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.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.     

Protein-protein interactions: principles, techniques, and their potential role in new drug development

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

Protein-Protein Interactions: Principles,Techniques, and their Potential Role in New Drug Development

Abstract

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

Liquid-liquid phase separation in biology: mechanisms,physiological functions and human diseases

Cells are compartmentalized by numerous membrane-enclosed organelles and membraneless compartments to ensure that a wide variety of cellular activities occur in a spatially and temporally controlled manner. The molecular mechanisms underlying the dynamics of membrane-bound organelles, such as their fusion and fission, vesicle-mediated trafficking and membrane contactmediated inter-organelle interactions, have been extensively characterized. However, the molecular details of the assembly and functions of membraneless compartments remain elusive. Mounting evidence has emerged recently that a large number of membraneless compartments, collectively called biomacromolecular condensates, are assembled via liquid-liquid phase separation(LLPS). Phase-separated condensates participate in various biological activities, including higher-order chromatin organization,gene expression, triage of misfolded or unwanted proteins for autophagic degradation, assembly of signaling clusters and actin-and microtubule-based cytoskeletal networks, asymmetric segregations of cell fate determinants and formation of pre-and post-synaptic density signaling assemblies. Biomacromolecular condensates can transition into different material states such as gel-like structures and solid aggregates. The material properties of condensates are crucial for fulfilment of their distinct functions, such as biochemical reaction centers, signaling hubs and supporting architectures. Cells have evolved multiple mechanisms to ensure that biomacromolecular condensates are assembled and disassembled in a tightly controlled manner. Aberrant phase separation and transition are causatively associated with a variety of human diseases such as neurodegenerative diseases and cancers. This review summarizes recent major progress in elucidating the roles of LLPS in various biological pathways and diseases.     

Possible involvement of transglutaminase-catalyzed reactions in the physiopathology of neurodegenerative diseases

Transglutaminases are ubiquitous enzymes, which catalyze post-translational modifications of proteins. Recently, transglutaminases and tranglutaminase-catalyzed post-translational modification of proteins have been shown to be involved in the molecular mechanisms responsible for several human diseases. Transglutaminase activity has been hypothesized to be involved also in the pathogenetic mechanisms responsible for human neurodegenerative diseases. Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, supranuclear palsy, Huntington’s disease and other polyglutamine diseases, are characterized in part by aberrant cerebral transglutaminase activity and by increased cross-linked proteins in affected brains. In this review, we focus on the possible molecular mechanisms by which transglutaminase activity could be involved in the pathogenesis of neurodegenerative diseases, and on the possible therapeutic effects of selective transglutaminase inhibitors for the cure of patients with diseases characterized by aberrant transglutaminase activity.     

Biological networks

Recent advances in high-throughput methods have provided us with a first glimpse of the overall structure of molecular interaction networks in biological systems. Ultimately, we expect that such information will change how we think about biological systems in a fundamental way. Instead of viewing the genetic parts list of an organism as a loose collection of biochemical activities, in the best case, we anticipate discrete networks of function to bridge the gap between genotype and phenotype, and to do so in a more profound way than the current qualitative classification of linked reactions into familiar pathways, such as glycolysis and the MAPK signal transduction cascades. At the present time, however, we are still far from a complete answer to the most basic question: what can we learn about biology by studying networks? Promising steps in this direction have come from such diverse approaches as mathematical analysis of global network structure, partitioning networks into functionally related modules and motifs, and even de novo design of networks. A complete picture will probably require integrating the data obtained from all of these approaches with modeling efforts at many different levels of detail.     

Identification of proteins binding to decursinol by chemical proteomics

Decursinol, found in the roots of Angelica gigas Nakai, has been traditionally used to treat anemia and other various diseases. Recently, numerous biological activities such as cytotoxic effect on leukemia cells, and antitumor, neuroprotection, and antibacterial activities have been reported for this compound. Although a number of proteins including protein kinase C, androgen receptor, and acetylcholinesterase were proposed as molecular targets responsible for the activities of decursinol, they are not enough to explain such a diverse biological activity mentioned above. In this study, we employed a chemical proteomic approach, leading to identification of seven proteins as potential proteins interacting with decursinol. Most of the proteins contain a defined ATP or nucleic acid binding domain and have been implied to be involved in the pathogenesis and progression of various human diseases including cancer, autoimmune disorders, or neurodegenerative diseases. The present results may provide clues to understand the molecular mechanism of the biological activities shown by decursinol, an anticancer natural product.     

Discovering functions and revealing mechanisms at molecular level from biological networks

With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.     

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.     

Application of Bayesian networks on large-scale biological data

The investigation of the interplay between genes, proteins, metabolites and diseases plays a central role in molecular and cellular biology. Whole genome sequencing has made it possible to examine the behavior of all the genes in a genome by high-throughput experimental techniques and to pinpoint molecular interactions on a genome-wide scale, which form the backbone of systems biology. In particular, Bayesian network (BN) is a powerful tool for the ab-initial identification of causal and non-causal relationships between biological factors directly from experimental data. However, scalability is a crucial issue when we try to apply BNs to infer such interactions. In this paper, we not only introduce the Bayesian network formalism and its applications in systems biology, but also review recent technical developments for scaling up or speeding up the structural learning of BNs, which is important for the discovery of causal knowledge from large-scale biological datasets. Specifically, we highlight the basic idea, relative pros and cons of each technique and discuss possible ways to combine different algorithms towards making BN learning more accurate and much faster.     

Topological features of cancer proteins in the human NR-RTK interaction network

Protein-protein interaction (PPI) network analysis has been considered as a useful approach to explore the mechanisms of complex diseases, such as cancer. To date, many proteins have been reported to involve in the development of cancer. Exploration of cancer proteins in the human PPI network may provide important biological information to uncover molecular mechanisms of cancer. Here, we have explored network characteristics (including degree, betweenness, clustering coefficient and shortest-path distance) of cancer proteins of the human nuclear and tyrosine kinases receptors network (NR-RTK) constructed in our earlier work. We found that the network topology of cancer proteins in this network have some specific features. Relative to the non-cancer proteins, the cancer proteins have likely higher degree, higher betweenness, similar clustering coefficient and similar shortest-path distance. Finally, we found that the cancer proteins were involved mainly in signalling pathways which dysfunction is directly related to cancer onset. These findings are helpful for cancer candidate protein prioritization and verification, and identification of key pathways involved in cancer disease.     

Serpinopathies and the conformational dementias

The serpin superfamily of serine proteinase inhibitors has a central role in controlling proteinases in many biological pathways in a wide range of species. The inhibitory function of the serpins involves a marked conformational transition, but this inherent molecular flexibility also renders the serpins susceptible to point mutations that result in aberrant intermolecular linkage and polymer formation. The effects of such protein aggregation are cumulative, with a progressive loss of cellular function that results in diseases as diverse as cirrhosis and emphysema. The recent recognition that mutations in a serpin can also result in late-onset dementia provides insights into changes that underlie other conformational diseases, such as the amyloidoses, the prion encephalopathies and Huntington and Alzheimer diseases.     

A high-throughput quantitative multiplex kinase assay for monitoring information flow in signaling networks: application to sepsis-apoptosis

To treat complex human diseases effectively, a systems-level approach is needed to understand the interplay of environmental cues, intracellular signals, and cellular behaviors that underlie disease states. This approach requires high-throughput, multiplex techniques that measure quantitative temporal variations of multiple protein activities in the intracellular signaling network. Here, we describe a single microtiter-based format that simultaneously quantifies protein kinase activities in the phosphatidylinositol 3-kinase pathway (Akt), nuclear factor-kappaB pathway (IKK), and three core mitogen-activated protein kinase pathways (ERK, JNK1, MK2). These parallel high-throughput assays are stringently linear, redundantly specific, reproducible, and sensitive compared with classical low-throughput techniques. When applied to a model of sepsis-induced colon epithelial apoptosis, this approach identified a late phase of Akt activity as a critical mediator of cell survival that quantitatively contributed to the efficacy of insulin as an anti-apoptotic cue. Thus, sampling parallel nodes in the intracellular signaling network identified part of the molecular mechanism underlying the efficacy of insulin in the treatment of human sepsis.     

Targeting the lipids LPA and S1P and their signalling pathways to inhibit tumour progression

The bioactive lipids lysophosphatidic acid (LPA) and sphingosine 1-phosphate (S1P), the enzymes that generate and degrade them, and the receptors that receive their signals are all potential therapeutic targets in cancer. LPA and S1P signalling pathways can modulate a range of cellular processes that contribute to tumourigenesis, such as proliferation and motility, and components of the signalling pathways often show aberrant expression and altered activity upon malignant transformation. This article reviews LPA- and S1P-mediated activities that might contribute to the aetiology of cancer, and examines the potential of the many antagonists that have been developed to inhibit LPA and S1P signalling pathways. In addition, the outcomes of various clinical trials using LPA- and S1P-associated targets in cancer and other diseases are described, and future directions are discussed.     

基于微阵列数据构建基因调控网络

随着微阵列数据的快速增长, 微阵列基因表达数据日益成为生物信息学研究的重要数据源。利用微阵列基因表达数据构建基因调控网络也成为一个研究热点。通过构建基因调控网络, 可以解读复杂的调控关系, 发现细胞内的调控模式, 并进而在系统尺度上理解生物学进程。近年来, 人们引入了多种算法来利用基因芯片数据构建基因调控网络。文章回顾了这些算法的发展历史, 尤其是其在理论和方法上的改进, 给出了一些相关的软件平台, 并预测了该领域可能的发展趋势。