Molecular Mechanisms Contributing to the Growth and Physiology of an Extremophile Cultured with Dielectric Heating.

The effect of microwave frequency electromagnetic fields on living microorganisms is an active and highly contested area of research. One of the major drawbacks to using mesophilic organisms to study microwave radiation effects is the unavoidable heating of the organism, which has limited the scale (<5 ml) and duration (<1 h) of experiments. However, the negative effects of heating a mesophile can be mitigated by employing thermophiles (organisms able to grow at temperatures of >60°C). This study identified changes in global gene expression profiles during the growth of Thermus scotoductus SA-01 at 65°C using dielectric (2.45 GHz, i.e., microwave) heating. RNA sequencing was performed on cultures at 8, 14, and 24 h after inoculation to determine the molecular mechanisms contributing to long-term cellular growth and survival under microwave heating conditions. Over the course of growth, genes associated with amino acid metabolism, carbohydrate metabolism, and defense mechanisms were upregulated; the number of repressed genes with unknown function increased; and at all time points, transposases were upregulated. Genes involved in cell wall biogenesis and elongation were also upregulated, consistent with the distinct elongated cell morphology observed after 24 h using microwave heating. Analysis of the global differential gene expression data enabled the identification of molecular processes specific to the response of T. scotoductus SA-01 to dielectric heating during growth. IMPORTANCE: The residual heating of living organisms in the microwave region of the electromagnetic spectrum has complicated the identification of radiation-only effects using microorganisms for 50 years. A majority of the previous experiments used either mature cells or short exposure times with low-energy high-frequency radiation. Using global differential gene expression data, we identified molecular processes unique to dielectric heating using Thermus scotoductus SA-01 cultured over 30 h in a commercial microwave digestor. Genes associated with amino acid metabolism, carbohydrate metabolism, and defense mechanisms were upregulated; the number of repressed genes with unknown function increased; and at all time points, transposases were upregulated. These findings serve as a platform for future studies with mesophiles in order to better understand the response of microorganisms to microwave radiation.

Investigation of a fiber optic surface plasmon spectroscopy in conjunction with conductivity as an in situ method for simultaneously monitoring changes in dissolved organic carbon and salinity in coastal waters.

A combination surface plasmon resonance (SPR) and conductivity sensor array was developed and implemented to demonstrate the ability to differentiate among changes in dissolved organic carbon (DOC) and salinity in coastal water. The array is capable of achieving sufficient spatial and temporal data density to better understand the cycling and fate of terrestrial DOC in coastal areas. DOC is the second largest source of bioreactive carbon in the environment and plays a key role in mediating microbial activity and generation of atmospheric CO(2). In the coastal areas, the salinity is also an important property in many applications, such as leak detection for landfill liners, saltwater intrusion to drinking water, marine environment monitoring, and seasonal climate prediction. Conductivity sensors are the industry standard for determining salinity in ocean systems. However, both conductivity and refractive index sensors, such as SPR spectroscopy based sensors, respond to salinity and DOC levels. To demonstrate the capability of the SPR sensor and a conductivity sensor to collect complimentary data useful in discrimination of salinity and DOC in coastal zone water, conductivity, SPR, and temperature data were collected during passage from the Juan de Fuca ridge area returning to the University of Washington docks.

Evolving window factor analysis-multivariate curve resolution with automated library matching for enhanced peak deconvolution in gas chromatography-mass spectrometry fuel data.

Fuel chromatography is inherently limited by the high complexity of petroleum fuel compositions. In practice, almost no fuel components are fully resolved in gas chromatography. This is due to both insufficient peak capacity for the large number of individual components within time and chromatographic efficiency constraints, and insufficient resolving power of the stationary phase in the gas chromatography column relative to the many structurally similar isomers or homologs present in petrochemical fuels. Multidimensional approaches, longer columns and slower heating rates can offer some benefits but will not necessarily fully resolve co-eluting fuel compounds, especially within reasonable analysis times. The following work details how deconvolved mass spectral loadings, combined with library matching, provide a quality metric against which to automatically evaluate results obtained from an experimental evolving window factor analysis-multivariate curve resolution deconvolution algorithm applied to gas chromatography-mass spectrometry data. This algorithm was evaluated in the context of trace component detection in synthetic fuel data sets, dodecane and tetradecane detection in petrochemical fuels, and the detection of natural products unlikely to be present in petrochemical fuels. In the case of the trace component detection challenge, the experimental algorithm outperformed a control algorithm that utilized a singular value-based quality metric. Meanwhile, when detecting dodecane, tetradecane, and natural products in petrochemical fuels, the experimental algorithm allowed for higher-quality compound identification results than could be obtained without peak deconvolution, thus reliably improving fuel component resolution in an automated fashion.

Analysis of Twenty-Two Performance Properties of Diesel, Gasoline, and Jet Fuels Using a Field-Portable Near-Infrared (NIR) Analyzer.

The change in custody of fuel shipments at depots, pipelines, and ports could benefit from an analyzer that could rapidly verify that properties are within specifications. To meet this need, the design requirements for a fuel analyzer based on near-infrared (NIR) spectroscopy, such as spectral region and resolution, were examined. It was found that the 1000 to 1600 nm region, containing the second CH overtone and combination vibrational modes of hydrocarbons, provided the best near-infrared to fuel property correlations when path length was taken into account, whereas 4 cm(-1) resolution provided only a modest improvement compared to 16 cm(-1) resolution when four or more latent variables were used. Based on these results, a field-portable near-infrared fuel analyzer was built that employed an incandescent light source, sample compartment optics to hold 2 mL glass sample vials with ∼1 cm path length, a transmission grating, and a 256 channel InGaAs detector that measured the above stated wavelength range with 5-6 nm (∼32 cm(-1)) resolution. The analyzer produced high signal-to-noise ratio (SNR) spectra of samples in 5 s. Twenty-two property correlation models were developed for diesel, gasoline, and jet fuels with root mean squared error of correlation - cross-validated values that compared favorably to corresponding ASTM reproducibility values. The standard deviations of predicted properties for repeat measurements at 4, 24, and 38℃ were often better than ASTM documented repeatability values. The analyzer and diesel property models were tested by measuring seven diesel samples at a local ASTM certification laboratory. The standard deviations between the analyzer determined values and the ASTM measured values for these samples were generally better than the model root mean squared error of correlation-cross-validated values for each property.

Pharmacodynamic behaviour of the selective cyclooxygenase-2 inhibitor lumiracoxib in the lipopolysaccharide-stimulated rat air pouch model.

PURPOSE: To investigate the pharmacodynamic behaviour of the selective cyclooxygenase-2 inhibitor, lumiracoxib, in the rat air pouch. METHODS: Air pouches were injected with lipopolysaccharide to stimulate prostaglandin E2 (PGE2) production 1h after lumiracoxib treatment. Pouch fluid samples were collected 6 or 24 h after lumiracoxib administration to measure PGE2 levels. Lumiracoxib concentrations in pouch fluid and plasma were measured by mass spectrometry. RESULTS: Oral administration of lumiracoxib resulted in dose-dependent inhibition of PGE2 production 6 and 24 h post-dose. The estimated ED50 values for inhibition of PGE2 production were 0.1 and 2.0 mg/kg at 6 and 24 h, respectively. Lumiracoxib concentrations in plasma and pouch fluid increased in proportion to dose. There was a strong positive correlation between lumiracoxib concentrations in plasma and pouch fluid compartments. Lumiracoxib concentrations were higher in plasma than in pouch fluid 6 h post-dose, but at 24 h post-dose, pouch fluid concentrations were > or =4-fold greater than plasma concentrations. CONCLUSIONS: Lumiracoxib readily enters the air pouch and persists in this extravascular compartment for a longer period of time than in plasma. This distribution profile may contribute to the ability of lumiracoxib to inhibit PGE2 production up to 24 h after dosing.

Improved peak selection strategy for automatically determining minute compositional changes in fuels by gas chromatography-mass spectrometry.

During the development of automated computational methods to detect minute compositional changes in fuels, it became apparent that peak selection through the spectral deconvolution of gas chromatography-mass spectrometry (GC-MS) data is limited by the complexity and noise levels inherent in the data. Specifically, current techniques are not capable of detecting minute, chemically relevant compositional differences with sufficient sensitivity. Therefore, an alternative peak selection strategy was developed based on spectral interpretation through interval-oriented parallel factor analysis (PARAFAC). It will be shown that this strategy outperforms the deconvolution-based peak selection strategy as well as two control strategies. Successful application of the PARAFAC-based method to detect minute chemical changes produced during microbiological growth in four different inoculated diesel fuels will be discussed.

N-alkyl urea hydroxamic acids as a new class of peptide deformylase inhibitors with antibacterial activity.

Peptide deformylase (PDF) is a prokaryotic metalloenzyme that is essential for bacterial growth and is a new target for the development of antibacterial agents. All previously reported PDF inhibitors with sufficient antibacterial activity share the structural feature of a 2-substituted alkanoyl at the P(1)' site. Using a combination of iterative parallel synthesis and traditional medicinal chemistry, we have identified a new class of PDF inhibitors with N-alkyl urea at the P(1)' site. Compounds with MICs of 200 micro M for matrilysin and other mammalian metalloproteases. Structure-activity relationship analysis identified preferred substitutions resulting in improved potency and decreased cytotoxity. One of the compounds (VRC4307) was cocrystallized with PDF, and the enzyme-inhibitor structure was determined at a resolution of 1.7 A. This structural information indicated that the urea compounds adopt a binding position similar to that previously determined for succinate hydroxamates. Two compounds, VRC4232 and VRC4307, displayed in vivo efficacy in a mouse protection assay, with 50% protective doses of 30.8 and 17.9 mg/kg of body weight, respectively. These N-alkyl urea hydroxamic acids provide a starting point for identifying new PDF inhibitors that can serve as antimicrobial agents.