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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.
Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.
The contractile vacuole (CV) is an osmoregulatory organelle found exclusively in algae and protists. In addition to expelling excessive water out of the cell, it also expels ions and other metabolites and thereby contributes to the cell's metabolic homeostasis. The interest in the CV reaches beyond its immediate cellular roles. The CV's function is tightly related to basic cellular processes such as membrane dynamics and vesicle budding and fusion; several physiological processes in animals, such as synaptic neurotransmission and blood filtration in the kidney, are related to the CV's function; and several pathogens, such as the causative agents of sleeping sickness, possess CVs, which may serve as pharmacological targets. The green alga Chlamydomonas reinhardtii has two CVs. They are the smallest known CVs in nature, and they remain relatively untouched in the CV-related literature. Many genes that have been shown to be related to the CV in other organisms have close homologues in C. reinhardtii. We attempted to silence some of these genes and observe the effect on the CV. One of our genes, VMP1, caused striking, severe phenotypes when silenced. Cells exhibited defective cytokinesis and aberrant morphologies. The CV, incidentally, remained unscathed. In addition, mutant cells showed some evidence of disrupted autophagy. Several important regulators of the cell cycle as well as autophagy were found to be underexpressed in the mutant. Lipidomic analysis revealed many meaningful changes between wild-type and mutant cells, reinforcing the compromised-autophagy observation. VMP1 is a singular protein, with homologues in numerous eukaryotic organisms (aside from fungi), but usually with no relatives in each particular genome. Since its first characterization in 2002 it has been associated with several cellular processes and functions, namely autophagy, programmed cell-death, secretion, cell adhesion, and organelle biogenesis. It has been implicated in several human diseases: pancreatitis, diabetes, and several types of cancer. Our results reiterate some of the observations in VMP1's six reported homologues, but, importantly, show for the first time an involvement of this protein in cell division. The mechanisms underlying this involvement in Chlamydomonas, as well as other key aspects, such as VMP1's subcellular localization and interaction partners, still await elucidation.