Actin assembly ruptures the nuclear envelope by prying the lamina away from nuclear pores and nuclear membranes in starfish oocytes.

The nucleus of oocytes (germinal vesicle) is unusually large and its nuclear envelope (NE) is densely packed with nuclear pore complexes (NPCs) stockpiled for embryonic development. We showed that breakdown of this specialized NE is mediated by an Arp2/3-nucleated F-actin 'shell' in starfish oocytes, in contrast to microtubule-driven tearing in mammalian fibroblasts. Here, we address the mechanism of F-actin-driven NE rupture by correlated live-cell, super-resolution and electron microscopy. We show that actin is nucleated within the lamina sprouting filopodia-like spikes towards the nuclear membranes. These F-actin spikes protrude pore-free nuclear membranes, whereas the adjoining membrane stretches accumulate NPCs associated with the still-intact lamina. Packed NPCs sort into a distinct membrane network, while breaks appear in ER-like, pore-free regions. Thereby, we reveal a new function for actin-mediated membrane shaping in nuclear rupture that is likely to have implications in other contexts such as nuclear rupture observed in cancer cells

The graphene sheet versus the 2DEG: a relativistic Fano spin-filter via STM and AFM tips

We explore theoretically the density of states (LDOS) probed by an STM tip of 2D systems hosting an adatom and a subsurface impurity,both capacitively coupled to AFM tips and traversed by antiparallel magnetic fields. Two kinds of setups are analyzed, a monolayer of graphene and a two-dimensional electron gas (2DEG). The AFM tips set the impurity levels at the Fermi energy, where two contrasting behaviors emerge: the Fano factor for the graphene diverges, while in the 2DEG it approaches zero. As result, the spin-degeneracy of the LDOS is lifted exclusively in the graphene system, in particular for the asymmetric regime of Fano interference. The aftermath of this limit is a counterintuitive phenomenon, which consists of a dominant Fano factor due to the subsurface impurity even with a stronger STM-adatom coupling. Thus we find a full polarized conductance, achievable just by displacing vertically the position of the STM tip. To the best knowledge, our work is the first to propose the Fano effect as the mechanism to filter spins in graphene. This feature arises from the massless Dirac electrons within the band structure and allows us to employ the graphene host as a relativistic Fano spin-filter

Transcriptional vs post-transcriptional regulation of the central carbon metabolism of E. coli

Transcriptomics data are currently one of the most available types of large-scale biological data. A large number of methods have been developed to improve constraint-based simulations using these data. We recently performed a systematic comparison of these methods and observed that, at least for central carbon metabolism, there is no significant improvement in the prediction of flux distributions when gene expression data is used. These results are consistent with recent studies, in different organisms, showing that central carbon metabolism is predominantly regulated at post-transcriptional levels. Central carbon metabolism provides the precursors for the production of multiple compounds used in industrial biotechnology. Hence, it is the main target for intervention in most rational strain design strategies. However, its complexity is still not completely understood. In this work, we analyze the role of allosteric regulation, one of the main mechanisms of post-transcriptional regulation, for the control of central carbon metabolism. We extend a model of central carbon metabolism of E. coli with allosteric interactions, revealing a hidden topology in metabolic networks. We use this model to integrate a multi-omic dataset containing transcript, protein, flux and metabolite levels to further dissect the contribution of different types of regulation for metabolic flux control in these central pathways. Situations of predominant allosteric control could be identified, highlighting the importance of this kind of regulation in central carbon metabolism

Modelling allosteric regulation for prediction of flux control in the central carbon metabolism of E. coli

Rational strain design is a fundamental step in the development of microbial cell factories. Multiple genetic manipulations are often required in order to redirect the metabolic flux towards a product of industrial interest. Most manipulation targets are focused on central carbon metabolism, which provides the molecular precursors and the energy required for other biochemical pathways. However, the complex regulation of those pathways is still not completely unraveled. Recent studies have shown that central carbon metabolism is mostly regulated at post-transcriptional levels. In this work, we explore the role of allosteric regulation in the control of metabolic fluxes. We begin by expanding a metabolic network reconstruction of the central carbon metabolism of E. coli with allosteric interaction information from relevant databases. This model is used to integrate a multi-omic dataset for this organism. We analyze the coordinated changes in enzyme, metabolite and flux levels between multiple experimental conditions, and observe cases where allosteric regulators have a major contribution in the metabolic flux changes. We then develop a method for systematic prediction of potential cases of allosteric control for given metabolic perturbations. This is a valuable approach for predicting coordinated flux changes that would not be predicted with a purely stoichiometric model representation.BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-00002

Modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli

Redesign of microbial metabolism is a critical step in biotechnology for the production of industrially relevant compounds. Central carbon metabolism provides the energy and building blocks required for cellular growth and synthesis of the desired byproducts and, consequently, it is the main target for intervention in most rational strain design approaches. However, the complexity of central carbon metabolism is still not completely understood. Recent studies in different organisms show that flux control in central carbon metabolism is predominantly regulated by non-transcriptional mechanisms, leaving post-translational modifications, allosteric regulation, and thermodynamics as main candidates. In this work, we extend a model of central carbon metabolism of E.coli with allosteric interactions in order to reveal a hidden topology in metabolic networks. We use this model to integrate a multi-omic dataset containing transcript, protein, flux and metabolite levels to further dissect and analyze the contribution of allosteric regulation for metabolic flux control

Implant Site Nexplanon Reaction?

Nexplanon (Schering-Plough Limited/Merck Sharp & Dohme Limited (MSD)) is a long active reversible contraceptive method that provides effective contraception for 3 years. It consists of a single, flexible, rod-shaped implant, containing 68 mg etonogestrel. It is 4 cm long, consists of an ethylene vinyl acetate copolymer, a non-absorbable material, and also contains 15 mg of barium sulfate, which makes it visible by X-ray. We describe a case of a 39-year-old woman who experienced a local reaction to the barium sulfate in Nexplanon. She was given medical treatment, but only the removal of the implant resolved the symptoms. After removal there was gradual improvement and 72 h later the patient was asymptomatic. Allergic reaction to barium sulfate is extremely rare: until now, there have only been two cases associated with Nexplanon described in the literature

Novel modeling formalisms and simulation tools in computational biosystems

Living organisms are complex systems that emerge from the fundamental building blocks of life. Systems Biology is a recent field of science that studies these complex phenomena at the cellular level (Kitano 2002). Understanding the mechanisms of the cell is essential for research and development in several areas such as drug discovery and biotechnological production. In the latter, metabolic engineering is used for building mutant microbial strains with increased productivity of compounds with industrial interest, such as biofuels (Stephanopoulos 1998). Using computational models of cellular metabolism, it is possible to systematically test and predict the optimal manipulations, such as gene knockouts, that produce the ideal phenotype for a specific application. These models are typically built in an iterative cycle of experiment and refinement, by multidisciplinary research teams that include biologists, engineers and computer scientists. The interconnection between different cellular processes, such as metabolism and genetic regulation, reflects the importance of the holistic approach claimed by the Systems Biology paradigm in replacement of traditional reductionist methods. Although most cellular components have been studied individually, the behavior of the cell emerges from the network-level interaction and requires an integrative analysis. Recent high–throughput methods have generated the so- called omics data (e.g.: genomics, transcriptomics, proteomics, metabolomics, fluxomics) that have allowed the reconstruction of biological networks (Palsson 2006). However, despite the great advances in the area, we are still far from a whole-cell computational model that is able to simulate all the components of a living cell. Due to the enormous size and complexity of intracellular biological networks, computational cell models tend to be partial and focused on the application of interest. Also, due to the multidisciplinarity of the field, these models are based on several different kinds of formalisms. Therefore, it is important to develop a framework with common modeling formalisms, analysis and simulation methods, that is able to accommodate different kinds biological networks, with different types of entities and their interactions, into genome-scale integrated models. Cells are composed by thousands of components that interact in myriad ways. Despite this intricate interconnection it is usual to divide and classify these networks according to biological function. The main types of networks are signaling, gene regulatory and metabolic. Signal transduction is a process for cellular communication where the cell receives and responds to external stimuli through signaling cascades (Gomperts et al. 2009; Albert and Wang 2009). These cascades affect gene regulation, which is the method for controlling gene expression, and consequently several cellular functions (Schlittand and Brazma 2007; Karlebach and Sgamir 2008). Many genes encode enzymes which are responsible for catalyzing biochemical reactions. The complex network of these reactions forms the cellular metabolism that sustains the cell’s growth and energy requirements (Steuer and Junker 2009; Palsson 2006). The objectives of this work, in the context of a PhD thesis, consist in re-search and selection of an appropriate modeling formalism to develop a framework for integration of different biological networks, with focus on regulatory and metabolic networks, and the implementation of suitable analysis, simulation and optimization methods. To achieve these goals, it is necessary to resolve many modeling issues, such as the integration of discrete and continuous events, representation of network topology, support for different levels of abstraction, lack of parameters and model complexity. This framework will be used for the implementation of an integrated model of E. coli, a widely used organism for industrial application

Design of a biosynthetic pathway for curcumin production in Escherichia coli

Curcumin is the yellow pigment from turmeric, a well known culinary spice produced from the herb Curcuma longa. Research over the last years has shown that curcumin presents a wide range of pharmacological effects, including anti-inflammatory, anti-oxidant and anticarcinogenic activity. Given its potential application in cancer treatment, there is an interest for industrial production of this natural compound. This work consists on a synthetic biology approach for the design of a heterologous pathway for curcumin synthesis in Escherichia coli, a widely used microbe in industrial biotechnology. Using pathway databases and literature research we have selected the best gene candidates for heterologous expression of a curcumin synthesis pathway in E. coli. The DNA sequences for these genes were retrieved from public databases and can be readily synthesized for insertion into the host using molecular biology techniques. The inclusion of this pathway in a recent genome-scale reconstruction of the metabolism of E. coli has enabled the in silico analysis of the production capabilities for this host. We have analysed the theoretical production yields and biomass growth under different experimental conditions. Using this model we have also searched for potential gene knockouts that partially redirect the metabolic flux to the heterologous pathway without compromising cellular growth. In overall, the methods used in this work allow the selection of the most suitable combination of experimental conditions and genetic manipulations for the design of an efficient biosynthetic pathway for curcumin production in E.coli

Uso do Implante de Etonogestrel Além da Duração Aprovada – Um Caso Clínico

The contraceptive implant is the most effective method of reversible contraception. Observational and trial data indicate that this method remains effective beyond the initially approved duration of use. We report a case of etonogestrel serum levels above the supposed threshold value for ovulation suppression nine years after implant insertion.info:eu-repo/semantics/publishedVersio

Large scale dynamic model reconstruction for the central carbon metabolism of escherichia coli

The major objective of metabolic engineering is the construction of industrially relevant microbial strains with desired properties. From an engineering perspective, dynamic mathematical modeling to quantitatively assess intracellular metabolism and predict the complex behavior of living cells is one of the most successful tools to achieve that goal. In this work, we present an expansion of the original E. coli dynamic model [1], which links the acetate metabolism and tricarboxylic acid cycle (TCA) with the phosphotransferase systems, the pentose-phosphate pathway and the glycolysis system based on mechanistic enzymatic rate equations. The kinetic information is collected from available database and literature, and is used as an initial guess for the global fitting. The results of the numeric simulations were in good agreement with the experimental results. Thus, the results are sufficiently good to prompt us to seek further experimental data for comparison with the simulations