Linear Motif-Mediated Interactions Have Contributed to the Evolution of Modularity in Complex Protein Interaction Networks

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.     

A predicted protein-protein interaction network of the filamentous fungus Neurospora crassa

The filamentous fungus Neurospora crassa is a leading model organism for circadian clock studies. Computational identification of a protein-protein interaction (PPI) network (also known as an interactome) in N. crassa can provide new insights into the cellular functions of proteins. Using two well-established bioinformatics methods (the interolog method and the domain interaction-based method), we predicted 27,588 PPIs among 3006 N. crassa proteins. To the best of our knowledge, this is the first identified interactome for N. crassa, although it remains problematic because of incomplete interactions and false positives. In particular, the established PPI network has provided clues to further decipher the molecular mechanism of circadian rhythmicity. For instance, we found that clock-controlled genes (ccgs) are more likely to act as bottlenecks in the established PPI network. We also identified an important module related to circadian oscillators, and some functional unknown proteins in this module may serve as potential candidates for new oscillators. Finally, all predicted PPIs were compiled into a user-friendly database server (NCPI), which is freely available at .     

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.     

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...     

Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction

Abdominal aortic aneurysm (AAA) is frequently lethal and has no effective pharmaceutical treatment, posing a great threat to human health. Previous bioinformatics studies of the mechanisms underlying AAA relied largely on the detection of direct protein-protein interactions (level-1 PPI) between the products of reported AAA-related genes. Thus, some proteins not suspected to be directly linked to previously reported genes of pivotal importance to AAA might have been missed. In this study, we constructed an indirect protein-protein interaction (level-2 PPI) network based on common interacting proteins encoded by known AAA-related genes and successfully predicted previously unreported AAA-related genes using this network. We used four methods to test and verify the performance of this level-2 PPI network: cross validation, human AAA mRNA chip array comparison, literature mining, and verification in a mouse CaPO₄ AAA model. We confirmed that the new level-2 PPI network is superior to the original level-1 PPI network and proved that the top 100 candidate genes predicted by the level-2 PPI network shared similar GO functions and KEGG pathways compared with positive genes.     

A signature predicting relapse based on integrated analysis on relapse-associated alternative mRNA splicing in I–III rectal cancer

Researches focusing on the effects of alternative splicing (AS) on relapse of rectal cancer is little and signature based on the AS is blank. In this study, bioinformatic analysis was performed to identify and analyze the relapse-associated ASs, a signature was also constructed. In conclusion, 829 relapse-associated ASs of 676 mRNA were identified. 603 proteins with 2119 interactions were involved in the PPI (protein-protein interactions) network. 43 relapse-associated ASs and 64 SFs (splicing factors) with 160 interactions were indicated. Finally, we built a robust signature to predict the relapse of I–III rectal cancer with a high AUC (0.98) of ROC at 1 year. Based on the ASs involved in the signature, 4 molecular subgroups that could distinguish the relapse rate in diverse groups were identified. Our research provided an overview of relapse-associated ASs in I–III rectal cancer.     

A kaleidoscopic view of the Arabidopsis core cell cycle interactome

Although protein-protein interaction (PPI) networks have been shown to offer a systems-wide view of cellular processes, only a few plant PPI maps are available. Recently, the core cell cycle of Arabidopsis thaliana has been analyzed by three independent PPI technologies, including yeast two-hybrid systems, bimolecular fluorescence complementation and tandem affinity purification. Here, we merge the three interactomes with literature-curated and computationally predicted interactions, paving the way for a comprehensive picture of the plant core cell cycle machinery. Platform-specific interactions unveil the strengths and weaknesses of each detection method and give insights into the nature of the interactions among cell cycle proteins. Moreover, comparison of the obtained data reveals that a complete interactome can only be obtained when multiple techniques are applied in parallel.     

Network cluster analysis of protein–protein interaction network identified biomarker for early onset colorectal cancer

Colorectal cancer (CRC) is a major cause of morbidity and mortality throughout the world. However, the genetic alterations and molecular mechanism of the early onset CRCs are not fully investigated. The present study aimed to characterize early onset CRC by analyzing its gene expression compared with normal controls and to identify network-based biomarkers of early onset CRC. The gene expression profiles of early onset CRC were downloaded from Gene Expression Omnibus and the differentially expressed genes (DEGs) in CRC patients were identified. Then, a protein–protein interaction (PPI) network was constructed and the clusters in PPI were analyzed by ClusterONE. Furthermore, the gene ontology functional analysis and pathway enrichment analysis were conducted to the modules in PPI network. A systems biology approach integrating microarray data and PPI was further applied to construct a PPI network in CRC. Total 631 DEGs were identified from the early onset CRC compared to healthy controls. These genes were found to be involved in several biological processes, including cell communication, cell proliferation, cell shape and apoptosis. Five functional modules which may play important roles in the initiation of early onset CRC were identified from the PPI network. Functional annotation revealed that these five modules were involved in the pathways of signal transduction, carcinogenesis and metastasis. The hub nodes of these five modules, CDC42, TEX11, QKI, CAV1 and FN1, may serve as the biomarkers of early onset CRC and have the potential to be targets for therapeutic intervention. However, further investigations are still needed to confirm our findings.     

A steady-state modeling approach to validate an in vivo mechanism of the GAL regulatory network in Saccharomyces cerevisiae.

Cellular regulation is a result of complex interactions arising from DNA-protein and protein-protein binding, autoregulation, and compartmentalization and shuttling of regulatory proteins. Experiments in molecular biology have identified these mechanisms recruited by a regulatory network. Mathematical models may be used to complement the knowledge-base provided by in vitro experimental methods. Interactions identified by in vitro experiments can lead to the hypothesis of multiple candidate models explaining the in vivo mechanism. The equilibrium dissociation constants for the various interactions and the total component concentration constitute constraints on the candidate models. In this work, we identify the most plausible in vivo network by comparing the output response to the experimental data. We demonstrate the methodology using the GAL system of Saccharomyces cerevisiae for which the steady-state analysis reveals that Gal3p neither dimerizes nor shuttles between the cytoplasm and the nucleus.     

Functional integrative levels in the human interactome recapitulate organ organization

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.     

Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.     

Extent of protein-protein interactions and quasi-equivalence in viral capsids

Viral capsids are composed of multiple copies of one or a few gene products that self-assemble on their own or in the presence of the viral genome and/or auxiliary proteins into closed shells (capsids). We have analyzed 75 high-resolution virus capsid structures by calculating the average fraction of the solvent-accessible surface area of the coat protein subunits buried in the viral capsids. This fraction ranges from 0 to 1 and represents a normalized protein-protein interaction (PPI) index and is a measure of the extent of protein-protein interactions. The PPI indices were used to compare the extent of association of subunits among different capsids. We further examined the variation of the PPI indices as a function of the molecular weight of the coat protein subunit and the capsid diameter. Our results suggest that the PPI indices in T=1 and pseudo-T=3 capsids vary linearly with the molecular weight of the subunit and capsid size. This is in contrast to quasi-equivalent capsids with T>or=3, where the extent of protein-protein interactions is relatively independent of the subunit and capsid sizes. The striking outcome of this analysis is the distinctive clustering of the "T=2" capsids, which are distinguished by higher subunit molecular weights and a much lower degree of protein-protein interactions. Furthermore, the calculated residual (R(sym)) of the fraction buried surface areas of the structurally unique subunits in capsids with T>1 was used to calculate the quasi-equivalence of different subunit environments.     

随机抽样对蛋白质相互作用网络度分布的影响

蛋白质相互作用(PPI)网络,是目前系统研究蛋白质层面细胞活动的重要工具,PPI网络的度分布是研究复杂网络性质的重要物理量。随着蛋白质相互作用数据量不断的增加,其数据的可靠性是否会对PPI网络的度分布造成影响,并不确定。本文对四种模式生物的PPI网络数据,按照5%的增长比率,随机删除PPI网络的边,来检验随机抽样对PPI网络度分布的影响。结果表明,各种实验数据的误差不会对真实PPI网络的度分布造成干扰,且在目前描述网络度分布的五种函数中,广延指数函数拟合程度最优。     

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.