Understanding regulatory mechanisms underlying stem cells helps to identify cancer biomarkers

Detection of biomarker genes play a crucial role in disease detection and treatment. Bioinformatics offers a variety of approaches for identification of biomarker genes which play key roles in complex diseases. These computational approaches enhance the insight derived from experiments and reduce the efforts of biologists and experimentalists. This is essentially achieved through prioritizing a set of genes with certain attributes. In this thesis, we show that understanding the regulatory mechanisms underlying stem cells helps to identify cancer biomarkers. We got inspired by the regulatory mechanisms of the pluripotency network in mouse embryonic stem cells and formulated the problem where a set of master regulatory genes in regulatory networks is identified with two combinatorial optimization problems namely as minimum dominating set and minimum connected dominating set in weakly and strongly connected components. Then we applied the developed methods to regulatory cancer networks to identify disease-associated genes and anti-cancer drug targets in breast cancer and hepatocellular carcinoma. As not all the nodes in the solutions are critical, we developed a prioritization method to rank a set of candidate genes which are related to a certain disease based on systematic analysis of the genes that are differentially expressed in tumor and normal conditions. Moreover, we demonstrated that the topological features in regulatory networks surrounding differentially expressed genes are highly consistent in terms of using the output of several analysis tools. We compared two randomization strategies for TF-miRNA co-regulatory networks to infer significant network motifs underlying cellular identity. We showed that the edge-type conserving method surpasses the non-conserving method in terms of biological relevance and centrality overlap. We presented several web servers and software packages that are publicly available at no cost. The Cytoscape plugin of minimum connected dominating set identifies a set of key regulatory genes in a user provided regulatory network based on a heuristic approach. The ILP formulations of minimum dominating set and minimum connected dominating set return the optimal solutions for the aforementioned problems. Our source code is publicly available. The web servers TFmiR and TFmiR2 construct disease-, tissue-, process-specific networks for the sets of deregulated genes and miRNAs provided by a user. They highlight topological hotspots and offer detection of three- and four-node FFL motifs as a separate web service for both organisms mouse and human.Die Gendetektion von Biomarkern spielt eine wesentliche Rolle bei der Erkennung und Behandlung von Krankheiten. Die Bioinformatik bietet eine Vielzahl von Ansätzen zur Identifizierung von Biomarker-Genen, die bei komplizierten Erkrankungen eine Schlüsselrolle spielen. Diese computerbasierten Ansätze verbessern die Erkenntnisse aus Experimenten und reduzieren den Aufwand von Biologen und Forschern. Dies wird hauptsächlich erreicht durch die Priorisierung einer Reihe von Genen mit bestimmten Attributen. In dieser Arbeit zeigen wir, dass die Identifizierung von Krebs-Biomarkern leichter gelingt, wenn wir die den Stammzellen zugrunde liegenden regulatorischen Mechanismen verstehen. Dazu angeregt wurden wir durch die regulatorischen Mechanismen des Pluripotenz-Netzwerks in embryonalen Maus-Stammzellen. Wir formulierten und haben das Problem der Identifizierung einer Reihe von Master-Regulator-Genen in regulatorischen Netzwerken mit zwei kombinatorischen Optimierungsproblemen, nämlich als minimal dominierende Menge und als minimal zusammenhängende dominierende Menge in schwach und stark verbundenen Komponenten. Die entwickelten Methoden haben wir dann auf regulatorische Krebsnetzwerke angewandt, um krankheitsassoziierte Gene und Zielproteine für Medikamenten gegen Brustkrebs und hepatozelluläres Karzinom zu identifizieren. Im Hinblick darauf, dass nicht alle Knoten in den Lösungen wesentlich sind, haben wir basierend auf der systematischen Analyse von Genen, die unterschiedlich bei Tumor- und Normalbedingungen reagieren, eine Priorisierungsmethode entwickelt, um einen Satz von Kandidatengenen in eine Reihenfolge zu bringen, die einer bestimmten Krankheit zugeordnet sind. Darüber hinaus haben wir gezeigt, dass die topologischen Eigenschaften in regulatorischen Netzwerken, die die deregulierte Gene umgeben, sehr einheitlich in Bezug auf den Einsatz verschiedener Analysewerkzeuge sind. Wir haben zwei Randomisierungsstrategien für TF-miRNA-Co-regulatorische Netzwerke verglichen, um signifikante Netzwerkmotive herauszufinden, welche zellulärer Identität zugrunde liegen. Wir haben gezeigt, dass die Edge-Type-Erhaltungsmethode, die nicht-erhaltende Methode in Bezug auf biologische Relevanz und zentrale Überlappung übertrifft. Wir haben mehrere Softwarepakete und Webserver vorgestellt, die allgemein und kostenlos zugänglich sind. Das Cytoscape Plugin für die Identififizierung, der minimal verbundener dominierenden Mengen identifiziert einen Satz von regulatorischen Schlüsselgenen in einem vom Benutzer bereitgestellten regulatorischen Netzwerk basierend auf einem heuristischen Ansatz. Die ILP Formulierungen, der minimal dominierenden Menge und der minimal verbundenen dominierenden Menge liefern die optimalen Lösungen für die oben vorgenannten Probleme. Unser Quellcode hierfür ist öffentlich verfügbar. Die Webserver TFmiR und TFmiR2 erzeugen Krankheits-, Gewebe- und prozessspezifische Netzwerke für die von einem Benutzer bereitgestellten deregulierten Gene und miRNAs. Außerdem verwenden die Webserver topologische Merkmale, um Hotspot-Knoten hervorzuheben und bieten die Erkennung von drei und vier Knoten FFL Motiven als separaten Web-Service für beide Organismen, Maus und Mensch

Identification of Biomarkers Controlling Cell Fate In Blood Cell Development

A blood cell lineage consists of several consecutive developmental stages starting from the pluri- or multipotent stem cell to a state of terminal differentiation. Despite their importance for human biology, the regulatory pathways and gene networks that govern these differentiation processes are not yet fully understood. This is in part due to challenges associated with delineating the interactions between transcription factors (TFs) and their corresponding target genes. A possible step forward in this case is provided by the increasing amount of expression data, as a basis for linking differentiation stages and gene activities. Here, we present a novel hierarchical approach to identify characteristic expression peak patterns that global regulators excert along the differentiation path of cell lineages. Based on such simple patterns, we identified cell state-specific marker genes and extracted TFs that likely drive their differentiation. Integration of the mean expression values of stage-specific “key player” genes yielded a distinct peaking pattern for each lineage that was used to identify further genes in the dataset which behave similarly. Incorporating the set of TFs that regulate these genes led to a set of stage-specific regulators that control the biological process of cell fate. As proof of concept, we considered two expression datasets covering key differentiation events in blood cell formation of mice

Topology Consistency of Disease-specific Differential Co-regulatory Networks

Background: Sets of differentially expressed genes often contain driver genes that induce disease processes. However, various methods for identifying differentially expressed genes yield quite different results. Thus, we investigated whether this affects the identification of key players in regulatory networks derived by downstream analysis from lists of differentially expressed genes. Results: While the overlap between the sets of significant differentially expressed genes determined by DESeq, edgeR, voom and VST was only 26% in liver hepatocellular carcinoma and 28% in breast invasive carcinoma, the topologies of the regulatory networks constructed using the TFmiR webserver for the different sets of differentially expressed genes were found to be highly consistent with respect to hub-degree nodes, minimum dominating set and minimum connected dominating set. Conclusions: The findings suggest that key genes identified in regulatory networks derived by systematic analysis of differentially expressed genes may be a more robust basis for understanding diseases processes than simply inspecting the lists of differentially expressed genes

Identification of molecular candidates which regulate calcium-dependent CD8+ T-cell cytotoxicity

Cytotoxic CD8+ T lymphocytes (CTL) eliminate infected cells or transformed tumor cells by releasing perforincontaining cytotoxic granules at the immunological synapse. The secretion of such granules depends on Ca2+- influx through store operated Ca2+ channels, formed by STIM (stromal interaction molecule)-activated Orai proteins. Whereas molecular mechanisms of the secretion machinery are well understood, much less is known about the molecular machinery that regulates the efficiency of Ca2+-dependent target cell killing. CTL killing efficiency is of high interest considering the number of studies on CD8+ T lymphocytes modified for clinical use. Here, we isolated total RNA from primary human cells: natural killer (NK) cells, non-stimulated CD8+ T-cells, and from Staphylococcus aureus enterotoxin A (SEA) stimulated CD8+ T-cells (SEA-CTL) and conducted whole genome expression profiling by microarray experiments. Based on differential expression analysis of the transcriptome data and analysis of master regulator genes, we identified 31 candidates which potentially regulate Ca2+-homeostasis in CTL. To investigate a putative function of these candidates in CTL cytotoxicity, we transfected either SEA-stimulated CTL (SEA-CTL) or antigen specific CD8+ T-cell clones (CTL-MART-1) with siRNAs specific against the identified candidates and analyzed the killing capacity using a real-time killing assay. In addition, we complemented the analysis by studying the effect of inhibitory substances acting on the candidate proteins if available. Finally, to unmask their involvement in Ca2+ dependent cytotoxicity, candidates were also analyzed under Ca2+-limiting conditions. Overall, we identified four hits, CCR5 (C-C chemokine receptor type five), KCNN4 (potassium calcium-activated channel subfamily N), RCAN3 (regulator of calcineurin) and BCL (Bcell lymphoma) 2 which clearly affect the efficiency of Ca2+ dependent cytotoxicity in CTL-MART-1 cells, CCR5, BCL2, and KCNN4 in a positive manner, and RCAN3 in a negative way

第8回千葉カルシウム代謝研究会 4.

Supplementary. This file includes the supplementary figures and tables mentioned in the paper. (PDF 274 kb

Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost

Additional file 2 of Identification of key player genes in gene regulatory networks

MCDS. This file includes the implementation of ILP formulation for MCDS problem using glpk solver. (SAGE 4 kb

Additional file 3 of Identification of key player genes in gene regulatory networks

User guide. This guide contains instructions for users to use the MDS and MCDS programs to find the optimal solution in a directed network. It also includes two GRNs from breast cancer network modules which can be used as input networks for ILP programs. (PDF 45 kb