Characterization of the mitochondrial active-site serine protein LACTB: filaments in the mitochondrial intermembrane space

Mitochondria have evolved from endosymbiotic alpha-proteobacteria. During the endosymbiotic process early eukaryotes dumped the major component of the bacterial cell wall, the peptidoglycan layer. Peptidoglycan is synthesized and maintained by active-site serine enzymes belonging to the penicillin-binding protein and the β-lactamase superfamily. Mammals harbor a protein named LACTB that shares sequence similarity with bacterial penicillin-binding proteins and β-lactamases. Since eukaryotes lack the synthesis machinery for peptidoglycan, the physiological role of LACTB is intriguing. Recently, LACTB has been validated in vivo to be causative for obesity, suggesting that LACTB is implicated in metabolic processes. The aim of this study was to investigate the phylogeny, structure, biochemistry and cell biology of LACTB in order to elucidate its physiological function. Phylogenetic analysis revealed that LACTB has evolved from penicillin binding-proteins present in the bacterial periplasmic space. A structural model of LACTB indicates that LACTB shares characteristic features common to all penicillin-binding proteins and β-lactamases. Recombinat LACTB protein expressed in E. coli was recovered in significant quantities. Biochemical and cell biology studies showed that LACTB is a soluble protein localized in the mitochondrial intermembrane space. Further analysis showed that LACTB preprotein underwent proteolytic processing disclosing an N-terminal tetrapeptide motif also found in a set of cell death-inducing proteins. Electron microscopy structural studies revealed that LACTB can polymerize to form stable filaments with lengths ranging from twenty to several hundred nanometers. These data suggest that LACTB filaments define a distinct microdomain in the intermembrane space. A possible role of LACTB filaments is proposed in the intramitochondrial membrane organization and microcompartmentation. The implications of these findings offer novel insight into the evolution of mitochondria. Further studies of the LACTB function might provide a tool to treat mitochondria-related metabolic diseases.

Formation of structure in dark energy cosmologies

Acceleration of the universe has been established but not explained. During the past few years precise cosmological experiments have confirmed the standard big bang scenario of a flat universe undergoing an inflationary expansion in its earliest stages, where the perturbations are generated that eventually form into galaxies and other structure in matter, most of which is non-baryonic dark matter. Curiously, the universe has presently entered into another period of acceleration. Such a result is inferred from observations of extra-galactic supernovae and is independently supported by the cosmic microwave background radiation and large scale structure data. It seems there is a positive cosmological constant speeding up the universal expansion of space. Then the vacuum energy density the constant describes should be about a dozen times the present energy density in visible matter, but particle physics scales are enormously larger than that. This is the cosmological constant problem, perhaps the greatest mystery of contemporary cosmology. In this thesis we will explore alternative agents of the acceleration. Generically, such are called dark energy. If some symmetry turns off vacuum energy, its value is not a problem but one needs some dark energy. Such could be a scalar field dynamically evolving in its potential, or some other exotic constituent exhibiting negative pressure. Another option is to assume that gravity at cosmological scales is not well described by general relativity. In a modified theory of gravity one might find the expansion rate increasing in a universe filled by just dark matter and baryons. Such possibilities are taken here under investigation. The main goal is to uncover observational consequences of different models of dark energy, the emphasis being on their implications for the formation of large-scale structure of the universe. Possible properties of dark energy are investigated using phenomenological paramaterizations, but several specific models are also considered in detail. Difficulties in unifying dark matter and dark energy into a single concept are pointed out. Considerable attention is on modifications of gravity resulting in second order field equations. It is shown that in a general class of such models the viable ones represent effectively the cosmological constant, while from another class one might find interesting modifications of the standard cosmological scenario yet allowed by observations. The thesis consists of seven research papers preceded by an introductory discussion.

Local structure of water studied by Compton scattering

This thesis presents a novel application of x-ray Compton scattering to structural studies of molecular liquids. Systematic Compton-scattering experiments on water have been carried out with unprecedented accuracy at third-generation synchrotron-radiation laboratories. The experiments focused on temperature effects in water, the water-to-ice phase transition, quantum isotope effects, and ion hydration. The experimental data is interpreted by comparison with both model computations and ab initio molecular-dynamics simulations. Accordingly, Compton scattering is found to provide unique intra- and intermolecular structural information. This thesis thus demonstrates the complementarity of the technique to traditional real-space probes for studies on the local structure of water and, more generally, molecular liquids.

Structure of Relativistic States

QCD factorization in the Bjorken limit allows to separate the long-distance physics from the hard subprocess. At leading twist, only one parton in each hadron is coherent with the hard subprocess. Higher twist effects increase as one of the active partons carries most of the longitudinal momentum of the hadron, x -> 1. In the Drell-Yan process \pi N -> \mu^- mu^+ + X, the polarization of the virtual photon is observed to change to longitudinal when the photon carries x_F > 0.6 of the pion. I define and study the Berger-Brodsky limit of Q^2 -> \infty with Q^2(1-x) fixed. A new kind of factorization holds in the Drell-Yan process in this limit, in which both pion valence quarks are coherent with the hard subprocess, the virtual photon is longitudinal rather than transverse, and the cross section is proportional to a multiparton distribution. Generalized parton distributions contain information on the longitudinal momentum and transverse position densities of partons in a hadron. Transverse charge densities are Fourier transforms of the electromagnetic form factors. I discuss the application of these methods to the QED electron, studying the form factors, charge densities and spin distributions of the leading order |e\gamma> Fock state in impact parameter and longitudinal momentum space. I show how the transverse shape of any virtual photon induced process, \gamma^*(q)+i -> f, may be measured. Qualitative arguments concerning the size of such transitions have been previously made in the literature, but without a precise analysis. Properly defined, the amplitudes and the cross section in impact parameter space provide information on the transverse shape of the transition process.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.