Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

石榴WRKY基因家族全基因组鉴定与表达分析

WRKY蛋白是一类在植物生长发育过程及生物与非生物胁迫过程中起重要调控作用的转录因子。该研究利用石榴全基因组数据,采用生物信息学的方法,对石榴WRKY转录因子家族成员蛋白理化性质、系统进化、基因结构、保守基序、顺式作用元件、蛋白互作及基因共表达和转录组表达模式进行系统分析。结果共鉴定出69个PgWRKY基因;分组鉴定和进化分析显示WRKY蛋白可分为Ⅰ、Ⅱ和Ⅲ共三大类型。顺式作用元件分析表明,PgWRKY基因广泛参与到非生物胁迫中;蛋白互作网络与共表达分析暗示PgWRKY基因在同一胁迫应答中可能作用一致并同时诱导表达;RNA-Seq数据分析表明,PgWRKY基因有一定的组织表达特异性,广泛参与植物营养、生殖生长以及根部逆境胁迫应答过程。     

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.     

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell''s state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato''s Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.