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The human gut is home for thousands of microbes that are important for human life. As most of these cannot be cultivated, metagenomics is an important means to understand this important community. To perform comparative metagenomic analysis of the human gut microbiome, I have developed SMASH (Simple metagenomic analysis shell), a computational pipeline. SMASH can also be used to assemble and analyze single genomes, and has been successfully applied to the bacterium Mycoplasma pneumoniae and the fungus Chaetomium thermophilum. In the context of the MetaHIT (Metagenomics of the human intestinal tract) consortium our group is participating in, I used SMASH to validate the assembly and to estimate the assembly error rate of 576.7 Gb metagenome sequence obtained using Illumina Solexa technology from fecal DNA of 124 European individuals. I also estimated the completeness of the gene catalogue containing 3.3 million open reading frames obtained from these metagenomes. Finally, I used SMASH to analyze human gut metagenomes of 39 individuals from 6 countries encompassing a wide range of host properties such as age, body mass index and disease states. We find that the variation in the gut microbiome is not continuous but stratified into enterotypes. Enterotypes are complex host-microbial symbiotic states that are not explained by host properties, nutritional habits or possible technical biases. The concept of enterotypes might have far reaching implications, for example, to explain different responses to diet or drug intake. We also find several functional markers in the human gut microbiome that correlate with a number of host properties such as body mass index, highlighting the need for functional analysis and raising hopes for the application of microbial markers as diagnostic or even prognostic tools for microbiota-associated human disorders.
In recent years high-throughput experiments provided a vast amount of data from all areas of molecular biology, including genomics, transcriptomics, proteomics and metabolomics. Its analysis using bioinformatics methods has developed accordingly, towards a systematic approach to understand how genes and their resulting proteins give rise to biological form and function. They interact with each other and with other molecules in highly complex structures, which are explored in network biology. The in-depth knowledge of genes and proteins obtained from high-throughput experiments can be complemented by the architecture of molecular networks to gain a deeper understanding of biological processes. This thesis provides methods and statistical analyses for the integration of molecular data into biological networks and the identification of functional modules, as well as its application to distinct biological data. The integrated network approach is implemented as a software package, termed BioNet, for the statistical language R. The package includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes and edges of these networks as well as methods for subnetwork search and visualisation. The exact algorithm is extensively tested in a simulation study and outperforms existing heuristic methods for the calculation of this NP-hard problem in accuracy and robustness. The variability of the resulting solutions is assessed on perturbed data, mimicking random or biased factors that obscure the biological signal, generated for the integrated data and the network. An optimal, robust module can be calculated using a consensus approach, based on a resampling method. It summarizes optimally an ensemble of solutions in a robust consensus module with the estimated variability indicated by confidence values for the nodes and edges. The approach is subsequently applied to two gene expression data sets. The first application analyses gene expression data for acute lymphoblastic leukaemia (ALL) and differences between the subgroups with and without an oncogenic BCR/ABL gene fusion. In a second application gene expression and survival data from diffuse large B-cell lymphomas are examined. The identified modules include and extend already existing gene lists and signatures by further significant genes and their interactions. The most important novelty is that these genes are determined and visualised in the context of their interactions as a functional module and not as a list of independent and unrelated transcripts. In a third application the integrative network approach is used to trace changes in tardigrade metabolism to identify pathways responsible for their extreme resistance to environmental changes and endurance in an inactive tun state. For the first time a metabolic network approach is proposed to detect shifts in metabolic pathways, integrating transcriptome and metabolite data. Concluding, the presented integrated network approach is an adequate technique to unite high-throughput experimental data for single molecules and their intermolecular dependencies. It is flexible to apply on diverse data, ranging from gene expression changes over metabolite abundances to protein modifications in a combination with a suitable molecular network. The exact algorithm is accurate and robust in comparison to heuristic approaches and delivers an optimal, robust solution in form of a consensus module with confidence values. By the integration of diverse sources of information and a simultaneous inspection of a molecular event from different points of view, new and exhaustive insights into biological processes can be acquired.
Diese Arbeit untersucht zelluläre Netzwerke mit dem Ziel, die so gewonnenen Einsichten medizinisch beziehungsweise biotechnologisch zu nutzen. Hierzu müssen zunächst Proteindomänen und wichtige regulatorische RNA Elemente erkannt werden. Dies geschieht für regulatorische Elemente in Nukleinsäuren am Beispiel von Iron Responsive Elements (IREs) in Staphylococcus aureus, wobei sich solche Elemente in viel versprechender Nähe zu exprimierten Sequenzen finden lassen (T. Dandekar, F. Du, H. Bertram (2001) Nonlinear Analysis 47(1): 225-34). Noch bedeutsamer als Ziele zur Medikamentenentwicklung gegen Parasiten sind Domänenunterschiede in Struktur und Sequenz bei Proteinen (T. Dandekar, F. Du, H. Bertram (2001) Nonlinear Analysis 47(1): 225-34). Ihre Identifikation wird am Beispiel eines potentiellen Transportproteins in Plasmodium falciparum exemplarisch dargestellt. Anschließend wird das Zusammenwirken von regulatorischen Elementen und Domänen in Netzwerken betrachtet (einschließlich experimenteller Daten). Dies kann einerseits zu allgemeineren Schlussfolgerungen über das Netzwerkverhalten führen, andererseits für konkrete Anwendungen genutzt werden. Als Beispiel wählten wir hier Redoxnetzwerke und die Bekämpfung von Plasmodien als Verursacher der Malaria. Da das gesamte Redoxnetzwerk einer lebenden Zelle mit Methoden der pH Wert Messung nur unzureichend zu erfassen ist, werden als alternative Messmethode für dieses Netzwerk Mikrokristalle der Glutathionreduktase als Indikatorsystem nach digitaler Verstärkung experimentell genutzt (H. Bertram, M. A. Keese, C. Boulin, R. H. Schirmer, R. Pepperkok, T. Dandekar (2002) Chemical Nanotechnology Talks III - Nano for Life Sciences). Um komplexe Redoxnetzwerke auch bioinformatisch zu modulieren, werden Verfahren der metabolischen Fluxanalyse vorgestellt und verbessert, um insbesondere ihrer Verzahnung besser gerecht zu werden und solche Netzwerke mit möglichst wenig elementaren Flussmoden zutreffend beschreiben zu können. Die Reduktion der Anzahl von Elementarmoden bei sehr großen metabolischen Netzwerken einer Zelle gelingt hier mit Hilfe unterschiedlicher Methoden und führt zu einer vereinfachten Darstellungsmöglichkeit komplexer Stoffwechselwege von Metaboliten. Dabei dient bei jeder dieser Methoden die biochemisch sinnvolle Definition von externen Metaboliten als Grundlage (T. Dandekar, F. Moldenhauer, S. Bulik, H. Bertram, S. Schuster (2003) Biosystems 70(3): 255-70). Allgemeiner werden Verfahren der Proteindomänenklassifikation sowie neue Strategien gegen mikrobielle Erreger betrachtet. In Bezug auf automatisierte Einteilung von Proteinen in Domänen wird ein neues System von Taylor (2002b) mit bekannten Systemen verglichen, die in unterschiedlichem Umfang menschlichen Eingriffs bedürfen (H. Bertram, T. Dandekar (2002) Chemtracts 15: 735-9). Außerdem wurde neben einer Arbeit über die verschiedenen Methoden aus den Daten eines Genoms Informationen über das metabolische Netzwerk der Zelle zu erlangen (H. Bertram, T. Dandekar (2004) it 46(1): 5-11) auch eine Übersicht über die Schwerpunkte der Bioinformatik in Würzburg zusammengestellt (H. Bertram, S. Balthasar, T. Dandekar (2003) Bioforum 1-2: 26-7). Schließlich wird beschrieben, wie die Pathogenomik und Virulenz von Bakterien der bioinformatischen Analyse zugänglich gemacht werden können (H. Bertram, S. Balthasar, T. Dandekar (2003) Bioforum Eur. 3: 157-9). Im letzten Teil wird die metabolische Fluxanalyse zur Identifikation neuer Strategien zur Bekämpfung von Plasmodien dargestellt: Beim Vergleich der Stoffwechselwege mit Glutathion und Thioredoxin in Plasmodium falciparum, Anopheles und Mensch geht es darum, gezielte Störungen im Stoffwechsel des Malariaerregers auszulösen und dabei den Wirt zu schonen. Es ergeben sich einige interessante Ansatzpunkte, deren medizinische Nutzung experimentell angestrebt werden kann.
Background: The frequency of the most observed cancer, Non Hodgkin Lymphoma (NHL), is further rising. Diffuse large B-cell lymphoma (DLBCL) is the most common of the NHLs. There are two subgroups of DLBCL with different gene expression patterns: ABC (“Activated B-like DLBCL”) and GCB (“Germinal Center B-like DLBCL”). Without therapy the patients often die within a few months, the ABC type exhibits the more aggressive behaviour. A further B-cell lymphoma is the Mantle cell lymphoma (MCL). It is rare and shows very poor prognosis. There is no cure yet. Methods: In this project these B-cell lymphomas were examined with methods from bioinformatics, to find new characteristics or undiscovered events on the molecular level. This would improve understanding and therapy of lymphomas. For this purpose we used survival, gene expression and comparative genomic hybridization (CGH) data. In some clinical studies, you get large data sets, from which one can reveal yet unknown trends. Results (MCL): The published proliferation signature correlates directly with survival. Exploratory analyses of gene expression and CGH data of MCL samples (n=71) revealed a valid grouping according to the median of the proliferation signature values. The second axis of correspondence analysis distinguishes between good and bad prognosis. Statistical testing (moderate t-test, Wilcoxon rank-sum test) showed differences in the cell cycle and delivered a network of kinases, which are responsible for the difference between good and bad prognosis. A set of seven genes (CENPE, CDC20, HPRT1, CDC2, BIRC5, ASPM, IGF2BP3) predicted, similarly well, survival patterns as proliferation signature with 20 genes. Furthermore, some bands could be associated with prognosis in the explorative analysis (chromosome 9: 9p24, 9p23, 9p22, 9p21, 9q33 and 9q34). Results (DLBCL): New normalization of gene expression data of DLBCL patients revealed better separation of risk groups by the 2002 published signature based predictor. We could achieve, similarly well, a separation with six genes. Exploratory analysis of gene expression data could confirm the subgroups ABC and GCB. We recognized a clear difference in early and late cell cycle stages of cell cycle genes, which can separate ABC and GCB. Classical lymphoma and best separating genes form a network, which can classify and explain the ABC and GCB groups. Together with gene sets which identify ABC and GCB we get a network, which can classify and explain the ABC and GCB groups (ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5; Altogether these findings are useful for diagnosis, prognosis and therapy (cytostatic drugs).
In this work models for molecular networks consisting of ordinary differential equations are extended by terms that include the interaction of the corresponding molecular network with the environment that the molecular network is embedded in. These terms model the effects of the external stimuli on the molecular network. The usability of this extension is demonstrated with a model of a circadian clock that is extended with certain terms and reproduces data from several experiments at the same time.
Once the model including external stimuli is set up, a framework is developed in order to calculate external stimuli that have a predefined desired effect on the molecular network. For this purpose the task of finding appropriate external stimuli is formulated as a mathematical optimal control problem for which in order to solve it a lot of mathematical methods are available. Several methods are discussed and worked out in order to calculate a solution for the corresponding optimal control problem. The application of the framework to find pharmacological intervention points or effective drug combinations is pointed out and discussed. Furthermore the framework is related to existing network analysis tools and their combination for network analysis in order to find dedicated external stimuli is discussed.
The total framework is verified with biological examples by comparing the calculated results with data from literature. For this purpose platelet aggregation is investigated based on a corresponding gene regulatory network and associated receptors are detected. Furthermore a transition from one to another type of T-helper cell is analyzed in a tumor setting where missing agents are calculated to induce the corresponding switch in vitro. Next a gene regulatory network of a myocardiocyte is investigated where it is shown how the presented framework can be used to compare different treatment strategies with respect to their beneficial effects and side effects quantitatively. Moreover a constitutively activated signaling pathway, which thus causes maleficent effects, is modeled and intervention points with corresponding treatment strategies are determined that steer the gene regulatory network from a pathological expression pattern to physiological one again.
Durch das Auftreten neuer Stämme resistenter Krankheitserreger ist die Suche nach neuartigen Wirkstoffen gegen diese, sich ständig weiter ausbreitende Bedrohung, dringend notwendig. Der interdisziplinäre Sonderforschungsbereich 630 der Universität Würzburg stellt sich dieser Aufgabe, indem hier neuartige Xenobiotika synthetisiert und auf ihre Wirksamkeit getestet werden. Die hier vorgelegte Dissertation fügt sich hierbei nahtlos in die verschiedenen Fachbereiche des SFB630 ein: Sie stellt eine Schnittstelle zwischen Synthese und Analyse der Effekte der im Rahmen des SFB630 synthetisierten Isochinolinalkaloid-Derivaten. Mit den hier angewandten bioinformatischen Methoden wurden zunächst die wichtigsten Stoffwechselwege von S. epidermidis R62A, S. aureus USA300 und menschlicher Zellen in sogenannten metabolischen Netzwerkmodellen nachgestellt. Basierend auf diesen Modellen konnten Enzymaktivitäten für verschiedene Szenarien an zugesetzten Xenobiotika berechnet werden. Die hierfür benötigten Daten wurden direkt aus Genexpressionsanalysen gewonnen. Die Validierung dieser Methode erfolgte durch Metabolommessungen. Hierfür wurde S. aureus USA300 mit verschiedenen Konzentrationen von IQ-143 behandelt und gemäß dem in dieser Dissertation vorgelegten Ernteprotokoll aufgearbeitet. Die Ergebnisse hieraus lassen darauf schließen, dass IQ-143 starke Effekte auf den Komplex 1 der Atmungskette ausübt – diese Resultate decken sich mit denen der metabolischen Netzwerkanalyse. Für den Wirkstoff IQ-238 ergaben sich trotz der strukturellen Ähnlichkeiten zu IQ-143 deutlich verschiedene Wirkeffekte: Dieser Stoff verursacht einen direkten Abfall der Enzymaktivitäten in der Glykolyse. Dadurch konnte eine unspezifische Toxizität dieser Stoffe basierend auf ihrer chemischen Struktur ausgeschlossen werden. Weiterhin konnten die bereits für IQ-143 und IQ-238 auf Bakterien angewandten Methoden erfolgreich zur Modellierung der Effekte von Methylenblau auf verschiedene resistente Stämme von P. falciparum 3D7 angewandt werden. Dadurch konnte gezeigt werden, dass Methylenblau in einer Kombination mit anderen Präparaten gegen diesen Parasiten zum einen die Wirkung des Primärpräparates verstärkt, zum anderen aber auch in gewissem Maße vorhandene Resistenzen gegen das Primärpräparat zu verringern vermag. Somit konnte durch die vorgelegte Arbeit eine Pipeline zur Identifizierung der metabolischen Effekte verschiedener Wirkstoffe auf unterschiedliche Krankheitserreger erstellt werden. Diese Pipeline kann jederzeit auf andere Organismen ausgeweitet werden und stellt somit einen wichtigen Ansatz um Netzwerkeffekte verschiedener, potentieller Medikamente aufzuklären.
An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications.
The phylum Tardigrada consists of about 1000 described species to date. The animals live in habitats within marine, freshwater and terrestrial ecosystems allover the world. Tardigrades are polyextremophiles. They are capable to resist extreme temperature, pressure or radiation. In the event of desiccation, tardigrades enter a so-called tun stage. The reason for their great tolerance capabilities against extreme environmental conditions is not discovered yet. Our Funcrypta project aims at finding answers to the question what mechanisms underlie these adaption capabilities particularly with regard to the species Milnesium tardigradum. The first part of this thesis describes the establishment of expressed sequence tags (ESTs) libraries for different stages of M. tardigradum. From proteomics data we bioinformatically identified 144 proteins with a known function and additionally 36 proteins which seemed to be specific for M. tardigradum. The generation of a comprehensive web-based database allows us to merge the proteome and transcriptome data. Therefore we created an annotation pipeline for the functional annotation of the protein and nucleotide sequences. Additionally, we clustered the obtained proteome dataset and identified some tardigrade-specific proteins (TSPs) which did not show homology to known proteins. Moreover, we examined the heat shock proteins of M. tardigradum and their different expression levels depending on the actual state of the animals. In further bioinformatical analyses of the whole data set, we discovered promising proteins and pathways which are described to be correlated with the stress tolerance, e.g. late embryogenesis abundant (LEA) proteins. Besides, we compared the tardigrades with nematodes, rotifers, yeast and man to identify shared and tardigrade specific stress pathways. An analysis of the 50 and 30 untranslated regions (UTRs) demonstrates a strong usage of stabilising motifs like the 15-lipoxygenase differentiation control element (15-LOX-DICE) but also reveals a lack of other common UTR motifs normally used, e.g. AU rich elements. The second part of this thesis focuses on the relatedness between several cryptic species within the tardigrade genus Paramacrobiotus. Therefore for the first time, we used the sequence-structure information of the internal transcribed spacer 2 (ITS2) as a phylogenetic marker in tardigrades. This allowed the description of three new species which were indistinguishable using morphological characters or common molecular markers like the 18S ribosomal ribonucleic acid (rRNA) or the Cytochrome c oxidase subunit I (COI). In a large in silico simulation study we also succeeded to show the benefit for the phylogenetic tree reconstruction by adding structure information to the ITS2 sequence. Next to the genus Paramacrobiotus we used the ITS2 to corroborate a monophyletic DO-group (Sphaeropleales) within the Chlorophyceae. Additionally we redesigned another comprehensive database—the ITS2 database resulting in a doubled number of sequence-structure pairs of the ITS2. In conclusion, this thesis shows the first insights (6 first author publications and 4 coauthor publications) into the reasons for the enormous adaption capabilities of tardigrades and offers a solution to the debate on the phylogenetic relatedness within the tardigrade genus Paramacrobiotus.
The topic of my doctorial research was the computational analysis of metagenomic data. A metagenome comprises the genomic information from all the microorganisms within a certain environment. The currently available metagenomic data sets cover only parts of these usually huge metagenomes due to the high technical and financial effort of such sequencing endeavors. During my thesis I developed bioinformatic tools and applied them to analyse genomic features of different metagenomic data sets and to search for enzymes of importance for biotechnology or pharmaceutical applications in those sequence collections. In these studies nine metagenomic projects (with up to 41 subsamples) were analysed. These samples originated from diverse environments like farm soil, acid mine drainage, microbial mats on whale bones, marine water, fresh water, water treatment sludges and the human gut flora. Additionally, data sets of conventionally retrieved sequence data were taken into account and compared with each other
Biological systems such as cells or whole organisms are governed by complex regulatory networks of transcription factors, hormones and other regulators which determine the behavior of the system depending on internal and external stimuli. In mathematical models of these networks, genes are represented by interacting “nodes” whose “value” represents the activity of the gene.
Control processes in these regulatory networks are challenging to elucidate and quantify. Previous control centrality metrics, which aim to mathematically capture the ability of individual nodes to control biological systems, have been found to suffer from problems regarding biological plausibility.
This thesis presents a new approach to control centrality in biological networks. Three types of network control are distinguished: Total control centrality quantifies the impact of gene mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signaling cascades (e.g control in mouse colon stem cells). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Well-defined network manipulations define all three centralities not only for nodes, but also for the interactions between them, enabling detailed insights into network pathways.
The calculation of the new metrics is made possible by substantial computational improvements in the simulation algorithms for several widely used mathematical modeling paradigms for genetic regulatory networks, which are implemented in the regulatory network simulation framework Jimena created for this thesis.
Applying the new metrics to biological networks and artificial random networks shows how these mathematical concepts correspond to experimentally verified gene functions and signaling pathways in immunity and cell differentiation. In contrast to controversial previous results even from the Barabási group, all results indicate that the ability to control biological networks resides in only few driver nodes characterized by a high number of connections to the rest of the network. Autoregulatory loops strongly increase the controllability of the network, i.e. its ability to control itself, and biological networks are characterized by high controllability in conjunction with high robustness against mutations, a combination that can be achieved best in sparsely connected networks with densities (i.e. connections to nodes ratios) around 2.0 - 3.0.
The new concepts are thus considerably narrowing the gap between network science and biology and can be used in various areas such as system modeling, plausibility trials and system analyses.
Medical applications discussed in this thesis include the search for oncogenes and pharmacological targets, as well their functional characterization.