The Biomolecular Interaction Network Database and related tools 2005 update

What have Data Standards ever done for us?

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies for both the field of molecular interaction and that of mass spectrometry for more than 20 years. This review explores some of the ways that the proteomics community has benefitted from the development of community standards and takes a look at some of the tools and resources that have been improved or developed as a result of the work of the HUPO-PSI.

Advances in Computational Methods for Protein–Protein Interaction Prediction

Protein–protein interactions (PPIs) are pivotal in various physiological processes inside biological entities. Accurate identification of PPIs holds paramount significance for comprehending biological processes, deciphering disease mechanisms, and advancing medical research. Given the costly and labor-intensive nature of experimental approaches, a multitude of computational methods have been devised to enable swift and large-scale PPI prediction. This review offers a thorough examination of recent strides in computational methodologies for PPI prediction, with a particular focus on the utilization of deep learning techniques within this domain. Alongside a systematic classification and discussion of relevant databases, feature extraction strategies, and prominent computational approaches, we conclude with a thorough analysis of current challenges and prospects for the future of this field.

Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases

Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.

Histone methyltransferases SDG33 and SDG34 regulate organ-specific nitrogen responses in tomato

Histone posttranslational modifications shape the chromatin landscape of the plant genome and affect gene expression in response to developmental and environmental cues. To date, the role of histone modifications in regulating plant responses to environmental nutrient availability, especially in agriculturally important species, remains largely unknown. We describe the functions of two histone lysine methyltransferases, SET Domain Group 33 (SDG33) and SDG34, in mediating nitrogen (N) responses of shoots and roots in tomato. By comparing the transcriptomes of CRISPR edited tomato lines sdg33 and sdg34 with wild-type plants under N-supplied and N-starved conditions, we uncovered that SDG33 and SDG34 regulate overlapping yet distinct downstream gene targets. In response to N level changes, both SDG33 and SDG34 mediate gene regulation in an organ-specific manner: in roots, SDG33 and SDG34 regulate a gene network including Nitrate Transporter 1.1 (NRT1.1) and Small Auxin Up-regulated RNA (SAUR) genes. In agreement with this, mutations in sdg33 or sdg34 abolish the root growth response triggered by an N-supply; In shoots, SDG33 and SDG34 affect the expression of photosynthesis genes and photosynthetic parameters in response to N. Our analysis thus revealed that SDG33 and SDG34 regulate N-responsive gene expression and physiological changes in an organ-specific manner, thus presenting previously unknown candidate genes as targets for selection and engineering to improve N uptake and usage in crop plants.

The Landscape of Virus-Host Protein–Protein Interaction Databases

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

The transcriptomic revolution and radiation biology

PURPOSE

This article will briefly review the origins and evolution of functional genomics, first describing the experimental technology, and then some of the approaches applied to data analysis and visualization. It will emphasize application of functional genomics to radiation biology, using examples from the author's work to illustrate several key types of analysis. It concludes with a look at non-coding RNA, alternative reading of the genome, and single-cell transcriptomics, some of the innovative areas that may help to shape future research in radiation biology and oncology.

CONCLUSIONS

Transcriptomic approaches have provided insight into many areas of radiation biology and medicine, and innovations in technology and data analysis approaches promise continued contributions to radiation science in the future.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Alternative Splicing Mechanisms Underlying Opioid-Induced Hyperalgesia

Prolonged use of opioids can cause opioid-induced hyperalgesia (OIH). The impact of alternative splicing on OIH remains partially characterized. A study of the absolute and relative modes of action of alternative splicing further the understanding of the molecular mechanisms underlying OIH. Differential absolute and relative isoform profiles were detected in the trigeminal ganglia and nucleus accumbens of mice presenting OIH behaviors elicited by chronic morphine administration relative to control mice. Genes that participate in glutamatergic synapse (e.g., Grip1, Grin1, Wnk3), myelin protein processes (e.g., Mbp, Mpz), and axon guidance presented absolute and relative splicing associated with OIH. Splicing of genes in the gonadotropin-releasing hormone receptor pathway was detected in the nucleus accumbens while splicing in the vascular endothelial growth factor, endogenous cannabinoid signaling, circadian clock system, and metabotropic glutamate receptor pathways was detected in the trigeminal ganglia. A notable finding was the prevalence of alternatively spliced transcription factors and regulators (e.g., Ciart, Ablim2, Pbx1, Arntl2) in the trigeminal ganglia. Insights into the nociceptive and antinociceptive modulatory action of Hnrnpk were gained. The results from our study highlight the impact of alternative splicing and transcriptional regulators on OIH and expose the need for isoform-level research to advance the understanding of morphine-associated hyperalgesia.

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Databases for Protein-Protein Interactions

Protein-protein interaction networks have a crucial role in biological processes. Proteins perform multiple functions in forming physical and functional interactions in cellular systems. Information concerning an enormous number of protein interactions in a wide range of species has accumulated and has been integrated into various resources for molecular biology and systems biology. This chapter provides a review of the representative databases and the major computational methods used for protein-protein interactions.

Identification of the Key Role of NF-κB Signaling Pathway in the Treatment of Osteoarthritis With Bushen Zhuangjin Decoction, a Verification Based on Network Pharmacology Approach

This study aimed to identify whether the NF-κB signaling pathway plays a key role in the treatment of osteoarthritis (OA) with Bushen Zhuangjin Decoction (BZD) based on a typical network pharmacology approach (NPA). Four sequential experiments were performed: 1) conventional high-performance liquid chromatography (HPLC), 2) preliminary observation of the therapeutic effects of BZD, 3) NPA using three OA-related gene expression profiles, and 4) verification of the key pathway identified by NPA. Only one HPLC-verified compound (paeoniflorin) was identified from the candidate compounds discovered by NPA. The genes verified in the preliminary observation were also identified by NPA. NPA identified a key role for the NF-κB signaling pathway in the treatment of OA with BZD, which was confirmed by conventional western blot analysis. This study identified and verified NF-κB signaling pathway as the most important inflammatory signaling pathway involved in the mechanisms of BZD for treating OA by comparing the NPA results with conventional methods. Our findings also indicate that NPA is a powerful tool for exploring the molecular targets of complex herbal formulations, such as BZD.

Review on Epidermal Growth Factor Receptor (EGFR) Structure, Signaling Pathways, Interactions, and Recent Updates of EGFR Inhibitors

The epidermal growth factor receptor (EGFR) belongs to the ERBB family of tyrosine kinase receptors. EGFR signaling cascade is a key regulator in cell proliferation, differentiation, division, survival, and cancer development. In this review, the EGFR structure and its mutations, signaling pathway, ligand binding and EGFR dimerization, EGF/EGFR interaction, and the progress in the development of EGFR inhibitors have been explored.

Predicting Protein-Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches

Rice blast, caused by the fungus Magnaporthe oryzae, is the most devastating disease affecting rice production. Identification of protein-protein interactions (PPIs) is a critical step toward understanding the molecular mechanisms underlying resistance to blast fungus in rice. In this study, we presented a computational framework for predicting plant-pathogen PPIs based on structural information. Compared with the sequence-based methods, the structure-based approach showed to be more powerful in discovering new PPIs between plants and pathogens. Using the structure-based method, we generated a global PPI network consisted of 2,018 interacting protein pairs involving 1,344 rice proteins and 418 blast fungus proteins. The network analysis showed that blast resistance genes were enriched in the PPI network. The network-based prediction enabled systematic discovery of new blast resistance genes in rice. The network provided a global map to help accelerate the identification of blast resistance genes and advance our understanding of plant-pathogen interactions.

Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies

Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known 3D structures of representative protein complexes. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue.

The current standard approach for estimating error in proteome-scale crosslinking-mass spectrometry datasets has severe limitations. A proposed set of data-quality metrics provides a more accurate assessment of error rate.

Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information

Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost and time-consuming. To solve the above problems, computational methods are developed to predict PPIs. Thus, in this paper, we present a method to predict PPIs using protein sequences. First, protein sequences are transformed into Position Weight Matrix (PWM), in which Scale-Invariant Feature Transform (SIFT) algorithm is used to extract features. Then Principal Component Analysis (PCA) is applied to reduce the dimension of features. At last, Weighted Extreme Learning Machine (WELM) classifier is employed to predict PPIs and a series of evaluation results are obtained. In our method, since SIFT and WELM are used to extract features and classify respectively, we called the proposed method SIFT-WELM. When applying the proposed method on three well-known PPIs datasets of Yeast, Human and Helicobacter.pylori, the average accuracies of our method using five-fold cross validation are obtained as high as 94.83, 97.60 and 83.64 percent, respectively. In order to evaluate the proposed approach properly, we compare it with Support Vector Machine (SVM) classifier and other recent-developed methods in different aspects. Moreover, the training time of our method is greatly shortened, which is obviously superior to the previous methods, such as SVM, ACC, PCVMZM and so on.

Lasting and Sex-Dependent Impact of Maternal Immune Activation on Molecular Pathways of the Amygdala

The prolonged and sex-dependent impact of maternal immune activation (MIA) during gestation on the molecular pathways of the amygdala, a brain region that influences social, emotional, and other behaviors, is only partially understood. To address this gap, we investigated the effects of viral-elicited MIA during gestation on the amygdala transcriptome of pigs, a species of high molecular and developmental homology to humans. Gene expression levels were measured using RNA-Seq on the amygdala for 3-week-old female and male offspring from MIA and control groups. Among the 403 genes that exhibited significant MIA effect, a prevalence of differentially expressed genes annotated to the neuroactive ligand-receptor pathway, glutamatergic functions, neuropeptide systems, and cilium morphogenesis were uncovered. Genes in these categories included corticotropin-releasing hormone receptor 2, glutamate metabotropic receptor 4, glycoprotein hormones, alpha polypeptide, parathyroid hormone 1 receptor, vasointestinal peptide receptor 2, neurotensin, proenkephalin, and gastrin-releasing peptide. These categories and genes have been associated with the MIA-related human neurodevelopmental disorders, including schizophrenia and autism spectrum disorders. Gene network reconstruction highlighted differential vulnerability to MIA effects between sexes. Our results advance the understanding necessary for the development of multifactorial therapies targeting immune modulation and neurochemical dysfunction that can ameliorate the effects of MIA on offspring behavior later in life.

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.

MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity

Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.

Association of functional variants and protein-to-protein physical interactions of human MutY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome

The human gene MUTYH codes for a DNA glycosylase involved in the repair of oxidative DNA damage. Faulty MUTYH protein activity causes the accumulation of G→T transversions due to unrepaired 8-oxoG:A mismatches. MUTYH germ-line mutations in humans are linked with a recessive form of Familial Adenomatous Polyposis (FAP) and colorectal cancer predisposition. We studied the repair capacity of variants identified in MUTYH-associated polyposis (MAP) patients. MAP is inherited in an autosomal recessive type due to mutations in MUTYH (Y165C, G382D, P54S, A22V, Q63R, G45D, S136P and N43S), indicating that both copies of the gene become inactivated. However, the parents of an individual with an autosomal recessive condition may serve as carriers, each harboring one copy of the mutated gene without showing signs or symptoms of MAP. Six protein partners have been associated with MUTYH, four via direct physical interactions, namely, hMSH6, hPCNA, hRPA1, and hAPEX1. We examined, for the first time, specific interactions of these protein partners with MAP-associated MUTYH mutants using molecular dynamics simulations. The approach provided tools for exploration of the conformational energy landscape accessible to protein partners. The investigation also determined the impact before and after energy minimization of protein-protein interactions and binding affinities of MUTYH wild type and mutant forms, as well as the interactions with other proteins. Taken together, this study provided new insights into the role of MUTYH and its interacting proteins in MAP.

UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

Identification of a Two-Gene (PML-EPB41) Signature With Independent Prognostic Value in Osteosarcoma

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis.

Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes.

Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA.

Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications.

Identification of TAF1, SAT1, and ARHGEF9 as DNA methylation biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. More than 90% of primary HCC is HCC. Hepatitis C virus (HCV) infection and alcohol consumption have been widely accepted as two major risk factors for developing HCC. Herein, we aimed to identify DNA methylation genes related to both HCV infection and alcohol consumption. In this study, we identified methylation genes that were associated with the risk of HCV infection and alcohol consumption, respectively, by a large-scale bioinformatic analysis. Through PPI network analysis, we revealed the associations between the two types of genes and found six hub genes-TAF1, SAT1, Phospholipase C-beta 2, FGD1, ARHGAP4, and ARHGEF9-that may be associated with both HCV infection and alcohol consumption. Gene Ontology enrichment analysis was used to analyze the function which these genes in the network enriched. Among them, TAF1, SAT1, and ARHGEF9 were methylated genes that have been found to be related to tumor progression in HCC patients. Through independent data sets, we verified the methylation pattern of these six genes in HCC samples that had both HCV infection and alcohol consumption risks. Furthermore, we found that three of the six methylated genes were also associated with the prognosis of HCC patients. To summarize, we identified six hub genes that were associated with both HCV infection and alcohol consumption in the progress of HCC. The six methylation genes that might play an important role in both HCV infection and alcohol consumption would be potential therapy targets for HCC.

Extended Human G-Protein Coupled Receptor Network: Cell-Type-Specific Analysis of G-Protein Coupled Receptor Signaling Pathways

G-protein coupled receptors (GPCRs) mediate crucial physiological functions in humans, have been implicated in an array of diseases, and are therefore prime drug targets. GPCRs signal via a multitude of pathways, mainly through G-proteins and β-arrestins, to regulate effectors responsible for cellular responses. The limited number of transducers results in different GPCRs exerting control on the same pathway, while the availability of signaling proteins in a cell defines the result of GPCR activation. The aim of this study was to construct the extended human GPCR network (hGPCRnet) and examine the effect that cell-type specificity has on GPCR signaling pathways. To achieve this, protein-protein interaction data between GPCRs, G-protein coupled receptor kinases (GRKs), Gα subunits, β-arrestins, and effectors were combined with protein expression data in cell types. This resulted in the hGPCRnet, a very large interconnected network, and similar cell-type-specific networks in which, distinct GPCR signaling pathways were formed. Finally, a user friendly web application, hGPCRnet ( http://bioinformatics.biol.uoa.gr/hGPCRnet ), was created to allow for the visualization and exploration of these networks and of GPCR signaling pathways. This work, and the resulting application, can be useful in further studies of GPCR function and pharmacology.

Biological Network Approaches and Applications in Rare Disease Studies

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.

Identification of multiple potent neutralizing and non-neutralizing antibodies against Epstein-Barr virus gp350 protein with potential for clinical application and as reagents for mapping immunodominant epitopes

Prevention of Epstein-Barr virus (EBV) infection has focused on generating neutralizing antibodies (nAbs) targeting the major envelope glycoprotein gp350/220 (gp350). In this study, we generated 23 hybridomas producing gp350-specific antibodies. We compared the candidate gp350-specific antibodies to the well-characterized nAb 72A1 by: (1) testing their ability to detect gp350 using enzyme-linked immunosorbent assay, flow cytometry, and immunoblot; (2) sequencing their heavy and light chain complementarity-determining regions (CDRs); (3) measuring the ability of each monoclonal antibody (mAb) to neutralize EBV infection in vitro; and (4) mapping the gp350 amino acids bound by the mAbs using competitive cell and linear peptide binding assays. We performed sequence analysis to identify 15 mAbs with CDR regions unique from those of murine 72A1 (m72A1). We observed antigen binding competition between biotinylated m72A1, serially diluted unlabeled gp350 nAbs (HB1, HB5, HB11, HB20), and our recently humanized 72A1, but not gp350 non-nAb (HB17) or anti-KSHV gH/gL antibody.

The UCSC Genome Browser database: 2018 update

The UCSC Genome Browser (https://genome.ucsc.edu) provides a web interface for exploring annotated genome assemblies. The assemblies and annotation tracks are updated on an ongoing basis—12 assemblies and more than 28 tracks were added in the past year. Two recent additions are a display of CRISPR/Cas9 guide sequences and an interactive navigator for gene interactions. Other upgrades from the past year include a command-line version of the Variant Annotation Integrator, support for Human Genome Variation Society variant nomenclature input and output, and a revised highlighting tool that now supports multiple simultaneous regions and colors.

Genome-wide functional association networks: background, data & state-of-the-art resources

The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.

The underlying pathophysiology association between the Type 2-diabetic and hepatocellular carcinoma

Type 2-diabetic (T2D) disease has been reported to increase the incidence of liver cancer, however, the underlying pathophysiology is still not fully understood. Here, we aimed to reveal the underlying pathophysiology association between the T2D and hepatocellular carcinoma (HCC) and, therefore, to find the possible therapeutic targets in the occurrence and development of HCC. The methylation microarray data of T2D and HCC were extracted from the Gene Expression Omnibus and The Cancer Genome Atlas. A total of 504 differentially methylated genes (DMGs) between T2D samples and the controls were identified, whereas 6269 DMGs were identified between HCC samples and the control groups. There were 336 DMGs coexisting in diabetes and HCC, among which 86 genes were comethylated genes. These genes were mostly enriched in pathways as glycosaminoglycan biosynthesis, fatty acid, and metabolic pathway as glycosaminoglycan degradation and thiamine, fructose and mannose. There were 250 DMGs that had differential methylation direction between T2D DMGs and HCC DMGs, and these genes were enriched in the Sphingolipid metabolism pathway and immune pathways through natural killer cell-mediated cytotoxicity and ak-STAT signaling pathway. Eight genes were found related to the occurrence and development of diabetes and HCC. Moreover, the result of protein-protein interaction network showed that CDKN1A gene was related to the prognosis of HCC. In summary, eight genes were found to be associated with the development of HCC and CDKN1A may serve as the potential prognostic gene for HCC.

Opioid-Induced Hyperalgesia Is Associated with Dysregulation of Circadian Rhythm and Adaptive Immune Pathways in the Mouse Trigeminal Ganglia and Nucleus Accumbens

The benefits of opioid-based treatments to mitigate chronic pain can be hindered by the side effects of opioid-induced hyperalgesia (OIH) that can lead to higher consumption and risk of addiction. The present study advances the understanding of the molecular mechanisms associated with OIH by comparing mice presenting OIH symptoms in response to chronic morphine exposure (OIH treatment) relative to control mice (CON treatment). Using RNA-Seq profiles, gene networks were inferred in the trigeminal ganglia (TG), a central nervous system region associated with pain signaling, and in the nucleus accumbens (NAc), a region associated with reward dependency. The biological process of nucleic acid processing was over-represented among the 122 genes that exhibited a region-dependent treatment effect. Within the 187 genes that exhibited a region-independent treatment effect, circadian rhythm processes were enriched among the genes over-expressed in OIH relative to CON mice. This enrichment was supported by the differential expression of the period circadian clock 2 and 3 genes (Per2 and Per3). Transcriptional regulators in the PAR bZip family that are influenced by the circadian clock and that modulate neurotransmission associated with pain and drug addiction were also over-expressed in OIH relative to CON mice. Also notable was the under-expression in OIH relative to CON mice of the Toll-like receptor, nuclear factor-kappa beta, and interferon gamma genes and enrichment of the adaptive immune processes. The results from the present study offer insights to advance the effective use of opioids for pain management while minimizing hyperalgesia.

Identification of Crucial Candidate Genes and Pathways in Glioblastoma Multiform by Bioinformatics Analysis

The present study aimed to investigate the molecular mechanisms underlying glioblastoma multiform (GBM) and its biomarkers. The differentially expressed genes (DEGs) were diagnosed using the limma software package. The ToppGene (ToppFun) was used to perform pathway and Gene Ontology (GO) enrichment analysis of the DEGs. Protein-protein interaction (PPI) networks, extracted modules, miRNA-target genes regulatory network and TF-target genes regulatory network were used to obtain insight into the actions of DEGs. Survival analysis for DEGs was carried out. A total of 590 DEGs, including 243 up regulated and 347 down regulated genes, were diagnosed between scrambled shRNA expression and Lin7A knock down. The up-regulated genes were enriched in ribosome, mitochondrial translation termination, translation, and peptide biosynthetic process. The down-regulated genes were enriched in focal adhesion, VEGFR3 signaling in lymphatic endothelium, extracellular matrix organization, and extracellular matrix. The current study screened the genes in the PPI network, extracted modules, miRNA-target genes regulatory network, and TF-target genes regulatory network with higher degrees as hub genes, which included NPM1, CUL4A, YIPF1, SHC1, AKT1, VLDLR, RPL14, P3H2, DTNA, FAM126B, RPL34, and MYL5. Survival analysis indicated that the high expression of RPL36A and MRPL35 were predicting longer survival of GBM, while high expression of AP1S1 and AKAP12 were predicting shorter survival of GBM. High expression of RPL36A and AP1S1 were associated with pathogenesis of GBM, while low expression of ALPL was associated with pathogenesis of GBM. In conclusion, the current study diagnosed DEGs between scrambled shRNA expression and Lin7A knock down samples, which could improve our understanding of the molecular mechanisms in the progression of GBM, and these crucial as well as new diagnostic markers might be used as therapeutic targets for GBM.

Protein-protein interaction network analysis of insecticide resistance molecular mechanism in Drosophila melanogaster

The problem of resistance has not been solved fundamentally at present, because the development speed of new insecticides can not keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. Here we collected seed genes and their interacting proteins involved in insecticide resistance molecular mechanism in Drosophila melanogaster by literature mining and the String database. We identified a total of 528 proteins and 13514 protein-protein interactions. The protein interaction network was constructed by String and Pajek, and we analyzed the topological properties, such as degree centrality and eigenvector centrality. Proteasome complexes and drug metabolism-cytochrome P450 were an enrichment by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This is the first time to explore the insecticide resistance molecular mechanism of D. melanogaster by the methods and tools of network biology, it can provide the bioinformatic foundation for further understanding the mechanisms of insecticide resistance.

Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma

Lung adenocarcinoma (LUAD) poses a significant threat to public health worldwide, while the genetic and epigenetic abnormalities involved in the oncogenesis of LUAD remains unknown. This study aimed to identify and validate key genes during the development and progression of LUAD by multiomics analysis. First, Empirical Analysis of Digital Gene Expression Data in R (EdgeR) was used to identify differentially regulated genes between normal samples and LUAD samples. Then significance analysis of microarrays (SAM) was used to identify differentially methylated genes and regulated microRNAs (miRNAs) between normal samples and LUAD samples. Following that, Kyoto Encyclopedia of Genes and Genomes (KEGG)-enrichment analysis was used to analyze the function that these genes enriched in. A total of 4,816 genes, 419 miRNAs, and 4,476 methylated genes that were significantly differentially expressed corresponding to the normal tissues in LUAD were obtained, and some of the pathways these genes enriched in were the same. Moreover, 255 genes differentially methylated and expressed at the same time were also found, and these 255 genes were the target genes of the miRNAs differentially expressed in LUAD. Finally, nine genes (BRCA1, COL1A1, ESR1, FGFR2, HNF4A, IGFBP3, MET, MMP3, and PAK1) network analysis, and two of which were found to be related to the survival of LUAD patients. In summary, a total of nine genes that may play important roles in the development of LUAD were identified, and two (PAK1 and FGFR2) of them can be served as prognostic biomarkers for LUAD patients. The genes found in this study played different roles in the tumor progression of LUAD, indicating these genes may be considered as potential target genes for LUAD treatment.

Antiviral activity of the mineralocorticoid receptor NR3C2 against Herpes simplex virus Type 1 (HSV-1) infection

Analysis of a genome-scale RNA interference screen of host factors affecting herpes simplex virus type 1 (HSV-1) revealed that the mineralocorticoid receptor (MR) inhibits HSV-1 replication. As a ligand-activated transcription factor the MR regulates sodium transport and blood pressure in the kidney in response to aldosterone, but roles have recently been elucidated for the MR in other cellular processes. Here, we show that the MR and other members of the mineralocorticoid signalling pathway including HSP90 and FKBP4, possess anti-viral activity against HSV-1 independent of their effect on sodium transport, as shown by sodium channel inhibitors. Expression of the MR is upregulated upon infection in an interferon (IFN) and viral transcriptional activator VP16-dependent fashion. Furthermore, the MR and VP16, together with the cellular co-activator Oct-1, transactivate the hormone response element (HRE) present in the MR promoter and those of its transcriptional targets. As the MR induces IFN expression, our data suggests the MR is involved in a positive feedback loop that controls HSV-1 infection.

Identification of CD28 and PTEN as novel prognostic markers for cervical cancer

Cervical cancer (CC) is the most common malignant tumor with poor clinical outcome among women. Identification of novel biomarkers could be beneficial for the clinical diagnosis and treatment of CC. This study aimed to identify prognostic biomarkers for the prediction of prognostic status of CC patients, and explore the effect of the corresponding methylated genes in the occurrence and development of CC. The methylation microarray data of CC was extracted from The Cancer Genome Atlas (TCGA) dataset. The methylation genes associated with the prognostic status were identified based on the information of the relapse-free survival (RFS) of the CC patients. The prognostic gene pairs were further identified. Then, the prognostic signature was identified by the forward search algorithm based on the C-index method. The results were validated by independent dataset. Finally, the functional analysis was performed on the methylation genes. A total of 276 methylation genes and 2508 gene pairs associated with the prognostic status of the CC were identified. A signature composed of eight methylation gene pairs was obtained to predict the prognostic status of cervical patients. A series of genes that played an important role in the occurrence and development of CC were obtained by the functional enrichment analysis. To summary, a prognostic signature consisting of eight methylation gene pairs was obtained. Of note, the CD28 and PTEN gene pair were found to play important roles in the occurrence and development of CC.

Genomic data integration systematically biases interactome mapping

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk

Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs.

Brain region-dependent gene networks associated with selective breeding for increased voluntary wheel-running behavior

Mouse lines selectively bred for high voluntary wheel-running behavior are helpful models for uncovering gene networks associated with increased motivation for physical activity and other reward-dependent behaviors. The fact that multiple brain regions are hypothesized to contribute to distinct behavior components necessitates the simultaneous study of these regions. The goals of this study were to identify brain-region dependent and independent gene expression patterns, regulators, and networks associated with increased voluntary wheel-running behavior. The cerebellum and striatum from a high voluntary running line and a non-selected control line were compared. Neuropeptide genes annotated to reward-dependent processes including neuropeptide S receptor 1 (Npsr1), neuropeptide Y (Npy), and proprotein convertase subtilisin/kexin type 9 (Pcsk9), and genes implicated in motor coordination including vitamin D receptor (Vdr) and keratin, type I cytoskeletal 25 (Krt25) were among the genes exhibiting activity line-by-region interaction effects. Genes annotated to the Parkinson pathway presented consistent line patterns, albeit at different orders of magnitude between brain regions, suggesting some parallel events in response to selection for high voluntary activity. The comparison of gene networks between brain regions highlighted genes including transcription factor AP-2-delta (Tfap2d), distal-less homeobox 5 gene (Dlx5) and sine oculis homeobox homolog 3 (Six3) that exhibited line differential expression in one brain region and are associated with reward-dependent behaviors. Transcription factors including En2, Stat6 and Eomes predominated among regulators of genes that differed in expression between lines. Results from the simultaneous study of striatum and cerebellum confirm the necessity to study molecular mechanisms associated with voluntary activity and reward-dependent behaviors in consideration of brain region dependencies.

Automatic extraction of protein-protein interactions using grammatical relationship graph

Background

Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm.

Methods

Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database.

Results

We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature.

Conclusions

We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.

The key genes underlying pathophysiology association between the type 2-diabetic and colorectal cancer

Although diabetes mellitus (DM) is reported as an independent risk factor for colorectal cancer (CRC) in many researches, the underlying pathophysiology is still unclear. We investigated the differentially expressed genes (DEGs) for the diabetes and CRC to reveal the underlying pathophysiological association between the type 2-diabetic (T2D) and CRC. Gene expression profiles for T2D (GSE55650), CRC (GSE8671), and Metformin treated cell lines (GSE67342) were downloaded from GEO database. The DEGs between T2D samples and their control samples were identified with t-test and variance analysis. After cluster analysis and functional enrichment analysis, protein-protein interaction (PPI) network was constructed to find potential genes for diabetes and CRC in Metformin's treatment. Totally, we identified 583 overlapped genes, 169 common DEGs, and 414 independent DEGs between T2D and CRC samples. The common genes contained 89 up-regulated (DEGs1) and 80 down-regulated genes (DEGs3); and independent DEGs contained 270 down-regulated genes (DEGs4) in diabetes and 144 down-regulated genes (DEGs2) in CRC. In enrichment analysis, the Ribosome pathway was significantly enriched by the independent DEGs. The common genes were mainly enriched in some inflammatory related pathways. Two target genes of Metformin were significantly interacted with six hub genes (HADHB, NDUFS3, TAF1, MYC, HNFF4A, and MAX) with significant changes in expression values (P < 0.05, t-test). To summary, it is suggested that the six hub genes might play important roles in the process of Metformin treatment for diabetes and CRC. However, specific pathology remains to be further studied.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

Background

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.

Results

In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far.

Conclusions

We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Interactome INSIDER: a structural interactome browser for genomic studies

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

The MIntAct Project and Molecular Interaction Databases

Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination.

Finding Common Protein Interaction Patterns Across Organisms

Protein interactions are an important resource to obtain an understanding of cell function. Recently, researchers have compared networks of interactions in order to understand network evolution. While current methods first infer homologs and then compare topologies, we here present a method which first searches for interesting topologies and then looks for homologs. PINA (protein interaction network analysis) takes the protein interaction networks of two organisms, scans both networks for subnetworks deemed interesting, and then tries to find orthologs among the interesting subnetworks. The application is very fast because orthology investigations are restricted to subnetworks like hubs and clusters that fulfill certain criteria regarding neighborhood and connectivity. Finally, the hubs or clusters found to be related can be visualized and analyzed according to protein annotation.

Public Databases and Software for the Pathway Analysis of Cancer Genomes

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways.