The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.     

Fine-Scale Dissection of Functional Protein Network Organization by Statistical Network Analysis

Revealing organizational principles of biological networks is an important goal of systems biology. In this study, we sought to analyze the dynamic organizational principles within the protein interaction network by studying the characteristics of individual neighborhoods of proteins within the network based on their gene expression as well as protein-protein interaction patterns. By clustering proteins into distinct groups based on their neighborhood gene expression characteristics, we identify several significant trends in the dynamic organization of the protein interaction network. We show that proteins with distinct neighborhood gene expression characteristics are positioned in specific localities in the protein interaction network thereby playing specific roles in the dynamic network connectivity. Remarkably, our analysis reveals a neighborhood characteristic that corresponds to the most centrally located group of proteins within the network. Further, we show that the connectivity pattern displayed by this group is consistent with the notion of “rich club connectivity” in complex networks. Importantly, our findings are largely reproducible in networks constructed using independent and different datasets.     

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

Background  

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Protein Connectivity and Protein Complexity Promotes Human Gene Duplicability in a Mutually Exclusive Manner

It has previously been reported that protein complexity (i.e. number of subunits in a protein complex) is negatively correlated to gene duplicability in yeast as well as in humans. However, unlike in yeast, protein connectivity in a protein–protein interaction network has a positive correlation with gene duplicability in human genes. In the present study, we have analyzed 1732 human and 1269 yeast proteins that are present both in a protein–protein interaction network as well as in a protein complex network. In the human case, we observed that both protein connectivity and protein complexity complement each other in a mutually exclusive manner over gene duplicability in a positive direction. Analysis of human haploinsufficient proteins and large protein complexes (complex size >10) shows that when protein connectivity does not have any direct association with gene duplicability, there exists a positive correlation between gene duplicability and protein complexity. The same trend, however, is not found in case of yeast, where both protein connectivity and protein complexity independently guide gene duplicability in the negative direction. We conclude that the higher rate of duplication of human genes may be attributed to organismal complexity either by increasing connectivity in the protein–protein interaction network or by increasing protein complexity.     

Global protein function prediction from protein-protein interaction networks

Determining protein function is one of the most challenging problems of the post-genomic era. The availability of entire genome sequences and of high-throughput capabilities to determine gene coexpression patterns has shifted the research focus from the study of single proteins or small complexes to that of the entire proteome. In this context, the search for reliable methods for assigning protein function is of primary importance. There are various approaches available for deducing the function of proteins of unknown function using information derived from sequence similarity or clustering patterns of co-regulated genes, phylogenetic profiles, protein-protein interactions (refs. 5-8 and Samanta, M.P. and Liang, S., unpublished data), and protein complexes. Here we propose the assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories. Function assignment is proteome-wide and is determined by the global connectivity pattern of the protein network. The approach results in multiple functional assignments, a consequence of the existence of multiple equivalent solutions. We apply the method to analyze the yeast Saccharomyces cerevisiae protein-protein interaction network. The robustness of the approach is tested in a system containing a high percentage of unclassified proteins and also in cases of deletion and insertion of specific protein interactions.     

利用基因共表达网络分析蛋白质的相互作用

基于Hom in的基因共表达网络的比较分析,发现人类基因共表达网络和蛋白质相互作用数据之间存在一定的相关性。采用基因本体论对这两个网络重叠区域进行基因分类后发现,这些编码的蛋白质主要集中在对刺激物的应答途径之中。通过对该途径中的蛋白质相互作用网络作图,获得了两个独立的功能模块。通过对模块中的基因分类和关键基因分析得出两者分别对应于内外源刺激物的应答功能。本研究对于利用不断丰富的核酸公共数据信息挖掘蛋白质相互作用的研究具有积极的促进作用。     

Diffusion kernel-based logistic regression models for protein function prediction

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.     

The relationships among microRNA regulation, intrinsically disordered regions, and other indicators of protein evolutionary rate

Many indicators of protein evolutionary rate have been proposed, but some of them are interrelated. The purpose of this study is to disentangle their correlations. We assess the strength of each indicator by controlling for the other indicators under study. We find that the number of microRNA (miRNA) types that regulate a gene is the strongest rate indicator (a negative correlation), followed by disorder content (the percentage of disordered regions in a protein, a positive correlation); the strength of disorder content as a rate indicator is substantially increased after controlling for the number of miRNA types. By dividing proteins into lowly and highly intrinsically disordered proteins (L-IDPs and H-IDPs), we find that proteins interacting with more H-IDPs tend to evolve more slowly, which largely explains the previous observation of a negative correlation between the number of protein-protein interactions and evolutionary rate. Moreover, all of the indicators examined here, except for the number of miRNA types, have different strengths in L-IDPs and in H-IDPs. Finally, the number of phosphorylation sites is weakly correlated with the number of miRNA types, and its strength as a rate indicator is substantially reduced when other indicators are considered. Our study reveals the relative strength of each rate indicator and increases our understanding of protein evolution.     

Characterization of microRNA-regulated protein-protein interaction network

We have performed topological analysis to elucidate the global correlation between microRNA (miRNA) regulation and protein-protein interaction network in human. The analysis showed that target genes of individual miRNA tend to be hubs and bottlenecks in the network. While proteins directly regulated by miRNA might not form a network module themselves, the miRNA-target genes and their interacting neighbors jointly showed significantly higher modularity. Our findings shed light on how miRNA may regulate the protein interaction network.     

Modification of gene duplicability during the evolution of protein interaction network

Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human) that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

基于MADS-box诱饵与蛋白质相互作用的拟南芥花瓣发育分子网络拓展

阐明花器官发育调控机理具重要的进化、发育和生态学意义。该文以拟南芥(Arabidopsis thaliana)花瓣发育为例, 整合蛋白质互作、亚细胞定位、基因芯片和基因功能注释等数据库, 通过组建蛋白质互作可信预测模型, 获得拟南芥花瓣蛋白质互作网络, 以含有MADS-box结构域蛋白为诱饵在网络中进行一级拓展, 得到含38个蛋白质和67对互作的拓展网络。基于拓展网络, DAVID基因功能注释表明, 多数蛋白质涉及的生物学过程与花发育调控相关; 提取到19个候选四元互作, 涉及ABCDE模型基因之外的8个基因, 其中含MADS-box结构域的AGL16可能是B类基因新成员或其冗余; SEU、LUH、CHR4、CHR11、CHR17和AT3G04960为拟南芥花瓣AP1-AP3-PI-SEP四聚体的候选靶标基因。研究结果为深入解析拟南芥花瓣发育分子调控网络奠定了基础。     

The zebrafish iguana locus encodes Dzip1, a novel zinc-finger protein required for proper regulation of Hedgehog signaling

Members of the Hedgehog (Hh) family of intercellular signaling molecules play crucial roles in animal development. Aberrant regulation of Hh signaling in humans causes developmental defects, and leads to various genetic disorders and cancers. We have characterized a novel regulator of Hh signaling through the analysis of the zebrafish midline mutant iguana (igu). Mutations in igu lead to reduced expression of Hh target genes in the ventral neural tube, similar to the phenotype seen in zebrafish mutants known to affect Hh signaling. Contradictory at first sight, igu mutations lead to expanded Hh target gene expression in somites. Genetic and pharmacological analyses revealed that the expression of Hh target genes in igu mutants requires Gli activator function but does not depend on Smoothened function. Our results show that the ability of Gli proteins to activate Hh target gene expression in response to Hh signals is generally reduced in igu mutants both in the neural tube and in somites. Although this reduced Hh signaling activity leads to a loss of Hh target gene expression in the neural tube, the same low levels of Hh signaling appear to be sufficient to activate Hh target genes throughout somites because of different threshold responses to Hh signals. We also show that Hh target gene expression in igu mutants is resistant to increased protein kinase A activity that normally represses Hh signaling. Together, our data indicate that igu mutations impair both the full activation of Gli proteins in response to Hh signals, and the negative regulation of Hh signaling in tissues more distant from the source of Hh. Positional cloning revealed that the igu locus encodes Dzip1, a novel intracellular protein that contains a single zinc-finger protein-protein interaction domain. Overexpression of Igu/Dzip1 proteins suggested that Igu/Dzip1 functions in a permissive way in the Hh signaling pathway. Taken together, our studies show that Igu/Dzip1 functions as a permissive factor that is required for the proper regulation of Hh target genes in response to Hh signals.     

Identifying Correlations between Chromosomal Proximity of Genes and Distance of Their Products in Protein-Protein Interaction Networks of Yeast

In this article we present evidence for a relationship between chromosome gene loci and the topological properties of the protein-protein interaction network corresponding to the set of genes under consideration. Specifically, for each chromosome of the Saccharomyces cerevisiae genome, the distribution of the intra-chromosome inter-gene distances was analyzed and a positive correlation with the distance among the corresponding proteins of the protein-protein interaction network was found. In order to study this relationship we used concepts based on non-parametric statistics and information theory.We provide statistical evidence that if two genes are closely located, then it is likely that their protein products are closely located in the protein-protein interaction network, or in other words, that they are involved in the same biological process.