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Developing an Immunochemical Fingerprinting Analysis for Identification of Salmonella


<p>The overall goal of this project is to develop an innovative method for identification and subtyping of Salmonella and to compare the discriminatory potential of the developed method with other molecular methods. This new method, based on profiling proteolytic fragments of Salmonella flagella antigens, is expected to be specific, rapid, and reliable. The specific objectives are to:</p>

<p>1. Develop analytical protocols for the Immunochemical Fingerprint Analysis (IFA).</p>

<p>2. Establish an IFA database of collected Salmonella serotypes.</p>

<p>3. Compare the relation and diversity of the IFA profiles with other molecular methods.</p>

More information

<p>Phase 1: Development of analytical protocols for IFA The IFA method to be developed consists of four major steps, antigen extraction, limited proteolysis, SDS-polyacrylamide gel electrophoresis, and Western blot. Each of these steps needs to be standardized in order to achieve reproducible results. In general, the flagella antigens will be extracted from enriched cultures using glycine-hydrochloride or urea method and treated with various proteinases. The resulting proteolytic fragments will be separated by SDS gels and transferred to nitrocellulose membranes. Western blots will be performed using flagella-specific monoclonal antibodies that have been produced in a previous project. The antigenic fragment profiles will be captured using an imaging system and analyzed using bioinformatics software. Flagella antigen preparation: Two procedures will be tested according to the lysis buffers used. Salmonella cultures will be centrifuged at 15,000 g for 30 min, resuspended in lysis buffer and incubated for 30 min at 37 ºC. The cells will then be homogenized using a bench mixer, in order to release the flagella filaments and then centrifuged at 15,000 g for 30 min. The flagellin-containing supernatant will be submitted to ultrafiltration with a 10 kD cut-off membrane to eliminate molecules with less than 10 kD from the culture medium produced by the bacterial strain. The recovered filtrates will be used for limited proteolysis. Limited proteolysis: Trials of limited proteolysis using trypsin, Arg-C, Lys-C, Glu-C, Asp-N and Proteinase K will be carried out. The enzyme to protein ratio will be optimized to achieve time-resolved cleavage with most diverse profiles. The protein concentration will be determined by UV absorbance. Aliquots will be withdrawn from the reaction mixture at specified time intervals for electrophoresis. Gel electrophoresis: Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) will be performed with a Criterion electrophoresis cell (Bio-Rad) according to manufacturer instructions. Proteins will be stained with SYPRO Ruby protein stain and protein contents will be determined by densitometry by using ChemiDoc MP imaging system and Quantity One software. Western Blot: The proteins will be transferred to nitrocellulose membranes using Trans-Blot Turbo Transfer System (Bio-Rad). The membrane will be blocked with 5% non-fat dry milk powder in PBS for 1 h at room temperature and incubated for 1 h with flagella-specific monoclonal antibodies. After washing with 0.05% Tween-20 in PBS (PBS-T), bound antibodies will be reacted with goat anti-mouse IgG conjugated with peroxidase. After incubation for 1 h with the conjugate and washing with PBS-T, bound antibodies will be detected with Chemiluminescent western blot detection kit (Bio-Rad). The membrane will be photographed with illumination from a ChemiDoc MP imaging system (Bio-Rad).</p><p>Phase 2: Establishment of an IFA database The IFA protocols developed in Phase 1 will be applied to establish a database of the fingerprint profiles of Salmonella enterica isolates. The genus Salmonella consists of only two species: S. enterica and S. bongori. Most Salmonella encountered in foodborne outbreaks are serotypes belonging to S. enterica which is further divided into six subspecies. The completion of IFA database will allow the comparison of fingerprint profiles of different Salmonella serotypes. The database is also an important tool for evaluating the similarity and diversity of IFA concurrently with other molecular method (PFGE) as planned in</p><p> Phase 3. IFA Database of Salmonella: Salmonella enterica isolates of known serotypes will be obtained from ATCC. These isolates will consist of at least eight different serovars of Salmonella enterica subsp. enterica historically associated with foodborne outbreaks. IFA fingerprint profiles of Salmonella serovas will be established according to the protocols developed in Phase 1. Analysis of IFA Fingerprint profiles: The program BioNumerics (Applied Maths) will be used to perform numerical analyses of fingerprint profiles. Strain relationships will be inferred by use of the Pearson product-moment correlation coefficient and unweighted pair-group with mathematical average clustering, and depicted in dendrogrammatic form. Reproducibility will be determined by assessing the mean similarity between duplicate profiles of 10 randomly selected strains. Individual fingerprinting types will be defined by application of this reproducibility cut-off value to the dendrogram containing all isolates. The discriminatory potential of the method will be assessed by use of algorithm discussed by Hunter (1990), in which values between 1.0 and 0 may be obtained, whereby the value of 1.0 denotes that all strains are differentiated into individual types and the value of 0 assigns all strains studied to the same type.Phase 3: Validation of the relation and diversity between IFA and PFGE profiles This will be the first attempt to correlate the IFA fingerprint profiles with serotyping method and to compare the similarity and diversity of fingerprint profiles between IFA and PFGE. In this phase of project, a database of PFGE fingerprint profiles will be created for the known serotypes as those used in establishing the IFA database. Analysis will be conducted to compare the similarity, diversity, discriminatory potential and relation of Salmonella fingerprint profiles between IFA and PFGE. PFGE database of Salmonella: PFGE fingerprint profiles of Salmonella isolates will be created. The PFGE protocols as described by Bender et al. (2001) will be followed. In brief, the plugs will be individually digested with XbaI and the digested DNA fragments will be separated with an electrophoresis apparatus (CHEF-DR III, Bio-Rad). A strain of S. enterica serotype typhimurium will be chosen as a standard. Gels will be run with the use of 0.5× TBE buffer at 14°C, a linear increase in switching times (from 10.3 to 64.0 seconds) over a period of 22 hours, a 120-degree switch angle, and a gradient of 6.0 V per centimeter. The gels will be stained with ethidium bromide solution and photographed with ultraviolet illumination from a ChemiDoc MP imaging system. Analysis of PFGE fingerprint profiles: The gel images will be processed and analyzed by BioNumerics software. The images will be normalized by use of standard molecular markers, and banding patterns will be compared. Similarity analysis will be performed using Dice coefficients, with a 1.0% band position tolerance and 1.56% optimization, and isolates will be separated into similarity clusters by the unweighted-pair group method using average linkages. Comparison of IFA and PFGE: The random forest classification algorithm will be used to distinguish the serotypes of samples based on their IFA and PFGE profiles (Zou et al., 2010). The IFA and PFGE profiles band classes of various sizes will be generated using BioNumerics software. The band classes will be coded as 1 and 0, representing the presence and absence of a band, respectively. In the classification analysis, the profiles will be partitioned into a training set and a separate test set. The model development involved two phases: (1) building of a classification model, including determination of the classification algorithm, identification of the most relevant features (band classes), and fitting of the prediction model to training data; and (2) assessment of the performance of the prediction model. The leave-one-out cross-validation approach will be used in the analysis and to evaluate the performance of the prediction model.</p>

Chen, Fur-Chi
Tennessee State University
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