Metal-coated concave cone in a fused-silica rod as a multi-function plasmonic element.

A collimated light beam parallel to the axis of a fused-quartz cylinder impinging on a 90° apex angle concave cone cut in a quartz rod is transformed into a cylindrical wave by total internal reflection. A thin metal film at the quartz-air interface enables excitation of the plasmon mode at the air side that can polarize the cylindrical wave and/or has the potential to monitor physical, chemical, or biological quantities or events at the inner wall of the cone. The present Letter first analyzes the plasmon coupling mechanism and conditions. It then describes the diamond-grinding technique achieving a smooth cone wall and the finest possible tip. The experimental evidence of the polarization conversion is brought on a diamond-grinded section of fused-silica rod and gold coating of the concave wall.

Cyclic Crack Growth in Chemically Tailored Isotropic Austenitic Steel Processed by Electron Beam Powder Bed Fusion.

The present study analyzes the cyclic crack propagation behavior in an austenitic steel processed by electron beam powder bed fusion (PBF-EB). The threshold value of crack growth as well as the crack growth behavior in the Paris regime were studied. In contrast to other austenitic steels, the building direction during PBF-EB did not affect the crack propagation rate, i.e., the crack growth rates perpendicular and parallel to the building direction were similar due to the isotropic microstructure characterized by equiaxed grains. Furthermore, the influence of significantly different building parameters was studied and, thereby, different energy inputs causing locally varying manganese content. Crack growth behavior was not affected by these changes. Even a compositional gradation within the same specimen, i.e., crack growth through an interface of areas with high and areas with low manganese content, did not lead to a significant change of the crack growth rate. Thus, the steel studied is characterized by a quite robust cyclic crack growth behavior independent from building direction and hardly affected by typical parameter deviations in the PBF-EB process.

Novel indirect co-culture of immortalised hepatocytes with monocyte derived macrophages is characterised by pro-inflammatory cytokine networks.

The liver is composed of different cell populations. Interactions of different cell populations can be investigated by a newly established indirect co-culture system consisting of immortalised primary human hepatocytes and human monocyte derived macrophages (MDMs). Using the time-dependent cytokine secretion of the co-cultures and single cultures, correlation networks (including the cytokines G-CSF, CCL3, MCP-1, CCL20, FGF, TGF-ß1, GM-CSF, IL-8 IL-6, IL-1ß, and IL-18) were generated and the correlations were validated by application of IL-8 and TNF-α-neutralising antibodies. The data reveal that IL-8 is crucial for the interaction between hepatocytes and macrophages in vitro. In addition, transcriptome analyses showed that a change in the ratio between macrophages and hepatocytes may trigger pro-inflammatory signalling pathways of the acute phase response and the complement system (release of, e.g., certain cyto- and chemokines). Using diclofenac and LPS showed that the release of cytokines is increasing with higher ratios of MDMs. Altogether, we could demonstrate that the current co-culture system is better suited to mirror the in vivo situation when compared to previously established co-culture systems composed of HepG2 and differentiated THP-1 cells. Further, our data reveal that the cytokine IL-8 is crucial for the interaction between hepatocytes and macrophages in vitro.

Determining the Damage and Failure Behaviour of Textile Reinforced Composites under Combined In-Plane and Out-of-Plane Loading.

The absence of sufficient knowledge of the heterogeneous damage behaviour of textile reinforced composites, especially under combined in-plane and out-of-plane loadings, requires the development of multi-scale experimental and numerical methods. In the scope of this paper, three different types of plain weave fabrics with increasing areal weight were considered to characterise the influence of ondulation and nesting effects on the damage behaviour. Therefore an advanced new biaxial testing method has been elaborated to experimentally determine the fracture resistance at the combined biaxial loads. Methods in image processing of the acquired in-situ CT data and micrographs have been utilised to obtain profound knowledge of the textile geometry and the distribution of the fibre volume content of each type. Combining the derived data of the idealised geometry with a numerical multi-scale approach was sufficient to determine the fracture resistances of predefined uniaxial and biaxial load paths. Thereby, CUNTZEmathsizesmall's three-dimensional failure mode concept was incorporated to predict damage and failure. The embedded element method was used to obtain a structured mesh of the complex textile geometries. The usage of statistical and visualisation methods contributed to a profound comprehension of the ondulation and nesting effects.

Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases.

BACKGROUND & AIMS: Recently, spatial-temporal/metabolic mathematical models have been established that allow the simulation of metabolic processes in tissues. We applied these models to decipher ammonia detoxification mechanisms in the liver. METHODS: An integrated metabolic-spatial-temporal model was used to generate hypotheses of ammonia metabolism. Predicted mechanisms were validated using time-resolved analyses of nitrogen metabolism, activity analyses, immunostaining and gene expression after induction of liver damage in mice. Moreover, blood from the portal vein, liver vein and mixed venous blood was analyzed in a time dependent manner. RESULTS: Modeling revealed an underestimation of ammonia consumption after liver damage when only the currently established mechanisms of ammonia detoxification were simulated. By iterative cycles of modeling and experiments, the reductive amidation of alpha-ketoglutarate (α-KG) via glutamate dehydrogenase (GDH) was identified as the lacking component. GDH is released from damaged hepatocytes into the blood where it consumes ammonia to generate glutamate, thereby providing systemic protection against hyperammonemia. This mechanism was exploited therapeutically in a mouse model of hyperammonemia by injecting GDH together with optimized doses of cofactors. Intravenous injection of GDH (720 U/kg), α-KG (280 mg/kg) and NADPH (180 mg/kg) reduced the elevated blood ammonia concentrations (>200 µM) to levels close to normal within only 15 min. CONCLUSION: If successfully translated to patients the GDH-based therapy might provide a less aggressive therapeutic alternative for patients with severe hyperammonemia.

Epitope Mapping Using Peptide Microarray in Autoantibody Profiling.

The use of peptide microarrays for epitope mapping of autoantibodies greatly facilitates the early diagnosis of allergic, cytotoxin-associated diseases and especially inflammatory diseases. A common approach to create the microarrays utilizes nitrocellulose-coated glass slides for peptide probe binding, which is based on surface adsorption. Advantages of this method include excellent peptide binding capacity and long-term stability. To ensure equal accessibility to all antibodies on the peptide microarray during epitope mapping, all probes are immobilized in a random manner, thus avoiding concentration-dependent effects on signal intensity.In this chapter, we provide a step-by-step protocol on how to construct the peptide microarrays and perform epitope mapping of autoantibodies using them. Finally we present a comparative approach for the evaluation of the data.

Optimality in the zonation of ammonia detoxification in rodent liver.

The rodent liver eliminates toxic ammonia. In mammals, three enzymes (or enzyme systems) are involved in this process: glutaminase, glutamine synthetase and the urea cycle enzymes, represented by carbamoyl phosphate synthetase. The distribution of these enzymes for optimal ammonia detoxification was determined by numerical optimization. This in silico approach predicted that the enzymes have to be zonated in order to achieve maximal removal of toxic ammonia and minimal changes in glutamine concentration. Using 13 compartments, representing hepatocytes, the following predictions were generated: glutamine synthetase is active only within a narrow pericentral zone. Glutaminase and carbamoyl phosphate synthetase are located in the periportal zone in a non-homogeneous distribution. This correlates well with the paradoxical observation that in a first step glutamine-bound ammonia is released (by glutaminase) although one of the functions of the liver is detoxification by ammonia fixation. The in silico approach correctly predicted the in vivo enzyme distributions also for non-physiological conditions (e.g. starvation) and during regeneration after tetrachloromethane (CCl4) intoxication. Metabolite concentrations of glutamine, ammonia and urea in each compartment, representing individual hepatocytes, were predicted. Finally, a sensitivity analysis showed a striking robustness of the results. These bioinformatics predictions were validated experimentally by immunohistochemistry and are supported by the literature. In summary, optimization approaches like the one applied can provide valuable explanations and high-quality predictions for in vivo enzyme and metabolite distributions in tissues and can reveal unknown metabolic functions.

Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.

Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.

Data- and knowledge-based modeling of gene regulatory networks: an update.

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.

Basic regulatory principles of Escherichia coli's electron transport chain for varying oxygen conditions.

For adaptation between anaerobic, micro-aerobic and aerobic conditions Escherichia coli's metabolism and in particular its electron transport chain (ETC) is highly regulated. Although it is known that the global transcriptional regulators FNR and ArcA are involved in oxygen response it is unclear how they interplay in the regulation of ETC enzymes under micro-aerobic chemostat conditions. Also, there are diverse results which and how quinones (oxidised/reduced, ubiquinone/other quinones) are controlling the ArcBA two-component system. In the following a mathematical model of the E. coli ETC linked to basic modules for substrate uptake, fermentation product excretion and biomass formation is introduced. The kinetic modelling focusses on regulatory principles of the ETC for varying oxygen conditions in glucose-limited continuous cultures. The model is based on the balance of electron donation (glucose) and acceptance (oxygen or other acceptors). Also, it is able to account for different chemostat conditions due to changed substrate concentrations and dilution rates. The parameter identification process is divided into an estimation and a validation step based on previously published and new experimental data. The model shows that experimentally observed, qualitatively different behaviour of the ubiquinone redox state and the ArcA activity profile in the micro-aerobic range for different experimental conditions can emerge from a single network structure. The network structure features a strong feed-forward effect from the FNR regulatory system to the ArcBA regulatory system via a common control of the dehydrogenases of the ETC. The model supports the hypothesis that ubiquinone but not ubiquinol plays a key role in determining the activity of ArcBA in a glucose-limited chemostat at micro-aerobic conditions.

Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration.

UNLABELLED: The impairment of hepatic metabolism due to liver injury has high systemic relevance. However, it is difficult to calculate the impairment of metabolic capacity from a specific pattern of liver damage with conventional techniques. We established an integrated metabolic spatial-temporal model (IM) using hepatic ammonia detoxification as a paradigm. First, a metabolic model (MM) based on mass balancing and mouse liver perfusion data was established to describe ammonia detoxification and its zonation. Next, the MM was combined with a spatial-temporal model simulating liver tissue damage and regeneration after CCl4 intoxication. The resulting IM simulated and visualized whether, where, and to what extent liver damage compromised ammonia detoxification. It allowed us to enter the extent and spatial patterns of liver damage and then calculate the outflow concentrations of ammonia, glutamine, and urea in the hepatic vein. The model was validated through comparisons with (1) published data for isolated, perfused livers with and without CCl4 intoxication and (2) a set of in vivo experiments. Using the experimentally determined portal concentrations of ammonia, the model adequately predicted metabolite concentrations over time in the hepatic vein during toxin-induced liver damage and regeneration in rodents. Further simulations, especially in combination with a simplified model of blood circulation with three ammonia-detoxifying compartments, indicated a yet unidentified process of ammonia consumption during liver regeneration and revealed unexpected concomitant changes in amino acid metabolism in the liver and at extrahepatic sites. CONCLUSION: The IM of hepatic ammonia detoxification considerably improves our understanding of the metabolic impact of liver disease and highlights the importance of integrated modeling approaches on the way toward virtual organisms.

Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0.

BACKGROUND: Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time. RESULTS: The NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed. CONCLUSIONS: NetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit.

A systems biology approach to analyse leaf carbohydrate metabolism in Arabidopsis thaliana.

Plant carbohydrate metabolism comprises numerous metabolite interconversions, some of which form cycles of metabolite degradation and re-synthesis and are thus referred to as futile cycles. In this study, we present a systems biology approach to analyse any possible regulatory principle that operates such futile cycles based on experimental data for sucrose (Scr) cycling in photosynthetically active leaves of the model plant Arabidopsis thaliana. Kinetic parameters of enzymatic steps in Scr cycling were identified by fitting model simulations to experimental data. A statistical analysis of the kinetic parameters and calculated flux rates allowed for estimation of the variability and supported the predictability of the model. A principal component analysis of the parameter results revealed the identifiability of the model parameters. We investigated the stability properties of Scr cycling and found that feedback inhibition of enzymes catalysing metabolite interconversions at different steps of the cycle have differential influence on stability. Applying this observation to futile cycling of Scr in leaf cells points to the enzyme hexokinase as an important regulator, while the step of Scr degradation by invertases appears subordinate.

Mathematical modeling of the central carbohydrate metabolism in Arabidopsis reveals a substantial regulatory influence of vacuolar invertase on whole plant carbon metabolism.

A mathematical model representing metabolite interconversions in the central carbohydrate metabolism of Arabidopsis (Arabidopsis thaliana) was developed to simulate the diurnal dynamics of primary carbon metabolism in a photosynthetically active plant leaf. The model groups enzymatic steps of central carbohydrate metabolism into blocks of interconverting reactions that link easily measurable quantities like CO(2) exchange and quasi-steady-state levels of soluble sugars and starch. When metabolite levels that fluctuate over diurnal cycles are used as a basic condition for simulation, turnover rates for the interconverting reactions can be calculated that approximate measured metabolite dynamics and yield kinetic parameters of interconverting reactions. We used experimental data for Arabidopsis wild-type plants, accession Columbia, and a mutant defective in vacuolar invertase, AtbetaFruct4, as input data. Reducing invertase activity to mutant levels in the wild-type model led to a correct prediction of increased sucrose levels. However, additional changes were needed to correctly simulate levels of hexoses and sugar phosphates, indicating that invertase knockout causes subsequent changes in other enzymatic parameters. Reduction of invertase activity caused a decline in photosynthesis and export of reduced carbon to associated metabolic pathways and sink organs (e.g. roots), which is in agreement with the reported contribution of vacuolar invertase to sink strength. According to model parameters, there is a role for invertase in leaves, where futile cycling of sucrose appears to have a buffering effect on the pools of sucrose, hexoses, and sugar phosphates. Our data demonstrate that modeling complex metabolic pathways is a useful tool to study the significance of single enzyme activities in complex, nonintuitive networks.