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Dynamic interactions and their changes are at the forefront of current research in bioinformatics and systems biology. This thesis focusses on two particular dynamic aspects of cellular adaptation: miRNA and metabolites.
miRNAs have an established role in hematopoiesis and megakaryocytopoiesis, and platelet miRNAs have potential as tools for understanding basic mechanisms of platelet function. The thesis highlights the possible role of miRNAs in regulating protein translation in platelet lifespan with relevance to platelet apoptosis and identifying involved pathways and potential key regulatory molecules. Furthermore, corresponding miRNA/target mRNAs in murine platelets are identified. Moreover, key miRNAs involved in aortic aneurysm are predicted by similar techniques. The clinical relevance of miRNAs as biomarkers, targets, resulting later translational therapeutics, and tissue specific restrictors of genes expression in cardiovascular diseases is also discussed.
In a second part of thesis we highlight the importance of scientific software solution development in metabolic modelling and how it can be helpful in bioinformatics tool development along with software feature analysis such as performed on metabolic flux analysis applications. We proposed the “Butterfly” approach to implement efficiently scientific software programming. Using this approach, software applications were developed for quantitative Metabolic Flux Analysis and efficient Mass Isotopomer Distribution Analysis (MIDA) in metabolic modelling as well as for data management. “LS-MIDA” allows easy and efficient MIDA analysis and, with a more powerful algorithm and database, the software “Isotopo” allows efficient analysis of metabolic flows, for instance in pathogenic bacteria (Salmonella, Listeria). All three approaches have been published (see Appendices).
Localization microscopy is a class of super-resolution fluorescence microscopy techniques. Localization microscopy methods are characterized by stochastic temporal isolation of fluorophore emission, i.e., making the fluorophores blink so rapidly that no two are
likely to be photoactive at the same time close to each other. Well-known localization microscopy methods include dSTORM}, STORM, PALM, FPALM, or GSDIM. The biological community has taken great interest in localization microscopy, since it can enhance the resolution of common fluorescence microscopy by an order of magnitude at little experimental cost.
However, localization microscopy has considerable computational cost since millions of individual stochastic emissions must be located with nanometer precision. The computational cost of this evaluation, and the organizational cost of implementing the complex algorithms, has impeded adoption of super-resolution microscopy for a long time.
In this work, I describe my algorithmic framework for evaluating localization microscopy data.
I demonstrate how my novel open-source software achieves real-time data evaluation, i.e., can evaluate data faster than the common experimental setups can capture them.
I show how this speed is attained on standard consumer-grade CPUs, removing the need for computing on expensive clusters or deploying graphics processing units.
The evaluation is performed with the widely accepted Gaussian PSF model and a Poissonian maximum-likelihood noise model.
I extend the computational model to show how robust, optimal two-color evaluation is realized, allowing correlative microscopy between multiple proteins or structures. By employing cubic B-splines, I show how the evaluation of three-dimensional samples can be made simple and robust, taking an important step towards precise imaging of micrometer-thick samples.
I uncover the behavior and limits of localization algorithms in the face of increasing emission densities.
Finally, I show up algorithms to extend localization microscopy to common biological problems.
I investigate cellular movement and motility by considering the in vitro movement of myosin-actin filaments. I show how SNAP-tag fusion proteins enable imaging with bright and stable organic fluorophores in live cells. By analyzing the internal structure of protein clusters, I show how localization microscopy can provide new quantitative approaches beyond pure imaging.
Applying microarray‐based techniques to study gene expression patterns: a bio‐computational approach
(2010)
The regulation and maintenance of iron homeostasis is critical to human health. As a constituent of hemoglobin, iron is essential for oxygen transport and significant iron deficiency leads to anemia. Eukaryotic cells require iron for survival and proliferation. Iron is part of hemoproteins, iron-sulfur (Fe-S) proteins, and other proteins with functional groups that require iron as a cofactor. At the cellular level, iron uptake, utilization, storage, and export are regulated at different molecular levels (transcriptional, mRNA stability, translational, and posttranslational). Iron regulatory proteins (IRPs) 1 and 2 post-transcriptionally control mammalian iron homeostasis by binding to iron-responsive elements (IREs), conserved RNA stem-loop structures located in the 5’- or 3‘- untranslated regions of genes involved in iron metabolism (e.g. FTH1, FTL, and TFRC). To identify novel IRE-containing mRNAs, we integrated biochemical, biocomputational, and microarray-based experimental approaches. Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Methods In this project response to the iron treatment was examined under different conditions using bioinformatical methods. This would improve our understanding of an iron regulatory network. For these purposes we used microarray gene expression data. To identify novel IRE-containing mRNAs biochemical, biocomputational, and microarray-based experimental approaches were integrated. IRP/IRE messenger ribonucleoproteins were immunoselected and their mRNA composition was analysed using an IronChip microarray enriched for genes predicted computationally to contain IRE-like motifs. Analysis of IronChip microarray data requires specialized tool which can use all advantages of a customized microarray platform. Novel decision-tree based algorithm was implemented using Perl in IronChip Evaluation Package (ICEP). Results IRE-like motifs were identified from genomic nucleic acid databases by an algorithm combining primary nucleic acid sequence and RNA structural criteria. Depending on the choice of constraining criteria, such computational screens tend to generate a large number of false positives. To refine the search and reduce the number of false positive hits, additional constraints were introduced. The refined screen yielded 15 IRE-like motifs. A second approach made use of a reported list of 230 IRE-like sequences obtained from screening UTR databases. We selected 6 out of these 230 entries based on the ability of the lower IRE stem to form at least 6 out of 7 bp. Corresponding ESTs were spotted onto the human or mouse versions of the IronChip and the results were analysed using ICEP. Our data show that the immunoselection/microarray strategy is a feasible approach for screening bioinformatically predicted IRE genes and the detection of novel IRE-containing mRNAs. In addition, we identified a novel IRE-containing gene CDC14A (Sanchez M, et al. 2006). The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip, but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls (Vainshtein Y, et al., 2010).
In this century new experimental and computational techniques are adding an enormous amount of information, revealing many biological mysteries. The complexities of biological systems still broach new questions. Till now the main approach to understand a system has been to divide it in components that can be studied. The upcoming new paradigm is to combine the pieces of information in order to understand it at a global level. In the present thesis we have tried to study infectious diseases with such a global ‘Systems Biology’ approach. In the first part the apoptosis pathway is analyzed. Apoptosis (Programmed cell death) is used as a counter measure in different infections, for example viral infections. The interactions between death domain containing proteins are studied to address the following questions: i) How specificity is maintained - showing that it is induced through adaptors, ii) how proliferation/ survival signals are induced during activation of apoptosis – suggesting the pivotal role of RIP. The model also allowed us to detect new possible interacting surfaces. The pathway is then studied at a global level in a time step simulation to understand the evolution of the topology of activators and inhibitors of the pathway. Signal processing is further modeled in detail for the apoptosis pathway in M. musculus to predict the concentration time course of effector caspases. Further, experimental measurements of caspase-3 and viability of cells validate the model. The second part focuses on the phagosome, an organelle which plays an essential role in removal of pathogens as exemplified by M. tuberculosis. Again the problem is addressed in two main sections: i) To understanding the processes that are inhibited by M. tuberculosis; we focused on the phospholipid network applying a time step simulation in section one, which plays an important role in inhibition or activation of actin polymerization on the phagosome membrane. ii) Furthermore, actin polymers are suggested to play a role in the fusion of the phagosome with lysosome. To check this hypothesis an in silico model was developed; we find that the search time is reduced by 5 fold in the presence of actin polymers. Further the effect of length of actin polymers, dimensions of lysosome, phagosome and other model parameter is analyzed. After studying a pathway and then an organelle, the next step was to move to the system. This was exemplified by the host pathogen interactions between Bordetella pertussis and Bordetella bronchiseptica. The limited availability of quantitative information was the crucial factor behind the choice of the model type. A Boolean model was developed which was used for a dynamic simulation. The results predict important factors playing a role in Bordetella pathology especially the importance of Th1 related responses and not Th2 related responses in the clearance of the pathogen. Some of the quantitative predictions have been counterchecked by experimental results such as the time course of infection in different mutants and wild type mice. All these computational models have been developed in presence of limited kinetic data. The success of these models has been validated by comparison with experimental observations. Comparative models studied in chapters 6 and 9 can be used to explore new host pathogen interactions. For example in chapter 6, the analysis of inhibitors and inhibitory paths in three organism leads to the identification of regulatory hotspots in complex organisms and in chapter 9 the identification of three phases in B. bronchiseptica and inhibition of IFN-γ by TTSS lead us to explore similar phases and inhibition of IFN-γ in B. pertussis. Further an important significance of these models is to identify new components playing an essential role in host-pathogen interactions. In silico deletions can point out such components which can be further analyzed by experimental mutations.
The internal transcribed spacer 2 (ITS2) of the ribosomal gene repeat is an increasingly important phylogenetic marker whose RNA secondary structure is widely conserved across eukaryotic organisms. The ITS2 database aims to be a comprehensive resource on ITS2 sequence and secondary structure, based on direct thermodynamic as well as homology modelled RNA folds. Results: (a) A rebuild of the original ITS2 database generation scripts applied to a current NCBI dataset reveal more than 60,000 ITS2 structures. This more than doubles the contents of the original database and triples it when including partial structures. (b) The end-user interface was rewritten, extended and now features user-defined homology modelling. (c) Other possible RNA structure discovery methods (namely suboptimal and shape folding) prove helpful but are not able to replace homology modelling. (d) A use case of the ITS2 database in conjunction with other tools developed at the department gave insight into molecular phylogenetic analysis with ITS2.
Im gleichen Maße wie informatisches Wissen mehr und mehr in den wissenschaftlichen Alltag aller Lebenswissenschaften Einzug gehalten hat, hat sich der Schwerpunkt bioinformatischer Forschung in stärker mathematisch und informatisch-orientierte Themengebiete verschoben. Bioinformatik heute ist mehr als die computergestützte Verarbeitung großer Mengen an biologischen Daten, sondern hat einen entscheidenden Fokus auf der Modellierung komplexer biologischer Systeme. Zur Anwendung kommen hierbei insbesondere Theorien aus dem Bereich der Stochastik und Statistik, des maschinellen Lernens und der theoretischen Informatik. In der vorliegenden Dissertation beschreibe ich in Fallstudien die systematische Modellierung biologischer Systeme aus einem informatisch - mathematischen Standpunkt unter Anwendung von Verfahren aus den genannten Teilbereichen und auf unterschiedlichen Ebenen biologischer Abstraktion. Ausgehend von der Sequenzinformation über Transkriptom, Metabolom und deren regulatorischer Interaktion hin zur Modellierung von Populationseffekten werden hierbei aktuelle biologische Fragestellungen mit mathematisch - informatischen Modellen und einer Vielzahl experimenteller Daten kombiniert. Ein besonderer Augenmerk liegt dabei auf dem Vorgang der Modellierung und des Modellbegriffs als solchem im Rahmen moderner bioinformatischer Forschung. Im Detail umfassen die Projekte (mehrere Publikationen) die Entwicklung eines neuen Ansatzes zur Einbettung und Visualisierung von Multiplen Sequenz- und Sequenz-Strukturalignments, illustriert am Beispiel eines Hemagglutininalignments unterschiedlicher H5N1 Varianten, sowie die Modellierung des Transkriptoms von A. thaliana, bei welchem mit Hilfe einer kernelisierten nicht-parametrischen Metaanalyse neue, an der Infektionsabwehr beteiligten, Gene ausfindig gemacht werden konnten. Desweiteren ist uns mit Hilfe unserer Software YANAsquare eine detaillierte Untersuchung des Metabolismus von L. monocytogenes unter Aktivierung des Transkriptionsfaktors prfA gelungen, dessen Vorhersagen durch experimentelle 13C Isotopologstudien belegt werden konnten. In einem Anschlußprojekt war der Zusammenhang zwischen Regulation des Metabolismus durch Regulation der Genexpression und der Fluxverteilung des metabolischen Steady- State-Netzwerks das Ziel. Die Modellierung eines komplexen organismischen Phänotyps, der Zellgrößenentwicklung der Diatomee Pseudo-nitzschia delicatissima, schließt die Untersuchungen ab.
Background
Phytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis.
Results
A novel open source system for the automated recognition of phytoplankton by the use of microscopy and image analysis was developed. It integrates the segmentation of the organisms from the background, the calculation of a large range of features, and a neural network for the classification of imaged organisms into different groups of plankton taxa. The analysis of samples containing 10 different taxa showed an average recognition rate of 94.7% and an average error rate of 5.5%. The presented system has a flexible framework which easily allows expanding it to include additional taxa in the future.
Conclusions
The implemented automated microscopy and the new open source image analysis system - PlanktoVision - showed classification results that were comparable or better than existing systems and the exclusion of non-plankton particles could be greatly improved. The software package is published as free software and is available to anyone to help make the analysis of water quality more reproducible and cost effective.
Zentrales Ziel dieser Arbeit war es, Methoden der Mikroskopie, Bildverarbeitung und Bilderkennung für die Charakterisierungen verschiedener Phyotplankter zu nutzen, um deren Analyse zu verbessern und zu vereinfachen.
Der erste Schwerpunkt der Arbeit lag auf der Analyse von Phytoplanktongemeinschaften, die im Rahmen der Überprüfung der Süßwasserqualität als Marker dienen. Die konventionelle Analyse ist dabei sehr aufwendig, da diese noch immer vollständig von Hand durchgeführt wird und hierfür speziell ausgebildetes Personal eingesetzt werden muss. Ziel war es, ein System zur automatischen Erkennung aufzubauen, um die Analyse vereinfachen zu können. Mit Hilfe von automatischer Mikroskopie war es möglich Plankter unterschiedlicher Ausdehnung durch die Integration mehrerer Schärfeebenen besser in einem Bild aufzunehmen. Weiterhin wurden verschiedene Fluoreszenzeigenschaften in die Analyse integriert. Mit einem für ImageJ erstellten Plugin können Organismen vom Hintergrund der Aufnahmen abgetrennt und eine Vielzahl von Merkmalen berechnet werden. Über das Training von neuralen Netzen wird die Unterscheidung von verschieden Gruppen von Planktontaxa möglich. Zudem können weitere Taxa einfach in die Analyse integriert und die Erkennung erweitert werden. Die erste Analyse von Mischproben, bestehend aus 10 verschiedenen Taxa, zeigte dabei eine durchschnittliche Erkennungsrate von 94.7% und eine durchschnittliche Falsch-Positiv Rate von 5.5%. Im Vergleich mit bestehenden Systemen konnte die Erkennungsrate verbessert und die Falsch Positiv Rate deutlich gesenkt werde. Bei einer Erweiterung des Datensatzes auf 22 Taxa wurde darauf geachtet, Arten zu verwenden, die verschiedene Stadien in ihrem Wachstum durchlaufen oder höhere Ähnlichkeiten zu den bereits vorhandenen Arten aufweisen, um evtl. Schwachstellen des Systemes erkennen zu können. Hier ergab sich eine gute Erkennungsrate (86.8%), bei der der Ausschluss von nicht-planktonischen Partikeln (11.9%) weiterhin verbessert war. Der Vergleich mit weiteren Klassifikationsverfahren zeigte, dass neuronale Netze anderen Verfahren bei dieser Problemstellung überlegen sind. Ähnlich gute Klassifikationsraten konnten durch Support Vektor Maschinen erzielt werden. Allerdings waren diese bei der Unterscheidung von unbekannten Partikeln dem neuralen Netz deutlich unterlegen.
Der zweite Abschnitt stellt die Entwicklung einer einfachen Methode zur Viabilitätsanalyse von Cyanobakterien, bei der keine weitere Behandlung der Proben notwendig ist, dar. Dabei wird die rote Chlorophyll - Autofluoreszenz als Marker für lebende Zellen und eine grüne unspezifische Fluoreszenz als Marker für tote Zellen genutzt. Der Assay wurde mit dem Modellorganismus Synechocystis sp. PCC 6803 etabliert und validiert. Die Auswahl eines geeigeneten Filtersets ermöglicht es beide Signale gleichzeitig anzuregen und zu beobachten und somit direkt zwischen lebendenden und toten Zellen zu unterscheiden. Die Ergebnisse zur Etablierung des Assays konnten durch Ausplattieren, Chlorophyllbestimmung und Bestimmung des Absorbtionsspektrums bestätigt werden. Durch den Einsatz von automatisierter Mikroskopie und einem neu erstellten ImageJ Plugin wurde eine sehr genaue und schnelle Analyse der Proben möglich. Der Einsatz beim Monitoring einer mutagenisierten Kultur zur Erhöhung der Temperaturtoleranz ermöglichte genaue und zeitnahe Einblicke in den Zustand der Kultur. Weitere Ergebnisse weisen darauf hin, dass die Kombination mit Absorptionsspektren es ermöglichen können bessere Einblicke in die Vitalität der Kultur zu erhalten.
Die Sequenzierungstechnologien entwickeln sich stetig weiter, dies ermöglicht eine zuvor nicht erreichte Ausbeute an experimentellen Daten und auch an Neuentwicklungen von zuvor nicht realisierbaren Experimenten. Zugleich werden spezifische Datenbanken, Algorithmen und Softwareprogramme entwickelt, um die neu entstandenen Daten zu analysieren. Während der Untersuchung bioinformatischer Methoden für die Identifizierung und Klassifizierung somatischer Mutationen in hämatologischen Erkrankungen, zeigte sich eine hohe Vielfalt an alternativen Softwaretools die für die jeweiligen Analyseschritte genutzt werden können. Derzeit existiert noch kein Standard zur effizienten Analyse von Mutationen aus Next-Generation-Sequencing (NGS)-Daten. Die unterschiedlichen Methoden und Pipelines generieren Kandidaten, die zum größten Anteil in allen Ansätzen identifiziert werden können, jedoch werden Software spezifische Kandidaten nicht einheitlich detektiert.
Um eine einheitliche und effiziente Analyse von NGS-Daten durchzuführen war im Rahmen dieser Arbeit die Entwicklung einer benutzerfreundlichen und einheitlichen Pipeline vorgesehen. Hierfür wurden zunächst die essentiellen Analysen wie die Identifizierung der Basen, die Alignierung und die Identifizierung der Mutationen untersucht. Des Weiteren wurden unter Berücksichtigung von Effizienz und Performance diverse verfügbare Softwaretools getestet, ausgewertet und sowohl mögliche Verbesserungen als auch Erleichterungen der bisherigen Analysen vorgestellt und diskutiert. Durch Mitwirken in Konsortien wie der klinischen Forschergruppe 216 (KFO 216) und International Cancer Genome Consortium (ICGC) oder auch bei Haus-internen Projekten wurden Datensätze zu den Entitäten Multiples Myelom (MM), Burkitt Lymphom (BL) und Follikuläres Lymphom (FL) erstellt und analysiert. Die Selektion geeigneter Softwaretools und die Generierung der Pipeline basieren auf komparativen Analysen dieser Daten, sowie auf geteilte Ergebnisse und Erfahrungen in der Literatur und auch in Foren. Durch die gezielte Entwicklung von Skripten konnten biologische und klinische Fragestellungen bearbeitet werden. Hierzu zählten eine einheitliche Annotation der Gennamen, sowie die Erstellung von Genmutations-Heatmaps mit nicht Variant-Calling-File (VCF)-Syntax konformen Dateien. Des Weiteren konnten nicht abgedeckte Regionen des Genoms in den NGS-Daten identifiziert und analysiert werden. Neue Projekte zur detaillierten Untersuchung der Verteilung von wiederkehrender Mutationen und Funktionsassays zu einzelnen Mutationskandidaten konnten basierend auf den Ergebnissen initiiert werden.
Durch eigens erstellte Python-Skripte konnte somit die Funktionalität der Pipeline erweitert werden und zu wichtigen Erkenntnissen bei der biologischen Interpretation der Sequenzierungsdaten führen, wie beispielsweise zu der Detektion von drei neuen molekularen Subgruppen im MM. Die Erweiterungen, der in dieser Arbeit entwickelten Pipeline verbesserte somit die Effizienz der Analyse und die Vergleichbarkeit unserer Daten. Des Weiteren konnte durch die Erstellung eines eigenen Skripts die Analyse von unbeachteten Regionen in den NGS-Daten erfolgen.
Insights into the evolution of protein domains give rise to improvements of function prediction
(2005)
The growing number of uncharacterised sequences in public databases has turned the prediction of protein function into a challenging research field. Traditional annotation methods are often error-prone due to the small subset of proteins with experimentally verified function. Goal of this thesis was to analyse the function and evolution of protein domains in order to understand molecular processes in the cell. The focus was on signalling domains of little understood function, as well as on functional sites of protein domains in general. Glucosaminidases (GlcNAcases) represent key enzymes in signal transduction pathways. Together with glucosamine transferases, they serve as molecular switches, similar to kinases and phosphatases. Little was known about the molecular function and structure of the GlcNAcases. In this thesis, the GlcNAcases were identified as remote homologues of N-acetyltransferases. By comparing the homologous sequences, I was able to predict functional sites of the GlcNAcase family and to identify the GlcNAcases as the first family member of the acetyltransferase superfamily with a distinct catalytic mechanism, which is not involved in the transfer of acetyl groups. In a similar approach, the sensor domain of a plant hormone receptor was studied. I was able to predict putative ligand-binding sites by comparing evolutionary constraints in functionally diverged subfamilies. Most of the putative ligand-binding sites have been experimentally confirmed in the meantime. Due to the importance of enzymes involved in cellular signalling, it seems impossible to find substitutions of catalytic amino acids that turn them catalytically inactive. Nevertheless, by scanning catalytic positions of the protein tyrosine phosphatase families, I found many inactive domains among single domain and tandem domain phosphatases in metazoan proteomes. In addition, I found that inactive phosphatases are conserved throughout evolution, which led to the question about the function of these catalytically inactive phosphatase domains. An analysis of evolutionary site rates of amino acid substitutions revealed a cluster of conserved residues in the apparently redundant domain of tandem phosphatases. This putative regulatory center might be responsible for the experimentally verified dimerization of the active and inactive domain in order to control the catalytic activity of the active phosphatase domain. Moreover, I detected a subgroup of inactive phosphatases, which presumably functions in substrate recognition, based on different evolutionary site rates within the phosphatase family. The characterization of these new regulatory modules in the phosphatase family raised the question whether inactivation of enzymes is a more general evolutionary mechanism to enlarge signalling pathways and whether inactive domains are also found in other enzyme families. A large-scale analysis of substitutions at catalytic positions of enzymatic domains was performed in this work. I identified many domains with inactivating substitutions in various enzyme families. Signalling domains harbour a particular high occurrence of catalytically inactive domains indicating that these domains have evolved to modulate existing regulatory pathways. Furthermore, it was shown that inactivation of enzymes by single substitutions happened multiple times independently in evolution. The surprising variability of amino acids at catalytic positions was decisive for a subsequent analysis of the diversity of functional sites in general. Using functional residues extracted from structural complexes I could show that functional sites of protein domains do not only vary in their type of amino acid but also in their structural location within the domain. In the process of evolution, protein domains have arisen from duplication events and subsequently adapted to new binding partners and developed new functions, which is reflected in the high variability of functional sites. However, great differences exist between domain families. The analysis demonstrated that functional sites of nuclear domains are more conserved than functional sites of extracellular domains. Furthermore, the type of ligand influences the degree of conservation, for example ion binding sites are more conserved than peptide binding sites. The work presented in this thesis has led to the detection of functional sites in various protein domains involved in signalling pathways and it has resulted in insights into the molecular function of those domains. In addition, properties of functional sites of protein domains were revealed. This knowledge can be used in the future to improve the prediction of protein function and to identify functional sites of proteins.