Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid approach for clinical bacterial identification. However, current protein-based commercial bacterial ID methods fall short when differentiating closely related species/strains. To address this shortcoming, we employed CeO2-catalyzed fragmentation of lipids to produce fatty acids using the energy inherent to the MALDI laser as a novel alternative to protein profiling. Fatty acid profiles collected from Enterobacteriaceae, Acinetobacter, and Listeria using CeO2-catalyzed metal oxide laser ionization (MOLI MS), processed by principal component analysis, and validated by leave–one-out cross-validation (CV), showed 100% correct classification at the species level and 98% at the strain level. In comparison, protein profile data from the same bacteria yielded 32%, 54% and 67% mean species-level accuracy using two MALDI-TOF MS platforms, respectively. In addition, several pathogens were misidentified by protein profiling as non-pathogens and vice versa. These results suggest novel CeO2-catalyzed lipid fragmentation readily produced (i) taxonomically tractable fatty acid profiles by MOLI MS, (ii) highly accurate bacterial classification and (iii) consistent strain-level ID for bacteria that were routinely misidentified by protein-based methods.

Periodontitis is one of the most prevalent threats to oral health as the most common cause of tooth loss. In order to perform e ective treatment, a clinical test that detect sites where disease activity is high and predicts periodontal tissue destruction is strongly desired, however, it is still di cult to prognose the periodontal tissue breakdown on the basis of conventional methods. e aim of this study is to examine the usefulness of gas chromatography/mass spectrometry (GC/MS), which could eventually be used for on-site analysis of metabolites in gingival crevicular uid (GCF) in order to objectively diagnose periodontitis at a molecular level. GCF samples were collected from two diseased sites (one site with a moderate pocket and another site with a deep pocket) from each patient and from clinically healthy sites of volunteers. Nineteen metabolites were identi ed using GC/MS. Total ion current chromatograms showed broad differences in metabolite peak patterns between GCF samples obtained from healthy sites, moderate-pocket sites, and deep-pocket sites. e intensity di erence of some metabolites was signi cant at sites with deep pockets compared to healthy sites. Additionally, metabolite intensities at moderate-pocket sites showed an intermediate pro le between the severely diseased sites and healthy sites, which suggested that periodontitis progression could be observed with a changing metabolite pro le. Principal component analysis con rmed these observations by clearly delineating healthy sites and sites with deep pockets. ese results suggest that metabolomic analysis of GCF could be useful for prediction and diagnosis of periodontal disease in a single visit from a patient and provides the groundwork for establishing a new, on-site diagnostic method for periodontitis.

Bacterial fatty acid profiling is a well-established technique for bacterial identification. Ten bacteria were analyzed using both positive- and negative-ion modes with a modified matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) approach using CaO as a matrix replacement (metal oxide laser ionization MS (MOLI MS)). The results show that reproducible lipid cleavage similar to thermal in situ tetramethyl ammonium hydroxide saponification/derivatization had occurred. Principal component analysis showed that replicates from each organism grouped in a unique space. Cross validation (CV) of spectra from both ionization modes resulted in greater than 94% validation of the data. When CV results were compared for the two ionization modes, negative-ion data produced a superior outcome. MOLI MS provides clinicians a rapid, reproducible and cost-effective bacterial diagnostic tool.

Biodiesel is regarded by many as a "greener" alternative fuel to petroleum diesel with potentially lower health risk. However, recent studies examining biodiesel particulate matter (PM) characteristics and health effects are contradictive, and typically utilize PM generated by passenger car engines in laboratory settings. There is a critical need to analyze diesel and biodiesel PM generated in a "real-world" setting where heavy duty-diesel (HDD) engines and commercially purchased fuel are utilized. This study compares the mass concentrations, chemical composition and cytotoxicity of real-world PM from combustion of both petroleum diesel and a waste grease 20% biodiesel blend (B20) at a community recycling center operating HDD nonroad equipment. PM was analyzed for metals, elemental/organic carbon (EC/OC), polycyclic aromatic hydrocarbons (PAHs), and nitro-polycyclic aromatic hydrocarbons (N-PAHs). Cytotoxicity in a human lung epithelial cell line (BEAS-2B) following 24h exposure to the real-world particles was also evaluated. On average, higher concentrations for both EC and OC were measured in diesel PM. B20 PM contained significantly higher levels of Cu and Mo whereas diesel PM contained significantly higher concentrations of Pb. Principal component analysis determined Mo, Cu, and Ni were the metals with the greatest loading factor, suggesting a unique pattern related to the B20 fuel source. Total PAH concentration during diesel fuel use was 1.9 times higher than during B20 operations; however, total N-PAH concentration was 3.3 times higher during B20 use. Diesel PM cytotoxicity was 8.5 times higher than B20 PM (p<0.05) in a BEAS-2B cell line. This study contributes novel data on real-world, nonroad engine sources of metals, PAH and N-PAH species, comparing tailpipe PM vs. PM collected inside the equipment cabin. Results suggest PM generated from burning petroleum diesel in nonroad engines may be more harmful to human health, but the links between exposure, composition and toxicity are not straightforward.

The results of this preliminary feasibility study show good precision and accuracy, and the fatty acid patterns are clearly distinctive for each of the ten species examined. The speed and ease of analysis and the high classification accuracy for this initial study indicate that DART is an effective method for bacterial fatty acid profiling.