Colicin U from Shigella boydii Forms Voltage-Dependent Pores.

Colicin U is a protein produced by the bacterium Shigella boydii (serovars 1 and 8). It exerts antibacterial activity against strains of the enterobacterial genera Shigella and Escherichia Here, we report that colicin U forms voltage-dependent pores in planar lipid membranes; its single-pore conductance was found to be about 22 pS in 1 M KCl at pH 6 under 80 mV in asolectin bilayers. In agreement with the high degree of homology between their C-terminal domains, colicin U shares some pore characteristics with the related colicins A and B. Colicin U pores are strongly pH dependent, and as we deduced from the activity of colicin U in planar membranes at different protein concentrations, they have a monomeric pore structure. However, in contrast to related colicins, we observed a very low cationic selectivity of colicin U pores (1.5/1 of K+/Cl- at pH 6) along with their atypical voltage gating. Finally, using nonelectrolytes, we determined the inner diameter of the pores to be in the range of 0.7 to 1 nm, which is similar to colicin Ia, but with a considerably different inner profile.IMPORTANCE Currently, a dramatic increase in antibiotic resistance is driving researchers to find new antimicrobial agents. The large group of toxins called bacteriocins appears to be very promising from this point of view, especially because their narrow killing spectrum allows specific targeting against selected bacterial strains. Colicins are a subgroup of bacteriocins that act on Gram-negative bacteria. To date, some colicins are commercially used for the treatment of animals (1) and tested as a component of engineered species-specific antimicrobial peptides, which are studied for the potential treatment of humans (2). Here, we present a thorough single-molecule study of colicin U which leads to a better understanding of its mode of action. It extends the range of characterized colicins available for possible future medical applications.

Daptomycin Pore Formation and Stoichiometry Depend on Membrane Potential of Target Membrane.

Daptomycin is a calcium-dependent lipodepsipeptide antibiotic clinically used to treat serious infections caused by Gram-positive pathogens. Its precise mode of action is somewhat controversial; the biggest issue is daptomycin pore formation, which we directly investigated here. We first performed a screening experiment using propidium iodide (PI) entry to Bacillus subtilis cells and chose the optimum and therapeutically relevant conditions (10 µg/ml daptomycin and 1.25 mM CaCl2) for the subsequent analyses. Using conductance measurements on planar lipid bilayers, we show that daptomycin forms nonuniform oligomeric pores with conductance ranging from 120 pS to 14 nS. The smallest conductance unit is probably a dimer; however, tetramers and pentamers occur in the membrane most frequently. Moreover, daptomycin pore-forming activity is exponentially dependent on the applied membrane voltage. We further analyzed the membrane-permeabilizing activity in B. subtilis cells using fluorescence methods [PI and DiSC3(5)]. Daptomycin most rapidly permeabilizes cells with high initial membrane potential and dissipates it within a few minutes. Low initial membrane potential hinders daptomycin pore formation.

Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review.

Background: Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success. Objective: We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation. Methods: Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods. Results: Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I2 = 98% favoring learning methods. Conclusion: Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.