SPIKE: a database of highly curated human signaling pathways

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer

Precision medicine has revolutionised cancer treatments; however, actionable biomarkers remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) framework for analysing the molecular and biomarker landscape of randomised controlled clinical trials. OncoBird identifies biomarkers based on single genes or mutually exclusive genetic alterations in isolation or in the context of tumour subtypes, and finally, assesses predictive components by their treatment interactions. Here, we utilise the open-label, randomised phase III trial (FIRE-3, AIO KRK-0306) in metastatic colorectal carcinoma patients, who received either cetuximab or bevacizumab in combination with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI). We systematically identify five biomarkers with predictive components, e.g., patients with tumours that carry chr20q amplifications or lack mutually exclusive ERK signalling mutations benefited from cetuximab compared to bevacizumab. In summary, OncoBird characterises the molecular landscape and outlines actionable biomarkers, which generalises to any molecularly characterised randomised controlled trial.

Computational Construction of Toxicant Signaling Networks

Humans and animals are regularly exposed to compounds that may have adverse effects on health. The Toxicity Forecaster (ToxCast) program was developed to use high throughput screening assays to quickly screen chemicals by measuring their effects on many biological end points. Many of these assays test for effects on cellular receptors and transcription factors (TFs), under the assumption that a toxicant may perturb normal signaling pathways in the cell. We hypothesized that we could reconstruct the intermediate proteins in these pathways that may be directly or indirectly affected by the toxicant, potentially revealing important physiological processes not yet tested for many chemicals. We integrate data from ToxCast with a human protein interactome to build toxicant signaling networks that contain physical and signaling protein interactions that may be affected as a result of toxicant exposure. To build these networks, we developed the EdgeLinker algorithm, which efficiently finds short paths in the interactome that connect the receptors to TFs for each toxicant. We performed multiple evaluations and found evidence suggesting that these signaling networks capture biologically relevant effects of toxicants. To aid in dissemination and interpretation, interactive visualizations of these networks are available at http://graphspace.org.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

Comprehensive pathway enrichment analysis workflows: COVID-19 case study

The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.

In vitro dexamethasone treatment does not induce alternative ATM transcripts in cells from Ataxia-Telangiectasia patients

Short term treatment with low doses of glucocorticoid analogues has been shown to ameliorate neurological symptoms in Ataxia–Telangiectasia (A–T), a rare autosomal recessive multisystem disease that mainly affects the cerebellum, immune system, and lungs. Molecular mechanisms underlying this clinical observation are unclear. We aimed at evaluating the effect of dexamethasone on the induction of alternative ATM transcripts (ATMdexa1). We showed that dexamethasone cannot induce an alternative ATM transcript in control and A–T lymphoblasts and primary fibroblasts, or in an ATM-knockout HeLa cell line. We also demonstrated that some of the reported readouts associated with ATMdexa1 are due to cellular artifacts and the direct induction of γH2AX by dexamethasone via DNA-PK. Finally, we suggest caution in interpreting dexamethasone effects in vitro for the results to be translated into a rational use of the drug in A–T patients.

IL-16 processing in sentinel node regulatory T cells is a factor in bladder cancer immunity

In the effort of developing new immunotherapies, the sentinel node (SN) has proven a promising source from which to harness an effective antitumour T cell response. However, tumour immune escape, a process in which regulatory T cells (Tregs) play a central role, remains a major limiting factor. Therefore, there is a clear need to increase the knowledge of Treg function and signalling in sentinel nodes. Here, we set out to explore whether the proteome in SN-resident T cells is altered by the tumour and to identify key proteins in SN T cell signalling, focusing on Tregs. Five patients with muscle-invasive urothelial bladder cancer were prospectively included. Mass spectrometry was performed on two patients, with validation and functional studies being performed on three additional patients and four healthy donors. At cystectomy, SN, non-SN lymph nodes and peripheral blood samples were collected from the patients and T cell subsets isolated through flow cytometry before downstream experiments. Proteomic analysis indicated that growth and immune signalling pathways are upregulated in SN-resident Tregs. Furthermore, centrality analysis identified the cytokine IL-16 to be central in the SN-Treg signalling network. We show that tumour-released factors, through activating caspase-3, increase Treg IL-16 processing into bioactive forms, reinforcing Treg suppressive capacity. In conclusion, we provide evidence that Tregs exposed to secreted factors from bladder tumours show increased immune and growth signalling and altered IL-16 processing which translates to enhanced Treg suppressive function, indicating altered IL-16 signalling as a novel tumour immune escape mechanism.

Ataxia-telangiectasia complicated with Hodgkin's lymphoma: A case report

BACKGROUND

Ataxia-telangiectasia (AT) is a rare, autosomal recessive, multisystem disorder. Because most clinicians have low awareness of the disease, only scarce reports of AT exist in the literature, especially of cases with lymphoma/leukemia.

CASE SUMMARY

A 7-year-old girl with a history of recurrent respiratory tract infections was referred to our department because of unstable walking for 5 years and enlarged neck nodes for 2-mo duration. Physical examination revealed scleral telangiectasia and cerebellar ataxia. Elevated alpha-fetoprotein, decreased serum immunoglobulin, and decreased T cell function were the major findings of laboratory examination. Histological analysis of cervical lymph node biopsy was suggestive of classical Hodgkin's lymphoma. Genetic examination showed heterozygous nucleotide variation of c.6679C>T and heterozygous nucleotide variation of c.5773 delG in the ATM gene; her parents were heterozygotes. The final diagnosis was AT with Hodgkin's lymphoma.

CONCLUSION

Clinicians should strengthen their understanding of AT diseases. Gene diagnosis plays an important role in its diagnosis and treatment.

Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways

Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

Integrating protein localization with automated signaling pathway reconstruction

Background

Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information.

Results

We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.

Conclusion

LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user-specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).

SignaLink: Multilayered Regulatory Networks

Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .

Endothelium-Independent Primitive Myxoid Vascularization Creates Invertebrate-Like Channels to Maintain Blood Supply in Optic Gliomas

Optic gliomas are brain tumors characterized by slow growth, progressive loss of vision, and limited therapeutic options. Optic gliomas contain various amounts of myxoid matrix, which can represent most of the tumor mass. We sought to investigate biological function and protein structure of the myxoid matrix in optic gliomas to identify novel therapeutic targets. We reviewed histological features and clinical imaging properties, analyzed vasculature by immunohistochemistry and electron microscopy, and performed liquid chromatography-mass spectrometry on optic gliomas, which varied in the amount of myxoid matrix. We found that although subtypes of optic gliomas are indistinguishable on imaging, the microvascular network of pilomyxoid astrocytoma, a subtype of optic glioma with abundant myxoid matrix, is characterized by the presence of endothelium-free channels in the myxoid matrix. These tumors show normal perfusion by clinical imaging and lack histological evidence of hemorrhage organization or thrombosis. The myxoid matrix is composed predominantly of the proteoglycan versican and its linking protein, a vertebrate hyaluronan and proteoglycan link protein 1. We propose that pediatric optic gliomas can maintain blood supply without endothelial cells by using invertebrate-like channels, which we termed primitive myxoid vascularization. Enzymatic targeting of the proteoglycan versican/hyaluronan and proteoglycan link protein 1 rich myxoid matrix, which is in direct contact with circulating blood, can provide novel therapeutic avenues for optic gliomas of childhood.

NaviCom: a web application to create interactive molecular network portraits using multi-level omics data

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease.

Database URL: NaviCom is available at https://navicom.curie.fr

Construction of a Comprehensive Protein-Protein Interaction Map for Vitiligo Disease to Identify Key Regulatory Elements: A Systemic Approach

Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo.

Ataxia telangiectasia: a review

Definition of the disease

Ataxia telangiectasia (A-T) is an autosomal recessive disorder primarily characterized by cerebellar degeneration, telangiectasia, immunodeficiency, cancer susceptibility and radiation sensitivity. A-T is often referred to as a genome instability or DNA damage response syndrome.

Epidemiology

The world-wide prevalence of A-T is estimated to be between 1 in 40,000 and 1 in 100,000 live births.

Clinical description

A-T is a complex disorder with substantial variability in the severity of features between affected individuals, and at different ages. Neurological symptoms most often first appear in early childhood when children begin to sit or walk. They have immunological abnormalities including immunoglobulin and antibody deficiencies and lymphopenia. People with A-T have an increased predisposition for cancers, particularly of lymphoid origin. Pulmonary disease and problems with feeding, swallowing and nutrition are common, and there also may be dermatological and endocrine manifestations.

Etiology

A-T is caused by mutations in the ATM (Ataxia Telangiectasia, Mutated) gene which encodes a protein of the same name. The primary role of the ATM protein is coordination of cellular signaling pathways in response to DNA double strand breaks, oxidative stress and other genotoxic stress.

Diagnosis

The diagnosis of A-T is usually suspected by the combination of neurologic clinical features (ataxia, abnormal control of eye movement, and postural instability) with one or more of the following which may vary in their appearance: telangiectasia, frequent sinopulmonary infections and specific laboratory abnormalities (e.g. IgA deficiency, lymphopenia especially affecting T lymphocytes and increased alpha-fetoprotein levels). Because certain neurological features may arise later, a diagnosis of A-T should be carefully considered for any ataxic child with an otherwise elusive diagnosis. A diagnosis of A-T can be confirmed by the finding of an absence or deficiency of the ATM protein or its kinase activity in cultured cell lines, and/or identification of the pathological mutations in the ATM gene.

Differential diagnosis

There are several other neurologic and rare disorders that physicians must consider when diagnosing A-T and that can be confused with A-T. Differentiation of these various disorders is often possible with clinical features and selected laboratory tests, including gene sequencing.

Antenatal diagnosis

Antenatal diagnosis can be performed if the pathological ATM mutations in that family have been identified in an affected child. In the absence of identifying mutations, antenatal diagnosis can be made by haplotype analysis if an unambiguous diagnosis of the affected child has been made through clinical and laboratory findings and/or ATM protein analysis.

Genetic counseling

Genetic counseling can help family members of a patient with A-T understand when genetic testing for A-T is feasible, and how the test results should be interpreted.

Management and prognosis

Treatment of the neurologic problems associated with A-T is symptomatic and supportive, as there are no treatments known to slow or stop the neurodegeneration. However, other manifestations of A-T, e.g. immunodeficiency, pulmonary disease, failure to thrive and diabetes can be treated effectively.

Pathways on demand: automated reconstruction of human signaling networks

Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present PATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.

Exon-centric regulation of ATM expression is population-dependent and amenable to antisense modification by pseudoexon targeting

ATM is an important cancer susceptibility gene that encodes a critical apical kinase of the DNA damage response (DDR) pathway. We show that a key nonsense-mediated RNA decay switch exon (NSE) in ATM is repressed by U2AF, PUF60 and hnRNPA1. The NSE activation was haplotype-specific and was most promoted by cytosine at rs609621 in the NSE 3' splice-site (3'ss), which is predominant in high cancer risk populations. NSE levels were deregulated in leukemias and were influenced by the identity of U2AF35 residue 34. We also identify splice-switching oligonucleotides (SSOs) that exploit competition of adjacent pseudoexons to modulate NSE levels. The U2AF-regulated exon usage in the ATM signalling pathway was centred on the MRN/ATM-CHEK2-CDC25-cdc2/cyclin-B axis and preferentially involved transcripts implicated in cancer-associated gene fusions and chromosomal translocations. These results reveal important links between 3'ss control and ATM-dependent responses to double-strand DNA breaks, demonstrate functional plasticity of intronic variants and illustrate versatility of intronic SSOs that target pseudo-3'ss to modify gene expression.

Identifying communities from multiplex biological networks

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).

A method for developing regulatory gene set networks to characterize complex biological systems

Background

Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations.

Results

In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease.

Conclusions

R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther).

Network-based approaches for drug response prediction and targeted therapy development in cancer

Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts

The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.

Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

PathPPI: an integrated dataset of human pathways and protein-protein interactions

Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at http://proteomeview.hupo.org.cn/PathPPI/PathPPI.html.

Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein-protein or gene-gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.

The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis

The diversity of the installed sequencing and microarray equipment make it increasingly difficult to compare and analyze the gene expression datasets obtained using the different methods. Many applications requiring high-quality and low error rates cannot make use of available data using traditional analytical approaches. Recently, we proposed a new concept of signalome-wide analysis of functional changes in the intracellular pathways termed OncoFinder, a bioinformatic tool for quantitative estimation of the signaling pathway activation (SPA). We also developed methods to compare the gene expression data obtained using multiple platforms and minimizing the error rates by mapping the gene expression data onto the known and custom signaling pathways. This technique for the first time makes it possible to analyze the functional features of intracellular regulation on a mathematical basis. In this study we show that the OncoFinder method significantly reduces the errors introduced by transcriptome-wide experimental techniques. We compared the gene expression data for the same biological samples obtained by both the next generation sequencing (NGS) and microarray methods. For these different techniques we demonstrate that there is virtually no correlation between the gene expression values for all datasets analyzed (R² < 0.1). In contrast, when the OncoFinder algorithm is applied to the data we observed clear-cut correlations between the NGS and microarray gene expression datasets. The SPA profiles obtained using NGS and microarray techniques were almost identical for the same biological samples allowing for the platform-agnostic analytical applications. We conclude that this feature of the OncoFinder enables to characterize the functional states of the transcriptomes and interactomes more accurately as before, which makes OncoFinder a method of choice for many applications including genetics, physiology, biomedicine, and molecular diagnostics.

Signaling hypergraphs

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

Parallel profiling of the transcriptome, cistrome, and epigenome in the cellular response to ionizing radiation

The DNA damage response (DDR) is a vast signaling network that is robustly activated by DNA double-strand breaks, the critical lesion induced by ionizing radiation (IR). Although much of this response operates at the protein level, a critical component of the network sustains many DDR branches by modulating the cellular transcriptome. Using deep sequencing, we delineated three layers in the transcriptional response to IR in human breast cancer cells: changes in the expression of genes encoding proteins or long noncoding RNAs, alterations in genomic binding by key transcription factors, and dynamics of epigenetic markers of active promoters and enhancers. We identified protein-coding and previously unidentified noncoding genes that were responsive to IR, and demonstrated that IR-induced transcriptional dynamics was mediated largely by the transcription factors p53 and nuclear factor κB (NF-κB) and was primarily dependent on the kinase ataxia-telangiectasia mutated (ATM). The resultant data set provides a rich resource for understanding a basic, underlying component of a critical cellular stress response.

Oncofinder, a new method for the analysis of intracellular signaling pathway activation using transcriptomic data

We propose a new biomathematical method, OncoFinder, for both quantitative and qualitative analysis of the intracellular signaling pathway activation (SPA). This method is universal and may be used for the analysis of any physiological, stress, malignancy and other perturbed conditions at the molecular level. In contrast to the other existing techniques for aggregation and generalization of the gene expression data for individual samples, we suggest to distinguish the positive/activator and negative/repressor role of every gene product in each pathway. We show that the relative importance of each gene product in a pathway can be assessed using kinetic models for “low-level” protein interactions. Although the importance factors for the pathway members cannot be so far established for most of the signaling pathways due to the lack of the required experimental data, we showed that ignoring these factors can be sometimes acceptable and that the simplified formula for SPA evaluation may be applied for many cases. We hope that due to its universal applicability, the method OncoFinder will be widely used by the researcher community.

Navigating the multilayered organization of eukaryotic signaling: a new trend in data integration

The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells

R-based software for the integration of pathway data into bioinformatic algorithms

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.

A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks

MOTIVATION

A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions.

RESULTS

Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms.

AVAILABILITY

ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pattern search in BioPAX models

Motivation: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult.

Results: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases.

Availability: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License.

Contact:patternsearch@cbio.mskcc.org

Supplementary information:Supplementary data are available at Bioinformatics online.

Altered transcriptome signature of phenotypically normal skin fibroblasts heterozygous for CDKN2A in familial melanoma: relevance to early intervention

Familial melanoma (FM) is a dominantly heritable cancer that is associated with mutations in the tumor suppressor CDKN2A/p16. In FM, a single inherited “hit” occurs in every somatic cell, enabling interrogation of cultured normal skin fibroblasts (SFs) from FM gene carriers as surrogates for the cell of tumor origin, namely the melanocyte. We compared the gene expression profile of SFs from FM individuals with two distinct CDKN2A/p16 mutations (V126D-p16 and R87P-p16) with the gene expression profile of SFs from age-matched individuals without p16 mutations and with no family history of melanoma. We show an altered transcriptome signature in normal SFs bearing a single-hit inherited mutation in the CDKN2A/p16 gene, wherein some of these abnormal alterations recapitulate changes observed in the corresponding cancer. Significantly, the extent of the alterations is mutation-site specific with the R87P-p16 mutation being more disruptive than the V126D-p16 mutation. We also examined changes in gene expression after exposure to ultraviolet (UV) radiation to define potential early biomarkers triggered by sun exposure. UV treatment of SFs from FM families induces distinct alterations in genes related to cell cycle regulation and DNA damage responses that are also reported to be dysregulated in melanoma. Importantly, these changes were diametrically opposed to UV-induced changes in SF from normal controls. We posit that changes identified in the transcriptome of SF from FM mutation carriers represent early events critical for melanoma development. As such, they may serve as specific biomarkers of increased risk as well as molecular targets for personalized prevention strategies in high-risk populations.

ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes--now with human data

Genome sequencing and transcriptomic profiling are two widely used approaches for the identification of human disease pathways. However, each approach typically provides a limited view of disease pathways: Genome sequencing can identify disease-related mutations but rarely reveals their mode-of-action, while transcriptomic assays do not reveal the series of events that lead to the transcriptomic change. ResponseNet is an integrative network-optimization approach that we developed to fill these gaps by highlighting major signaling and regulatory molecular interaction paths that connect disease-related mutations and genes. The ResponseNet web-server provides a user-friendly interface to ResponseNet. Specifically, users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction subnetwork connecting them, that is biased toward regulatory and signaling pathways. ResponseNet2.0 enhances the functionality of the ResponseNet web-server in two important ways. First, it supports analysis of human data by offering a human interactome composed of proteins, genes and micro-RNAs. Second, it offers a new informative view of the output, including a randomization analysis, to help users assess the biological relevance of the output subnetwork. ResponseNet2.0 is available at http://netbio.bgu.ac.il/respnet .

Interferon-beta induces distinct gene expression response patterns in human monocytes versus T cells

Background

Monocytes, which are key players in innate immunity, are outnumbered by neutrophils and lymphocytes among peripheral white blood cells. The cytokine interferon-β (IFN-β) is widely used as an immunomodulatory drug for multiple sclerosis and its functional pathways in peripheral blood mononuclear cells (PBMCs) have been previously described. The aim of the present study was to identify novel, cell-specific IFN-β functions and pathways in tumor necrosis factor (TNF)-α-activated monocytes that may have been missed in studies using PBMCs.

Methodology/Principal Findings

Whole genome gene expression profiles of human monocytes and T cells were compared following in vitro priming to TNF-α and overnight exposure to IFN-β. Statistical analyses of the gene expression data revealed a cell-type-specific change of 699 transcripts, 667 monocyte-specific transcripts, 21 T cell-specific transcripts and 11 transcripts with either a difference in the response direction or a difference in the magnitude of response. RT-PCR revealed a set of differentially expressed genes (DEGs), exhibiting responses to IFN-β that are modulated by TNF-α in monocytes, such as RIPK2 and CD83, but not in T cells or PBMCs. Known IFN-β promoter response elements, such as ISRE, were enriched in T cell DEGs but not in monocyte DEGs. The overall directionality of the gene expression regulation by IFN-β was different in T cells and monocytes, with up-regulation more prevalent in T cells, and a similar extent of up and down-regulation recorded in monocytes.

Conclusions

By focusing on the response of distinct cell types and by evaluating the combined effects of two cytokines with pro and anti-inflammatory activities, we were able to present two new findings First, new IFN-β response pathways and genes, some of which were monocytes specific; second, a cell-specific modulation of the IFN-β response transcriptome by TNF-α.

p53 induces transcriptional and translational programs to suppress cell proliferation and growth

BACKGROUND

Cell growth and proliferation are tightly connected to ensure that appropriately sized daughter cells are generated following mitosis. Energy stress blocks cell growth and proliferation, a critical response for survival under extreme conditions. Excessive oncogenic stress leads to p53 activation and the induction of senescence, an irreversible state of cell-cycle arrest and a critical component in the suppression of tumorigenesis. Nutrient-sensing and mitogenic cues converge on a major signaling node, which regulates the activity of the mTOR kinase. Although transcriptional responses to energy and oncogenic stresses have been examined by many gene-expression experiments, a global exploration of the modulation of mRNA translation in response to these conditions is lacking.

RESULTS

We combine RNA sequencing and ribosomal profiling analyses to systematically delineate modes of transcriptional and translational regulation induced in response to conditions of limited energy, oncogenic stress and cellular transformation. We detect a key role for mTOR and p53 in these distinct physiological states, and provide the first genome-wide demonstration that p53 activation results in mTOR inhibition and a consequent global repression of protein translation. We confirm the role of the direct p53 target genes Sestrin1 and Sestrin2 in this response, as part of the broad modulation of gene expression induced by p53 activation.

CONCLUSIONS

We delineate a bimodal tumor-suppressive regulatory program activated by p53, in which cell-cycle arrest is imposed mainly at the transcriptional level, whereas cell growth inhibition is enforced by global repression of the translation machinery.