Protein-protein interactions essentials: key concepts to building and analyzing interactome networks

The Interchromatin Compartment Participates in the Structural and Functional Organization of the Cell Nucleus

This article focuses on the role of the interchromatin compartment (IC) in shaping nuclear landscapes. The IC is connected with nuclear pore complexes (NPCs) and harbors splicing speckles and nuclear bodies. It is postulated that the IC provides routes for imported transcription factors to target sites, for export routes of mRNA as ribonucleoproteins toward NPCs, as well as for the intranuclear passage of regulatory RNAs from sites of transcription to remote functional sites (IC hypothesis). IC channels are lined by less-compacted euchromatin, called the perichromatin region (PR). The PR and IC together form the active nuclear compartment (ANC). The ANC is co-aligned with the inactive nuclear compartment (INC), comprising more compacted heterochromatin. It is postulated that the INC is accessible for individual transcription factors, but inaccessible for larger macromolecular aggregates (limited accessibility hypothesis). This functional nuclear organization depends on still unexplored movements of genes and regulatory sequences between the two compartments.

Integration of genetic variants and gene network for drug repurposing in colorectal cancer

Even though many genetic risk loci for human diseases have been identified and comprehensively cataloged, strategies to guide clinical research by integrating the extensive results of genetic studies and biological resources are still limited. Moreover, integrative analyses that provide novel insights into disease biology are expected to be especially useful for drug discovery. Herein, we used text mining of genetic studies on colorectal cancer (CRC) and assigned biological annotations to identified risk genes in order to discover novel drug targets and potential drugs for repurposing. Risk genes for CRC were obtained from PubMed text mining, and for each gene, six functional and bioinformatic annotations were analyzed. The annotations include missense mutations, cis-expression quantitative trait loci (cis-eQTL), molecular pathway analyses, protein-protein interactions (PPIs), a genetic overlap with knockout mouse phenotypes, and primary immunodeficiency (PID). We then prioritized the biological risk candidate genes according to a scoring system of the six functional annotations. Each functional annotation was assigned one point, and those genes with a score ≥2 were designated "biological CRC risk genes". Using this method, we revealed 82 biological CRC risk genes, which were mapped to 128 genes in an expanded PPI network. Further utilizing DrugBank and the Therapeutic Target Database, we found 21 genes in our list that are targeted by 166 candidate drugs. Based on data from ClinicalTrials.gov and literature review, we found four known target genes with six drugs for clinical treatment in CRC, and three target genes with nine drugs supported by previous preclinical results in CRC. Additionally, 12 genes are targeted by 32 drugs approved for other indications, which can possibly be repurposed for CRC treatment. Finally, analysis from Connectivity Map (CMap) showed that 18 drugs have a high potential for CRC.

Protein-protein interaction site prediction through combining local and global features with deep neural networks

MOTIVATION

Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction.

RESULTS

A new end-to-end deep learning framework, named DeepPPISP, through combining local contextual and global sequence features, is proposed for PPI site prediction. For local contextual features, we use a sliding window to capture features of neighbors of a target amino acid as in previous studies. For global sequence features, a text convolutional neural network is applied to extract features from the whole protein sequence. Then the local contextual and global sequence features are combined to predict PPI sites. By integrating local contextual and global sequence features, DeepPPISP achieves the state-of-the-art performance, which is better than the other competing methods. In order to investigate if global sequence features are helpful in our deep learning model, we remove or change some components in DeepPPISP. Detailed analyses show that global sequence features play important roles in DeepPPISP.

AVAILABILITY AND IMPLEMENTATION

The DeepPPISP web server is available at http://bioinformatics.csu.edu.cn/PPISP/. The source code can be obtained from https://github.com/CSUBioGroup/DeepPPISP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

Investigation and Prediction of Human Interactome Based on Quantitative Features

Protein is one of the most significant components of all living creatures. All significant and essential biological structures and functions relies on proteins and their respective biological functions. However, proteins cannot perform their unique biological significance independently. They have to interact with each other to realize the complicated biological processes in all living creatures including human beings. In other words, proteins depend on interactions (protein-protein interactions) to realize their significant effects. Thus, the significance comparison and quantitative contribution of candidate PPI features must be determined urgently. According to previous studies, 258 physical and chemical characteristics of proteins have been reported and confirmed to definitively affect the interaction efficiency of the related proteins. Among such features, essential physiochemical features of proteins like stoichiometric balance, protein abundance, molecular weight and charge distribution have been validated to be quite significant and irreplaceable for protein-protein interactions (PPIs). Therefore, in this study, we, on one hand, presented a novel computational framework to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and on the other hand, built a quantitative decision-rule system to evaluate the potential PPIs under real conditions with random forest (RF) and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs. The main datasets and codes can be downloaded at https://github.com/xypan1232/Mass-PPI.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

Study of genetic correlation between children's sleep and obesity

Laboratory and epidemiological studies have shown that short sleep time is associated with obesity. In this study, we conducted a post-GWAS analysis to test genetic correlation between children's sleep and obesity due to linkage disequilibrium (LD) SNPs, shared genes and pathways. Our analysis showed that genetic heritability was 0.14 (p-value = 0.0005) and 0.41 (p-value = 1.18E-24) for children's sleep and obesity, respectively, but genetic correlation due to LD SNPs was insignificant. Gene associations at children's GWAS were measured based on SNP associations and ranked by their uniform score (U-score). After adjusting for gene size, measured as the number of independent SNPs, children's sleep and obesity GWAS had significant gene correlation (r = 0.23). Pathway enrichment analysis showed that "Suz12 target genes" was the significant pathway for both children's sleep and obesity; pathways were significantly shared among top enriched pathways with an OR of 8.1-59.4; and significant correlation coefficient of pathway U-score was r = 0.36. Analysis of sleep time and obesity GWAS variants for all ages in the NHGRI-EBI GWAS Catalog also presented significant pathway correlation (r = 0.30). The "PAX3-FOXO1 target genes" was the significant pathway for all-age obesity phenotype and ranked as the second top associated pathway for all-age sleep time. Our study suggested that genetic correlation of children's sleep time and obesity is attributed to genes with pleiotropy effects and common pathway regulations that may contain only weak SNP associations.

Dynamic rewiring of the human interactome by interferon signaling

BACKGROUND

The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.

RESULTS

We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis.

CONCLUSIONS

Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Comparative study of gene expression profiles rooted in acute myocardial infarction and ischemic/reperfusion rat models

Mining data in depth of genome-wide sequencing data generated from pathological target tissues under disease conditions is necessary for seeking novel functional genes, and developing more biological study directions for the field. Based on our previous published RNA-seq data generated from acute myocardial ischemia and ischemia-reperfusion in rat heart, we re-analysed these two data sets using bioinformatics tools. All these raw fastq files were extracted from Illumina BCL using the Illumina CASAVA program. Four groups were obtained: UD (genes up-regulated in MI but down-regulated in I/R injury), DU (genes down-regulated in MI but up-regulated in I/R injury), UU (genes both up-regulated in MI and I/R injury), and DD (genes both down-regulated in MI and I/R injury) groups. The results showed that 304 common genes in the UD group, 236 common genes in the DU group, 318 common genes in the UU group, and 159 common genes in the DD group detected by comparing data sets of the MI and the I/R injury. We then listed the top 30 DEGs for each group, and carried out GO and KEGG analyses for enrichment and pathway studies for those top expressed genes. Further analysis of INTERPRO Protein Domains and Features enriched by DEGs showed that 20% of the Domains enriched were related to c-type lectin, and 17% of these domains are related to neurotransmitter-gated ion-channel. 15% of PFAM Protein Domains were about Neurotransmitter-gated ion-channel. There were only 8 SMART Protein Domains DEGs enriched and 37.5% of which were concerned about leucine-rich. Collagen involvement in Reactome Pathways accounted for 22.7%. We found that only a few DEGs in these two disease conditions have been reported in the literatures, suggesting that there are many new genes would be considered in the future studies. These analyses would provide some information for seeking more novel targets of these two clinic diseases, acute myocardial ischemia and myocardial ischemia/reperfusion.

Information Theory in Computational Biology: Where We Stand Today

"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

CSN: unsupervised approach for inferring biological networks based on the genome alone

BACKGROUND

Most organisms cannot be cultivated, as they live in unique ecological conditions that cannot be mimicked in the lab. Understanding the functionality of those organisms' genes and their interactions by performing large-scale measurements of transcription levels, protein-protein interactions or metabolism, is extremely difficult and, in some cases, impossible. Thus, efficient algorithms for deciphering genome functionality based only on the genomic sequences with no other experimental measurements are needed.

RESULTS

In this study, we describe a novel algorithm that infers gene networks that we name Common Substring Network (CSN). The algorithm enables inferring novel regulatory relations among genes based only on the genomic sequence of a given organism and partial homolog/ortholog-based functional annotation. It can specifically infer the functional annotation of genes with unknown homology. This approach is based on the assumption that related genes, not necessarily homologs, tend to share sub-sequences, which may be related to common regulatory mechanisms, similar functionality of encoded proteins, common evolutionary history, and more. We demonstrate that CSNs, which are based on S. cerevisiae and E. coli genomes, have properties similar to 'traditional' biological networks inferred from experiments. Highly expressed genes tend to have higher degree nodes in the CSN, genes with similar protein functionality tend to be closer, and the CSN graph exhibits a power-law degree distribution. Also, we show how the CSN can be used for predicting gene interactions and functions.

CONCLUSIONS

The reported results suggest that 'silent' code inside the transcript can help to predict central features of biological networks and gene function. This approach can help researchers to understand the genome of novel microorganisms, analyze metagenomic data, and can help to decipher new gene functions.

AVAILABILITY

Our MATLAB implementation of CSN is available at https://www.cs.tau.ac.il/~tamirtul/CSN-Autogen.

In Situ Detection of Ribonucleoprotein Complex Assembly in the C. elegans Germline using Proximity Ligation Assay

Understanding when and where protein-protein interactions (PPIs) occur is critical to understanding protein function in the cell and how broader processes such as development are affected. The Caenorhabditis elegans germline is a great model system for studying PPIs that are related to the regulation of stem cells, meiosis, and development. There are a variety of well-developed techniques that allow proteins of interest to be tagged for recognition by standard antibodies, making this system advantageous for proximity ligation assay (PLA) reactions. As a result, the PLA is able to show where PPIs occur in a spatial and temporal manner in germlines more effectively than alternative approaches. Described here is a protocol for the application and quantification of this technology to probe PPIs in the C. elegans germline.

Uncovering the mechanism of the effects of Paeoniae Radix Alba on iron-deficiency anaemia through a network pharmacology-based strategy

BACKGROUND

Paeoniae Radix Alba, the root of the plant Paeonia lactiflora Pall, is a common blood-enriching drug in traditional Chinese medicine. Its effectiveness in the clinical treatment of anaemia is remarkable, but its potential pharmacologic mechanism has not been clarified.

METHODS

In this study, the potential pharmacologic mechanism of Paeoniae Radix Alba in the treatment of iron-deficiency anaemia was preliminarily elucidated through systematic and comprehensive network pharmacology.

RESULTS

Specifically, we obtained 15 candidate active ingredients from among 146 chemical components in Paeoniae Radix Alba. The ingredients were predicted to target 77 genes associated with iron-deficiency anaemia. In-depth analyses of these targets revealed that they were mostly associated with energy metabolism, cell proliferation, and stress responses, suggesting that Paeoniae Radix Alba helps alleviate iron-deficiency anaemia by affecting these processes. In addition, we conducted a core target analysis and a cluster analysis of protein-protein interaction (PPI) networks. The results showed that four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway and the AGE-RAGE signalling pathway in diabetic complications, may be major pathways associated with the ameliorative effects of Paeoniae Radix Alba on iron-deficiency anaemia. Moreover, molecular docking verified the credibility of the network for molecular target prediction.

CONCLUSIONS

Overall, this study predicted the functional ingredients in Paeoniae Radix Alba and their targets and uncovered the mechanism of action of this drug, providing new insights for advanced research on Paeoniae Radix Alba and other traditional Chinese medicines.

microRNA-9 might be a novel protective factor for osteoarthritis patients

BACKGROUND

The study aimed to identify the targeting genes and miRNAs using the microarray expression profile dataset for Osteoarthritis (OA) patients. Differentially expressed genes (DEGs) between OA and control samples were identified using Bayes method of limma package. Subsequently, a protein-protein interaction (PPI) network was constructed. miRNAs and transcription factor (TFs) based on DEGs in PPI network were identified using Webgestalt and ENCODE, respectively. Finally, MCODE, Gene Ontology (GO) function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. The expressions of several DEGs and predicted miRNAs in OA rats were detected by RT-PCR.

RESULTS

A total of 594 DEGs were identified. In PPI network, there were 313 upregulated DEGs and 22 downregulated DEGs. Besides, the regulatory relationships included 467 upregulated interactions and 85 downregulated interactions (miR-124A → QKI and MAP 1B) between miRNA and DEGs in PPI network. The module from downregulated DEGs-TFs-miRNA networks was mainly enriched to low-density lipoprotein particle clearance, response to linoleic acid, and small molecule metabolic process BP terms. Moreover, QKI, MAP 1B mRNA and miR-9 expressions were significantly reduced in OA rats.

CONCLUSION

miR-9 might be a protective factor for OA patients via inhibiting proliferation and differentiation of cartilage progenitor cells. miR-124A might play an important role in progression of OA through targeting QKI and MAP 1B.

Bifunctional Non-Canonical Amino Acids: Combining Photo-Crosslinking with Click Chemistry

Genetic code expansion is a powerful tool for the study of protein interactions, as it allows for the site-specific incorporation of a photoreactive group via non-canonical amino acids. Recently, several groups have published bifunctional amino acids that carry a handle for click chemistry in addition to the photo-crosslinker. This allows for the specific labeling of crosslinked proteins and therefore the pulldown of peptides for further analysis. This review describes the properties and advantages of different bifunctional amino acids, and gives an overview about current and future applications.

Biological Assembly of Modular Protein Building Blocks as Sensing, Delivery, and Therapeutic Agents

Nature has evolved a wide range of strategies to create self-assembled protein nanostructures with structurally defined architectures that serve a myriad of highly specialized biological functions. With the advent of biological tools for site-specific protein modifications and de novo protein design, a wide range of customized protein nanocarriers have been created using both natural and synthetic biological building blocks to mimic these native designs for targeted biomedical applications. In this review, different design frameworks and synthetic decoration strategies for achieving these functional protein nanostructures are summarized. Key attributes of these designer protein nanostructures, their unique functions, and their impact on biosensing and therapeutic applications are discussed.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

AMPK Interactome Reveals New Function in Non-homologous End Joining DNA Repair

Adenosine monophosphate-activated protein kinase (AMPK) is an obligate heterotrimer that consists of a catalytic subunit (α) and two regulatory subunits (β and γ). AMPK is a key enzyme in the regulation of cellular energy homeostasis. It has been well studied and is known to function in many cellular pathways. However, the interactome of AMPK has not yet been systematically established, although protein-protein interaction is critically important for protein function and regulation. Here, we used tandem-affinity purification, coupled with mass spectrometry (TAP-MS) analysis, to determine the interactome of AMPK and its functions. We conducted a TAP-MS analysis of all seven AMPK subunits. We identified 138 candidate high-confidence interacting proteins (HCIPs) of AMPK, which allowed us to build an interaction network of AMPK complexes. Five candidate AMPK-binding proteins were experimentally validated, underlining the reliability of our data set. Furthermore, we demonstrated that AMPK acts with a strong AMPK-binding protein, Artemis, in non-homologous end joining. Collectively, our study established the first AMPK interactome and uncovered a new function of AMPK in DNA repair.

HDAC8 inhibitor attenuates airway responses to antigen stimulus through synchronously suppressing galectin-3 expression and reducing macrophage-2 polarization

BACKGROUND

This study was to investigate of the mechanism by which histone deacetylase (HDAC) 8 inhibitor ameliorated airway hyperresponsiveness (AHR) and allergic airway inflammation.

METHODS

Mice were sensitized and then treated with budesonide (BUD) or PCI-34051 (PCI) prior to exposing to normal saline (NS) or ovalbumin (OVA). The raw264.7 cells were treated with interleukin (IL)-4 and PCI or shRNA alone. Repetitive measurements of enhanced pause (Penh) were executed by increasing concentrations of acetyl-β-methacholine chloride (0 - 50 mg/ml). Cells in bronchoalveolar lavage fluid (BALF) and pathological changes of lungs were examined, respectively. The expression levels of HDAC8, Galecitn (Gal)-3, CD68, CD86, CD163, Arg1 and NOS2 in lungs were measured. Co-regulation of HDAC8 and Gal-3 proteins was observed by immunofluorescence staining and co-immunoprecipitation assay (Co-IP).

RESULTS

Significant increases in Penh and IL-4 level were detected with a large inflammatory infiltrate, comprised predominantly of macrophages and eosinophils, into the BALF in OVA-exposed lungs. HDAC8, Gal-3, CD68, CD86, CD163, Arg1 and NOS2 proteins were over-expressed with the significant changes in the Arg1 and NOS2 mRNA levels in the lungs and the IL-4-treated cells. PCI intervention obviously reduced the counts of CD163+ cells. Furthermore, Gal-3 knockdown suppressed Arg1 expression in the cells. Immunofluorescence staining displayed simultaneous changes in HDAC8 and Gal-3 expression in the investigated samples. Treatment with PCI resulted in synchronous reduction of HDAC8 and Gal-3 expression in the Co-IP complexes.

CONCLUSIONS

The HDAC8 inhibitor ameliorates AHR and airway inflammation in animal model of allergic asthma through reducing HDAC8-Gal-3 interaction and M2 macrophage polarization.

Network Diffusion Promotes the Integrative Analysis of Multiple Omics

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion—also referred to as network propagation—has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.

A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

The mammalian two-hybrid system as a powerful tool for high-throughput drug screening

Protein-protein interactions (PPIs) are the backbone of signaling pathways, responsible for the basis of cell communication and, when deregulated, several diseases. Consequently, identifying and modulating PPIs can unravel the pathophysiological mechanisms of diseases. The two-hybrid system, particularly the mammalian two-hybrid system (MTH), is an efficient technique to validate PPIs ex vivo. Combining MTH with high-throughput screening has a huge advantage in biomedical research. In this review, we describe methodologies developed from MTH and the role of these adaptations in PPI discovery. We also highlight the powerful contribution of MTH to the identification of disease-related PPIs and its use in the development of potential new drug screens.

The pathogen Moniliophthora perniciosa promotes differential proteomic modulation of cacao genotypes with contrasting resistance to witches´ broom disease

BACKGROUND

Witches' broom disease (WBD) of cacao (Theobroma cacao L.), caused by Moniliophthora perniciosa, is the most important limiting factor for the cacao production in Brazil. Hence, the development of cacao genotypes with durable resistance is the key challenge for control the disease. Proteomic methods are often used to study the interactions between hosts and pathogens, therefore helping classical plant breeding projects on the development of resistant genotypes. The present study compared the proteomic alterations between two cacao genotypes standard for WBD resistance and susceptibility, in response to M. perniciosa infection at 72 h and 45 days post-inoculation; respectively the very early stages of the biotrophic and necrotrophic stages of the cacao x M. perniciosa interaction.

RESULTS

A total of 554 proteins were identified, being 246 in the susceptible Catongo and 308 in the resistant TSH1188 genotypes. The identified proteins were involved mainly in metabolism, energy, defense and oxidative stress. The resistant genotype showed more expressed proteins with more variability associated with stress and defense, while the susceptible genotype exhibited more repressed proteins. Among these proteins, stand out pathogenesis related proteins (PRs), oxidative stress regulation related proteins, and trypsin inhibitors. Interaction networks were predicted, and a complex protein-protein interaction was observed. Some proteins showed a high number of interactions, suggesting that those proteins may function as cross-talkers between these biological functions.

CONCLUSIONS

We present the first study reporting the proteomic alterations of resistant and susceptible genotypes in the T. cacao x M. perniciosa pathosystem. The important altered proteins identified in the present study are related to key biologic functions in resistance, such as oxidative stress, especially in the resistant genotype TSH1188, that showed a strong mechanism of detoxification. Also, the positive regulation of defense and stress proteins were more evident in this genotype. Proteins with significant roles against fungal plant pathogens, such as chitinases, trypsin inhibitors and PR 5 were also identified, and they may be good resistance markers. Finally, important biological functions, such as stress and defense, photosynthesis, oxidative stress and carbohydrate metabolism were differentially impacted with M. perniciosa infection in each genotype.

Analyzing IDPs in Interactomes

Intrinsically disordered proteins (IDPs) and regions (IDRs) are commonly found in all proteomes analyzed so far. These proteins/regions are subject to numerous posttranslational modifications (PTMs) and alternative splicing, are involved in a wide range of cellular functions, and often facilitate protein-protein interactions (PPIs). Some of these proteins contain molecular recognition features (MoRFs), which are IDRs that bind to partner proteins and undergo disorder-to-order transitions. Although many IDPs/IDRs can fold upon binding, a large fraction of these proteins are known to maintain significant amounts of disorder in their bound states. Being well-recognized interaction specialists, IDPs/IDRs can participate in one-to-many and many-to-one interactions, where one IDP/IDR binds to multiple partners potentially gaining very different structures in the bound state, or where multiple unrelated IDPs/IDRs bind to one partner. As a result, IDPs frequently serve as hubs (i.e., proteins with many links) in complex PPI networks. The goal of this chapter is to describe computational and bioinformatics tools that can be used to look at the disorder status of proteins within a given PPI network and also to gain some knowledge on the disorder-based functionality of the members of this network. To this end, description is provided for some of the use of UniProt and DisProt databases, several databases generating PPI networks (BioGRID, IntAct, DIP, MINT, HPRD, APID, KEGG, and STRING), Composition profiler, some tools for the per-residue disorder predictions (PONDR^® VLXT, PONDR^® VL3, PONDR^® VSL2, PONDR-FIT, and IUPred), binary disorder classifiers CH-plot and CDF-plot and their combined CH-CDF analysis, web-based tools for the visualization of disorder distribution in a query protein (D²P² and MobiDB), as well as some tools for evaluation disorder-based functionality of proteins (ANCHOR, MoRFpred, DEPP, and ModPred).

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Background

Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.

Results

In this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.

Conclusion

Experimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

Reversible biotinylation of purified proteins for measuring protein-protein interactions

Measuring protein-protein interactions using purified proteins in vitro is one of the most frequently used approach to understand the biochemical and mechanistic details of cellular signaling pathways. Typically, affinity tags are genetically fused to proteins of interest, and they are used to capture and detect them. However, in some cases, fusion of bulky affinity tags might present a significant limitation in these experiments, especially if the regions in close proximity of tags are involved in protein-protein interactions. Here, we present a step-by-step protocol for an alternative approach that involves reversible biotinylation of purified proteins using a simple chemical-conjugation of cleavable biotin moiety. Biotinylated proteins can be directly used as bait for selective immobilization on solid support for measuring protein-protein interactions. Furthermore, biotinylation of protein of interest also allows specific detection in standard biochemical assays. This simple, straightforward and modular protocol can be directly adapted and applied to facilitate the detection of novel protein-protein interactions as well as measuring apparent affinities of such interactions.

WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

Background

In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes.

Method

We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models.

Results

Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation.

Conclusion

We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks.

Availability

The free WIPER API is described at discovery.informatics.uab.edu/wiper/.

Bioinformatics analysis on differentially expressed genes of alveolar macrophage in IPF

Objective: This study aimed to explore the differentially expressed genes (DEGs) of pulmonary macrophages in human idiopathic pulmonary fibrosis (IPF) by bioinformatics, and elaborate on IPF on the gene level. Methods: The gene expression profile GSE49072 was downloaded from the gene expression omnibus (GEO) database. Genes of alveolar macrophages between normal volunteers and patients diagnosed as IPF were analyzed by GEO2R tools. Gene ontology (GO) and pathway enrichment analyses of genes were performed in the database for annotation, visualization and integrated discovery (DAVID) database, followed by functional annotation and protein-protein interaction (PPI) network construction in String website. Finally, the results were analyzed in a comprehensive way. Results: A total of 551 DEGs, including 205 down-regulated and 346 up-regulated were identified. The expression of 209875_s_at (secreted phosphoprotein 1, SPP1) and 214146_s_at (pro-platelet basic protein, PPBP) genes are the most significant in upregulated genes. DEGs in the MAPK（mitogen-activated protein kinase） signaling pathway and chemokine signaling pathway play important roles in the development of IPF. Conclusions: The up-regulation of genes such as SPP1 and PPBP affect the secretion of alveolar macrophages, thereby speeding up the process of fibrosis.

Expanding the understanding of the heterogeneous nature of melanoma with bioinformatics and disorder-based proteomics

The past few decades show that incidences of melanoma are on the rise. The risk associated with this disease is an interplay between genetic and host factors and sun exposure. While scientific progress in the treatment of melanoma is remarkable, additional research is needed to improve patient outcomes and to better understand the heterogenous nature of this disease. Fortunately, as the clinical community enters the era of "big data" and personalized medicine, the rise of bioinformatics that stems from recent advances in high throughout profiling of biological information offers potential for innovative treatment options. This study aims to provide an example of the usefulness of bioinformatics and disorder-based proteomics to identify the molecular pathway in melanoma, garner information on selected proteins from this pathway and uncover their intrinsically disordered proteins regions (IDPRs) and investigate functionality implicated in these IDPRs. The present study provides a new look at the melanoma heterogeneity and suggests that, in addition to the well-established genetic heterogeneity of melanoma, there is another level of heterogeneity that lies within the conformational ensembles that stem from intrinsic disorder in melanoma-related proteins. The hope is that these insights will inspire future drug discovery campaigns.

Sequence-Defined Macrocycles for Understanding and Controlling the Build-up of Hierarchical Order in Self-Assembled 2D Arrays

Anfinsen's dogma that sequence dictates structure is fundamental to understanding the activity and assembly of proteins. This idea has been applied to all manner of oligomers but not to the behavior of cyclic oligomers, aka macrocycles. We do this here by providing the first proofs that sequence controls the hierarchical assembly of nonbiological macrocycles, in this case, at graphite surfaces. To design macrocycles with one (AAA), two (AAB), or three (ABC) different carbazole units, we needed to subvert the synthetic preferences for one-pot macrocyclizations. We developed a new stepwise synthesis with sequence-defined targets made in 11, 17, and 22 steps with 25, 10, and 5% yields, respectively. The linear build up of primary sequence (1°) also enabled a thermal Huisgen cycloaddition to proceed regioselectively for the first time using geometric control. The resulting macrocycles are planar (2° structure) and form H-bonded dimers (3°) at surfaces. Primary sequences encoded into the suite of tricarb macrocycles were shown by scanning-tunneling microscopy (STM) to impact the next levels of supramolecular ordering (4°) and 2D crystalline polymorphs (5°) at solution-graphite interfaces. STM imaging of an AAB macrocycle revealed the formation of a new gap phase that was inaccessible using only C3-symmetric macrocycles. STM imaging of two additional sequence-controlled macrocycles (AAD, ABE) allowed us to identify the factors driving the formation of this new polymorph. This demonstration of how sequence controls the hierarchical patterning of macrocycles raises the importance of stepwise syntheses relative to one-pot macrocyclizations to offer new approaches for greater understanding and control of hierarchical assembly.

Suppression of the Peripheral Immune System Limits the Central Immune Response Following Cuprizone-Feeding: Relevance to Modelling Multiple Sclerosis

Cuprizone (CPZ) preferentially affects oligodendrocytes (OLG), resulting in demyelination. To investigate whether central oligodendrocytosis and gliosis triggered an adaptive immune response, the impact of combining a standard (0.2%) or low (0.1%) dose of ingested CPZ with disruption of the blood brain barrier (BBB), using pertussis toxin (PT), was assessed in mice. 0.2% CPZ(±PT) for 5 weeks produced oligodendrocytosis, demyelination and gliosis plus marked splenic atrophy (37%) and reduced levels of CD4 (44%) and CD8 (61%). Conversely, 0.1% CPZ(±PT) produced a similar oligodendrocytosis, demyelination and gliosis but a smaller reduction in splenic CD4 (11%) and CD8 (14%) levels and no splenic atrophy. Long-term feeding of 0.1% CPZ(±PT) for 12 weeks produced similar reductions in CD4 (27%) and CD8 (43%), as well as splenic atrophy (33%), as seen with 0.2% CPZ(±PT) for 5 weeks. Collectively, these results suggest that 0.1% CPZ for 5 weeks may be a more promising model to study the ‘inside-out’ theory of Multiple Sclerosis (MS). However, neither CD4 nor CD8 were detected in the brain in CPZ±PT groups, indicating that CPZ-mediated suppression of peripheral immune organs is a major impediment to studying the ‘inside-out’ role of the adaptive immune system in this model over long time periods. Notably, CPZ(±PT)-feeding induced changes in the brain proteome related to the suppression of immune function, cellular metabolism, synaptic function and cellular structure/organization, indicating that demyelinating conditions, such as MS, can be initiated in the absence of adaptive immune system involvement.

In-Cell Protein-Protein Contacts: Transient Interactions in the Crowd

Proteins in vivo are immersed in a crowded environment of water, ions, metabolites, and macromolecules. In-cell experiments highlight how transient weak protein-protein interactions promote (via functional "quinary structure") or hinder (via competitive binding or "sticking") complex formation. Computational models of the cytoplasm are expensive. We tackle this challenge with an all-atom model of a small volume of the E. coli cytoplasm to simulate protein-protein contacts up to the 5 μs time scale on the special-purpose supercomputer Anton 2. We use three CHARMM-derived force fields: C22*, C36m, and C36mCU (with CUFIX corrections). We find that both C36m and C36mCU form smaller contact surfaces than C22*. Although CUFIX was developed to reduce protein-protein sticking, larger contacts are observed with C36mCU than C36m. We show that the lifespan Δt of protein-protein contacts obeys a power law distribution between 0.03 and 3 μs, with ∼90% of all contacts lasting <1 μs (similar to the time scale for downhill folding).

Computational Analysis of Non-covalent Interactions in Phycocyanin Subunit Interfaces

Protein-protein interactions are an important phenomenon in biological processes and functions. We used the manually curated non-redundant dataset of 118 phycocyanin interfaces to gain additional insight into this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck. Our observations indicate that there is a relatively high composition of hydrophobic residues at the interfaces. Most of the interface residues are clustered at the middle of the range which we call "standard-size" interfaces. Furthermore, the multiple interaction patterns founded in the present study indicate that more than half of the residues involved in these interactions participate in multiple and water-bridged hydrogen bonds. Thus, hydrogen bonds contribute maximally towards the stability of protein-protein complexes. The analysis shows that hydrogen bond energies contribute to about 88 % to the total energy and it also increases with interface size. Van der Waals (vdW) energy contributes to 9.3 %±1.7 % on average in these complexes. Moreover, there is about 1.9 %±1.5 % contribution by electrostatic energy. Nevertheless, the role by vdW and electrostatic energy could not be ignored in interface binding. Results show that the total binding energy is more for large phycocyanin interfaces. The normalized energy per residue was less than -16 kJ mol^-1 , while most of them have energy in the range from -6 to -14 kJ mol^-1 . The non-covalent interacting residues in these proteins were found to be highly conserved. Obtained results might contribute to the understanding of structural stability of this class of evolutionary essential proteins with increased practical application and future designs of novel protein-bioactive compound interactions.

Bottlenecks in Proteomics: An Update

Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to sample preparation, sample fractionation, sample analysis, data analysis & interpretation and biological significance. Here we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.

Measuring rank robustness in scored protein interaction networks

BACKGROUND

Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.

RESULTS

We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.

CONCLUSION

Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Functional Cross-Talk between Adenosine and Metabotropic Glutamate Receptors

Abstract: G-protein coupled receptors are transmembrane proteins widely expressed in cells and their transduction pathways are mediated by controlling second messenger levels through different G-protein interactions. Many of these receptors have been described as involved in the physiopathology of neurodegenerative diseases and even considered as potential targets for the design of novel therapeutic strategies. Endogenous and synthetic allosteric and orthosteric selective ligands are able to modulate GPCRs at both gene and protein expression levels and can also modify their physiological function. GPCRs that coexist in the same cells can homo- and heteromerize, therefore, modulating their function. Adenosine receptors are GPCRs which stimulate or inhibit adenylyl cyclase activity through Gi/Gs protein and are involved in the control of neurotransmitter release as glutamate. In turn, metabotropic glutamate receptors are also GPCRs which inhibit adenylyl cyclase or stimulate phospholipase C activities through Gi or Gq proteins, respectively. In recent years, evidence of crosstalk mechanisms be-tween different GPCRs have been described. The aim of the present review was to summarize the described mechanisms of interaction and crosstalking between adenosine and metabotropic glutamate receptors, mainly of group I, in both in vitro and in vivo systems, and their possible use for the design of novel ligands for the treatment of neurodegenerative diseases.

Protein-Protein Interactions in Candida albicans

Despite being one of the most important human fungal pathogens, Candida albicans has not been studied extensively at the level of protein-protein interactions (PPIs) and data on PPIs are not readily available in online databases. In January 2018, the database called "Biological General Repository for Interaction Datasets (BioGRID)" that contains the most PPIs for C. albicans, only documented 188 physical or direct PPIs (release 3.4.156) while several more can be found in the literature. Other databases such as the String database, the Molecular INTeraction Database (MINT), and the Database for Interacting Proteins (DIP) database contain even fewer interactions or do not even include C. albicans as a searchable term. Because of the non-canonical codon usage of C. albicans where CUG is translated as serine rather than leucine, it is often problematic to use the yeast two-hybrid system in Saccharomyces cerevisiae to study C. albicans PPIs. However, studying PPIs is crucial to gain a thorough understanding of the function of proteins, biological processes and pathways. PPIs can also be potential drug targets. To aid in creating PPI networks and updating the BioGRID, we performed an exhaustive literature search in order to provide, in an accessible format, a more extensive list of known PPIs in C. albicans.

TCSBN: a database of tissue and cancer specific biological networks

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

Identification of biomarkers associated with progression and prognosis in bladder cancer via co-expression analysis

BACKGROUND

Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy.

OBJECTIVE

To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer.

METHODS

Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses.

RESULTS

Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction.

CONCLUSIONS

This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.

In silico analyses of CD14 molecule reveal significant evolutionary diversity, potentially associated with speciation and variable immune response in mammals

The cluster differentiation gene (CD14) is a family of monocyte differentiating genes that works in conjunction with lipopolysaccharide binding protein, forming a complex with TLR4 or LY96 to mediate innate immune response to pathogens. In this paper, we used different computational methods to elucidate the evolution of CD14 gene coding region in 14 mammalian species. Our analyses identified leucine-rich repeats as the only significant domain across the CD14 protein of the 14 species, presenting with frequencies ranging from one to four. Importantly, we found signal peptides located at mutational hotspots demonstrating that this gene is conserved across these species. Out of the 10 selected variants analyzed in this study, only six were predicted to possess significant deleterious effect. Our predicted protein interactome showed a significant varying protein-protein interaction with CD14 protein across the species. This may be important for drug target and therapeutic manipulation for the treatment of many diseases. We conclude that these results contribute to our understanding of the CD14 molecular evolution, which underlays varying species response to complex disease traits.

Protein-Protein Interactions of Phosphodiesterases

BACKGROUND

Phosphodiesterases (PDEs) are enzymes that play a key role in terminating cyclic nucleotides signalling by catalysing the hydrolysis of 3', 5'- cyclic adenosine monophosphate (cAMP) and/or 3', 5' cyclic guanosine monophosphate (cGMP), the second messengers within the cell that transport the signals produced by extracellular signalling molecules which are unable to get into the cells. However, PDEs are proteins which do not operate alone but in complexes that made up of a many proteins.

OBJECTIVE

This review highlights some of the general characteristics of PDEs and focuses mainly on the Protein-Protein Interactions (PPIs) of selected PDE enzymes. The objective is to review the role of PPIs in the specific mechanism for activation and thereby regulation of certain biological functions of PDEs.

METHODS

The article discusses some of the PPIs of selected PDEs as reported in recent scientific literature. These interactions are critical for understanding the biological role of the target PDE.

RESULTS

The PPIs have shown that each PDE has a specific mechanism for activation and thereby regulation a certain biological function.

CONCLUSION

Targeting of PDEs to specific regions of the cell is based on the interaction with other proteins where each PDE enzyme binds with specific protein(s) via PPIs.

Analysis of risk factors for colon cancer progression

Purpose: This study aimed to find risk factors for colon cancer progression with bioinformatics methods, and validated by clinical patients.

Methods: Differentially expressed genes (DEGs) between colon cancer tissues and normal colon tissues were extracted from The Cancer Genome Atlas (TCGA) database using R software, amounted to 8,051. DEGs between pathologic stage I+II and stage III+IV amounted to 373, and were compared with DEGs of cancer/normal analyzed above to get the intersection of both. Ninety-six intersected DEGs were identified and defined as progressive DEGs of colon cancer. Then these 96 progressive DEGs were studied by Gene ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis using the DAVID database and visualizing by R software. A protein–protein interaction (PPI) network and functional modules were established using the STRING database. Further, an overall survival (OS) curve was drawn via the GEPIA website based on the CGA database and six progressive DEGs were found to be involved with OS of colon cancer patients. The Linkedomics website was used for detailed analysis of specific subsets of TNM.

Results: Pregnancy specific glycoprotein (PSG), vitamin digestion, and absorption were confirmed to promote the progression of colon cancer. Furthermore, NTF4 was found to be associated with both OS and each subset of TNM; therefore, defined as a key risk factor for colon cancer progression. Further analysis of NTF4 expression using clinical data showed it acted as a key risk factor and diagnosis marker for colon cancer progression.

Conclusion: NTF4 is a risk factor contributing to colon cancer progression and associated with overall survival.