Publications

Current lipidomic profiling methods rely mainly on MS to identify unknown lipids within a complex sample. We describe a new approach, involving LC×MS/MS (liquid chromatography×tandem MS) analysis of sphingolipids based on both mass and hydrophobicity, and use this method to characterize the SM (sphingomyelin), ceramide and GalCer (galactosylceramide) content of hippocampus from AD (Alzheimer's disease) and control subjects. Using a mathematical relationship we exclude the influence of sphingolipid mass on retention time, and generate two-dimensional plots that facilitate accurate visualization and characterization of the different ceramide moieties within a given sphingolipid class, because related molecules align horizontally or vertically on the plots. Major brain GalCer species that differ in mass by only 0.04 Da were easily differentiated on the basis of their hydrophobicity. The importance of our method's capacity to define all of the major GalCer species in the brain samples is illustrated by the novel observation that the proportion of GalCer with hydroxylated fatty acids increased approximately 2-fold in the hippocampus of AD patients, compared with age- and gender-matched controls. This suggests activation of fatty acid hydroxylase in AD. Our method greatly improves the clarity of data obtained in a lipid profiling experiment and can be expanded to other lipid classes.

It is known that the human immune proteins APOBEC3G and -F (hA3G/F) can inhibit Vif-deficient HIV by G-to-A mutation; however, the roles of these enzymes in the evolution of HIV are debated. We argue that if evolutionary pressure from hA3G/F exists there should be evidence of their imprint on the HIV genome in the form of (i) underrepresentation of hA3G/F target motifs (e.g., TGGG [targeted position is underlined]) and overrepresentation of product motifs (e.g., TAGG) and/or (ii) an increase in the ratio of nonsynonymous to synonymous (NS/S) G-to-A changes among hA3G/F target motifs and a decrease of NS/S A-to-G changes among hA3G/F product motifs. To test the first hypothesis, we studied the representation of hA3G/F target and product motifs in 1,932 complete HIV-1 genomes using Markov models. We found that the highly targeted motifs are not underrepresented and their product motifs are not overrepresented. To test the second hypothesis, we determined the NS/S G↔A changes among the hA3G/F target and product motifs in 1,540 complete sets of nine HIV-1 genes. The NS/S changes did not show an increasing/decreasing trend within the target/product motifs, but the NS/S changes within the motif AG was exceptionally low. We observed the same pattern by analyzing 740 human genes. Given that hA3G/F do not act on the human genome, this suggests a small NS/S change within AG has arisen by other mechanisms. We therefore find no evidence of an evolutionary footprint of hA3G/F. We postulate several mechanisms to explain why the HIV-1 genome does not contain the hA3G/F footprint.

The probability density functions of amount ratios of compounds (total codeine/total morphine, 6-monoacetylemorphine/total morphine, papaverine/total morphine, and noscapine/total morphine) from the analysis of seized heroin, originating from known world regions (South East Asia, South West Asia, South America, Mexico) allows calculation of likelihood ratios for 'unknown' samples. Application of Bayes Theorem with a suitable prior probability, for example the frequency of a particular region in the database, leads to the probability that a particular profile comes from a given target region. Data from 2549 seizures of heroin at Australia's border illustrates the method, and results are compared with simple HS1 ratio approaches for assigning geographical origin. The method can be implemented in a spreadsheet and gives more refined intelligence of the origins of seized drugs than simple ranges.

Gas chromatography using a highly polar column combined with field ionization mass spectrometry (FI-MS) is used as a comprehensive two-dimensional (2D) separation approach to analyze mixtures of fatty acid methyl esters (FAMEs). A unique ordered pattern and classification of FAMEs is obtained in a 2D GC x FI-MS separation plot based on the number of carbons, the degree of unsaturation, and a combination of both by which the geometrical, positional, and structural isomers group together. FAMEs with different chain length but identical geometry, position, and degree of unsaturation follow linear patterns. These subclassifications (linear functions) can provide information about the geometry, position, and structure of unsaturation of an unknown FAME. Non-FAMEs and FAMEs with different functional groups are identified using the ordered separation pattern of the FAMEs in the GC x FI-MS plot and the exact mass data from the FI-MS mode. Measurement of exact mass also acts as a high-resolution separation technique to separate overlapping peaks. The method is illustrated by application to samples of fish, canola, and biodiesel oils and standard mixtures of 37 FAMEs and of alpha-linolenic acid methyl ester geometrical isomers. A great wealth of information is achieved in a single run.

Orthogonal acceleration time-of-flight (oa-TOF) mass spectrometry (MS) was coupled to gas chromatography (GC) to measure ion yields (ratio of ion counts to number of neutrals entering the ion source) and signal-to-noise (S/N) in the electron ionization (EI) mode (hard ionization) as well as in the soft ionization modes of chemical ionization (CI), electron capture negative ion chemical ionization (NICI) and field ionization (FI). Mass accuracies of the EI and FI modes were also investigated. Sixteen structurally diverse volatile organic compounds were chosen for this study. The oa-TOF mass analyzer is highly suited for FI MS and provided an opportunity to compare the sensitivity of this ionization method to the more conventional ionization methods. Compared to the widely used quadrupole mass filter, the oa-TOF platform offers significantly greater mass accuracy and therefore the possibility of determining the empirical formula of analytes. The findings of this study showed that, for the instrument used, EI generated the most ions with the exception of compounds able to form negative ions readily. Lower ion yields in the FI mode were generally observed but the chromatograms displayed greater S/N and in many cases gave spectra dominated by a molecular ion. Ion counts in CI are limited by the very small apertures required to maintain sufficiently high pressures in the ionization chamber. Mass accuracy for molecular and fragment ions was attainable at close to manufacturer's specifications, thus providing useful information on molecular ions and neutral losses. The data presented also suggests a potentially useful instrumental combination would result if EI and FI spectra could be collected simultaneously or in alternate scans during GC/MS.

There is a consensus that electron impact ionization mass spectrometry is not capable of discriminating among geometrical isomers of unsaturated fatty acid methyl esters (and in general olefinic compounds). In this paper, we report the identification of all eight geometrical isomers of alpha-linolenic acid, one of the few essential omega-3 fatty acids that has attracted great attention, using low-energy electron ionization mass spectrometry. Three electron energies 70, 50, and 30 eV were studied and the mass spectrum of each isomer was obtained from the analysis of different concentrations of a standard mixture of alpha-linolenic acid methyl ester geometrical isomers to ensure the robustness of the method. Principal component analysis was employed to model the complex variation of m/z intensities across the isomers. Only using the data of 30 eV energy was complete differentiation among geometrical isomers observed. The unique cleavage pattern of the alpha-linolenic acid methyl ester isomers leading to a benzenium ion structure is discussed and general fragmentation rules are derived using the mass spectra of over 300 compounds with different kinds and levels of unsaturation. Application of the proposed method is not limited to alpha-linolenic acid. It can potentially be used to identify the geometrical isomers of any compounds with an olefinic chain.

Parallel factor analysis (PARAFAC) was used to analyze data from the high throughput screening of an array of organometallic rhodium and iridium complexes as catalysts for the intramolecular hydroamination of 2-(2-phenylethynyl)aniline to give 2-phenylindole. The progress of the hydroamination reactions was monitored using UV-visible spectroscopy. The overlapped UV-visible spectra of the mixture of starting material, product and solvent in the samples taken at different times were deconvoluted using PARAFAC. Unique PARAFAC models led to close approximations of the actual UV-visible spectra of the compounds in the mixture. The performance of the catalysts was then compared by estimating the final concentration of the starting material and product using PARAFAC loadings. A library of 63 complexes generated in situ was examined in a single experiment using this methodology. The complexes were generated from combinations of seven ligands (bis(N-methyl2-imidazolyl)methane, bis(1-pyrazolyl)methane, 1,10-phenanthroline, N,N'-bis(p-tolyl)diazabutadiene, N,N'-bis(p-tolyl)1,2-dimethyldiazabutadiene, N,N'-bis(mesityl)1,2-dimethyldiazabutadiene and bis(2,4,6-trimethylphenylimino)acenapthene) and nine metal precursors ([Ir(COD)Cl](2) (COD = 1,5-cyclooctadiene), [Ir(CO)(2)Cl](n), [Ir(COE)(2)Cl](2), [IrCp*Cl(2)](2) (Cp* = 1,2,3,4,5-pentamethylcyclopentadiene), [Rh(COD)Cl](2), [Rh(CO)(2)Cl](2), [Rh(COE)(2)Cl](2), [RhCp*Cl(2)](2) and [RhCpCl(2)](2)) (Cp = cyclopentadiene)). The proposed method can be used for the fast screening of arrays of metal complexes for identifying effective catalysts, providing information that can augment traditional methods used for the analysis of catalyzed reactions.

American Standards for Testing and Materials method (ASTM5739-00) and Nordtest methodology, as the two major approaches for identifying the source of spilled oils using gas chromatography-mass spectrometry (GC-MS) data, are critically compared and a new method based on multi-way parallel factor analysis (PARAFAC2) is proposed. The new approach exploits both ASTM and Nordtest methodologies by using the entire extracted ion chromatogram (EIC) and taking into account the concentration diversities of different compound classes, respectively. A multi-way data preprocessing is proposed to preserve the diagnostic properties of the original GC-MS data, which are destroyed in the ASTM method by normalizing the EICs individually. Petroleum oils, in particular diesel oils, that are difficult to classify using current methods are shown to be excellent candidates for PARAFAC2 in which EIC matrices of different sizes can be analyzed simultaneously. A diesel oil sample from an oil spill and seven very similar suspect diesel source oils, which had undergone controlled weathering for 2-15 days, were compared by this method. 79% of pairwise group comparisons were separated, in contrast to the method in which EICs were each normalized to 100, which gave 32% separation of the comparisons.

A peptide-modified electrode array with a different peptide on each electrode is compared with a single electrode modified with many peptides for the voltammetric measurement of concentrations of Cu(2+), Cd(2+) and Pb(2+) in solution. The single gold electrode was modified simultaneously with peptides Gly-Gly-His, glutathione and angiotensin I each coupled to thioctic acid, and thioctic acid itself, and the calibration of mixtures of ions was compared with previously published data from an array of four sensors each with an individual modification. Calibration at the multi-peptide single-electrode sensor was by two-way partial least squares (voltammetric current measured with variables 'sample' x 'potential') and for the electrode array by three-way NPLS1 ('sample' x 'potential' x 'electrode'). The advantage of designing experiments to yield multi-way data is demonstrated and discussed.

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC-MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC-MS data set (MSxGCxsample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC-MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.