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Alle Organismen sind für ihr Überleben auf Metalle angewiesen. Hierbei gibt es für jedes Metall einen Konzentrationsbereich, der das Optimum zwischen Metallmangel, -bedarf und -toxizität darstellt. Es gilt mittlerweile als erwiesen, dass alle Organismen zur Aufrechterhaltung des Metallgleichgewichts ein komplexes Netzwerk von Proteinen und niedermolekularen Verbindungen entwickelt haben. Die molekularen Komponenten dieses Netzwerks sind nur zu einem Teil bekannt und charakterisiert: In den letzten Jahren wurden einige Proteinfamilien identifiziert, deren Mitglieder Metalle durch Lipidmembranen transportieren. Eine dieser Metalltransporterfamilien ist die Cation Diffusion Facilitator (CDF)-Familie: Alle charakterisierten Mitglieder exportieren Metalle aus dem Zytoplasma – entweder in zelluläre Kompartimente oder aus der Zelle heraus. Von den zwölf Mitgliedern dieser Familie in Arabidopsis thaliana (A. thaliana) – Metall Toleranz Protein (MTP)-1 bis -12 – wurden bisher AtMTP1 und AtMTP3 charakterisiert. In dieser Arbeit wird die Charakterisierung von AtMTP2 beschrieben. Wie die homologen Proteine AtMTP1 und AtMTP3 führt AtMTP2 zu Zn-Toleranz, wenn es heterolog in Zn-sensitiven Hefemutanten exprimiert wird. Mit AtMTP2 transformierte Hefemutanten zeigten darüber hinaus erhöhte Co-Toleranz. Expression von chimären AtMTP2/GFP Fusionsproteinen in Hefe, A.thaliana protoplasten und in stabil transformierten A.thalinana Planzenlinien deutet auf Lokalisation of AtMTP2 in Membranen des Endoplasmatischen Retikulums (ER) hin, wenn GFP an den C-Terminus von MTP2 fusioniert wird. Fusion of GFP an den N-Terminus von AtMTP2 führte zu Lokalisation in der vakuolären Membran, was wahrscheinlichsten auf Fehllokalisierung durch Maskierung eines ER-Retentionsmotivs (XXRR) am N-Terminus von AtMTP2 zurückgeht. Dies legt nahe, dass AtMTP2 die erwähnten Metalle in das Endomembransystem der Zelle transportieren kann. Eine gewebespezifische Lokalisierung wurde mit Pflanzen durchgeführt, die das β-Glucuronidase (GUS)-Reporterprotein bzw. chimäre Fusionsproteine aus EGFP und AtMTP2 unter Kontrolle des nativen pMTP2-Promotors exprimierten. Diese Experimente bestätigten zum einen, dass der pMTP2-Promotor nur unter Zn-Defizienz aktiv ist. GUS-Aktivität wurde unter diesen Bedingungen in zwei Zonen der Wurzelspitze beobachtet: in den isodiametrischen Zellen der meristematischen Zone und in der beginnenden Wurzelhaarzone. Darüber hinaus konnte gezeigt werden, dass die EGFP-Fusionsproteine unter Kontrolle des nativen pMTP2-Promotors nur in epidermalen Zellen exprimiert werden. Für eine homozygote Knockout- Linie, mtp2-S3, konnte bisher kein eindeutiger Phänotyp identifiziert werden. Auf Grundlage der bisher durchgeführten Charakterisierung von AtMTP2 erscheinen zwei Modelle der Funktion von AtMTP2 in der Pflanze möglich: AtMTP2 könnte essentiell für die Versorgung des ER mit Zn unter Zn-Mangelbedingungen sein. Hierfür spricht, dass AtMTP2 in jungen, teilungsaktiven und damit Zn-benötigenden Wurzelzonen exprimiert wird. Die auf die Epidermis beschränkte Lokalisation könnte bei diesem Modell auf die Möglichkeit der zwischenzellulären Zn-Verteilung innerhalb des ER über Desmotubules hindeuten. Alternativ könnte AtMTP2 eine Funktion bei der Detoxifizierung von Zn unter Zn-Schock Bedingungen haben: Es ist bekannt, dass unter Zn- Mangelbedingungen die Expression der zellulären Zn-Aufnahmesysteme hochreguliert wird. Wenn nun die Zn-Verfügbarkeit im Boden z. B durch eine pH-Änderung innerhalb kurzer Zeit stark ansteigt, besteht die Notwendigkeit der Entgiftung von Zn innerhalb der Zelle, bis der starke Einstrom von Zn ins Zytoplasma durch die Deaktivierung der Zn-Aufnahmesysteme und einer geringeren Expression in der Pflanze gedrosselt ist. Ein ähnlicher Mechanismus wurde in der Bäckerhefe S. cerevisae beschrieben, in der darüber hinaus ein Zn-Transporter verstärkt exprimiert wird, der Zn durch Transport in die Vakuole entgiften kann. Es ist durchaus möglich, dass in Arabidopsis AtMTP2 die Zn-Detoxifizierung unter diesen speziellen Bedingungen durch Zn-Transport in das ER oder die Vakuole vermittelt. Zur Identifikation weiterer Komponenten des Metallhomöostasenetzwerks sind verschiedene Ansätze denkbar. In dieser Arbeit wurde in Hefe ein heterologer Screen durchgeführt, um Interaktoren für vier Mitglieder der Arabidopsis-CDF-Familie zu identifizieren. Unter den 11 im Hefesystem bestätigten Kandidaten befindet sich mit AtSPL1 ein AtMTP1-Interaktionskandidat, der möglicherweise eine Rolle bei der Cu-,Zn-Homöostase spielt. Als wahrscheinliche AtMTP3-Interaktionskandidaten wurde die c”-Untereinheit der vakuolären H+-ATPase AtVHA identifiziert sowie mit AtNPSN13 ein Protein, das vermutlich eine Rolle bei Fusionen von Vesikeln mit Zielmembranen spielt. Ein anderer Ansatz zur Identifikation neuer Metallhomöostasegene ist die vergleichende Elementanalyse von natürlichen oder mutagenisierten Pflanzenpopulationen. Voraussetzung für diesen Ansatz ist die schnelle und genaue Analyse des Elementgehalts von Pflanzen. Eine etablierte Methode zur simultanen Bestimmung von bis zu 65 Elementen in einer Probe ist die Inductively Coupled Plasma Optical Emission Spectrometry (ICP OES). Der limitierende Faktor für einen hohen Probendurchsatz ist die Notwendigkeit, Proben für die Analyse zu verflüssigen. Eine alternative Methode der Probenzuführung zum Analysegerät ist die elektrothermale Verdampfung (ETV) der Probe. Zur weitgehend automatisierten Analyse von Pflanzenmaterial mit minimiertem Arbeitsaufwand wurde eine Methode entwickelt, die auf der Kopplung der ETV mit der ICP OES basiert.
The highly conserved protein complex containing the Target of Rapamycin (TOR) kinase is known to integrate intra- and extra-cellular stimuli controlling nutrient allocation and cellular growth. This thesis describes three studies aimed to understand how TOR signaling pathway influences carbon and nitrogen metabolism in Chlamydomonas reinhardtii. The first study presents a time-resolved analysis of the molecular and physiological features across the diurnal cycle. The inhibition of TOR leads to 50% reduction in growth followed by nonlinear delays in the cell cycle progression. The metabolomics analysis showed that the growth repression is mainly driven by differential carbon partitioning between anabolic and catabolic processes. Furthermore, the high accumulation of nitrogen-containing compounds indicated that TOR kinase controls the carbon to nitrogen balance of the cell, which is responsible for biomass accumulation, growth and cell cycle progression. In the second study the cause of the high accumulation of amino acids is explained. For this purpose, the effect of TOR inhibition on Chlamydomonas was examined under different growth regimes using stable 13C- and 15N-isotope labeling. The data clearly showed that an increased nitrogen uptake is induced within minutes after the inhibition of TOR. Interestingly, this increased N-influx is accompanied by increased activities of nitrogen assimilating enzymes. Accordingly, it was concluded that TOR inhibition induces de-novo amino acid synthesis in Chlamydomonas. The recognition of this novel process opened an array of questions regarding potential links between central metabolism and TOR signaling. Therefore a detailed phosphoproteomics study was conducted to identify the potential substrates of TOR pathway regulating central metabolism. Interestingly, some of the key enzymes involved in carbon metabolism as well as amino acid synthesis exhibited significant changes in the phosphosite intensities immediately after TOR inhibition. Altogether, these studies provide a) detailed insights to metabolic response of Chlamydomonas to TOR inhibition, b) identification of a novel process causing rapid upshifts in amino acid levels upon TOR inhibition and c) finally highlight potential targets of TOR signaling regulating changes in central metabolism. Further biochemical and molecular investigations could confirm these observations and advance the understanding of growth signaling in microalgae.
The advent of large-scale and high-throughput technologies has recently caused a shift in focus in contemporary biology from decades of reductionism towards a more systemic view. Alongside the availability of genome sequences the exploration of organisms utilizing such approach should give rise to a more comprehensive understanding of complex systems. Domestication and intensive breeding of crop plants has led to a parallel narrowing of their genetic basis. The potential to improve crops by conventional breeding using elite cultivars is therefore rather limited and molecular technologies, such as marker assisted selection (MAS) are currently being exploited to re-introduce allelic variance from wild species. Molecular breeding strategies have mostly focused on the introduction of yield or resistance related traits to date. However given that medical research has highlighted the importance of crop compositional quality in the human diet this research field is rapidly becoming more important. Chemical composition of biological tissues can be efficiently assessed by metabolite profiling techniques, which allow the multivariate detection of metabolites of a given biological sample. Here, a GC/MS metabolite profiling approach has been applied to investigate natural variation of tomatoes with respect to the chemical composition of their fruits. The establishment of a mass spectral and retention index (MSRI) library was a prerequisite for this work in order to establish a framework for the identification of metabolites from a complex mixture. As mass spectral and retention index information is highly important for the metabolomics community this library was made publicly available. Metabolite profiling of tomato wild species revealed large differences in the chemical composition, especially of amino and organic acids, as well as on the sugar composition and secondary metabolites. Intriguingly, the analysis of a set of S. pennellii introgression lines (IL) identified 889 quantitative trait loci of compositional quality and 326 yield-associated traits. These traits are characterized by increases/decreases not only of single metabolites but also of entire metabolic pathways, thus highlighting the potential of this approach in uncovering novel aspects of metabolic regulation. Finally the biosynthetic pathway of the phenylalanine-derived fruit volatiles phenylethanol and phenylacetaldehyde was elucidated via a combination of metabolic profiling of natural variation, stable isotope tracer experiments and reverse genetic experimentation.
This dissertation aimed to determine differential expressed miRNAs in the context of chronic pain in polyneuropathy. For this purpose, patients with chronic painful polyneuropathy were compared with age matched healthy patients. Taken together, all miRNA pre library preparation quality controls were successful and none of the samples was identified as an outlier or excluded for library preparation. Pre sequencing quality control showed that library preparation worked for all samples as well as that all samples were free of adapter dimers after BluePippin size selection and reached the minimum molarity for further processing. Thus, all samples were subjected to sequencing. The sequencing control parameters were in their optimal range and resulted in valid sequencing results with strong sample to sample correlation for all samples. The resulting FASTQ file of each miRNA library was analyzed and used to perform a differential expression analysis. The differentially expressed and filtered miRNAs were subjected to miRDB to perform a target prediction. Three of those four miRNAs were downregulated: hsa-miR-3135b, hsa-miR-584-5p and hsa-miR-12136, while one was upregulated: hsa-miR-550a-3p. miRNA target prediction showed that chronic pain in polyneuropathy might be the result of a combination of miRNA mediated high blood flow/pressure and neural activity dysregulations/disbalances. Thus, leading to the promising conclusion that these four miRNAs could serve as potential biomarkers for the diagnosis of chronic pain in polyneuropathy.
Since TRPV1 seems to be one of the major contributors of nociception and is associated with neuropathic pain, the influence of PKA phosphorylated ARMS on the sensitivity of TRPV1 as well as the part of AKAP79 during PKA phosphorylation of ARMS was characterized. Therefore, possible PKA-sites in the sequence of ARMS were identified. This revealed five canonical PKA-sites: S882, T903, S1251/52, S1439/40 and S1526/27. The single PKA-site mutants of ARMS revealed that PKA-mediated ARMS phosphorylation seems not to influence the interaction rate of TRPV1/ARMS. While phosphorylation of ARMST903 does not increase the interaction rate with TRPV1, ARMSS1526/27 is probably not phosphorylated and leads to an increased interaction rate. The calcium flux measurements indicated that the higher the interaction rate of TRPV1/ARMS, the lower the EC50 for capsaicin of TRPV1, independent of the PKA phosphorylation status of ARMS. In addition, the western blot analysis confirmed the previously observed TRPV1/ARMS interaction. More importantly, AKAP79 seems to be involved in the TRPV1/ARMS/PKA signaling complex. To overcome the problem of ARMS-mediated TRPV1 sensitization by interaction, ARMS was silenced by shRNA. ARMS silencing resulted in a restored TRPV1 desensitization without affecting the TRPV1 expression and therefore could be used as new topical therapeutic analgesic alternative to stop ARMS mediated TRPV1 sensitization.
In the context of ecological risk assessment of chemicals, individual-based population models hold great potential to increase the ecological realism of current regulatory risk assessment procedures. However, developing and parameterizing such models is time-consuming and often ad hoc. Using standardized, tested submodels of individual organisms would make individual-based modelling more efficient and coherent. In this thesis, I explored whether Dynamic Energy Budget (DEB) theory is suitable for being used as a standard submodel in individual-based models, both for ecological risk assessment and theoretical population ecology. First, I developed a generic implementation of DEB theory in an individual-based modeling (IBM) context: DEB-IBM. Using the DEB-IBM framework I tested the ability of the DEB theory to predict population-level dynamics from the properties of individuals. We used Daphnia magna as a model species, where data at the individual level was available to parameterize the model, and population-level predictions were compared against independent data from controlled population experiments. We found that DEB theory successfully predicted population growth rates and peak densities of experimental Daphnia populations in multiple experimental settings, but failed to capture the decline phase, when the available food per Daphnia was low. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detecting gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology. In addition to theoretical explorations, we tested the potential of DEB theory combined with IBMs to extrapolate effects of chemical stress from the individual to population level. For this we used information at the individual level on the effect of 3,4-dichloroanailine on Daphnia. The individual data suggested direct effects on reproduction but no significant effects on growth. Assuming such direct effects on reproduction, the model was able to accurately predict the population response to increasing concentrations of 3,4-dichloroaniline. We conclude that DEB theory combined with IBMs holds great potential for standardized ecological risk assessment based on ecological models.
Investigating the role of fluorinated amino acids on protein structure and function using simulation
(2018)
Biofilms are complex living materials that form as bacteria get embedded in a matrix of self-produced protein and polysaccharide fibres. The formation of a network of extracellular biopolymer fibres contributes to the cohesion of the biofilm by promoting cell-cell attachment and by mediating biofilm-substrate interactions. This sessile mode of bacteria growth has been well studied by microbiologists to prevent the detrimental effects of biofilms in medical and industrial settings. Indeed, biofilms are associated with increased antibiotic resistance in bacterial infections, and they can also cause clogging of pipelines or promote bio-corrosion. However, biofilms also gained interest from biophysics due to their ability to form complex morphological patterns during growth. Recently, the emerging field of engineered living materials investigates biofilm mechanical properties at multiple length scales and leverages the tools of synthetic biology to tune the functions of their constitutive biopolymers.
This doctoral thesis aims at clarifying how the morphogenesis of Escherichia coli (E. coli) biofilms is influenced by their growth dynamics and mechanical properties. To address this question, I used methods from cell mechanics and materials science. I first studied how biological activity in biofilms gives rise to non-uniform growth patterns. In a second study, I investigated how E. coli biofilm morphogenesis and its mechanical properties adapt to an environmental stimulus, namely the water content of their substrate. Finally, I estimated how the mechanical properties of E. coli biofilms are altered when the bacteria express different extracellular biopolymers.
On nutritive hydrogels, micron-sized E. coli cells can build centimetre-large biofilms. During this process, bacterial proliferation and matrix production introduce mechanical stresses in the biofilm, which release through the formation of macroscopic wrinkles and delaminated buckles. To relate these biological and mechanical phenomena, I used time-lapse fluorescence imaging to track cell and matrix surface densities through the early and late stages of E. coli biofilm growth. Colocalization of high cell and matrix densities at the periphery precede the onset of mechanical instabilities at this annular region. Early growth is detected at this outer annulus, which was analysed by adding fluorescent microspheres to the bacterial inoculum. But only when high rates of matrix production are present in the biofilm centre, does overall biofilm spreading initiate along the solid-air interface. By tracking larger fluorescent particles for a long time, I could distinguish several kinematic stages of E. coli biofilm expansion and observed a transition from non-linear to linear velocity profiles, which precedes the emergence of wrinkles at the biofilm periphery. Decomposing particle velocities to their radial and circumferential components revealed a last kinematic stage, where biofilm movement is mostly directed towards the radial delaminated buckles, which verticalize. The resulting compressive strains computed in these regions were observed to substantially deform the underlying agar substrates. The co-localization of higher cell and matrix densities towards an annular region and the succession of several kinematic stages are thus expected to promote the emergence of mechanical instabilities at the biofilm periphery. These experimental findings are predicted to advance future modelling approaches of biofilm morphogenesis.
E. coli biofilm morphogenesis is further anticipated to depend on external stimuli from the environment. To clarify how the water could be used to tune biofilm material properties, we quantified E. coli biofilm growth, wrinkling dynamics and rigidity as a function of the water content of the nutritive substrates. Time-lapse microscopy and computational image analysis revealed that substrates with high water content promote biofilm spreading kinetics, while substrates with low water content promote biofilm wrinkling. The wrinkles observed on biofilm cross-sections appeared more bent on substrates with high water content, while they tended to be more vertical on substrates with low water content. Both wet and dry biomass, accumulated over 4 days of culture, were larger in biofilms cultured on substrates with high water content, despite extra porosity within the matrix layer. Finally, the micro-indentation analysis revealed that substrates with low water content supported the formation of stiffer biofilms. This study shows that E. coli biofilms respond to the water content of their substrate, which might be used for tuning their material properties in view of further applications.
Biofilm material properties further depend on the composition and structure of the matrix of extracellular proteins and polysaccharides. In particular, E. coli biofilms were suggested to present tissue-like elasticity due to a dense fibre network consisting of amyloid curli and phosphoethanolamine-modified cellulose. To understand the contribution of these components to the emergent mechanical properties of E. coli biofilms, we performed micro-indentation on biofilms grown from bacteria of several strains. Besides showing higher dry masses, larger spreading diameters and slightly reduced water contents, biofilms expressing both main matrix components also presented high rigidities in the range of several hundred kPa, similar to biofilms containing only curli fibres. In contrast, a lack of amyloid curli fibres provides much higher adhesive energies and more viscoelastic fluid-like material behaviour. Therefore, the combination of amyloid curli and phosphoethanolamine-modified cellulose fibres implies the formation of a composite material whereby the amyloid curli fibres provide rigidity to E. coli biofilms, whereas the phosphoethanolamine-modified cellulose rather acts as a glue. These findings motivate further studies involving purified versions of these protein and polysaccharide components to better understand how their interactions benefit biofilm functions.
All three studies depict different aspects of biofilm morphogenesis, which are interrelated. The first work reveals the correlation between non-uniform biological activities and the emergence of mechanical instabilities in the biofilm. The second work acknowledges the adaptive nature of E. coli biofilm morphogenesis and its mechanical properties to an environmental stimulus, namely water. Finally, the last study reveals the complementary role of the individual matrix components in the formation of a stable biofilm material, which not only forms complex morphologies but also functions as a protective shield for the bacteria it contains. Our experimental findings on E. coli biofilm morphogenesis and their mechanical properties can have further implications for fundamental and applied biofilm research fields.
Mathematical modeling of biological phenomena has experienced increasing interest since new high-throughput technologies give access to growing amounts of molecular data. These modeling approaches are especially able to test hypotheses which are not yet experimentally accessible or guide an experimental setup. One particular attempt investigates the evolutionary dynamics responsible for today's composition of organisms. Computer simulations either propose an evolutionary mechanism and thus reproduce a recent finding or rebuild an evolutionary process in order to learn about its mechanism. The quest for evolutionary fingerprints in metabolic and gene-coexpression networks is the central topic of this cumulative thesis based on four published articles. An understanding of the actual origin of life will probably remain an insoluble problem. However, one can argue that after a first simple metabolism has evolved, the further evolution of metabolism occurred in parallel with the evolution of the sequences of the catalyzing enzymes. Indications of such a coevolution can be found when correlating the change in sequence between two enzymes with their distance on the metabolic network which is obtained from the KEGG database. We observe that there exists a small but significant correlation primarily on nearest neighbors. This indicates that enzymes catalyzing subsequent reactions tend to be descended from the same precursor. Since this correlation is relatively small one can at least assume that, if new enzymes are no "genetic children" of the previous enzymes, they certainly be descended from any of the already existing ones. Following this hypothesis, we introduce a model of enzyme-pathway coevolution. By iteratively adding enzymes, this model explores the metabolic network in a manner similar to diffusion. With implementation of an Gillespie-like algorithm we are able to introduce a tunable parameter that controls the weight of sequence similarity when choosing a new enzyme. Furthermore, this method also defines a time difference between successive evolutionary innovations in terms of a new enzyme. Overall, these simulations generate putative time-courses of the evolutionary walk on the metabolic network. By a time-series analysis, we find that the acquisition of new enzymes appears in bursts which are pronounced when the influence of the sequence similarity is higher. This behavior strongly resembles punctuated equilibrium which denotes the observation that new species tend to appear in bursts as well rather than in a gradual manner. Thus, our model helps to establish a better understanding of punctuated equilibrium giving a potential description at molecular level. From the time-courses we also extract a tentative order of new enzymes, metabolites, and even organisms. The consistence of this order with previous findings provides evidence for the validity of our approach. While the sequence of a gene is actually subject to mutations, its expression profile might also indirectly change through the evolutionary events in the cellular interplay. Gene coexpression data is simply accessible by microarray experiments and commonly illustrated using coexpression networks where genes are nodes and get linked once they show a significant coexpression. Since the large number of genes makes an illustration of the entire coexpression network difficult, clustering helps to show the network on a metalevel. Various clustering techniques already exist. However, we introduce a novel one which maintains control of the cluster sizes and thus assures proper visual inspection. An application of the method on Arabidopsis thaliana reveals that genes causing a severe phenotype often show a functional uniqueness in their network vicinity. This leads to 20 genes of so far unknown phenotype which are however suggested to be essential for plant growth. Of these, six indeed provoke such a severe phenotype, shown by mutant analysis. By an inspection of the degree distribution of the A.thaliana coexpression network, we identified two characteristics. The distribution deviates from the frequently observed power-law by a sharp truncation which follows after an over-representation of highly connected nodes. For a better understanding, we developed an evolutionary model which mimics the growth of a coexpression network by gene duplication which underlies a strong selection criterion, and slight mutational changes in the expression profile. Despite the simplicity of our assumption, we can reproduce the observed properties in A.thaliana as well as in E.coli and S.cerevisiae. The over-representation of high-degree nodes could be identified with mutually well connected genes of similar functional families: zinc fingers (PF00096), flagella, and ribosomes respectively. In conclusion, these four manuscripts demonstrate the usefulness of mathematical models and statistical tools as a source of new biological insight. While the clustering approach of gene coexpression data leads to the phenotypic characterization of so far unknown genes and thus supports genome annotation, our model approaches offer explanations for observed properties of the coexpression network and furthermore substantiate punctuated equilibrium as an evolutionary process by a deeper understanding of an underlying molecular mechanism.