RESUMEN

Salmonella spp. is one of the most isolated microorganisms reported to be responsible for human foodborne diseases and death. Water constitutes a major reservoir where the Salmonella spp. can persist and go undetected when present in low numbers. In this study, we assessed the viability of 12 serotypes of Salmonella enterica subsp. enterica for 160 days in nuclease-free water at 4 and 25°C using flow cytometry and Tryptic Soy Agar (TSA) plate counts. The results show that all 12 serotypes remain viable after 160 days in distilled water using flow cytometry, whereas traditional plate counts failed to detect ten serotypes incubated at 25°C. Moreover, the findings demonstrate that 4°C constitutes a more favorable environment where Salmonella can remain viable for prolonged periods without nutrients. Under such conditions, however, Salmonella exhibits a higher susceptibility to all tested antibiotics and benzalkonium chloride (BZK). The pre-enrichment with Universal Pre-enrichment Broth (UP) and 1/10 × Tryptic Soy broth (1/10 × TSB) resuscitated all tested serotypes on TSA plates, nevertheless cell size decreased after 160 days. Furthermore, phenotype microarray (PM) analysis of S. Inverness and S. Enteritidis combined with principal component analysis (PCA) revealed an inter-individual variability in serotypes with their phenotype characteristics, and the impact of long-term storage at 4 and 25°C for 160 days in nuclease-free water. This study provides an insight to Salmonella spp. long-term survivability at different temperatures and highlights the need for powerful tools to detect this microorganism to reduce the risk of disease transmission of foodborne pathogens via nuclease-free water.

RESUMEN

Burkholderia cepacia complex (BCC) contamination has resulted in recalls of non-sterile pharmaceutical products. The fast, sensitive, and specific detection of BCC is critical for ensuring the quality and safety of pharmaceutical products. In this study, a rapid flow cytometry-based detection method was developed using a fluorescence-labeled oligonucleotide Kef probe that specifically binds a KefB/KefC membrane protein sequence within BCC. Optimal conditions of a 1 nM Kef probe concentration at a 60 °C hybridization temperature for 30 min were determined and applied for the flow cytometry assay. The true-positive rate (sensitivity) and true-negative rate (specificity) of the Kef probe assay were 90% (18 positive out of 20 BCC species) and 88.9% (16 negative out of 18 non-BCC), respectively. The detection limit for B. cenocepacia AU1054 with the Kef probe flow cytometry assay in nuclease-free water was 1 CFU/mL. The average cell counts using the Kef probe assay from a concentration of 10 µg/mL chlorhexidine gluconate and 50 µg/mL benzalkonium chloride were similar to those of the RAPID-B total plate count (TPC). We demonstrate the potential of Kef probe flow cytometry as a more sensitive alternative to culture-based methods for detecting BCC in non-sterilized pharmaceutical raw materials and products with regards to water-based environments.

RESUMEN

Ralstonia pickettii is an emerging global opportunistic pathogen. Here, we report the 5.3-Mbp draft genome sequence of R. pickettii NCTR106, isolated from milk carton paperboard obtained from a commercial paper mill. The genome sequence carries two beta-lactamase genes similar to those reported in R. pickettii isolates collected from a hospital.

RESUMEN

The presence of Burkholderia cepacia complex (BCC) strains has resulted in recalls of pharmaceutical products, since these opportunistic pathogens can cause serious infections. Rapid and sensitive diagnostic methods to detect BCC are crucial to determine contamination levels. We evaluated bacterial cultures, real-time PCR (qPCR), droplet digital PCR (ddPCR), and flow cytometry to detect BCC in nuclease-free water, in chlorhexidine gluconate (CHX) and benzalkonium chloride (BZK) solutions. Twenty BCC strains were each suspended (1, 10, 100, and 1000 CFU/ml) in autoclaved nuclease-free water, 10 µg/ml CHX, and 50 µg/ml BZK. Five replicates of each strain were tested at each concentration (20 strains × 4 concentrations × 5 replicates = 400 tests) to detect BCC using the aforementioned four methods. We demonstrated the potential of ddPCR and flow cytometry as more sensitive alternatives to culture-based methods to detect BCC in autoclaved nuclease-free water and antiseptics samples.

Asunto(s)

RESUMEN

Very low cell count detection of Escherichia coli O157:H7 in foods is critical, since an infective dose for this pathogen may be only 10 cells, and fewer still for vulnerable populations. A flow cytometer is able to detect and count individual cells of a target bacterium, in this case E. coli O157:H7. The challenge is to find the single cell in a complex matrix like raw spinach. To find that cell requires growing it as quickly as possible to a number sufficiently in excess of matrix background that identification is certain. The experimental design for this work was that of a U.S. Food and Drug Administration (FDA) In-House Level 3 validation executed in the technology's originating laboratory. Using non-selective enrichment broth, 6.5 h incubation at 42°C, centrifugation for target cell concentration, and a highly selective E. coli O157 fluorescent antibody tag, the cytometry method proved more sensitive than a reference regulatory method (p = 0.01) for detecting a single target cell, one E. coli O157:H7 cell, in 25 g of spinach. It counted that cell's daughters with at least 38× signal-to-noise ratio, analyzing 25 samples in total-time-to-results of 9 h.

RESUMEN

Detection of microbial contamination in foods before they go on to the market can help prevent the occurrence of foodborne illness outbreaks. Current methods for the detection of Escherichia coli are limited by time-consuming procedures, which include multiple culture incubation steps, and require several days to get results. This unit describes the development of an improved rapid flow-cytometry-based detection method that has greater sensitivity and specificity. This method requires less time-to-results (TTR) and can detect a small number of E. coli in the presence of large numbers of other bacteria. Clear step-by-step protocols for cell concentration determination, sample preparation, and flow cytometric analysis are provided. © 2017 by John Wiley & Sons, Inc.

Asunto(s)

RESUMEN

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Asunto(s)

RESUMEN

BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

Asunto(s)

RESUMEN

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Asunto(s)

RESUMEN

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.

Asunto(s)

RESUMEN

RATIONALE: Rapid sub-species characterization of pathogens is required for timely responses in outbreak situations. Pyrolysis mass spectrometry (PyMS) has the potential to be used for this purpose. METHODS: However, in order to make PyMS practical for traceback applications, certain improvements related to spectrum reproducibility and data acquisition speed were required. The main objectives of this study were to facilitate fast detection (<30 min to analyze 6 samples, including preparation) and sub-species-level bacterial characterization based on pattern recognition of mass spectral fingerprints acquired from whole cells volatilized and ionized at atmospheric pressure. An AccuTOF DART mass spectrometer was re-engineered to permit ionization of low-volatility bacteria by means of Plasma Jet Ionization (PJI), in which an electric discharge, and, by extension, a plasma beam, impinges on sample cells. RESULTS: Instrumental improvements and spectral acquisition methodology are described. Performance of the re-engineered system was assessed using a small challenge set comprised of assorted bacterial isolates differing in identity by varying amounts. In general, the spectral patterns obtained allowed differentiation of all samples tested, including those of the same genus and species but different serotypes. CONCLUSIONS: Fluctuations of ±15% in bacterial cell concentrations did not substantially compromise replicate spectra reproducibility.

Asunto(s)

RESUMEN

The Bacteriological Analytical Manual (BAM) method currently used by the United States Food and Drug Administration (FDA) to detect Escherichia coli O157:H7 in spinach was systematically compared to a new flow cytometry based method. This Food and Drug Administration (FDA) level 2 external laboratory validation study was designed to determine the latter method's sensitivity and speed for analysis of this pathogen in raw spinach. Detection of target cell inoculations with a low cell count is critical, since enterohemorrhagic strains of E. coli require an infective dose of as few as 10 cells (Schmid-Hempel and Frank, 2007). Although, according to the FDA, the infectious dose is unknown (Food and Drug Administration, 1993). Therefore, the inoculation level into the spinach, a total of 2.0±2.6 viable E. coli O157 cells, was specified to yield between 25% and 75% detection by the new method, out of 20 samples (10 positives and 10 negatives). This criterion was met in that the new method detected 60% of the nominally positive samples; the corresponding sensitivity of the reference method was 50%. For both methods the most likely explanation for false negatives was that no viable cells were actually introduced into the sample. In this validation study, the flow cytometry method was equal to the BAM in sensitivity and far superior in speed.

Asunto(s)

RESUMEN

RATIONALE: The identification of bacteria based on mass spectra produced by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) has become routine since its introduction in 1996. The major drawback is that bacterial patterns produced by MALDI are dependent on sample preparation prior to analysis. This results in poor reproducibility in identifying bacterial types and between laboratories. The need for a more broadly applicable and useful sample handling procedure is warranted. METHODS: Thymol was added to the suspension solvent of bacteria prior to MALDI analysis. The suspension solvent consisted of ethanol, water and TFA. The bacterium was added to the thymol suspension solvent and heated. An aliquot of the bacterial suspension was mixed directly with the matrix solution at a 9:1 ratio, matrix/bacteria solution, respectively. The mixture was then placed on the MALDI plate and allowed to air dry before MALDI analysis. RESULTS: The thymol method improved the quality of spectra and number of peaks when compared to other sample preparation procedures studied. The bacterium-identifying biomarkers assigned to four strains of E. coli were statistically 95% reproducible analyzed on three separate days. The thymol method successfully differentiated between the four E. coli strains. In addition, the thymol procedure could identify nine out of ten S. enterica serovars over a 3-day period and nine S. Typhimurium strains from the other ten serovars 90% of the time over the same period. CONCLUSIONS: The thymol method can identify certain bacteria at the sub-species level and yield reproducible results over time. It improves the quality of spectra by increasing the number of peaks when compared to the other sample preparation methods assessed in this study. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Asunto(s)

RESUMEN

Modified 3D-SDAR fingerprints combining (13)C and (15)N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD-). The best performing 3D-QSDAR model utilized a grid with a density of 8ppm×8ppm in the C-C region, 8ppm×20ppm in the C-N region and 20ppm×20ppm in the N-N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy=0.70, sensitivity=0.73 and specificity=0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5-7.5Å distant from an amino-group. The presence of a second aromatic ring separated by a 4-5Å spacer from the first ring and at a distance of between 5.5Å and 7Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.

Asunto(s)

RESUMEN

Foodborne illnesses occur in both industrialized and developing countries, and may be increasing due to rapidly evolving food production practices. Yet some primary tools used to assess food safety are decades, if not centuries, old. To improve the time to result for food safety assessment a sensitive flow cytometer based system to detect microbial contamination was developed. By eliminating background fluorescence and improving signal to noise the assays accurately measure bacterial load or specifically identify pathogens. These assays provide results in minutes or, if sensitivity to one cell in a complex matrix is required, after several hours enrichment. Conventional assessments of food safety require 48 to 56 hours. The assays described within are linear over 5 orders of magnitude with results identical to culture plates, and report live and dead microorganisms. This system offers a powerful approach to real-time assessment of food safety, useful for industry self-monitoring and regulatory inspection.

Asunto(s)

RESUMEN

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test = 0.801) offered an improvement of about 7.5% (R(2) test consensus = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Asunto(s)

RESUMEN

Multiple validation techniques (Y-scrambling, complete training/test set randomization, determination of the dependence of R2test on the number of randomization cycles, etc.) aimed to improve the reliability of the modeling process were utilized and their effect on the statistical parameters of the models was evaluated. A consensus partial least squares (PLS)-similarity based k-nearest neighbors (KNN) model utilizing 3D-SDAR (three dimensional spectral data-activity relationship) fingerprint descriptors for prediction of the log(1/EC50) values of a dataset of 94 aryl hydrocarbon receptor binders was developed. This consensus model was constructed from a PLS model utilizing 10 ppm x 10 ppm x 0.5 Å bins and 7 latent variables (R2test of 0.617), and a KNN model using 2 ppm x 2 ppm x 0.5 Å bins and 6 neighbors (R2test of 0.622). Compared to individual models, improvement in predictive performance of approximately 10.5% (R2test of 0.685) was observed. Further experiments indicated that this improvement is likely an outcome of the complementarity of the information contained in 3D-SDAR matrices of different granularity. For similarly sized data sets of Aryl hydrocarbon (AhR) binders the consensus KNN and PLS models compare favorably to earlier reports. The ability of 3D-QSDAR (three dimensional quantitative spectral data-activity relationship) to provide structural interpretation was illustrated by a projection of the most frequently occurring bins on the standard coordinate space, thus allowing identification of structural features related to toxicity.

RESUMEN

A flow cytometric method (RAPID-B™) with detection sensitivity of one viable cell of Escherichia coli serotype O157:H7 in fresh spinach (Spinacia oleracea) was developed and evaluated. The major impediment to achieving this performance was mistaking autofluorescing spinach particles for tagged target cells. Following a 5 h non-selective enrichment, artificially inoculated samples were photobleached, using phloxine B as a photosensitizer. Samples were centrifuged at high speed to concentrate target cells, then gradient centrifuged to separate them from matrix debris. In external laboratory experiments, RAPID-B and the reference method both correctly detected E. coli O157:H7 at inoculations of ca. 15 cells. In a follow-up study, after 4 cell inoculations of positives and 6 h enrichment, RAPID-B correctly identified 92% of 25 samples. The RAPID-B method limit of detection (LOD) was one cell in 25 g. It proved superior to the reference method (which incorporated real time-PCR, selective enrichment, and culture plating elements) in accuracy and speed.

Asunto(s)

RESUMEN

An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

Asunto(s)