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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.
Leaves exhibit cells with varying degrees of shape complexity along the proximodistal axis. Heterogeneities in growth directions within individual cells bring about such complexity in cell shape. Highly complex and interconnected gene regulatory networks and signaling pathways have been identified to govern these processes. In addition, the organization of cytoskeletal networks and cell wall mechanical properties greatly influences the regulation of cell shape. Research has shown that microtubules are involved in regulating cellulose deposition and direc-tion of cell growth. However, comprehensive analysis of the regulation of the actin cytoskele-ton in cell shape regulation has not been well studied.
This thesis provides evidence that actin regulates aspects of cell growth, division, and direction-al expansion that impacts morphogenesis of developing leaves. The jigsaw puzzle piece mor-phology of epidermal pavement cells further serves as an ideal system to investigate the com-plex process of morphogenetic processes occurring at the cellular level. Here we have em-ployed live cell based imaging studies to track the development of pavement cells in actin com-promised conditions. Genetic perturbation of two predominantly expressed vegetative actin genes ACTIN2 and ACTIN7 results in delayed emergence of the cellular protrusions in pave-ment cells. Perturbation of actin also impacted the organization of microtubule in these cells that is known to promote emergence of cellular protrusions. Further, live-cell imaging of actin or-ganization revealed a correlation with cell shape, suggesting that actin plays a role in influencing pavement cell morphogenesis.
In addition, disruption of actin leads to an increase in cell size along the leaf midrib, with cells being highly anisotropic due to reduced cell division. The reduction of cell division further im-pacted the morphology of the entire leaf, with the mutant leaves being more curved. These re-sults suggests that actin plays a pivotal role in regulating morphogenesis at the cellular and tis-sue scales thereby providing valuable insights into the role of the actin cytoskeleton in plant morphogenesis.
Mars is one of the best candidates among planetary bodies for supporting life. The presence of water in the form of ice and atmospheric vapour together with the availability of biogenic elements and energy are indicators of the possibility of hosting life as we know it. The occurrence of permanently frozen ground – permafrost, is a common phenomenon on Mars and it shows multiple morphological analogies with terrestrial permafrost. Despite the extreme inhospitable conditions, highly diverse microbial communities inhabit terrestrial permafrost in large numbers. Among these are methanogenic archaea, which are anaerobic chemotrophic microorganisms that meet many of the metabolic and physiological requirements for survival on the martian subsurface. Moreover, methanogens from Siberian permafrost are extremely resistant against different types of physiological stresses as well as simulated martian thermo-physical and subsurface conditions, making them promising model organisms for potential life on Mars. The main aims of this investigation are to assess the survival of methanogenic archaea under Mars conditions, focusing on methanogens from Siberian permafrost, and to characterize their biosignatures by means of Raman spectroscopy, a powerful technology for microbial identification that will be used in the ExoMars mission. For this purpose, methanogens from Siberian permafrost and non-permafrost habitats were subjected to simulated martian desiccation by exposure to an ultra-low subfreezing temperature (-80ºC) and to Mars regolith (S-MRS and P-MRS) and atmospheric analogues. They were also exposed to different concentrations of perchlorate, a strong oxidant found in martian soils. Moreover, the biosignatures of methanogens were characterized at the single-cell level using confocal Raman microspectroscopy (CRM). The results showed survival and methane production in all methanogenic strains under simulated martian desiccation. After exposure to subfreezing temperatures, Siberian permafrost strains had a faster metabolic recovery, whereas the membranes of non-permafrost methanogens remained intact to a greater extent. The strain Methanosarcina soligelidi SMA-21 from Siberian permafrost showed significantly higher methane production rates than all other strains after the exposure to martian soil and atmospheric analogues, and all strains survived the presence of perchlorate at the concentration on Mars. Furthermore, CRM analyses revealed remarkable differences in the overall chemical composition of permafrost and non-permafrost strains of methanogens, regardless of their phylogenetic relationship. The convergence of the chemical composition in non-sister permafrost strains may be the consequence of adaptations to the environment, and could explain their greater resistance compared to the non-permafrost strains. As part of this study, Raman spectroscopy was evaluated as an analytical technique for remote detection of methanogens embedded in a mineral matrix. This thesis contributes to the understanding of the survival limits of methanogenic archaea under simulated martian conditions to further assess the hypothetical existence of life similar to methanogens on the martian subsurface. In addition, the overall chemical composition of methanogens was characterized for the first time by means of confocal Raman microspectroscopy, with potential implications for astrobiological research.