Colonization of carbapenem-resistant Klebsiella pneumoniae in a sink-drain model biofilm system

Evaluating the antibacterial efficacy of a silver nanocomposite surface coating against nosocomial pathogens as an antibiofilm strategy to prevent hospital infections

Antimicrobial nanocoatings may be a means of preventing nosocomial infections, which account for significant morbidity and mortality. The role of hospital sink traps in these infections is also increasingly appreciated. We describe the preparation, material characterization and antibacterial activity of a pipe cement-based silver nanocoating applied to unplasticized polyvinyl chloride, a material widely used in wastewater plumbing. Three-dimensional surface topography imaging and scanning electron microscopy showed increased roughness in all surface finishes versus control, with grinding producing the roughest surfaces. Silver stability within nanocoatings was >99.89% in deionized water and bacteriological media seeded with bacteria. The nanocoating exhibited potent antibiofilm (99.82-100% inhibition) and antiplanktonic (99.59-99.99% killing) activity against three representative bacterial species and a microbial community recovered from hospital sink traps. Hospital sink trap microbiota were characterized by sequencing the 16S rRNA gene, revealing the presence of opportunistic pathogens from genera including Pseudomonas, Enterobacter and Clostridioides. In a benchtop model sink trap system, nanocoating antibiofilm activity against this community remained significant after 11 days but waned following 25 days. Silver nanocoated disks in real-world sink traps in two university buildings had a limited antibiofilm effect, even though in vitro experiments using microbial communities recovered from the same traps demonstrated that the nanocoating was effective, reducing biofilm formation by >99.6% and killing >98% of planktonic bacteria. We propose that conditioning films forming in the complex conditions of real-world sink traps negatively impact nanocoating performance, which may have wider relevance to development of antimicrobial nanocoatings that are not tested in the real-world.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Novel Drexlerviridae bacteriophage KMI8 with specific lytic activity against Klebsiella michiganensis and its biofilms

The bacterial genus Klebsiella includes the closely related species K. michiganensis, K. oxytoca and K. pneumoniae, which are capable of causing severe disease in humans. In this report we describe the isolation, genomic and functional characterisation of the lytic bacteriophage KMI8 specific for K. michiganensis. KMI8 belongs to the family Drexlerviridae, and has a novel genome which shares very little homology (71.89% identity over a query cover of only 8%) with that of its closest related bacteriophages (Klebsiella bacteriophage LF20 (MW417503.1); Klebsiella bacteriophage 066039 (MW042802.1). KMI8, which possess a putative endosialidase (depolymerase) enzyme, was shown to be capable of degrading mono-biofilms of a strain of K. michiganensis that carried the polysaccharide capsule KL70 locus. This is the first report of a lytic bacteriophage for K. michiganensis, which is capable of breaking down a biofilm of this species.