Expression Analysis of Host-Induced Genes and Proteins of Potato Pathogen Pectobacterium atrosepticum

Pectobacterium atrosepticum on Gram-negatiivinen bakteeri, joka aiheuttaa perunan tyvi- ja märkämätää. P. atrosepticum bakteerin optimilämpötila on melko alhainen ja se on yleinen lauhkeilla alueilla. Tyvimätä leviää pääasiassa siemenperunan välityksellä ja siksi se on ongelma erityisesti siemenperunan tuotannossa. P. atrosepticum kannan SCRI1043 genomi on julkaistu ja sitä tutkitaan malliorganismina märkä- ja tyvimädän taudinaiheuttamisen ymmärtämiseksi. Tämä opportunistinen taudinaiheuttaja voi elää isäntäkasvissa kuukausia piilevänä, aiheuttamatta näkyviä oireita. Suotuisissa olosuhteissa bakteerit alkavat jakautua ja tuottaa kasvin kudoksia hajottavia entsyymejä. Mädäntyvä kasvimassa tarjoaa ravinteita bakteerien kasvuun ja mahdollistaa isäntäkasvin asuttamisen. Soluseiniä hajottavien entsyymien merkitys taudinaiheuttamisessa on hyvin tunnettu, mutta oireettomasta jaksosta ja taudin alkuvaiheista tiedätään vain vähän. Bakteerin genomi sisältää monia toksiineja, adhesiineja, hemolysiineja ja muita proteiineja, joilla saattaa olla merkitys taudinaiheuttamisessa. Tässä työssä käytettiin proteomiikkaa ja mikrosiruanalysiä P. atrosepticum bakteerin erittyvien proteiinien ja geeniekspression tutkimiseen. Proteiinit, jotka eritetään ulos bakteerista, toimivat todennäköisesti taudinaiheuttamisessa, koska ne ovat suorassa kontaktissa isäntäkasvin kanssa. Analyysit suoritettiin olosuhteissa, jotka muistuttavat kasvin soluvälitilaa: matala pH, vähän ravinteita ja matala lämpötila. Isäntäkasvin läsnäolon vaikutusta proteiinien tuottoon ja geeniekspressioon tutkittiin lisäämällä perunauutetta kasvatusalustaan. Tutkimuksessa tunnistettiin P. atrosepticum bakteerin monia jo tunnettuja ja mahdollisesti taudinaiheuttamiseen liittyviä proteiineja. Perunauute lisäsi hiljattain tunnistetun, proteiinien eritysreittiä (tyyppi VI sekreetio, T6SS) koodaavien geenien ilmentymistä. Lisäksi bakteerin havaittiin erittävän useita T6SS:n liittyviä proteiineja kasvualustaan, johon oli lisätty perunauutetta. T6SS:n merkitys bakteereille on vielä epäselvä ja sen vaikutuksesta taudinaiheuttamiseen on julkaistu ristiriitaisia tuloksia. Märkä- ja tyvimädän ymmärtäminen molekulaarisella tasolla luo pohjan tautien kontrollointiin tähtäävään soveltavaan tutkimukseen. Tämä tutkimus lisää tietoa kasvi-patogeeni- interaktiosta ja sitä voidaan tulevaisuudessa käyttää hyväksi esimerkiksi diagnostiikassa, resistenttien perunalajikkeiden jalostuksessa tai viljely- ja varastointiolosuhteiden parantamisessa.

HPLC analysis of plant sterol oxidation products

Increased interest in the cholesterol-lowering effect of plant sterols has led to development of plant sterol-enriched foods. When products are enriched, the safety of the added components must be evaluated. In the case of plant sterols, oxidation is the reaction of main concern. In vitro studies have indicated that cholesterol oxides may have harmful effects. Due their structural similarity, plant sterol oxidation products may have similar health implications. This study concentrated on developing high-performance liquid chromatography (HPLC) methods that enable the investigation of formation of both primary and secondary oxidation products and thus can be used for oxidation mechanism studies of plant sterols. The applicability of the methods for following the oxidation reactions of plant sterols was evaluated by using oxidized stigmasterol and sterol mixture as model samples. An HPLC method with ultraviolet and fluorescence detection (HPLC-UV-FL) was developed. It allowed the specific detection of hydroperoxides with FL detection after post-column reagent addition. The formation of primary and secondary oxidation products and amount of unoxidized sterol could be followed by using UV detection. With the HPLC-UV-FL method, separation between oxides was essential and oxides of only one plant sterol could be quantified in one run. Quantification with UV can lead to inaccuracy of the results since the number of double bonds had effect on the UV absorbance. In the case of liquid chromatography-mass spectrometry (LC-MS), separation of oxides with different functionalities was important because some oxides of the same sterol have similar molecular weight and moreover epimers have similar fragmentation behaviour. On the other hand, coelution of different plant sterol oxides with the same functional group was acceptable since they differ in molecular weights. Results revealed that all studied plant sterols and cholesterol seem to have similar fragmentation behaviour, with only relative ion abundances being slightly different. The major advantage of MS detection coupled with LC separation is the capability to analyse totally or partly coeluting analytes if these have different molecular weights. The HPLC-UV-FL and LC-MS methods were demonstrated to be suitable for studying the photo-oxidation and thermo-oxidation reactions of plant sterols. The HPLC-UV-FL method was able to show different formation rates of hydroperoxides during photo-oxidation. The method also confirmed that plant sterols have similar photo-oxidation behaviour to cholesterol. When thermo-oxidation of plant sterols was investigated by HPLC-UV-FL and LC-MS, the results revealed that the formation and decomposition of individual hydroperoxides and secondary oxidation products could be studied. The methods used revealed that all of the plant sterols had similar thermo-oxidation behaviour when compared with each other, and the predominant reactions and oxidation rates were temperature dependent. Overall, these findings showed that with these LC methods the oxidation mechanisms of plant sterols can be examined in detail, including the formation and degradation of individual hydroperoxides and secondary oxidation products, with less sample pretreatment and without derivatization.

Topological Quantum Computation - An Analysis of an Anyon Model Based on Quantum Double Symmetries

There exists various suggestions for building a functional and a fault-tolerant large-scale quantum computer. Topological quantum computation is a more exotic suggestion, which makes use of the properties of quasiparticles manifest only in certain two-dimensional systems. These so called anyons exhibit topological degrees of freedom, which, in principle, can be used to execute quantum computation with intrinsic fault-tolerance. This feature is the main incentive to study topological quantum computation. The objective of this thesis is to provide an accessible introduction to the theory. In this thesis one has considered the theory of anyons arising in two-dimensional quantum mechanical systems, which are described by gauge theories based on so called quantum double symmetries. The quasiparticles are shown to exhibit interactions and carry quantum numbers, which are both of topological nature. Particularly, it is found that the addition of the quantum numbers is not unique, but that the fusion of the quasiparticles is described by a non-trivial fusion algebra. It is discussed how this property can be used to encode quantum information in a manner which is intrinsically protected from decoherence and how one could, in principle, perform quantum computation by braiding the quasiparticles. As an example of the presented general discussion, the particle spectrum and the fusion algebra of an anyon model based on the gauge group S_3 are explicitly derived. The fusion algebra is found to branch into multiple proper subalgebras and the simplest one of them is chosen as a model for an illustrative demonstration. The different steps of a topological quantum computation are outlined and the computational power of the model is assessed. It turns out that the chosen model is not universal for quantum computation. However, because the objective was a demonstration of the theory with explicit calculations, none of the other more complicated fusion subalgebras were considered. Studying their applicability for quantum computation could be a topic of further research.

Use of non-specific and specific interactions in the analysis of testosterone and related compounds by capillary electromigration techniques

Determination of testosterone and related compounds in body fluids is of utmost importance in doping control and the diagnosis of many diseases. Capillary electromigration techniques are a relatively new approach for steroid research. Owing to their electrical neutrality, however, separation of steroids by capillary electromigration techniques requires the use of charged electrolyte additives that interact with the steroids either specifically or non-specifically. The analysis of testosterone and related steroids by non-specific micellar electrokinetic chromatography (MEKC) was investigated in this study. The partial filling (PF) technique was employed, being suitable for detection by both ultraviolet spectrophotometry (UV) and electrospray ionization mass spectrometry (ESI-MS). Efficient, quantitative PF-MEKC UV methods for steroid standards were developed through the use of optimized pseudostationary phases comprising surfactants and cyclodextrins. PF-MEKC UV proved to be a more sensitive, efficient and repeatable method for the steroids than PF-MEKC ESI-MS. It was discovered that in PF-MEKC analyses of electrically neutral steroids, ESI-MS interfacing sets significant limitations not only on the chemistry affecting the ionization and detection processes, but also on the separation. The new PF-MEKC UV method was successfully employed in the determination of testosterone in male urine samples after microscale immunoaffinity solid-phase extraction (IA-SPE). The IA-SPE method, relying on specific interactions between testosterone and a recombinant anti-testosterone Fab fragment, is the first such method described for testosterone. Finally, new data for interactions between steroids and human and bovine serum albumins were obtained through the use of affinity capillary electrophoresis. A new algorithm for the calculation of association constants between proteins and neutral ligands is introduced.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Bayesian analysis of community dynamics

Elucidating the mechanisms responsible for the patterns of species abundance, diversity, and distribution within and across ecological systems is a fundamental research focus in ecology. Species abundance patterns are shaped in a convoluted way by interplays between inter-/intra-specific interactions, environmental forcing, demographic stochasticity, and dispersal. Comprehensive models and suitable inferential and computational tools for teasing out these different factors are quite limited, even though such tools are critically needed to guide the implementation of management and conservation strategies, the efficacy of which rests on a realistic evaluation of the underlying mechanisms. This is even more so in the prevailing context of concerns over climate change progress and its potential impacts on ecosystems. This thesis utilized the flexible hierarchical Bayesian modelling framework in combination with the computer intensive methods known as Markov chain Monte Carlo, to develop methodologies for identifying and evaluating the factors that control the structure and dynamics of ecological communities. These methodologies were used to analyze data from a range of taxa: macro-moths (Lepidoptera), fish, crustaceans, birds, and rodents. Environmental stochasticity emerged as the most important driver of community dynamics, followed by density dependent regulation; the influence of inter-specific interactions on community-level variances was broadly minor. This thesis contributes to the understanding of the mechanisms underlying the structure and dynamics of ecological communities, by showing directly that environmental fluctuations rather than inter-specific competition dominate the dynamics of several systems. This finding emphasizes the need to better understand how species are affected by the environment and acknowledge species differences in their responses to environmental heterogeneity, if we are to effectively model and predict their dynamics (e.g. for management and conservation purposes). The thesis also proposes a model-based approach to integrating the niche and neutral perspectives on community structure and dynamics, making it possible for the relative importance of each category of factors to be evaluated in light of field data.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis. In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.