Biomolecule-corona formation confers resistance of bacteria to nanoparticle-induced killing: Implications for the design of improved nanoantibiotics.

Multidrug-resistant bacterial infections are a global health threat. Nanoparticles are thus investigated as novel antibacterial agents for clinical practice, including wound dressings and implants. We report that nanoparticles' bactericidal activity strongly depends on their physical binding to pathogens, including multidrug-resistant primary clinical isolates, such as Staphylococcus aureus, Klebsiella pneumoniae or Enterococcus faecalis. Using controllable nanoparticle models, we found that nanoparticle-pathogen complex formation was enhanced by small nanoparticle size rather than material or charge, and was prevented by 'stealth' modifications. Nanoparticles seem to preferentially bind to Gram-positive pathogens, such as Listeria monocytogenes, S. aureus or Streptococcus pyrogenes, correlating with enhanced antibacterial activity. Bacterial resistance to metal-based nanoparticles was mediated by biomolecule coronas acquired in pathophysiological environments, such as wounds, the lung, or the blood system. Biomolecule corona formation reduced nanoparticles' binding to pathogens, but did not impact nanoparticle dissolution. Our results provide a mechanistic explanation why nano-sized antibiotics may show reduced activity in clinically relevant environments, and may inspire future nanoantibiotic designs with improved and potentially pathogen-specific activity.

Insights into the biology of acidophilic members of the Acidiferrobacteraceae family derived from comparative genomic analyses.

The family Acidiferrobacteraceae (order Acidiferrobacterales) currently contains Gram negative, neutrophilic sulfur oxidizers such as Sulfuricaulis and Sulfurifustis, as well as acidophilic iron and sulfur oxidizers belonging to the Acidiferrobacter genus. The diversity and taxonomy of the genus Acidiferrobacter has remained poorly explored. Although several metagenome and bioleaching studies have identified its presence worldwide, only two strains, namely Acidiferrobacter thiooxydans DSM 2932T, and Acidiferrobacter spp. SP3/III have been isolated and made publically available. Using 16S rRNA sequence data publically available for the Acidiferrobacteraceae, we herein shed light into the molecular taxonomy of this family. Results obtained support the presence of three clades Acidiferrobacter, Sulfuricaulis and Sulfurifustis. Genomic analyses of the genome sequences of A. thiooxydansT and Acidiferrobacter spp. SP3/III indicate that ANI relatedness between the SPIII/3 strain and A. thiooxydansT is below 95-96%, supporting the classification of strain SP3/III as a new species within this genus. In addition, approximately 70% of Acidiferrobacter sp. SPIII/3 predicted genes have a conserved ortholog in A. thiooxydans strains. A comparative analysis of iron, sulfur oxidation pathways, genome plasticity and cell-cell communication mechanisms of Acidiferrobacter spp. are also discussed.

Multiplicative approximations, optimal hypervolume distributions, and the choice of the reference point.

Many optimization problems arising in applications have to consider several objective functions at the same time. Evolutionary algorithms seem to be a very natural choice for dealing with multi-objective problems as the population of such an algorithm can be used to represent the trade-offs with respect to the given objective functions. In this paper, we contribute to the theoretical understanding of evolutionary algorithms for multi-objective problems. We consider indicator-based algorithms whose goal is to maximize the hypervolume for a given problem by distributing [Formula: see text] points on the Pareto front. To gain new theoretical insights into the behavior of hypervolume-based algorithms, we compare their optimization goal to the goal of achieving an optimal multiplicative approximation ratio. Our studies are carried out for different Pareto front shapes of bi-objective problems. For the class of linear fronts and a class of convex fronts, we prove that maximizing the hypervolume gives the best possible approximation ratio when assuming that the extreme points have to be included in both distributions of the points on the Pareto front. Furthermore, we investigate the choice of the reference point on the approximation behavior of hypervolume-based approaches and examine Pareto fronts of different shapes by numerical calculations.