Interaction between Intrinsically Disordered Proteins Frequently Occurs in a Human Protein-Protein Interaction Network

Intrinsic protein disorder is a widespread phenomenon characterised by a lack of stable three-dimensional structures and is considered to play an important role in protein-protein interactions (PPIs). This study examined the genome-wide preference of disorder in PPIs by using exhaustive disorder prediction in human PPIs. We categorised the PPIs into three types (interaction between disordered proteins, interaction between structured proteins, and interaction between a disordered protein and a structured protein) with regard to the flexibility of molecular recognition and compared these three interaction types in an existing human PPI network with those in a randomised network. Although the structured regions were expected to become the identifiers for binding recognition, this comparative analysis revealed unexpected results. The occurrence of interactions between disordered proteins was significantly frequent, and that between a disordered protein and a structured protein was significantly infrequent. We found that this propensity was much stronger in interactions between nonhub proteins. We also analysed the interaction types from a functional standpoint by using GO, which revealed that the interaction between disordered proteins frequently occurred in cellular processes, regulation, and metabolic processes. The number of interactions, especially in metabolic processes between disordered proteins, was 1.8 times as large as that in the randomised network. Another analysis conducted by using KEGG pathways provided results where several signaling pathways and disease-related pathways included many interactions between disordered proteins. All of these analyses suggest that human PPIs preferably occur between disordered proteins and that the flexibility of the interacting protein pairs may play an important role in human PPI networks.     

LEVELNET to visualize,explore, and compare protein–protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein–protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.     

Identification of key processes underlying cancer phenotypes using biologic pathway analysis

Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically. Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes. Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways - Trka Receptor, Apoptosis response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin - whose differences robustly distinguish diverse tumor types from corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state. Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome. This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.     

Domain-mediated protein interaction prediction: From genome to network

Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.     

基于双聚类挖掘癌症共享的基因功能模块

基因多效性是癌症遗传机制中的普遍现象, 但罕见系统性的分析。文章提出采用双聚类挖掘基因功能模块的新思路探索癌症的共享分子机制和不同癌症间的关系。获取20种癌症的基因表达数据, 应用改良t检验和倍数法筛选出至少在两种癌症中差异表达的基因, 得到10417×20的数据矩阵; 采用双聚类方法获得22个癌症共享的基因簇; 进一步富集分析得到17个基因功能模块(Bonferroni校正后P<0.05), 主要参与有丝分裂染色单体分离的调控、细胞分化、免疫和炎症反应、胶原纤维组织等生物过程; 主要执行ATP结合和微管活动、MHCⅡ类受体活性、肽链内切酶抑制活性等分子功能; 活动区域主要在细胞骨架、染色体、MHCⅡ蛋白质复合体、中间丝蛋白、胶原纤维等。基于模块构建癌症相关网络, 显示胃癌、卵巢腺癌、宫颈鳞癌和间皮瘤等之间相关程度较高, 而两种血液系统癌症(急性髓细胞性白血病与多发性骨髓瘤)分子机制与其他癌症存在较大差异。可见癌症共享的基因功能模块与多种生物机制有关, 癌症之间相似性可能与组织起源、共同的致癌机制等有关。文章提出的基因多效性分析方法有助于解释人类复杂性疾病的共享分子机制。     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

Targeting secret handshakes of biological processes for novel drug development

In multicellular organisms, several biological processes control the rise and fall of life. Different cell types communicate and co-operate in response to different stimulus through cell to cell signaling and regulate biologic processes in the cell/organism. Signaling in multicellular organism has to be made very secretly so that only the target cell responds to the signal. Of all the biomolecules, nature chose mainly proteins for secret delivery of information both inside and outside the cell. During cell signaling, proteins physically interact and shake hands for transfer of secret information by a phenomenon called as protein–protein interactions (PPIs). In both, extra and intracellular signaling processes PPIs play a crucial role. PPIs involved in cellular signaling are the primary cause for cell proliferation, differentiation, movement, metabolism, death and various other biological processes not mentioned here. These secret handshakes are very specific for specific functions. Any alterations/malfunctions in particular PPIs results in diseased condition. An overview of signaling pathways and importance of PPIs in cellular function and possibilities of targeting PPIs for novel drug development are discussed in this review.     

Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models

Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.     

Exploring the protein–protein interaction landscape in plants

Protein–protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.     

Current concepts in ovarian epithelial tumorigenesis: correlation between morphological and molecular data

Ovarian carcinoma is the most lethal gynaecological malignancy, most tumours being advanced at presentation. However, little is known about precursor lesions and the cell of origin of epithelial ovarian malignancy. In this review, the proposed cell of origin is discussed as well as recent molecular data relating to ovarian cancers of different morphological types. It is stressed that ovarian carcinoma is a heterogeneous group of neoplasms with several different morphological types, each with their own underlying molecular genetic events. Recent data suggest that mucinous ovarian cancers and a small subset of serous cancers (low grade ovarian serous carcinoma) develop through a well-defined adenoma-carcinoma sequence while the much more common high grade ovarian serous carcinoma develops de novo from the ovarian surface epithelium or the epithelium of cortical inclusion cysts. The realisation that various morphological types of epithelial ovarian cancer are associated with different molecular genetic events is a major advance in the study of ovarian cancer. It can be anticipated that this will lead to the development of specific therapeutic agents of value against a specific tumour type.     

Determining protein–protein functional associations by functional rules based on gene ontology and KEGG pathway

Protein–protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.     

Expression array technology in the diagnosis and treatment of breast cancer

The most common group of cancers among American women involves malignancies of the breast. Breast cancer is a complex disease, involving several different types of tissues and specific cells with various functions, that is categorized into many distinct subtypes. Microarray analysis has recently revealed that different biological subtypes of breast cancer are accompanied by differences in their specific gene expression profile. Because breast tissue (and breast cancer) is heterogeneous, microarray analysis may provide clinicians with a better understanding of how to treat each specific case. Thus, microarray analysis may translate basic research data into more confident diagnoses, specifically designed treatment regimens geared to each patient's needs, and better clinical prognoses.     

A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations

Background

One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks.

Results

Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D–domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ?=?2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer.

Conclusions

Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease-associated proteins and their mutations. We believe that our method is useful to reconstruct structurally resolved PPI networks for interpreting structural genomics and disease associations.

     

X-linked inhibitor of apoptosis protein (XIAP) mediates cancer cell motility via Rho GDP dissociation inhibitor (RhoGDI)-dependent regulation of the cytoskeleton

X-linked inhibitor of apoptosis protein (XIAP) overexpression has been found to be associated with malignant cancer progression and aggression in individuals with many types of cancers. However, the molecular basis of XIAP in the regulation of cancer cell biological behavior remains largely unknown. In this study, we found that a deficiency of XIAP expression in human cancer cells by either knock-out or knockdown leads to a marked reduction in β-actin polymerization and cytoskeleton formation. Consistently, cell migration and invasion were also decreased in XIAP-deficient cells compared with parental wild-type cells. Subsequent studies demonstrated that the regulation of cell motility by XIAP depends on its interaction with the Rho GDP dissociation inhibitor (RhoGDI) via the XIAP RING domain. Furthermore, XIAP was found to negatively regulate RhoGDI SUMOylation, which might affect its activity in controlling cell motility. Collectively, our studies provide novel insights into the molecular mechanisms by which XIAP regulates cancer invasion and offer a further theoretical basis for setting XIAP as a potential prognostic marker and specific target for treatment of cancers with metastatic properties.