Metagenomic Sequencing and Quantitative Real-Time PCR for Fecal Pollution Assessment in an Urban Watershed.

Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log10 copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.

Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters.

Oral administration of drug products is a strict requirement in many medical indications. Therefore, bioavailability prediction models are of high importance for prioritization of compound candidates in the drug discovery process. However, oral exposure and bioavailability are difficult to predict, as they are the result of various highly complex factors and/or processes influenced by the physicochemical properties of a compound, such as solubility, lipophilicity, or charge state, as well as by interactions with the organism, for instance, metabolism or membrane permeation. In this study, we assess whether it is possible to predict intravenous (iv) or oral drug exposure and oral bioavailability in rats. As input parameters, we use (i) six experimentally determined in vitro and physicochemical endpoints, namely, membrane permeation, free fraction, metabolic stability, solubility, pKa value, and lipophilicity; (ii) the outputs of six in silico absorption, distribution, metabolism, and excretion models trained on the same endpoints, or (iii) the chemical structure encoded as fingerprints or simplified molecular input line entry system strings. The underlying data set for the models is an unprecedented collection of almost 1900 data points with high-quality in vivo experiments performed in rats. We find that drug exposure after iv administration can be predicted similarly well using hybrid models with in vitro- or in silico-predicted endpoints as inputs, with fold change errors (FCE) of 2.28 and 2.08, respectively. The FCEs for exposure after oral administration are higher, and here, the prediction from in vitro inputs performs significantly better in comparison to in silico-based models with FCEs of 3.49 and 2.40, respectively, most probably reflecting the higher complexity of oral bioavailability. Simplifying the prediction task to a binary alert for low oral bioavailability, based only on chemical structure, we achieve accuracy and precision close to 70%.

Identification of vancomycin interaction with Enterococcus faecalis within 30 min of interaction time using Raman spectroscopy.

Vancomycin is an important glycopeptide antibiotic which is used to treat serious infections caused by Gram-positive bacteria. However, during the last years, a tremendous rise in vancomycin resistances, especially among Enterococci, was reported, making fast diagnostic methods inevitable. In this contribution, we apply Raman spectroscopy to systematically characterize vancomycin-enterococci interactions over a time span of 90 min using a sensitive Enterococcus faecalis strain and two different vancomycin concentrations above the minimal inhibitory concentration (MIC). Successful action of the drug on the pathogen could be observed already after 30 min of interaction time. Characteristic spectral changes are visualized with the help of multivariate statistical analysis (linear discriminant analysis and partial least squares regressions). Those changes were employed to train a statistical model to predict vancomycin treatment based on the Raman spectra. The robustness of the model was tested using data recorded by an independent operator. Classification accuracies of >90 % were obtained for vancomycin concentrations in the lower range of a typical trough serum concentration recommended for most patients during appropriate vancomycin therapy. Characterization of drug-pathogen interactions by means of label-free spectroscopic methods, such as Raman spectroscopy, can provide the knowledge base for innovative and fast susceptibility tests which could speed up microbiological analysis as well as finding applications in novel antibiotic screenings assays. Graphical Abstract E. faecalis is incubated with vancomycin and characterized by means of Raman spectroscopy after different time points. Characteristic spectral changes reveal efficient vancomycin-enterococci-interaction.

Extracellular zinc protects against acidosis-induced injury of cells expressing Ca2+-permeable acid-sensing ion channels.

Acidosis is a common feature of neurological conditions including brain ischemia, epileptic seizures, and neurotrauma. Activation of Ca(2+)-permeable acid-sensing ion channels (ASIC1a) is involved in acidosis-mediated ischemic brain injury. Zn(2+) is a divalent cation concentrated in nerve terminals in various brain regions, and is released into the extracellular space during excitatory stimulation. Our previous studies have demonstrated that the activities of ASIC1a containing channels and acid-induced increased intracellular Ca(2+) concentrations are inhibited dramatically by the physiological concentration of extracellular Zn(2+). In this report, we demonstrate that decreasing the concentration of the extracellular Zn(2+) significantly enhances acid-induced injury of HEK 293 cells, a cell line expressing homomeric ASIC1a-like channels, whereas increasing the concentration of extracellular Zn(2+) appears to be protective. Although increased concentrations of intracellular Zn(2+) have been shown to be detrimental to neurons, our findings may suggest that the physiological concentration of extracellular Zn(2+) might play a protective role in acidosis-induced, ASIC1a-mediated neuronal injury.

Neuroprotection in ischemia: blocking calcium-permeable acid-sensing ion channels.

Ca2+ toxicity remains the central focus of ischemic brain injury. The mechanism by which toxic Ca2+ loading of cells occurs in the ischemic brain has become less clear as multiple human trials of glutamate antagonists have failed to show effective neuroprotection in stroke. Acidosis is a common feature of ischemia and is assumed to play a critical role in brain injury; however, the mechanism(s) remain ill defined. Here, we show that acidosis activates Ca2+ -permeable acid-sensing ion channels (ASICs), inducing glutamate receptor-independent, Ca2+ -dependent, neuronal injury inhibited by ASIC blockers. Cells lacking endogenous ASICs are resistant to acid injury, while transfection of Ca2+ -permeable ASIC1a establishes sensitivity. In focal ischemia, intracerebroventricular injection of ASIC1a blockers or knockout of the ASIC1a gene protects the brain from ischemic injury and does so more potently than glutamate antagonism. Thus, acidosis injures the brain via membrane receptor-based mechanisms with resultant toxicity of [Ca2+]i, disclosing new potential therapeutic targets for stroke.