The PluriNetWork: An Electronic Representation of the Network Underlying Pluripotency in Mouse, and Its Applications

BACKGROUND: Analysis of the mechanisms underlying pluripotency and reprogramming would benefit substantially from easy access to an electronic network of genes, proteins and mechanisms. Moreover, interpreting gene expression data needs to move beyond just the identification of the up-/downregulation of key genes and of overrepresented processes and pathways, towards clarifying the essential effects of the experiment in molecular terms. METHODOLOGY/PRINCIPAL FINDINGS: We have assembled a network of 574 molecular interactions, stimulations and inhibitions, based on a collection of research data from 177 publications until June 2010, involving 274 mouse genes/proteins, all in a standard electronic format, enabling analyses by readily available software such as Cytoscape and its plugins. The network includes the core circuit of Oct4 (Pou5f1), Sox2 and Nanog, its periphery (such as Stat3, Klf4, Esrrb, and c-Myc), connections to upstream signaling pathways (such as Activin, WNT, FGF, BMP, Insulin, Notch and LIF), and epigenetic regulators as well as some other relevant genes/proteins, such as proteins involved in nuclear import/export. We describe the general properties of the network, as well as a Gene Ontology analysis of the genes included. We use several expression data sets to condense the network to a set of network links that are affected in the course of an experiment, yielding hypotheses about the underlying mechanisms. CONCLUSIONS/SIGNIFICANCE: We have initiated an electronic data repository that will be useful to understand pluripotency and to facilitate the interpretation of high-throughput data. To keep up with the growth of knowledge on the fundamental processes of pluripotency and reprogramming, we suggest to combine Wiki and social networking software towards a community curation system that is easy to use and flexible, and tailored to provide a benefit for the scientist, and to improve communication and exchange of research results. A PluriNetWork tutorial is available at http://www.ibima.med.uni-rostock.de/IBIMA/PluriNetWork/

ExprEssence - Revealing the essence of differential experimental data in the context of an interaction/regulation net-work

<p>Abstract</p> <p>Background</p> <p>Experimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.</p> <p>Results</p> <p>To address this challenge, we developed a method, implemented as a Cytoscape plugin called <it>ExprEssence</it>. As input we take a network of interaction, stimulation and/or inhibition links between genes/proteins, and differential data, such as gene expression data, tracking an intervention or development in time. We condense the network, highlighting those links across which the largest changes can be observed. Highlighting is based on a simple formula inspired by the law of mass action. We can interactively modify the threshold for highlighting and instantaneously visualize results. We applied <it>ExprEssence </it>to three scenarios describing kidney podocyte biology, pluripotency and ageing: 1) We identify putative processes involved in podocyte (de-)differentiation and validate one prediction experimentally. 2) We predict and validate the expression level of a transcription factor involved in pluripotency. 3) Finally, we generate plausible hypotheses on the role of apoptosis, cell cycle deregulation and DNA repair in ageing data obtained from the hippocampus.</p> <p>Conclusion</p> <p>Reducing the size of gene/protein networks to the few links affected by large changes allows to screen for putative mechanistic relationships among the genes/proteins that are involved in adaptation to different experimental conditions, yielding important hypotheses, insights and suggestions for new experiments. We note that we do not focus on the identification of 'active subnetworks'. Instead we focus on the identification of single links (which may or may not form subnetworks), and these single links are much easier to validate experimentally than submodules. <it>ExprEssence </it>is available at <url>http://sourceforge.net/projects/expressence/</url>.</p

Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts

Accumulating evidence suggests that dysregulation of hypoxia-regulated transcriptional mechanisms is involved in development of chronic kidney diseases (CKD). However, it remains unclear how hypoxia-induced transcription factors (HIFs) and subsequent biological processes contribute to CKD development and progression. In our study, genome-wide expression profiles of more than 200 renal biopsies from patients with different CKD stages revealed significant correlation of HIF-target genes with eGFR in glomeruli and tubulointerstitium. These correlations were positive and negative and in part compartment-specific. Microarrays of proximal tubular cells and podocytes with stable HIF1α and/or HIF2α suppression displayed cell type-specific HIF1/HIF2-dependencies as well as dysregulation of several pathways. WGCNA analysis identified gene sets that were highly coregulated within modules. Characterization of the modules revealed common as well as cell group- and condition-specific pathways, GO-Terms and transcription factors. Gene expression analysis of the hypoxia-interconnected pathways in patients with different CKD stages revealed an increased dysregulation with loss of renal function. In conclusion, our data clearly point to a compartment- and cell type-specific dysregulation of hypoxia-associated gene transcripts and might help to improve the understanding of hypoxia, HIF dysregulation, and transcriptional program response in CKD

Über die Differentielle Analyse von Protein-Protein-Interaktionsnetzwerken

Die dem Leben zugrundeliegenden Prozesse sind hochkomplex. Sie werden zu einem Großteil durch Proteine umgesetzt. Diese spielen eine tragende Rolle für die morphologische Struktur und Vielfalt sowie Spezifität der Fähigkeiten der verschiedenen Zelltypen. Jedoch wirken Proteine nicht isoliert für sich allein sondern indem sie miteinander oder mit anderen Molekülen in der Zelle (DNA, Metabolite, Signalstoffe etc.) wechselwirken. Gerät dieses Geflecht von aufeinander abgestimmten Wechselwirkungen aus dem Gleichgewicht, kann das eine Ursache für Erkrankungen sein. Die Kenntnis über fehlregulierte Interaktionen kann dabei helfen, die betreffende Krankheit besser zu verstehen und gegen sie zu intervenieren. Die vorliegende Dissertation beschäftigt sich mit der Identifizierung von solch differentiell regulierten Interaktionen. Im Rahmen der Arbeit wurde eine Methode mit dem Namen ExprEssence entwickelt, welche diejenigen Interaktionen in einem Protein-Protein-Interaktionsnetzwerk identifiziert, die sich zwischen zwei verglichenen Zuständen (z.B. krank versus gesund) am stärksten unterscheiden. Ziel ist es, das Netzwerk auf die wesentlichen Unterschiede zwischen den zwei untersuchten Zuständen zu reduzieren. Hierzu werden Genexpressions- oder Proteomdaten der beiden Zustände in das bereits bestehende Netzwerk integriert. Aus diesen Daten wird die Stärke/Häufigkeit des Auftretens der einzelnen Interaktionen des Netzwerks geschätzt. Die Interaktionen, deren Interaktionsstärken sich zwischen den betrachteten Zuständen am stärksten unterscheiden, werden beibehalten – die restlichen Interaktionen werden verworfen. Dies ergibt ein verkleinertes Subnetzwerk, das aus jenen Interaktionen besteht, die am stärksten differentiell reguliert sind. Diese Interaktionen und ihre Proteine sind Kandidaten für eine Erklärung der biologischen Unterschiede der betrachteten Zustände auf molekularem Niveau. Die Methode wurde auf verschiedene biologische Fragestellungen angewandt und mit anderen ähnlichen Methoden verglichen. Bei der Untersuchung der Unterschiede zwischen Erfolg und Misserfolg einer chemotherapeutischen Brustkrebstherapie konnte beispielsweise gezeigt werden, dass das mit ExprEssence erstellte Subnetzwerk einen stärkeren Bezug zu den bereits bekannten Therapieerfolg-relevanten Mechanismen aufweist als die Methoden, mit denen ExprEssence verglichen wurde. Weiterhin wurde im Subnetzwerk eine möglicherweise für den Therapieerfolg relevante Interaktion identifiziert, die in diesem Zusammenhang bisher nicht betrachtet wurde. Deren Bedeutung konnte in der experimentellen Nachverfolgung weiter untermauert werden. Einen weiteren Schwerpunkt der Arbeit bildete die Untersuchung des Interaktoms eines spezialisierten Zelltyps der Niere – des Podozyten. Dieser Zelltyp ist essentiell für die Filtrationskompetenz der Niere. Ein Interaktionsnetzwerk mit spezifisch für den Podozyten relevanten Interaktion gib es bisher nicht. Daher wurde ein Podozyten-spezifisches Protein-Protein-Interaktionsnetzwerk aus wissenschaftlichen Veröffentlichungen zusammengestellt und öffentlich verfügbar gemacht. Genexpressionsdaten vielfältiger Art, beispielsweise von Podozyten in verschiedenen Entwicklungsstadien oder in Zellkultur, wurden in das Netzwerk integriert und mit ExprEssence analysiert. So konnte beispielsweise gezeigt werden, dass die Dedifferenzierung von in Kultur gehaltenen Podozyten nicht dem Umkehrweg der zuvor durchlaufenen Differenzierung entspricht. Neben ExprEssence wurde weitere Software entwickelt, die die Anwendbarkeit von ExprEssence erweitert – MovieMaker und ExprEsSector. Mit MovieMaker werden die Übergänge zwischen den betrachteten Zuständen nachvollziehbarer visualisiert. ExprEsSector bildet die Vereinigungs- und Schnittmengen-Netzwerke von ExprEssence-Subnetzwerken. So können beispielsweise verschiedenen Krankheiten gemeinsame Veränderungen vom Normalzustand identifiziert werden. Ist für eine Krankheit bereits ein Therapieansatz vorhanden, der auf eine fehlregulierte Interaktion einwirkt, und ist diese Interaktion auch in der anderen Krankheit gleichartig differentiell reguliert, kann geprüft werden, ob diese Therapie auf die zweite Krankheit übertragen werden kann. Neben der Vorstellung und Diskussion der erzielten Ergebnisse, wird auch auf methodisch bedingte Nachteile eingegangen. Es werden Strategien aufgezeigt, wie die negativen Einflüsse möglichst minimiert werden können oder wie sie bei der Bewertung der Ergebnisse zu berücksichtigen sind. In Anbetracht der immer schneller ansteigenden Menge biologischer Daten ist es eine wesentliche Herausforderung geworden, aus diesen die essentiellen Informationen zu extrahieren. Der integrative Ansatz der Verknüpfung von Informationen verschiedener Quellen wurde mit ExprEssence und den Erweiterungen MovieMaker und ExprEsSector in einem Konzept zur Identifizierung zustandsrelevanter molekularer Mechanismen in intuitiv leicht erfassbarer Form umgesetzt.Biological processes are highly complex and are realized in large parts by proteins, which make up the morphological structure as well as diversity and specificity of different cell types. Proteins do not act in an isolated manner but by interacting with each other or other molecules in the cell (DNA, metabolites, signal molecules etc.). Disturbance of this interplay can cause diseases. Knowing which interactions are deregulated can help to better understand and intervene against the diseases. In this doctoral thesis, a method is developed to identify such differentially regulated interactions. This method has been implemented as a software named ExprEssence. It identifies such interactions from a protein protein interaction network that are changing most strongly between two compared conditions (e.g. diseased versus control). The aim is to condense the network such that it contains only the essential differences between both compared states. For this, transcriptome or proteome data are mapped onto the network and the level of interaction strength is estimated for each protein interaction. Interactions that are changing most strongly are kept, the other interactions are removed. The remaining interactions and their proteins are candidates for explanation of biological differences on a molecular level. The method has been applied to several biological questions and the results have been compared with other methods. Investigating differences between success and non-success of a breast cancer chemotherapy, we could show that the ExprEssence-based subnetwork had a stronger focus on already known mechanisms compared to two other methods, ExprEssence has been compared with. Further, a putatively relevant interaction was identified which has not yet been discussed in this context. The significance of this interaction could be corroborated by further practical experiments. A further focus of this thesis was defined by the work with podocytes. This type of cells is essential for filtration in the kidney. A protein interaction network with podocyte-relevant interactions (PodNet) was set up, as such a network was not available, so far. PodNet is an expert-curated network based on literature search. Transcriptome data of diverse nature (e.g. podocytes of different developmental stages or in cell culture) were integrated into PodNet and analyzed with ExprEssence. We could show that cultured podocytes dedifferentiate without going back to earlier developmental stages. Besides ExprEssence, futher software was developed which extends the applicability of ExprEssence: MovieMaker visualizes the transition between observed states in a comprehensible manner. ExprEsSector determines the intersection and union networks of ExprEssence-condensed networks, which helps to identify common changes of several diseases against healthy controls. In case there is a known treatment against one disease which affects a deregulated interaction and in case this interaction is also differentially regulated in the same manner in the other disease, one can check whether the therapy of the first disease could be transferred to the second disease. Besides introducing and discussing the results, also methodical drawbacks are examined. Strategies are disclosed of how to cope with them. In consideration of the increasing amounts of biological data, extracting the essential information has become a major challenge. The integrative approach of combining information from various sources was implemented in an intuitively easily manageable concept with ExprEssence and its additional tools MovieMaker and ExprEsSector

Differential Network Analysis Applied to Preoperative Breast Cancer Chemotherapy Response

<div><p>In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of the underlying molecular mechanisms. Ever increasing amounts of high throughput data become available for integration into functional networks. In this study, we applied our software tool ExprEssence to identify specific mechanisms relevant for TFAC therapy response, from a gene/protein interaction network. We contrasted the resulting active subnetwork to the subnetworks of two other such methods, OptDis and KeyPathwayMiner. We could show that the ExprEssence subnetwork is more related to the mechanistic functional principles of TFAC therapy than the subnetworks of the other two methods despite the simplicity of ExprEssence. We were able to validate our method by recovering known mechanisms and as an application example of our method, we identified a mechanism that may further explain the synergism between paclitaxel and doxorubicin in TFAC treatment: Paclitaxel may attenuate MELK gene expression, resulting in lower levels of its target MYBL2, already associated with doxorubicin synergism in hepatocellular carcinoma cell lines. We tested our hypothesis in three breast cancer cell lines, confirming it in part. In particular, the predicted effect on MYBL2 could be validated, and a synergistic effect of paclitaxel and doxorubicin could be demonstrated in the breast cancer cell lines SKBR3 and MCF-7.</p></div

ExprEssence-condensed network describing the 16 most and 16 least active interactions between the E40 genes/proteins.

<p>For each gene, its mean expression level is visualized for non-responders (left) and responders (right) by color (green for low, white for intermediate, red for high expression). Interactions between the genes/proteins are represented by a line. Stimulations are indicated by an arrow on the target, inhibitions by a t-bar. The up- (red) and down-regulation (green) of interactions are also colorcoded. Full gene names can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784.s007" target="_blank">Table S1</a>.</p

Selected breast cancer subtypes with their most common marker profile, their overall prevalence and a representative human cell line with these molecular features.

<p>This table was compiled from different sources <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784-Carey1" target="_blank">[45]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784-Yang1" target="_blank">[48]</a>. ER: Estrogen receptor; PR: Progesterone receptor; HER2: human epidermal growth factor receptor 2; +: positive; −: negative.</p

Expression analysis of MYBL2 protein after treatment with paclitaxel (Taxol, T) and doxorubicin (Adriamycin, A) in several cell lines by Western blotting (non-tumorigenic cell line MCF-10A and breast cancer cell lines MCF-7, BT-20 and SKBR3).

<p>Single treatment with T or A for 48(T (48 h); A (48 h)), combined treatment for 48 h (T + A (48 h)) or successive treatment for each for 24 h (T (24 h), A (24 h) was applied. Quantification of western blotting results was carried out with individual passaged cells for at least three times. Representative western blots were displayed on top of the graphs. Proliferative alterations were detected against Proliferating Cell Nuclear Antigen (PCNA). Loading controls were labeling of the house keeping protein <i>β</i>-actin and stain-free imaging of the SDS-PAGEs prior blotting procedure. Mean ± SD values (n = 3). * : <i>p</i><0.05; ** : <i>p</i><0.01; * * * : <i>p</i><0.001 as compared to control treatment (unpaired t test).</p

Expression levels of MELK and MYBL2 protein in the non-tumorigenic cell line MCF-10A in contrast to the breast cancer cell lines MCF-7, BT-20 and SKBR3 detected by immunofluorescence.

<p>Note that MELK protein levels were below detection threshold while MYBL2 protein was abundant in all cell lines. The strongest MYBL2 signal was reached in the cell line SKBR3. MELK and MYBL2 protein: green; cell nuclei: blue.</p