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Neuropsychiatric disorders are complex, highly heritable but incompletely understood disorders. The clinical and genetic heterogeneity of these disorders poses a significant challenge to the identification of disorder related biomarkers. Besides significant progress in unveiling the genetic basis of these disorders, the underlying causes and biological mechanisms remain obscure. With the advancement in the array, sequencing, and big data technologies, a huge amount of data is generated from individuals across different platforms and in various data structures. But there is a paucity of bioinformatics tools that can integrate this plethora of data. Therefore, there is a need to develop an integrative bioinformatics data analysis tool that combines biological and clinical data from different data types to better understand the underlying genetics.
This thesis presents a bioinformatics pipeline implementing data from different platforms to provide a thorough understanding of the genetic etiology of a neuropsychiatric quantitative as well as a qualitative trait of interest. Throughout the thesis, we present two aspects: one is the development and architecture of the bioinformatics pipeline named MApping the Genetics of neuropsychiatric traits to the molecular NETworks of the human brain (MAGNET). The other part demonstrates the implementation and usefulness of MAGNET analysing large Autism Spectrum Disorder (ASD) cohorts.
MAGNET is a freely available command-line tool available on GitHub (https://github.com/SheenYo/MAGNET). It is implemented within one framework using data integration approaches based on state-of-the-art algorithms and software to ultimately identify the genes and pathways genetically associated with a trait of interest. MAGNET provides an edge over the existing tools since it performs a comprehensive analysis taking care of the data handling and parsing steps necessary to communicate between the different APIs (Application Program Interface). Thus, this avoids the in-between data handling steps required by researchers to provide output from one analysis to the next. Moreover, depending on the size of the dataset users can deduce important information regarding their trait of interest within a time frame of a few days. Besides gaining insights into genetic associations, one of the central features is the mapping of the associated genes onto developing human brain implementing transcriptome data of 16 different brain regions starting from the 5th post-conceptional week to over 40 years of age.
In the second part as proof of concept, we implemented MAGNET on two ASD cohorts. ASD is a group of psychiatric disorders. Clinically, ASD is characterized by the following psychopathology: A) limitations in social interaction and communication, and B) restricted, repetitive behavior. The etiology of this disorder is extremely complex due to its heterogeneous clinical traits and genetics. Therefore, to date, no reliable biomarkers are identified. Here, the aim is to characterize the genetic architecture of ASD taking into account the two aforementioned ASD diagnostic domains. As well as to investigate if these domains are genetically linked or independent of each other. Moreover, we addressed the question if these traits share genetic risk with the categorical diagnosis of ASD and how much of the phenotypic variance of these traits can be explained by the underlying genetics.
We included affected individuals from two ASD cohorts, i.e. the Autism Genome Project (AGP) and a German cohort consisting of 2,735 and 705 families respectively. MAGNET was applied to each of the ASD subdomains as a quantitative dependent variable. MAGNET is divided into five main sections i.e. (1) quality check of the genotype data, (2) imputation of missing genotype data, (3) association analysis of genotype and trait data, (4) gene-based analysis, and (5) enrichment analysis using gene expression data from the human brain.
MAGNET was applied to each of the individual traits in each cohort to perform quality control of the genetic data and imputed the missing data in an automated fashion. MAGNET identified 292 known and new ASD risk genes. These genes were subsequently assigned to biological signaling pathways and gene ontologies via MAGNET. The underlying biological mechanisms converged with respect to neuronal transmission and development processes. By reconciling these genes with the transcriptome of the developing human brain, MAGNET was able to identify that the significant genes associated with the subdomains are expressed at specific time points in brain areas such as the hippocampus, amygdala, and cortical regions. Further, we found that ASD subdomains related to domain A but not
to domain B have a shared genetic etiology.
Das adaptive Immunsystem schützt den Menschen vor extra- wie auch intrakorporal auftretenden Pathogenen und Krebszellen. Die Funktionalität dieses Prozesses geht hierbei auf die Interaktion und Kooperation einer Vielzahl verschiedener Zelltypen des Körpers zurück und ist vorwiegend innerhalb der Lymphknoten lokalisiert. Ist auch nur ein Bestandteil dieses sensiblen Prozesses gestört, kann dies zu einem teilweisen oder vollständigen Verlust der immunologischen Fitness des Menschen führen. Daher war es das Ziel dieser Arbeit, solche Aberrationen des humanen Lymphknotengewebes umfassend digital-pathologisch zu detektieren und zu definieren.
Hierfür wurde zunächst eine digitale Gewebedatenbank etabliert. Diese basiert auf dem im Rahmen dieser Arbeit implementierten Content-Management-System Digital Tissue Management Suite. Weiterhin wurde die Software Feature analysis in tissue histomorphometry entwickelt, welche die Analyse von zweidimensionalen whole slide images ermöglicht. Hierbei werden Methoden aus dem Bereich Computer Vision und Graphentheorie eingesetzt, um morphologische und distributionale Eigenschaften der Zelltypen des Lymphknotens zu charakterisieren. Darüber hinaus enthält diese Software Plug-ins zur Visualisierung und statistischen Analyse der Daten.
Aufbauend auf der eigens implementierten, digitalen Infrastruktur, in Kombination mit der Software Imaris wurden zweidimensional und dreidimensional gescannte, reaktive und neoplastische Gewebeproben digital phänotypisiert. Hierbei konnten neue mechanische Barrieren zur Kompartimentalisierung der Keimzentren aufgeklärt werden. Weiterhin konnte der Erhalt des quantitativen Verhältnisses einzelner Zellpopulationen innerhalb der Keimzentren beschrieben werden. Ausgehend von den reaktiven Phänotypen des Lymphknotens, wurden pathophysiologische Aberrationen in verschiedenen lymphatischen Neoplasien untersucht. Hierbei konnte gezeigt werden, dass speziell die strukturelle Destruktion häufig mit einer morphologischen Veränderung der fibroblastischen Retikulumzellen einhergeht.
Neben strukturellen Veränderungen sind auch zytologische Veränderungen der Tumormikroumgebung zu verzeichnen. Eine besondere Rolle spielen hierbei sogenannte Tumor-assoziierte Makrophagen. Im Rahmen dieser Arbeit konnte gezeigt werden, dass speziell Makrophagen in der Tumormikroumgebung des diffus großzelligen B-Zell-Lymphoms und der chronisch lymphatischen Leukämie spezifische pathophysiologische Veränderungen aufzeigen. Auch konnte gezeigt werden, dass genetische Änderungen neoplastischer B-Zellen mit einer generellen Reduktion der CD20-Antigendichte einhergehen.
Zusammenfassend ermöglichten die Ergebnisse die Generierung eines umfassenden digital-pathologischen Profils des klassischen Hodgkin-Lymphoms. Hierbei konnten morphologische Veränderungen neoplastischer, CD30-positiver Hodgkin-Reed-Sternberg-Zellen validiert und beschrieben werden. Auch konnten pathologische Veränderungen des Konnektoms und der Tumormikroumgebung dieser Zellen parametrisiert und quantifiziert werden. Abschließend wurde unter Anwendung eines Random forest-Klassifikators die diagnostische Potenz digital-pathologischer Profile evaluiert und validiert.
Proteins are biological macromolecules playing essential roles in all living organisms.
Proteins often bind with each other forming complexes to fulfill their function. Such protein complexes assemble along an ordered pathway. An assembled protein complex can often be divided into structural and functional modules. Knowing the order of assembly and the modules of a protein complex is important to understand biological processes and treat diseases related to misassembly.
Typical structures of the Protein Data Bank (PDB) contain two to three subunits and a few thousand atoms. Recent developments have led to large protein complexes being resolved. The increasing number and size of the protein complexes demand for computational assistance for the visualization and analysis. One such large protein complex is respiratory complex I accounting for 45 subunits in Homo sapiens.
Complex I is a well understood protein complex that served as case study to validate our methods.
Our aim was to analyze time-resolved Molecular Dynamics (MD) simulation data, identify modules of a protein complex and generate hypotheses for the assembly pathway of a protein complex. For that purpose, we abstracted the topology of protein complexes to Complex Graphs of the Protein Topology Graph Library (PTGL). The subunits are represented as vertices, and spatial contacts as edges. The edges are weighted with the number of contacts based on a distance threshold. This allowed us to apply graph-theoretic methods to visualize and analyze protein complexes.
We extended the implementations of two methods to achieve a computation of Complex Graphs in feasible runtimes. The first method skipped checks for contacts using the information which residues are sequential neighbors. We extended the method to protein complexes and structures containing ligands. The second method introduced spheres encompassing all atoms of a subunit and skipped the check for contacts if the corresponding spheres do not overlap. Both methods combined allowed skipping up to 93 % of the checks for contacts for sample complexes of 40 subunits compared to up to 10 % of the previous implementation. We showed that the runtime of the combined method scaled linearly with the number of atoms compared to a non-linear scaling of the previous implementation We implemented a third method fixing the assignment of an orientation to secondary structure elements. We placed a three-dimensional vector in each secondary structure element and computed the angle between secondary structure elements to assign an orientation. This method sped up the runtime especially for large structures, such as the capsid of human immunodeficiency virus, for which the runtime decreased from 43 to less than 9 hours.
The feasible runtimes allowed us to investigate two data sets of MD trajectories of respiratory complex I of Thermus thermophilus that we received. The data sets differ only by whether ubiquinone is bound to the complex. We implemented a pipeline, PTGLdynamics, to compute the contacts and Complex Graphs for all time steps of the trajectories. We investigated different methods to track changes of contacts during the simulation and created a heat map put onto the three-dimensional structure visualizing the changes. We also created line plots to visualize the changes of contacts over the course of the simulation. Both visualizations helped spotting outstandingly flexible or rigid regions of the structure or time points of the simulation in which major dynamics occur.
We introduced normalizations of the edge weights of Complex Graphs for identi-fying modules and predicting the assembly pathway. The idea is to normalize the number of contacts for the number of residues of a subunit. We defined five different normalizations.
To identify structural and functional modules, we applied the Leiden graph clustering algorithm to the Complex Graphs of respiratory complex I and the respiratory supercomplex. We examined the results for the different normalizations of the weights of the Complex Graphs. The absolute edge weight produced the best result identifying three of four modules that have been defined in the literature for respiratory complex I.
We applied agglomerative hierarchical clustering to the edges of a Complex Graph to create hypotheses of the assembly pathway. The rationale was that subunits with an extensive interface in the final structure assemble early. We tested our method against two existing methods on a data set of 21 proteins with reported assembly pathways. Our prediction outperformed the other methods and ran in feasible runtimes of a few minutes at most.
We also tested our method on respiratory complex I, the respiratory supercomplex and the respiratory megacomplex. We compared the results for the different normalizations with an assembly pathway of respiratory complex I described in the literature. We transformed the assembly pathways to dendrograms and compared the predictions to the reference using the Robinson-Foulds distance and clustering information distance. We analyzed the landscape of the clustering information distance by generating random dendrograms and showed that our result is far better than expected at random. We showed in a detailed analysis that the assembly prediction using one normalization was able to capture key features of the assembly pathway that has been proposed in the literature.
In conclusion, we presented different applications of graph theory to automatically analyze the topology of protein complexes. Our programs run in feasible runtimes even for large complexes. We showed that graph-theoretic modeling of the protein structure can be used to analyze MD simulation data, identify modules of protein complexes and predict assembly pathways.
Local protein synthesis has re-defined our ideas on the basic cellular mechanisms that underlie synaptic plasticity and memory formation. The population of messenger RNAs that are localised to dendrites, however, remains sparsely identified. Furthermore, neuronal morphological complexity and spatial compartmentalisation require efficient mechanisms for messenger RNA localisation and control over translational efficiency or transcript stability. 3’ untranslated regions, downstream from stop codons, are recognised for providing binding platforms for many regulatory units, thus encoding the processing of the above processes. The hippocampus, a part of the brain involved in the formation, organisation and storage of memories, provides a natural platform to investigate patterns of RNA localisation. The hippocampus comprises tissue layers, which naturally separate the principle neuronal cell bodies from their processes (axons and dendrites). Identifying the full-complement of localised transcripts and associated 3’UTR isoforms is of great importance to understand both basic neuronal functions and principles of synaptic plasticity. These findings can be used to study the properties of neuronal networks as well as to understand how these networks malfunction in neuronal diseases.
Here, deep sequencing is used to identify the mRNAs resident in the synaptic neuropil in the hippocampus. Analysis of a neuropil data set yields a list of 8,379 transcripts of which 2,550 are localised in dendrites and/or axons. Using a fluorescent barcode strategy to label individual mRNAs shows that the relative abundance of different mRNAs in the neuropil varies over 5 orders of magnitude. High-resolution in situ hybridisation validated the presence of mRNAs in both cultured neurons and hippocampal slices. Among the many mRNAs identified, a large fraction of known synaptic proteins including signaling molecules, scaffolds and receptors is discovered. These results reveal a previously unappreciated enormous potential for the local protein synthesis machinery to supply, maintain and modify the dendritic and synaptic proteome.
Using advances in library preparation for next generation sequencing experiments, the diversity of 3’UTR isoforms present in localised transcripts from the rat hippocampus is examined. The obtained results indicate that there is an increase in 3’UTR heterogeneity and 3’UTR length in neuronal tissue. The evolutionary importance of the 3’UTR diversity and correlation with changes in species,tissue and cell complexity is investigated. The conducted analysis reveals the population of 3’UTR isoforms required for transcript localisation in overall neuronal transcriptome as well as the regulatory elements and binding sites specific for neuronal compartments. The configuration of poly(A) signals is correlated with gene function and can be further exploit to determine similar mechanisms for alternative polyadenylation.
Usage of custom specified methods for next-generation sequencing as well as novel approaches for RNA quantification and visualisation necessitate the development and implementation of new downstream analytic methods. Library methods for data-mining transcripts annotation, expression and ontology relations is provided. Usage of a specialised search engine targeting key features of previous experiments is proposed. A processing pipeline for NanoString technology, defining experimental quality and exploiting methods for data normalisation is developed. High-resolution in situ images are analysed by custom application, showing a correlation between RNA quantity and spatial distribution. The vast variety of bioinformatic methods included in this work indicates the importance of downstream analysis to reach biological conclusions. Maintaining the integrability and modularity of our implementations is of great priority, as the dynamic nature of many experimental techniques requires constant improvement in computational analysis.
Die digitale Pathologie ist ein neues, aber stetig wachsendes, Feld in der Medizin. Die kontinuierliche Entwicklung von verbesserten digitalen Scannern erlaubt heute das Abscannen von kompletten Gewebeschnitten und Whole Slide Images gewinnen an Bedeutung. Ziel dieser Arbeit ist die Methodenentwicklung zur Analyse von Whole Slide Images des klassischen Hodgkin Lymphoms. Das Hodgkin-Lymphom, oder Morbus Hodgkin, ist eine Tumorerkrankung des Lymphsystems, bei der die monoklonalen Tumorzellen in der Regel von B-Lymphozyten im Vorläuferstadium abstammen.
Etwas mehr als 9.000 Hodgkin-Lymphom-Fälle werden jährlich in den USA diagnostiziert. Zwar ist die 5-Jahre-Überlebensrate für Hodgkin-Lymphome mit 85,3 % vergleichsweise hoch, dennoch werden etwa 1.100 Todesfälle pro Jahr in den USA registriert. Auf mikroskopischer Ebene sind die Hodgkin-Reed-Sternberg Zellen (HRS-Zellen) typisch für das klassische Hodgkin Lymphom. HRS-Zellen haben einen oder mehrere Zellkerne, die stark vergrößert sind und eine grobe Chromatinstruktur aufweisen. Immunhistologisch gibt es für HRS-Zellen charakterisierende Marker, so sind HRS-Zellen positiv für den Aktivierungsmarker CD30.
Neben der konventionellen Mikroskopie, ermöglichen Scanner das Digitalisieren von ganzen Objektträgern (Whole Slide Image). Whole Slide Images werden bisher wenig in der Routinediagnostik eingesetzt. Ein großer Vorteil von digitalisierten Gewebeschnitten bietet sich bei der computergestützten Analyse. Automatisierte Bildanalyseverfahren wie Zellerkennung können Pathologen bei der Diagnose unterstützen, indem sie umfassende Statistiken zur Anzahl und Verteilung von immungefärbten Zellen bereitstellen.
Die untersuchten immunohistologischen Bilder wurden vom Dr. Senckenbergisches Institut für Pathologie des Universitätsklinikums Frankfurt bereit gestellt. Die betrachteten Gewebeschnitte sind gegen CD30 immungefärbt, einem Membranrezeptor, welcher in HRS-Zellen und aktivierten Lymphozyten exprimiert wird. Die Gewebeschnitte wurden mit einem Aperio ScanScope slide scanner digitalisiert und liegen mit einer hohen Auflösung von 0,25 μm pro Pixel vor. Bei den vorliegenden Gewebeschnittgrößen ergeben sich Bilder mit bis zu 90.000 x 90.000 Pixeln.
Der untersuchte Bilddatensatz umfasst 35 Bilder von Lymphknotengewebeschnitten der drei Krankheitsbilder: Gemischtzelliges klassisches Hodgkinlymphom, noduläres klassisches Hodgkinlymphom und Lymphadenitis. Die Bildverarbeitungspipeline wurden teils neu implementiert, teils von etablierten Bilderkennungssoftware und -bibliotheken wie CellProfiler und Java Advanced Imaging verwendet. CD30-positive Zellobjekte werden in den Gewebeschnitten automatisiert erkannt und neben der globalen Position im Whole Slide Image weitere Morphologiedeskriptoren berechnet, wie Fläche, Feret-Durchmesser, Exzentrität und Solidität. Die Zellerkennung zeigt mit 84 % eine hohe Präzision und mit 95 % eine sehr gute Sensitivität.
Es konnte gezeigt werden, dass in Lymphadenitisfällen im Schnitt deutlich weniger CD30- positive Zellen präsent sind als in klassisches Hodgkinlymphom. Während hier im Schnitt nur rund 3.000 Zellen gefunden wurden, lag der Durchschnitt für das Mischtyp klassisches Hodgkinlymphom bei rund 19.000 CD30 positiven Zellen. Während die CD30-positiven Zellen in Lymphadenitisfällen relativ gleichmäßig verteilt sind, bilden diese in klassischen Hodgkinlymphom-Fällen Zellcluster höherer Dichte.
Die berechneten Morphologiedeskriptoren bieten die Möglichkeit die Gewebeschnitte und den Krankheitsverlauf näher zu beschreiben. Zudem sind bisher Größe und Erscheinungsbild der HRS-Zellen hauptsächlich anhand manuell ausgewählter Zellen bestimmt worden. Ein Maß für die Ausdehnung der Zellen ist der maximale Feret-Durchmesser. Bei CD30-Zellen im klassischen Hodgkinlymphom liegt dieser im Durchschnitt bei 20 μm und ist somit deutlich größer als die durchschnittlich gemessenen 15 μm in Lymphadenitis.
Es wurde ein graphentheoretischer Ansatz gewählt, um die CD30 positiven Zellen und ihre räumliche Nachbarschaft zu modellieren. In CD30-Zellgraphen von klassischen Hodgkinlymphom-Gewebeschnitten ist der durchschnittliche Knotengrad gegenüber den von Lymphadenitis-Bildern stark erhöht. Der Vergleich mit Zufallsgraphen zeigt, dass die beobachteten Knotengradverteilungen nicht für eine zufällige Verteilung der Zellen im Gewebeschnitt sprechen. Eigenschaften und Verteilung von Communities in CD30-Zellgraphen können hinzugenommen werden, um klassisches Hodgkinlymphom Gewebeschnitte näher zu charakterisieren.
Diese Arbeit zeigt, dass die Auswertung von Whole Slide Image unterstützend zur Verbesserung der Diagnose möglich ist. Die mehr als 400.000 automatisch erkannten CD30-positiven Zellobjekte wurden morphologisch beschrieben, und zusammen mit ihrer Position im Gewebeschnitt ist die Betrachtung wichtiger Eigenschaften des klassischen Hodgkinlymphoms realisierbar. Zellgraphen können durch weitere Zelltypen erweitert werden und auf andere Krankheitsbilder angewendet werden.
Biological ageing is a degenerative and irreversible process, ultimately leading to death of the organism. The process is complex and under the control of genetic, environmental and stochastic traits. Although many theories have been established during the last decades, none of these are able to fully describe the complex mechanisms, which lead to ageing. Generally, biological processes and environmental factors lead to molecular damage and an accumulation of impaired cellular components. In contrast, counteracting surveillance systems are effective, including repair, remodelling and degradation of damaged or impaired components, respectively. Nevertheless, at some point these systems are no longer effective, either because the increasing amount of molecular damages can not longer be removed efficiently or because the repairing and removing mechanisms themselves become affected by impairing effects. The organism finally declines and dies. To investigate and to understand these counteracting mechanisms and the complex interplay of decline and maintenance, holistic and systems biological investigations are required. Hence, the processes which lead to ageing in the fungal model organism Podospora anserina, had been analysed using different advanced bioinformatics methods. In contrast to many other ageing models, P. anserina exhibits a short lifespan, a less biochemical complexity and it provides a good accessibility for genetic manipulations.
To achieve a general overview on the different biochemical processes, which are affected during ageing in P. anserina, an initial comprehensive investigation was applied, which aimed to reveal genes significantly regulated and expressed in an age-dependent manner. This investigation was based on an age-dependent transcriptome analysis. Sophisticated and comprehensive analyses revealed different age-related pathways and indicated that especially autophagy may play a crucial role during ageing. For example, it was found that the expression of autophagy-associated genes increases in the course of ageing.
Subsequently, to investigate and to characterise the autophagy pathway, its associated single components and their interactions, Path2PPI, a new bioinformatics approach, was developed. Path2PPI enables the prediction of protein-protein interaction networks of particular pathways by means of a homology comparison approach and was applied to construct the protein-protein interaction network of autophagy in P. anserina.
The predicted network was extended by experimental data, comprising the transcriptome data as well as newly generated protein-protein interaction data achieved from a yeast two-hybrid analysis. Using different mathematical and statistical methods the topological properties of the constructed network had been compared with those of randomly generated networks to approve its biological significance. In addition, based on this topological and functional analysis, the most important proteins were determined and functional modules were identified, which correspond to the different sub-pathways of autophagy. Due to the integrated transcriptome data the autophagy network could be linked to the ageing process. For example, different proteins had been identified, which genes are continuously up- or down-regulated during ageing and it was shown for the first time that autophagy-associated genes are significantly often co-expressed during ageing.
The presented biological network provides a systems biological view on autophagy and enables further studies, which aim to analyse the relationship of autophagy and ageing. Furthermore, it allows the investigation of potential methods for intervention into the ageing process and to extend the healthy lifespan of P. anserina as well as of other eukaryotic organisms, in particular humans.
Mathematical modeling of Arabidopsis thaliana with focus on network decomposition and reduction
(2014)
Systems biology has become an important research field during the last decade. It focusses on the understanding of the systems which emit the measured data. An important part of this research field is the network analysis, investigating biological networks. An essential point of the inspection of these network models is their validation, i.e., the successful comparison of predicted properties to measured data. Here especially Petri nets have shown their usefulness as modeling technique, coming with sound analysis methods and an intuitive representation of biological network data.
A very important tool for network validation is the analysis of the Transition-invariants (TI), which represent possible steady-state pathways, and the investigation of the liveness property. The computational complexity of the determination of both, TI and liveness property, often hamper their investigation.
To investigate this issue, a metabolic network model is created. It describes the core metabolism of Arabidopsis thaliana, and it is solely based on data from the literature. The model is too complex to determine the TI and the liveness property.
Several strategies are followed to enable an analysis and validation of the network. A network decomposition is utilized in two different ways: manually, motivated by idea to preserve the integrity of biological pathways, and automatically, motivated by the idea to minimize the number of crossing edges. As a decomposition may not be preserving important properties like the coveredness, a network reduction approach is suggested, which is mathematically proven to conserve these important properties. To deal with the large amount of data coming from the TI analysis, new organizational structures are proposed. The liveness property is investigated by reducing the complexity of the calculation method and adapting it to biological networks.
The results obtained by these approaches suggest a valid network model. In conclusion, the proposed approaches and strategies can be used in combination to allow the validation and analysis of highly complex biological networks.
Reactive oxygen species are a class of naturally occurring, highly reactive molecules that change the structure and function of macromolecules. This can often lead to irreversible intracellular damage. Conversely, they can also cause reversible changes through post-translational modification of proteins which are utilized in the cell for signaling. Most of these modifications occur on specific cysteines. Which structural and physicochemical features contribute to the sensitivity of cysteines to redox modification is currently unclear. Here, I investigated the in uence of protein structural and sequence features on the modifiability of proteins and specific cysteines therein using statistical and machine learning methods. I found several strong structural predictors for redox modification, such as a higher accessibility to the cytosol and a high number of positively charged amino acids in the close vicinity. I detected a high frequency of other post-translational modifications, such as phosphorylation and ubiquitination, near modified cysteines. Distribution of secondary structure elements appears to play a major role in the modifiability of proteins. Utilizing these features, I created models to predict the presence of redox modifiable cysteines in proteins, including human mitochondrial complex I, NKG2E natural killer cell receptors and proximal tubule cell proteins, and compared some of these predictions to earlier experimental results.
This work investigated the applicability of global pairwise sequence alignment to the detection of functional analogues in virtual screening. This variant of sequence comparison was developed for the identification of homologue proteins based on amino acid or nucleotide sequences. Because of the significant differences between biopolymers and small molecules several aspects of this approach for sequence comparison had to be adapted. All proposed concepts were implemented as the ‘Pharmacophore Alignment Search Tool’ (PhAST) and evaluated in retrospective experiments on the COBRA dataset in version 6.1. The aim to identify functional analogues raised the necessity for identification and classification of functional properties in molecular structures. This was realized by fragment-based atom-typing, where one out of nine functional properties was assigned to each non-hydrogen atom in a structure. These properties were pre-assigned to atoms in the fragments. Whenever a fragment matched a substructure in a molecule, the assigned properties were transferred from fragment atoms to structure atoms. Each functional property was represented by exactly one symbol. Unlike amino acid or nucleotide sequences, small drug-like molecules contain branches and cycles. This was a major obstacle in the application of sequence alignment to virtual screening, since this technique can only be applied to linear sequences of symbols. The best linearization technique was shown to be Minimum Volume Embedding. To the best of knowledge, this work represents the first application of dimensionality reduction to graph linearization. Sequence alignment relies on a scoring system that rates symbol equivalences (matches) and differences (mismatches) based on functional properties that correspond to rated symbols. Existing scoring schemes are applicable only to amino acids and nucleotides. In this work, scoring schemes for functional properties in drug-like molecules were developed based on property frequencies and isofunctionality judged from chemical experience, pairwise sequence alignments, pairwise kernel-based assignments and stochastic optimization. The scoring system based on property frequencies and isofunctionality proved to be the most powerful (measured in enrichment capability). All developed scoring systems performed superior compared to simple scoring approaches that rate matches and mismatches uniformly. The frameworks proposed for score calculations can be used to guide modifications to the atom-typing in promising directions. The scoring system was further modified to allow for emphasis on particular symbols in a sequence. It was proven that the application of weights to symbols that correspond to key interaction points important to receptor-ligand-interaction significantly improves screening capabilities of PhAST. It was demonstrated that the systematic application of weights to all sequence positions in retrospective experiments can be used for pharmacophore elucidation. A scoring system based on structural instead of functional similarity was investigated and found to be suitable for similarity searches in shape-constrained datasets. Three methods for similarity assessment based on alignments were evaluated: Sequence identity, alignment score and significance. PhAST achieved significantly higher enrichment with alignment scores compared to sequence identity. p-values as significance estimates were calculated in a combination of Marcov Chain Monte Carlo Simulation and Importance Sampling. p-values were adapted to library size in a Bonferroni correction, yielding E-values. A significance threshold of an E-value of 1*10-5 was proposed for the application in prospective screenings. PhAST was compared to state-of-the-art methods for virtual screening. The unweighted version was shown to exhibit comparable enrichment capabilities. Compound rankings obtained with PhAST were proven to be complementary to those of other methods. The application to three-dimensional instead of two-dimensional molecular representations resulted in altered compound rankings without increased enrichment. PhAST was employed in two prospective applications. A screening for non-nucleoside analogue inhibitors of bacterial thymidin kinase yielded a hit with a distinct structural framework but only weak activity. The search for drugs not member of the NSAID (non-steroidal anti-inflammatory drug) class as modulators of gamma-secretase resulted in a potent modulator with clear structural distiction from the reference compound. The calculation of significance estimates, emphasizing on key interactions, the pharmacophore elucidation capabilities and the unique compound rannkings set PhAST apart from other screening techniques.
Antimicrobial resistance became a serious threat to the worldwide public health in this century. A better understanding of the mechanisms, by which bacteria infect host cells and how the host counteracts against the invading pathogens, is an important subject of current research. Intracellular bacteria of the Salmonella genus have been frequently used as a model system for bacterial infections. Salmonella are ingested by contaminated food or water and cause gastroenteritis and typhoid fever in animals and humans. Once inside the gastrointestinal tract, Salmonella can invade intestinal epithelial cells. The host cell can fight against intracellular pathogens by a process called xenophagy. For complex systems, such as processes involved in the bacterial infection of cells, computational systems biology provides approaches to describe mathematically how these intertwined mechanisms in the cell function. Computational systems biology allows the analysis of biological systems at different levels of abstraction. Functional dependencies as well as dynamic behavior can be studied. In this thesis, we used the Petri net formalism to gain a better insight into bacterial infections and host defense mechanisms and to predict cellular behavior that can be tested experimentally. We also focused on the development of new computational methods.
In this work, the first realization of a mathematical model of the xenophagic capturing of Salmonella enterica serovar Typhimurium in epithelial cells was developed. The mathematical model expressed in the Petri net formalism was constructed in an iterative way of modeling and analyses. For the model verification, we analyzed the Petri net, including a computational performance of knockout experiments named in silico knockouts, which was established in this work. The in silico knockouts of the proposed Petri net are consistent with the published experimental perturbation studies and, thus, ensures the biological credibility of the Petri net. In silico knockouts that have not been experimentally investigated yet provide hypotheses for future investigations of the pathway.
To study the dynamic behavior of an epithelial cell infected with Salmonella enterica serovar Typhimurium, a stochastic Petri net was constructed. In experimental research, a decision like "Which incubation time is needed to infect half of the epithelial cells with Salmonella?" is based on experience or practicability. A mathematical model can help to answer these questions and improve experimental design. The stochastic Petri net models the cell at different stages of the Salmonella infection. We parameterized the model by a set of experimental data derived from different literature sources. The kinetic parameters of the stochastic Petri net determine the time evolution of the bacterial infection of a cell. The model captures the stochastic variation and heterogeneity of the intracellular Salmonella population of a single cell over time. The stochastic Petri net is a valuable tool to examine the dynamics of Salmonella infections in epithelial cells and generate valuable information for experimental design.
In the last part of this thesis, a novel theoretical method was introduced to perform knockout experiments in silico. The new concept of in silico knockouts is based on the computation of signal flows at steady state and allows the determination of knockout behavior that is comparable to experimental perturbation behavior. In this context, we established the concept of Manatee invariants and demonstrated the suitability of their application for in silico knockouts by reflecting biological dependencies from the signal initiation to the response. As a proof of principle, we applied the proposed concept of in silico knockouts to the Petri net of the xenophagic recognition of Salmonella. To enable the application of in silico knockouts for the scientific community, we implemented the novel method in the software isiKnock. isiKnock allows the automatized performance and visualization of in silico knockouts in signaling pathways expressed in the Petri net formalism. In conclusion, the knockout analysis provides a valuable method to verify computational models of signaling pathways, to detect inconsistencies in the current knowledge of a pathway, and to predict unknown pathway behavior.
In summary, the main contributions of this thesis are the Petri net of the xenophagic capturing of Salmonella enterica serovar Typhimurium in epithelial cells to study the knockout behavior and the stochastic Petri net of an epithelial cell infected with Salmonella enterica serovar Typhimurium to analyze the infection dynamics. Moreover, we established a new method for in silico knockouts, including the concept of Manatee invariants and the software isiKnock. The results of these studies are useful to a better understanding of bacterial infections and provide valuable model analysis techniques for the field of computational systems biology.