Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Recent Progress on the Analysis of Power-law Features in Complex Cellular Networks

Complex interactions between different kinds of bio-molecules and essential nutrients are responsible for cellular functions. Rapid advances in theoretical modeling and experimental analyses have shown that drastically different biological and non-biological networks share a common architecture. That is, the probability that a selected node in the network has exactly k edges decays as a power-law. This finding has definitely opened an intense research and debate on the origin and implications of this ubiquitous pattern. In this review, we describe the recent progress on the emergence of power-law distributions in cellular networks. We first show the internal characteristics of the observed complex networks uncovered using graph theory. We then briefly review some works that have significantly contributed to the theoretical analysis of cellular networks and systems, from metabolic and protein networks to gene expression profiles. This prevalent topology observed in so many diverse biological systems suggests the existence of generic laws and organizing principles behind the cellular networks.     

Chaperones as integrators of cellular networks: changes of cellular integrity in stress and diseases

The complex integrity of the cells and its sudden, but often predictable changes can be described and understood by the topology and dynamism of cellular networks. All these networks undergo both local and global rearrangements during stress and development of diseases. Here, we illustrate this by showing the stress-induced structural rearrangement of the yeast protein-protein interaction network (interactome). In an unstressed state, the yeast interactome is highly compact, and the centrally organized modules have a large overlap. During stress, several original modules became more separated, and a number of novel modules also appear. A few basic functions such as theproteasome preserve their central position; however, several functions with high energy demand, such the cell-cycle regulation loose their original centrality during stress. A number of key stress-dependent protein complexes, such as the disaggregation-specific chaperone, Hsp104 gain centrality in the stressed yeast interactome. Molecular chaperones, heat shock, or stress proteins became established as key elements in our molecular understanding of the cellular stress response. Chaperones form complex interaction networks (the chaperome) with each other and their partners. Here, we show that the human chaperome recovers the segregation of protein synthesis-coupled and stress-related chaperones observed in yeast recently. Examination of yeast and human interactomes shows that chaperones 1) are intermodular integrators of protein-protein interaction networks, which 2) often bridge hubs and 3) are favorite candidates for extensive phosphorylation. Moreover, chaperones 4) become more central in the organization of the isolated modules of the stressed yeast protein-protein interaction network, which highlights their importance in the decoupling and recoupling of network modules during and after stress. Chaperone-mediated evolvability of cellular networks may play a key role in cellular adaptation during stress and various polygenic and chronic diseases, such as cancer, diabetes or neurodegeneration.     

植物蛋白质翻译后修饰组学研究进展

蛋白质翻译后修饰(Protein post-translational modification,PTMs)是一种重要的细胞调控机制,通过在蛋白质的氨基酸侧链上共价结合一些化学小分子基团来调节蛋白质的活性、结构、定位和蛋白质间的互作关系,从而精细调控蛋白质生物学功能的动态变化。PTMs是植物对环境变化最快、最早的反应之一,是植物蛋白质组多样性的关键机制,在植物生长发育和对环境适应中起重要作用。主要介绍了近年来植物磷酸化、乙酰化、琥珀酰化、糖基化、泛素化、巴豆酰化、S-亚硝基化及2-羟基异丁酰化等PTMs研究进展,旨为认识植物PTMs的关键生物学功能和研究前景提供参考。     

Effects of different components of diversity on productivity in artificial plant communities

In an experiment on artificial plant communities, the effects of three components of plant diversity—plant species diversity, plant functional group diversity and plant functional diversity—on community productivity and soil water content were compared. We found that simple regression analysis showed a positive diversity effect on ecosystem processes (productivity and soil water content). However, when three components of diversity were included in the multiple regression analyses, the results showed that functional group diversity and functional diversity had more important effects on productivity and resource use efficiency. These results suggested that, compared with species number, functional differences among species and the range of functional traits carried by plants are the basis of biodiversity effects on ecosystem functioning. These diversity effects of increasing functional group diversity or functional diversity were likely because species differing greatly in size, life form, phenology and capacity to capture and use resources efficiently in diverse communities realize complementary resource use in temporal, spatial, and biological ways.     

Analyzing protein–protein interactions in the post-interactomic era. Are we ready for the endgame?

Mapping protein–protein interactions in genome-wide scales revealed thousands of novel binding partners in each of the explored model organisms. Organizing these hits in comprehensive ways is becoming increasingly important for systems biology approaches to understand complex cellular processes and diseases. However, proteome wide interaction techniques and their resulting global networks are not revealing the topologies of networks that are truly operating in the cell. In this short review I will discuss which prerequisites have to be fulfilled and which experimental methods might be practicable to translate primary protein interaction data into network presentations that help in understanding cellular processes.     

The plant cell wall: Biosynthesis,construction, and functions

The plant cell wall is composed of multiple biopolymers, representing one of the most complex structural networks in nature. Hundreds of genes are involved in building such a natural masterpiece. However, the plant cell wall is the least understood cellular structure in plants. Due to great progress in plant functional genomics,manyachievementshavebeenmadein uncovering cell wall biosynthesis, assembly, and architecture, as well as cell wall regulation and signaling. Such information has significantly advanced our understanding of the roles of the cell wall in many biological and physiological processes and has enhanced our utilization of cell wall materials. The use of cutting-edge technologies such as single-molecule imaging,nuclear magnetic resonance spectroscopy, and atomic force microscopy has provided much insight into the plant cell wall as an intricate nanoscale network, opening up unprecedented possibilities for cell wall research. In this review,we summarize the major advances made in understanding the cell wall in this era of functional genomics, including the latest findings on the biosynthesis, construction, and functions of the cell wall.     

Semantic integration to identify overlapping functional modules in protein interaction networks

Background  

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.     

植物MAP激酶级联途径研究进展

MAP激酶(促分裂原活化蛋白激酶)级联途径可以将不同的细胞膜感受器与细胞应答联系起来,响应各种生物以及非生物胁迫,在植物激素信号以及细胞分裂和发育过程中发挥着重要的作用.为有效地传递各种特异信号,MAP激酶级联相互交叉形成复杂的信号传递网络.近年来,随着功能获得型突变体、功能缺失型突变体的获得以及其它一些新技术的应用,进一步阐明了MAP激酶级联途径在信号传导过程中的功能和作用.本文主要对植物MAPK级联途径在信号传导过程中交叉串通以及复杂性的最新研究结果进行综述.     

植物生物钟及其调控生长发育的研究进展

作为植物细胞内部的授时机制, 生物钟系统主要包括信号输入、核心振荡器和信号输出3个主要部分。该系统通过感受外界光照和温度等环境因子的昼夜周期性变化动态, 协调植物生长发育、代谢与生理反应, 赋予植物对生存环境的适应性。植物生物钟系统的核心振荡器通过多层级调控复杂的下游信号转导网络来参与调节植物生长发育及对生物与非生物胁迫的适应性。该文概述了近年来生物钟核心振荡器及其调控植物生长发育过程诸方面的研究进展, 并初步提出了植物时间生物学研究领域一些亟待解决的科学问题, 以期为生物钟领域的研究成果在作物分子育种方面的利用提供理论借鉴。     

Enhanced functional information from predicted protein networks

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.     

Unveiling protein functions through the dynamics of the interaction network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.     

Inferring gene-phenotype associations via global protein complex network propagation

Background

Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.

Methodology/Principal Findings

We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.

Conclusions/Significance

Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.     

A systems‐level approach to mapping the telomere length maintenance gene circuitry

The ends of eukaryotic chromosomes are protected by telomeres, nucleoprotein structures that are essential for chromosomal stability and integrity. Understanding how telomere length is controlled has significant medical implications, especially in the fields of aging and cancer. Two recent systematic genome‐wide surveys measuring the telomere length of deleted mutants in the yeast Saccharomyces cerevisiae have identified hundreds of telomere length maintenance (TLM) genes, which span a large array of functional categories and different localizations within the cell. This study presents a novel general method that integrates large‐scale screening mutant data with protein–protein interaction information to rigorously chart the cellular subnetwork underlying the function investigated. Applying this method to the yeast telomere length control data, we identify pathways that connect the TLM proteins to the telomere‐processing machinery, and predict new TLM genes and their effect on telomere length. We experimentally validate some of these predictions, demonstrating that our method is remarkably accurate. Our results both uncover the complex cellular network underlying TLM and validate a new method for inferring such networks.     

Plant myosins

Summary Plant myosins are motor proteins that bind to the external surfaces of organelles and interact with the cytoskeletal protein actin (as actin microfilaments), which organizes and directs intracellular movement. Recent progress in physiological, biochemical, immunological, and genetical studies of plant myosin has revealed considerable information about the structures and functions of these important molecules. This article briefly reviews the history of plant myosin research, summarizes recent progress, and highlights directions for future research. Dedicated to the memory of the late Professor Noburo Kamiya (1913–1999)