Halenaquinol Blocks Staphylococcal Protein A Anchoring on Cell Wall Surface by Inhibiting Sortase A in Staphylococcus aureus.

Sortase A (SrtA) is a cysteine transpeptidase that binds to the periplasmic membrane and plays a crucial role in attaching surface proteins, including staphylococcal protein A (SpA), to the peptidoglycan cell wall. Six pentacyclic polyketides (1-6) were isolated from the marine sponge Xestospongia sp., and their structures were elucidated using spectroscopic techniques and by comparing them to previously reported data. Among them, halenaquinol (2) was found to be the most potent SrtA inhibitor, with an IC50 of 13.94 µM (4.66 µg/mL). Semi-quantitative reverse transcription PCR data suggest that halenaquinol does not inhibit the transcription of srtA and spA, while Western blot analysis and immunofluorescence microscopy images suggest that it blocks the cell wall surface anchoring of SpA by inhibiting the activity of SrtA. The onset and magnitude of the inhibition of SpA anchoring on the cell wall surface in S. aureus that has been treated with halenaquinol at a value 8× that of the IC50 of SrtA are comparable to those for an srtA-deletion mutant. These findings contribute to the understanding of the mechanism by which marine-derived pentacyclic polyketides inhibit SrtA, highlighting their potential as anti-infective agents targeting S. aureus virulence.

Exploring the Diverse Landscape of Biaryl-Containing Peptides Generated by Cytochrome P450 Macrocyclases.

Cytochrome P450 enzymes (P450s) catalyze diverse oxidative cross-coupling reactions between aromatic substrates in the natural product biosynthesis. Specifically, P450s install distinct biaryl macrocyclic linkages in three families of ribosomally synthesized and post-translationally modified peptides (RiPPs). However, the chemical diversity of biaryl-containing macrocyclic RiPPs remains largely unexplored. Here, we demonstrate that P450s have the capability to generate diverse biaryl linkages on RiPPs, collectively named "cyptides". Homology-based genome mining for P450 macrocyclases revealed 19 novel groups of homologous biosynthetic gene clusters (BGCs) with distinct aromatic residue patterns in the precursor peptides. Using the P450-modified precursor peptides heterologously coexpressed with corresponding P450s in Escherichia coli, we determined the NMR structures of three novel biaryl-containing peptides─the enzymatic products, roseovertin (1), rubrin (2), and lapparbin (3)─and confirmed the formation of three unprecedented or rare biaryl linkages: Trp C-7'-to-His N-τ in 1, Trp C-7'-to-Tyr C-6 in 2, and Tyr C-6-to-Trp N-1' in 3. Biochemical characterization indicated that certain P450s in these pathways have a relaxed substrate specificity. Overall, our studies suggest that P450 macrocyclases have evolved to create diverse biaryl linkages in RiPPs, promoting the exploration of a broader chemical space for biaryl-containing peptides encoded in bacterial genomes.

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials.

In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been actively studied for industrial applications such as scrap metal recycling. However, in machine learning models, a training set of the used samples may not cover the diversity of the scrap metal encountered in field measurements. Moreover, differences in experimental configuration, where laboratory standards and real samples are analyzed in situ, may lead to a wider gap in the distribution of training and test sets, dramatically reducing the performance of the LIBS-based fast classification system for real samples. To address these challenges, we propose a two-step Aug2Tran model. First, we augment the SRM dataset by synthesizing spectra of unobserved types through attenuation of dominant peaks corresponding to sample composition and generating spectra depending on the target sample using a generative adversarial network. Second, we used the augmented SRM dataset to build a robust real-time classification model with a convolutional neural network, which is further customized for the target scrap metal with limited measurements through transfer learning. For evaluation, SRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass, are measured with a typical setup to form the SRM dataset. For testing, scrap metal from actual industrial fields is experimented with three different configurations, resulting in eight different test datasets. The experimental results show that the proposed scheme produces an average classification accuracy of 98.25% for the three experimental conditions, as high as the results of the conventional scheme with three separately trained and executed models. Additionally, the proposed model improves the classification accuracy of arbitrarily shaped static or moving samples with various surface contaminations and compositions, and even for differing ranges of charted intensities and wavelengths. Therefore, the proposed Aug2Tran model can be used as a systematic model for scrap metal classification with generalizability and ease of implementation.

Association between Doppler flow of atrial fibrillatory contraction and recurrence of atrial fibrillation after electrical cardioversion.

BACKGROUND: Left atrial fibrillatory contraction (Afc) flow can be frequently observed interspersed between two successive mitral E waves in patients with atrial fibrillation (AF). The aim of this study was to test the hypothesis that Afc is related to the maintenance of sinus rhythm after electrical cardioversion for AF. METHODS: In this retrospective study, the records of a total of 137 patients with AF who underwent successful electrical cardioversion were examined. Conventional echocardiographic measurements, including left atrial volume index (LAVI), were obtained, and the appearance of Afc flow was also evaluated before cardioversion. Patients were followed to a clinical end point defined as recurrent AF during the study period. RESULTS: AF recurrence was noted in 100 patients (73%) over a mean follow-up period of 5 months. The patients with recurrent AF had greater LAVI and left atrial dimensions and had a lower frequency of Afc flow (57.0% vs 86.5%, P < .001): both the velocity and velocity-time integral (VTI) of Afc flow significantly decreased. Receiver operating characteristic curve analysis showed that the Afc flow VTI and velocity had stronger associations with AF recurrence than did LAVI (areas under the curve: VTI, 0.96; velocity, 0.86; LAVI, 0.71). A VTI of 3.1 cm and velocity of 32 cm/sec for Afc flow were the best cutoff values for AF recurrence. Afc flow VTI (hazard ratio, 0.70; 95% confidence interval, 0.51-0.96) and velocity (hazard ratio, 0.97; 95% confidence interval, 0.94-0.99) were significantly related to AF recurrence in a multivariate Cox regression analysis. CONCLUSIONS: Return of AF after successful electrical cardioversion may be associated with Afc Doppler flow velocity and VTI measured immediately before cardioversion.

The efficacy of the cystatin C based glomerular filtration rate in the estimation of safe contrast media volume.

BACKGROUND AND OBJECTIVES: The risk of contrast-induced nephropathy (CIN) is significantly influenced by baseline renal function and the amount of contrast media (CM). We evaluated the usefulness of the cystatin C (CyC) based estimated glomerular filtration rate (eGFRCyC) in the prediction of CIN and to determine the safe CM dosage. SUBJECTS AND METHODS: We prospectively enrolled a total of 723 patients who received percutaneous coronary intervention (PCI) and investigated the clinical factors associated with the development of CIN. Renal function was calculated as eGFRCyC and a modified diet in the renal disease (MDRD) equation, respectively. Systemic exposure of CM was calculated as CM volume to eGFR ratio. We conducted a regression analysis to evaluate the predictive role of CM volume to eGFRCyC for the risk of CIN. RESULTS: The incidence of CIN was 4.0% (29/723). The patients with CIN had a lower hemoglobin level, decreased renal function, and a higher CyC value, and had greater CM exposure. Through multivariate regression analyses, hemoglobin {odds ratio (OR) 0.743, p=0.032}, CM volume/eGFRCyC (OR 1.697, p=0.006) and CM volume/MDRD (OR 2.275, p<0.001) were found to be independent predictors for CIN. In the receiver operating characteristic curve analysis, fair discrimination for CIN was found at a CM volume/eGFRCyC level of 4.493 (C-statics=0.814), and at this value, the sensitivity and specificity were 79.3% and 80.0%, respectively. CONCLUSION: Both the CM volume/MDRD and CM volume/eGFRCyC method would be simple, useful indicators for determining the safe CM-dose based on eGFR value before PCI. However, there was no significantly different predictive value between creatinine and CyC based GFR estimations.