Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies

Die rheumatoide Arthritis (RA) und die Multiple Sklerose (MS) werden allgemein als Autoimmunkrankheiten eingestuft. Zur Behandlung dieser Krankheiten werden immunmodulatorische Medikamente eingesetzt, etwa TNF-alpha-Blocker (z.B. Etanercept) im Falle der RA und IFN-beta-Präparate (z.B. Betaferon und Avonex) im Falle der MS. Bis heute sind die molekularen Mechanismen dieser Therapien weitestgehend unbekannt. Zudem ist ihre Wirksamkeit und Verträglichkeit bei einigen Patienten unzureichend. In dieser Arbeit wurde die transkriptionelle Antwort im Blut von Patienten auf jede dieser drei Therapien untersucht, um die Wirkungsweise dieser Medikamente besser zu verstehen. Dabei wurden Methoden der Netzwerkinferenz eingesetzt, mit dem Ziel, die genregulatorischen Netzwerke (GRNs) der in ihrer Expression veränderten Gene zu rekonstruieren. Ausgangspunkt dieser Analysen war jeweils ein Genexpressions- Datensatz. Daraus wurden zunächst Gene gefiltert, die nach Therapiebeginn hoch- oder herunterreguliert sind. Anschließend wurden die genregulatorischen Regionen dieser Gene auf Transkriptionsfaktor-Bindestellen (TFBS) analysiert. Um schließlich GRN-Modelle abzuleiten, wurde ein neuer Netzwerkinferenz-Algorithmus (TILAR) verwendet. TILAR unterscheidet zwischen Genen und TF und beschreibt die regulatorischen Effekte zwischen diesen durch ein lineares Gleichungssystem. TILAR erlaubt dabei Vorwissen über Gen-TF- und TF-Gen-Interaktionen einzubeziehen. Im Ergebnis wurden komplexe Netzwerkstrukturen rekonstruiert, welche die regulatorischen Beziehungen zwischen den Genen beschreiben, die im Verlauf der Therapien differentiell exprimiert sind. Für die Etanercept-Therapie wurde ein Teilnetz gefunden, das Gene enthält, die niedrigere Expressionslevel bei RA-Patienten zeigen, die sehr gut auf das Medikament ansprechen. Die Analyse von GRNs kann somit zu einem besseren Verständnis Therapie-assoziierter Prozesse beitragen und transkriptionelle Unterschiede zwischen Patienten aufzeigen

Exploration of large molecular datasets using global gene networks : computational methods and tools

Defining gene expression profiles and mapping complex interactions between molecular regulators and proteins is a key for understanding biological processes and the functional properties of cells, which is therefore, the focus on numerous experimental studies. Small-scale biochemical analyses deliver high-quality data, but lack coverage, whereas high throughput sequencing reveals thousands of interactions which can be error-prone and require proper computational methods to discover true relations. Furthermore, all these approaches usually focus on one type of interaction at a time. This makes experimental mapping of the genome-wide network a cost and time-intensive procedure. In the first part of the thesis, I present the developed network analysis tools for exploring large- scale datasets in the context of a global network of functional coupling. Paper I introduces NEArender, a method for performing pathway analysis and determines the relations between gene sets using a global network. Traditionally, pathway analysis did not consider network relations, thereby covering a minor part of the whole picture. Placing the gene sets in the context of a network provides additional information for pathway analysis, which reveals a more comprehensive picture. Paper II presents EviNet, a user-friendly web interface for using NEArender algorithm. The user can either input gene lists or manage and integrate highly complex experimental designs via the interactive Venn diagram-based interface. The web resource provides access to biological networks and pathways from multiple public or users’ own resources. The analysis typically takes seconds or minutes, and the results are presented in a graphic and tabular format. Paper III describes NEAmarker, a method to predict anti-cancer drug targets from enrichment scores calculated by NEArender, thus presenting a practical usage of network enrichment tool. The method can integrate data from multiple omics platforms to model drug sensitivity with enrichment variables. In parallel, alternative methods for pathway enrichment analysis were benchmarked in the paper. The second part of the thesis is focused on identifying spatial and temporal mechanisms that govern the formation of neural cell diversity in the developing brain. High-throughput platforms for RNA- and ChIP-sequencing were applied to provide data for studying the underlying biological hypothesis at the genome-wide scale. In Paper IV, I defined the role of the transcription factor Foxa2 during the specification and differentiation of floor plate cells of the ventral neural tube. By RNA-seq analyses of Foxa2-/- cells, a large set of candidate genes involved in floor plate differentiation were identified. Analysis of Foxa2 ChIP-seq dataset suggested that Foxa2 directly regulated more than 250 genes expressed by the floor plate and identified Rfx4 and Ascl1 as co-regulators of many floor plate genes. Experimental studies suggested a cooperative activator function for Foxa2 and Rfx4 and a suppressive role for Ascl1 in spatially constraining floor plate induction. Paper V addresses how time is measured during sequential specification of neurons from multipotent progenitor cells during the development of ventral hindbrain. An underlying timer circuitry which leads to the sequential generation of motor neurons and serotonergic neurons has been identified by integrating experimental and computational data modeling

A network module for the perseus software for computational proteomics facilitates proteome interaction graph analysis

Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org.publishedVersio

Gene regulatory network inference in human pathogenic fungi

Pathogenic fungi are a serious threat to people with impeded immune system, especially during organ transplantation and HIV infections. As the number of treatments that include a weakening of the patients immune system increase, so does the number of fungal infections. Often, the infection is opportunistic, meaning the pathogen already lives as a commensal in the host and uses the weak immune system to spread out and starts to colonise different parts of the host. These infections can lead to systemic, life-threatening infections, lowering the survival rate of the often already weakened host. Two of the most common human pathogens are Candida albicans and Aspergillus fumigatus. While C. albicans is a commensal and part of the healthy human flora, it can turn to an opportunistic pathogen, once the hosts immune system fails to contain it. Conidia of A. fumigatus are inhaled by humans every day and removed again by the immune system. In a weakened host, A. fumigatus can colonise the lung of the host and spread to other parts of the body, which can lead to fatal results, if no treatment is administered. The first part of this thesis aims to study the gene regulatory network of C. albicans on a genome-wide level, with a scale-free distribution of node degrees. These networks can be used to identify genes with central regulatory functions, called hubs, which are possible drug targets and can be the starting point for future studies. The modeling process included a large set of gene expression data measured by microarrays, the use of prior knowledge and a automatically harvested gold standard for the evaluation of the results. The final model is used to identify several hubs and is also able to reproduce current knowledge. A focused small-scale gene regulatory network is inferred for A. fumigatus while it is treated with the clinically applied drug caspofungin. The chapter describes the process from mapping of the RNA-Seq data over the selection of candidate genes and the harvest of prior knowledge to the application of the NetGenerator tool. A network model of 26 genes is tested for robustness against noise and used to identify a so far unknown cross-talk between to key regulators of major drug response pathways in A. fumigatus, which could be experimentally verified by the collaboration partner. Both, the large- and the small-scale network inference are later compared to give guidance on the correct application depending on the scientific question. To further study the influence of drug treatment on A. fumigatus caspofungin treatment was paired with the use of humidimycin, which does not have antifungal properties on its own, but seems to enhance the effect of caspofungin. Analysis of differential expression and clustering revealed that the combination of the two drugs lowers the number of differentially expressed genes in A. fumigatus, giving hints on how the enhancing effect of humidimycin works on the genetic level.Pathogene Pilze stellen eine ernste Bedrohung für Menschen mit geschwächtem Immunsystem dar. Die betrifft insbesondere Menschen während Organtransplantationen und HIV-Infizierte. Mit der steigenden Anzahl von Behandlungen, bei denen eine Schwächung des Immunsystems einhergeht, steigt auch die Anzahl der Pilzinfektionen. Diese sind häufig opportunistisch, was bedeutet, das der Pathogen bereits als Nutznießer im Wirt lebt und ein geschwächtes Immunsystem nutzt, um sich auszubreiten. Dies kann zu systematischen, lebensbedrohenden Infektionen führen, welche die Überlebenswahrscheinlichkeit des oft bereits geschwächten Wirts weiter senkt. Zwei der am weitesten verbreiteten Pathogene sind Candida albicans und Aspergillus fumigatus. Während C. albicans gewöhnlich als Teil der gesunden menschlichen Flora lebt, ohne Schaden anzurichten, kann es sich zu einem opportunistischem Pathogen entwickeln, sobald das Immunsystem des Wirts ihn nicht mehr eindämmen kann. Sporen von A. fumigatus werden von Menschen jeden Tag eingeatmet und vom Immunsystem wieder entfernt. In einem geschwächtem Wirt kann A. fumigatus die Lunge besiedeln und sich auf andere Teile des Körpers ausbreiten. Ohne Behandlung kann dies tödliche Folgen für den Wirt haben. Der erste Teil dieser Doktorarbeit zielt auf die Untersuchung der genregulatorischen Netzwerke von C. albicans auf genomweiter Ebene ab. Dabei wurden Netzwerke mit einer skalenfreien Verteilung der Kantengrade erzeugt. Diese Netzwerke können dafür verwendet werden, Gene mit zentraler regulatorischer Funktion zu identifizieren. Diese so genannten Hubs sind mögliche Zielgene für Medikamente und können der Anfang für zukünftige Studien sein. Die Modellierung enthält die Verwendung von Vorwissen und ein automatisch gesammelter Goldstandard zu Evaluierung der Ergebnisse. Das endgültige Modell wird benutzt um verschiedene Hubs zu identifizieren und ist auch in der Lage, aktuelles Wissen wiederzugeben. Darüber hinaus wird ein fokussiertes genregulatorisches Netzwerk für A. fumigatus erstellt, während es mit dem klinischem Medikament Caspofungin behandelt wird. Hier beschrieben wird der Vorgang von der Kartierung der RNA-Seq-Daten über die Auswahl der Kandidatengene und das Sammeln von Vorwissen zu der Anwendung des NetGenerator Programms. Ein Netzwerkmodel aus 26 Genen wird bezüglich seiner Robustheit gegen Rauschen in den Daten und fehlendes Vorwissen getestet. Dabei wird eine bisher unbekannte Regulation zwischen zwei zentralen Genen gefunden, welche für die Stressantwort gegen Medikamente in A. fumigatus verantwortlich sind. Diese Regulation konnte experimentell durch Kollaborationspartner bestätigt werden. Sowohl die genomweite, als auch die fokussierte Netzwerkinfernz werden anschließend verglichen, um Hinweise für ihre korrekte Anwendung zu geben, abhängig von der biologischen Fragestellung. Um den Einfluß von Medikamenten auf A. fumigatus weiter zu untersuchen, wurde die Kombination von Caspofungin mit Humidimycin untersucht. Humidimycin besitzt selbst keine antifungielle Wirkung, scheint jedoch die Wirkung von Caspofungin zu verstärken. Eine Analyse der differentiell exprimierten Gene und Clustering zeigte, das die Kombination beider Medikamente die Anzahl der differentiell exprimierten Gene gegenüber der Einzelbehandlung mit Caspofungin verringert. Dies gibt Hinweise darauf, wie der verstärkende Effekt von Humidimycin auf Genebene funktioniert

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Development and application of software and algorithms for network approaches to proteomics data analysis

The cells making up all living organisms integrate external and internal signals to carry out the functions of life. Dysregulation of signaling can lead to a variety of grave diseases, including cancer [Slamon et al., 1987]. In order to understand signal transduction, one has to identify and characterize the main constituents of cellular signaling cascades. Proteins are involved in most cellular processes and form the major class of biomolecules responsible for signal transduction. Post-translational modifications (PTMs) of proteins can modulate their enzymatic activity and their protein-protein interactions (PPIs) which in turn can ultimately lead to changes in protein expression. Classical biochemistry has approached the study of proteins, PTMs and interaction from a reductionist view. The abundance, stability and localization of proteins was studied one protein at a time, following the one gene-one protein-one function paradigm [Beadle and Tatum, 1941]. Pathways were considered to be linear, where signals would be transmitted from a gene to proteins, eventually resulting in a specific phenotype. Establishing the crucial link between genotype and phenotype remains challenging despite great advances in omics technologies, such as liquid chromatography (LC)-mass spectrometry (MS) that allow for the system-wide interrogation of proteins. Systems and network biology [Barabási and Oltvai, 2004, Bensimon et al., 2012, Jørgensen and Locard-Paulet, 2012, Choudhary and Mann, 2010] aims to transform modern biology by utilizing omics technologies to understand and uncover the various complex networks that govern the cell. The first detected large-scale biological networks have been found to be highly structured and non-random [Albert and Barabási, 2002]. Furthermore, these are assembled from functional and topological modules. The smallest topological modules are formed by the direct physical interactions within protein-protein and protein-RNA complexes. These molecular machines are able to perform a diverse array of cellular functions, such as transcription and degradation [Alberts, 1998]. Members of functional modules are not required to have a direct physical interaction. Instead, such modules also include proteins with temporal co-regulation throughout the cell cycle [Olsen et al., 2010], or following the circadian day-night rhythm [Robles et al., 2014]. The signaling pathways that make up the cellular network [Jordan et al., 2000] are assembled from a hierarchy of these smaller modules [Barabási and Oltvai, 2004]. The regulation of these modules through dynamic rewiring enables the cell to respond to internal an external stimuli. The main challenge in network biology is to develop techniques to probe the topology of various biological networks, to identify topological and functional modules, and to understand their assembly and dynamic rewiring. LC-MS has become a powerful experimental platform that addresses all these challenges directly [Bensimon et al., 2012], and has long been used to study a wide range of biomolecules that participate in the cellular network. The field of proteomics in particular, which is concerned with the identification and characterization of the proteins in the cell, has been revolutionized by recent technological advances in MS. Proteomics experiments are used not only to quantify peptides and proteins, but also to uncover the edges of the cellular network, by screening for physical PPIs in a global [Hein et al., 2015] or condition specific manner [Kloet et al., 2016]. Crucial for the interpretation of the large-scale data generated by MS experiments is the development of software tools that aid researchers in translating raw measurements into biological insights. The MaxQuant and Perseus platforms were designed for this exact purpose. The aim of this thesis was to develop software tools for the analysis of MS-based proteomics data with a focus on network biology and apply the developed tools to study cellular signaling. The first step was the extension of the Perseus software with network data structures and activities. The new network module allows for the sideby-side analysis of matrices and networks inside an interactive workflow and is described in article 1. We subsequently apply the newly developed software to study the circadian phosphoproteome of cortical synapses (see article 2). In parallel we aimed to improve the analysis of large datasets by adapting the previously Windows-only MaxQuant software to the Linux operating system, which is more prevalent in high performance computing environments (see article 3)

Interactomic and Pharmacological Insights on Human Sirt-1

Sirt-1 is defined as a nuclear protein involved in the molecular mechanisms of inflammation and neurodegeneration through the de-acetylation of many different substrates even if experimental data in mouse suggest both its cytoplasmatic presence and nucleo-cytoplasmic shuttling upon oxidative stress. Since the experimental structure of human Sirt-1 has not yet been reported, we have modeled its 3D structure, highlighted that it is composed by four different structural regions: N-terminal region, allosteric site, catalytic core and C-terminal region, and underlined that the two terminal regions have high intrinsic disorder propensity and numerous putative phosphorylation sites. Many different papers report experimental studies related to its functional activators because Sirt-1 is implicated in various diseases and cancers. The aim of this article is (i) to present interactomic studies based human Sirt-1 to understand its most important functional relationships in the light of the gene–protein interactions that control major metabolic pathways and (ii) to show by docking studies how this protein binds some activator molecules in order to evidence structural determinants, physico-chemical features and those residues involved in the formation of complexes

The Attributed Pi Calculus with Priorities

International audienceWe present the attributed $\pi$-calculus for modeling concurrent systems with interaction constraints depending on the values of attributes of processes. The $\pi$-calculus serves as a constraint language underlying the $\pi$-calculus. Interaction constraints subsume priorities, by which to express global aspects of populations. We present a nondeterministic and a stochastic semantics for the attributed $\pi$-calculus. We show how to encode the $\pi$-calculus with priorities and polyadic synchronization $\pi$@ and thus dynamic compartments, as well as the stochastic $\pi$-calculus with concurrent objects spico. We illustrate the usefulness of the attributed $\pi$-calculus for modeling biological systems at two particular examples: Euglena’s spatial movement in phototaxis, and cooperative protein binding in gene regulation of bacteriophage lambda. Furthermore, population-based model is supported beside individual-based modeling. A stochastic simulation algorithm for the attributed $\pi$-calculus is derived from its stochastic semantics. We have implemented a simulator and present experimental results, that confirm the practical relevance of our approach