The dental calculus metabolome in modern and historic samples.

INTRODUCTION: Dental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens. OBJECTIVE: We present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach. METHODS: Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography-MS (GC-MS) and UPLC-MS/MS for further characterization of metabolites and lipids. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss. RESULTS: Dipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples. CONCLUSIONS: The results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies

Assessment of QCM array schemes for mixture identification: Citrus scented odors

© 2016 The Royal Society of Chemistry. QCM sensor arrays are promising systems for volatile complex mixture analysis. Such mixtures, sometimes termed odors, can prove to be challenging targets for accurate identification using conventional approaches. As a result, development of novel gas sensing systems and materials for identification of complex mixtures has garnered much interest in recent years. Herein, we present a comparative study between traditional and alternative quartz crystal microbalance (QCM) array sensing schemes for complex mixture identification. In this study, several citrus scented odors were chosen for identification using three different QCM sensing scheme. A traditional multisensor array (MSA) scheme was compared to a recently introduced virtual sensor array (VSA) scheme and identification results were found to be comparable (84-91% to 73-98% accurate). In addition, a new sensing scheme developed by combining complementary MSA and VSA schemes is introduced. In this regard, a virtual multisensor array (V-MSA), with enhanced data density, allowed accurate identification (100%) of complex mixtures (odor samples) over multiple concentrations. While each method employed is promising, the newly presented V-MSA scheme is superior to each of the previously presented array sensing methods for complex mixture analysis. To the best of our knowledge, this is the first report of a QCM V-MSA

QCM virtual multisensor array for fuel discrimination and detection of gasoline adulteration

© 2017 Elsevier Ltd Herein, a simplistic quartz crystal microbalance (QCM) approach for discrimination of petroleum based fuels is presented. In this regard, a quartz crystal microbalance (QCM) virtual multisensor array (V-MSA) was employed to discriminate between different petroleum based fuels and to detect gasoline adulteration with high accuracy. First, an ionic liquid based V-MSA was used to discriminate between four fuel types (petroleum ether, gasoline, kerosene, and diesel). Subsequently, the system was used to successfully discriminate between three gasoline grades as a precursor for studies of gasoline adulteration. Finally, the system was used to detect and determine the nature of several gasoline adulterants at different v/v ratios (1%, 10%, 20% and 40%). Excellent accuracy (100%) was achieved for each study extolling the potential of this approach. This report represents the first example of a QCM sensor array utilized for detection of gasoline adulteration

The Marburg Virus VP24 Protein Interacts with Keap1 to Activate the Cytoprotective Antioxidant Response Pathway

Kelch-like ECH-associated protein 1 (Keap1) is a ubiquitin E3 ligase specificity factor that targets transcription factor nuclear factor (erythroid-derived 2)-like 2 (Nrf2) for ubiquitination and degradation. Disrupting Keap1-Nrf2 interaction stabilizes Nrf2, resulting in Nrf2 nuclear accumulation, binding to antioxidant response elements (AREs), and transcription of cytoprotective genes. Marburg virus (MARV) is a zoonotic pathogen that likely uses bats as reservoir hosts. We demonstrate that MARV protein VP24 (mVP24) binds the Kelch domain of either human or bat Keap1. This binding is of high affinity and 1:1 stoichiometry and activates Nrf2. Modeling based on the Zaire ebolavirus (EBOV) VP24 (eVP24) structure identified in mVP24 an acidic loop (K-loop) critical for Keap1 interaction. Transfer of the K-loop to eVP24, which otherwise does not bind Keap1, confers Keap1 binding and Nrf2 activation, and infection by MARV, but not EBOV, activates ARE gene expression. Therefore, MARV targets Keap1 to activate Nrf2-induced cytoprotective responses during infection

QCM virtual multisensor array for fuel discrimination and detection of gasoline adulteration

© 2017 Elsevier Ltd Herein, a simplistic quartz crystal microbalance (QCM) approach for discrimination of petroleum based fuels is presented. In this regard, a quartz crystal microbalance (QCM) virtual multisensor array (V-MSA) was employed to discriminate between different petroleum based fuels and to detect gasoline adulteration with high accuracy. First, an ionic liquid based V-MSA was used to discriminate between four fuel types (petroleum ether, gasoline, kerosene, and diesel). Subsequently, the system was used to successfully discriminate between three gasoline grades as a precursor for studies of gasoline adulteration. Finally, the system was used to detect and determine the nature of several gasoline adulterants at different v/v ratios (1%, 10%, 20% and 40%). Excellent accuracy (100%) was achieved for each study extolling the potential of this approach. This report represents the first example of a QCM sensor array utilized for detection of gasoline adulteration