2,078 research outputs found

    Large-Scale Kinetic Analyses of Protein-Protein Interactions: Advancing the Understanding of Post Translational Modifications in Biological Regulation

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    abstract: Signal transduction networks comprising protein-protein interactions (PPIs) mediate homeostatic, diseased, and therapeutic cellular responses. Mapping these networks has primarily focused on identifying interactors, but less is known about the interaction affinity, rates of interaction or their regulation. To better understand the extent of the annotated human interactome, I first examined > 2500 protein interactions within the B cell receptor (BCR) signaling pathway using a current, cutting-edge bioluminescence-based platform called “NanoBRET” that is capable of analyzing transient and stable interactions in high throughput. Eighty-three percent (83%) of the detected interactions have not been previously reported, indicating that much of the BCR pathway is still unexplored. Unfortunately, NanoBRET, as with all other high throughput methods, cannot determine binding kinetics or affinities. To address this shortcoming, I developed a hybrid platform that characterizes > 400 PPIs quantitatively and simultaneously in 12,000 PPIs in the BCR signaling pathway, revealing unique kinetic mechanisms that are employed by proteins, phosphorylation and activation states to regulate PPIs. In one example, activation of the GTPase RAC1 with nonhydrolyzable GTP-γS minimally affected its binding affinities with phosphorylated proteins but increased, on average, its on- and off-rates by 4 orders of magnitude for one-third of its interactions. In contrast, this phenomenon occurred with virtually all unphosphorylated proteins. The majority of the interactions (85%) were novel, sharing 40% of the same interactions as NanoBRET as well as detecting 55% more interactions than NanoBRET. In addition, I further validated four novel interactions identified by NAPPA-SPRi using SDS-PAGE migration and Western blot analyses. In one case, we have the first evidence of a direct enzyme-substrate interaction between two well-known proto-oncogenes that are abnormally regulated in > 30% of cancers, PI3K and MYC. Herein, PI3K is demonstrated to phosphorylate MYC at serine 62, a phosphosite that increases the stability of MYC. This study provides valuable insight into how PPIs, phosphorylation, and GTPase activation regulate the BCR signal transduction pathway. In addition, these methods could be applied toward understanding other signaling pathways, pathogen-host interactions, and the effect of protein mutations on protein interactions.Dissertation/ThesisDoctoral Dissertation Biological Design 201

    MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

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    Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

    Activity of microRNAs and transcription factors in Gene Regulatory Networks

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    In biological research, diverse high-throughput techniques enable the investigation of whole systems at the molecular level. The development of new methods and algorithms is necessary to analyze and interpret measurements of gene and protein expression and of interactions between genes and proteins. One of the challenges is the integrated analysis of gene expression and the associated regulation mechanisms. The two most important types of regulators, transcription factors (TFs) and microRNAs (miRNAs), often cooperate in complex networks at the transcriptional and post-transcriptional level and, thus, enable a combinatorial and highly complex regulation of cellular processes. For instance, TFs activate and inhibit the expression of other genes including other TFs whereas miRNAs can post-transcriptionally induce the degradation of transcribed RNA and impair the translation of mRNA into proteins. The identification of gene regulatory networks (GRNs) is mandatory in order to understand the underlying control mechanisms. The expression of regulators is itself regulated, i.e. activating or inhibiting regulators in varying conditions and perturbations. Thus, measurements of gene expression following targeted perturbations (knockouts or overexpressions) of these regulators are of particular importance. The prediction of the activity states of the regulators and the prediction of the target genes are first important steps towards the construction of GRNs. This thesis deals with these first bioinformatics steps to construct GRNs. Targets of TFs and miRNAs are determined as comprehensively and accurately as possible. The activity state of regulators is predicted for specific high-throughput data and specific contexts using appropriate statistical approaches. Moreover, (parts of) GRNs are inferred, which lead to explanations of given measurements. The thesis describes new approaches for these tasks together with accompanying evaluations and validations. This immediately defines the three main goals of the current thesis: 1. The development of a comprehensive database of regulator-target relation. Regulators and targets are retrieved from public repositories, extracted from the literature via text mining and collected into the miRSel database. In addition, relations can be predicted using various published methods. In order to determine the activity states of regulators (see 2.) and to infer GRNs (3.) comprehensive and accurate regulator-target relations are required. It could be shown that text mining enables the reliable extraction of miRNA, gene, and protein names as well as their relations from scientific free texts. Overall, the miRSel contains about three times more relations for the model organisms human, mouse, and rat as compared to state-of-the-art databases (e.g. TarBase, one of the currently most used resources for miRNA-target relations). 2. The prediction of activity states of regulators based on improved target sets. In order to investigate mechanisms of gene regulation, the experimental contexts have to be determined in which the respective regulators become active. A regulator is predicted as active based on appropriate statistical tests applied to the expression values of its set of target genes. For this task various gene set enrichment (GSE) methods have been proposed. Unfortunately, before an actual experiment it is unknown which genes are affected. The missing standard-of-truth so far has prevented the systematic assessment and evaluation of GSE tests. In contrast, the trigger of gene expression changes is of course known for experiments where a particular regulator has been directly perturbed (i.e. by knockout, transfection, or overexpression). Based on such datasets, we have systematically evaluated 12 current GSE tests. In our analysis ANOVA and the Wilcoxon test performed best. 3. The prediction of regulation cascades. Using gene expression measurements and given regulator-target relations (e.g. from the miRSel database) GRNs are derived. GSE tests are applied to determine TFs and miRNAs that change their activity as cellular response to an overexpressed miRNA. Gene regulatory networks can constructed iteratively. Our models show how miRNAs trigger gene expression changes: either directly or indirectly via cascades of miRNA-TF, miRNA-kinase-TF as well as TF-TF relations. In this thesis we focus on measurements which have been obtained after overexpression of miRNAs. Surprisingly, a number of cancer relevant miRNAs influence a common core of TFs which are involved in processes such as proliferation and apoptosis

    Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

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    The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

    QPath: a method for querying pathways in a protein-protein interaction network

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    BACKGROUND: Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks. RESULTS: We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly. CONCLUSION: Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data

    Contextual Analysis of Gene Expression Data

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    As measurement of gene expression using microarrays has become a standard high throughput method in molecular biology, the analysis of gene expression data is still a very active area of research in bioinformatics and statistics. Despite some issues in quality and reproducibility of microarray and derived data, they are still considered as one of the most promising experimental techniques for the understanding of complex molecular mechanisms. This work approaches the problem of expression data analysis using contextual information. While all analyses must be based on sound statistical data processing, it is also important to include biological knowledge to arrive at biologically interpretable results. After giving an introduction and some biological background, in chapter 2 some standard methods for the analysis of microarray data including normalization, computation of differentially expressed genes, and clustering are reviewed. The first source of context information that is used to aid in the interpretation of the data, is functional annotation of genes. Such information is often represented using ontologies such as gene ontology. GO annotations are provided by many gene and protein databases and have been used to find functional groups that are significantly enriched in differentially expressed, or otherwise conspicuous genes. In gene clustering approaches, functional annotations have been used to find enriched functional classes within each cluster. In chapter 3, a clustering method for the samples of an expression data set is described that uses GO annotations during the clustering process in order to find functional classes that imply a particularly strong separation of the samples. The resulting clusters can be interpreted more easily in terms of GO classes. The clustering method was developed in joint work with Henning Redestig. More complex biological information that covers interactions between biological objects is contained in networks. Such networks can be obtained from public databases of metabolic pathways, signaling cascades, transcription factor binding sites, or high-throughput measurements for the detection of protein-protein interactions such as yeast two hybrid experiments. Furthermore, networks can be inferred using literature mining approaches or network inference from expression data. The information contained in such networks is very heterogenous with respect to the type, the quality and the completeness of the contained data. ToPNet, a software tool for the interactive analysis of networks and gene expression data has been developed in cooperation with Daniel Hanisch. The basic analysis and visualization methods as well as some important concepts of this tool are described in chapter 4. In order to access the heterogeneous data represented as networks with annotated experimental data and functions, it is important to provide advanced querying functionality. Pathway queries allow the formulation of network templates that can include functional annotations as well as expression data. The pathway search algorithm finds all instances of the template in a given network. In order to do so, a special case of the well known subgraph isomorphism problem has to be solved. Although the algorithm has exponential running time in the worst case, some implementation tricks make it run fast enough for practical purposes. Often, a pathway query has many matching instances, and it is important to assess the statistical significance of the individual instances with respect to expression data or other criteria. In chapter 5 the pathway query language and the pathway search algorithm are described in detail and some theoretical properties are derived. Furthermore, some scoring methods that have been implemented are described. The possibility of combining different scoring schemes for different parts of the query result in very flexible scoring capabilities. In chapter 6, some applications of the methods are described, using public data sets as well as data sets from research projects. On the basis of the well studied public data sets, it is demonstrated that the methods yield biologically meaningful results. The other analyses show how new hypotheses can be generated in more complex biological systems, but the validation of these hypotheses can only be provided by new experiments. Finally, an outlook is given on how the presented methods can contribute to ongoing research efforts in the area of expression data analysis, their applicability to other types of data (such as proteomics data) and their possible extensions.Während die Messung von RNA-Konzentrationen mittels Microarrays eine Standardtechnik zur genomweiten Bestimmung von Genexpressionswerten geworden ist, ist die Analyse der dabei gewonnenen Daten immer noch ein Gebiet äußerst aktiver Forschung. Trotz einiger Probleme bezüglich der Reproduzierbarkeit von Microarray- und davon abgeleiteten Daten werden diese als eine der vielversprechendsten Technologien zur Aufklärung komplexer molekularer Mechanismen angesehen. Diese Arbeit beschäftigt sich mit dem Problem der Expressionsdatenanalyse mit Hilfe von Kontextinformationen. Alle Analysen müssen auf solider Statistik beruhen, aber es ist außerdem wichtig, biologisches Wissen einzubeziehen, um biologisch interpretierbare Ergebnisse zu erhalten. Nach einer Einleitung und einigem biologischen Hintergrund werden in Kapitel 2 einige Standardmethoden zur Analyse von Expressionsdaten vorgestellt, wie z.B. Normalisierung, Berechnung differenziell exprimierter Gene sowie Clustering. Die erste Quelle von Kontextinformationen, die zur besseren Interpretation der Daten herangezogen wird, ist funktionale Annotation von Genen. Solche Informationen werden oft mit Hilfe von Ontologien wie z.B. der Gene Ontology dargestellt. GO Annotationen werden von vielen Gen- und Proteindatenbanken zur Verfügung gestellt und werden unter anderem benutzt, um Funktionen zu finden, die signifikant angereichert sind an differenziell exprimierten oder aus anderen Gründen auffälligen Genen. Bei Clusteringmethoden werden funktionale Annotationen benutzt, um in den gefundenen Clustern angereicherte Funktionen zu identifizieren. In Kapitel 3 wird ein neues Clusterverfahren für Proben in Expressionsdatensätzen vorgestellt, das GO Annotationen während des Clustering benutzt, um Funktionen zu finden, anhand derer die Expressionsdaten besonders deutlich getrennt werden können. Die resultierenden Cluster können mit Hilfe der GO Annotationen leichter interpretiert werden. Die Clusteringmethode wurde in Zusammenarbeit mit Henning Redestig entwickelt. Komplexere biologische Informationen, die auch die Interaktionen zwischen biologischen Objekten beinhaltet, sind in Netzwerken enthalten. Solche Netzwerke können aus öffentlichen Datenbanken von metabolischen Pfaden, Signalkaskaden, Bindestellen von Transkriptionsfaktoren, aber auch aus Hochdurchsatzexperimenten wie der Yeast Two Hybrid Methode gewonnen werden. Außerdem können Netzwerke durch die automatische Auswertung wissenschaftlicher Literatur oder Inferenz aus Expressionsdaten gewonnen werden. Die Information, die in solchen Netzwerken enthalten ist, ist sehr verschieden in Bezug auf die Art, die Qualität und die Vollständigkeit der Daten. ToPNet, ein Computerprogramm zur interaktiven Analyse von Netzwerken und Genexpressionsdaten, wurde gemeinsam mit Daniel Hanisch entwickelt. Die grundlegenden Analyse und Visualisierungsmethoden sowie einige wichtige Konzepte dieses Programms werden in Kapitel 4 beschrieben. Um auf die verschiedenartigen Daten zugreifen zu können, die durch Netzwerke mit funktionalen Annotationen sowie Expressionsdaten repräsentiert werden, ist es wichtig, flexible und mächtige Anfragefunktionalität zur Verfügung zu stellen. Pathway queries erlauben die Beschreibung von Netzwerkmustern, die funktionale Annotationen sowie Expressionsdaten enthalten. Der pathway search Algorithmus findet alle Instanzen des Musters in einem gegebenen Netzwerk. Dazu muss ein Spezialfall des bekannten Subgraph-Isomorphie-Problems gelöst werden. Obwohl der Algorithmus im schlechtesten Fall exponentielle Laufzeit in der Größe des Musters hat, läuft er durch einige Implementationstricks schnell genug für praktische Anwendungen. Oft hat eine pathway query viele Instanzen, so dass es wichtig ist, die statistische Signifikanz der einzelnen Instanzen in Hinblick auf Expressionsdaten oder andere Kriterien zu bestimmen. In Kapitel 5 werden die Anfragesprache pathway query language sowie der pathway search Algorithmus im Detail vorgestellt und einige theoretische Eigenschaften gezeigt. Außerdem werden einige implementierte Scoring-Methoden beschrieben. Die Möglichkeit, verschiedene Teile der Anfrage mit verschiedenen Scoring-Methoden zu bewerten und zu einem Gesamtscore zusammenzufassen, erlaubt äußerst flexible Bewertungen der Instanzen. In Kapitel 6 werden einige Anwendungen der vorgestellten Methoden beschrieben, die auf öffentlichen Datensätzen sowie Datensätzen aus Forschungsprojekten beruhen. Mit Hilfe der gut untersuchten öffentlichen Datensätze wird gezeigt, dass die Methoden biologisch sinnvolle Ergebnisse liefern. Die anderen Analysen zeigen, wie neue Hypothesen in komplexeren biologischen Systemen generiert werden können, die jedoch nur mit Hilfe von weiteren biologischen Experimenten validiert werden könnten. Schließlich wird ein Ausblick gegeben, was die vorgestellten Methoden zur laufenden Forschung im Bereich der Expressionsdatenanalyse beitragen können, wie sie auf andere Daten angewendet werden können und welche Erweiterungen denkbar und wünschenswert sind

    From pathway to regulon in Arabidopsis

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    Combined bioinformatic approaches, using genomic and transcriptomic data, are applied to investigate the fatty acid biosynthesis pathway, at the molecular level, and in the context of the system biology of Arabidopsis. Fatty acids are essential components of all known bacterial and eukaryotic cells with critical role in cells as energy reserves and the metabolic precursors for biological membranes. The pathway for fatty acid synthesis seems to be conserved across all living systems. Acetyl-CoA carboxylase, a member of a superfamily of biotin-dependent enzymes, catalyzes the first committed step of the fatty acid biosynthesis pathway. Phylogenetic study exposed complex and intertwined evolutionary histories of this family, with multiple domain fusions and rearrangements. As revealed by meta-analysis of a wide array of Arabidopsis transcriptomic data, fatty acid biosynthesis is transcriptionally regulated, and this regulation not only extends across all pathway reactions, but also some substrate- and cofactor-producing reactions, thus defining a major transcriptionally co-regulated pathway. Meta-analysis of the transcriptome is extended to find groups of coexpressed genes (also called modules, or regulons) in the Arabidopsis genome. Major functionally-coherent gene groups were identified. These comprise development, information processing, defense, and metabolism, as well as tissue- and organelle-specific processes
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