Showing papers by "Arun K. Bhunia published in 2009"

Lactobacillus delbrueckii ssp. bulgaricus B-30892 can inhibit cytotoxic effects and adhesion of pathogenic Clostridium difficile to Caco-2 cells

Abstract: Probiotic microorganisms are receiving increasing interest for use in the prevention, treatment, or dietary management of certain diseases, including antibiotic-associated diarrhea (AAD). Clostridium difficile is the most common cause of AAD and the resulting C. difficile – mediated infection (CDI), is potentially deadly. C. difficile associated diarrhea (CDAD) is manifested by severe inflammation and colitis, mostly due to the release of two exotoxins by C. difficile causing destruction of epithelial cells in the intestine. The aim of this study was to determine the effect of probiotic bacteria Lactobacillus delbrueckii ssp. bulgaricus B-30892 (LDB B-30892) on C. difficile-mediated cytotoxicity using Caco-2 cells as a model. Experiments were carried out to test if the cytotoxicity induced by C. difficile- conditioned-medium on Caco-2 cells can be altered by cell-free supernatant (CFS) from LDB B-30892 in different dilutions (1:2 to 1:2048). In a similar experimental setup, comparative evaluations of other probiotic strains were made by contrasting the results from these strains with the results from LDB B-30892, specifically the ability to affect C. difficile induced cytotoxicity on Caco-2 monolayers. Adhesion assays followed by quantitative analysis by Giemsa staining were conducted to test if the CFSs from LDB B-30892 and other probiotic test strains have the capability to alter the adhesion of C. difficile to the Caco-2 monolayer. Experiments were also performed to evaluate if LDB B-30892 or its released components have any bactericidal effect on C. difficile. Co-culturing of LDB B-30892 with C. difficile inhibited the C. difficile- mediated cytotoxicity on Caco-2 cells. When CFS from LDB B-30892-C. difficile co-culture was administered (up to a dilution of 1:16) on Caco-2 monolayer, there were no signs of cytotoxicity. When CFS from separately grown LDB B-30892 was mixed with the cell-free toxin preparation (CFT) of separately cultured C. difficile, the LDB B-30892 CFS was inhibitory to C. difficile CFT-mediated cytotoxicity at a ratio of 1:8 (LDB B-30892 CFS:C. difficile CFT). We failed to find any similar inhibition of C. difficile- mediated cytotoxicity when other probiotic organisms were tested in parallel to LDB B-30892. Our data of cytotoxicity experiments suggest that LDB B-30892 releases one or more bioactive component(s) into the CFS, which neutralizes the cytotoxicity induced by C. difficile, probably by inactivating its toxin(s). Our data also indicate that CFS from LDB B-30892 reduced the adhesion of C. difficile by 81%, which is significantly (P <0.01) higher than all other probiotic organisms tested in this study. This study reveals the very first findings that Lactobacillus delbrueckii ssp. bulgaricus B-30892 (LDB B-30892) can eliminate C. difficile-mediated cytotoxicity, using Caco-2 cells as a model. The study also demonstrates that LDB B-30892 can reduce the colonization of C. difficile cells in colorectal cells. More study is warranted to elucidate the specific mechanism of action of such reduction of cytotoxicity and colonization.

Evanescent wave fiber optic biosensor for salmonella detection in food.

Abstract: Salmonella enterica is a major food-borne pathogen of world-wide concern. Sensitive and rapid detection methods to assess product safety before retail distribution are highly desirable. Since Salmonella is most commonly associated with poultry products, an evanescent wave fiber-optic assay was developed to detect Salmonella in shell egg and chicken breast and data were compared with a time-resolved fluorescence (TRF) assay. Anti-Salmonella polyclonal antibody was immobilized onto the surface of an optical fiber using biotin-avidin interactions to capture Salmonella. Alexa Fluor 647-conjugated antibody (MAb 2F-11) was used as the reporter. Detection occurred when an evanescent wave from a laser (635 nm) excited the Alexa Fluor and the fluorescence was measured by a laser-spectrofluorometer at 710 nm. The biosensor was specific for Salmonella and the limit of detection was established to be 103 cfu/mL in pure culture and 104 cfu/mL with egg and chicken breast samples when spiked with 102 cfu/mL after 2–6 h of enrichment. The results indicate that the performance of the fiber-optic sensor is comparable to TRF, and can be completed in less than 8 h, providing an alternative to the current detection methods.

Targeted Capture of Pathogenic Bacteria Using a Mammalian Cell Receptor Coupled with Dielectrophoresis on a Biochip

Abstract: Efficient capture of target analyte on biosensor platforms is a prerequisite for reliable and specific detection of pathogenic microorganisms in a microfluidic chip. Antibodies have been widely used as ligands; however, because of their occasional unsatisfactory performance, a search for alternative receptors is underway. Heat shock protein 60 (Hsp60), a eukaryotic mitochondrial chaperon protein is a receptor for Listeria adhesion protein (LAP) during Listeria monocytogenes infection. This paper reports application of biotinylated Hsp60 as a capture molecule for living (viable) L. monocytogenes in a microfluidic environment. Hsp60, immobilized on the surface of streptavidin-coated silicon dioxide exhibited specific capture of pathogenic Listeria against a background of other Listeria species, Salmonella, Escherichia, Bacillus, Pseudomonas, Serratia, Hafnia, Enterobacter, Citrobacter, and Lactobacillus. The capture efficiency of L. monocytogenes was 83 times greater than another Listeria receptor, the mono...

Salmonella enterica serovar Typhimurium adhesion and cytotoxicity during epithelial cell stress is reduced by Lactobacillus rhamnosus GG

Abstract: Background: Physiological stressors may alter susceptibility of the host intestinal epithelium to infection by enteric pathogens. In the current study, cytotoxic effect, adhesion and invasion of Salmonella enterica serovar Typhimurium (S. Typhimurium) to Caco-2 cells exposed to thermal stress (41°C, 1 h) was investigated. Probiotic bacteria have been shown to reduce interaction of pathogens with the epithelium under non-stress conditions and may have a significant effect on epithelial viability during infection; however, probiotic effect on pathogen interaction with epithelial cells under physiological stress is not known. Therefore, we investigated the influence of Lactobacillus rhamnosus GG and Lactobacillus gasseri on Salmonella adhesion and Salmonella-induced cytotoxicity of Caco-2 cells subjected to thermal stress. Results: Thermal stress increased the cytotoxic effect of both S. Typhimurium (P = 0.0001) and nonpathogenic E. coli K12 (P = 0.004) to Caco-2 cells, and resulted in greater susceptibility of cell monolayers to S. Typhimurium adhesion (P = 0.001). Thermal stress had no significant impact on inflammatory cytokines released by Caco-2 cells, although exposure to S. Typhimurium resulted in greater than 80% increase in production of IL-6 and IL-8. Blocking S. Typhimurium with anti-ShdA antibody prior to exposure of Salmonella decreased adhesion (P = 0.01) to non-stressed and thermal-stressed Caco-2 cells. Pre-exposure of Caco-2 cells to L. rhamnosus GG significantly reduced Salmonella-induced cytotoxicity (P = 0.001) and Salmonella adhesion (P = 0.001) to Caco2 cells during thermal stress, while L. gasseri had no effect. Conclusion: Results suggest that thermal stress increases susceptibility of intestinal epithelial Caco-2 cells to Salmonella adhesion, and increases the cytotoxic effect of Salmonella during infection. Use of L. rhamnosus GG as a probiotic may reduce the severity of infection during epithelial cell stress. Mechanisms by which thermal stress increases susceptibility to S. Typhimurium colonization and by which L. rhamnosus GG limits the severity of infection remain to be elucidated.

System automation for a bacterial colony detection and identification instrument via forward scattering

Abstract: A system design and automation of a microbiological instrument that locates bacterial colonies and captures the forward-scattering signatures are presented. The proposed instrument integrates three major components: a colony locator, a forward scatterometer and a motion controller. The colony locator utilizes an off-axis light source to illuminate a Petri dish and an IEEE1394 camera to capture the diffusively scattered light to provide the number of bacterial colonies and two-dimensional coordinate information of the bacterial colonies with the help of a segmentation algorithm with region-growing. Then the Petri dish is automatically aligned with the respective centroid coordinate with a trajectory optimization method, such as the Traveling Salesman Algorithm. The forward scatterometer automatically computes the scattered laser beam from a monochromatic image sensor via quadrant intensity balancing and quantitatively determines the centeredness of the forward-scattering pattern. The final scattering signatures are stored to be analyzed to provide rapid identification and classification of the bacterial samples.

Learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology

Abstract: For a training dataset with a nonexhaustive list of classes, i.e. some classes are not yet known and hence are not represented, the resulting learning problem is ill-defined. In this case a sample from a missing class is incorrectly classified to one of the existing classes. For some applications the cost of misclassifying a sample could be negligible. However, the significance of this problem can better be acknowledged when the potentially undesirable consequences of incorrectly classifying a food pathogen as a nonpathogen are considered. Our research is directed towards the real-time detection of food pathogens using optical-scattering technology. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. These spectral signatures contain descriptive characteristics of bacterial colonies, which can be used to identify bacteria cultures in real time. One bottleneck that remains to be addressed is the nonexhaustive nature of the training library. It is very difficult if not impractical to collect samples from all possible bacteria colonies and construct a digital library with an exhaustive set of scatter signatures. This study deals with the real-time detection of samples from a missing class and the associated problem of learning with a nonexhaustive training dataset. Our proposed method assumes a common prior for the set of all classes, known and missing. The parameters of the prior are estimated from the samples of the known classes. This prior is then used to generate a large number of samples to simulate the space of missing classes. Finally a Bayesian maximum likelihood classifier is implemented using samples from real as well as simulated classes. Experiments performed with samples collected for 28 bacteria subclasses favor the proposed approach over the state of the art.

Rapid Detection of Salmonella enteritidis in Pork Samples with Impedimetric Biosensor: Effect of Electrode Spacing on Sensitivity

Abstract: Frequent outbreaks of foodborne illness have been increasing the awareness of food safety. Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Some immunological, rapid assays are developed, but these assays still require prolonged enrichment steps. Recently developed biosensors have shown potential for the rapid detection of foodborne pathogens. In this study, an impedimetric biosensor was developed for rapid detection of Salmonella entritidis in food sample. To develop the biosensor, an interdigitated microelectrode (IME) was fabricated by using a semiconductor fabrication process. Anti-Salmonella antibodies were immobilized based on neutravidin-biotin binding on the surface of the IME to form an active sensing layer. To evaluate the effect of electrode gap on sensitivity of the sensor, 3 types of sensors with different electrode gap sizes (2, 5, and 10 ㎛) were fabricated and tested. The impedimetric biosensor could detect 10³ CFU/㎖ of Salmonella in pork meat extract with an incubation time of 5 min. This method may provide a simple, rapid, and sensitive method to detect foodborne pathogens.

Development of a real-time system of monitoring bacterial colony growth and registering the forward-scattering pattern

Abstract: Early detection and classification of pathogenic bacteria species is crucial to food safety. The previous BARDOT (BActeria Rapid Detection by using Optical light scattering Technology) system is capable of classifying the bacterial colonies of around 1~1.5mm diameter within 24~36 hours of incubation. However, in order to further reduce the detection time and synchronize the detection operation with the bacterial cultivation, a micro-incubator is developed that not only grows bacteria at 37°C but also enables forward scatterometry. This new design feature enables us to continuously characterize the light scattering patterns of the bacterial colonies throughout their growing stages. Some experimental results from this new system are demonstrated and compared with the images obtained from phase contrast microscopy and a confocal displacement meter to show the possibility of earlier identification of bacteria species. Moreover, this paper also explains the updated optical and mechanical modules for the beam waist control to accommodate the smaller bacteria colony detection.

Prediction of the light scattering patterns from bacteria colonies by a time-resolved reaction-diffusion model and the scalar diffraction theory

Abstract: In order to maximize the utility of the optical scattering technology in the area of bacterial colony identification, it is necessary to have a thorough understanding of how bacteria species grow into different morphological aggregation and subsequently function as distinctive optical amplitude and phase modulators to alter the incoming Gaussian laser beam. In this paper, a 2-dimentional reaction-diffusion (RD) model with nutrient concentration, diffusion coefficient, and agar hardness as variables is investigated to explain the correlation between the various environmental parameters and the distinctive morphological aggregations formed by different bacteria species. More importantly, the morphological change of the bacterial colony against time is demonstrated by this model, which is able to characterize the spatio-temporal patterns formed by the bacteria colonies over their entire growth curve. The bacteria population density information obtained from the RD model is mathematically converted to the amplitude/phase modulation factor used in the scalar diffraction theory which predicts the light scattering patterns for bacterial colonies. The conclusions drawn from the RD model combined with the scalar diffraction theory are useful in guiding the design of the optical scattering instrument aiming at bacteria colony detection and classification.

Morphotypic analysis and classification of bacteria and bacterial colonies using laser light-scattering, pattern recognition, and machine-learning system

Abstract: Light scattering is one of the most fundamental optical processes whereby electromagnetic waves are forced to deviate from a straight trajectory by non-uniformities in the medium that they traverse. This presentation summarizes our recent research on application of light-scatter measurements paired with machine learning and pattern recognition methodologies for label-free classification of bioparticles. Two separate examples of light scatter-based techniques are discussed: forward-scatter measurements of bacterial colonies in an imaging system, and flow cytometry measurements of scatter signals formed by individual bacterial particles. Recently, we have reported a first practical implementation of a system capable of label-free classification and recognition of pathogenic species of Listeria , Salmonella, Vibrio, Staphylococcus, and E. coli using forward-scatter patterns produced by bacterial colonies irradiated with laser light. Individual bacteria in flow also form complex patterns dependent on particle size, shape, refraction index, density, and morphology. Although commercial flow cytometers allow scatter measurement at two angles this rudimentary approach cannot be used to separate populations of bioparticles of similar shape, size, or structure. The custom-built system used in the presented work collects axial light-loss and scatter signals at five carefully chosen angles. Experimental results obtained from colony scanner, as well from the extended cytometry instrument, were used to train the pattern-recognition algorithm. The results demonstrate that information provided by scatter alone may be sufficient to recognize various bioparticles with 90-99% success rate, both in flow and in imaging systems.

Non-exhaustive Learning for Bacteria Detection

Abstract: Technologies for rapid detection and classification of bacterial pathogens are crucial for securing the food supply. A light-scattering sensor recently developed for real-time detection and identification of colonies of multiple pathogens has shown great promise for distinguishing bacteria cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio, and Escherichia. Unlike traditional testing methods, this new technology does not require a labeling reagent or biochemical processing. The classification approach currently used with this technology relies on supervised learning. For an accurate detection and classification of bacterial pathogens, the training library used to train the classifier should consist of samples of all possible forms of the pathogens. Construction of such a training library is impractical if not impossible due to the high mutation rate that characterizes some of the infectious agents. In this study we propose a Bayesian approach to advance this sensor technology to allow for the detection of new classes/subclasses of bacteria, which do not exist in the training library. Learning with a nonexhaustive training library is an ill-defined problem. We assume Gaussian distributions for bacteria subclasses and implement a maximum likelihood classifier. A pair of conjugate priors based on Wishart distribution is defined and the covariance matrices are estimated by the posterior mean. A new sample is classified into one of the existing set of classes if the maximum of the likelihoods is above a designated threshold. If not, the sample is considered a novelty, i.e. a sample of a potentially new class. We compare the proposed approach with a benchmark support estimation technique as well as a simulated Bayesian modelling approach recently proposed.