Physical Characterization and Cellular Toxicity Studies of Commercial NiO Nanoparticles.

Nickel oxide (NiO) nanoparticles from several manufacturers with different reported sizes and surface coatings were characterized prior to assessing their cellular toxicity. The physical characterization of these particles revealed that sizes often varied from those reported by the supplier, and that particles were heavily agglomerated when dispersed in water, resulting in a smaller surface area and larger hydrodynamic diameter upon dispersion. Cytotoxicity testing of these materials showed differences between samples; however, correlation of these differences with the physical properties of the materials was not conclusive. Generally, particles with higher surface area and smaller hydrodynamic diameter were more cytotoxic. While all samples produced an increase in reactive oxygen species (ROS), there was no correlation between the magnitude of the increase in ROS and the difference in cytotoxicity between different materials.

Characterization of Commercial Metal Oxide Nanomaterials: Crystalline Phase, Particle Size and Specific Surface Area.

Physical chemical characterization of nanomaterials is critical to assessing quality control during production, evaluating the impact of material properties on human health and the environment, and developing regulatory frameworks for their use. We have investigated a set of 29 nanomaterials from four metal oxide families (aluminum, copper, titanium and zinc) with a focus on the measurands that are important for the basic characterization of dry nanomaterials and the determination of the dose metrics for nanotoxicology. These include crystalline phase and crystallite size, measured by powder X-ray diffraction, particle shape and size distributions from transmission electron microscopy, and specific surface area, measured by gas adsorption. The results are compared to the nominal data provided by the manufacturer, where available. While the crystalline phase data are generally reliable, data on minor components that may impact toxicity is often lacking. The crystal and particle size data highlight the issues in obtaining size measurements of materials with broad size distributions and significant levels of aggregation, and indicate that reliance on nominal values provided by the manufacturer is frequently inadequate for toxicological studies aimed at identifying differences between nanoforms. The data will be used for the development of models and strategies for grouping and read-across to support regulatory human health and environmental assessments of metal oxide nanomaterials.

Particle Size Distributions for Cellulose Nanocrystals Measured by Transmission Electron Microscopy: An Interlaboratory Comparison.

Particle size is a key parameter that must be measured to ensure reproducible production of cellulose nanocrystals (CNCs) and to achieve reliable performance metrics for specific CNC applications. Nevertheless, size measurements for CNCs are challenging due to their broad size distribution, irregular rod-shaped particles, and propensity to aggregate and agglomerate. We report an interlaboratory comparison (ILC) that tests transmission electron microscopy (TEM) protocols for image acquisition and analysis. Samples of CNCs were prepared on TEM grids in a single laboratory, and detailed data acquisition and analysis protocols were provided to participants. CNCs were imaged and the size of individual particles was analyzed in 10 participating laboratories that represent a cross section of academic, industrial, and government laboratories with varying levels of experience with imaging CNCs. The data for each laboratory were fit to a skew normal distribution that accommodates the variability in central location and distribution width and asymmetries for the various datasets. Consensus values were obtained by modeling the variation between laboratories using a skew normal distribution. This approach gave consensus distributions with values for mean, standard deviation, and shape factor of 95.8, 38.2, and 6.3 nm for length and 7.7, 2.2, and 2.9 nm for width, respectively. Comparison of the degree of overlap between distributions for individual laboratories indicates that differences in imaging resolution contribute to the variation in measured widths. We conclude that the selection of individual CNCs for analysis and the variability in CNC agglomeration and staining are the main factors that lead to variations in measured length and width between laboratories.

Selective embedment of silver nanocrystals into spatially segregated domains in thin polymer films for controlled fabrication of functional nanocomposites.

Fabrication of polymer-nanoparticle nanocomposites typically relies on mixing nanoparticle and polymer solutions, which renders little control over nanoparticle incorporation, and homogeneity of the resulting composite material. This work focuses on the thermally induced embedment of monocrystalline silver nanocubes (AgNCs) into polymer surfaces. The AgNCs are initially deposited through a Langmuir approach onto films of immiscible blended polymer films, which allows fine control over nanoparticle density and aggregation state. This nanoparticle/polymer composite is then heated above the glass transition temperature (T g) of a polymer, which initiates the irreversible embedding of the AgNCs. The immiscible ternary polymer films featured discrete domains (with different T gs), which were altered by changing the amount of polystyrene, poly(2-vinylpyridine) and poly(methyl methacrylate) within the polymer solution. The T g dependence of the embedding process allowed the selective embedment of AgNCs into discrete polymer domains. The process was monitored in real time by using spatially separated hybrid plasmon modes, through peak shifts observed in a UV-vis spectrum. Enhanced surface confinement was observed for certain tripolymer films when compared to polystyrene-AgNC nanocomposites, due to changes in the surface energy within the blend. This work brings interesting insight on nanoparticle-blended polymer interactions and provides a fairly universal approach for the fabrication of these polymer-metal nanoparticle nanocomposites, which is of particular interest in fields that require fine control over nanoparticle incorporation within segregated polymer domains.

Segregation of the Anodic Microbial Communities in a Microbial Fuel Cell Cascade.

Metabolic interactions within microbial communities are essential for the efficient degradation of complex organic compounds, and underpin natural phenomena driven by microorganisms, such as the recycling of carbon-, nitrogen-, and sulfur-containing molecules. These metabolic interactions ultimately determine the function, activity and stability of the community, and therefore their understanding would be essential to steer processes where microbial communities are involved. This is exploited in the design of microbial fuel cells (MFCs), bioelectrochemical devices that convert the chemical energy present in substrates into electrical energy through the metabolic activity of microorganisms, either single species or communities. In this work, we analyzed the evolution of the microbial community structure in a cascade of MFCs inoculated with an anaerobic microbial community and continuously fed with a complex medium. The analysis of the composition of the anodic communities revealed the establishment of different communities in the anodes of the hydraulically connected MFCs, with a decrease in the abundance of fermentative taxa and a concurrent increase in respiratory taxa along the cascade. The analysis of the metabolites in the anodic suspension showed a metabolic shift between the first and last MFC, confirming the segregation of the anodic communities. Those results suggest a metabolic interaction mechanism between the predominant fermentative bacteria at the first stages of the cascade and the anaerobic respiratory electrogenic population in the latter stages, which is reflected in the observed increase in power output. We show that our experimental system represents an ideal platform for optimization of processes where the degradation of complex substrates is involved, as well as a potential tool for the study of metabolic interactions in complex microbial communities.

Straw N-halamines: evaluation in single and multistage filtration systems.

New N-halamines (I-Cl and II-Cl) based on cellulose extracted from rice straw have been evaluated in single and multistage filtration systems against bacteria and viruses. Escherichia coli and Staphylococcus aureus were used as examples of Gram-negative and Gram-positive bacteria respectively while PRD1 bacteriophage was used as an example for viruses. II-Cl has achieved 9 log reductions in viable counts against E. coli in 2 h and S. aureus in 1h while it has achieved 7 log reductions against PRD1 in 5 h. The particle size of prepared materials was modified as well as the flow rate through the filtration systems. The antimicrobial activity of modified cellulose was proved to be comparable to some synthetic biocidal polymers from the same type in similar water treatment systems.

Acorn: a grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface.

BACKGROUND: Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. RESULTS: Here, we present Acorn, an open source (GNU GPL) grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users) increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a web server allowing constraint based simulations of the genome scale metabolic reaction networks of E. coli, S. cerevisiae and M. tuberculosis. CONCLUSIONS: Acorn is a free software package, which can be installed by research groups to create a web based environment for computer simulations of genome scale metabolic reaction networks. It facilitates shared access to models and creation of publicly available constraint based modelling resources.

Genome-wide transcriptomic analysis of the response to nitrogen limitation in Streptomyces coelicolor A3(2).

BACKGROUND: The present study represents a genome-wide transcriptomic analysis of the response of the model streptomycete Streptomyces coelicolor A3(2) M145 to fermentor culture in Modified Evans Media limited, respectively, for nitrogen, phosphate and carbon undertaken as part of the ActinoGEN consortium to provide a publicly available reference microarray dataset. FINDINGS: A microarray dataset using samples from two replicate cultures for each nutrient limitation was generated. In this report our analysis has focused on the genes which are significantly differentially expressed, as determined by Rank Products Analysis, between samples from matched time points correlated by growth phase for the three pairs of differently limited culture datasets. With a few exceptions, genes are only significantly differentially expressed between the N6/N7 time points and their corresponding time points in the C and P-limited cultures, with the vast majority of the differentially expressed genes being more highly expressed in the N-limited cultures. Our analysis of these genes indicated expression of several members of the GlnR regulon are induced upon nitrogen limitation, as assayed for by [NH4+] measurements, and we are able to identify several additional genes not present in the GlnR regulon whose expression is induced in response to nitrogen limitation. We also note SCO3327 which encodes a small protein (32 amino acid residues) unusually rich in the basic amino acids lysine (31.25%) and arginine (25%) is significantly differentially expressed in the nitrogen limited cultures. Additionally, we investigate the expression of known members of the GlnR regulon and the relationship between gene organization and expression for the SCO2486-SCO2487 and SCO5583-SCO5585 operons. CONCLUSIONS: We provide a list of genes whose expression is differentially expressed in low nitrogen culture conditions, including a putative nitrogen storage protein encoded by SCO3327. Our list includes several genes whose expression patterns are similar to up-regulated members of the GlnR regulon and are induced in response to nitrogen limitation. These genes represent likely targets for future studies into the nitrogen starvation response in Streptomyces coelicolor.

SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks.

UNLABELLED: Constraint-based modeling of genome-scale metabolic networks has been successfully used in numerous applications such as prediction of gene essentiality and metabolic engineering. We present SurreyFBA, which provides constraint-based simulations and network map visualization in a free, stand-alone software. In addition to basic simulation protocols, the tool also implements the analysis of minimal substrate and product sets, which is useful for metabolic engineering and prediction of nutritional requirements in complex in vivo environments, but not available in other commonly used programs. The SurreyFBA is based on a command line interface to the GLPK solver distributed as binary and source code for the three major operating systems. The command line tool, implemented in C++, is easily executed within scripting languages used in the bioinformatics community and provides efficient implementation of tasks requiring iterative calls to the linear programming solver. SurreyFBA includes JyMet, a graphics user interface allowing spreadsheet-based model presentation, visualization of numerical results on metabolic networks represented in the Petri net convention, as well as in charts and plots. AVAILABILITY: SurreyFBA is distributed under GNU GPL license and available from http://sysbio3.fhms.surrey.ac.uk/SurreyFBA.zip.

New approach to produce water free of bacteria, viruses, and halogens in a recyclable system.

The antimicrobial activity of a new cross-linked N-halamine polymer against bacteria and viruses was evaluated. The polymer achieved a 9-log(10) reduction of bacteria (both Escherichia coli and Staphylococcus aureus) in 1.5 h and a 5-log(10) reduction of bacteriophage PRD1 in 3 h. At the same time, the ability of the nonhalogenated polymer to trap halide ions was examined. The polymer was incorporated into a multifiltration system to study the ability to produce water free of bacteria, viruses, and halide ions. The antimicrobial activity, useful lifetime, halide ion level, and recycling possibilities of the system were quantified on a laboratory scale. A design for a large-scale multifiltration system based on this polymer is proposed.

Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production.

Using flux variability analysis of a genome scale metabolic network of Streptomyces coelicolor, a series of reactions were identified, from disparate pathways that could be combined into an actinorhodin-generating mini-network. Candidate process feed nutrients that might be expected to influence this network were used in process simulations and in silico predictions compared to experimental findings. Ranking potential process feeds by flux balance analysis optimisation, using either growth or antibiotic production as objective function, did not correlate with experimental actinorhodin yields in fed processes. However, the effect of the feeds on glucose assimilation rate (using glucose uptake as objective function) ranked them in the same order as in vivo antibiotic production efficiency, consistent with results of a robustness analysis of the effect of glucose assimilation on actinorhodin production.

GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism.

BACKGROUND: An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis. RESULTS: GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available. CONCLUSION: The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.

Manipulation of the physiology of clavulanic acid production in Streptomyces clavuligerus.

This paper reports a novel use of cluster analysis for the identification of intermediary metabolites that are produced at rates closely correlated with those of antibiotic biosynthesis. This information was used to devise culture feeds resulting in enhanced production of clavulanic acid, an antibiotic of current worldwide commercial interest. The feeding strategies apparently alleviated a rate-limiting supply of the C3 precursor of clavulanic acid. C3 limitation may be a consequence of unusual nitrogen and carbon metabolism in Streptomyces clavuligerus. This approach has potential as a generic method for influencing biosynthetic pathway fluxes using feeds without knowledge of the biosynthetic pathway.