LOL is a novel secondary metabolite gene cluster associated with the production of loline alkaloids (saturated 1-aminopyrrolizidine alkaloids with an oxygen bridge) exclusively in closely related grass-endophyte species in the genera Epichloë and Neotyphodium. In this study I characterize the LOL cluster in E. festucae, including the presentation of sequence corresponding to 10 individual lol genes as well as defining the boundaries of the cluster and evaluation of the genomic DNA region flanking LOL in E. festucae. In addition to characterizing the LOL cluster in E. festucae, I present LOL sequence from two additional species, Neotyphodium coenophialum and Neotyphodium sp. PauTG-1. Together with two recently published LOL clusters from N. uncinatum, these data allow for a powerful phylogenetic comparison of five clusters from four closely related species. There is a high degree of microsynteny (conserved gene order and orientation) among the five LOL clusters, allowing us to predict potential transcriptional co-regulatory binding motifs in lol promoter regions. The relatedness of LOL clusters is especially interesting in light of the history of interspecific hybridizations that generated the asexual, Neotyphodium lineages. In fact, three of the clusters appear to have been introduced to different Neotyphodium species by the same ancestral Epichloë species, for which present day isolates are no longer able to produce lolines. To address the evolutionary origins of the cluster we have investigated the phylogenetic relationships of particular lol ORFs to their paralogous primary metabolism genes (and gene families) from endophytes, other fungi and even other kingdoms. I present extensive evidence that at least two individual lol genes have evolved from primary metabolism genes within the fungal ancestors of endophytes, rather than being introduced via horizontal gene transfer. I also present complementation studies in Neurospora crassa exploring the functional divergence of one lol gene from its primary metabolism paralog. While it is clear that these insecticidal compounds should convey a selective advantage to the fungus and its host, thus explaining preservation of the trait, this analysis provides an exploration into the evolutionary origin and maintenance of the genes that comprise the LOL and the cluster itself.

The aim of this dissertation is to investigate the distinctive character and variations in the landscape portraits in Synkkä yksinpuhelu, by the Finnish author Olavi Paavolainen, as well as investigate the significance of the landscape portraits for the work as a whole. Paavolainen calls his work a war diary. It comprises the years 1939-1944 when Finland suffered first the Winter War (Russo-Finnish War) and then the Continuation War with the Soviet Union. The author served at the front during the first year of the Continuation War and then afterwards at general staff headquaters. In the first part of the work the author focuses on describing the Karelian landscape which had become the battlefield. The latter part brings out the war time political events in Finland and in other parts of the warring world. As a form, the diary gives the author possibilities to use texts with various styles and content. In general, Synkkä yksinpuhelu can be said to contain history, autobiography, political and cultural essays, landscape portraits and travel sketches. The landscape portraits assume a central position in the book because of the scope, about a third of the total pages. In these portraits a few of the main themes of the work are developed. At the same time these themes build an antithetical relationship: nature creates and preserves life while war annihilates it. A number of the portraits, for exemple, descriptions of the moon and burial places, are a recurrent motif, giving the text structure and strengthening the theme of impermanence. In an exteded sense, Paavolainen's own concept of the thinking landscape can be used to characterize his portraits because the surrounding landscapes communicate his own moods and thoughts. This manner of describing nature ties together schools of art, such as romanticism, symbolism, expressionism and surrealism, where it is characteristic to allow the outer world to reflect the inner state of the soul. Paavolainen makes numerous references to works, authors and artists from the 19th and 20th centuries in his portraits. Using means such as irony and antithesis and with a sprinkling of ambivalence, the intertextual and interartistic relations illuminate the author's attitudes towards prevailing conditions. They also accentuate his thoughts about the purpose of existence.

Long terminal repeats (LTRs) are non-coding repeats flanking the protein-coding genes of LTR retrotransposons. The variability of LTRs poses a challenge in studying them. Hidden Markov models (HMMs), probabilistic models widely used in pattern recognition, are useful in dealing with this variability. The aim of this work was mainly to study LTRs of retroviruses and LTR retrotransposons using HMMs. Paper I describes the methodology of HMM modelling applied to different groups of LTRs from exogenous retroviruses (XRVs) and endogenous retroviruses (ERVs). The detection capabilities of HMMs were assessed and were found to be high for homogeneous groups of LTRs. The alignments generated by the HMMs displayed conserved motifs some of which could be related to known functions of XRVs. The common features of the different groups of retroviral LTRs were investigated by combining them into a single alignment. They were the short inverted terminal repeats TG and CA and three AT-rich stretches which provide retroviruses with TATA boxes and AATAAA polyadenylation signals. In Paper II, phylogenetic trees of three groups of retroviral LTRs were constructed by using HMM-based alignments. The LTR trees were consistent with trees based on other retroviral genes suggesting co-evolution between LTRs and these genes. In Paper III, the methods in Paper I and II were extended to LTRs from other retrotransposon groups, covering much of the diversity of all known LTRs. For the first time an LTR phylogeny could be achieved. There were no major disagreement between the LTR tree and trees based on three different domains of the Pol gene. The conserved LTR structure of paper I was found to apply to all LTRs. Putative Integrase recognition motifs extended up to 12 bp beyond the short inverted repeats TG/CA. Paper IV is a review article describing the use of sequence similarity and structural markers for the taxonomy of ERVs. ERVs were originally classified into three classes according to the length of the target site duplication. While this classification is useful it does not include all ERVs. A naming convention based on previous ERV and XRV nomenclature but taking into account newer information is advocated in order to provide a practical yet coherent scheme in dealing with new unclassified ERV sequences. Paper V gives an overview of bioinformatics tools for studies of ERVs and of retroviral evolution before and after endogenization. It gives some examples of recent integrations in vertebrate genomes and discusses pathogenicity of human ERVs including their possible relation to cancers. In conclusion, HMMs were able to successfully detect and align LTRs. Progress was made in understanding their conserved structure and phylogeny. The methods developed in this thesis could be applied to different kinds of non-coding DNA sequence element.

The key to the complexity of higher organisms lies not in the number of protein coding genes they carry, but rather in the intrinsic complexity of the gene regulatory networks. The major effectors of transcriptional regulation are proteins called transcription factors, and in this thesis four papers describing genome-wide studies of seven such factors are presented, together with studies on components of the chromatin and transcriptome. In Paper I, we optimized a large-scale in vivo method, ChIP-chip, to study protein – DNA interactions using microarrays. The metabolic-disease related transcription factors USF1, HNF4a and FOXA2 were studied in 1 % of the genome, and a surprising number of binding sites were found, mostly far from annotated genes. In Paper II, a novel sequencing based method, ChIP-seq, was applied to FOXA2, HNF4a and GABPa, allowing a true genome-wide view of binding sites. A large overlap between the datasets were seen, and molecular interactions were verified in vivo. Using a ChIP-seq specific motif discovery method, we identified both the expected motifs and several for co-localized transcription factors. In Paper III, we identified and studied a novel transcription factor, ZBED6, using the ChIP-seq method. Here, we went from one known binding site to several hundred sites throughout the mouse genome. Finally, in Paper IV, we studied the chromatin landscape by deep sequencing of nucleosomal DNA, and further used RNA-sequencing to quantify expression levels, and extended the knowledge about the binding profiles for the transcription factors NFY and TCF7L2.

Cette étude répond à l'intitulé suivant : La fiction romanesque de la postmodernité et ses labyrinthes. L'exemple des textes d'Alain Robbe-Grillet (France, 1922-2008), de Juan José Saer (Argentine, 1937-2005) et de Boubacar Boris Diop (Sénégal, 1947-). Elle se propose de cerner, sous l'autorité méthodologique de la poétique textuelle, les diverses modalités par lesquelles le motif du labyrinthe s'impose comme substrat privilégié de la poétique du récit chez trois écrivains appartenant à des aires linguistiques et culturelles différentes. Le choix et l'examen des textes de ces trois écrivains reposent sur la volonté de montrer le déploiement de ce motif dans l'organisation interne de la fiction romanesque postmoderne. En effet, intégré dans la dynamique interne de l'oeuvre de manière très diversifiée, le motif du labyrinthe surgit dans toutes les problématiques qui traversent l'écriture romanesque postmoderne. Par commodité méthodologique et rigueur scientifique, cette étude se déploie suivant trois axes complémentaires. Tandis que le premier qui s'intitule " Le labyrinthe comme invariant obsessionnel de la fiction romanesque de la postmodernité " s'attache à faire ressortir les diverses facettes sous lesquelles ce motif se déploie dans la dynamique textuelle du récit fictionnel postmoderne ; le deuxième qui a pour titre " Le labyrinthe comme modalité constitutive de la narrativité du récit : la fiction romanesque de la postmodernité à l'épreuve de la discontinuité ", s'attèle quant à lui, à examiner en quoi ce motif participe d'un projet d'écriture qui revendique la discontinuité comme principe narratif ; enfin, le troisième qui a pour intitulé " Le labyrinthe comme modalité caractéristique de l'interdiscursivité et de l'intergénéricité de la dynamique textuelle : la fiction romanesque de la postmodernité à l'épreuve de la polyphonie ", entreprend pour sa part de démontrer en quoi ce motif est constitutif de la polyphonie qui gouverne la dynamique textuelle du récit fictionnel postmoderne.

My thesis concerns spectroscopic studies (NMR, CD and fluorescence) of peptides with functions in biotechnology and biology, and their interactions with a model membrane (large unilamellar phospholipid vesicles). The resorufin-based arsenical hairpin binder (ReAsH) bound to a short peptide is a useful fluorescent tag for genetic labeling of proteins in living cells. A hairpin structure with some resemblance to type II β-turn was determined by NMR structure calculations (Paper I). Cell-penetrating peptides (CPPs) are short (30-35 residues), often rich in basic amino acids such as Arg. They can pass through the cell membrane and deliver bioactive cargoes, making them useful for biotechnical and pharmacological applications. The mechanisms of cellular uptake and membrane translocation are under debate. Understanding the mechanistic aspects of CPPs is the major focus of Papers II, III, and IV. The effect of the pyrenebutyrate (PB) on the cellular uptake, membrane translocation and perturbation of several CPPs from different subgroups was investigated (Paper II). We concluded that both charge and hydrophobicity of the CPP affect the cellular uptake and membrane translocation efficiency. Endosomal escape is a crucial challenge for the CPP applications. We modeled the endosome and endosomal escape for different CPPs to investigate the corresponding molecular mechanisms (Papers III and IV). Hydrophobic CPPs were able to translocate across the model membrane in the presence of a pH gradient, produced by bacteriorhodopsin proton pumping, whereas a smaller effect was observed for hydrophilic CPPs. Dynorphin A (Dyn A) peptide mutations are associated with neurodegenerative disorders, without involvement of the opioid receptors. The non-opioid activities of Dyn A may involve membrane perturbations. Model membrane-perturbations by three Dyn A mutants were investigated (Paper V). The results showed effects to different degrees largely in accordance with their neurotoxic effects. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.

Protein sequence motifs are gathering more and more attention in the field of sequence analysis. The recurring patterns have the potential to determine the conformation, function and activities of the proteins. In our work, we obtained protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is essential. We use two granular computing models, Fuzzy Improved K-means (FIK) and Fuzzy Greedy K-means (FGK), in order to efficiently generate protein motif information. After that, we develop an efficient Super Granular SVM Feature Elimination model to further extract the motif information. During the motifs searching process, setting up a fixed window size in advance may simplify the computational complexity and increase the efficiency. However, due to the fixed size, our model may deliver a number of similar motifs simply shifted by some bases or including mismatches. We develop a new strategy named Positional Association Super-Rule to confront the problem of motifs generated from a fixed window size. It is a combination approach of the super-rule analysis and a novel Positional Association Rule algorithm. We use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified HHK clustering, which requires no parameter setup to identify the similarities and dissimilarities between the motifs. The positional association rule is created and applied to search similar motifs that are shifted some residues. By analyzing the motifs results generated by our approaches, we realize that these motifs are not only significant in sequence area, but also in secondary structure similarity and biochemical properties.

Influenza B virus hemagglutinin (HA) is a major surface glycoprotein with frequent amino-acid substitutions. However, the roles of antibody selection in the amino-acid substitutions of HA were still poorly understood. An analysis was conducted on a total of 271 HA 1 sequences of influenza B virus strains isolated during 1940∼2007 finding positively selected sites all located in the four major epitopes (120-loop, 150-loop, 160-loop and 190-helix) supporting a predominant role of antibody selection in HA evolution. Of particular significance is the involvement of the 120-loop in positive selection. Influenza B virus HA continues to evolve into new sublineages, within which the four major epitopes were targeted selectively in positive selection. Thus, any newly emerging strains need to be placed in the context of their evolutionary history in order to understand and predict their epidemic potential. As key epigenetic regulators, polycomb group (PcG) proteins are responsible for the control of cell proliferation and differentiation as well as stem cell pluripotency and self-renewal. To facilitate experimental identification of PcG target genes, which are poorly understood, we propose a novel computational method, EpiPredictor , which models transcription factor interaction using a non-linear kernel. The resulting targets suggests that multiple transcription factor networking at the cis -regulatory elements is critical for PcG recruitment, while high GC content and high conservation level are also important features of PcG target genes. To try to translate the EpiPredictor into human data, we performed a computational study utilizing 22 human genome-wide CHIP data to identify DNA motifs and genome features that would potentially specify PRC2 using five motif discovery algorithms, Jaspar known transcription binding motifs, and other whole genome data. We have found multiple motifs within the various subgroups of experimental categories that have much higher enrichment against CHIP identified gene promoter than among random gene promoters. Specifically, we have identified Low CpG content CpG Islands (LeG's) as being critical in the separation of Cancer cell line identified targets from Embryonic Stem cell line identified targets. Additionally, there are differences between human and mouse ES cell predictions using the same motifs and features suggesting relevant evolutionary divergence.

This thesis investigates the correlations between short protein peptide sequences and local tertiary structures. In particular, it introduces a novel algorithm for partitioning short protein segments into clusters of local sequence-structure motifs, and demonstrates that these motif clusters contain useful structural information via two applications to structural prediction. The first application utilizes motif clusters to predict local protein tertiary structures. A novel dynamic programming algorithm that performs comparably with some of the best existing algorithms is described. The second application exploits the capability of motif clusters in recognizing regular secondary structures to improve the performance of secondary structure prediction based on Support Vector Machines. Empirical results show significant improvement in overall prediction accuracy with no performance degradation in any specific aspect being measured. The encouraging results obtained illustrate the great potential of using local sequence-structure motifs to tackle protein structure predictions and possibly other important problems in computational biology.