Nicolas Alcaraz

KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

BackgroundOver the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.ResultsWe present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.ConclusionWith KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.

KeyPathwayMiner: Detecting Case-Specific Biological Pathways Using Expression Data

Abstract Recent advances in systems biology have provided us with massive amounts of pathway data that describe the interplay of genes and their products. The resulting biological networks can be modeled as graphs. By means of “omics” technologies, such as microarrays, the activity of genes and proteins can be measured. Here, data from microarray experiments is integrated with the network data to gain deeper insights into gene expression. We introduce KeyPathwayMiner, a method that enables the extraction and visualization of interesting subpathways given the results of a series of gene expression studies. We aim to detect highly connected subnetworks in which most genes or proteins show similar patterns of expression. Specifically, given network and gene expression data, KeyPathwayMiner identifies those maximal subgraphs where all but k nodes of the subnetwork are expressed similarly in all but l cases in the gene expression data. Since identifying these subgraphs is computationally intensive, we developed a heuristic algorithm based on Ant Colony Optimization. We implemented KeyPathwayMiner as a plug-in for Cytoscape. Our computational model is related to a strategy presented by Ulitsky et al. in 2008. Consequently, we used the same data sets for evaluation. KeyPathwayMiner is available online at http://keypathwayminer.mpi-inf.mpg.de .

KeyPathwayMinerWeb: online multi-omics network enrichment

We present KeyPathwayMinerWeb, the first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein–protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles. The web interface at (http://keypathwayminer.compbio.sdu.dk) implements all core functionalities of the KeyPathwayMiner tool set such as data integration, input of background knowledge, batch runs for parameter optimization and visualization of extracted pathways. In addition to an intuitive web interface, we also implemented a RESTful API that now enables other online developers to integrate network enrichment as a web service into their own platforms.

On the performance of de novo pathway enrichment

De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.Computational biology: Evaluation of network-based pathway enrichment toolsDe novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules.

De novo pathway-based biomarker identification

Abstract Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

Change in autoantibody and cytokine responses during the evolution of neuromyelitis optica in patients with systemic lupus erythematosus: A preliminary study.

Background: Neuromyelitis optica (NMO)–systemic lupus erythematosus (SLE) association is a rare condition characterized by multiple autoantibodies. Objective: To examine if, during the evolution of NMO, anti-AQP4 responses are part of polyclonal B cell activation, and if T cell responses contribute. Methods: In 19 samples of six patients who developed NMO during SLE, we examined the correlation of AQP4-IgG1 and IgM with (i) anti-MOG IgG and IgM, (ii) anti-nuclear, anti-nucleosome and anti-dsDNA IgG antibodies, (iii) cytokines and chemokines in the serum and (iv) longitudinal relation to NMO relapses/remission. Results: AQP4-IgG1 was present 1–2–5 years before the first NMO relapse. During relapse, AQP4-IgG1, ANA, anti-dsDNA and anti-nucleosome antibodies were elevated. Anti-MOG IgG/IgM and AQP4-IgM antibodies were not detected. AQP4-IgG1 antibodies correlated with concentration of anti-nucleosome, IFN-γ,interferon-gamma-induced CCL10/IP-10 and CCL17/TARC (p<0.05, respectively). CCL17/TARC correlated with levels of anti-nucleosome and anti-dsDNA (p<0.05, respectively). Compared to healthy subjects, concentration of IFN-γ and CCL17/TARC was higher in NMO/SLE (p<0.05). Conclusions: AQP4-IgG1 antibodies are present in the sera years before the first NMO attack in patients with SLE; elevation of anti-AQP4 is part of a polyclonal B cell response during NMO relapses; in spite of multiple autoantibodies in the serum, MOG antibodies were not present; Th1 responses accompany autoantibody responses in NMO/SLE.

Robust de novo pathway enrichment with KeyPathwayMiner 5

Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.

Elucidation of altered pathways in tumor-initiating cells of triple-negative breast cancer: A useful cell model system for drug screening

A limited number of cancer cells within a tumor are thought to have self‐renewing and tumor‐initiating capabilities that produce the remaining cancer cells in a heterogeneous tumor mass. Elucidation of central pathways preferentially used by tumor‐initiating cells/cancer stem cells (CSCs) may allow their exploitation as potential cancer therapy targets. We used single cell cloning to isolate and characterize four isogenic cell clones from a triple‐negative breast cancer cell line; two exhibited mesenchymal‐like and two epithelial‐like characteristics. Within these pairs, one, but not the other, resulted in tumors in immunodeficient NOD/Shi‐scid/IL‐2 Rγ null mice and efficiently formed mammospheres. Quantitative proteomics and phosphoproteomics were used to map signaling pathways associated with the tumor‐initiating ability. Signaling associated with apoptosis was suppressed in tumor‐initiating versus nontumorigenic counterparts with pro‐apoptotic proteins, such as Bcl2‐associated agonist of cell death (BAD), FAS‐associated death domain protein (FADD), and myeloid differentiation primary response protein (MYD88), downregulated in tumor‐initiating epithelial‐like cells. Functional studies confirmed significantly lower apoptosis in tumor‐initiating versus nontumorigenic cells. Moreover, central pathways, including β‐catenin and nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB)‐related signaling, exhibited increased activation in the tumor‐initiating cells. To evaluate the CSC model as a tool for drug screening, we assessed the effect of separately blocking NF‐κB and Wnt/β‐catenin signaling and found markedly reduced mammosphere formation, particularly for tumor‐initiating cells. Similar reduction was also observed using patient‐derived primary cancer cells. Furthermore, blocking NF‐κB signaling in mice transplanted with tumor‐initiating cells significantly reduced tumor outgrowth. Our study demonstrates that suppressed apoptosis, activation of pathways associated with cell viability, and CSCs are the major differences between tumor‐initiating and nontumorigenic cells independent of their epithelial‐like/mesenchymal‐like phenotype. These altered pathways may provide targets for future drug development to eliminate CSCs, and the cell model may be a useful tool in such drug screenings. Stem Cells 2017;35:1898–1912

A Simulated Annealing Algorithm for Maximum Common Edge Subgraph Detection in Biological Networks

Network alignment is a challenging computational problem that identifies node or edge mappings between two or more networks, with the aim to unravel common patterns among them. Pairwise network alignment is already intractable, making multiple network comparison even more difficult. Here, we introduce a heuristic algorithm for the multiple maximum common edge subgraph problem that is able to detect large common substructures shared across multiple, real-world size networks efficiently. Our algorithm uses a combination of iterated local search, simulated annealing and a pheromone-based perturbation strategy. We implemented multiple local search strategies and annealing schedules, that were evaluated on a range of synthetic networks and real protein-protein interaction networks. Our method is parallelized and well-suited to exploit current multi-core CPU architectures. While it is generic, we apply it to unravel a biochemical backbone inherent in different species, modeled as multiple maximum common subgraphs.

The GBAF chromatin remodeling complex binds H3K27ac and mediates enhancer transcription

H3K27ac is associated with regulatory active enhancers, but its exact role in enhancer function remains elusive. Using mass spectrometry-based interaction proteomics, we identified the Super Elongation Complex (SEC) and GBAF, a non-canonical GLTSCR1L- and BRD9-containing SWI/SNF chromatin remodeling complex, to be major interactors of H3K27ac. We systematically characterized the composition of GBAF and the conserved GLTSCR1/1L ‘GiBAF’-domain, which we found to be responsible for GBAF complex formation and GLTSCR1L nuclear localization. Inhibition of the bromodomain of BRD9 revealed interaction between GLTSCR1L and H3K27ac to be BRD9-dependent and led to GLTSCR1L dislocation from its preferred binding sites at H3K27ac-associated enhancers. GLTSCR1L disassociation from chromatin resulted in genome-wide downregulation of enhancer transcription while leaving most mRNA expression levels unchanged, except for reduced mRNA levels from loci topologically linked to affected enhancers. Our results indicate that GBAF is an enhancer-associated chromatin remodeler important for transcriptional and regulatory activity of enhancers. Graphical abstract