Frederick S. Fry

Potential Use of DNA Barcodes in Regulatory Science : Applications of the Regulatory Fish Encyclopedia

The use of a DNA-based identification system (DNA barcoding) founded on the mitochondrial gene cytochrome c oxidase subunit I (COI) was investigated for updating the U.S. Food and Drug Administration Regulatory Fish Encyclopedia (RFE; http://www.cfsan.fda.gov/-frf/rfe0.html). The RFE is a compilation of data used to identify fish species. It was compiled to help regulators identify species substitution that could result in potential adverse health consequences or could be a source of economic fraud. For each of many aquatic species commonly sold in the United States, the RFE includes high-resolution photographs of whole fish and their marketed product forms and species-specific biochemical patterns for authenticated fish species. These patterns currently include data from isoelectric focusing studies. In this article, we describe the generation of DNA barcodes for 172 individual authenticated fish representing 72 species from 27 families contained in the RFE. These barcode sequences can be used as an additional identification resource. In a blind study, 60 unknown fish muscle samples were barcoded, and the results were compared with the RFE barcode reference library. All 60 samples were correctly identified to species based on the barcoding data. Our study indicates that DNA barcoding can be a powerful tool for species identification and has broad potential applications.

Detection and Identification of Bacteria in a Juice Matrix with Fourier Transform–Near Infrared Spectroscopy and Multivariate Analysis

The use of Fourier transform-near infrared (FT-NIR) spectroscopy combined with multivariate pattern recognition techniques was evaluated to address the need for a fast and sensitive method for the detection of bacterial contamination in liquids. The complex cellular composition of bacteria produces FT-NIR vibrational transitions (overtone and combination bands), forming the basis for identification and subtyping. A database including strains of Escherichia coil. Pseudomonas aeruginosa. Bacillus subtilis, Bacillus cereus, and Bacillus thuringiensis was built, with special care taken to optimize sample preparation. The bacterial cells were treated with 70% (vol/vol) ethanol to enhance safe handling of pathogenic strains and then concentrated on an aluminum oxide membrane to obtain a thin bacterial film. This simple membrane filtration procedure generated reproducible FT-NIR spectra that allowed for the rapid discrimination among closely related strains. Principal component analysis and soft independent modeling of class analogy of transformed spectra in the region 5,100 to 4,400 cm -1 were able to discriminate between bacterial species. Spectroscopic analysis of apple juices inoculated with different strains of E. coli at approximately 10 5 CFU/ml showed that FT-NIR spectral features are consistent with bacterial contamination and soft independent modeling of class analogy correctly predicted the identity of the contaminant as strains of E. coli. FT-NIR in conjunction with multivariate techniques can be used for the rapid and accurate evaluation of potential bacterial contamination in liquids with minimal sample manipulation, and hence limited exposure of the laboratory worker to the agents.

Differentiation of Whole Bacterial Cells Based on High-Throughput Microarray Chip Printing and Infrared Microspectroscopic Readout

Using robotic automation, a microarray printing protocol for whole bacterial cells was developed for subsequent label-free and nondestructive infrared microspectroscopic detection. Using this contact microspotting system, 24 microorganisms were printed on zinc selenide slides; these were 6 species of Listeria, 10 species of Vibrio, 2 strains of Photobacterium damselae, Yersinia enterocolitica 289, Bacillus cereus ATCC 14529, Staphylococcus aureus, ATCC 19075 (serotype 104 B), Shigella sonnei 20143, Klebsiella pneumoniae KP73, Enterobacter cloacae, Citrobacter freundii 200, and Escherichia coli. Microarrays consisting of separate spots of bacterial deposits gave consistent and reproducible infrared spectra, which were differentiated by unsupervised pattern recognition algorithms. Two multivariate analysis algorithms, principal component analysis and hierarchical cluster analysis, successfully separated most, but not all, the bacteria investigated down to the species level.

A discriminant based charge deconvolution analysis pipeline for protein profiling of whole cell extracts using liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry.

A discriminant based charge deconvolution analysis pipeline is proposed. The molecular weight determination (MoWeD) charge deconvolution method was applied directly to the discrimination rules obtained by the fuzzy rule-building expert system (FuRES) pattern classifier. This approach was demonstrated with synthetic electrospray ionization-mass spectra. Identification of the tentative protein biomarkers by bacterial cell extracts of Salmonella enterica serovar typhimurium strains A1 and A19 by liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) was also demonstrated. The data analysis time was reduced by applying this approach. In addition, this method was less affected by noise and baseline drift.

Near infrared chemical imaging for high throughput screening of food bacteria

Introduction A ssuring food safety is an ongoing endeavour in the food industry and there is a need for fast, reliable tools to screen for pathogens from both naturally-occurring and intentional contamination. FT-IR, Raman and NIR spectroscopy have been successfully applied in food and clinical microbiology in the last two decades. Many reports have shown the possibility of identifi cation of bacteria and yeasts, down to the strain level in some cases (for reviews, see References 1 and 2). However, the sheer numbers of possibilities that must be considered in a general microbiological library and the biochemical similarity exhibited by all microorganisms have hindered the widespread deployment of these techniques. Unsupervised multivariate classifi cation schemes have shown good success when small data sets of bacteria were considered, but calibrated systems and neural networks have been favoured when larger numbers of organisms are involved. In the latter case, it becomes important to include a reasonable complement of organisms of interest in the calibration set, which in turn involves both a signifi cant time commitment for data acquisition as well as refi ned data processing strategies to sort through the thousands of samples of biochemically-similar organisms. Finally, careful consideration of critical instrument issues is needed to ensure calibration transfer. The size of the database imposes a smaller burden when its scope is limited to the microbial fl ora of food. Indeed, the target food product is always known at the time of sampling and most foods are only potentially contaminated by a fairly small number of pathogens. Of course, fi nding a totally unexpected organism is possible and may suggest intentional contamination and this dictates a requirement for some way to fl ag and trap these anomalies in an analytical system. In this work, we built on the original fi ndings that bacteria exhibit NIR spectra that are characteristic enough for identifi cation and developed a high throughput approach with signifi cant potential as a screening tool. The basis of this application is that NIR spectroscopy using a focal-plane array detector provides the additional and critical high throughput advantage required for a useful screening tool and food-specifi c bacterial arrays, or “ID cards”, can be created by placing references directly on the sampling device, thereby eliminating calibration transfer issues. Chemical imaging is often used to obtain spatially-resolved chemical information about inhomogeneous systems. This approach takes advantage of the spatial dimensions of chemical imaging to perform parallel spectral acquisition of about 42 different bacterial samples at once.

Application of a novel hydrophilic infrared-transparent membrane to the differentiation between microcolonies of Enterobacter sakazakii and Klebsiella pneumoniae.

A proof-of-concept study is reported for the differentiation between microcolonies of Enterobacter sakazakii and Klebsiella pneumoniae by means of a novel sample preparation for infrared (IR) analysis. A disposable, IR-transparent, microporous (0.2-microm pores), hydrophobic, polyethylene (PE) membrane (51 microm thick) was plasma treated under an oxygen atmosphere and used to (i) filter (or print microarrays of) dilute aqueous foodborne bacterial suspensions and (ii) subsequently grow bacterial microcolonies when the treated, hydrophilic PE membrane was placed over brain heart infusion agar medium and incubated. Because this unique membrane is transparent to IR light, isolated microcolonies (200 microm) of bacterial cells grown on this PE substrate for the first time could be directly fingerprinted by IR microspectroscopy in the transmission mode. Hence, time-consuming bacterial cell transfer from culture plates to an IR sample holder for subsequent measurement by IR spectroscopy was eliminated. Multivariate analysis of the observed IR spectra for microcolonies allowed the rapid differentiation between E. sakazakii and K. pneumoniae.

DNA subtyping and pattern analysis methods for bacterial pathogens: Who's who in an outbreak

Abstract We have only scratched the surface of subtyping data analysis. To date, most subtyping analyses, whether the patterns are from ribotyping or pulsed-field gel electrophoresis, have been done visually. In the future, image analysis and statistical tools will be applied to create subtyping databases of high reliability and predictive ability. Such tools will advance the field of molecular epidemiology as well as have implications for the forensic analysis of DNA profiles.