Clinically Approved Drugs Inhibit the Staphylococcus aureus Multidrug NorA Efflux Pump and Reduce Biofilm Formation

Staphylococcus aureus has acquired resistance to antibiotics since their first use. The S. aureus protein NorA, an efflux pump belonging to the major facilitator superfamily (MFS), contributes to resistance to fluoroquinolones (e.g., ciprofloxacin), biocides, dyes, quaternary ammonium compounds, and antiseptics. Different compounds have been identified as potential efflux pump inhibitors (EPIs) of NorA that result in increased intracellular concentration of antibiotics, restoring their antibacterial activity and cell susceptibility. However, none of the currently known EPIs have been approved for clinical use, probably due to their toxicity profiles. In the present study, we screened approved drugs for possible efflux pump inhibition. By screening a compound library of approximately 1200 different drugs, we identified nilotinib, a tyrosine kinase inhibitor, as showing the best efflux pump inhibitory activity, with a fractional inhibitory concentration index of 0.1875, indicating synergism with ciprofloxacin, and a minimum effective concentration as low as 0.195 μM. Moreover, at 0.39 μM, nilotinib, in combination with 8 μg/mL of ciprofloxacin, led to a significant reduction in biofilm formation and preformed mature biofilms. This is the first description of an approved drug that can be used as an efflux pump inhibitor and to reduce biofilms formation at clinically achievable concentrations

Development of the skeleton in the dwarf clawed frog Pseudhymenochirus merlini (Amphibia: Anura: Pipidae)

In the present study, we investigate the larval development and metamorphic changes of the skeleton of the small, West African pipid frog Pseudhymenochirus merlini Chabanaud, 1920 for the first time . Specimens were cleared and differentially stained for bone and cartilage and the presence or absence of individual bony elements was recorded. Pseudhymenochirus merlini is overall similar in larval morphology and development to its sister taxon Hymenochirus, but shows differences in ossification sequence. Furthermore, Pseudhymenochirus and Hymenochirus differ from other pipids by a reduction of the vertebral column to just six presacral vertebrae. This is apparently the result of a modification of the first two vertebrae and a forward shift of the articulation of the pelvic girdle with the vertebral column by at least one vertebra compared to other pipids. The peculiar skeletal characteristics of Pseudhymenochirus and Hymenochirus do not seem to be a result of miniaturization as often suggested

A Novel Computerized Cell Count Algorithm for Biofilm Analysis

<div><p>Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.</p></div

Comparison of simulated images with genuine biofilms.

<p>(A) <i>S</i>. <i>aureus</i> (cocci) biofilm and (B) <i>P</i>. <i>aeruginosa</i> (rods) biofilm. Simulations of 10,000 coccal (C) or rod (D) cells at a minimum and maximum declension = 1. All 2D-images of single layers are shown as section of similar resolution with an approximately edge length of 42 μm.</p

Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.

<p>Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.</p

Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.

<p>Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.</p

Comparison of a 2D and 3D analysis by qBA of an <i>E</i>. <i>coli</i> biofilm.

<p>(A) Analyzed biofilm layers scanned by CLSM (green and red channels overlapping). (B) Histogram of the viable (green) and dead (red) cells estimated in a 2D (dotted lines) and 3D (solid lines) setting. (C) Allocated (red crosses) local grayscale maxima in three neighboring layers (as indicated by the red dotted square in A). Biofilm images in A and B were processed by increasing the intensity and contrast of the signals for illustrative purpose.</p

Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.

<p>Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.</p

Principals of image processing and adaptive segmentation and binarization.

<p>(A) 2D CLSM image of one Z-layer; (B) Schematic example of a 3D grayscale histogram section (<i>g</i> = f[x, y]); (C) Schematic illustration of the Z-allocation of the cells; (D) 2D projection image of all Z-layers and the local grayscale maxima (indicated as red crosses); (E) Schematic illustration of the window adjustment (<i>w</i>, <i>w)</i> by prolongation and local intensity; (F) Segmented image by seeded region growing algorithm.</p