Mutations in the small subunit of the Drosophila U2AF splicing factor cause lethality and developmental defects.

The essential eukaryotic pre-mRNA splicing factor U2AF (U2 small nuclear ribonucleoprotein auxiliary factor) is required to specify the 3' splice at an early step in spliceosome assembly. U2AF binds site-specifically to the intron polypyrimidine tract and recruits U2 small nuclear ribonucleoprotein to the branch site. Human U2AF (hU2AF) is a heterodimer composed of a large (hU2AF65) and small (hU2AF35) subunit. Although these proteins associate in a tight complex, the biochemical requirement for U2AF activity can be satisfied solely by the large subunit. The requirement for the small subunit in splicing has remained enigmatic. No biochemical activity has been found for hU2AF35 and it has been implicated in splicing only indirectly by its interaction with known splicing factors. In the absence of a biochemical assay, we have taken a genetic approach to investigate the function of the small subunit in the fruit fly Drosophila melanogaster. A cDNA clone encoding the small subunit of Drosophila U2AF (dU2AF38) has been isolated and sequenced. The dU2AF38 protein is highly homologous to hU2AF35 containing a conserved central arginine- and serine-rich (RS) domain. A recessive P-element insertion mutation affecting dU2AF38 causes a reduction in viability and fertility and morphological bristle defects. Consistent with a general role in splicing, a null allele of dU2AF38 is fully penetrant recessive lethal, like null alleles of the Drosophila U2AF large subunit.

Identification of ciliary neurotrophic factor (CNTF) residues essential for leukemia inhibitory factor receptor binding and generation of CNTF receptor antagonists.

Ciliary neurotrophic factor (CNTF) drives the sequential assembly of a receptor complex containing the ligand-specific alpha-receptor subunit (CNTFR alpha) and the signal transducers gp130 and leukemia inhibitory factor receptor-beta (LIFR). The D1 structural motif, located at the beginning of the D-helix of human CNTF, contains two amino acid residues, F152 and K155, which are conserved among all cytokines that signal through LIFR. The functional importance of these residues was assessed by alanine mutagenesis. Substitution of either F152 or K155 with alanine was found to specifically inhibit cytokine interaction with LIFR without affecting binding to CNTFR alpha or gp130. The resulting variants behaved as partial agonists with varying degrees of residual bioactivity in different cell-based assays. Simultaneous alanine substitution of both F152 and K155 totally abolished biological activity. Combining these mutations with amino acid substitutions in the D-helix, which enhance binding affinity for the CNTFR alpha, gave rise to a potent competitive CNTF receptor antagonist. This protein constitutes a new tool for studies of CNTF function in normal physiology and disease.

Hepatocyte growth factor is a coupling factor for osteoclasts and osteoblasts in vitro.

Hepatocyte growth factor (HGF), also known as scatter factor, is a powerful motogen, mitogen, and morphogen produced by cells of mesodermal origin, acting on epithelial and endothelial cells. Its receptor is the tyrosine kinase encoded by the c-MET protooncogene. We show that the HGF receptor is expressed by human primary osteoclasts, by osteoclast-like cell lines, and by osteoblasts. In both cell lineages, HGF stimulation triggers the receptor kinase activity and autophosphorylation. In osteoclasts, HGF receptor activation is followed by increase in intracellular Ca2+ concentration and by activation of the pp60c-Src kinase. HGF induces changes in osteoclast shape and stimulates chemotactic migration and DNA replication. Osteoblasts respond to HGF by entering the cell cycle, as indicated by stimulation of DNA synthesis. Interestingly, osteoclasts were found to synthesize and secrete biologically active HGF. These data strongly suggest the possibility of an autocrine regulation of the osteoclast by HGF and a paracrine regulation of the osteoblast by the HGF produced by the osteoclast.

Modeling and optimization of populations subject to time-dependent mutation.

It has become clear that many organisms possess the ability to regulate their mutation rate in response to environmental conditions. So the question of finding an optimal mutation rate must be replaced by that of finding an optimal mutation schedule. We show that this task cannot be accomplished with standard population-dynamic models. We then develop a "hybrid" model for populations experiencing time-dependent mutation that treats population growth as deterministic but the time of first appearance of new variants as stochastic. We show that the hybrid model agrees well with a Monte Carlo simulation. From this model, we derive a deterministic approximation, a "threshold" model, that is similar to standard population dynamic models but differs in the initial rate of generation of new mutants. We use these techniques to model antibody affinity maturation by somatic hypermutation. We had previously shown that the optimal mutation schedule for the deterministic threshold model is phasic, with periods of mutation between intervals of mutation-free growth. To establish the validity of this schedule, we now show that the phasic schedule that optimizes the deterministic threshold model significantly improves upon the best constant-rate schedule for the hybrid and Monte Carlo models.

Exogenous vascular endothelial growth factor induces malformed and hyperfused vessels during embryonic neovascularization.

Vascular endothelial growth factor (VEGF) is a potent and specific endothelial mitogen that is able to induce angiogenesis in vivo [Leung, D. W., Cachianes, G., Kuang, W.-J., Goeddel, D. V. & Ferrara, N. (1989) Science 246 1306-1309]. To determine if VEGF also influences the behavior of primordial endothelial cells, we used an in vivo vascular assay based on the de novo formation of vessels. Japanese quail embryos injected with nanomolar quantities of the 165-residue form of VEGF at the onset of vasculogenesis exhibited profoundly altered vessel development. In fact, the overall patterning of the vascular network was abnormal in all VEGF-injected embryos. The malformations were attributable to two specific endothelial cell activities: (i) inappropriate neovascularization in normally avascular areas and (ii) the unregulated, excessive fusion of vessels. In the first instance, supernumerary vessels directly linked the inflow channel of the heart to the aortic outflow channel. The second aberrant activity led to the formation of vessels with abnormally large lumens. Ultimately, unregulated vessel fusion generated massive vascular sacs that obliterated the identity of individual vessels. These observations show that exogenous VEGF has an impact on the behavior of primordial endothelial cells engaged in vasculogenesis, and they strongly suggest that endogenous VEGF is important in vascular patterning and regulation of vessel size (lumen formation).

Simulação e modelagem computacional de dados de espalhamento à baixos ângulos - enfoque em estruturas de alta simetria

Esta tese apresenta uma abordagem para a criação rápida de modelos em diferentes geometrias (complexas ou de alta simetria) com objetivo de calcular a correspondente intensidade espalhada, podendo esta ser utilizada na descrição de experimentos de es- palhamento à baixos ângulos. A modelagem pode ser realizada com mais de 100 geome- trias catalogadas em um Banco de Dados, além da possibilidade de construir estruturas a partir de posições aleatórias distribuídas na superfície de uma esfera. Em todos os casos os modelos são gerados por meio do método de elementos finitos compondo uma única geometria, ou ainda, compondo diferentes geometrias, combinadas entre si a partir de um número baixo de parâmetros. Para realizar essa tarefa foi desenvolvido um programa em Fortran, chamado de Polygen, que permite modelar geometrias convexas em diferentes formas, como sólidos, cascas, ou ainda com esferas ou estruturas do tipo DNA nas arestas, além de usar esses modelos para simular a curva de intensidade espalhada para sistemas orientados e aleatoriamente orientados. A curva de intensidade de espalhamento é calculada por meio da equação de Debye e os parâmetros que compõe cada um dos modelos, podem ser otimizados pelo ajuste contra dados experimentais, por meio de métodos de minimização baseados em simulated annealing, Levenberg-Marquardt e algorítmicos genéticos. A minimização permite ajustar os parâmetros do modelo (ou composição de modelos) como tamanho, densidade eletrônica, raio das subunidades, entre outros, contribuindo para fornecer uma nova ferramenta para modelagem e análise de dados de espalhamento. Em outra etapa desta tese, é apresentado o design de modelos atomísticos e a sua respectiva simulação por Dinâmica Molecular. A geometria de dois sistemas auto-organizado de DNA na forma de octaedro truncado, um com linkers de 7 Adeninas e outro com linkers de ATATATA, foram escolhidas para realizar a modelagem atomística e a simulação por Dinâmica Molecular. Para este sistema são apresentados os resultados de Root Mean Square Deviations (RMSD), Root Mean Square Fluctuations (RMSF), raio de giro, torção das hélices duplas de DNA além da avaliação das ligações de Hidrogênio, todos obtidos por meio da análise de uma trajetória de 50 ns.

Gene cloning, molecular modeling, and phylogenetics of serine protease P32 and serine carboxypeptidase SCP1 from nematophagous fungi Pochonia rubescens and Pochonia chlamydosporia

The fungi Pochonia chlamydosporia and Pochonia rubescens are parasites of nematode eggs and thus are biocontrol agents of nematodes. Proteolytic enzymes such as the S8 proteases VCP1 and P32, secreted during the pathogenesis of nematode eggs, are major virulence factors in these fungi. Recently, expression of these enzymes and of SCP1, a new putative S10 carboxypeptidase, was detected during endophytic colonization of barley roots by these fungi. In our study, we cloned the genomic and mRNA sequences encoding P32 from P. rubescens and SCP1 from P. chlamydosporia. P32 showed a high homology with the serine proteases Pr1A from the entomopathogenic fungus Metarhizium anisopliae and VCP1 from P. chlamydosporia (86% and 76% identity, respectively). However, the catalytic pocket of P32 showed differences in the amino acids of the substrate-recognition sites compared with the catalytic pockets of Pr1A and VCP1 proteases. Phylogenetic analysis of P32 suggests a common ancestor with protease Pr1A. SCP1 displays the characteristic features of a member of the S10 family of serine proteases. Phylogenetic comparisons show that SCP1 and other carboxypeptidases from filamentous fungi have an origin different from that of yeast vacuolar serine carboxypeptidases. Understanding protease genes from nematophagous fungi is crucial for enhancing the biocontrol potential of these organisms.

Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research

The microfoundations research agenda presents an expanded theoretical perspective because it considers individuals, their characteristics, and their interactions as relevant variables to help us understand firm-level strategic issues. However, microfoundations empirical research faces unique challenges because processes take place at different levels of analysis and these multilevel processes must be considered simultaneously. We describe multilevel modeling and mixed methods as methodological approaches whose use will allow for theoretical advancements. We describe key issues regarding the use of these two types of methods and, more importantly, discuss pressing substantive questions and topics that can be addressed with each of these methodological approaches with the goal of making theoretical advancements regarding the microfoundations research agenda and strategic management studies in general.

The Theoretical Framework and Methodology to Estimate the Farm Labour and Other Factor-Derived Demand and Output Supply Systems. Factor Markets Working Document No. 44, May 2013

This paper provides a conceptual framework for the estimation of the farm labour and other factor-derived demand and output supply systems. In order to analyse the drivers of labour demand in agriculture and account for the impact of policies on those decisions, it is necessary to acknowledge the interaction between the different factor markets. For this purpose, we present a review of the theoretical background to primal and dual representations of production and some empirical literature that has made use of derived demand systems. The main focus of the empirical work is to study the effect of market distortions in one market, through inefficient pricing, on the demand for other inputs. Therefore, own-price and cross-price elasticities of demand become key variables in the analysis. The dual cost function is selected as the most appropriate approach, where input prices are assumed to be exogenous. A commonly employed specification – and one that is particularly convenient due to its flexible form – is the translog cost function. The analysis consists of estimating the system of cost-share equations, in order to obtain the derived demand functions for inputs. Thus, the elasticities of factor substitution can be used to examine the complementarity/substitutability between inputs.

‘The oil friendship’: the state of and prospects for Russian-Chinese energy cooperation. OSW Commentary NUMBER 197|17.02.2016

The oil sector has been the major element of Russian-Chinese energy cooperation. The years 2013–2015 saw a significant increase in the volume of crude oil exported by Russia. In 2015, China became the main importer of Russian oil; Russia became the second largest supplier of oil to the Chinese market, after Saudi Arabia. From Beijing’s perspective, supplies of Russian oil are of strategic importance because the main supply routes are overland routes. Russia, for its part, is interested in boosting its export because of its deteriorating position on the European market, which hitherto has been considered a strategic market.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.