Kenneth W. Widmer

Transcriptome Analysis of Genes Controlled by luxS/Autoinducer-2 in Salmonella enterica Serovar Typhimurium

The enteric pathogen Salmonella enterica serovar Typhimurium uses autoinducer-2 (AI-2) as a signaling molecule. AI-2 requires the luxS gene for its synthesis. The regulation of global gene expression in Salmonella Typhimurium by luxS/AI-2 is currently not known; therefore, the focus of this study was to elucidate the global gene expression patterns in Salmonella Typhimurium as regulated by luxS/AI-2. The genes controlled by luxS/AI-2 were identified using microarrays with RNA samples from wild-type (WT) Salmonella Typhimurium and its isogenic DeltaluxS mutant, in two growth conditions (presence and absence of glucose) at mid-log and early stationary phases. The results indicate that luxS/AI-2 has very different effects in Salmonella Typhimurium depending on the stage of cell growth and the levels of glucose. Genes with p < or = 0.05 were considered to be significantly expressed differentially between WT and DeltaluxS mutant. In the mid-log phase of growth, AI-2 activity was higher (1500-fold) in the presence of glucose than in its absence (450-fold). There was differential gene expression of 13 genes between the WT and its isogenic DeltaluxS mutant in the presence of glucose and 547 genes in its absence. In early stationary phase, AI-2 activity was higher (650-fold) in the presence of glucose than in its absence (1.5-fold). In the presence of glucose, 16 genes were differentially expressed, and in its absence, 60 genes were differentially expressed. Our microarray study indicates that both luxS and AI-2 could play a vital role in several cellular processes including metabolism, biofilm formation, transcription, translation, transport, and binding proteins, signal transduction, and regulatory functions in addition to previously identified functions. Phenotypic analysis of DeltaluxS mutant confirmed the microarray results and revealed that luxS did not influence growth but played a role in the biofilm formation and motility.

Identification of ground beef-derived fatty acid inhibitors of autoinducer-2-based cell signaling.

Autoinducer-2 (AI-2) molecules are used by several microorganisms to modulate various processes, including bioluminescence, biofilm formation, and virulence expression. Certain food matrices, including ground beef extracts, possess compounds capable of inhibiting AI-2 activity. In the present study, we identified and characterized these AI-2 inhibitors from ground beef extract using hexane solvent extraction and gas chromatography. Gas chromatographic analysis revealed the presence of several fatty acids such as palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:omega9), and linoleic acid (C18:omega6) that were capable of inhibiting AI-2 activity. These fatty acids were tested (using Vibrio harveyi BB170 and MM32 reporter strains) at different concentrations (1, 5, and 10 mM) to identify differences in the level of AI-2 activity inhibition. AI-2 inhibition ranged from 25 to 90%. A mixture of these fatty acids (prepared at concentrations equivalent to those present in the ground beef extract) produced 52 to 65% inhibition of AI-2 activity. The fatty acid mixture also negatively influenced Escherichia coli K-12 biofilm formation. These results demonstrate that both medium- and long-chain fatty acids in ground beef have the ability to interfere with AI-2-based cell signaling.

Antibiotic resistance profiles of Escherichia coli isolated from rural and urban environments

Abstract Sludge and waste water samples from a variety of locations (in the United States and from one location in Mexico) were evaluated to determine whether multiple antibiotic resistant resistance patterns correlated with location and type of waste. The occurrence of antibiotic resistant strains of Escherichia coli was dependent upon site among the eleven locations sampled (P < 0.001). The E. coli strains from digested municipal sewage sludge from El Paso, Texas and those from an El Paso army hospital were resistant to the widest range of antibiotics and were resistant to a greater number of antibiotics than any other site (P < 0.01). When isolates from undigested waste water samples were analyzed, there was no significant difference (P ≥ 0.05) between those obtained from two neighboring cities located on either side of the US‐Mexico border. There was no significant difference in the number of antibiotics to which isolates were resistant when comparing digested sewage sludge samples from rural and urba...

Survival of Salmonella typhimurium in soil and liquid microcosms amended with clinoptilolite compounds

The objectives of this study were to screen the effect of different clinoptilolite compounds on S. typhimurium survival and to compare the most effective compounds as amendments using agricultural soil and aqueous microcosm conditions. These microcosms were inoculated with the bacterial culture and at periodic intervals (over 12–30 days) viable Salmonella populations were enumerated using selective media. In screening studies Salmonella populations were found to be lower from the smaller mesh clinoptilolite compounds than from wood-chip sources or larger mesh clinoptilolite compounds at the end of 14 days. However, the response was highly correlated with the change in moisture content. In soil microcosm studies where moisture content was kept constant, there was an overall reduction of about 4 log units but there was no significant difference between the unamended control and the treatments. When smaller mesh clinoptilolites were evaluated in phosphate saline buffer there was a significantly lower (P < 0·05) number of viable S. typhimurium than in the unamended treatments. However, when the clinoptilolite compounds were sterilized prior to use, it was observed that the bacterial populations were protected from viability loss when compared with the unamended control.

Use of Artificial Neural Networks To Accurately Identify Cryptosporidium Oocyst and Giardia Cyst Images

ABSTRACT Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400× (total magnification), processed, and converted into a binary numerical array. A variety of “negative” images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously “unseen” images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.

Identification of Cryptosporidium parvum Oocysts by an Artificial Neural Network Approach

ABSTRACT Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts. A total of 525 digitized images of immunofluorescently labeled oocysts, fluorescent microspheres, and other miscellaneous nonoocyst images were employed in the training of the ANN. The images were cropped to a 36- by 36-pixel image, and the cropped images were placed into two categories, oocyst and nonoocyst images. The images were converted to grayscale and processed into a histogram of gray color pixel intensity. Commercially available software was used to develop and train the ANN. The networks were optimized by varying the number of training images, number of hidden neurons, and a combination of these two parameters. The network performance was then evaluated using a set of 362 unique testing images which the network had never “seen” before. Under optimized conditions, the correct identification of authentic oocyst images ranged from 81 to 97%, and the correct identification of nonoocyst images ranged from 78 to 82%, depending on the type of fluorescent antibody that was employed. The results indicate that the ANN developed were able to generalize the training images and subsequently discern previously unseen oocyst images efficiently and reproducibly. Thus, ANN can be used to reduce human errors associated with the microscopic detection of Cryptosporidium oocysts.

Fatty acid modulation of autoinducer (AI-2) influenced growth and macrophage invasion by Salmonella Typhimurium.

Autoinducer-2 (AI-2) is a small molecule that is involved in bacterial cell-to-cell signaling whose precursor formation is mediated by luxS. A luxS mutant of Salmonella Typhimurium PJ002 (ΔluxS) was grown in glucose-containing M-9 minimal medium supplemented with varying concentrations (1×, 10×, and 100×) of long-chain fatty acids (linoleic acid, oleic acid, palmitic acid, and stearic acid) to study the influence of fatty acids on growth rate and macrophage invasion. Additionally, in vitro synthesized AI-2 was added to this medium to identify the influence of AI-2 on S. Typhimurium PJ002 (ΔluxS) growth rate and macrophage invasion. The growth rate constant (k) for each experimental treatment was determined based on OD₆₀₀ values recorded during 12 h of incubation. There was a significant (p=0.01) increase in the growth rate of S. Typhimurium PJ002 (ΔluxS) in the presence of AI-2 when compared to the phosphate-buffered saline (PBS) control. However, fatty acids either singly or in a mixture were unable to influence AI-2s effect on growth rate. The presence of AI-2 significantly (p=0.02) decreased the invasiveness of S. Typhimurium PJ002 (ΔluxS) towards the murine macrophage cell line, RAW 264.7. However, the fatty acid mixture was able to reverse this reduction and restore invasiveness to background levels. These results suggest that, while AI-2 may enhance the growth rate and reduce macrophage invasion by the luxS mutant S. Typhimurium PJ002 (ΔluxS), fatty acids may influence the virulence in S. Typhimurium (PJ002) by modulating AI-2 activity.