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Bacteria are an integral part of modern biotechnology. They are used to make a variety of products, such as foods, drugs, as well as a multitude of chemicals. In order to increase their production rates molecular biotechnology offers many tuning points, starting from the selection of an applicable host, over its geno- and phenotypical characterization, followed by genetic manipulations for an optimized metabolism and stabilisation of production processes. This work comprises the optimization of Bacillus subtilis as an expression system. It describes the steps taken for selection and genomic characterization of the B. subtilis wild type strain ATCC 6051, the subsequent optimizations of the strain in respect to growth and productivity, as well as the characterization of its behaviour in a variety of cultivation conditions. The B. subtilis strain most commonly found in laboratories around the world is the first sequenced Gram-positive organism B. subtilis 168. Zeigler et al. showed that strain 168 is not a real wild type. Instead it was created through random mutagenesis with X-rays and selected for transformability. This strain has been used as the basis for popular B. subtilis strains in heterologous gene expression such as the extracellular protease deficient WB strains. Growth experiments showed the real wild type strain ATCC 6051 to be superior to its mutated ancestor 168, making it a solid basis for the construction of an optimized B. subtilis expression system. In order to gain a full understanding of the genomic and corresponding physiological differences between the two systems, B. subtilis ATCC 6051 was sequenced and compared to the genome of B. Subtilis 168. Several variations on geno- and phenotypic level could be revealed, that resulted in particular from genes involved in natural competency, the metabolism of amino acids and chemotaxis. This genomically well characterized B. subtilis ATCC 6051 was improved in respect to its application as an expression host. Improvements were achieved through the inactivation of both sporulation and reduction of autolysis, leading to a more robust behaviour during the overproduction and secretion of a reporter enzyme. A positive effect on the activity of an acetoin induced promoter by the addition of second copies for its transcription factors SigmaL and AcoR could be observed. Anaerobic zones and areas with excess glucose caused by insufficient mixing are common conditions in large scale bioprocesses and lead to oscillating conditions for the cells. In turn, this oscillation provokes an excretion of so called overflow metabolites, which can negatively affect the bacterial productivity. Detailed scientific characterizations of industrial scale processes under such oscillating conditions are scarce due to the high costs and logistics involved. A B. Subtilis sporulation mutant was thus examined in respect to its extra- and intracellular metabolites in a scale-down, two-compartment reactor giving hints about conditions the host is exposed to and how it reacts. To improve tolerance thresholds and utilization capacity for such metabolites in B. subtilis, the glyoxylate cycle was transferred from its close relative Bacillus licheniformis into the genome of B. subtilis. This feature enabled our B. subtilis ACE mutant to grow on acetate. The improved strain showed higher tolerance towards excess glucose in a fed-batch as well as higher productivity during the expression of a reporter enzyme in comparison to the wild type. The ACE strain and B. licheniformis showed an increased formation of glycolate during growth with the glyoxylate cycle. This with regard to bacteria undescribed metabolite seems to play a role as a by-product of the glyoxylate cycle. Summarizing, this thesis deals with the characterization and optimization of B. subtilis for growth on overflow metabolites, enhancements of the acoA-expression system and the influence of sporulation and lysis mutants on its activity. Complementary, the host was begun to be characterized in respect to its behaviour in industrial scale processes.
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.