Nutrition and bone metabolism : In vivo effects of inorganic phosphate on rats and in vitro effects of bioactive tripeptides on human osteoblasts

Nutrition affects bone health throughout life. To optimize peak bone mass development and maintenance, it is important to pay attention to the dietary factors that enhance and impair bone metabolism. In this study, the in vivo effects of inorganic dietary phosphate and the in vitro effects of bioactive tripeptides, IPP, VPP and LKP were investigated. Dietary phosphate intake is increased through the use of convenience foods and soft drinks rich in phosphate-containing food additives. Our results show that increased dietary phosphate intake hinders mineral deposition in cortical bone and diminishes bone mineral density (BMD) in the aged skeleton in a rodent model (Study I). In the growing skeleton (Study II), increased phosphate intake was observed to reduce bone material and structural properties, leading to diminished bone strength. Studies I and II revealed that a low Ca:P ratio has negative effects on the mature and growing rat skeleton even when calcium intake is sufficient. High dietary protein intake is beneficial for bone health. Protein is essential for bone turnover and matrix formation. In addition, hydrolysis of proteins in the gastrointestinal tract produces short peptides that possess a biological function beyond that of being tissue building blocks. The effects of three bioactive tripeptides, IPP, VPP and LKP, were assessed in short- and long-term in vitro experiments. Short-term treatment (24 h) with tripeptide IPP, VPP or LKP influenced osteoblast gene expression (Study III). IPP in particular, regulates genes associated with cell differentiation, cell growth and cell signal transduction. The upregulation of these genes indicates that IPP enhances osteoblast proliferation and differentiation. Long-term treatment with IPP enhanced osteoblast gene expression in favour of bone formation and increased mineralization (Study IV). The in vivo effects of IPP on osteoblast differentiation might differ since eating frequency drives food consumption, and protein degradation products, such as bioactive peptides, are available periodically, not continuously as in this study. To sum up, Studies I and II raise concern about the appropriate amount of dietary phosphate to support bone health as excess is harmful. Studies III and IV in turn, support findings of the beneficial effects of dietary protein on bone and provide a mechanistic explanation since cell proliferation and osteoblast function were improved by treatment with bioactive tripeptide IPP.

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.

Algorithms for 13C metabolic flux analysis

The metabolism of an organism consists of a network of biochemical reactions that transform small molecules, or metabolites, into others in order to produce energy and building blocks for essential macromolecules. The goal of metabolic flux analysis is to uncover the rates, or the fluxes, of those biochemical reactions. In a steady state, the sum of the fluxes that produce an internal metabolite is equal to the sum of the fluxes that consume the same molecule. Thus the steady state imposes linear balance constraints to the fluxes. In general, the balance constraints imposed by the steady state are not sufficient to uncover all the fluxes of a metabolic network. The fluxes through cycles and alternative pathways between the same source and target metabolites remain unknown. More information about the fluxes can be obtained from isotopic labelling experiments, where a cell population is fed with labelled nutrients, such as glucose that contains 13C atoms. Labels are then transferred by biochemical reactions to other metabolites. The relative abundances of different labelling patterns in internal metabolites depend on the fluxes of pathways producing them. Thus, the relative abundances of different labelling patterns contain information about the fluxes that cannot be uncovered from the balance constraints derived from the steady state. The field of research that estimates the fluxes utilizing the measured constraints to the relative abundances of different labelling patterns induced by 13C labelled nutrients is called 13C metabolic flux analysis. There exist two approaches of 13C metabolic flux analysis. In the optimization approach, a non-linear optimization task, where candidate fluxes are iteratively generated until they fit to the measured abundances of different labelling patterns, is constructed. In the direct approach, linear balance constraints given by the steady state are augmented with linear constraints derived from the abundances of different labelling patterns of metabolites. Thus, mathematically involved non-linear optimization methods that can get stuck to the local optima can be avoided. On the other hand, the direct approach may require more measurement data than the optimization approach to obtain the same flux information. Furthermore, the optimization framework can easily be applied regardless of the labelling measurement technology and with all network topologies. In this thesis we present a formal computational framework for direct 13C metabolic flux analysis. The aim of our study is to construct as many linear constraints to the fluxes from the 13C labelling measurements using only computational methods that avoid non-linear techniques and are independent from the type of measurement data, the labelling of external nutrients and the topology of the metabolic network. The presented framework is the first representative of the direct approach for 13C metabolic flux analysis that is free from restricting assumptions made about these parameters.In our framework, measurement data is first propagated from the measured metabolites to other metabolites. The propagation is facilitated by the flow analysis of metabolite fragments in the network. Then new linear constraints to the fluxes are derived from the propagated data by applying the techniques of linear algebra.Based on the results of the fragment flow analysis, we also present an experiment planning method that selects sets of metabolites whose relative abundances of different labelling patterns are most useful for 13C metabolic flux analysis. Furthermore, we give computational tools to process raw 13C labelling data produced by tandem mass spectrometry to a form suitable for 13C metabolic flux analysis.

The viral coat protein is regulated by HSP70 and HSP40 in Potato virus A infection

Plus-stranded (plus) RNA viruses multiply within a cellular environment as tightly integrated units and rely on the genetic information carried within their genomes for multiplication and, hence, persistence. The minimal genomes of plus RNA viruses are unable to encode the molecular machineries that are required for virus multiplication. This sets requisites for the virus, which must form compatible interactions with host components during multiplication to successfully utilize primary metabolites as building blocks or metabolic energy, and to divert the protein synthesis machinery for production of viral proteins. In fact, the emerging picture of a virus-infected cell displays tight integration with the virus, from simple host and virus protein interactions through to major changes in the physiological state of the host cell. This study set out to develop a method for the identification of host components, mainly host proteins, that interact with proteins of Potato virus A (PVA; Potyvirus) during infection. This goal was approached by developing affinity-tag based methods for the purification of viral proteins complexed with associated host proteins from infected plants. Using this method, host membrane-associated viral ribonucleoprotein (RNP) complexes were obtained, and several host and viral proteins could be identified as components of these complexes. One of the host proteins identified using this strategy was a member of the heat shock protein 70 (HSP70) family, and this protein was chosen for further analysis. To enable the analysis of viral gene expression, a second method was developed based on Agrobacterium-mediated virus genome delivery into plant cells, and detection of virally expressed Renilla luciferase (RLUC) as a quantitative measure of viral gene expression. Using this method, it was observed that down-regulation of HSP70 caused a PVA coat protein (CP)-mediated defect associated with replication. Further experimentation suggested that CP can inhibit viral gene expression and that a distinct translational activity coupled to replication, referred to as replication-associated translation (RAT), exists. Unlike translation of replication-deficient viral RNA, RAT was dependent on HSP70 and its co-chaperone CPIP. HSP70 and CPIP together regulated CP turnover by promoting its modification by ubiquitin. Based on these results, an HSP70 and CPIP-driven mechanism that functions to regulate CP during viral RNA replication and/or translation is proposed, possibly to prevent premature particle assembly caused by CP association with viral RNA.

Radiation effects in supported nanoparticles

Nanotechnology applications are entering the market in increasing numbers, nanoparticles being among the main classes of materials used. Particles can be used, e.g., for catalysing chemical reactions, such as is done in car exhaust catalysts today. They can also modify the optical and electronic properties of materials or be used as building blocks for thin film coatings on a variety of surfaces. To develop materials for specific applications, an intricate control of the particle properties, structure, size and shape is required. All these depend on a multitude of factors from methods of synthesis and deposition to post-processing. This thesis addresses the control of nanoparticle structure by low-energy cluster beam deposition and post-synthesis ion irradiation. Cluster deposition in high vacuum offers a method for obtaining precisely controlled cluster-assembled materials with minimal contamination. Due to the clusters small size, however, the cluster-surface interaction may drastically change the cluster properties on deposition. In this thesis, the deposition process of metal and alloy clusters on metallic surfaces is modelled using molecular dynamics simulations, and the mechanisms influencing cluster structure are identified. Two mechanisms, mechanical melting upon deposition and thermally activated dislocation motion, are shown to determine whether a deposited cluster will align epitaxially with its support. The semiconductor industry has used ion irradiation as a tool to modify material properties for decades. Irradiation can be used for doping, patterning surfaces, and inducing chemical ordering in alloys, just to give a few examples. The irradiation response of nanoparticles has, however, remained an almost uncharted territory. Although irradiation effects in nanoparticles embedded inside solid matrices have been studied, almost no work has been done on supported particles. In this thesis, the response of supported nanoparticles is studied systematically for heavy and light ion irradiation. The processes leading to damage production are identified and models are developed for both types of irradiation. In recent experiments, helium irradiation has been shown to induce a phase transformation from multiply twinned to single-crystalline nanoparticles in bimetallic alloys, but the nature of the transition has remained unknown. The alloys for which the effect has been observed are CuAu and FePt. It is shown in this thesis that transient amorphization leads to the observed transition and that while CuAu and FePt do not amorphize upon irradiation in bulk or as thin films, they readily do so as nanoparticles. This is the first time such an effect is demonstrated with supported particles, not embedded in a matrix where mixing is always an issue. An understanding of the above physical processes is essential, if nanoparticles are to be used in applications in an optimal way. This thesis clarifies the mechanisms which control particle morphology, and paves way for the synthesis of nanostructured materials tailored for specific applications.

Sampled longest common prefix array

When augmented with the longest common prefix (LCP) array and some other structures, the suffix array can solve many string processing problems in optimal time and space. A compressed representation of the LCP array is also one of the main building blocks in many compressed suffix tree proposals. In this paper, we describe a new compressed LCP representation: the sampled LCP array. We show that when used with a compressed suffix array (CSA), the sampled LCP array often offers better time/space trade-offs than the existing alternatives. We also show how to construct the compressed representations of the LCP array directly from a CSA

The Nature and Change of Bonds in Industrial Business Relationships

A focus on cooperative industrial business relationships has become increasingly important in studies of industrial relationships. If the relationships between companies are strong it is usually a sign that companies will cooperate for a longer time and that may affect companies’ competitive and financial strength positively. As a result the bonds between companies become more important. This is due to the fact that bonds are building blocks of relationships and thus affect the stability in the cooperation between companies. Bond strength affect relationship strength. A framework regarding how bonds develop and change in an industrial business relationship has been developed in the study. Episodes affect the bonds in the relationship strengthening or weakening the bonds in the relationship or preserving status quo. Routine or critical episodes may lead to the strengthening or weakening of bonds as well as the preservation of status quo. The method used for analyzing bond strength trying to grasp the nature and change of bonds was invented by systematically following the elements of the definitions of bonds. A system with tables was drawn up in order to find out if the bond was weak, of medium strength or strong. Bonds are important regulators of industrial business relationships. By influencing the bonds one may have possibilities to strengthen or weaken the business relationship. Strengthen the business relationship in order to increase business and revenue and weaken the relationship in order to terminate business where the revenue is low or where there may be other problems in the relationship. By measuring the strength of different bonds it can be possible to strengthen weak bonds in order to strengthen the relationship. By using bond management it is possible to strategically strengthen or weaken the bonds between the cooperating companies in order to strengthen the cooperation and tie the customer or supplier to the company or weaken the cooperation in order to terminate the relationship. The instrument for the management of bonds is to use the created bond audit in order to know which bonds resources should be focused on in order to increase or decrease their strength.

Optical Tweezers for Single Molecule Biology

Molecular machinery on the micro-scale, believed to be the fundamental building blocks of life, involve forces of 1-100 pN and movements of nanometers to micrometers. Micromechanical single-molecule experiments seek to understand the physics of nucleic acids, molecular motors, and other biological systems through direct measurement of forces and displacements. Optical tweezers are a popular choice among several complementary techniques for sensitive force-spectroscopy in the field of single molecule biology. The main objective of this thesis was to design and construct an optical tweezers instrument capable of investigating the physics of molecular motors and mechanisms of protein/nucleic-acid interactions on the single-molecule level. A double-trap optical tweezers instrument incorporating acousto-optic trap-steering, two independent detection channels, and a real-time digital controller was built. A numerical simulation and a theoretical study was performed to assess the signal-to-noise ratio in a constant-force molecular motor stepping experiment. Real-time feedback control of optical tweezers was explored in three studies. Position-clamping was implemented and compared to theoretical models using both proportional and predictive control. A force-clamp was implemented and tested with a DNA-tether in presence of the enzyme lambda exonuclease. The results of the study indicate that the presented models describing signal-to-noise ratio in constant-force experiments and feedback control experiments in optical tweezers agree well with experimental data. The effective trap stiffness can be increased by an order of magnitude using the presented position-clamping method. The force-clamp can be used for constant-force experiments, and the results from a proof-of-principle experiment, in which the enzyme lambda exonuclease converts double-stranded DNA to single-stranded DNA, agree with previous research. The main objective of the thesis was thus achieved. The developed instrument and presented results on feedback control serve as a stepping stone for future contributions to the growing field of single molecule biology.

Irradiation effects in graphene and related materials

Nanomaterials with a hexagonally ordered atomic structure, e.g., graphene, carbon and boron nitride nanotubes, and white graphene (a monolayer of hexagonal boron nitride) possess many impressive properties. For example, the mechanical stiffness and strength of these materials are unprecedented. Also, the extraordinary electronic properties of graphene and carbon nanotubes suggest that these materials may serve as building blocks of next generation electronics. However, the properties of pristine materials are not always what is needed in applications, but careful manipulation of their atomic structure, e.g., via particle irradiation can be used to tailor the properties. On the other hand, inadvertently introduced defects can deteriorate the useful properties of these materials in radiation hostile environments, such as outer space. In this thesis, defect production via energetic particle bombardment in the aforementioned materials is investigated. The effects of ion irradiation on multi-walled carbon and boron nitride nanotubes are studied experimentally by first conducting controlled irradiation treatments of the samples using an ion accelerator and subsequently characterizing the induced changes by transmission electron microscopy and Raman spectroscopy. The usefulness of the characterization methods is critically evaluated and a damage grading scale is proposed, based on transmission electron microscopy images. Theoretical predictions are made on defect production in graphene and white graphene under particle bombardment. A stochastic model based on first-principles molecular dynamics simulations is used together with electron irradiation experiments for understanding the formation of peculiar triangular defect structures in white graphene. An extensive set of classical molecular dynamics simulations is conducted, in order to study defect production under ion irradiation in graphene and white graphene. In the experimental studies the response of carbon and boron nitride multi-walled nanotubes to irradiation with a wide range of ion types, energies and fluences is explored. The stabilities of these structures under ion irradiation are investigated, as well as the issue of how the mechanism of energy transfer affects the irradiation-induced damage. An irradiation fluence of 5.5x10^15 ions/cm^2 with 40 keV Ar+ ions is established to be sufficient to amorphize a multi-walled nanotube. In the case of 350 keV He+ ion irradiation, where most of the energy transfer happens through inelastic collisions between the ion and the target electrons, an irradiation fluence of 1.4x10^17 ions/cm^2 heavily damages carbon nanotubes, whereas a larger irradiation fluence of 1.2x10^18 ions/cm^2 leaves a boron nitride nanotube in much better condition, indicating that carbon nanotubes might be more susceptible to damage via electronic excitations than their boron nitride counterparts. An elevated temperature was discovered to considerably reduce the accumulated damage created by energetic ions in both carbon and boron nitride nanotubes, attributed to enhanced defect mobility and efficient recombination at high temperatures. Additionally, cobalt nanorods encapsulated inside multi-walled carbon nanotubes were observed to transform into spherical nanoparticles after ion irradiation at an elevated temperature, which can be explained by the inverse Ostwald ripening effect. The simulation studies on ion irradiation of the hexagonal monolayers yielded quantitative estimates on types and abundances of defects produced within a large range of irradiation parameters. He, Ne, Ar, Kr, Xe, and Ga ions were considered in the simulations with kinetic energies ranging from 35 eV to 10 MeV, and the role of the angle of incidence of the ions was studied in detail. A stochastic model was developed for utilizing the large amount of data produced by the molecular dynamics simulations. It was discovered that a high degree of selectivity over the types and abundances of defects can be achieved by carefully selecting the irradiation parameters, which can be of great use when precise pattering of graphene or white graphene using focused ion beams is planned.