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The soil living, Gram-positive bacterium Bacillus subtilis is frequently exposed to a wide variety of stress and starvation conditions in its natural environment. In order to survive under these environmental and energy stresses, the bacterium acquired a general stress response mechanism mediated by the alternative sigma factor, SigB. A wide-variety of stress conditions such as environmental stress conditions like ethanol stress, heat stress, oxidative stress, osmotic stress or limitation of glucose, oxygen, phosphate etc.; and low temperature growth induce this SigB-dependent general stress response. Though much is known about the mechanisms of activation of this general stress response, the conditions that induce the SigB regulon and its general functions, the definition of the structure of the SigB regulon is not completely clear. The SigB-dependent general stress regulon has previously been characterized by proteomic approaches as well as DNA-array based expression studies. Genome-wide expression studies performed by Price, Petersohn and Helmann defined the SigB regulon containing well above 100 target genes, however the overlapping list of target genes contains only 67 members. The differences between these studies probably result from the different strains, growth conditions, array platforms and experimental setups used in these studies. The first part of this work presents a targeted microarray analysis, which was performed to gain a better understanding of the structure of the general stress regulon. This is the first study analyzing the gene expression of a wild type strain and its isogenic sigB mutant strain for almost all known SigB inducing conditions, using the same array platform. Furthermore, the kinetics of the gene expression of 252 putative SigB-dependent genes and 36 appropriate control genes were recorded. The data were analyzed using Random Forest, a machine-learning algorithm, by incorporating the knowledge of previous studies. Two Random Forest models were designed in this study. The “expression RF” model was designed to identify genes showing expression differences between wild type and sigB mutant and the “kinetic RF” model to identify genes having a SigB-dependent expression kinetic, but is subject to secondary regulators next to SigB influencing their expression in the sigB mutant. The random forest classification using the “expression RF” model identified 166 genes as SigB regulon members based on the expression differences between the wild type and the sigB mutant strain. A variable importance plot showing the impact during the classification process within the “expression RF” could assign a hierarchy to the stress conditions investigated in this study. This hierarchy suggested all the RsbU-dependent environmental stresses to have higher impact on SigB-dependent gene expression compared to the RsbP-dependent energy stresses. The “kinetic RF” model identified 30 additional genes, having additional regulators next to SigB. The SigB dependency of the 30 genes identified by the “kinetic RF” model was validated by screening for SigB promoter motifs within the upstream region of these genes. The hierarchical clustering of the obtained motifs scores with the expression ratios of the SigB regulon members predicted in the current work revealed that only a subset of genes displayed correlation of gene expression values and sequence motifs. As this observation is not true for all sets of genes, it cannot be generalized that gene expression is only correlated with the corresponding motif scores. In total 196 SigB regulon members could be classified by this targeted oligo nucleotide microarray study. The majority of these regulon members were preceded by a putative SigB promoter motif either identified previously or predicted in the current work. The inclusion of the broad range of stress conditions, from environmental stresses to energy limiting conditions enabled a more detailed characterization of the structure of the general stress regulon of B. subtilis. The implementation of machine learning algorithms allowed the prediction with a minimum number of false-positives. In the second part of this work a high resolution tiling array analysis for the majority of growth conditions, stresses and changes in carbon sources supply was exploited for the screening for new SigB targets within already annotated or newly annotated RNA features. Thereby 133 previously un-annotated RNA features, which were completely new, were assigned to the SigB regulon. 50 of these 133 new features encode antisense RNAs which can have potential influence on the transcription / translation of their sense RNAs targets. A set of 282 annotated genes were indentified to be SigB regulon members, comparison with the targeted oligo nucleotide study, 90 genes were newly identified and not known to be SigB-dependent before. The analysis of the expression levels of these genes by k-means clustering revealed a cluster of 32 genes having low induction levels in all SigB-inducing conditions, although the majority of these genes possess a well-conserved SigB promoter motif. However, all these genes are probably subject to the control of regulators other than SigB, which might mask the typical strong SigB-dependent induction in the analyzed stress conditions. The analysis of the expression levels of the SigB regulon under a variety of conditions, revealed the SigB-dependent expression in conditions such as growth on plates, in swarming cells, biofilm formation and growth on glycerol as a carbon source. The possible reason for the induction of the SigB regulon during growth on plates and in swarming cells was supposed to be due to scarcity of the nutrients on plates, e.g. glucose limitation. SigB-dependent genes were likely induced during growth on glycerol due to the oxygen limitation that arose during the growth. However, induction of the SigB regulon during biofilm formation is assumed to be due to the phosphate limitation. The description of these new SigB activating stimuli gains support from the fact that the majority of the SigB-dependent genes were induced under these growth conditions. In addition to the general stress response, B. subtilis cells have stress specific adaptive mechanisms such as osmotic response, which was addressed in the third part of this dissertation. The frequent flooding and drying of the soil triggered osmotic stress, one of the most common stress conditions encountered by soil bacteria. Bacterial cells are equipped with osmo-specific adaptation responses in which specific regulation of a set of genes is used to maintain proper cellular function. It was known from previous studies that a large set of genes were influenced in expression by salt shock as well as growth at high osmolarity. Detailed analysis of the tiling array data revealed 467 differentially regulated newly annotated features during salt shock and 251 newly annotated features that were expressed at a different level during continuous growth at high versus low osmolarity. A comparison of the studies that used the sigB knockout mutant with the tiling array study also provided support for the sigma factor competition in control of the expression of osmo-adaptive genes. The level of induction of specific osmo-adaptive genes was much higher in the sigB mutant strain compared to the wild type strain. Furthermore, the tiling array data revealed a SigB-dependent antisense RNA S1290 upstream of the opuB operon that transports choline to the cell. The presence of this antisense RNA had a potential impact on the transcription of the opuB operon, during salt shock. In agreement with the previous studies, the tiling array data assigned the osmotically regulated proHJ operon to the SigE regulon, with a SigE promoter upstream. In addition, the significantly higher percentage of proline among spore coat proteins also supports the assumption that osmotic synthesis of proline might play a role during the generation of spores. In conclusion, the tiling array data revealed newly annotated RNA features that are regulated during the general stress response as well as the osmotic response of the cell. The current work identifies new conditions that induce the majority of SigB-dependent genes as well as the new features that regulate the osmotically induced genes.
Myokardiale Erkrankungen gehören zu den häufigsten Todesursachen. Das Verständnis der molekularen, patho-physiologischen Ereignisse ist somit entscheidend für die Suche nach geeigneten Therapien. Um einen Einblick in zelluläre Ereignisse der kardialen Erkrankungen zu erhalten, sollten in der hier vorgelegten Arbeit Genexpressionsanalysen unter Verwendung von DNA-Mikroarrays durchgeführt werden. Den Schwerpunkt dieser Arbeit bildete die Charakterisierung der Immunadsorptionstherapie (IA/IgG-Therapie), die eine Therapieoption der dilatativen Kardiomyopathie (DCM) darstellt. Anhand von Genexpressionsanalysen immunadsorbierter Patienten sollte der Therapieeffekt analysiert werden. Dafür wurde zunächst die Etablierung eines Protokolls zur Isolation von Protein und RNA aus humanen endomyokardialen Biopsien von DCM-Patienten nötig. Um die mit der Therapie verbundenen Genexpressionsänderungen einordnen zu können, sollte zunächst das Genexpressionsmuster von 47 DCM-Patienten mit Patienten ohne Einschränkung der Pumpfunktion (Kontrollen) verglichen und mittels Real-time PCR validiert werden. Die Betrachtung dieser DCM-Patienten und Patienten, die eine normale Pumpfunktion aufwiesen, ergab 649 Gene mit krankheitsbedingt veränderter Expression. Zu diesen gehören neben bekannten Herzinsuffizienz-Markern (BNP, ANP, MYH6) Gene, die Proteine des Protein-Ubiquitinylierungssystem kodieren oder an der Ausprägung von Hypoxie und Fibrose (Connective tissue growth factor) beteiligt sind und auf eine Dysregulation des Proteinabbaus, erhöhten oxidativen Stress und Matrix-Remodeling hinweisen. Darüber hinaus ist aufgrund der geringeren Expression von Genen, die der oxidativen Phosphorylierung und Glykolyse zuzuordnen sind, von einer Energielimitation in DCM-Patienten auszugehen. Unter Berücksichtigung von verschiedenen klinischen Parametern bestand weiterhin die Aufgabe, den Einfluss dieser Parameter auf die Genexpression zu klären. Während wenig Korrelationen zu Body-Mass-Index (BMI), Alter und Krankheitszeitraum auftrat, wurde eine Häufung von Genen festgestellt, deren Expression zur LVEF und LVIDd korreliert. Gene, die Regulatoren mit myokardialer Funktion kodieren (ADRA1A, ADRB2, PLN, RYR2), zeigten gleichzeitig Korrelationen zur LVEF und dem LVIDd (p<0,05). Darüber hinaus sollte das Genexpressionsprofil von Patienten, die von der IA/IgG-Therapie profitieren (Responder) mit den Patienten ohne Therapieerfolg (Nonresponder) vor und 6 Monate nach der IA/IgG-Therapie verglichen werden. In der Subgruppe der Responder wurde für 171 Gene eine signifikant unterschiedliche Expression bestimmt, während die Zahl der durch die Therapie in ihrer Expressionshöhe betroffenen Gene in Nonrespondern mit 72 wesentlich geringer ausfiel. Gene, die sowohl in Respondern nach Therapie als auch krankheitsbedingt verändert waren, konnten kaum beobachtet werden, so dass andere Mechanismen für den Therapieeffekt verantwortlich sein müssen. Neben einer geringeren Expression des ACE2 wurde auch eine Abnahme Fibrose-assoziierter Gene wie CTGF, Fibronectin und Collagen 1A2 in Respondern nach IA/IgG-Therapie beobachtet. Zudem war eine signifikante LVIDd-Abnahme in Respondern zu verzeichnen, die in Nonrespondern nicht zu erkennen war. In Nonrespondern wurde nach IA/IgG-Therapie dagegen die verminderte Expression einiger Komplementfaktoren beobachtet. Zudem bestand die Aufgabe in der Suche nach Genexpressionsänderungen immunadsorbierter DCM-Patienten, die mit der Änderung klinischer Parameter korrelieren. Weiterhin sollte im Rahmen dieser Arbeit eine beschreibende Signatur definiert werden, die eine Vorhersage des Therapieerfolges für den individuellen DCM-Patienten vor Durchführung der IA/IgG-Therapie ermöglicht. Unter Verwendung des Resamplings (Crossvalidierung) wurde mit Hilfe einer Support Vector Machine eine Signatur von 25 Genen definiert, die eine Klassifizierung der Subgruppen mit einer Fehlerrate von 3,7% erlaubt. Die in Abhängigkeit verschiedener, myokardialer Parameter gezeigten Genexpressionsunterschiede in DCM-Patienten spiegeln die Dynamik der Erkrankung wider. Der Einfluss der IA/IgG-Therapie auf die Genexpression von Patienten, die an der DCM erkrankt sind, betrifft eine Vielzahl von Genen verschiedener Kategorien. Korrelationsanalysen zeigen, dass ein Zusammenhang zwischen Genexpressionsänderung und den Änderungen der Parameter LVEF, LVIDd und Inflammation bestehen. Mit der Definition von Patienten, die von der IA/IgG-Therapie profitieren, wurden auch Unterschiede bezüglich klinischer Parameter (LVIDd, Zeitraum der Erkrankung) deutlich, die zum Verständnis der hier dargestellten Genexpressionsunterschiede beitragen. Die generierten Daten bieten neben dem besseren Verständnis der im Myokard ablaufenden Prozesse eine Reihe von Ansatzpunkten für weitere Untersuchungen, wie zum Beispiel zur Rolle des IGF-1-Signalweges oder des Protein-Ubiquitinylierungssystems bei der Ausprägung der DCM, die auch zu neuen Therapieansätzen beitragen können.