Jon G. Wilkes

Rapid identification of intact whole bacteria based on spectral patterns using matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry.

Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) was investigated as a method for the rapid identification of whole bacteria, either by comparison with archived reference spectra or by co-analysis with cultures of known bacteria. Bacteria were sampled from colonies on an agar plate, mixed with the matrix, air-dried, and introduced in batches into the mass spectrometer for analysis. In the first experiment, both bacterial strains that had been previously analyzed to obtain reference spectra and other strains that had not been analyzed were blind-numbered and their spectra were obtained. Those strains that matched reference spectra were found to be correctly identified. A second experiment involved co-analysis of reference strains and bind-numbered strains under identical conditions; species-specific identification was demonstrated by comparison of spectra of the blind-numbered strains with those of the standards. In all of the spectra obtained in these experiments, each bacterial strain showed a few characteristic high-mass ions which are thought to be derived from bacterial proteins. This work represents the first reported instance of successful bacterial chemotaxonomy by MALDI-TOFMS analysis of whole cells. For the strains tested, the method is rapid and simple.

Sample preparation for the analysis of flavors and off-flavors in foods

Off-flavors in foods may originate from environmental pollutants, the growth of microorganisms, oxidation of lipids, or endogenous enzymatic decomposition in the foods. The chromatographic analysis of flavors and off-flavors in foods usually requires that the samples first be processed to remove as many interfering compounds as possible. For analysis of foods by gas chromatography (GC), sample preparation may include mincing, homogenation, centrifugation, distillation, simple solvent extraction, supercritical fluid extraction, pressurized-fluid extraction, microwave-assisted extraction, Soxhlet extraction, or methylation. For high-performance liquid chromatography of amines in fish, cheese, sausage and olive oil or aldehydes in fruit juice, sample preparation may include solvent extraction and derivatization. Headspace GC analysis of orange juice, fish, dehydrated potatoes, and milk requires almost no sample preparation. Purge-and-trap GC analysis of dairy products, seafoods, and garlic may require heating, microwave-mediated distillation, purging the sample with inert gases and trapping the analytes with Tenax or C18, thermal desorption, cryofocusing, or elution with ethyl acetate. Solid-phase microextraction GC analysis of spices, milk and fish can involve microwave-mediated distillation, and usually requires adsorption on poly(dimethyl)siloxane or electrodeposition on fibers followed by thermal desorption. For short-path thermal desorption GC analysis of spices, herbs, coffee, peanuts, candy, mushrooms, beverages, olive oil, honey, and milk, samples are placed in a glass-lined stainless steel thermal desorption tube, which is purged with helium and then heated gradually to desorb the volatiles for analysis. Few of the methods that are available for analysis of food flavors and off-flavors can be described simultaneously as cheap, easy and good.

Sample preparation and high-resolution separation of mycotoxins possessing carboxyl groups

The chromatographic analysis of carboxyl-containing mycotoxins, such as fumonisin B1, ochratoxin A, and citrinin, presents a continual challenge. Toxins must first be extracted from foods or tissues and then cleaned up before chromatographic separation and detection. Liquid-liquid extraction efficiencies for some carboxylic mycotoxins are marginal for spiked samples and uncertain for incurred residues. Immunoaffinity columns may be useful for concentrating mycotoxins from samples before chromatography. In almost every case, more than one analytical method must be used to confirm the identification of the mycotoxin. The fumonisins are especially troublesome to analyze because they are relatively insoluble in organic solvents, they are not separated easily by gas chromatography, and they do not respond to the usual absorbance or fluorescence detectors used in liquid chromatography. Fluorescence derivatization and electrospray liquid chromatography-mass spectrometry have now made it possible to detect trace levels of mycotoxins. The purity of mycotoxin standards for toxicological studies can be determined by liquid chromatography with either an evaporative light scattering detector or electrospray mass spectrometer. New developments in capillary electrophoresis, nonporous microsphere liquid chromatography, and detection methods for low-volatility compounds show promise for improving the analysis of mycotoxins in the future.

Models of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls Binding Affinity to the Aryl Hydrocarbon Receptor Developed Using 13C NMR Data

Quantitative spectroscopic data-activity relationship (QSDAR) models for polychlorinated dibenzofurans (PCDFs), dibenzodioxins (PCDDs), and biphenyls (PCBs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. All the models were based on multiple linear regression of comparative spectral analysis (CoSA) between compounds. A 1.0 ppm resolution CoSA model for 26 PCDF compounds based on chemical shifts in five bins had an explained variance (r(2)) of 0.93 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.90. A 2.0 ppm resolution CoSA model for 14 PCDD compounds based on chemical shifts in five bins had an r(2) of 0.91 and a q(2) of 0.81. The 1.0 ppm resolution CoSA model for 12 PCB compounds based on chemical shifts in five bins had an r(2) of 0.87 and a q(2) of 0.45. The models with more compounds had a better q(2) because there are more multiple chemical shift populated bins available on which to base the linear regression. A 1.0 ppm resolution CoSA model for all 52 compounds that was based on chemical shifts in 12 bins had an r(2) of 0.85 and q(2) of 0.71. A canonical variance analysis of the 1.0 ppm CoSA model for all 52 compounds when they were separated into 27 strong binding and 25 weak binding compounds was 98% correct. Conventional quantitative structure-activity relationship (QSAR) modeling suffer from errors introduced by the assumptions and approximations involved in calculated electrostatic potentials and the molecular alignment process. QSDAR modeling is not limited by such errors since electrostatic potential calculations and molecular alignment are not done. The QSDAR models provide a rapid, simple and valid way to model the PCDF, PCDD, and PCB binding activity in relation to the aryl hydrocarbon receptor (AhR).

Fragmentation and charge transfer in gas-phase complexes of divalent metal ions with acetonitrile

The development of electrospray has enabled generation of gas-phase multiply charged metal ion complexes with various solvent molecules. These species exhibit rich fragmentation chemistry, involving competition among neutral ligand loss, ligand cleavage, and dissociative electron and proton transfer. Acetonitrile is a common aprotic solvent. Here we present a comprehensive MS/MS study on acetonitrile complexes of divalent metal cations. We measured the critical sizes below which dissociation channels other than the trivial neutral evaporation become operative and minimum sizes at which dications remain stable against charge reduction. For all sizes between the two, low-energy fragmentation patterns have been elucidated in detail.

Use of 13C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity.

We have developed a spectroscopic data-activity relationship (SDAR) model based on 13C NMR spectral data for 30 estrogenic chemicals whose relative binding affinities (RBA) are available for the alpha (ERalpha) and beta (ERbeta) estrogen receptors. The SDAR models segregated the 30 compounds into strong and medium binding affinities. The SDAR model gave a leave-one-out (LOO) cross-validation of 90%. Two compounds that were classified incorrectly in the SDAR model were in the transition zone between classifications. Real and predicted 13C NMR chemical shifts were used with test compounds to evaluate the predictive behavior of the SDAR model. The 13C NMR SDAR model using predicted 13C NMR data for the test compounds provides a rapid, reliable, and simple way to screen whether a compound binds to the estrogen receptors.

13C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. Three of the models were based on comparative spectral analysis (CoSA), and the two other models were based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model based on five principal components had an explained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.71. A CoSASA model that used the assigned (13)C NMR chemical shifts from a steroidal backbone at five selected positions gave an r(2) of 0.75 and a q(2) of 0.66. The (13)C NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structural parameter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models is that (13)C NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR models calculated electrostatic potentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model the steroid inhibitor activity in relation to the aromatase enzyme.

An Integrated Flow Cytometry-Based System for Real-Time, High Sensitivity Bacterial Detection and Identification

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.

Determination of fumonisins B1, B2, B3 and B4 by high-performance liquid chromatography with evaporative light-scattering detection

Fumonisins B1, B2, B3 and B4 (FB1-FB4), a group of mycotoxins produced by the fungus Fusarium moniliforme, were separated by HPLC using an analytical-scale, base-deactivated C8 column and a gradient of trifluoroacetic acid buffer (pH 2.7) and acetonitrile. An evaporative light-scattering detector was used to detect the fumonisin peaks. A semi-preparative-scale, base-deactivated C8 column with a 1:14 mobile phase split facilitated the purification of analytical standards of FB.

Developing 13C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroid binding to the corticosteroid binding globulin

We have developed four quantitative spectrometric data-activity relationship (QSDAR) models for 30 steroids binding to corticosteroid binding globulin, based on comparative spectral analysis (CoSA) of simulated 13C nuclear magnetic resonance (NMR) data. A QSDAR model based on 3 spectral bins had an explained variance (r2) of 0.80 and a cross-validated variance (q2) of 0.78. Another QSDAR model using the 3 atoms from the comparative structurally assigned spectral analysis (CoSASA) of simulated 13C NMR on a steroid backbone template gave an explained variance (r2) of 0.80 and a cross-validated variance (q2) of 0.73. Positions 3 and 14 from the steroid backbone template have correlations with the relative binding activity to corticosteroid binding globulin that are greater than 0.52. The explained correlation and cross-validated correlation of these QSDAR models are as good as previously published quantitative structure-activity relationship (QSAR), self-organizing map (SOM) and electrotopological state (E-state) models. One reason that the cross-validated variance of QSDAR models were as good as the other models is that simulated 13C NMR spectral data are more accurate than the errors introduced by the assumptions and approximations used in calculated electrostatic potentials, E-states, HE-states, and the molecular alignment process of QSAR modeling. The QSDAR models developed provide a rapid, simple way to predict the binding activity of a steroid to corticosteroid binding globulin.