Hubs In Protein-protein Interaction Networks Research Articles

Structural characterisation of amyloidogenic intrinsically disordered zinc finger protein isoforms DPF3b and DPF3a

Double PHD fingers 3 (DPF3) is a zinc finger protein, found in the BAF chromatin remodelling complex, and is involved in the regulation of gene expression. Two DPF3 isoforms have been identified, respectively named DPF3b and DPF3a. Very limited structural information is available for these isoforms, and their specific functionality still remains poorly studied. In a previous work, we have demonstrated the first evidence of DPF3a being a disordered protein sensitive to amyloid fibrillation. Intrinsically disordered proteins (IDPs) lack a defined tertiary structure, existing as a dynamic conformational ensemble, allowing them to act as hubs in protein-protein interaction networks. In the present study, we have more thoroughly characterised DPF3a in vitro behaviour, as well as unravelled and compared the structural properties of the DPF3b isoform, using an array of predictors and biophysical techniques. Predictions, spectroscopy, and dynamic light scattering have revealed a high content in disorder: prevalence of random coil, aromatic residues partially to fully exposed to the solvent, and large hydrodynamic diameters. DPF3a appears to be more disordered than DPF3b, and exhibits more expanded conformations. Furthermore, we have shown that they both time-dependently aggregate into amyloid fibrils, as revealed by typical circular dichroism, deep-blue autofluorescence, and amyloid-dye binding assay fingerprints. Although spectroscopic and microscopic analyses have unveiled that they share a similar aggregation pathway, DPF3a fibrillates at a faster rate, likely through reordering of its C-terminal domain.

Intrinsically disordered proteins/regions and insight into their biomolecular interactions

Proteins may vary from being rigid to having flexible regions to being completely disordered, either as an intrinsically disordered protein (IDP) or having specific intrinsically disordered regions (IDRs). IDPs/IDRs can form complexes otherwise impossible, such as wrapping around the binding partner, hence providing the plasticity needed for achieving assemblies with specific functions. IDRs can exhibit promiscuity, using the same region in the sequence to bind multiple partners, and act as hubs in protein-protein interaction network (an essential part of the cell signalling network). Disorder-to-order transition on binding provides specificity with affinity, optimum for reversibility of the binding, thus offering suitability for regulation and signalling processes. IDRs interactions may be modulated by the environment or covalent modifications; mis-signalling or their unnatural or non-native folding may lead to diseases. This article aims to provide an overview of structural heterogeneity, as seen in IDPs/IDRs, and their role in biological recognition, binding and function.

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key genes for ACP early diagnosis and treatment. Datasets GSE94349 and GSE68015 were obtained from Gene Expression Omnibus database. Consensus clustering was applied to discover the gene clusters in the expression data of GSE94349 and functional enrichment analysis was performed on gene set in each cluster. The protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes, and hubs were selected. Support vector machine (SVM) model was built based on the signature genes identified from enrichment analysis and PPI network. Dataset GSE94349 was used for training and testing, and GSE68015 was used for validation. Besides, RT-qPCR analysis was performed to analyze the expression of signature genes in ACP samples compared with normal controls. Seven gene clusters were discovered in the differentially expressed genes identified from GSE94349 dataset. Enrichment analysis of each cluster identified 25 pathways that highly associated with ACP. PPI network was built and 46 hubs were determined. Twenty-five pathway-related genes that overlapped with the hubs in PPI network were used as signatures to establish the SVM diagnosis model for ACP. The prediction accuracy of SVM model for training, testing, and validation data were 94, 85, and 74%, respectively. The expression of CDH1, CCL2, ITGA2, COL8A1, COL6A2, and COL6A3 were significantly upregulated in ACP tumor samples, while CAMK2A, RIMS1, NEFL, SYT1, and STX1A were significantly downregulated, which were consistent with the differentially expressed gene analysis. SVM model is a promising classification tool for screening and early diagnosis of ACP. The ACP-related pathways and signature genes will advance our knowledge of ACP pathogenesis and benefit the therapy improvement.

Properties of genes essential for mouse development.

Essential genes are those that are critical for life. In the specific case of the mouse, they are the set of genes whose deletion means that a mouse is unable to survive after birth. As such, they are the key minimal set of genes needed for all the steps of development to produce an organism capable of life ex utero. We explored a wide range of sequence and functional features to characterise essential (lethal) and non-essential (viable) genes in mice. Experimental data curated manually identified 1301 essential genes and 3451 viable genes. Very many sequence features show highly significant differences between essential and viable mouse genes. Essential genes generally encode complex proteins, with multiple domains and many introns. These genes tend to be: long, highly expressed, old and evolutionarily conserved. These genes tend to encode ligases, transferases, phosphorylated proteins, intracellular proteins, nuclear proteins, and hubs in protein-protein interaction networks. They are involved with regulating protein-protein interactions, gene expression and metabolic processes, cell morphogenesis, cell division, cell proliferation, DNA replication, cell differentiation, DNA repair and transcription, cell differentiation and embryonic development. Viable genes tend to encode: membrane proteins or secreted proteins, and are associated with functions such as cellular communication, apoptosis, behaviour and immune response, as well as housekeeping and tissue specific functions. Viable genes are linked to transport, ion channels, signal transduction, calcium binding and lipid binding, consistent with their location in membranes and involvement with cell-cell communication. From the analysis of the composite features of essential and viable genes, we conclude that essential genes tend to be required for intracellular functions, and viable genes tend to be involved with extracellular functions and cell-cell communication. Knowledge of the features that are over-represented in essential genes allows for a deeper understanding of the functions and processes implemented during mammalian development.

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

Are protein hubs faster folders? Exploration based on Escherichia coli proteome.

Protein hubs in protein-protein interaction network are especially important due to their central roles in the entire network. Despite of their importance, the folding kinetics of hub proteins in comparison with non-hubs is still unknown. In this work, the folding rates for protein hubs and non-hubs were predicted and compared for the interactome of Escherichia coli K12, and the results showed that hub proteins fold faster than non-hub proteins. A possible explanation might be that protein hubs have more and fast-folding structural conformations than non-hubs, which leads to the notion of "hub of hubs" in the protein conformation space. It was found that the sequence and structure features relevant to protein folding rates are also different between hub and non-hub proteins. Moreover, the interacting proteins tend to have similar folding rates. These results gave insightful implications for understanding the interplay between the mechanisms of protein folding and interaction.

Co-expression and co-localization of hub proteins and their partners are encoded in protein sequence

Spatiotemporal coordination is a critical factor in biological processes. Some hubs in protein-protein interaction networks tend to be co-expressed and co-localized with their partners more strongly than others, a difference which is arguably related to functional differences between the hubs. Based on numerous analyses of yeast hubs, it has been suggested that differences in co-expression and co-localization are reflected in the structural and molecular characteristics of the hubs. We hypothesized that if indeed differences in co-expression and co-localization are encoded in the molecular characteristics of the protein, it may be possible to predict the tendency for co-expression and co-localization of human hubs based on features learned from systematically characterized yeast hubs. Thus, we trained a prediction algorithm on hubs from yeast that were classified as either strongly or weakly co-expressed and co-localized with their partners, and applied the trained model to 800 human hub proteins. We found that the algorithm significantly distinguishes between human hubs that are co-expressed and co-localized with their partners and hubs that are not. The prediction is based on sequence derived features such as "stickiness", i.e. the existence of multiple putative binding sites that enable multiple simultaneous interactions, "plasticity", i.e. the existence of predicted structural disorder which conjecturally allows for multiple consecutive interactions with the same binding site and predicted subcellular localization. These results suggest that spatiotemporal dynamics is encoded, at least in part, in the amino acid sequence of the protein and that this encoding is similar in yeast and in human.

Correlation of Genomic Features With Dynamic Modularity in the Yeast Interactome: A View From the Structural Perspective

The idea of the existence of date and party hubs in protein-protein interaction networks has been debated since it was proposed in 2004. Based on the incorporation of the information extracted from known three-dimensional structures of protein interactions, we revisited the properties associated with date and party hubs previously identified. The correlation of genomic essentiality, gene coexpression, and functional semantic similarity with date and party hubs were examined. The number of interaction interfaces associated with each hub was taken into account. The results suggested that the identification of date and party hubs based on their network connectivity and expression profiles with interaction partners may be incomplete. The number of interaction interfaces could play an important role in examining functional and topological properties associated with each hub protein. The observation is robust to the choice of degree cutoffs for hubs. Furthermore, we found that while singlish-interface hubs seem to correspond mostly to date hub, it appears that there is no significant difference between the proportions of multi-interface proteins categorized as date and as party hubs.

Exploring the Differences in Evolutionary Rates between Monogenic and Polygenic Disease Genes in Human

Comparative analyses on disease and nondisease (ND) genes have greatly facilitated the understanding of human diseases. However, most studies have grouped all the disease genes together and have performed comparative analyses with other ND genes. Thus, the molecular mechanism of disease on which disease genes can be separated into monogenic and polygenic diseases (MDs and PDs) has been ignored in earlier studies. Here, we report a comprehensive study of PD and MD genes with respect to ND genes. Our work shows that MD genes are more conserved than PD genes and that ND genes are themselves more conserved than both classes of disease genes. By separating the ND genes into housekeeping and other genes, it was found that housekeeping genes are the most conserved among all categories of genes, whereas other ND genes show an evolutionary rate intermediate between MD and PD genes. Although PD genes have a higher number of interacting partners than MD and ND genes, the reasons for their higher evolutionary rate require explanation. We provide evidences that the faster evolutionary rate of PD genes is influenced by 1) the predominance of date hubs in protein-protein interaction network, 2) the higher number of disorder residues, 3) the lower expression level, and 4) the involvement with more regulatory processes. Logistic regression analysis suggests that the relative importance of the four individual factors in determining the evolutionary rate variation among the four classes of proteins is in the order of mRNA expression level > presence of party/date hubs > disorder > involvement of proteins in core/regulatory processes.

The role of charged surface residues in the binding ability of small hubs in protein-protein interaction networks.

Hubs are highly connected proteins in a protein-protein interaction network. Previous work has implicated disordered domains and high surface charge as the properties significant in the ability of hubs to bind multiple proteins. While conformational flexibility of disordered domains plays an important role in the binding ability of large hubs, high surface charge is the dominant property in small hubs. In this study, we further investigate the role of the high surface charge in the binding ability of small hubs in the absence of disordered domains. Using multipole expansion, we find that the charges are highly distributed over the hub surfaces. Residue enrichment studies show that the charged residues in hubs are more prevalent on the exposed surface, with the exception of Arg, which is predominantly found at the interface, as compared to non-hubs. This suggests that the charged residues act primarily from the exposed surface rather than the interface to affect the binding ability of small hubs. They do this through (i) enhanced intra-molecular electrostatic interactions to lower the desolvation penalty, (ii) indirect long – range intermolecular interactions with charged residues on the partner proteins for better complementarity and electrostatic steering, and (iii) increased solubility for enhanced diffusion-controlled rate of binding. Along with Arg, we also find a high prevalence of polar residues Tyr, Gln and His and the hydrophobic residue Met at the interfaces of hubs, all of which have the ability to form multiple types of interactions, indicating that the interfaces of hubs are optimized to participate in multiple interactions.