SOS-LUX- and LAC-FLUORO-TEST for the quantification of genotoxic and/or cytotoxic effects of heavy metal salts

The constantly increasing industrial, agricultural and domestic activities with their growing risk of contaminating fresh water and ground water by discharged compounds lead to an increasing concern focused on the quality of water. Because of their simplicity and sensitivity, bacterial tests play an important role in the detection and screening of genotoxins or cytotoxins in water.One of those bacterial tests is the SOS-LUX- and LAC-FLUORO-TEST, which is a combination of two bioassays, that simultaneously measures the genotoxicity (SOS-LUX-TEST) and the cytotoxicity (LAC-FLUORO-TEST) of substances and mixtures of substances.The SOS-LUX-TEST is based on genetically modified Salmonella typhimurium TA1535 bacteria, which have been transformed with the plasmid pPLS-1 carrying the promoterless lux operon of Photobacterium leiognathi as reporter element under the control of a DNA damage-dependent SOS promoter from ColD as sensing element. This system reacts in a dose-dependent manner to agents which induce DNA damages inside these bacterial cells with the production of bioluminescence that can easily be measured.The analogous LAC-FLUORO-TEST has been developed for the detection of cellular responses to cytotoxins. It is based on the constitutive expression of green fluorescent protein (GFP) mediated by the bacterial protein expression vector pGFPuv (Clontech, Palo Alto, USA). In response to cytotoxic agents, this system reacts with a dose-dependent reduction of GFP-fluorescence.A panel of recombinant S. typhimurium strains carrying either the SOS-LUX plasmid or the fluorescence-mediating lac-GFPuv plasmid was used to determine in parallel on one microplate the genotoxic and the cytotoxic potential of heavy metal salts like K₂Cr₂O₇, CrCl₃, ZnSO₄, CuSO₄, NiSO₄, KH₂AsO₄ and As₂O₃ at the same time. Light and fluorescence emission of untreated and chemical-treated cells were measured in a microplate luminometer-fluorometer-photometer combination and the luminescence induction as well as the fluorescence reduction were used to determine the genotoxic and/or cytotoxic potential of the heavy metal salts.     

Rapid, highly efficient extraction and purification of membrane proteins using a microfluidic continuous-flow based aqueous two-phase system

Membrane proteins play essential roles in regulating various fundamental cellular functions. To investigate membrane proteins, extraction and purification are usually prerequisite steps. Here, we demonstrated a microfluidic aqueous PEG/detergent two-phase system for the purification of membrane proteins from crude cell extract, which replaced the conventional discontinuous agitation method with continuous extraction in laminar flows, resulting in significantly increased extraction speed and efficiency. To evaluate this system, different separation and detection methods were used to identify the purified proteins, such as capillary electrophoresis, SDS-PAGE and nano-HPLC-MS/MS. Swiss-Prot database with Mascot search engine was used to search for membrane proteins from random selected bands of SDS-PAGE. Results indicated that efficient purification of membrane proteins can be achieved within 5-7s and approximately 90% of the purified proteins were membrane proteins (the highest extraction efficiency reported up to date), including membrane-associated proteins and integral membrane proteins with multiple transmembrane domains. Compared to conventional approaches, this new method had advantages of greater specific surface area, minimal emulsification, reduced sample consumption and analysis time. We expect the developed method to be potentially useful in membrane protein purifications, facilitating the investigation of membrane proteomics.     

A single-cell encapsulation method based on a microfluidic multi-step droplet splitting system

Single cell analysis is of great significance to understand the physiological activity of organisms.Microfluidic droplet is an ideal analytical platform for single-cell analysis. We developed a microfluidic droplet splitting system integrated with a flow-focusing structure and multi-step splitting structures to form 8-line droplets and encapsulate single cells in the droplets. Droplet generation frequency reached1021 Hz with the aqueous phase flow rate of 1 m L/min and the oil phase flow rate of 15 mL /min. Relative standard deviation of the droplet size was less than 5% in a single channel, while less than 6% in all the8 channels. The system was used for encapsulating human whole blood cells. A single-cell encapsulation efficiency of 31% was obtained with the blood cell concentration of 2.5× 10~4cells/mL, and the multicellular droplet percentage was only 1.3%. The multi-step droplet splitting system for single cell encapsulation featured simple structure and high throughput.     

European ring exercise on water toxicity using different bioluminescence inhibition tests based on Vibrio fischeri, in support to the implementation of the water framework directive

An inter-laboratory comparison exercise was conducted under the European Union funded project entitled: Screening Methods for Water Data Information in Support of the Implementation of the Water Framework Directive (SWIFT-WFD) and coordinated by the Consejo Superior de Investigaciones Científicas (CSIC), in order to evaluate the reproducibility of different toxicity tests based on the bioluminescence inhibition of Vibrio fischeri, for the rapid water toxicity assessment.For the first time, this type of exercise has been organized in Europe, and using different tests based on the same principle. In this exercise, 10 laboratories from 8 countries (Austria, Cyprus, Germany, Greece, Italy, Portugal, Romania, and Spain) took place, and a total number of 360 samples were distributed.During the exercise, six series of six samples were analyzed along 5 months. Every batch of samples was composed by three real samples and three standard solutions. The real samples were: a raw influent and the effluent of a wastewater treatment plant (WWTP), and a sample from a first settlement of the WWTP spiked with a mixture of toxicant standards.A final number of 330 (91.7%) samples was analyzed, 3300 values in duplicate were collected, and the results for each sample were expressed as the 50% effective concentration (EC₅₀) values calculated through five points of dilution inhibition curves, after 5 and 15 min of incubation times.A statistical study was initiated using 660 results. The mean values, standard deviations (σ), variances (σ²), and upper and lower warning limits (UWL and LWL) were obtained, using the EC₅₀ values calculated with the result from the participating laboratories.The main objectives of this toxicity ring study were to evaluate the repeatability (r) and reproducibility (R) when different laboratories conduct the test, the influence of complex matrix samples, the variability between different tests based on the same principle, and to determine the rate at which participating laboratories successfully completed tests initiated.In this exercise, the 3.93% toxicity values were outliers according with the Z-score values and the Dixon test. The samples with the greater number of outliers were those with the smallest variability coefficient, corresponding to the greater and the smaller toxicity level.No relation was found through the cluster analysis, between the final results and the different commercial devices involved. Testing by multiple commercial devices did not appear to reduce the precision of the results, and the variability coefficient for the exercise was nearby to the average value for past editions carried out at national level, where the different participants used the same commercial device.Stability of samples was also followed during the exercise. While statistical significance differences were not found for the greater part of samples, for the sample from the WWTP influent, a significant decrease of the toxicity value was found along this study. Nevertheless, this was a type of sample with a high toxicity level during all the exercise.On the other hand, in order to obtain the chemical characterization of real samples, those were analyzed by chromatographic techniques, using different sequential solid phase extraction (SSPE) procedures, followed by liquid chromatography coupled with mass spectrometry (LC-MS), and gas chromatography-mass spectrometry (GC-MS). Good agreement was found between the chemical analysis results and the toxicity level of the samples.     

Continuous on-line concentration based on dynamic pH junction for trimethoprim and sulfamethoxazole by microfluidic capillary electrophoresis combined with flow injection analysis system

A novel, rapid and continuous on-line concentration approach based on dynamic pH junction for the analysis of trimethoprim (TMP) and sulfamethoxazole (SMZ) by microfluidic capillary electrophoresis (CE) combined with flow injection analysis is developed in this paper. Stacking is due to decreases in the velocity of analytes when migrating from the low-pH sample zone (sample was dissolved in 50 mM HCl) to a relatively high-pH buffer (30 mM phosphate buffer, pH 8.5) filled in the capillary. This results in 2.9-4.7-fold improvement in concentration sensitivity relative to conventional capillary electrophoresis methods. The separation could be achieved within 2 min and sample throughput rate can reach up to 38 h(-1).     

Spectrophotometric determination of dopamine in microliter scale using microfluidic system based on polymeric technology

In this paper we report two simple and sensitive spectrophotometric procedures for the determination of dopamine in microfluidic system based on poly(dimethylsiloxane) (PDMS) technology and comparison of their interference-susceptibility. The analytical reactions and measurements were carried out at ambient temperature in a microreactor of total volume 6 μl coupled with a spectrophotometric flow-through cuvette.     

Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network

Coal ash fusion temperature is important to boiler designers and operators of power plants. Fusion temperature is determined by the chemical composition of coal ash, however, their relationships are not precisely known. A novel neural network, ACO-BP neural network, is used to model coal ash fusion temperature based on its chemical composition. Ant colony optimization (ACO) is an ecological system algorithm, which draws its inspiration from the foraging behavior of real ants. A three-layer network is designed with 10 hidden nodes. The oxide contents consist of the inputs of the network and the fusion temperature is the output. Data on 80 typical Chinese coal ash samples were used for training and testing. Results show that ACO-BP neural network can obtain better performance compared with empirical formulas and BP neural network. The well-trained neural network can be used as a useful tool to predict coal ash fusion temperature according to the oxide contents of the coal ash.     

Gene network coherence based on prior knowledge using direct and indirect relationships

Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships.We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/.The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation.     

Discovery of network motifs based on induced subgraphs using a dynamic expansion tree

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.     

Separation of organic acid compounds from biological samples by zinc oxide nanoparticles–chitosan using genetic algorithm based on response surface methodology and artificial neural network

In this study, zinc oxide nanoparticles–chitosan based on solid phase extraction and high performance liquid chromatography was developed for the separation of organic compounds including citric, tartaric and oxalic acids from biological samples. For simulation and optimization of this method, the hybrids of genetic algorithm with response surface methodology (RSM) and artificial neural network (ANN) have been used. The predictive capability and generalization of both predictive models (RSM and ANN) were compared by unseen data. The results have shown the superiority of ANN compared with RSM. At the optimum conditions, the limits of detections of 2.2–2.9 µg L⁻¹ were obtained for the analytes. The developed procedure was then applied to the extraction and determination of organic acid compounds from biological samples. Copyright © 2014 John Wiley & Sons, Ltd.