Gut physiology mediates a trade-off between adaptation to malnutrition and susceptibility to food-borne pathogens.

The animal gut plays a central role in tackling two common ecological challenges, nutrient shortage and food-borne parasites, the former by efficient digestion and nutrient absorption, the latter by acting as an immune organ and a barrier. It remains unknown whether these functions can be independently optimised by evolution, or whether they interfere with each other. We report that Drosophila melanogaster populations adapted during 160 generations of experimental evolution to chronic larval malnutrition became more susceptible to intestinal infection with the opportunistic bacterial pathogen Pseudomonas entomophila. However, they do not show suppressed immune response or higher bacterial loads. Rather, their increased susceptibility to P. entomophila is largely mediated by an elevated predisposition to loss of intestinal barrier integrity upon infection. These results may reflect a trade-off between the efficiency of nutrient extraction from poor food and the protective function of the gut, in particular its tolerance to pathogen-induced damage.

Mechanisms of OGT-mediated HCF-1 protein maturation

Post-translational protein modifications are crucial for many fundamental cellular and extracellular processes and greatly contribute to the complexity of organisms. Human HCF-1 is a transcriptional co-regulator that undergoes complex protein maturation involving reversible and irreversible post-translational modifications. Upon synthesis as a large precursor protein, HCF-1 undergoes extensive reversible glycosylation with β-N-acetylglucosamine giving rise to O-linked-β-N-acetylglucosamine (O-GlcNAc) modified serines and threonines. HCF-1 also undergoes irreversible site-specific proteolysis, which is important for one of HCF-1's major functions - the regulation of the cell-division cycle. HCF-1 O-GlcNAcylation and site-specific proteolysis are both catalyzed by a single enzyme with an unusual dual enzymatic activity, the O-GlcNAc transferase (OGT). HCF-1 is cleaved by OGT at any of six highly conserved 26 amino acid repeated sequences (HCF-1PRO repeats), but the mechanisms and the substrate requirements for OGT-mediated cleavage are not understood. In the present work, I characterized substrate requirements for OGT-mediated cleavage and O-GlcNAcylation of HCF-1. I identified key elements within the HCF-1PRO-repeat sequence that are important for proteolysis. Remarkably, an invariant single amino acid side-chain within the HCF-1PRO-repeat sequence displays particular OGT-binding properties and is essential for proteolysis. Additionally, I characterized substrate requirements for proteolysis outside of the HCF-1PRO repeat and identified a novel, highly O-GlcNAcylated OGT-binding sequence that enhances cleavage of the first HCF-1PRO repeat. These results link OGT association and its O-GlcNAcylation activities to HCF-1PRO-repeat proteolysis.

Sidelobe elimination for generalized synthetic discriminant functions by a two-filter correlation and subsequent postprocessing of the intensity distributions

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

Biological interpretation of genome-wide association studies using predicted gene functions.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Implementing Role Based Access Control into the Enterprise Environment to Support Business and IT Functions

The thesis studies role based access control and its suitability in the enterprise environment. The aim is to research how extensively role based access control can be implemented in the case organization and how it support organization’s business and IT functions. This study points out the enterprise’s needs for access control, factors of access control in the enterprise environment and requirements for implementation and the benefits and challenges it brings along. To find the scope how extensively role based access control can be implemented into the case organization, firstly is examined the actual state of access control. Secondly is defined a rudimentary desired state (how things should be) and thirdly completed it by using the results of the implementation of role based access control application. The study results the role model for case organization unit, and the building blocks and the framework for the organization wide implementation. Ultimate value for organization is delivered by facilitating the normal operations of the organization whilst protecting its information assets.

Embryonic gene expression of Coregonus palaea (whitefish) under pathogen stress as analyzed by high-throughput RNA-sequencing.

Most fishes produce free-living embryos that are exposed to environmental stressors immediately following fertilization, including pathogenic microorganisms. Initial immune protection of embryos involves the chorion, as a protective barrier, and maternally-allocated antimicrobial compounds. At later developmental stages, host-genetic effects influence susceptibility and tolerance, suggesting a direct interaction between embryo genes and pathogens. So far, only a few host genes could be identified that correlate with embryonic survival under pathogen stress in salmonids. Here, we utilized high-throughput RNA-sequencing in order to describe the transcriptional response of a non-model fish, the Alpine whitefish Coregonus palaea, to infection, both in terms of host genes that are likely manipulated by the pathogen, and those involved in an early putative immune response. Embryos were produced in vitro, raised individually, and exposed at the late-eyed stage to a virulent strain of the opportunistic fish pathogen Pseudomonas fluorescens. The pseudomonad increased embryonic mortality and affected gene expression substantially. For example, essential, upregulated metabolic pathways in embryos under pathogen stress included ion binding pathways, aminoacyl-tRNA-biosynthesis, and the production of arginine and proline, most probably mediated by the pathogen for its proliferation. Most prominently downregulated transcripts comprised the biosynthesis of unsaturated fatty acids, the citrate cycle, and various isoforms of b-cell transcription factors. These factors have been shown to play a significant role in host blood cell differentiation and renewal. With regard to specific immune functions, differentially expressed transcripts mapped to the complement cascade, MHC class I and II, TNF-alpha, and T-cell differentiation proteins. The results of this study reveal insights into how P. fluorescens impairs the development of whitefish embryos and set a foundation for future studies investigating host pathogen interactions in fish embryos.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Suberization-the second life of an endodermal cell.

The endodermis is the innermost cortical cell layer that surrounds the central vasculature and deposits an apoplastic diffusion barrier known as the Casparian strip. Although discovered 150 years ago, the underlying mechanisms responsible for formation of the Casparian strips have only recently been investigated. However, the fate of the endodermal cell goes further than formation of Casparian strips as they undergo a second level of differentiation, defined by deposition of suberin as a secondary cell wall. The presence and function of endodermal suberin in root barriers has remained enigmatic, as its role in barrier formation is not clear, especially in respect to the already existing Casparian strips. In this review, we present recent advances in the understanding of suberin synthesis, transport to the secondary cell wall, developmental features and functions. We focus on some of the major unknown questions revolving the function of endodermal suberin, which we now have the means to investigate. We further provide thoughts on how this knowledge might expand our current models on the developmental and physiological adaptation of root in response to the environment.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

New insights into cellular prion protein (PrPc) functions: the 'ying and yang' of a relevant protein.

The conversion of cellular prion protein (PrPc), a GPI-anchored protein, into a protease-K-resistant and infective form (generally termed PrPsc) is mainly responsible for Transmissible Spongiform Encephalopathies (TSEs), characterized by neuronal degeneration and progressive loss of basic brain functions. Although PrPc is expressed by a wide range of tissues throughout the body, the complete repertoire of its functions has not been fully determined. Recent studies have confirmed its participation in basic physiological processes such as cell proliferation and the regulation of cellular homeostasis. Other studies indicate that PrPc interacts with several molecules to activate signaling cascades with a high number of cellular effects. To determine PrPc functions, transgenic mouse models have been generated in the last decade. In particular, mice lacking specific domains of the PrPc protein have revealed the contribution of these domains to neurodegenerative processes. A dual role of PrPc has been shown, since most authors report protective roles for this protein while others describe pro-apoptotic functions. In this review, we summarize new findings on PrPc functions, especially those related to neural degeneration and cell signaling.

Two-year follow-op of cognitive functions in Schizophrenia spectrum disorders of adolescent patients treated with electroconvulsive therapy

Objective: The aim of the current study was to investigate the long-term cognitive effects of electroconvulsive therapy (ECT) in a sample of adolescent patients in whom schizophrenia spectrum disorders were diagnosed. Methods: The sample was composed of nine adolescent subjects in whom schizophrenia or schizoaffective disorder was diagnosed according to DSM-IV-TR criteria on whom ECT was conducted (ECT group) and nine adolescent subjects matched by age, socioeconomic status, and diagnostic and Positive and Negative Syndrome Scale (PANSS) total score at baseline on whom ECT was not conducted (NECT group). Clinical and neuropsychological assessments were carried out at baseline before ECT treatment and at 2-year follow-up. Results: Significant differences were found between groups in the number of unsuccessful medication trials. No statistically significant differences were found between the ECT group and theNECT group in either severity as assessed by the PANSS, or in any cognitive variables at baseline.At follow-up, both groups showed significant improvement in clinical variables (subscales of positive, general, and total scores of PANSS and Clinical Global Impressions-Improvement). In the cognitive assessment at follow-up, significant improvement was found in both groups in the semantic category of verbal fluency task and digits forward. However, no significant differences were found between groups in any clinical or cognitive variable at follow-up. Repeated measures analysis found no significant interaction of time · group in any clinical or neuropsychological measures. Conclusions: The current study showed no significant differences in change over time in clinical or neuropsychological variables between the ECT group and the NECT group at 2-year follow-up. Thus, ECT did not show any negative influence on long-term neuropsychological variables in our sample.