它主要通过调节细胞周期蛋白依赖性激酶复合物特异性信号传递引发细胞周期阻滞诱导细胞凋亡,或通过调控细胞信号转导通路,如ROS、Ca2+、PKC、JNK介导的线粒体信号通路和TRAIL-R1(DR4)介导的死亡受体信号通路等诱导肿瘤细胞凋亡。     

CheA, CheW, and CheY are required for chemotaxis to oxygen and sugars of the phosphotransferase system in Escherichia coli.

We carried out studies with Escherichia coli to determine the site at which the methylation-independent pathways for taxis to oxygen and to sugars of the phosphoenolpyruvate:sugar phosphotransferase transport system converge with the methylation-dependent chemotaxis pathways. Using genetic reconstitution of the pathways in a null strain, we determined that all pathways examined required the products of the genes cheA, cheW, and cheY. Thus, we conclude that both the methylation-independent and methylation-dependent pathways converge at CheA, the histidine kinase product of cheA.     

PathAligner

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.     

Retrograde trafficking and plasma membrane recycling pathways of the budding yeast Saccharomyces cerevisiae

The endosomal system functions as a network of protein and lipid sorting stations that receives molecules from endocytic and secretory pathways and directs them to the lysosome for degradation, or exports them from the endosome via retrograde trafficking or plasma membrane recycling pathways. Retrograde trafficking pathways describe endosome‐to‐Golgi transport while plasma membrane recycling pathways describe trafficking routes that return endocytosed molecules to the plasma membrane. These pathways are crucial for lysosome biogenesis, nutrient acquisition and homeostasis and for the physiological functions of many types of specialized cells. Retrograde and recycling sorting machineries of eukaryotic cells were identified chiefly through genetic screens using the budding yeast Saccharomyces cerevisiae system and discovered to be highly conserved in structures and functions. In this review, we discuss advances regarding retrograde trafficking and recycling pathways, including new discoveries that challenge existing ideas about the organization of the endosomal system, as well as how these pathways intersect with cellular homeostasis pathways.     

Bisphenol A (BPA) and cell signaling pathways

Bisphenol A (BPA; 4,4′-isopropylidenediphenol) is an endocrine disruptor that is used as a material for the production of phenol resins, polyacrylates, polyesters, epoxy resins, and polycarbonate plastics. Endocrine-disruptive or toxic effects of BPA on living organisms through a number of cell signaling pathways have been reported. BPA induces carcinogenesis, reproductive toxicity, abnormal inflammatory or immune response, and developmental disorders of brain or nervous system through various cell signaling pathways. This review considers the literature concerning BPA and its association with cancer-related cell signaling pathways, reproductive toxicity-related cell signaling pathways, inflammatory or immune response-related cell signaling pathways, and brain and nervous system-related cell signaling pathways.