Protein–protein interaction networks improve the proteomics data interpretation in induced apoptosis

Evaluation of: Deighton RF, Kerr LE, Short DM et al. Network generation enhances interpretation of proteomics data from induced apoptosis. Proteomics DOI: 10.1002/pmic.200900112 (2010) (Epub ahead of print).

The huge ongoing improvements in proteomics technologies, including the development of high-throughput mass spectrometry, are resulting in ever increasing information on protein behavior during cellular processes. The exponential accumulation of proteomics data has the promise to advance biomedical sciences by shedding light on the most important events that regulate mammalian cells under normal and pathophysiological conditions. This may provide practical insights that will impact medical practice and therapy, and may permit the development of a new generation of personalized therapeutics. Proteomics, as a powerful tool, creates numerous opportunities as well as challenges. At the different stages, data interpretation requires proteomics analysis, various tools to help deal with large proteomics data banks and the extraction of more functional information. Network analysis tools facilitate proteomics data interpretation and predict protein functions, functional interactions and in silica identification of intracellular pathways. The work reported by Deighton and colleagues illustrates an example of improving proteomics data interpretation by network generation. The authors used ingenuity pathway analysis to generate a protein network predicting direct and indirect interaction between 13 proteins found to be affected by staurosporine treatment. Importantly, the authors highlight the caution required when interpreting the results from a small number of proteins analyzed using network analysis tools.     

Protein–protein interactions of huntingtin in the hippocampus

Molecular Biology - Huntingtin (HTT) occurs in the neuronal cytoplasm and can interact with structural elements of synapses. Huntington’s disease (HD) results from pathological expansion of a...     

Advances in the study of protein–DNA interaction

Protein-DNA interaction plays an important role in many biological processes. The classical methods and the novel technologies advanced have been developed for the interaction of protein-DNA. Recent developments of these methods and research achievements have been reviewed in this paper.     

Protein–protein interaction and SNP analysis in intraductal papillary mucinous neoplasm

Intraductal papillary mucinous neoplasm (IPMN) is a type of tumor that grows within the pancreatic ducts. It is a progress from hyperplasia to intraductal adenoma (IPMA), to noninvasive carcinoma, and ultimately to invasive carcinoma (IPMC). The objective of this study was to explore the molecular mechanism of the progression from IPMA to IPMC. By using the GSE19650 affymetrix microarray data accessible from Gene Expression Omnibus (GEO) database, we first identified the differentially expressed genes (DEGs) between IPMA and IPMC, followed by the protein–protein interaction and single-nucleotide polymorphism (SNP) analysis of the DEGs. Our study identified thousands of DEGs which involved regulation of cell cycle and apoptosis in this progression from IPMA to IPMC. Protein–protein interaction network construction found that MYC, IL6ST, NR3C1, CREBBP, GATA1 and LRP1 might play an important role in the progression. Furthermore, the SNP analysis confirmed the association between BRAC1 and pancreas cancer. In conclusion, our data provide a comprehensive bioinformatics analysis of genes and pathways which may be involved in the progression of IPMN from IPMA to IPMC.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

GRP75经由Raf/Mek/Erk1/2信号转导通路抑制缺糖诱导的细胞凋亡

本文旨在探讨促存活信号通路Raf/Mek/Erk1/2是否参与了葡萄糖调节蛋白75(glucose-regulated protein75,GRP75)对缺糖诱导的细胞凋亡的抑制作用。GRP75过表达的PC12细胞给予Raf/Mek/Erk1/2通路抑制剂U0126预处理之后,无糖培养6、12和24h,同时以DMSO预处理的GRP75过表达PC12细胞组为对照。Western blot检测Erk1/2的磷酸化和表达水平,MTT实验检测细胞存活率,Hoechst 33258染色观察凋亡细胞核的形态学改变,流式细胞仪检测细胞亚二倍体峰,免疫荧光检测细胞色素c(cytochrome c,Cytc)向胞浆的弥散情况。结果显示:U0126在没有影响Erk1/2表达水平的前提下,阻断了GRP75对Erk1/2磷酸化水平的维持;U0126处理组的凋亡率明显高于对照组;U0126处理组Cytc从线粒体向胞浆释放的时间明显早于对照组,同时Cytc向胞浆的弥散程度大于对照组。以上结果提示,U0126通过抑制Erk1/2磷酸化,阻断了缺糖状态下GRP75对Cytc释放和细胞凋亡的抑制作用,这表明GRP75是通过Raf/Mek/Er...     

On the functional and structural characterization of hubs in protein–protein interaction networks

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein–Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Interactome of signaling networks in wheat: the protein–protein interaction between TaRAR1 and TaSGT1

RAR1 and SGT1 are required for development and disease resistance in plants. In many cases, RAR1 and SGT1 regulate the resistance (R)-gene-mediated defense signaling pathways. Lr21 is the first identified NBS-LRR-type R protein in wheat and is required for resistance to the leaf rust pathogen. The Lr21-mediated signaling pathways require the wheat homologs of RAR1, SGT1, and HSP90. However, the molecular mechanisms of the Lr21-mediated signaling networks remain unknown. Here I present the DNA and protein sequences of TaRAR1 and TaSGT1, and demonstrate for the first time a direct protein-protein interaction between them.     

ChikvInt: a Chikungunya virus–host protein–protein interaction database

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral–host protein–protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com .     

The protein–protein interaction network of the human Sirtuin family

Protein–protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein–protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.     

Protein–Protein and Protein–Membrane Associations in the Lignin Pathway

Supramolecular organization of enzymes is proposed to orchestrate metabolic complexity and help channel intermediates in different pathways. Phenylpropanoid metabolism has to direct up to 30% of the carbon fixed by plants to the biosynthesis of lignin precursors. Effective coupling of the enzymes in the pathway thus seems to be required. Subcellular localization, mobility, protein–protein, and protein–membrane interactions of four consecutive enzymes around the main branch point leading to lignin precursors was investigated in leaf tissues of Nicotiana benthamiana and cells of Arabidopsis thaliana. CYP73A5 and CYP98A3, the two Arabidopsis cytochrome P450s (P450s) catalyzing para- and meta-hydroxylations of the phenolic ring of monolignols were found to colocalize in the endoplasmic reticulum (ER) and to form homo- and heteromers. They moved along with the fast remodeling plant ER, but their lateral diffusion on the ER surface was restricted, likely due to association with other ER proteins. The connecting soluble enzyme hydroxycinnamoyltransferase (HCT), was found partially associated with the ER. Both HCT and the 4-coumaroyl-CoA ligase relocalized closer to the membrane upon P450 expression. Fluorescence lifetime imaging microscopy supports P450 colocalization and interaction with the soluble proteins, enhanced by the expression of the partner proteins. Protein relocalization was further enhanced in tissues undergoing wound repair. CYP98A3 was the most effective in driving protein association.     

Inhibitors of the p53/hdm2 protein–protein interaction—path to the clinic

A growing number of the elements identified in intracellular signaling events that affect cell growth and transformation are proteins that physically interact with each other via domains or specifically recognized amino acid sequences. Some of these intracellular protein–protein interactions are attractive targets for anticancer targeted therapy, but progress in this field has been compromised by the paucity of compounds with suitable biological profiles and pharmacological properties. This Letter covers salient achievements in the identification and development of inhibitors of the p53–hdm2 protein–protein interaction, and highlights different screening techniques and structure-based design approaches that may be brought to bear on the discovery and development of inhibitors of other therapeutically relevant intracellular protein–protein interactions.     

Dissecting water binding sites at protein–protein interfaces: a lesson from the atomic structures in the Protein Data Bank

We dissect the protein–protein interfaces into water preservation (WP), water hydration (WH) and water dehydration (WD) sites by comparing the water-mediated hydrogen bonds (H-bond) in the bound and unbound states of the interacting subunits. Upon subunit complexation, if a H-bond between an interface water and a protein polar group is retained, we assign it as WP site; if it is lost, we assign it as WD site and if a new H-bond is created, we assign it as WH site. We find that the density of WD sites is highest followed by WH and WP sites except in antigen and (or) antibody complexes, where the density of WH sites is highest followed by WD and WP sites. Furthermore, we find that WP sites are the most conserved followed by WD and WH sites in all class of complexes except in antigen and (or) antibody complexes, where WD sites are the most conserved followed by WH and WP sites. A significant number of WP and WH sites are involved in water bridges that stabilize the subunit interactions. At WH sites, the residues involved in water bridges are significantly better conserved than the other residues. However, no such difference is observed at WP sites. Interestingly, WD sites are generally replaced with direct H-bonds upon subunit complexation. Significantly, we observe many water-mediated H-bonds remain preserved in spite of large conformational changes upon subunit complexation. These findings have implications in predicting and engineering water binding sites at protein–protein interfaces.     

Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein–protein interaction network

Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene’s propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein–protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.     

Protein–protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis

Under stressful conditions, the non-model marine microalga Tetraselmis subcordiformis can accumulate a substantial amount of starch, making it a potential feedstock for the production of fuel ethanol. Investigating the interactions of the enzymes and the regulatory factors involved in starch metabolism will provide potential genetic manipulation targets for optimising the starch productivity of T. subcordiformis. For this reason, the proteome of T. subcordiformis was utilised to predict the first protein–protein interaction (PPI) network for this marine alga based on orthologous interactions, mainly from the general PPI repositories. Different methods were introduced to evaluate the credibility of the predicted interactome, including the confidence value of each PPI pair and Pfam-based and subcellular location-based enrichment analysis. Functional subnetworks analysis suggested that the two enzymes involved in starch metabolism, starch phosphorylase and trehalose-phosphate synthase may be the potential ideal genetic engineering targets.