Canine Insulinoma (A Case Report)

Canine Insulinoma is a disease of old dogs which results from a functional neoplasm involving the beta cells of the islets of Langherhans of the pancreas. These are the same cells that are involved in diabetes mellitus where insulin production is decreased instead of increased. The insulinoma dog is usually admitted with signs of weakness, ataxia, restlessness, ravenous appetite, and convulsions

ICM cooling, AGN feedback and BCG properties of galaxy groups-Five properties where groups differ from clusters

Using Chandra data for a sample of 26 galaxy groups, we constrained the central cooling times (CCTs) of the ICM and classified the groups as strong cool-core (SCC), weak cool-core (WCC) and non-cool-core (NCC) based on their CCTs. The total radio luminosity of the brightest cluster galaxy (BCG) was obtained using radio catalog data and literature, which was compared to the CCT to understand the link between gas cooling and radio output. We determined K-band luminosities of the BCG with 2MASS data, and used it to constrain the masses of the SMBH, which were then compared to the radio output. We also tested for correlations between the BCG luminosity and the overall X-ray luminosity and mass of the group. The observed cool-core/non-cool-core fractions for groups are comparable to those of clusters. However, notable differences are seen. For clusters, all SCCs have a central temperature drop, but for groups, this is not the case as some SCCs have centrally rising temperature profiles. While for the cluster sample, all SCC clusters have a central radio source as opposed to only 45% of the NCCs, for the group sample, all NCC groups have a central radio source as opposed to 77% of the SCC groups. For clusters, there are indications of an anticorrelation trend between radio luminosity and CCT which is absent for the groups. Indications of a trend of radio luminosity with black hole mass observed in SCC clusters is absent for groups. The strong correlation observed between the BCG luminosity and the cluster X-ray luminosity/cluster mass weakens significantly for groups. We conclude that there are important differences between clusters and groups within the ICM cooling/AGN feedback paradigm.Comment: Accepted for publication in Astronomy and Astrophysic

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

[Letter to editor] Shedding of bevacizumab in tumour cells derived extracellular vesicles as a new therapeutic escape mechanism in glioblastoma

Glioblastoma (GBM) is the most aggressive type of primary brain tumours. Anti-angiogenic therapies (AAT), such as bevacizumab, have been developed to target the tumour blood supply. However, GBM presents mechanisms of escape from AAT activity, including a speculated direct effect of AAT on GBM cells. Furthermore, bevacizumab can alter the intercellular communication of GBM cells with their direct microenvironment. Extracellular vesicles (EVs) have been recently described as main acts in the GBM microenvironment, allowing tumour and stromal cells to exchange genetic and proteomic material. Herein, we examined and described the alterations in the EVs produced by GBM cells following bevacizumab treatment. Interestingly, bevacizumab that is able to neutralise GBM cells-derived VEGF-A, was found to be directly captured by GBM cells and eventually sorted at the surface of the respective EVs. We also identified early endosomes as potential pathways involved in the bevacizumab internalisation by GBM cells. Via MS analysis, we observed that treatment with bevacizumab induces changes in the EVs proteomic content, which are associated with tumour progression and therapeutic resistance. Accordingly, inhibition of EVs production by GBM cells improved the anti-tumour effect of bevacizumab. Together, this data suggests of a potential new mechanism of GBM escape from bevacizumab activity

Accessing the solid electrolyte interphase on silicon anodes for lithium ion batteries in situ through transmission soft X ray absorption spectroscopy

Silicon offers nine times higher theoretical storage capacity than commercial graphite anodes for Li ion batteries. For cycling stability, the electrolyte needs to be kinetically stabilized by the so called Solid Electrolyte Interphase SEI , a layer which ideally forms once from decomposition products of the electrolyte. While it works for graphite, the SEI on silicon fails to stabilize the electrolyte sufficiently, partly due to the large volume changes upon de lithiation. To investigate the SEI on silicon anodes, we developed a novel approach for X ray Absorption Spectroscopy XAS , that puts a twist to conventional SiNx window based liquid cells by utilizing a deliberately induced gas bubble to form a soft X ray transparent electrolyte layer. We demonstrate our approach to allow transmission XAS in the soft X ray regime on liquids and electrode thin film materials under in situ conditions. In our case XAS study of the SEI on silicon anodes, we reveal the main SEI constituents as Li acetate, Li ethylene di carbonate or Li ethylene mono carbonate, Li acetylacetonate, LiOH, and LiF. Additionally, we see evidence for aldehyde species which we attribute to possible liquid inclusions within a porous SEI morphology.We consider our method an appropriate tool for the successful engineering of a stable, efficient SEI in the futur

Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology

Aerobic glycolysis is a seemingly wasteful mode of ATP production that is seen both in rapidly proliferating mammalian cells and highly active contracting muscles, but whether there is a common origin for its presence in these widely different systems is unknown. To study this issue, here we develop a model of human central metabolism that incorporates a solvent capacity constraint of metabolic enzymes and mitochondria, accounting for their occupied volume densities, while assuming glucose and/or fatty acid utilization. The model demonstrates that activation of aerobic glycolysis is favored above a threshold metabolic rate in both rapidly proliferating cells and heavily contracting muscles, because it provides higher ATP yield per volume density than mitochondrial oxidative phosphorylation. In the case of muscle physiology, the model also predicts that before the lactate switch, fatty acid oxidation increases, reaches a maximum, and then decreases to zero with concomitant increase in glucose utilization, in agreement with the empirical evidence. These results are further corroborated by a larger scale model, including biosynthesis of major cell biomass components. The larger scale model also predicts that in proliferating cells the lactate switch is accompanied by activation of glutaminolysis, another distinctive feature of the Warburg effect. In conclusion, intracellular molecular crowding is a fundamental constraint for cell metabolism in both rapidly proliferating- and non-proliferating cells with high metabolic demand. Addition of this constraint to metabolic flux balance models can explain several observations of mammalian cell metabolism under steady state conditions

The link between accretion mode and environment in radio-loud active galaxies

The interactions between radio-loud AGN and their environments play an important rôle in galaxy and cluster evolution. Recent work has demonstrated fundamental differences between high- and low-excitation radio galaxies (HERGs and LERGs), and shown that they may have different relationships with their environments. In the Chandra Large Project ERA (Environments of Radio-loud AGN), we made the first systematic X-ray environmental study of the cluster environments of radio galaxies at a single epoch (z ~ 0.5), and found tentative evidence for a correlation between radio luminosity and cluster X-ray luminosity. We also found that this relationship appeared to be driven by the LERG subpopulation. We have now repeated the analysis with a low-redshift sample (z ~ 0.1), and found strong correlations between radio luminosity and environment richness and between radio luminosity and central density for the LERGs but not for the HERGs. These results are consistent with models in which the HERGs are fuelled from accretion discs maintained from local reservoirs of gas, while LERGs are fuelled more directly by gas ingested from the intracluster medium. Comparing the samples, we found that although the maximum environment richness of the HERG environments is similar in both samples, there are poorer HERG environments in the z ~ 0.1 sample than in the z ~ 0.5 sample. We have therefore tentative evidence of evolution of the HERG environments. We found no differences between the LERG subsamples for the two epochs, as would be expected if radio and cluster luminosities are related.Peer reviewedFinal Accepted Versio

Development of a high-throughput ex-vivo burn wound model using porcine skin, and its application to evaluate new approaches to control wound infection

Biofilm formation in wounds is considered a major barrier to successful treatment, and has been associated with the transition of wounds to a chronic non-healing state. Here, we present a novel laboratory model of wound biofilm formation using ex-vivo porcine skin and a custom burn wound array device. The model supports high-throughput studies of biofilm formation and is compatible with a range of established methods for monitoring bacterial growth, biofilm formation, and gene expression. We demonstrate the use of this model by evaluating the potential for bacteriophage to control biofilm formation by Staphylococcus aureus, and for population density dependant expression of S. aureus virulence factors (regulated by the Accessory Gene Regulator, agr) to signal clinically relevant wound infection. Enumeration of colony forming units and metabolic activity using the XTT assay, confirmed growth of bacteria in wounds and showed a significant reduction in viable cells after phage treatment. Confocal laser scanning microscopy confirmed the growth of biofilms in wounds, and showed phage treatment could significantly reduce the formation of these communities. Evaluation of agr activity by qRT-PCR showed an increase in activity during growth in wound models for most strains. Activation of a prototype infection-responsive dressing designed to provide a visual signal of wound infection, was related to increased agr activity. In all assays, excellent reproducibility was observed between replicates using this mode

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches